Measuring small business dynamics and employment with private-sector real-time data
中文摘要
本文提出一种新的方法论，用以将真实的商业开张与关闭同私营部门数据中的样本流失区分开来，并借助关于单个企业活动的高频补充信息来评估由此得到的估计结果的代表性。该方法论既生成仅使用同期信息的实时估计，也生成在额外信息可获得时将其纳入的回顾性估计，反映了时效性与准确性之间的根本性权衡。该方法论被应用于新冠疫情期间广泛使用的小企业实时样本，以展示其在极端情形下的有用性。这一应用凸显了恰当处理企业开张与关闭的重要性，同时就疫情期间的小企业动态得出两点重要洞见：(i) 在疫情初期，面对面服务部门的小企业就业波动幅度大于规模较大企业的就业，主要原因是临时性关闭的激增；(ii) 延迟获得薪资保护计划贷款显著增加了小企业的关闭，但对持续经营企业的就业影响甚微，这表明该计划的效力主要通过防止关闭而非保住经营中企业的岗位来实现。
Abstract
This paper proposes a novel methodology to distinguish true business openings and closings from sample churn in private-sector data and to evaluate the representativeness of the resulting estimates by leveraging supplementary high-frequency information on individual business activity. The methodology produces both real-time estimates using only concurrent information and retrospective estimates that incorporate additional information as it becomes available, reflecting a fundamental trade-off between timeliness and accuracy. The methodology is applied to a real-time sample of small businesses widely used during the COVID-19 pandemic to demonstrate its usefulness under extreme circumstances. The application highlights the importance of properly accounting for business openings and closings and at the same time yields two important insights about small business dynamics during the pandemic: (i) small business employment in in-person service sectors experienced larger swings at the beginning of the pandemic than employment of larger businesses, primarily due to a spike in temporary closings; (ii) delayed access to loans from the Paycheck Protection Program significantly increased small business closings but had minimal impact on employment of continuing businesses, suggesting the program’s effectiveness operated primarily through preventing closures rather than preserving jobs at operating businesses.