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Impacts of the COVID‐19 outbreak on older‐age cohorts in European Labor Markets: A machine learning exploration of vulnerable groups
We identify vulnerable groups through the examination of their employment status in the face of the initial coronavirus disease 2019 (COVID‐19) shock through the application of tree‐based ensemble machine learning algorithms on a sample of individuals over 50 years old. The present study elaborates...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9115518/ http://dx.doi.org/10.1111/rsp3.12520 |
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author | Celbiş, Mehmet Güney Wong, Pui‐hang Kourtit, Karima Nijkamp, Peter |
author_facet | Celbiş, Mehmet Güney Wong, Pui‐hang Kourtit, Karima Nijkamp, Peter |
author_sort | Celbiş, Mehmet Güney |
collection | PubMed |
description | We identify vulnerable groups through the examination of their employment status in the face of the initial coronavirus disease 2019 (COVID‐19) shock through the application of tree‐based ensemble machine learning algorithms on a sample of individuals over 50 years old. The present study elaborates on the findings through various interpretable machine learning techniques, namely Shapley values, individual conditional expectations, partial dependences, and variable importance scores. The structure of the data obtained from the Survey of Health, Aging and Retirement in Europe (SHARE) dataset enables us to specifically observe the before versus the after effects of the pandemic shock on individual job status in spatial labor markets. We identify small but distinct subgroups that may require particular policy interventions. We find that the persons in these groups are prone to pandemic‐related job loss owing to different sets of individual‐level factors such as employment type and sector, age, education, and prepandemic health status in addition to location‐specific factors such as drops in mobility and stringency policies affecting particular regions or countries. |
format | Online Article Text |
id | pubmed-9115518 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91155182022-05-18 Impacts of the COVID‐19 outbreak on older‐age cohorts in European Labor Markets: A machine learning exploration of vulnerable groups Celbiş, Mehmet Güney Wong, Pui‐hang Kourtit, Karima Nijkamp, Peter Regional Science Policy & Practice Original Articles We identify vulnerable groups through the examination of their employment status in the face of the initial coronavirus disease 2019 (COVID‐19) shock through the application of tree‐based ensemble machine learning algorithms on a sample of individuals over 50 years old. The present study elaborates on the findings through various interpretable machine learning techniques, namely Shapley values, individual conditional expectations, partial dependences, and variable importance scores. The structure of the data obtained from the Survey of Health, Aging and Retirement in Europe (SHARE) dataset enables us to specifically observe the before versus the after effects of the pandemic shock on individual job status in spatial labor markets. We identify small but distinct subgroups that may require particular policy interventions. We find that the persons in these groups are prone to pandemic‐related job loss owing to different sets of individual‐level factors such as employment type and sector, age, education, and prepandemic health status in addition to location‐specific factors such as drops in mobility and stringency policies affecting particular regions or countries. John Wiley and Sons Inc. 2022-03-21 /pmc/articles/PMC9115518/ http://dx.doi.org/10.1111/rsp3.12520 Text en © 2022 The Authors. Regional Science Policy & Practice published by John Wiley & Sons Ltd on behalf of Regional Science Association International. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Original Articles Celbiş, Mehmet Güney Wong, Pui‐hang Kourtit, Karima Nijkamp, Peter Impacts of the COVID‐19 outbreak on older‐age cohorts in European Labor Markets: A machine learning exploration of vulnerable groups |
title | Impacts of the COVID‐19 outbreak on older‐age cohorts in European Labor Markets: A machine learning exploration of vulnerable groups |
title_full | Impacts of the COVID‐19 outbreak on older‐age cohorts in European Labor Markets: A machine learning exploration of vulnerable groups |
title_fullStr | Impacts of the COVID‐19 outbreak on older‐age cohorts in European Labor Markets: A machine learning exploration of vulnerable groups |
title_full_unstemmed | Impacts of the COVID‐19 outbreak on older‐age cohorts in European Labor Markets: A machine learning exploration of vulnerable groups |
title_short | Impacts of the COVID‐19 outbreak on older‐age cohorts in European Labor Markets: A machine learning exploration of vulnerable groups |
title_sort | impacts of the covid‐19 outbreak on older‐age cohorts in european labor markets: a machine learning exploration of vulnerable groups |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9115518/ http://dx.doi.org/10.1111/rsp3.12520 |
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