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

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Listed:
  • Tomaz Cajner
  • Leland D. Crane
  • Ryan A. Decker
  • Adrian Hamins-Puertolas
  • Christopher Kurz

Abstract

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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  • 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 Working Papers 26033, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:26033
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    1. Cooper, Russell & Haltiwanger, John & Willis, Jonathan L., 2015. "Dynamics of labor demand: Evidence from plant-level observations and aggregate implications," Research in Economics, Elsevier, vol. 69(1), pages 37-50.
    2. Alan B. Krueger & Kenneth N. Fortson, 2003. "Do Markets Respond More to More Reliable Labor Market Data? A Test of Market Rationality," Journal of the European Economic Association, MIT Press, vol. 1(4), pages 931-957, June.
    3. Aruoba, S. Borağan & Diebold, Francis X. & Nalewaik, Jeremy & Schorfheide, Frank & Song, Dongho, 2016. "Improving GDP measurement: A measurement-error perspective," Journal of Econometrics, Elsevier, vol. 191(2), pages 384-397.
    4. Brad Hershbein & Lisa B. Kahn, 2018. "Do Recessions Accelerate Routine-Biased Technological Change? Evidence from Vacancy Postings," American Economic Review, American Economic Association, vol. 108(7), pages 1737-1772, July.
    5. S. Boragan Aruoba & Francis X. Diebold & Jeremy J. Nalewaik & Frank Schorfheide & Dongho Song, 2011. "Improving GDP measurement: a forecast combination perspective," Working Papers 11-41, Federal Reserve Bank of Philadelphia.
    6. John Grigsby & Erik Hurst & Ahu Yildirmaz, 2021. "Aggregate Nominal Wage Adjustments: New Evidence from Administrative Payroll Data," American Economic Review, American Economic Association, vol. 111(2), pages 428-471, February.
    7. Aditya Aladangady & Shifrah Aron-Dine & Wendy E. Dunn & Laura J. Feiveson & Paul Lengermann & Claudia R. Sahm, 2016. "The Effect of Hurricane Matthew on Consumer Spending," FEDS Notes 2016-12-02, Board of Governors of the Federal Reserve System (U.S.).
    8. Xiaohong Chen & Norman R. Swanson (ed.), 2013. "Recent Advances and Future Directions in Causality, Prediction, and Specification Analysis," Springer Books, Springer, edition 127, number 978-1-4614-1653-1, April.
    9. 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.
    10. Ryan Decker & John Haltiwanger & Ron Jarmin & Javier Miranda, 2014. "The Role of Entrepreneurship in US Job Creation and Economic Dynamism," Journal of Economic Perspectives, American Economic Association, vol. 28(3), pages 3-24, Summer.
    11. Alberto Cavallo & Roberto Rigobon, 2016. "The Billion Prices Project: Using Online Prices for Measurement and Research," Journal of Economic Perspectives, American Economic Association, vol. 30(2), pages 151-178, Spring.
    12. Keith R. Phillips & Christopher Slijk, 2015. "ADP payroll processing data can provide early look at Texas job growth," Southwest Economy, Federal Reserve Bank of Dallas, issue Q2, pages 10-13.
    13. Tomaz Cajner & Leland D. Crane & Ryan A. 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.).
    14. repec:pri:cepsud:88krueger is not listed on IDEAS
    15. Joshua H. Gallin & Raven S. Molloy & Eric R. Nielsen & Paul A. Smith & Kamila Sommer, 2018. "Measuring Aggregate Housing Wealth : New Insights from an Automated Valuation Model," Finance and Economics Discussion Series 2018-064, Board of Governors of the Federal Reserve System (U.S.).
    16. Alan B. Krueger & Kenneth N. Fortson, 2003. "Do Markets Respond More to More Reliable Labor Market Data? A Test of Market Rationality," Journal of the European Economic Association, MIT Press, vol. 1(4), pages 931-957, June.
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    Cited by:

    1. Tomaz Cajner & Leland D. Crane & Ryan A. 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.).
    2. Gallin, Joshua & Molloy, Raven & Nielsen, Eric & Smith, Paul & Sommer, Kamila, 2021. "Measuring aggregate housing wealth: New insights from machine learning ☆," Journal of Housing Economics, Elsevier, vol. 51(C).
    3. Eiji Goto & Jan P.A.M. Jacobs & Tara M. Sinclair & Simon van Norden, 2021. "Employment Reconciliation and Nowcasting," Working Papers 2021-007, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    4. Tomaz Cajner & Andrew Figura & Brendan M. Price & David Ratner & Alison E. Weingarden, 2020. "Reconciling Unemployment Claims with Job Losses in the First Months of the COVID-19 Crisis," Finance and Economics Discussion Series 2020-055, Board of Governors of the Federal Reserve System (U.S.).
    5. Jerome H. Powell, 2019. "Data-Dependent Monetary Policy in an Evolving Economy : A speech at \"Trucks and Terabytes: Integrating the 'Old' and 'New' Economies\" 61st Annual Meeting of the National Association for Bu," Speech 1093, Board of Governors of the Federal Reserve System (U.S.).
    6. Kohei Matsumura & Yusuke Oh & Tomohiro Sugo & Koji Takahashi, "undated". "Nowcasting Economic Activity with Mobility Data," Bank of Japan Working Paper Series 21-E-2, Bank of Japan.

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    More about this item

    JEL classification:

    • 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
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts
    • J2 - Labor and Demographic Economics - - Demand and Supply of Labor

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