Does institutional quality affect non-performing loans in the MENA countries? A comparative analysis between GCC and Non-GCC countries

Does institutional quality affect non-performing loans in the MENA countries? 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A comparative analysis between GCC and Non-GCC countries Abdelaziz Hakimi, Hichem Saidi, Soumaya Saidi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6153859/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Institutional quality plays a critical role in controlling nonperforming loans (NPLs) by ensuring strong legal frameworks, effective regulations, and good governance in the banking sector. The primary objective of this article is to evaluate how institutional quality indicators influence NPLs in the MENA region. To explore this relationship, we employed the System Generalized Method of Moments (SGMM) approach to estimate a dynamic panel model based on data from 70 banks across 12 MENA countries, observed from 2010 to 2022. The findings indicate that controlling corruption is a crucial factor in reducing NPLs in both the MENA region and its sub-regions. In terms of government stability and the rule of law, these factors significantly lower NPLs in the Gulf Cooperation Council countries (GCC), however, they appear to have the opposite effect in the non-Gulf Cooperation Council countries (non-GCC). The results of the sensitive analysis proved that banks faced a lower level of NPLs when the institutional quality improved in all the regions studied. The results of this paper have substantial implications. Strengthening institutional frameworks can enhance banking stability by reducing default risks and ensuring efficient loan recovery mechanisms. Additionally, Policymakers should prioritize regulatory reforms and anti-corruption measures to mitigate the adverse effects of weak institutions on nonperforming loan accumulation. JEL codes : D73, G18, G21, G28 Business and commerce/Business and management Business and commerce/Finance Institutional Quality Corruption Government Stability Rule of Law Non-performing Loans MENA Region SGMM 1. Introduction Banks are powerful levers for essential infrastructure in economic development. They provide financial resources to all sectors of an economy, stimulating investments and facilitating the flow of funds between savers and borrowers throughout the economic cycle. According to modern financial intermediation theory, banks exist to fulfill two fundamental functions: providing liquidity and transforming risk (Bhattacharya and Thakor, 1993 ). However, the banking sector is generally exposed to a major issue of loan non-repayment that leads to substantial losses on its assets. This could lead to operational disruptions and widespread bankruptcy fears, potentially triggering a financial crisis (Reinhart and Rogoff, 2011 ). Indeed, banks are often exposed to credit risk as measured by the non-performing loans (NPLs) ratio, which can jeopardize both the stability of the banking sector and the overall financial system (Hakimi et al., 2020 ). Furthermore, this risk is the most heavily criticized because it can potentially deteriorate economic growth (Boudriga et al., 2008 ). Therefore, the banking sector needs to comprehend the key factors contributing to rising NPLs levels. In the aftermath of the 2008 financial crisis, empirical research has focused heavily on credit risk factors. They explained the NPLs via banking-specific characteristics, macroeconomic factors, and industry-specific factors (Karismaulia et al., 2023 ; Naili and Lahrichi, 2022 ; Jabbouri et al., 2022 ; Radivojevic et al., 2019; Kumar et al., 2018 ; Chaibi and Ftiti, 2015 ; Makri et al., 2014 ). Another line of research has examined the influence of institutional quality on credit risk, particularly emphasizing the adverse effects of corruption, which is closely associated with heightened uncertainty. Theoretically, poor institutional quality, as reflected in government institutions, can encourage harmful and unethical behaviors among economic and financial actors. It exacerbates the problem of information asymmetry, further increasing the opacity of the financial sector (Levine, 2004 ). This issue becomes even more pronounced in countries with poor institutional quality, which consequently fosters "abuse of functions." As a result, weak institutional quality creates uncertainty for both domestic and foreign economic agents, jeopardizing the attainment of established economic objectives. Empirical studies have identified specific institutional indicators, such as corruption, government stability, and the rule of law, as significant determinants of non-performing loans (NPLs). This relationship has been supported by the findings of various scholars, including Ofria and Mucciardi, 2024; MVK and Maitra, 2023 ; Goyal et al., 2023 ; Hakimi et al., 2022 ; Polat, 2018 ; Bougatef, 2016 ; Goal and Hassan, 2011; Park, 2012 and Boudriga et al., 2008 ). Effective institutional quality is an important tool for economic stability and control since it helps to mitigate banking risks, ensure financial stability, and reduce the likelihood of crises. This is accomplished primarily through two key mechanisms: corruption control and the rule of law (Uddin et al., 2019 ; Murshad and Saadat, 2018). Corruption influences bank balance sheets, contributing to the 2008 global financial crisis (Park, 2012 ). Bank corruption allows bankers to exploit institutional weaknesses to approve loans for unqualified or high-risk borrowers (Djankov et al., 2007 ). Furthermore, in countries with poor institutional quality, bank directors perceive the risk of prosecution as relatively low, which can increase the likelihood of loan defaults (Lambsdorff and Teksoz, 2004). The persistence of this perception, combined with weak legal enforcement and a culture of impunity, incentivizes bankers to engage in bribery without properly evaluating borrowers' creditworthiness. Consequently, this practice contributes to more NPLs (Uddin et al., 2019 ; Gjeci and Marinc, 2018). This study aims to answer the following research question: Does institutional quality affect credit risk in the MENA region and its sub-regions? To investigate this, the authors used a dynamic panel model with data from 70 banks in the MENA region. The findings, estimated through the SGMM approach, show that strengthening corruption control in the MENA region is associated with a reduction in NPLs. In the GCC countries, control of corruption, government stability, and the rule of law are the key institutional factors that enhance credit quality and reduce NPLs. In contrast, in non-GCC countries, while control of corruption helps lower NPLs, government stability and the rule of law are associated with a higher NPL ratio. The sensitivity analysis results confirm that as institutional quality improves, NPLs levels decline. This study contributes to the existing body of literature for several key reasons. First, it highlights the importance of understanding how the quality of the institutional environment influences nonperforming loans (NPLs) in the MENA region. A comparative analysis was carried out between the sub-regions of this area: GCC countries and non-GCC countries. Second, the current study utilizes three WGI indicators (control of corruption, government stability, and rule of law) to identify which factor reduces the NPLs across MENA, GCC, and non-GCC countries. Lastly, it adopts a holistic approach by constructing a composite index, providing a comprehensive perspective on the relationship between institutional quality and NPLs. The current article is organized as follows. The second section summarizes the relevant prior studies on this subject. The third section presents the data and empirical approach. The fourth section outlines the empirical results. The fifth and last sections provide a sensitive analysis. Robustness check is given in section 6 . Section 7 concludes and addresses some policy recommendations. 2. Literature review and hypotheses development Although bank loans are the main products of the financial sector that stimulate economies (Naili and Lahrichi, 2020 ), non-productive loans act as "financial pollution" harming economic development (Makri et al., 2014 ). Credit risk is one of the banking risks that harm profitability and banking stability (Alnabulsi et al., 2023 ). Hence, its increase poses a serious threat not only to the stability of banks but also to the financial system and the economy in general. The existing literature on the relationship between institutional quality and non-performing loans has demonstrated that the effective functioning of institutions largely depends on the quality of their surrounding environment (Boulanouar et al., 2021 ; Anastasiou et al., 2019 ; Hasan and Ashfaq, 2021 ). For example, Tran et al. ( 2023 ) concluded that better quality is crucial for improving banking stability in emerging and developing countries. Also, Awdeh and El-Moussawi ( 2022 ) showed that strong institutional quality and political stability promote the expansion of bank credit while reducing the likelihood of credit tightening. In this context, Bermpei et al. ( 2018 ) found that banks opting for high-quality institutional environments tend to limit their risky activities. Similarly, Goyal et al. ( 2023 ) demonstrated that the volume of non-performing loans (NPLs) only decreases when the institutional environment improves, both in developed and developing countries, during the period 2010–2020. They also noted that corruption undermines market competitiveness, leading to inefficiencies in credit contracts. Tehulu ( 2022 ) emphasized that institutional quality promotes credit growth for microfinance institutions (MFIs), thereby encouraging risk-averse behavior in politically stable environments. Furthermore, MFIs grant more credit when the rule of law is strong. In this section, we discuss relevant prior studies that focus on the key institutional determinants of non-performing loans (NPLs). 