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

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.

The Effect of Financial Inclusion and Competitiveness on Financial Stability: Why Financial Regulation Matters in Developing Countries?

“…Competitiveness drives banks to take more risks and increases the availability of financial resources in the economy (Albaity et al 2019). Authors such as Albaity et al (2019), Jenkins et al (2021), and Klapper and Lusardi (2020) use banks' defaulter loans divided by total loans in their studies to measure credit risk and point out bad credit loans as the main factor for bank instability. We will use the same variable to represent credit risk.…”

“…As for banks' profitability, we use return on equity (ROE) as an indicator, due to the greater availability of data. The return on capital indicates a bank's ability to generate profits from its resources, and it is expected that the most profitable banks will have more resources to use in financial inclusion programs, as well as the ability to better risk assessment techniques (Jenkins et al 2021;Song et al 2021).…”

“…We use bank overhead costs to represent bank efficiency, as excess costs represent the mismanagement and inefficiency of the organization when costs are higher than revenues (Jenkins et al 2021;Liu et al 2020). The use of this variable is based on the expectation that banks that mismanage their resources are likely to take on more risk.…”

“…The various theoretical and empirical studies use, as a proxy for bank size, the logarithm of bank assets (Albaity et al 2019;Feghali et al 2021;Jenkins et al 2021), so in this study, we follow the same logic. The size of the bank can cause positive or negative effects on credit risk and consequently on its ability to offer financial services and products, through two routes.…”

“…The first is relative to the greater availability of resources to evaluate and monitor borrowing customers and, with this, to mitigate risk exposure. The other route is relative to a greater risk assumption, because they understand that they are too big to fail (Albaity et al 2019;Feghali et al 2021;Jenkins et al 2021;Son et al 2020).…”