Selected article for: "long short term and machine learning approach"

Author: Karimov, Shyngys; Konings, Jozef
Title: How lockdown causes a missing generation of start-ups and jobs
  • Cord-id: xfmnsvpz
  • Document date: 2021_8_21
  • ID: xfmnsvpz
    Snippet: This paper explores the impact of the COVID-19 lockdown on aggregate employment in Belgium. To this end, we use microdata of all Belgian firms and apply a machine learning-based approach to simulate the impact of the lockdown on employment growth under various economic scenarios. In doing so, we distinguish between start-ups and incumbent firms with both short and long-term effects. In the short term, we expect to see significant losses of employment coming mainly from mature incumbent firms. In
    Document: This paper explores the impact of the COVID-19 lockdown on aggregate employment in Belgium. To this end, we use microdata of all Belgian firms and apply a machine learning-based approach to simulate the impact of the lockdown on employment growth under various economic scenarios. In doing so, we distinguish between start-ups and incumbent firms with both short and long-term effects. In the short term, we expect to see significant losses of employment coming mainly from mature incumbent firms. In the long term, the missing generation of start-ups formed during the lockdown will have a significant and growing effect of slowing down the employment growth even a decade after the lockdown.

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