Navigating ESG rating divergence: Implications for labor investment efficiency and firm adaptation strategy

Liangpeng Wu, Yujing Tang, Lei Meng, Qingyuan Zhu and Dequn Zhou

Global Finance Journal, 2025, vol. 67, issue C

Abstract: In today's business environment, information asymmetry significantly impedes improvements in corporate labor investment efficiency (LIE), whereas high-quality environmental, social, and governance (ESG) disclosures are widely regarded as an effective means to reduce this issue. However, the rapid expansion of ESG rating agencies has led to considerable discrepancies in ESG evaluations, with the same firm often receiving divergent scores, even on identical indicators, from different agencies. The consequences of such inconsistencies remain underexplored. This study examines the effects of ESG rating divergence (ESGRD) on LIE, as well as the adaptive strategies firms adopt in response. We employ a double machine learning model using ESG ratings from six leading agencies, combined with panel data from Chinese A-share listed companies spanning 2003 to 2021. Our findings indicate that ESGRD has a significant short-term positive effect on LIE. In response, firms tend to increase their debt levels as a short-term coping strategy rather than reduce their workforce. Specifically, for every 1 % increase in ESGRD, firms in the 75–100th percentile range increase their liabilities by approximately 76,800 yuan, whereas those in the 50–75th percentile range increase liabilities by about 31,100 yuan. Moreover, the impact of ESGRD on LIE exhibits substantial heterogeneity across firms with different characteristics.

Keywords: Environmental, social, governance; ESG; ESG rating divergence; Labor investment efficiency; Firm adaptation; Double machine learning; DML (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:glofin:v:67:y:2025:i:c:s1044028325000687

DOI: 10.1016/j.gfj.2025.101141

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