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Improving the Accuracy of Economic Measurement with Multiple Data Sources: The Case of Payroll Employment Data

Tomaz Cajner, Leland D. Crane, Ryan A. Decker, Adrian Hamins-Puertolas, Christopher Kurz


This chapter is a preliminary draft unless otherwise noted. It may not have been subjected to the formal review process of the NBER. This page will be updated as the chapter is revised.

Chapter in forthcoming NBER book Big Data for 21st Century Economic Statistics, Katharine G. Abraham, Ron S. Jarmin, Brian Moyer, and Matthew D. Shapiro
Conference held March 15-16, 2019
Forthcoming from University of Chicago Press
in NBER Book Series Studies in Income and Wealth

This paper combines information from two sources of U.S. private payroll employment to increase the accuracy of real-time measurement of the labor market. The sources are the Current Employment Statistics (CES) from BLS and microdata from the payroll processing firm ADP. We briefly describe the ADP-derived data series, compare it to the BLS data, and describe an exercise that benchmarks the data series to an employment census. The CES and the ADP employment data are each derived from roughly equal-sized samples. We argue that combining CES and ADP data series reduces the measurement error inherent in both data sources. In particular, we infer “true” unobserved payroll employment growth using a state-space model and find that the optimal predictor of the unobserved state puts approximately equal weight on the CES and ADP-derived series. Moreover, the estimated state contains information about future readings of payroll employment.

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This chapter first appeared as NBER working paper w26033, Improving the Accuracy of Economic Measurement with Multiple Data Sources: The Case of Payroll Employment Data, Tomaz Cajner, Leland D. Crane, Ryan A. Decker, Adrian Hamins-Puertolas, Christopher Kurz
 
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