IDEAS home Printed from https://ideas.repec.org/a/iza/izawol/journl2019n95.html
   My bibliography  Save this article

The importance and challenges of measuring work hours

Author

Listed:
  • Jay Stewart

    (Bureau of Labor Statistics, USA, and IZA, Germany)

  • Harley Frazis

    (Bureau of Labor Statistics, USA)

Abstract

Work hours are key components in estimating productivity growth and hourly wages as well as being a useful cyclical indicator in their own right, so measuring them correctly is important. The US Bureau of Labor Statistics (BLS) collects data on work hours in several surveys and publishes four widely used series that measure average weekly hours. The series tell different stories about average weekly hours and trends in those hours but qualitatively similar stories about the cyclical behavior of work hours. The research summarized here explains the differences in levels, but only some of the differences in trends.

Suggested Citation

  • Jay Stewart & Harley Frazis, 2019. "The importance and challenges of measuring work hours," IZA World of Labor, Institute of Labor Economics (IZA), pages 1-95, July.
  • Handle: RePEc:iza:izawol:journl:2019:n:95
    as

    Download full text from publisher

    File URL: https://wol.iza.org/uploads/articles/95/pdfs/importance-and-challenges-of-measuring-work-hours.pdf
    Download Restriction: no

    File URL: https://wol.iza.org/articles/importance-and-challenges-of-measuring-work-hours
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Katharine G. Abraham & Aaron Maitland & Suzanne M. Bianchi, 2006. "Non-response in the American Time Use Survey: Who Is Missing from the Data and How Much Does It Matter?," NBER Technical Working Papers 0328, National Bureau of Economic Research, Inc.
    2. Jens Bonke, 2005. "Paid Work and Unpaid Work: Diary Information Versus Questionnaire Information," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 70(3), pages 349-368, February.
    3. Harley Frazis & Jay Stewart, 2010. "Why Do BLS Hours Series Tell Different Stories About Trends in Hours Worked?," NBER Chapters, in: Labor in the New Economy, pages 343-372, National Bureau of Economic Research, Inc.
    4. Michael C. Burda & Daniel S. Hamermesh & Jay Stewart, 2013. "Cyclical Variation in Labor Hours and Productivity Using the ATUS," American Economic Review, American Economic Association, vol. 103(3), pages 99-104, May.
    5. Harley Frazis & Jay Stewart, 2010. "Why Do BLS Hours Series Tell Different Stories About Trends in Hours Worked?," NBER Chapters, in: Labor in the New Economy, pages 343-372, National Bureau of Economic Research, Inc.
    6. Stephanie Aaronson & Andrew Figura, 2010. "How Biased Are Measures Of Cyclical Movements In Productivity And Hours?," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 56(3), pages 539-558, September.
    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. L. Rachel Ngai & Orhun Sevinc, 2020. "A Multisector Perspective on Wage Stagnation," Discussion Papers 2026, Centre for Macroeconomics (CFM).
    2. Heath,Rachel & Mansuri,Ghazala & Rijkers,Bob & Seitz,William Hutchins & Sharma,Dhiraj, 2020. "Measuring Employment : Experimental Evidence from Urban Ghana," Policy Research Working Paper Series 9263, The World Bank.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andrew S. Green, 2017. "Hours Off the Clock," Working Papers 17-44, Center for Economic Studies, U.S. Census Bureau.
    2. Jens Bonke & Mette Deding & Mette Lausten & Leslie S. Stratton, 2008. "Intra‐Household Specialization in Housework in the United States and Denmark," Social Science Quarterly, Southwestern Social Science Association, vol. 89(4), pages 1023-1043, December.
    3. Daniel Borowczyk-Martins & Etienne Lalé, 2019. "Employment Adjustment and Part-Time Work: Lessons from the United States and the United Kingdom," American Economic Journal: Macroeconomics, American Economic Association, vol. 11(1), pages 389-435, January.
    4. Michael C. Burda & Daniel S. Hamermesh & Jay Stewart, 2013. "Cyclical Variation in Labor Hours and Productivity Using the ATUS," American Economic Review, American Economic Association, vol. 103(3), pages 99-104, May.