2.1 Corruption Corruption is a global issue that severely hinders economic and financial development by deteriorating loan quality and increasing NPLs (Park, 2012 ). It poses a major obstacle to company operations, raising costs and weakening the stability of the banking sector, which, in turn, harms economic growth through NPLs (Lombardi et al., 2019 ; Son et al., 2020 ). The banking sector is particularly vulnerable to corruption, as it fosters an uneven distribution of opportunities and resources, erodes client trust, and ultimately raises the likelihood of defaults (Masrom et al., 2023 ). Systemic corruption supports a culture of excessive banking risk-taking (Chen et al., 2015 ), while bribery plays a major role in allowing companies to obtain unproductive loans. (Chen et al., 2013). In a corrupt environment, Murdock et al. ( 2023 ) showed that banks face a decrease in performance and an increase in credit risk. Their study also highlighted that small and medium-sized banks, seeking to maximize their returns in corrupt regions, often reduce their level of liquidity. To mitigate these risks, Jenkins et al. ( 2021 ) suggested that highly corrupt countries enforce stricter regulations, such as requiring larger provisions for banks with high credit risk. Research on the relationship between corruption and NPLs produced mixed results. Some studies discovered that NPLs are positively influenced by corruption (Ofria and Mucciardi, 2024; Jenkins et al., 2021 ; Bougatef, 2016 ). Analyzing the international context from 2000 to 2017, Hasan and Ashfaq ( 2021 ) applied the GMM approach to prove that corruption significantly exacerbates NPLs. In contracts, Hakimi et al. ( 2022 ) used data from 38 banks in the MENA region from 2004 to 2015. They discovered that the effect of corruption becomes significantly negative beyond a threshold of 2.22. Similarly, Boudriga et al. ( 2008 ) observed that the greater level of NPLs can be lowered by strengthening the fight against corruption and improving the quality of regulation. In Saudi Arabia, Polat ( 2018 ) reported an inverse association but found no significant link in Turkey. Hypothesis (H1): Corruption increases the level of NPLs. 2.2 Government stability The literature highlights an ambivalent relationship between governmental stability and the level of NPLs. A stable government encourages investment, ensures the proper functioning of banks, and thereby contributes to reducing NPLs while mitigating the adverse effects of corruption (Hakimi et al., 2022 ). Similarly, Adem ( 2022 ) underlines that political stability decreases credit risk, whereas instability increases it. MVK and Maitra (2024) used the SGMM approach to demonstrate that the more stable the political system, the lower the level of non-performing loans. However, Alnabulsi et al. ( 2022 ) analyzed a panel of 74 MENA banks from 2005 to 2020 using the GMM technique. Their research found a strong positive relationship between political stability and bank risk-taking. According to behavioral finance theory, banks often assume greater risks and engage in high-risk projects to enhance profitability, as political stability fosters a more stable financial sector. However, in such conditions, banks seek higher profitability, which drives them to take more risks and invest in high-risk projects. Nevertheless, an unstable political situation can create information asymmetry, leading to a decline in lending activities (Awdeh and Hamadi, 2019). Moreover, credit-granting decisions are likely to be influenced by political pressures exerted by interest groups (Goyal et al., 2023 ). This supports the findings of Ahmed et al. ( 2021 ), who analyzed data from 20 Pakistani banks over the period 2008–2018 using the GMM method, and demonstrated that political risk plays a key role in worsening the NPL problem in the Pakistani context. This outcome is attributed to Pakistan’s chronic political instability since its independence in 1947. Furthermore, according to Bhattarai ( 2014 ), political instability leads to an increase in NPLs, as perceived by bankers. Such instability also heightens credit risk by exacerbating the effects of corruption (Shaffer, 2008 ). Greater political exposure reflects varying levels of information asymmetry between lenders and borrowers, thereby increasing debt costs (Francis et al., 2014 ). Additionally, public banks generate more NPLs due to political lobbying and administrative pressures that hinder their decision-making process (Boudriga et al., 2008 ). Finally, political stability is a key determinant of bank lending, as banks are the primary financiers of the private sector (Firth et al., 2009). Hypothesis (H2): Political stability reduces the level of NPLs. 2.3 Rule of law The literature examining the relationship between the rule of law and non-performing loans is scant. Certain studies confirm the inverse relationship (Murshed and Saadet, 2018; Boudriga et al., 2008 ). Based on a sample of 74 MENA banks from 2005 to 2020, Alnabulsi et al. ( 2022 ) used the two-step GMM method to prove that a strong application of the law significantly reduces NPLs, as a strong rule of law ensures both the protection of lenders' and borrowers' rights and the enforcement of credit supply rules. Furthermore, Hakimi et al. ( 2020 ) found that implementing a strong, strict, and powerful law-enforcement role decreases a borrower's moral hazard by improving credit agreements and creditor rights, resulting in a lower likelihood of default. Furthermore, Gjeci and Marinc (2018) highlighted a negative and significant relationship between the interaction term corruption- rule of law and NPLs. This suggests that a solid and well-applied legal framework helps mitigate the detrimental impact of corruption on the quality of the credit portfolio. A positive but non-significant relationship was demonstrated by Kinateder et al. ( 2021 ) and Park ( 2012 ). Hypothesis (H3): The rule of law reduces the level of NPLs. 3. Empirical design 3.1 Data To investigate whether institutional quality affects NPLs in the MENA region and its sub-sample of GCC/ non-GCC countries, we utilize a dataset from 2010–2022. This period was chosen since it encompasses both the aftermath of the financial crisis and the post-revolutionary period in certain MENA countries. The initial sample includes 181 banks from 15 countries observed between 2000 and 2022. Then, all banks and countries that did not report institutional quality and NPLs data for more than three years were excluded. As a result, the final sample is composed of 70 banks located in 12 MENA countries, which were further divided into six GCC countries and six non-GCC countries. Data on institutional quality was taken from the Worldwide Governance Indicators of Kaufman et al. (2010), providing information on the country's institutional environment. This WGI, published by the World Bank, includes six governance indicators that measure the country's governance quality: control of corruption, political stability, rule of law, Voice and Accountability, Government Effectiveness, and regulatory Quality. The WGI score ranges from − 2.5 to 2.5, with a higher score indicating better institutional quality. NPLs data and all the bank-specific data were obtained from the Refinitiv Eikon database, which are bank size (Karismaulia et al., 2023 ), bank capital (Saliba et al., 2023 ), liquidity risk (Naili and Lahrichi, 2022 ), bank performance (Jabbouri et al., 2022 ), and bank diversification (Hakimi and Khemiri, 2024 ). Macroeconomic variables, such as economic growth (Beck et al., 2015 ) and inflation rate (Polat, 2018 ), were collected from the World Bank site. The bank industry-specific used in this study is the concentration (Karadima and Louri, 2021), which was retrieved from the Global Financial Development database. 