IDEAS home Printed from https://ideas.repec.org/p/fip/fedcwp/1438.html
   My bibliography  Save this paper

Assessing the Change in Labor Market Conditions

Author

Abstract

This paper describes a dynamic factor model of 19 U.S. labor market indicators, covering the broad categories of unemployment and underemployment, employment, workweeks, wages, vacancies, hiring, layoffs, quits, and surveys of consumers? and businesses? perceptions. The resulting labor market conditions index (LMCI) is a useful tool for gauging the change in labor market conditions. In addition, the model provides a way to organize discussions of the signal value of different labor market indicators in situations when they might be sending diverse signals. The model takes the greatest signal from private payroll employment and the unemployment rate. Other infl uential indicators include the insured unemployment rate, consumers? perceptions of job availability, and help-wanted advertising. Through the lens of the LMCI, labor market conditions have improved at a moderate pace over the past several years, albeit with some notable variation along the way. In addition, from the perspective of the model, the unemployment rate declined a bit faster over the past two years than was consistent with the other indicators.

Suggested Citation

  • Hess T. Chung & Bruce Fallick & Christopher J. Nekarda & David Ratner, 2015. "Assessing the Change in Labor Market Conditions," Working Papers (Old Series) 1438, Federal Reserve Bank of Cleveland.
    • Handle: RePEc:fip:fedcwp:1438
    DOI: 10.26509/frbc-wp-201438
    as

    Download full text from publisher

    File URL: https://doi.org/10.26509/frbc-wp-201438
    File Function: Persistent link
    Download Restriction: no
    File URL: https://www.clevelandfed.org/-/media/project/clevelandfedtenant/clevelandfedsite/publications/working-papers/2014/wp-1438-assessing-the-changes-in-labor-market-conditions-pdf.pdf
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.26509/frbc-wp-201438?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    2. Michelle L. Barnes & Ryan Chahrour & Giovanni P. Olivei & Gaoyan Tang, 2007. "A principal components approach to estimating labor market pressure and its implications for inflation," Public Policy Brief, Federal Reserve Bank of Boston.
    3. Reichlin, Lucrezia & Giannone, Domenico & Small, David, 2005. "Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases," CEPR Discussion Papers 5178, C.E.P.R. Discussion Papers.
    4. Michael Elsby & Bart Hobijn & Ayşegül Şahin, 2013. "On the Importance of the Participation Margin for Market Fluctuations," Working Paper Series 2013-05, Federal Reserve Bank of San Francisco.
    5. Davis, Steven J. & Faberman, R. Jason & Haltiwanger, John, 2012. "Labor market flows in the cross section and over time," Journal of Monetary Economics, Elsevier, vol. 59(1), pages 1-18.
    6. Stephanie Aaronson & Tomaz Cajner & Bruce Fallick & Felix Galbis-Reig & Christopher Smith & William Wascher, 2014. "Labor Force Participation: Recent Developments and Future Prospects," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 45(2 (Fall)), pages 197-275.
    7. Anne E. Polivka & Stephen M. Miller, 1998. "The CPS after the Redesign: Refocusing the Economic Lens," NBER Chapters, in: Labor Statistics Measurement Issues, pages 249-289, National Bureau of Economic Research, Inc.
    8. Thomas J. Sargent & Christopher A. Sims, 1977. "Business cycle modeling without pretending to have too much a priori economic theory," Working Papers 55, Federal Reserve Bank of Minneapolis.
    9. Barnichon, Regis, 2010. "Building a composite Help-Wanted Index," Economics Letters, Elsevier, vol. 109(3), pages 175-178, December.
    10. Craig S. Hakkio & Jonathan L. Willis, 2013. "Assessing labor market conditions: the level of activity and the speed of improvement," Macro Bulletin, Federal Reserve Bank of Kansas City, issue july18, pages 1-2, July.
    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. 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.
    2. Stephanie Aaronson & Tomaz Cajner & Bruce Fallick & Felix Galbis-Reig & Christopher Smith & William Wascher, 2014. "Labor Force Participation: Recent Developments and Future Prospects," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 45(2 (Fall)), pages 197-275.
    3. Albuquerque, Bruno & Baumann, Ursel, 2017. "Will US inflation awake from the dead? The role of slack and non-linearities in the Phillips curve," Journal of Policy Modeling, Elsevier, vol. 39(2), pages 247-271.
    4. L. Ferrara. & G. Sestieri., 2014. "US labour market and monetary policy: current debates and challenges," Quarterly selection of articles - Bulletin de la Banque de France, Banque de France, issue 36, pages 111-129, winter.
    5. Troy Gilchrist & Bart Hobijn, 2021. "The Divergent Signals about Labor Market Slack," FRBSF Economic Letter, Federal Reserve Bank of San Francisco, vol. 2021(15), pages 01-06, June.
    6. Jed Armstrong & Günes Kamber & Özer Karagedikli, 2016. "Developing a labour utilisation composite index for New Zealand," Reserve Bank of New Zealand Analytical Notes series AN2016/04, Reserve Bank of New Zealand.
    7. Salamaliki, Paraskevi, 2019. "Assessing labor market conditions in Greece: a note," MPRA Paper 97559, University Library of Munich, Germany.
    8. Simona Malovaná & Martin Hodula & Jan Frait, 2021. "What Does Really Drive Consumer Confidence?," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 155(3), pages 885-913, June.

