IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-04273887.html

Predicting the contribution of artificial intelligence to unemployment rates: an artificial neural network approach

Author

Listed:
  • Mihai Mutascu

    (LEO - Laboratoire d'Économie d'Orleans [2022-...] - UO - Université d'Orléans - UT - Université de Tours - UCA - Université Clermont Auvergne, Zeppelin University, UVT - Universitatea de Vest din Timișoara [România] = West University of Timișoara [Romania] = Université Ouest de Timișoara [Roumanie])

  • Scott Hegerty

    (Northeastern Illinois University)

Abstract

As technological innovations gain the capacity to replace human labour, it is increasingly possible that artificial intelligence can lead to higher unemployment rates. This paper is devoted to forecasting unemployment that is based on artificial intelligence as an input of interest by using an artificial neural network learning process. The simulation is performed based on a sample including 23 of the most high-tech and developed economies, over the period from 1998 to 2016. The proposed artificial neural network with one layer and 10 neurons offers good results in terms of unemployment prediction, with an overall coefficient of determination of 0.912. Artificial intelligence input is a top contributor to the prediction of unemployment, along with foreign direct investment, total population, labour productivity, and lagged unemployment. Inflation and government size register a modest contribution. This suggests that forecasts that include this new variable will be more accurate.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Mihai Mutascu & Scott Hegerty, 2023. "Predicting the contribution of artificial intelligence to unemployment rates: an artificial neural network approach," Post-Print hal-04273887, HAL.
    • Handle: RePEc:hal:journl:hal-04273887
    DOI: 10.1007/s12197-023-09616-z
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    Other versions of this item:

    More about this item

    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
    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

    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:hal:journl:hal-04273887. 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.