3.2 Empirical approach, model specification, and variables definition Our database includes an individual dimension (i) that exceeds the temporal dimension (t). Consequently, we opted for estimation using a dynamic panel model. This model considers that the dependent variable (Y i,t ) depends not only on the explanatory variables (X i,t ) but also on its lagged values (Y i,t−1 ). The System Generalized Method of Moments (SGMM) is particularly well-suited for modeling in the financial sector, which often faces endogeneity issues. It should be noted that our panel is unbalanced, and the SGMM method effectively addresses the bias introduced by missing data, unlike fixed or random effects approaches (Hakimi et al., 2022 ; Tissaoui et al. 2024). The econometric model was structured as follows : Where the ΣQI i represents a matrix of institutional quality indicators used in this study, including Control of corruption (CCORR), political stability (GOVS), and role of law (RLAW). The credit risk is assessed using the non-performing ratio, a critical indicator of financial health. Table 1 outlines the definitions and measurements of the variables. Table 1 Variables definition and measures Variables Definitions Measures Dependent variable NPLs Non-performing loans Non-performing loans to total loans ratio (%). Institutional quality CORR Control of Corruption Score that ranges from [-2.5 to 2.5], with 2.5 indicating very high control of corruption. GOVS Government stability Score that ranges from [-2.5 to 2.5], with 2.5 indicating high political stability. RLAW Rule of law Score that ranges from [-2.5 to 2.5], with 2.5 indicating strong rule of law Bank specifics SIZE Bank size The natural logarithm of the total assets of each bank CAP Capital Equity to total assets (%). LTD Liquidity risk Loan-to-deposit ratio, (%). ROA Bank performance Return on assets (ROA), (%) NII Bank diversification Non-interest income as a percentage of total assets. Industry specific CONC Concentration Share of the five biggest banks' assets to all banks' assets (%). Macroeconomic environment GDP Economic growth Annual GDP growth rate, (%) INF Inflation rate Annual growth of CPI, (%) Table 1 : Variables definition and measures 4. Empirical results In this section, we discuss the main findings on the relationship between institutional quality and NPLs. First, we begin with a summary of descriptive statistics and the correlation matrix. Second, we present our results for the MENA region, followed by a comparative analysis of the GCC and non-GCC sub-regions. Finally, we conclude with a sensitivity analysis, where we construct an institutional quality index using the PCA approach. 4.1 Summary statistics and correlation matrix To summarize the characteristics of the data used in this article, Table 2 provides some descriptive statistics. This step is essential to understanding and producing additional information about the evolution of data across countries and time. Table 2 Descriptive statistics Variable Mean Std, Dev, Min Max NPLs 7.30 11.80 4% 261% CCORR 0.208 0.551 -1.248 1.4 GOVS -0.2 0.803 -2.007 1.224 RLAW 0.232 0.407 -1.103 0.978 SIZE 23.737 1.223 20.942 26.512 CAP 16.70 4.60 3.50 42.90 ROA 1.40 0.80 -3.80 6.30 LTD 98.7630 5.84 1.40 162.00 NII 38.438 17.26 9.552 96 CONC 82.197 13.36 56.035 100 PIB 3.12 4.07 -2.4% 19.592 INF 4.833 10.958 -3.749 29.506 Starting with the dependent variable, the average level of NPLs is 7.30%, with a maximum value of 261% while the minimum value is 4%. For the institutional quality indicators, the average control of corruption is 0.208, with a maximum of 1.400 recorded by Qatar in 2010, while the minimum value is (-1.248) recorded by Lebanon in 2021. The average level of political stability is -0.200, indicating that the MENA region suffers from government instability. This indicator shows a maximum value of 1.224 recorded by Qatar in 2012 and a minimum value (-2.007) observed by Turkey in 2016. The rule of law average is 0.232, with a maximum value of 0.978 by Qatar in 2020 and a minimum (-1.103) recorded by Lebanon in 2022. With regard to the bank-specific, the average bank size equals 23.737, with the largest size being 26.512 and the weakest size being 20.942. The mean of bank capital and performance is 16.7% and 1.4%, with the maximum levels being 42.9% and 6.3%. For the liquidity risk, the loans-to-deposits ratio average is 98.76%, with a maximum of 162%, indicating weak liquidity, while the minimum level is 1.4%, referring to higher liquidity. Concerning bank diversification, the average is equal to 38.43%, the highest level was 96%, while the weak level was 9.55%. The highest level of bank concentration in the MENA region was 100%, while the weakest level was 56.03%, and the average value is equal to 82.19%. About the macroeconomic variables, the findings indicate that the average value of economic growth is equal to 3.12%, with the highest rate being 19.59% and the weakest growth rate being (-2.4%). The statistics show that the average value of the inflation rate is equal to 4.83% with a maximum value of 29.5% and a minimum value of (-3.74%). Table 2 : Descriptive statistics By examining the coefficient of correlation (Table 3 ), the correlation matrix reveals a strong positive and significant correlation, above 70%, between the institutional quality indicators. The variable (RDR) is highly correlated with (SGOUV) and (CCORR) with coefficients of (86.66%) and (87.56%). The correlation between (SGOUV) and (CCORR) reaches (79.20%). To resolve the multicollinearity problem, we must integrate our institutional quality variables separately into our model. Table 3 Correlation matrix NPLs CCORR GOVS RLAW SIZE CAP ROA LTD NII CONC PIB INF NPLs 1.0000 CCORR -0.0913* 1.0000 0.0091 GOVS -0.1516* 0.7920* 1.0000 0.0000 0.0000 RLAW -0.1132* 0.8756* 0.8666* 1.0000 0.0012 0.0000 0.0000 SIZE -0.1554* 0.2152* 0.1247* 0.1217* 1.0000 0.0001 0.0000 0.0015 0.0019 CAP -0.1971* 0.2670* 0.2418* 0.2723* 0.3864* 1.0000 0.0000 0.0000 0.0000 0.0000 0.0000 ROA -0.1673* 0.0424 -0.0041 -0.0019 0.1784* 0.1897* 1.0000 0.0000 0.2011 0.9023 0.9555 0.0000 0.0000 LTD -0.0495 -0.0185 -0.0864* -0.0752* -0.0516 0.0457 0.0944* 1.0000 0.1631 0.5909 0.0119 0.0286 0.1982 0.2034 0.0060 NII -0.1580* 0.1140* 0.1241* 0.1118* 0.3082* 0.2494* -0.0595 0.1403* 1.0000 0.0015 0.0171 0.0094 0.0193 0.0000 0.0000 0.2144 0.0044 CONC -0.1518* 0.3078* 0.6165* 0.6026* -0.0683 0.3430* 0.0185 -0.1439* 0.0415 1.0000 0.0001 0.0000 0.0000 0.0000 0.1300 0.0000 0.6197 0.0001 0.4272 PIB -0.0628 0.1254* 0.0257 0.0300 0.0699 0.0756* 0.3077* 0.0572 0.1250* -0.1685* 1.0000 0.0735 0.0002 0.4395 0.3656 0.0752 0.0303 0.0000 0.0964 0.0089 0.0000 INF 0.0612 -0.3531* -0.3427* -0.4371* 0.0058 -0.0630 0.1216* 0.0869* -0.1697* -0.4352* 0.0281 1.0000 0.0808 0.0000 0.0000 0.0000 0.8831 0.0711 0.0002 0.0115 0.0004 0.0000 0.3981 *, indicates the level of significance at 5% Table 3 : Correlation matrix 4.2. The Impact of institutional quality indicators on NPLs in the MENA region Given the results in Table 4 , the Sargan overidentification and autocorrelation tests do not reject the null hypothesis of the validity of the overidentification restrictions and the absence of correlation. The AR (2) and Sargan tests had p-values above 5%. Table 4 The Impact of institutional quality indicators on NPLs in the MENA region NPLs M1 : CCORR M2 : GOVS M3 : RLAW Coef. Coef. Coef. NPLs(-1) -0.122 -0.83 0.053 0.41 0.12 1.25 SIZE 0.024 1.31 0.037 1.6 0.031 1.36 CAP 0.498 1.63 0.241 0.92 0.221 0.92 ROA -2.575 -1.85* -2.445 -1.660* -2.354 -1.55 LTD 0.021 5.82*** 0.02 4.830*** 0.019 6.190*** NII 0.0001 0.08 0.0001 -0.16 0.0001 -0.19 CONC -0.01 -3.39*** -0.01 -3.720*** -0.012 -3.850*** PIB 0.007 2.36** 0.006 1.840* 0.006 1.930* INF 0.027 2.92*** 0.02 2.500** 0.017 2.79 CCORR -0.097 -1.91* — — GOVS — 0.035 1.56 — RLAW — — 0.021 0.2 _cons 0.208 0.41 -0.041 -0.07 0.299 0.43 AR(1) 0.97473 0.57195 0.66912 Prob 0.3297 0.5674 0.5034 AR(2) 0.62453 -0.32453 -0.42561 Prob 0.5546 0.6758 0.7546 Sargan test 7.4511 8.7821 8.9199 Prob 0.3835 0.2687 0.2585 Obs 682 682 682 ***,** and * indicate the level of significance at 1%, 5% and 10% Empirical results show that only control of corruption (CCORR) reduces credit risk in the MENA region. Specifically, an improvement of 10% in control of corruption significantly lowers the NPLs by 9.7%. A corrupt system undermines financial control, allowing insolvent debtors to obtain loans through personal relationships with bankers, favoritism, or bribes. Corruption frequently encourages bankers to manipulate loan applications and approve high-risk loans for personal benefit. Furthermore, corruption encourages irresponsible behavior, especially when corrupt officials protect bankers and borrowers from punishment, increasing NPLs. Conversely, a strong anti-corruption framework enhances financial supervision by implementing rigorous and transparent procedures. This system mitigates fraudulent behaviors while ensuring more efficient resource allocation. Furthermore, strict sanctions against acts of corruption deter illicit behaviors, reducing financial risks and contributing to the stability and reliability of the banking system. Thus, our findings confirm those of Murshed and Saadet (2018) and Boudriga et al. ( 2008 ). The results confirm the first hypothesis (H1). The findings reveal that bank performance, as measured by ROA, significantly decreases the volume of NPLs by 10% in MENA banks (M1 and M2). Highly profitable banks tend to minimize non-performing loans by establishing solid systems for loan monitoring and rigorous borrower credibility assessments, thereby mitigating credit risk. Additionally, these banks often maintain a diversified loan portfolio, which helps reduce overall risk exposure. They also typically hold sufficient provisioning reserves to cover potential losses in the event of defaults. These results are consistent with the studies of Jabbouri et al. ( 2022 ) and Jenkins et al. ( 2021 ). Unlike the effect of ROA, the loan-to-deposit ratio (LTD) has a positive and significant association with NPLs at 1%. Credit risk and liquidity risk are recognized as reciprocal risks, with an increase in liquidity risk increasing credit risk, and vice versa (Hakimi and Khemiri, 2024 ). Banks often solve a lack of liquidity by raising credit interest rates to generate higher income and mitigate liquidity risk. However, this strategy may jeopardize borrowers' repayment capacity, increasing the NPL ratio. This result confirms those of Boussaada et al. ( 2024 ) and Anastasion et al. (2019). Table 4 demonstrates that concentration (CONC) has a significant negative impact on non-performing loans (NPLs) in the MENA region. In a concentrated banking sector, banks enhance their risk management strategies, reduce expenses, provide competitive interest rates, and strengthen sector monitoring and supervision. Dominant banks contribute to greater stability by selecting highly creditworthy borrowers, thereby reducing credit risk. These findings align with those of Hakimi and Khemiri ( 2024 ). Regarding macroeconomic variables, GDP and inflation rate are positively and significantly correlated with NPLs in the MENA region. During periods of strong economic growth, the increase in credit demand pushes banks to relax their lending conditions. However, corruption can exacerbate credit risk by encouraging borrowers to provide inaccurate information, complicating decision-making and leading to a rise in non-performing loans. These findings are consistent with those of Radivojevic et al. (2019). Furthermore, during an inflationary period, central banks typically raise interest rates to counter inflationary pressures. However, this can negatively impact loan costs and borrowers' ability to repay loans, making them vulnerable to rising financial charges. As a result, higher inflation rates contribute to an increase in non-performing loans. These observations are similar to those of Polat ( 2018 ). Table 4 : The Impact of institutional quality indicators on NPLs in the MENA region 5. The Impact of institutional quality indicators on NPLs : A sensitivity analysis between GCC and non-GCC countries As sensitivity analysis to evaluate whether institutional quality indicators influence credit risk, we intend to look into this relationship in the MENA sub-region: GCC and non-GCC countries. Our sample of 70 banks was divided into 42 banks from the six GCC countries and 28 banks belonging to six non-GCC countries. Sargan overidentification and autocorrelation tests had p-values greater than 5%, thus do not reject the null hypothesis of the validity of the overidentification restrictions and the absence of correlation. Similar to the findings of the MENA region, we found that the control of corruption and bank performance (ROA) significantly decreased the NPLs ratio in both GCC and non-GCC countries. In contrast, results displayed in Table 5 show that government stability significantly decreases the level of NPLs for GCC banks, while it has the opposite effect in non-GCC countries. A period of strong political stability encourages all economic actors, including financial institutions, to adopt more opportunistic strategies towards the economy. Stability creates an excess of confidence and encourages banks to relax credit agreement conditions by reducing requirements related to borrowers' solvency and guarantees, increasing NPLs. Political stability can distort economic reality and encourage risky investments by promoting excessive access to credit, making them vulnerable to subsequent economic shocks. Even in a politically stable environment, the economy might experience periods of stagnation that deteriorate borrowers' financial situations, accumulating NPLs. Our findings corroborate those of Alnabulsi et al. ( 2022 ). The second hypothesis (H2) has been confirmed only in the GCC countries. Table 5 The Impact of institutional quality indicators on NPLs in the GCC/non-GCC countries NPLs GCC Non-GCC M1 : CCORR M2 : GOVS M3 : RLAW M1 : CCORR M2 : GOVS M3 : RLAW Coef. Coef. Coef. Coef. Coef. Coef. NPLs (-1) 0.530 2.580*** 0.688 3.800*** 0.756 4.880*** 0.122 2.59*** 0.140 3.37*** 0.056 1.24 SIZE 0.031 2.220** 0.041 3.670*** 0.038 2.630*** -0.023 -3.35*** -0.030 -2.98*** -0.022 4.21*** CAP -0.244 -0.570 -0.332 -0.930 -0.677 -2.070** -0.008 -1.04 -0.028 -2.78*** -0.024 -2.02** ROA -4.262 -1.650* -2.544 -1.540 -1.462 -0.870 -1.812 − 3.58*** -2.051 -2.84*** -1.749 -3.26*** LTD -0.001 -0.270 0.004 0.590 0.001 0.150 0.0003 1.98** -0.0002 -1.34 -0.00007 -0.25 NII 0.000 -0.320 0.000 0.750 0.000 0.350 -0.003 -4.37*** -0.003 -6.66*** -0.002 -5.36*** CONC 0.015 1.500 0.011 1.670* 0.010 2.110** 0.001 1.51 -0.001 -2.17** -0.0002 -0.84 PIB -0.014 -1.930* -0.009 -1.520 -0.014 -3.130*** -0.003 -15.40*** -0.003 -7.28*** -0.002 -11.79*** INF -0.016 -1.080 -0.009 -0.710 -0.029 -2.870*** 0.001 7.71*** 0.000 0.33 0.001 3.09*** CCORR -0.145 -2.350** — — -0.258 -6.41*** — — GOVS — -0.079 -3.080*** — — 0.047 5.09*** — RLAW — — -0.353 -3.310*** — — 0.195 10.99*** _cons -1.841 2.750*** -1.621 3.350*** -1.375 -2.270** 0.756 4.48*** 0.987 4.04*** 0.768 6.25*** AR(1) -1.1194 -1.2147 -1.1775 -1.0615 -1.8076 -0.77409 Prob 0.2630 0.2245 0.2390 0.2884 0.4389 0.4389 AR(2) -0.5784 -0.32472 -0.3964 -0.50667 -1.0556 -1.3757 Prob 0.6674 0.5541 0.5539 0.6124 0.2911 0.1689 Sargan test 9.2127 7.9446 8.0325 20.913 16.358 18.629 Prob 0.2377 0.3375 0.3297 1.0000 1.0000 1.0000 Obs 436 436 436 292 292 292 ***,** and * indicate the level of significance at 1%, 5% and 10% Contrary to the results of the whole sample, we found that the role of low negatively and significantly affects the NPLs ratio in the GCC countries. Whereas this effect is the opposite in the non-GCC countries. An ineffective regulatory framework leads bankers and borrowers to disregard contracts, fostering unclear or insufficient standards and regulations for provisioning requirements and borrower creditworthiness assessments. Additionally, a poor legal system complicates the swift enforcement of guarantees and impedes efficient decision-making. In a corrupt environment, poor adherence to the rule of law fosters unethical practices and weakens the fair enforcement of regulations. This dynamic motivates lenders and borrowers to engage in excessive risk-taking, confident that they will face any penalties. These findings contradicted those of Murshed and Saadet (2018) and Alnabulsi et al. ( 2022 ). The results confirm the third hypothesis (H3) in GCC countries, but it is rejected in non-GCC countries. We also found that the more the lag of NPLs (-1) increases, the more the current NPLs increase across the two sub-regions. Furthermore, the large bank size increases significantly the level of NPLs in the GCC countries. In contrast, it was found to have a negative effect on NPLs in the non-GCC countries. Large banks may attempt to improve the profitability of their assets by expanding the volume of loans and loosening lending standards, frequently without paying adequate attention to default risks. Furthermore, these banks may confront managerial challenges arising from the complexities of communication and coordination between managers and administrators. However, as stated by Jabbouri et al. ( 2022 ), large banks tend to maintain low levels of non-performing loans, attributed to their advanced risk management processes and tools. These conclusions correspond with the results of Hakimi and Khemiri ( 2024 ) and Chaibi and Ftiti ( 2015 ). In the GCC and non-GCC countries, the higher the capital adequacy ratio, the lower the credit risk. Better-capitalized banks are generally exposed to less credit risk and are more resilient to unexpected losses. Overcapitalized banks employ highly efficient credit management strategies to address information asymmetry issues. They implement more effective measures to prevent non-productive loans and preserve their capital reserves, which are used in case of risk. These findings are similar to those of Naili and Lahrichi ( 2022 ) and Alnabulsi et al. ( 2022 ). Bank diversification has a negative and significant impact on the amount of NPLs. Diversified banks generate a large portion of income from non-lending activities, allowing them to better manage their credit risk exposure. Moreover, diversifying banking activities promotes a distribution of risks across different sectors, thereby reducing the accumulation of non-performing loans (NPLs). Furthermore, a diverse source of income enhances banks' resilience in the face of financial crises, allowing them to maintain strict and prudent credit standards when providing loans, thus contributing to NPL reduction. These findings align with the conclusions of Saliba et al. ( 2023 ). In contrast to the effect of bank concentration on NPLs in the MENA region, it significantly increases the level of NPLs in GCC countries. Banking concentration increases NPLs by reducing competition, weakening market discipline, and encouraging dominant banks to engage in riskier lending. These banks often focus their operations on specific sectors, making them particularly vulnerable to economic downturns in those industries. Additionally, the "too big to fail" phenomenon fosters excessive risk-taking, as banks expect government intervention during crises. A lack of innovation in risk management and lending to high-risk borrowers further exacerbates the likelihood of loan defaults. As a result, banking concentration heightens financial risks and accelerates the accumulation of non-performing assets. These results support those of Canh et al. ( 2020 ). Concerning the macroeconomic factors, findings indicate that the GDP significantly decreases the NPLs ratio in both GCC and non-GCC countries, while the inflation rate reduces the NPLs ratio only in the GCC banks. During economic expansion, rising household incomes enhance their purchasing power, boosting corporate profitability. This cycle improves borrowers' financial stability, thereby lowering default risks. Conversely, during periods of high inflation, diminished purchasing power weakens the financial capacity of both households and businesses. In response, banks implement stricter