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Using Payroll Processor Microdata to Measure Aggregate Labor Market Activity

Tomaz Cajner, Leland Crane, Ryan Decker, Adrian Hamins-Puertolas, Christopher J. Kurz and Tyler Radler

No 2018-005, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (US)

Abstract: We show that high-frequency private payroll microdata can help forecast labor market conditions. Payroll employment is perhaps the most reliable real-time indicator of the business cycle and is therefore closely followed by policymakers, academia, and financial markets. Government statistical agencies have long served as the primary suppliers of information on the labor market and will continue to do so for the foreseeable future. That said, sources of “big data” are becoming increasingly available through collaborations with private businesses engaged in commercial activities that record economic activity on a granular, frequent, and timely basis. One such data source is generated by the firm ADP, which processes payrolls for about one fifth of the U.S. private sector workforce. We evaluate the efficacy of these data to create new statistics that complement existing measures. In particular, we develop a set of weekly aggregate employment indexes from 2000 to 2017, which allows us to measure employment at a higher frequency than is currently possible. The extensive coverage of the ADP data—similar in terms of private employment to the BLS CES sample—implies potentially high information value of these data, and our results confirm this conjecture. Indeed, the timeliness and frequency of the ADP payroll microdata substantially improves forecast accuracy for both current-month employment and revisions to the BLS CES data.

Keywords: Consumption, saving, production, employment, and investment; Labor supply and demand; Forecasting (search for similar items in EconPapers)
JEL-codes: J2 J11 C53 C55 C81 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big and nep-lma
Date: 2018-01-17
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DOI: 10.17016/FEDS.2018.005

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