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Does corruption control enhance ESG-induced firm value? Insights from machine learning analysis

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  • Malik, Mahfuja
  • Mamun, Khawaja
  • Osman, Syed Muhammad Ishraque

Abstract

This study adopts advanced causal machine learning (ML) techniques to investigate the impact of country-level corruption on the market valuation of firms’ environmental, social, and governance (ESG) performance. By employing double-debiased machine learning (DML) and linear regression analysis, we find that ESG performance positively influences firm value. This positive relationship is more pronounced for firms operating in countries with lower levels of corruption. The use of DML enhances effect identification and yields findings that closely align with those derived from linear regression, thereby providing robust support for the pivotal role of corruption control in enhancing ESG-induced firm value.

Suggested Citation

  • Malik, Mahfuja & Mamun, Khawaja & Osman, Syed Muhammad Ishraque, 2025. "Does corruption control enhance ESG-induced firm value? Insights from machine learning analysis," Finance Research Letters, Elsevier, vol. 72(C).
    • Handle: RePEc:eee:finlet:v:72:y:2025:i:c:s1544612324016015
    DOI: 10.1016/j.frl.2024.106572
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    More about this item

    Keywords

    Machine learning; Causal inferences; ESG performance; Firm value; Corruption control;
    All these keywords.

    JEL classification:

    • M14 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Corporate Culture; Diversity; Social Responsibility
    • M48 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Government Policy and Regulation
    • H80 - Public Economics - - Miscellaneous Issues - - - General

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