credit policies and tighten loan approval criteria. Additionally, reduced income levels lead to a decline in credit demand, indirectly curbing the accumulation of non-performing loans while promoting stricter risk management practices. Our results confirm those of Hakimi et al. (2023) and Jabbouri et al. ( 2022 ). Table 5 : The Impact of institutional quality indicators on NPLs in the GCC/non-GCC countries 6. Robustness check: The use of an institutional quality index Although the importance of the quality of the institutional environment has been recognized, which can influence the stability, regulation, and supervision of the banking system and thus improve the behavior of banks, there is little evidence that the Worldwide Governance Indicators (WGI) as a whole are directly linked to a reduction in NPLs. Based on the results obtained in the MENA region, the GCC, and non-GCC countries, we have built an institutional quality index (IQ index) from the following six WGI: control of corruption (CCORR), political stability (GOVS), rule of law (RLAW), Voice and Accountability (VA), Government Effectiveness (EG), and regulatory Quality (QR). This study takes a more holistic approach using the Principal Component Analysis (PCA) method. Table 6 shows a strong correlation below 70% between all the indicators in the QI_index. As a result, we will employ the PCA method of Hotelling and Thurstone (1934), which has its main advantage of isolating the common component (Goyal et al., 2023 ). Furthermore, PCA aids in the resolution of multicollinearity concerns that build when numerous highly correlated variables are included separately in the same regression (Wooldridge, 2010 ). Table 6 Correlation matrix of Institutional quality indicators CCORR EG GOVS QR RLAW VA CCORR 1.0000 EG 0.9248* 0.0000 1.0000 GOVS 0.8014* 0.0000 0.6814* 0.0000 1.0000 QR 0.8216* 0.0000 0.8955* 0.0000 0.6541* 0.0000 1.0000 RLAW 0.9041* 0.0000 0.8413* 0.0000 0.8371* 0.0000 0.8325* 0.0000 1.0000 VA -0.2886* 0.0003 -0.2760* 0.0005 -0.2442* 0.0021 -0.3279* 0.0000 -0.3297* 0.0000 1.0000 *, indicates the level of significance at 5% Table 6 : Correlation matrix of Institutional quality indicators The PCA technique consists of two main steps. The first uses the Min-Max approach to normalize each institutional quality indicator (Hakimi et al. 2021 ). This recently transforms data values within a given range, usually between 0 and 1. This phase minimizes the data's dimensionality while keeping the most important information. VN k is the normalized vector of each indicator during period t, with min (QI) and max (QI) representing the minimum and maximum values of each indicator, respectively. The second step is to ponderate each normalized indicator equally, using coefficients equal to 1/N, to ensure that all variables have uniform influence. This approach is easier, more practical, and more equitable, allowing for a consistent analysis of the variables' relevance without bias and avoiding weighting biases. Once the QI_index has been created, the new values will range from 0 to 1, with 0 representing the poorest institutional quality and 1 being the best. Here, N is the number of WGI used to build the QI_index. Table 7 shows the effect of the institutional quality index on NPLs in the MENA region, GCC, and non-GCC countries. The findings reveal that the QI_index significantly decreases NPLs in both GCC and non-GCC countries. By maintaining stable and transparent governance, institutional quality minimizes the volume of non-performing loans (NPLs), hence mitigating political and economic instability. In the case of a default, strong institutions enhance the chances of debt recovery by ensuring an efficient judiciary and protecting creditors' rights. Additionally, strict banking regulations and effective risk management promote diligent credit monitoring, which in turn minimizes excessive risk-taking. Ultimately, a stable economy supported by strong institutions enhances borrowers' ability to repay their debts, thereby lowering the likelihood of default. Our findings align with those of Goyal et al. ( 2023 ). Table 7 The Impact of Institutional Quality Index on NPLs in the MENA, GCC and non-GCC countries NPLs MENA GCC non-GCC QI_index Coef. QI_index Coef. QI_index Coef. NPLs(-1) -0.026 -0.230 0.736 4.470*** 0.083 1.78 SIZE 0.032 1.410 0.029 1.360 -0.041 -6.32*** CAP 0.220 0.860 -0.916 -3.090*** -0.001 -0.11 ROA -2.208 -1.340 -0.326 -0.170 -2.794 -3.38*** LTD 0.020 6.060*** -0.001 -0.410 -0.001 -2.62*** NII 0.0001 -0.240 0.000 -0.610 -0.003 -5.57*** CONC -0.009 -4.200*** 0.026 4.070*** -0.002 -4.74*** PIB 0.005 1.630 -0.020 -4.290*** -0.002 -4.87*** INF 0.016 2.560** -0.041 -4.310*** 0.001 5.90*** QI_INDEX 0.195 1.200 -0.901 -3.580*** -1.087 12.65*** _cons -0.032 -0.050 -2.896 -3.640*** 0.746 3.84*** AR(1) 0.70371 -0.98762 -1.0794 Prob 0.4816 0.3233 0.2804 AR(2) 0.34789 -0.03541 -1.2557 Prob 0.5789 0.7845 0.2092 Sargan Test 8.8847 7.3309 21.825 Prob 0.2610 0.3953 1.0000 Obs 682 436 292 *** and ** indicate the level of significance at 1% and 5% Regarding the effect of control variables, no significant effect has been noticed compared to the results displayed in Tables (4) and (5). Table 7 : The Impact of Institutional Quality Index on NPLs in the MENA, GCC and non-GCC countries Conclusion and Policy Recommendations To visualize the influence of institutional quality on NPLs in the MENA region, we estimated a dynamic panel model using the SGMM approach. To conduct a comparative study, we divided our sample into GCC and non-CCG countries using the same empirical approach. Comparing the results for all the regions in our sample, we observe that the impact of institutional quality on credit risk differs with the region, although there are some similarities. The control of corruption has a significant negative impact on NPLs in all our regions. Government stability has a negative and significant effect on NPLs in GCC countries, whereas it positively and significantly influences NPLs in non-GCC nations. Similarly, the rule of law significantly reduces NPLs in GCC countries but increases them in non-GCC countries. The institutional quality index has a negative and significant impact on NPLs in both GCC and NGCC countries but shows no significant effect on NPLs in the MENA region. The disparities in the results indicate that each region has unique institutional characteristics and responds differently to banking credit risk. Nevertheless, all regions place significant emphasis on combating corruption. In conclusion, the findings highlight that enhancing institutional quality is essential for mitigating credit risk across the MENA region and its sub-regions (GCC and non-GCC countries). To ensure the stability of the financial sector, maintain the continuity of its activities, and effectively manage credit risks, it is crucial to establish a strong and stable institutional environment. This environment must emphasize combating corruption by reducing uncertainty and ensuring the consistent enforcement of rules. Additionally, it is vital to develop an institutional framework that fosters sustainable financial and economic development in both GCC and non-GCC countries. This can be achieved by enhancing political and governmental stability, reinforcing legal stability, and ensuring sufficient legal protection for creditors' rights, particularly concerning the guarantees required. The current study has a few limitations. First, some MENA countries were excluded due to the unavailability of institutional quality data. Second, a comparative analysis of MENA countries based on the nature of institutional quality indicators (whether poor or good) was not explored. Lastly, the potential moderating role of corporate social responsibility in the relationship between institutional quality and credit risk was not examined. These limitations present opportunities for future research. Declarations Data availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The author(s) declare no competing interests. Ethical approval This article does not contain any studies with human participants performed by any of the authors. Informed consent This article does not contain any studies with human participants performed by any of the authors. Author contributions SS and AH conceptualized the study, obtained the data, conducted the data analysis and drafted the paper. AH and HS contributed to the model development and results interpretation. SS contributed to the literature review, the formation and compilation of conclusion. All authors read and approved the final manuscript. References Adem, M. (2022). Determinants of Credit Risk in Ethiopian Banking Industry: Does Political Stability Matter? 