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

TL;DR

This paper develops a machine learning-based lexicon to measure and analyze corporate human capital disclosures, providing tools and data for researchers to improve understanding and reporting of human capital management.

Contribution

It introduces a comprehensive HC-related lexicon, shares the data and code, and demonstrates how to use these tools for research and analysis of corporate disclosures.

Findings

Created a multidimensional HC lexicon with five subcategories.

Shared datasets, code, and examples for applying the lexicon.

Facilitated future research on HC disclosure and management.

Abstract

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.