根据与工人档案的接近程度对职业进行排序。

Q1 Mathematics Swiss Journal of Economics and Statistics Pub Date : 2024-01-01 Epub Date: 2024-07-25 DOI:10.1186/s41937-024-00125-2
Mirjam Bächli, Hélène Benghalem, Doriana Tinello, Damaris Aschwanden, Sascha Zuber, Matthias Kliegel, Michele Pellizzari, Rafael Lalive
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引用次数: 0

摘要

信息摩擦使得求职者很难找到新的就业机会。我们提出了一种方法,根据职业与求职者个人资料的接近程度进行排序,从而提供针对个人的职业推荐。我们确定了一组十二项技能、能力和工作方式,这些技能、能力和工作方式捕捉到了所有职业以工人为导向的要求,并讨论了如何利用在线问题和任务来衡量这些项目。我们使用与工人相关的测量项目与职业要求之间的欧氏距离来衡量求职者与职业之间的接近程度。我们的研究表明,求职者的个人资料与其失业前的职业之间的接近程度可以预测他们更换职业的意向,从而表明我们的方法捕捉到了有意义的不匹配概念。我们还表明,我们的方法所产生的推荐与错配求职者之前的职业不同,从而有可能扩大他们的搜索范围。
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Ranking occupations by their proximity to workers' profiles.

Information friction makes it difficult for job seekers to find new employment opportunities. We propose a method for providing individual-specific occupation recommendations by ranking occupations based on their proximity to the worker's profile. We identify a set of twelve skills, abilities and work styles that capture the worker-oriented requirements of all occupations and discuss how to measure these items using online questions and tasks. We use the Euclidean distance between the measured items pertaining to a worker and the requirements of an occupation to measure the proximity between job seekers and occupations. We show that the proximity between job seekers' profiles and their preunemployment occupation predicts their intention to change occupations, thus suggesting that our method captures a meaningful conceptualization of mismatch. We also show that our method generates recommendations that differ from the previous occupations of mismatched job seekers, thereby potentially expanding their search scope.

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来源期刊
Swiss Journal of Economics and Statistics
Swiss Journal of Economics and Statistics Mathematics-Statistics and Probability
CiteScore
5.20
自引率
0.00%
发文量
18
审稿时长
15 weeks
期刊最新文献
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