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Cyclicality of labour market search: a new big data approach

This paper exploits big data on online activity from the job exchange of the German Federal Employment Agency and its internal placement-software to generate measures for search activity of employers and job seekers and—as a novel feature—for placement activity of employment agencies. In addition, t...

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Autor principal: Hutter, Christian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7826156/
https://www.ncbi.nlm.nih.gov/pubmed/33521508
http://dx.doi.org/10.1186/s12651-020-00283-9
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author Hutter, Christian
author_facet Hutter, Christian
author_sort Hutter, Christian
collection PubMed
description This paper exploits big data on online activity from the job exchange of the German Federal Employment Agency and its internal placement-software to generate measures for search activity of employers and job seekers and—as a novel feature—for placement activity of employment agencies. In addition, the average search perimeter in the job seekers’ search profiles can be measured. The data are used to estimate the behaviour of the search and placement activities during the business and labour market cycle and their seasonal patterns. The results show that the search activities of firms and employment agencies are procyclical. By contrast, job seekers’ search intensity shows a countercyclical pattern, at least before the COVID-19 crisis.
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spelling pubmed-78261562021-01-25 Cyclicality of labour market search: a new big data approach Hutter, Christian J Labour Mark Res Original Article This paper exploits big data on online activity from the job exchange of the German Federal Employment Agency and its internal placement-software to generate measures for search activity of employers and job seekers and—as a novel feature—for placement activity of employment agencies. In addition, the average search perimeter in the job seekers’ search profiles can be measured. The data are used to estimate the behaviour of the search and placement activities during the business and labour market cycle and their seasonal patterns. The results show that the search activities of firms and employment agencies are procyclical. By contrast, job seekers’ search intensity shows a countercyclical pattern, at least before the COVID-19 crisis. Springer Berlin Heidelberg 2021-01-23 2021 /pmc/articles/PMC7826156/ /pubmed/33521508 http://dx.doi.org/10.1186/s12651-020-00283-9 Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Original Article
Hutter, Christian
Cyclicality of labour market search: a new big data approach
title Cyclicality of labour market search: a new big data approach
title_full Cyclicality of labour market search: a new big data approach
title_fullStr Cyclicality of labour market search: a new big data approach
title_full_unstemmed Cyclicality of labour market search: a new big data approach
title_short Cyclicality of labour market search: a new big data approach
title_sort cyclicality of labour market search: a new big data approach
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7826156/
https://www.ncbi.nlm.nih.gov/pubmed/33521508
http://dx.doi.org/10.1186/s12651-020-00283-9
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