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A comparative analysis between GCC and Non-GCC countries","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBanks are powerful levers for essential infrastructure in economic development. They provide financial resources to all sectors of an economy, stimulating investments and facilitating the flow of funds between savers and borrowers throughout the economic cycle. According to modern financial intermediation theory, banks exist to fulfill two fundamental functions: providing liquidity and transforming risk (Bhattacharya and Thakor, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). However, the banking sector is generally exposed to a major issue of loan non-repayment that leads to substantial losses on its assets. This could lead to operational disruptions and widespread bankruptcy fears, potentially triggering a financial crisis (Reinhart and Rogoff, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIndeed, banks are often exposed to credit risk as measured by the non-performing loans (NPLs) ratio, which can jeopardize both the stability of the banking sector and the overall financial system (Hakimi et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, this risk is the most heavily criticized because it can potentially deteriorate economic growth (Boudriga et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Therefore, the banking sector needs to comprehend the key factors contributing to rising NPLs levels. In the aftermath of the 2008 financial crisis, empirical research has focused heavily on credit risk factors. They explained the NPLs via banking-specific characteristics, macroeconomic factors, and industry-specific factors (Karismaulia et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Naili and Lahrichi, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jabbouri et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Radivojevic et al., 2019; Kumar et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Chaibi and Ftiti, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Makri et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnother line of research has examined the influence of institutional quality on credit risk, particularly emphasizing the adverse effects of corruption, which is closely associated with heightened uncertainty. Theoretically, poor institutional quality, as reflected in government institutions, can encourage harmful and unethical behaviors among economic and financial actors. It exacerbates the problem of information asymmetry, further increasing the opacity of the financial sector (Levine, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). This issue becomes even more pronounced in countries with poor institutional quality, which consequently fosters \"abuse of functions.\" As a result, weak institutional quality creates uncertainty for both domestic and foreign economic agents, jeopardizing the attainment of established economic objectives.\u003c/p\u003e \u003cp\u003eEmpirical studies have identified specific institutional indicators, such as corruption, government stability, and the rule of law, as significant determinants of non-performing loans (NPLs). This relationship has been supported by the findings of various scholars, including Ofria and Mucciardi, 2024; MVK and Maitra, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Goyal et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hakimi et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Polat, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Bougatef, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Goal and Hassan, 2011; Park, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2012\u003c/span\u003e and Boudriga et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEffective institutional quality is an important tool for economic stability and control since it helps to mitigate banking risks, ensure financial stability, and reduce the likelihood of crises. This is accomplished primarily through two key mechanisms: corruption control and the rule of law (Uddin et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Murshad and Saadat, 2018). Corruption influences bank balance sheets, contributing to the 2008 global financial crisis (Park, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Bank corruption allows bankers to exploit institutional weaknesses to approve loans for unqualified or high-risk borrowers (Djankov et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Furthermore, in countries with poor institutional quality, bank directors perceive the risk of prosecution as relatively low, which can increase the likelihood of loan defaults (Lambsdorff and Teksoz, 2004). The persistence of this perception, combined with weak legal enforcement and a culture of impunity, incentivizes bankers to engage in bribery without properly evaluating borrowers' creditworthiness. Consequently, this practice contributes to more NPLs (Uddin et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Gjeci and Marinc, 2018).\u003c/p\u003e \u003cp\u003eThis study aims to answer the following research question: Does institutional quality affect credit risk in the MENA region and its sub-regions? To investigate this, the authors used a dynamic panel model with data from 70 banks in the MENA region. The findings, estimated through the SGMM approach, show that strengthening corruption control in the MENA region is associated with a reduction in NPLs. In the GCC countries, control of corruption, government stability, and the rule of law are the key institutional factors that enhance credit quality and reduce NPLs. In contrast, in non-GCC countries, while control of corruption helps lower NPLs, government stability and the rule of law are associated with a higher NPL ratio. The sensitivity analysis results confirm that as institutional quality improves, NPLs levels decline.\u003c/p\u003e \u003cp\u003eThis study contributes to the existing body of literature for several key reasons. First, it highlights the importance of understanding how the quality of the institutional environment influences nonperforming loans (NPLs) in the MENA region. A comparative analysis was carried out between the sub-regions of this area: GCC countries and non-GCC countries. Second, the current study utilizes three WGI indicators (control of corruption, government stability, and rule of law) to identify which factor reduces the NPLs across MENA, GCC, and non-GCC countries. Lastly, it adopts a holistic approach by constructing a composite index, providing a comprehensive perspective on the relationship between institutional quality and NPLs.\u003c/p\u003e \u003cp\u003eThe current article is organized as follows. The second section summarizes the relevant prior studies on this subject. The third section presents the data and empirical approach. The fourth section outlines the empirical results. The fifth and last sections provide a sensitive analysis. Robustness check is given in section \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Section \u003cspan refid=\"Sec13\" class=\"InternalRef\"\u003e7\u003c/span\u003e concludes and addresses some policy recommendations.\u003c/p\u003e"},{"header":"2. Literature review and hypotheses development","content":"\u003cp\u003eAlthough bank loans are the main products of the financial sector that stimulate economies (Naili and Lahrichi, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), non-productive loans act as \"financial pollution\" harming economic development (Makri et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Credit risk is one of the banking risks that harm profitability and banking stability (Alnabulsi et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Hence, its increase poses a serious threat not only to the stability of banks but also to the financial system and the economy in general.\u003c/p\u003e \u003cp\u003eThe existing literature on the relationship between institutional quality and non-performing loans has demonstrated that the effective functioning of institutions largely depends on the quality of their surrounding environment (Boulanouar et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Anastasiou et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hasan and Ashfaq, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For example, Tran et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) concluded that better quality is crucial for improving banking stability in emerging and developing countries. Also, Awdeh and El-Moussawi (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) showed that strong institutional quality and political stability promote the expansion of bank credit while reducing the likelihood of credit tightening. In this context, Bermpei et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) found that banks opting for high-quality institutional environments tend to limit their risky activities. Similarly, Goyal et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) demonstrated that the volume of non-performing loans (NPLs) only decreases when the institutional environment improves, both in developed and developing countries, during the period 2010\u0026ndash;2020. They also noted that corruption undermines market competitiveness, leading to inefficiencies in credit contracts. Tehulu (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) emphasized that institutional quality promotes credit growth for microfinance institutions (MFIs), thereby encouraging risk-averse behavior in politically stable environments. Furthermore, MFIs grant more credit when the rule of law is strong.\u003c/p\u003e \u003cp\u003eIn this section, we discuss relevant prior studies that focus on the key institutional determinants of non-performing loans (NPLs).