    5. Julien Champagne & André Kurmann & Jay Stewart, 2016. "Reconciling the Differences in Aggregate U.S. Wage Series," Staff Working Papers 16-1, Bank of Canada.
    6. Champagne, Julien & Kurmann, André & Stewart, Jay, 2017. "Reconciling the divergence in aggregate U.S. wage series," Labour Economics, Elsevier, vol. 49(C), pages 27-41.
    7. Lachowska, Marta & Mas, Alexandre & Woodbury, Stephen A., 2022. "How reliable are administrative reports of paid work hours?," Labour Economics, Elsevier, vol. 75(C).
    8. Loukas Karabarbounis, 2014. "The Labor Wedge: MRS vs. MPN," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 17(2), pages 206-223, April.
    9. Alexander Bick & Bettina Brüggemann & Nicola Fuchs-Schündeln, 2019. "Data Revisions of Aggregate Hours Worked: Implications for the Europe-U.S. Hours Gap," Review, Federal Reserve Bank of St. Louis, vol. 101(1), pages 45-56.
    10. Alexander Bick & Bettina Brüggemann & Nicola Fuchs‐Schündeln, 2019. "Hours Worked in Europe and the United States: New Data, New Answers," Scandinavian Journal of Economics, Wiley Blackwell, vol. 121(4), pages 1381-1416, October.
    11. Michael C. Burda & Katie R. Genadek & Daniel S. Hamermesh, 2020. "Unemployment and Effort at Work," Economica, London School of Economics and Political Science, vol. 87(347), pages 662-681, July.
    12. Daniel S. Hamermesh & Katie R. Genadek & Michael C. Burda, 2021. "Racial/Ethnic Differences in Non-Work at Work," ILR Review, Cornell University, ILR School, vol. 74(2), pages 272-292, March.
    13. Billari, Francesco C. & Giuntella, Osea & Stella, Luca, 2018. "Broadband internet, digital temptations, and sleep," Journal of Economic Behavior & Organization, Elsevier, vol. 153(C), pages 58-76.
    14. Jordi Galí & Thijs van Rens, 2021. "The Vanishing Procyclicality of Labour Productivity [Why have business cycle fluctuations become less volatile?]," The Economic Journal, Royal Economic Society, vol. 131(633), pages 302-326.
    15. Michael Osei Mireku & Alina Rodriguez, 2021. "Sleep Duration and Waking Activities in Relation to the National Sleep Foundation’s Recommendations: An Analysis of US Population Sleep Patterns from 2015 to 2017," IJERPH, MDPI, vol. 18(11), pages 1-15, June.
    16. Scharadin, Benjamin, 2022. "The efficacy of the dependent care deduction at maintaining diet quality," Food Policy, Elsevier, vol. 107(C).
    17. Ferranna, Maddalena & Sevilla, J.P. & Zucker, Leo & Bloom, David E., 2022. "Patterns of Time Use among Older People," IZA Discussion Papers 15227, Institute of Labor Economics (IZA).
    18. Joshua Graff Zivin & Matthew Neidell, 2014. "Temperature and the Allocation of Time: Implications for Climate Change," Journal of Labor Economics, University of Chicago Press, vol. 32(1), pages 1-26.
    19. Giménez-Nadal, José Ignacio & Velilla, Jorge & Ortega, Raquel, 2022. "Revisiting excess commuting and self-employment: The case of Latin America," GLO Discussion Paper Series 1179, Global Labor Organization (GLO).
    20. Yuta Masuda & Lea Fortmann & Mary Gugerty & Marla Smith-Nilson & Joseph Cook, 2014. "Pictorial Approaches for Measuring Time Use in Rural Ethiopia," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 115(1), pages 467-482, January.

    More about this item

    Keywords

    hours of work; household surveys; establishment surveys; time-use surveys; productivity; hourly wages;
    All these keywords.

    JEL classification:

    • J22 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Time Allocation and Labor Supply
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • E23 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Production

    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:iza:izawol:journl:2019:n:95. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Institute of Labor Economics (IZA) (email available below). General contact details of provider: https://edirc.repec.org/data/izaaade.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.