基于就业存续关系的工人-企业匹配质量度量研究
Human Capital in Context: What Employment Survival Reveals About Worker-Firm Match
AI 导读
一篇 arXiv 论文提出用双塔神经网络从就业关系的存续中学习工人-企业匹配质量，将双方职业历史与企业特征嵌入同一空间，以余弦相似度度量匹配程度。模型基于覆盖 1.01 亿工人和 840 万企业的超十亿人期数据估计，匹配质量提升 0.1 可使离职几率降低 48%。匹配更好的工人收入更高，员工匹配度高的企业增长更快、更盈利，且专利更多、更有价值。
正文 · 原文
Abstract:There is no best worker in general, only a best worker for a particular firm. We develop a measure of worker-firm match quality that treats fit as a property of the pair, learned from the survival of employment relationships. A two-tower neural network embeds workers, from their career histories and current positions, and firms, from their characteristics and business descriptions, into a common space, and match quality is the cosine similarity of the two vectors. The measure takes its functional form from the skill-weights model of firm-specific human capital, and its only estimated objective, a separation hazard, from the view of the job match as an experience good. Estimated on more than a billion person-periods covering 101 million workers and 8.4 million firms, a 0.1 increase in match quality lowers the odds of separation by 48%. About a quarter of the variance of match quality is specific to the worker-firm pair, which worker-, firm-, and occupation-level measures set to zero by construction. Workers earn more when better matched, especially early in a relationship, and firms whose workforces score higher grow faster, become more profitable, and are issued more, and more valuable, patents over the next five years than their industry peers. Because it scores pairs that never formed, the measure also predicts where movers go, and acquirers record a larger share of the price as goodwill when the target's workers would fit the acquirer better than they fit the target.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); General Economics (econ.GN) |
| Cite as: | arXiv:2611.16058 [cs.LG] |
| (or arXiv:2611.16058v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2611.16058 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jungho Choi [view email]
[v1]
Wed, 11 Nov 2026 22:13:36 UTC (64 KB)
来源：arXiv cs.AI · arxiv.org