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Forecasting performance of workforce reskilling programmes

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  • Evan Hurwitz
  • George Cevora

Abstract

Estimating success rates for programmes aiming to reintegrate theunemployed into the workforce is essential for good stewardship of publicfinances. At the current moment, the methods used for this task arebased on the historical performance of comparable programmes. In lightof Brexit and Covid-19 simultaneously causing a shock to the labourmarket in the UK we developed an estimation method that is basedon fundamental factors involved - workforce demand and supply - asopposed to the historical values which are quickly becoming irrelevant.With an average error of 3.9% of the re-integration success rate, ourmodel outperforms the best benchmark known to us by 53%

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  • Evan Hurwitz & George Cevora, 2021. "Forecasting performance of workforce reskilling programmes," Papers 2107.10001, arXiv.org.
    • Handle: RePEc:arx:papers:2107.10001
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    References listed on IDEAS

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    1. Fabian Stephany & Otto Kassi & Uma Rani & Vili Lehdonvirta, 2021. "Online Labour Index 2020: New ways to measure the world's remote freelancing market," Papers 2105.09148, arXiv.org, revised Jun 2021.
    2. Adrian Chadi, 2010. "How to Distinguish Voluntary from Involuntary Unemployment: On the Relationship between the Willingness to Work and Unemployment‐Induced Unhappiness," Kyklos, Wiley Blackwell, vol. 63(3), pages 317-329, August.
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