Mahfuja Malik, Khawaja Mamun, Syed Muhammad Ishraque Osman
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引用次数: 0
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.
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