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Corruption\u003c/h2\u003e \u003cp\u003eCorruption is a global issue that severely hinders economic and financial development by deteriorating loan quality and increasing NPLs (Park, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). It poses a major obstacle to company operations, raising costs and weakening the stability of the banking sector, which, in turn, harms economic growth through NPLs (Lombardi et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Son et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe banking sector is particularly vulnerable to corruption, as it fosters an uneven distribution of opportunities and resources, erodes client trust, and ultimately raises the likelihood of defaults (Masrom et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Systemic corruption supports a culture of excessive banking risk-taking (Chen et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), while bribery plays a major role in allowing companies to obtain unproductive loans. (Chen et al., 2013).\u003c/p\u003e \u003cp\u003eIn a corrupt environment, Murdock et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) showed that banks face a decrease in performance and an increase in credit risk. Their study also highlighted that small and medium-sized banks, seeking to maximize their returns in corrupt regions, often reduce their level of liquidity. To mitigate these risks, Jenkins et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) suggested that highly corrupt countries enforce stricter regulations, such as requiring larger provisions for banks with high credit risk.\u003c/p\u003e \u003cp\u003eResearch on the relationship between corruption and NPLs produced mixed results. Some studies discovered that NPLs are positively influenced by corruption (Ofria and Mucciardi, 2024; Jenkins et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Bougatef, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Analyzing the international context from 2000 to 2017, Hasan and Ashfaq (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) applied the GMM approach to prove that corruption significantly exacerbates NPLs. In contracts, Hakimi et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) used data from 38 banks in the MENA region from 2004 to 2015. They discovered that the effect of corruption becomes significantly negative beyond a threshold of 2.22. Similarly, Boudriga et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) observed that the greater level of NPLs can be lowered by strengthening the fight against corruption and improving the quality of regulation. In Saudi Arabia, Polat (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) reported an inverse association but found no significant link in Turkey.\u003c/p\u003e \u003cp\u003e \u003cem\u003eHypothesis (H1): Corruption increases the level of NPLs.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Government stability\u003c/h2\u003e \u003cp\u003eThe literature highlights an ambivalent relationship between governmental stability and the level of NPLs. A stable government encourages investment, ensures the proper functioning of banks, and thereby contributes to reducing NPLs while mitigating the adverse effects of corruption (Hakimi et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Similarly, Adem (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) underlines that political stability decreases credit risk, whereas instability increases it. MVK and Maitra (2024) used the SGMM approach to demonstrate that the more stable the political system, the lower the level of non-performing loans. However, Alnabulsi et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) analyzed a panel of 74 MENA banks from 2005 to 2020 using the GMM technique. Their research found a strong positive relationship between political stability and bank risk-taking. According to behavioral finance theory, banks often assume greater risks and engage in high-risk projects to enhance profitability, as political stability fosters a more stable financial sector. However, in such conditions, banks seek higher profitability, which drives them to take more risks and invest in high-risk projects.\u003c/p\u003e \u003cp\u003eNevertheless, an unstable political situation can create information asymmetry, leading to a decline in lending activities (Awdeh and Hamadi, 2019). Moreover, credit-granting decisions are likely to be influenced by political pressures exerted by interest groups (Goyal et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This supports the findings of Ahmed et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who analyzed data from 20 Pakistani banks over the period 2008\u0026ndash;2018 using the GMM method, and demonstrated that political risk plays a key role in worsening the NPL problem in the Pakistani context. This outcome is attributed to Pakistan\u0026rsquo;s chronic political instability since its independence in 1947.\u003c/p\u003e \u003cp\u003eFurthermore, according to Bhattarai (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), political instability leads to an increase in NPLs, as perceived by bankers. Such instability also heightens credit risk by exacerbating the effects of corruption (Shaffer, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Greater political exposure reflects varying levels of information asymmetry between lenders and borrowers, thereby increasing debt costs (Francis et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Additionally, public banks generate more NPLs due to political lobbying and administrative pressures that hinder their decision-making process (Boudriga et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Finally, political stability is a key determinant of bank lending, as banks are the primary financiers of the private sector (Firth et al., 2009).\u003c/p\u003e \u003cp\u003e \u003cem\u003eHypothesis (H2): Political stability reduces the level of NPLs.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Rule of law\u003c/h2\u003e \u003cp\u003eThe literature examining the relationship between the rule of law and non-performing loans is scant. Certain studies confirm the inverse relationship (Murshed and Saadet, 2018; Boudriga et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Based on a sample of 74 MENA banks from 2005 to 2020, Alnabulsi et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) used the two-step GMM method to prove that a strong application of the law significantly reduces NPLs, as a strong rule of law ensures both the protection of lenders' and borrowers' rights and the enforcement of credit supply rules. Furthermore, Hakimi et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found that implementing a strong, strict, and powerful law-enforcement role decreases a borrower's moral hazard by improving credit agreements and creditor rights, resulting in a lower likelihood of default. Furthermore, Gjeci and Marinc (2018) highlighted a negative and significant relationship between the interaction term corruption- rule of law and NPLs. This suggests that a solid and well-applied legal framework helps mitigate the detrimental impact of corruption on the quality of the credit portfolio. A positive but non-significant relationship was demonstrated by Kinateder et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Park (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eHypothesis (H3): The rule of law reduces the level of NPLs.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Empirical design","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Data\u003c/h2\u003e\n \u003cp\u003eTo investigate whether institutional quality affects NPLs in the MENA region and its sub-sample of GCC/ non-GCC countries, we utilize a dataset from 2010\u0026ndash;2022. This period was chosen since it encompasses both the aftermath of the financial crisis and the post-revolutionary period in certain MENA countries. The initial sample includes 181 banks from 15 countries observed between 2000 and 2022. Then, all banks and countries that did not report institutional quality and NPLs data for more than three years were excluded. As a result, the final sample is composed of 70 banks located in 12 MENA countries, which were further divided into six GCC countries and six non-GCC countries.\u003c/p\u003e\n \u003cp\u003eData on institutional quality was taken from the Worldwide Governance Indicators of Kaufman et al. (2010), providing information on the country\u0026apos;s institutional environment. This WGI, published by the World Bank, includes six governance indicators that measure the country\u0026apos;s governance quality: control of corruption, political stability, rule of law, Voice and Accountability, Government Effectiveness, and regulatory Quality. The WGI score ranges from \u0026minus;\u0026thinsp;2.5 to 2.5, with a higher score indicating better institutional quality.\u003c/p\u003e\n \u003cp\u003eNPLs data and all the bank-specific data were obtained from the Refinitiv Eikon database, which are bank size (Karismaulia et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), bank capital (Saliba et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), liquidity risk (Naili and Lahrichi, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), bank performance (Jabbouri et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), and bank diversification (Hakimi and Khemiri, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Macroeconomic variables, such as economic growth (Beck et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) and inflation rate (Polat, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e), were collected from the World Bank site. The bank industry-specific used in this study is the concentration (Karadima and Louri, 2021), which was retrieved from the Global Financial Development database.