根据其与工人的个人资料的接近程度对职业进行排名
Mirjam Bächli1, Hélène Benghalem1, Doriana Tinello2
1Department of Economics, University of Lausanne, Lausanne, Switzerland.
Swiss journal of economics and statistics
|July 29, 2024
概括
求职者面临的信息摩擦. 这项研究引入了个性化职业推的新方法,利用技能和能力的接近性来扩大求职范围并减少不匹配.
科学领域:
- 劳动经济学 劳动经济学
- 职业科学 职业科学 职业科学
- 人与计算机的交互
背景情况:
- 信息摩擦阻碍了有效的求职和职业转型.
- 现有的职业推方法可能无法充分捕捉个人特定需求和个人资料.
- 了解工人与职业的接近性对于改善就业结果至关重要.
研究的目的:
- 开发和验证一种产生个人特定职业建议的方法.
- 量化求职者的个人资料与职业要求之间的接近程度.
- 评估拟议方法在预测改变工作意图和扩大搜索范围方面的有效性.
主要方法:
- 确定适用于所有职业的十二个关键面向工人的要求 (技能,能力,工作方式).
- 通过在线问题和任务来测量这些需求,以创建个别工人的个人资料.
- 计算工人个人资料和职业要求之间的欧几里德距离,以确定接近度.
- 通过将其与求职者改变职业的意图相关联,验证近距离测量.
主要成果:
- 求职者的个人资料与他们以前的职业之间的近距离测量成功预测了他们改变工作的意图,这表明有意义地捕捉了职业不匹配.
- 拟议的方法产生了与以前的不匹配求职者的角色不同的职业建议.
- 这表明该方法可以有效地确定合适的替代职业道路.
结论:
- 开发的方法为个性化职业建议提供了数据驱动的方法,解决了就业市场的信息摩擦.
- 通过量化员工和职业的近距离,该系统可以帮助求职者发现超越他们直接经验的相关机会.
- 这种方法有可能扩大求职策略,改善就业匹配结果.
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