IDEAS home Printed from https://ideas.repec.org/a/wly/japmet/v39y2024i3p462-480.html
   My bibliography  Save this article

Advance layoff notices and aggregate job loss

Author

Listed:
  • Pawel M. Krolikowski
  • Kurt G. Lunsford

Abstract

We collect data from Worker Adjustment and Retraining Notification (WARN) Act notices and establish their usefulness as an indicator of aggregate job loss. The number of workers affected by WARN notices (“WARN layoffs”) leads state‐level initial unemployment insurance claims and unemployment rate (UR) and private employment changes. WARN layoffs comove with aggregate layoffs from Mass Layoff Statistics and the Job Openings and Labor Turnover Survey but are timelier and cover a longer sample. In a vector autoregression, changes in WARN layoffs lead UR changes and job separations. Finally, they improve pseudo real‐time forecasts of the UR and private employment changes.

Suggested Citation

  • Pawel M. Krolikowski & Kurt G. Lunsford, 2024. "Advance layoff notices and aggregate job loss," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 462-480, April.
  • Handle: RePEc:wly:japmet:v:39:y:2024:i:3:p:462-480
    DOI: 10.1002/jae.3032
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/jae.3032
    Download Restriction: no

    File URL: https://libkey.io/10.1002/jae.3032?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. Runkle, David E, 1987. "Vector Autoregressions and Reality," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(4), pages 437-442, October.
    2. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    3. Rebecca M. Blank & David E. Card, 1991. "Recent Trends in Insured and Uninsured Unemployment: Is There an Explanation?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 106(4), pages 1157-1189.
    4. Melvyn G. Coles & Ali Moghaddasi Kelishomi, 2018. "Do Job Destruction Shocks Matter in the Theory of Unemployment?," American Economic Journal: Macroeconomics, American Economic Association, vol. 10(3), pages 118-136, July.
    5. Robert Shimer, 2012. "Reassessing the Ins and Outs of Unemployment," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 15(2), pages 127-148, April.
    6. Gary Solon & Ryan Michaels & Michael W. L. Elsby, 2009. "The Ins and Outs of Cyclical Unemployment," American Economic Journal: Macroeconomics, American Economic Association, vol. 1(1), pages 84-110, January.
    7. Gary Solon & Steven J. Haider & Jeffrey M. Wooldridge, 2015. "What Are We Weighting For?," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 301-316.
    8. Barnichon, Regis, 2012. "Vacancy posting, job separation and unemployment fluctuations," Journal of Economic Dynamics and Control, Elsevier, vol. 36(3), pages 315-330.
    9. Domenico Ferraro, 2018. "The Asymmetric Cyclical Behavior of the U.S. Labor Market," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 30, pages 145-162, October.
    10. Marta Bańbura & Michele Modugno, 2014. "Maximum Likelihood Estimation Of Factor Models On Datasets With Arbitrary Pattern Of Missing Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(1), pages 133-160, January.
    11. Danny Pfeffermann & Richard Tiller, 2005. "Bootstrap Approximation to Prediction MSE for State–Space Models with Estimated Parameters," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(6), pages 893-916, November.
    12. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November.
    13. 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.
    14. Camacho Maximo & Lovcha Yuliya & Quiros Gabriel Perez, 2015. "Can we use seasonally adjusted variables in dynamic factor models?," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(3), pages 377-391, June.
    15. Domenico Ferraro, 2018. "The Asymmetric Cyclical Behavior of the U.S. Labor Market," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 30, pages 145-162, October.
    16. Runkle, David E, 1987. "Vector Autoregressions and Reality: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(4), pages 454-454, October.
    17. Steven J. Davis, 2008. "The Decline of Job Loss and Why It Matters," American Economic Review, American Economic Association, vol. 98(2), pages 263-267, May.
    18. Raffaella Giacomini & Barbara Rossi, 2010. "Forecast comparisons in unstable environments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 595-620.
    19. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    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. Leland D. Crane & Emily Green & Molly Harnish & Will McClennan & Paul E. Soto & Betsy Vrankovich & Jacob Williams, 2024. "Tracking Real Time Layoffs with SEC Filings: A Preliminary Investigation," Finance and Economics Discussion Series 2024-020, Board of Governors of the Federal Reserve System (U.S.).

