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Measuring Corporate Human Capital Disclosures: Lexicon, Data, Code, and Research Opportunities

arXiv 2506.10155 ↗First posted 11 Jun 2025cs.CL cs.AI cs.LGcited by 0

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Abstract (arXiv)

Human capital (HC) is increasingly important to corporate value creation. Unlike other assets, however, HC is not currently subject to well-defined measurement or disclosure rules. We use a machine learning algorithm (word2vec) trained on a confirmed set of HC disclosures to develop a comprehensive list of HC-related keywords classified into five subcategories (DEI; health and safety; labor relations and culture; compensation and benefits; and demographics and other) that capture the multidimensional nature of HC management. We share our lexicon, corporate HC disclosures, and the Python code used to develop the lexicon, and we provide detailed examples of using our data and code, including for fine-tuning a BERT model. Researchers can use our HC lexicon (or modify the code to capture another construct of interest) with their samples of corporate communications to address pertinent HC questions. We close with a discussion of future research opportunities related to HC management and disclosure.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 56 references that are in the map. First posted on arXiv 11 Jun 2025.

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References · 4

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
  3. 2021ClimateBert: A Pretrained Language Model for Climate-Related Textcited by 90
  4. 2021"Garbage In, Garbage Out" Revisited: What Do Machine Learning Application Papers Report About Human-Labeled Training Data?cited by 24

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