We thank Sinem Buber, Mita Goldar and Matthew Levin from ADP for their support on the project. We also thank Steve Davis, Jan Eberly and Jonathan Parker for comments on prior drafts. As part of the University of Chicago data use contract, ADP reviewed the paper prior to distribution with the sole focus of making sure that the paper did not release information that would compromise the privacy of their clients or reveal proprietary information about the ADP business model. The views expressed in the paper are the authors' and do not necessarily reflect the views of ADP or the National Bureau of Economic Research. Additionally, the analysis and conclusions set forth here are those of the authors and do not indicate concurrence by other members of the research staff or the Board of Governors. At least one co-author has disclosed a financial relationship of potential relevance for this research. Further information is available online at http://www.nber.org/papers/w27159.ack NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

The National Establishment Time Series (NETS) is a private sector source of U.S. business microdata. Researchers have used state-specific NETS extracts for many years, but relatively little is known about the accuracy and representativeness of the nationwide NETS sample. We explore the properties of NETS as compared to official U.S. data on business activity: The Census Bureau's County Business Patterns (CBP) and Nonemployer Statistics (NES) and the Bureau of Labor Statistics' Quarterly Census of Employment and Wages (QCEW). We find that the NETS universe does not cover the entirety of the Census-based employer and nonemployer universes, but given certain restrictions NETS can be made to mimic official employer datasets with reasonable precision. The largest differences between NETS employer data and official sources are among small establishments, where imputation is prevalent in NETS. The most stringent of our proposed sample restrictions still allows scope that covers about three quarters of U.S. private sector employment. We conclude that NETS microdata can be useful and convenient for studying static business activity in high detail.

Many traditional official statistics are not suitable for measuring high-frequency developments that evolve over the course of weeks, not months. In this paper, we track the labor market effects of the COVID-19 pandemic with weekly payroll employment series based on microdata from ADP. These data are available essentially in real-time, and allow us to track both aggregate and industry effects. Cumulative losses in paid employment through April 4 are currently estimated at 18 million; just during the two weeks between March 14 and March 28 the U.S. economy lost about 13 million paid jobs. For comparison, during the entire Great Recession less than 9 million private payroll employment jobs were lost. In the current crisis, the most affected sector is leisure and hospitality, which has so far lost or furloughed about 30 percent of employment, or roughly 4 million jobs.