    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. Francisco Lasso-Valderrama & Héctor M. Zárate-Solano, 2019. "Forecasting the Colombian Unemployment Rate Using Labour Force Flows," Borradores de Economia 1073, Banco de la Republica de Colombia.
    2. Barnichon, Regis & Garda, Paula, 2016. "Forecasting unemployment across countries: The ins and outs," European Economic Review, Elsevier, vol. 84(C), pages 165-183.
    3. Barbara Rossi, 2019. "Forecasting in the presence of instabilities: How do we know whether models predict well and how to improve them," Economics Working Papers 1711, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2021.
    4. Christian Glocker & Serguei Kaniovski, 2022. "Macroeconometric forecasting using a cluster of dynamic factor models," Empirical Economics, Springer, vol. 63(1), pages 43-91, July.
    5. Byron Botha & Tim Olds & Geordie Reid & Daan Steenkamp & Rossouw van Jaarsveld, 2021. "Nowcasting South African gross domestic product using a suite of statistical models," South African Journal of Economics, Economic Society of South Africa, vol. 89(4), pages 526-554, December.
    6. Simona Delle Chiaie & Laurent Ferrara & Domenico Giannone, 2022. "Common factors of commodity prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 461-476, April.
    7. Nikoleta Anesti & Ana Beatriz Galvão & Silvia Miranda‐Agrippino, 2022. "Uncertain Kingdom: Nowcasting Gross Domestic Product and its revisions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 42-62, January.
    8. Byron Botha & Geordie Reid & Tim Olds & Daan Steenkamp & Rossouw van Jaarsveld, 2021. "Nowcasting South African GDP using a suite of statistical models," Working Papers 11001, South African Reserve Bank.
    9. Hendry, David F. & Hubrich, Kirstin, 2006. "Forecasting economic aggregates by disaggregates," Working Paper Series 589, European Central Bank.
    10. Mogliani, Matteo & Darné, Olivier & Pluyaud, Bertrand, 2017. "The new MIBA model: Real-time nowcasting of French GDP using the Banque de France's monthly business survey," Economic Modelling, Elsevier, vol. 64(C), pages 26-39.
    11. Faust, Jon & Wright, Jonathan H., 2013. "Forecasting Inflation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 2-56, Elsevier.
    12. Norman R. Swanson & Weiqi Xiong & Xiye Yang, 2020. "Predicting interest rates using shrinkage methods, real‐time diffusion indexes, and model combinations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(5), pages 587-613, August.
    13. Eleni Kalamara & Arthur Turrell & Chris Redl & George Kapetanios & Sujit Kapadia, 2022. "Making text count: Economic forecasting using newspaper text," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 896-919, August.
    14. 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.
    15. Régis Barnichon, 2009. "Demand-driven job separation: reconciling search models with the ins and outs of unemployment," Finance and Economics Discussion Series 2009-24, Board of Governors of the Federal Reserve System (U.S.).
    16. Laura Coroneo & Fabrizio Iacone, 2020. "Comparing predictive accuracy in small samples using fixed‐smoothing asymptotics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(4), pages 391-409, June.
    17. Cepni, Oguzhan & Clements, Michael P., 2024. "How local is the local inflation factor? Evidence from emerging European countries," International Journal of Forecasting, Elsevier, vol. 40(1), pages 160-183.
    18. Reichenbacher, Michael & Schuster, Philipp, 2022. "Size-adapted bond liquidity measures and their asset pricing implications," Journal of Financial Economics, Elsevier, vol. 146(2), pages 425-443.
    19. Eric Hillebrand & Jakob Guldbæk Mikkelsen & Lars Spreng & Giovanni Urga, 2023. "Exchange rates and macroeconomic fundamentals: Evidence of instabilities from time‐varying factor loadings," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(6), pages 857-877, September.
    20. Albis, Manuel Leonard F. & Mapa, Dennis S., 2014. "Bayesian Averaging of Classical Estimates in Asymmetric Vector Autoregressive (AVAR) Models," MPRA Paper 55902, University Library of Munich, Germany.

    More about this item

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • J63 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Turnover; Vacancies; Layoffs
    • J65 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment Insurance; Severance Pay; Plant Closings

    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:wly:japmet:v:39:y:2024:i:3:p:462-480. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/0883-7252/ .

    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.