The United States labor market exhibits a persistent coexistence of high job vacancy rates and prolonged unemployment duration, a pattern that standard labor market theory struggles to explain. This paper argues that a non-trivial portion of contemporary frictional unemployment is artificially induced by automated recruitment systems that rely on deterministic keyword-based screening. Drawing on labor economics, information asymmetry theory, and prior work on algorithmic hiring, we formalize this phenomenon as artificial frictional unemployment arising from semantic misinterpretation of candidate competencies. We evaluate this claim using controlled simulations that compare legacy keyword-based screening with semantic matching based on high-dimensional vector representations of resumes and job descriptions. The results demonstrate substantial improvements in recall and overall matching efficiency without a corresponding loss in precision. Building on these findings, the paper proposes a candidate-side workforce operating architecture that standardizes, verifies, and semantically aligns human capital signals while remaining interoperable with existing recruitment infrastructure. The findings highlight the economic costs of outdated hiring systems and the potential gains from improving semantic alignment in labor market matching.

翻译：美国劳动力市场持续呈现出高职位空缺率与长期失业持续时间并存的现象，这一模式是标准劳动力市场理论难以解释的。本文认为，当代摩擦性失业中有相当一部分是由依赖确定性关键词筛选的自动化招聘系统人为导致的。借鉴劳动经济学、信息不对称理论以及先前关于算法招聘的研究，我们将此现象形式化为因对候选人能力的语义误解而产生的人为摩擦性失业。我们通过对比传统关键词筛选与基于简历和职位描述的高维向量表示的语义匹配的受控模拟来评估这一主张。结果表明，在召回率和整体匹配效率方面均有显著提升，且未伴随精确度的相应损失。基于这些发现，本文提出了一种候选人侧劳动力运营架构，该架构能够标准化、验证并语义对齐人力资本信号，同时保持与现有招聘基础设施的互操作性。研究结果凸显了过时招聘系统的经济成本，以及通过改善劳动力市场匹配中的语义对齐所能获得的潜在收益。