IZA Discussion Paper No. 15515

Measuring Small Business Dynamics and Employment with Private-Sector Real-Time Data

The COVID-19 pandemic has led to an explosion of research using private-sector datasets to measure business dynamics and employment in real-time. Yet questions remain about the representativeness of these datasets and how to distinguish business openings and closings from sample churn – i.e., sample entry of already operating businesses and sample exits of businesses that continue operating. This paper proposes new methods to address these issues and applies them to the case of Homebase, a real-time dataset of mostly small service-sector sector businesses that has been used extensively in the literature to study the effects of the pandemic. We match the Homebase establishment records with information on business activity from Safegraph, Google, and Facebook to assess the representativeness of the data and to estimate the probability of business closings and openings among sample exits and entries. We then exploit the high frequency / geographic detail of the data to study whether small service-sector businesses have been hit harder by the pandemic than larger firms, and the extent to which the Paycheck Protection Program (PPP) helped small businesses keep their workforce employed. We find that our real-time estimates of small business dynamics and employment during the pandemic are remarkably representative and closely fit population counterparts from administrative data that have recently become available. Distinguishing business closings and openings from sample churn is critical for these results. We also find that while employment by small businesses contracted more severely in the beginning of the pandemic than employment of larger businesses, it also recovered more strongly thereafter. In turn, our estimates suggests that the rapid rollout of PPP loans significantly mitigated the negative employment effects of the pandemic. Business closings and openings are a key driver for both results, thus underlining the importance of properly correcting for sample churn.

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