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Using Payroll Processor Microdata to Measure Aggregate Labor Market Activity

Author

Listed:
  • Tomaz Cajner
  • Leland Crane
  • Ryan Decker
  • Adrian Hamins-Puertolas
  • Christopher J. Kurz
  • Tyler Radler

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.

Suggested Citation

  • Tomaz Cajner & Leland Crane & Ryan Decker & Adrian Hamins-Puertolas & Christopher J. Kurz & Tyler Radler, 2018. "Using Payroll Processor Microdata to Measure Aggregate Labor Market Activity," Finance and Economics Discussion Series 2018-005, Board of Governors of the Federal Reserve System (U.S.).
  • Handle: RePEc:fip:fedgfe:2018-05
    DOI: 10.17016/FEDS.2018.005
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    File URL: https://www.federalreserve.gov/econres/feds/files/2018005pap.pdf
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    References listed on IDEAS

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    Cited by:

    1. Tomaz Cajner & Leland D. Crane & Ryan A. Decker & Adrian Hamins-Puertolas & Christopher Kurz, 2019. "Improving the Accuracy of Economic Measurement with Multiple Data Sources: The Case of Payroll Employment Data," NBER Chapters, in: Big Data for Twenty-First Century Economic Statistics, National Bureau of Economic Research, Inc.
    2. Tomaz Cajner & Leland Crane & Ryan Decker & Adrian Hamins-Puertolas & Christopher J. Kurz, 2020. "Tracking Labor Market Developments during the COVID-19 Pandemic: A Preliminary Assessment," Finance and Economics Discussion Series 2020-030, Board of Governors of the Federal Reserve System (U.S.).
    3. Aditya Aladangady & Shifrah Aron-Dine & Wendy Dunn & Laura Feiveson & Paul Lengermann & Claudia Sahm, 2021. "From Transactions Data to Economic Statistics: Constructing Real-Time, High-Frequency, Geographic Measures of Consumer Spending," NBER Chapters, in: Big Data for Twenty-First Century Economic Statistics, National Bureau of Economic Research, Inc.
    4. Tomaz Cajner & Leland D. Crane & Ryan A. Decker & John Grigsby & Adrian Hamins-Puertolas & Erik Hurst & Christopher Johann Kurz & Ahu Yildirmaz, 2020. "The U.S. Labor Market During the Beginning of the Pandemic Recession," Working Papers 2020-58_Revision, Becker Friedman Institute for Research In Economics.

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    More about this item

    Keywords

    Consumption; saving; production; employment; and investment; Labor supply and demand; Forecasting;
    All these keywords.

    JEL classification:

    • J2 - Labor and Demographic Economics - - Demand and Supply of Labor
    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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