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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

    (Board of Governors of the Federal Reserve System (U.S.))

  • 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 (US).
  • 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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    1. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    2. Clark, Todd E. & West, Kenneth D., 2006. "Using out-of-sample mean squared prediction errors to test the martingale difference hypothesis," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 155-186.
    3. Teresa C Fort & John Haltiwanger & Ron S Jarmin & Javier Miranda, 2013. "How Firms Respond to Business Cycles: The Role of Firm Age and Firm Size," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 61(3), pages 520-559, August.
    4. Christopher Slijk & Keith R. Phillips, 2015. "ADP payroll processing data can provide early look at Texas job growth," Southwest Economy, Federal Reserve Bank of Dallas, pages 10-13.
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    6. Chang, Andrew C. & Hanson, Tyler J., 2016. "The accuracy of forecasts prepared for the Federal Open Market Committee," Journal of Economics and Business, Elsevier, vol. 83(C), pages 23-43.
    7. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
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    10. Allan W. Gregory & Hui Zhu, 2014. "Testing the value of lead information in forecasting monthly changes in employment from the Bureau of Labor Statistics," Applied Financial Economics, Taylor & Francis Journals, vol. 24(7), pages 505-514, April.
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    Citations

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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 21st Century Economic Statistics, National Bureau of Economic Research, Inc.
    2. repec:nbr:nberch:14267 is not listed on IDEAS
    3. Aditya Aladangady & Shifrah Aron-Dine & Wendy Dunn & Laura Feiveson & Paul Lengermann & Claudia Sahm, 2019. "From Transactions Data to Economic Statistics: Constructing Real-Time, High-Frequency, Geographic Measures of Consumer Spending," NBER Chapters, in: Big Data for 21st Century Economic Statistics, National Bureau of Economic Research, Inc.

    More about this item

    Keywords

    Consumption; saving; production; employment; and investment; Labor supply and demand; Forecasting;

    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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