Department of Geography, Environment and Spatial Sciences, Michigan State University, East Lansing, Michigan, United States of America.
International School of Economics in Tbilisi (ISET), Tbilisi State University, Tbilisi, Georgia.
PLoS One. 2023 Sep 18;18(9):e0291428. doi: 10.1371/journal.pone.0291428. eCollection 2023.
The fast-changing labor market highlights the need for an in-depth understanding of occupational mobility impacted by technological change. However, we lack a multidimensional classification scheme that considers similarities of occupations comprehensively, which prevents us from predicting employment trends and mobility across occupations. This study fills the gap by examining employment trends based on similarities between occupations.
We first demonstrated a new method that clusters 756 occupation titles based on knowledge, skills, abilities, education, experience, training, activities, values, and interests. We used the Principal Component Analysis to categorize occupations in the Standard Occupational Classification, which is grouped into a four-level hierarchy. Then, we paired the occupation clusters with the occupational employment projections provided by the U.S. Bureau of Labor Statistics. We analyzed how employment would change and what factors affect the employment changes within occupation groups. Particularly, we specified factors related to technological changes.
The results reveal that technological change accounts for significant job losses in some clusters. This poses occupational mobility challenges for workers in these jobs at present. Job losses for nearly 60% of current employment will occur in low-skill, low-wage occupational groups. Meanwhile, many mid-skilled and highly skilled jobs are projected to grow in the next ten years.
Our results demonstrate the utility of our occupational classification scheme. Furthermore, it suggests a critical need for skills upgrading and workforce development for workers in declining jobs. Special attention should be paid to vulnerable workers, such as older individuals and minorities.
快速变化的劳动力市场凸显了深入了解受技术变革影响的职业流动的必要性。然而，我们缺乏一个多维的分类方案，该方案全面考虑职业的相似性，这使得我们无法预测职业间的就业趋势和流动。本研究通过考察基于职业相似性的就业趋势来填补这一空白。
我们首先展示了一种新方法，该方法基于知识、技能、能力、教育、经验、培训、活动、价值观和兴趣对 756 个职业名称进行聚类。我们使用主成分分析对标准职业分类中的职业进行分类，该分类被分为四级层次结构。然后，我们将职业集群与美国劳工统计局提供的职业就业预测相匹配。我们分析了就业将如何变化以及哪些因素会影响职业群体内部的就业变化。特别是，我们指定了与技术变革相关的因素。
结果表明，技术变革导致一些职业集群的就业岗位大量流失。这给目前从事这些工作的劳动者带来了职业流动的挑战。目前，近 60%的就业岗位将流失，主要集中在低技能、低工资的职业群体。与此同时，未来十年，许多中等技能和高技能工作预计将增长。
我们的研究结果证明了我们的职业分类方案的实用性。此外，它表明，对于那些就业岗位不断减少的劳动者，技能提升和劳动力发展的需求非常迫切。应特别关注弱势劳动者，如老年人和少数族裔。
