IDEAS home Printed from https://ideas.repec.org/a/eee/jmacro/v47y2016ipbp200-213.html
   My bibliography  Save this article

Why are initial estimates of productivity growth so unreliable?

Author

Listed:
  • Jacobs, Jan P.A.M.
  • van Norden, Simon

Abstract

This paper argues that initial estimates of productivity growth will tend to be much less reliable than those of most other macroeconomic aggregates, such as output or employment growth. Two distinct factors complicate productivity measurement. (1) When production increases, factor inputs typically increase as well. Productivity growth is therefore typically less variable than output growth, meaning that measurement errors will tend to be relatively more important. (2) Revisions to published estimates of production and factor inputs tend to be less highly correlated than the published estimates themselves. This further increases the impact of data revisions on published productivity estimates.

Suggested Citation

  • Jacobs, Jan P.A.M. & van Norden, Simon, 2016. "Why are initial estimates of productivity growth so unreliable?," Journal of Macroeconomics, Elsevier, vol. 47(PB), pages 200-213.
  • Handle: RePEc:eee:jmacro:v:47:y:2016:i:pb:p:200-213
    DOI: 10.1016/j.jmacro.2015.11.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0164070415001421
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. van Norden, Simon, 2011. "Current trends in the analysis of Canadian productivity growth," The North American Journal of Economics and Finance, Elsevier, vol. 22(1), pages 5-25, January.
    2. Jacob A. Mincer & Victor Zarnowitz, 1969. "The Evaluation of Economic Forecasts," NBER Chapters, in: Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance, pages 3-46, National Bureau of Economic Research, Inc.
    3. S. Boragan Aruoba, 2008. "Data Revisions Are Not Well Behaved," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(2-3), pages 319-340, March.
    4. Richard G. Anderson & Kevin L. Kliesen, 2006. "The 1990s acceleration in labor productivity: causes and measurement," Review, Federal Reserve Bank of St. Louis, issue may, pages 181-202.
    5. Chad Syverson, 2011. "What Determines Productivity?," Journal of Economic Literature, American Economic Association, vol. 49(2), pages 326-365, June.
    6. Robert J. Gordon, 2000. "Does the "New Economy" Measure Up to the Great Inventions of the Past?," Journal of Economic Perspectives, American Economic Association, vol. 14(4), pages 49-74, Fall.
    7. Kahn, James A. & Rich, Robert W., 2007. "Tracking the new economy: Using growth theory to detect changes in trend productivity," Journal of Monetary Economics, Elsevier, vol. 54(6), pages 1670-1701, September.
    8. Athanasios Orphanides, 2001. "Monetary Policy Rules Based on Real-Time Data," American Economic Review, American Economic Association, vol. 91(4), pages 964-985, September.
    9. Swanson, Norman R. & van Dijk, Dick, 2006. "Are Statistical Reporting Agencies Getting It Right? Data Rationality and Business Cycle Asymmetry," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 24-42, January.
    10. Lawrence Slifman & Carol Corrado, 1999. "Decomposition of Productivity and Unit Costs," American Economic Review, American Economic Association, vol. 89(2), pages 328-332, May.
    11. Faust, Jon & Rogers, John H & Wright, Jonathan H, 2005. "News and Noise in G-7 GDP Announcements," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 403-419, June.
    12. Jin, Hui & Jorgenson, Dale W., 2010. "Econometric modeling of technical change," Journal of Econometrics, Elsevier, vol. 157(2), pages 205-219, August.
    13. Messina, Jeffrey D. & Sinclair, Tara M. & Stekler, Herman, 2015. "What can we learn from revisions to the Greenbook forecasts?," Journal of Macroeconomics, Elsevier, vol. 45(C), pages 54-62.
    14. Faust, Jon & Wright, Jonathan H., 2009. "Comparing Greenbook and Reduced Form Forecasts Using a Large Realtime Dataset," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 468-479.
    15. Edge, Rochelle M. & Laubach, Thomas & Williams, John C., 2007. "Learning and shifts in long-run productivity growth," Journal of Monetary Economics, Elsevier, vol. 54(8), pages 2421-2438, November.
    16. S. Boragan Aruoba, 2004. "Data Uncertainty in General Equilibrium," Computing in Economics and Finance 2004 131, Society for Computational Economics.
    17. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
    18. N. Gregory Mankiw & Matthew D. Shapiro, 1986. "News or Noise? An Analysis of GNP Revisions," NBER Working Papers 1939, National Bureau of Economic Research, Inc.
    19. Dean Croushore, 2011. "Frontiers of Real-Time Data Analysis," Journal of Economic Literature, American Economic Association, vol. 49(1), pages 72-100, March.
    20. Richard G. Anderson & Kevin L. Kliesen, 2010. "FOMC learning and productivity growth (1985-2003): a reading of the record," Review, Federal Reserve Bank of St. Louis, issue mar, pages 129-154.
    21. Jacobs, Jan P.A.M. & van Norden, Simon, 2011. "Modeling data revisions: Measurement error and dynamics of "true" values," Journal of Econometrics, Elsevier, vol. 161(2), pages 101-109, April.
    22. Field, Alexander J., 2010. "The Procyclical Behavior of Total Factor Productivity in the United States, 1890–2004," The Journal of Economic History, Cambridge University Press, vol. 70(2), pages 326-350, June.
    23. Richard G Anderson & Kevin L Kliesen, 2012. "How Does the FOMC Learn About Economic Revolutions? Evidence from the New Economy Era, 1994–2001," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 47(1), pages 27-56, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hecq, Alain & Jacobs, Jan P.A.M. & Stamatogiannis, Michalis P., 2019. "Testing for news and noise in non-stationary time series subject to multiple historical revisions," Journal of Macroeconomics, Elsevier, vol. 60(C), pages 396-407.

    More about this item

    Keywords

    Productivity; Real-time analysis; Data revisions; Greenbook projections;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jmacro:v:47:y:2016:i:pb:p:200-213. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/locate/inca/622617 .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.