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The impact of corruption on commercial banks' credit risk: Evidence from a panel quantile regression

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  • Hatice Jenkins
  • Ezuldeen Alshareef
  • Amer Mohamad

Abstract

This study explores the impact of countrywide corruption on the credit risk of commercial banks with different levels of credit risk. It applies the quantile regression (QR) estimation method for a panel data of 191 commercial banks, from 18 MENAP countries, between the years 2011–2018. The research finding indicates that corruption significantly exacerbates the problem of bad loans of banks. Furthermore, the QR results reveal that corruption does not affect all banks at the same level. Banks in higher quantiles (i.e., higher credit risk banks) appear to be affected more than the ones in lower quantiles (i.e., lower credit risk banks).Banks with high credit risk appear to be more vulnerable to corruption than banks with low credit risk.

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  • Hatice Jenkins & Ezuldeen Alshareef & Amer Mohamad, 2023. "The impact of corruption on commercial banks' credit risk: Evidence from a panel quantile regression," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1364-1375, April.
    • Handle: RePEc:wly:ijfiec:v:28:y:2023:i:2:p:1364-1375
    DOI: 10.1002/ijfe.2481
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