    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. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    2. Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2012. "A Quasi–Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1014-1024, November.
    3. Aruoba, S. BoraÄŸan & Diebold, Francis X. & Scotti, Chiara, 2009. "Real-Time Measurement of Business Conditions," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 417-427.
    4. Brandyn Bok & Daniele Caratelli & Domenico Giannone & Argia M. Sbordone & Andrea Tambalotti, 2018. "Macroeconomic Nowcasting and Forecasting with Big Data," Annual Review of Economics, Annual Reviews, vol. 10(1), pages 615-643, August.
    5. Ma, Tao & Zhou, Zhou & Antoniou, Constantinos, 2018. "Dynamic factor model for network traffic state forecast," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 281-317.
    6. Jonas Krampe & Luca Margaritella, 2021. "Factor Models with Sparse VAR Idiosyncratic Components," Papers 2112.07149, arXiv.org, revised May 2022.
    7. Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013. "Now-Casting and the Real-Time Data Flow," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 195-237, Elsevier.
    8. Bragoli, Daniela & Modugno, Michele, 2017. "A now-casting model for Canada: Do U.S. variables matter?," International Journal of Forecasting, Elsevier, vol. 33(4), pages 786-800.
    9. Cimadomo, Jacopo & Giannone, Domenico & Lenza, Michele & Monti, Francesca & Sokol, Andrej, 2022. "Nowcasting with large Bayesian vector autoregressions," Journal of Econometrics, Elsevier, vol. 231(2), pages 500-519.
    10. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    11. Antonello D’Agostino & Domenico Giannone & Michele Lenza & Michele Modugno, 2016. "Nowcasting Business Cycles: A Bayesian Approach to Dynamic Heterogeneous Factor Models," Advances in Econometrics, in: Dynamic Factor Models, volume 35, pages 569-594, Emerald Group Publishing Limited.
    12. George Kapetanios & Massimiliano Marcellino, 2009. "A parametric estimation method for dynamic factor models of large dimensions," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(2), pages 208-238, March.
    13. Modugno, Michele & Soybilgen, Barış & Yazgan, Ege, 2016. "Nowcasting Turkish GDP and news decomposition," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1369-1384.
    14. Jin, Sainan & Miao, Ke & Su, Liangjun, 2021. "On factor models with random missing: EM estimation, inference, and cross validation," Journal of Econometrics, Elsevier, vol. 222(1), pages 745-777.
    15. Antolín-Díaz, Juan & Drechsel, Thomas & Petrella, Ivan, 2024. "Advances in nowcasting economic activity: The role of heterogeneous dynamics and fat tails," Journal of Econometrics, Elsevier, vol. 238(2).
    16. Bork, Lasse, 2009. "Estimating US Monetary Policy Shocks Using a Factor-Augmented Vector Autoregression: An EM Algorithm Approach," Finance Research Group Working Papers F-2009-03, University of Aarhus, Aarhus School of Business, Department of Business Studies.
    17. Stephanie Aaronson & Tomaz Cajner & Bruce Fallick & Felix Galbis-Reig & Christopher Smith & William Wascher, 2014. "Labor Force Participation: Recent Developments and Future Prospects," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 45(2 (Fall)), pages 197-275.
    18. Pilar Poncela & Esther Ruiz, 2016. "Small- Versus Big-Data Factor Extraction in Dynamic Factor Models: An Empirical Assessment," Advances in Econometrics, in: Dynamic Factor Models, volume 35, pages 401-434, Emerald Group Publishing Limited.
    19. Daniel J. Lewis & Karel Mertens & James H. Stock & Mihir Trivedi, 2022. "Measuring real activity using a weekly economic index," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 667-687, June.
    20. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2010. "Are disaggregate data useful for factor analysis in forecasting French GDP?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 132-144.

    More about this item

    Keywords

    LMCI; U.S. labor market; dynamic factor models; unemployment rates; employment;
    All these keywords.

    JEL classification:

    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • E66 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - General Outlook and Conditions
    • J20 - Labor and Demographic Economics - - Demand and Supply of Labor - - - General
    • J6 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:fip:fedcwp:1438. 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: 4D Library (email available below). General contact details of provider: https://edirc.repec.org/data/frbclus.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.