IDEAS home Printed from https://ideas.repec.org/a/taf/rsocec/v79y2021i2p380-412.html
   My bibliography  Save this article

What is structural about unemployment in OECD countries?

Author

Listed:
  • Philipp Heimberger

Abstract

While established estimates of ‘structural’ unemployment are regularly assumed to be a valid proxy for their unobservable theoretical postulate, this paper sets out to study their actual econometric determinants. Based on a data set for 23 OECD countries over the time period 1985–2013, the panel regression results suggest that standard institutional labor market indicators – such as employment protection legislation, trade union density, tax wedge, minimum wages – largely underperform in explaining measures of ‘structural’ unemployment, but macroeconomic factors – in particular capital accumulation, but also the long-term real interest rate – are essential determinants. The available macroeconometric evidence does not support the view that labor market institutions are at the heart of increased ‘structural’ unemployment in OECD economies. To understand the development of unemployment in OECD countries, researchers and policy-makers should primarily consider macroeconomic factors and focus on capital accumulation.

Suggested Citation

  • Philipp Heimberger, 2021. "What is structural about unemployment in OECD countries?," Review of Social Economy, Taylor & Francis Journals, vol. 79(2), pages 380-412, April.
    • Handle: RePEc:taf:rsocec:v:79:y:2021:i:2:p:380-412
    DOI: 10.1080/00346764.2019.1678067
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00346764.2019.1678067
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00346764.2019.1678067?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
    ---><---

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

    Citations

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


    Cited by:

    1. Mihai Mutascu & Scott W. Hegerty, 2023. "Predicting the contribution of artificial intelligence to unemployment rates: an artificial neural network approach," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 47(2), pages 400-416, June.
    2. Chletsos, Michael & Sintos, Andreas, 2023. "The effects of IMF conditional programs on the unemployment rate," European Journal of Political Economy, Elsevier, vol. 76(C).

    More about this item

    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:taf:rsocec:v:79:y:2021:i:2:p:380-412. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RRSE20 .

    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.