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.

中文翻译：

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腐败控制是否提高了 ESG 诱导的公司价值？来自机器学习分析的见解


本研究采用先进的因果机器学习 （ML） 技术来调查国家层面的腐败对公司环境、社会和治理 （ESG） 绩效市场估值的影响。通过采用双重去偏机器学习 （DML） 和线性回归分析，我们发现 ESG 绩效对公司价值产生积极影响。这种正向关系对于在腐败水平较低的国家运营的公司来说更为明显。DML 的使用增强了效果识别，并产生了与线性回归得出的结果密切相关的结果，从而为腐败控制在提高 ESG 诱导的公司价值方面的关键作用提供了强有力的支持。