Does engaging in ESG practices improve banks’ performance in Jordan?

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Assessing Environmental, Social, and Governance (ESG) practices in the banking sector is becoming increasingly important. This study aims to analyze the correlation between ESG scores and the performance of banks. The ESG data were gathered using a Bloomberg database. Using fixed-effect estimation for a static model, this study examines a balanced panel sample of 15 Jordanian-listed banks from 2009 to 2023. Based on multivariate regression, the study outcomes suggest that Jordanian banks with higher ESG scores perform better in operating and market performance. Stakeholder theory supports this. Accordingly, the R2 values for the study models were 23.9% for the ROA model and 18.7% for Tobin’s Q, respectively, showing the high explanatory power of both models. Therefore, an increase of one point in ESG scores leads to a corresponding rise in ROA and Tobin’s Q 0.496 and 0.370, respectively. Regarding control variables, leverage has a negative correlation coefficient of –0.169 and –0.253, respectively, in both the ROA and Tobin’s Q models. According to the ROA model, a one-unit increase in bank size leads to a 0.309-unit increase in bank performance and a 0.115-unit increase, according to Tobin’s Q model. Similarly, as the bank ages by one year, its performance improves, with the ROA and Tobin’s Q models showing increases of 0.216 and 0.116 units, respectively. Additionally, the financial development showed correlation coefficients of 0.108 and 0.045 for the ROA and Tobin’s Q models, respectively. However, the ESG committee does not affect the performance of banks.

Acknowledgment
This research was funded through the annual funding track by the Deanship of Scientific Research, from the Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [Grant NO. KFU242703].

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    • Table 1. Variables description & definitions
    • Table 2. Descriptive statistics
    • Table 3. Correlation analysis
    • Table 4. Estimation results for ROA & Tobin’s Q models
    • Table 5. Estimation results for ROA & Tobin’s Q models without control variables
    • Table 6. Estimation results for ROE model
    • Conceptualization
      Marwan Mansour, Mo’taz Al Zobi, Ibrahim Alnohoud, Almothanna Abu Allan
    • Data curation
      Marwan Mansour, Mo’taz Al Zobi, Ibrahim Alnohoud, Almothanna Abu Allan, Abedulwale Khassawneh, Mohamed Saad
    • Formal Analysis
      Marwan Mansour, Ibrahim Alnohoud
    • Funding acquisition
      Marwan Mansour, Mo’taz Al Zobi, Ibrahim Alnohoud, Almothanna Abu Allan, Abedulwale Khassawneh, Mohamed Saad
    • Investigation
      Marwan Mansour, Almothanna Abu Allan, Abedulwale Khassawneh, Mohamed Saad
    • Methodology
      Marwan Mansour, Mohamed Saad
    • Resources
      Marwan Mansour, Mo’taz Al Zobi, Ibrahim Alnohoud, Almothanna Abu Allan, Abedulwale Khassawneh, Mohamed Saad
    • Validation
      Marwan Mansour, Ibrahim Alnohoud, Abedulwale Khassawneh, Mohamed Saad
    • Visualization
      Marwan Mansour, Mo’taz Al Zobi, Abedulwale Khassawneh, Mohamed Saad
    • Writing – original draft
      Marwan Mansour
    • Writing – review & editing
      Marwan Mansour, Mo’taz Al Zobi, Ibrahim Alnohoud, Almothanna Abu Allan, Abedulwale Khassawneh, Mohamed Saad
    • Project administration
      Mo’taz Al Zobi, Ibrahim Alnohoud, Almothanna Abu Allan, Abedulwale Khassawneh, Mohamed Saad
    • Software
      Mo’taz Al Zobi, Ibrahim Alnohoud, Abedulwale Khassawneh, Mohamed Saad
    • Supervision
      Mo’taz Al Zobi, Almothanna Abu Allan, Mohamed Saad