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Empirical approach, model specification, and variables definition\u003c/h2\u003e\n \u003cp\u003eOur database includes an individual dimension (i) that exceeds the temporal dimension (t). Consequently, we opted for estimation using a dynamic panel model. This model considers that the dependent variable (Y\u003csub\u003ei,t\u003c/sub\u003e) depends not only on the explanatory variables (X\u003csub\u003ei,t\u003c/sub\u003e) but also on its lagged values (Y\u003csub\u003ei,t\u0026minus;1\u003c/sub\u003e). The System Generalized Method of Moments (SGMM) is particularly well-suited for modeling in the financial sector, which often faces endogeneity issues. It should be noted that our panel is unbalanced, and the SGMM method effectively addresses the bias introduced by missing data, unlike fixed or random effects approaches (Hakimi et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e ; Tissaoui et al. 2024). The econometric model was structured as follows :\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eWhere the \u0026Sigma;QI\u003csub\u003ei\u003c/sub\u003e represents a matrix of institutional quality indicators used in this study, including Control of corruption (CCORR), political stability (GOVS), and role of law (RLAW). The credit risk is assessed using the non-performing ratio, a critical indicator of financial health. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e outlines the definitions and measurements of the variables.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eVariables definition and measures\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDefinitions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMeasures\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eDependent variable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNPLs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-performing loans\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-performing loans to total loans ratio (%).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eInstitutional quality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCORR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl of Corruption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScore that ranges from [-2.5 to 2.5], with 2.5 indicating very high control of corruption.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGOVS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGovernment stability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScore that ranges from [-2.5 to 2.5], with 2.5 indicating high political stability.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRLAW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRule of law\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScore that ranges from [-2.5 to 2.5], with 2.5 indicating strong rule of law\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eBank specifics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSIZE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBank size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe natural logarithm of the total assets of each bank\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCapital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEquity to total assets (%).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLTD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiquidity risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLoan-to-deposit ratio, (%).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eROA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBank performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReturn on assets (ROA), (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBank diversification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-interest income as a percentage of total assets.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndustry specific\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCONC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConcentration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShare of the five biggest banks\u0026apos; assets to all banks\u0026apos; assets (%).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eMacroeconomic environment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEconomic growth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnnual GDP growth rate, (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eINF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInflation rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnnual growth of CPI, (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e: \u003cstrong\u003eVariables definition and measures\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Empirical results","content":"\u003cp\u003eIn this section, we discuss the main findings on the relationship between institutional quality and NPLs. First, we begin with a summary of descriptive statistics and the correlation matrix. Second, we present our results for the MENA region, followed by a comparative analysis of the GCC and non-GCC sub-regions. Finally, we conclude with a sensitivity analysis, where we construct an institutional quality index using the PCA approach.\u003c/p\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Summary statistics and correlation matrix\u003c/h2\u003e\n \u003cp\u003eTo summarize the characteristics of the data used in this article, Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e provides some descriptive statistics. This step is essential to understanding and producing additional information about the evolution of data across countries and time.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive statistics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd, Dev,\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNPLs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e261%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCCORR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGOVS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.224\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRLAW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.978\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSIZE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.512\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eROA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLTD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98.7630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e162.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCONC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePIB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.592\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eINF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.506\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eStarting with the dependent variable, the average level of NPLs is 7.30%, with a maximum value of 261% while the minimum value is 4%. For the institutional quality indicators, the average control of corruption is 0.208, with a maximum of 1.400 recorded by Qatar in 2010, while the minimum value is (-1.248) recorded by Lebanon in 2021. The average level of political stability is -0.200, indicating that the MENA region suffers from government instability. This indicator shows a maximum value of 1.224 recorded by Qatar in 2012 and a minimum value (-2.007) observed by Turkey in 2016. The rule of law average is 0.232, with a maximum value of 0.978 by Qatar in 2020 and a minimum (-1.103) recorded by Lebanon in 2022.\u003c/p\u003e\n \u003cp\u003eWith regard to the bank-specific, the average bank size equals 23.737, with the largest size being 26.512 and the weakest size being 20.942. The mean of bank capital and performance is 16.7% and 1.4%, with the maximum levels being 42.9% and 6.3%. For the liquidity risk, the loans-to-deposits ratio average is 98.76%, with a maximum of 162%, indicating weak liquidity, while the minimum level is 1.4%, referring to higher liquidity. Concerning bank diversification, the average is equal to 38.43%, the highest level was 96%, while the weak level was 9.55%. The highest level of bank concentration in the MENA region was 100%, while the weakest level was 56.03%, and the average value is equal to 82.19%. About the macroeconomic variables, the findings indicate that the average value of economic growth is equal to 3.12%, with the highest rate being 19.59% and the weakest growth rate being (-2.4%). The statistics show that the average value of the inflation rate is equal to 4.83% with a maximum value of 29.5% and a minimum value of (-3.74%).\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e: \u003cstrong\u003eDescriptive statistics\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eBy examining the coefficient of correlation (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), the correlation matrix reveals a strong positive and significant correlation, above 70%, between the institutional quality indicators. The variable (RDR) is highly correlated with (SGOUV) and (CCORR) with coefficients of (86.66%) and (87.56%). The correlation between (SGOUV) and (CCORR) reaches (79.20%). To resolve the multicollinearity problem, we must integrate our institutional quality variables separately into our model.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelation matrix\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"13\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNPLs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCCORR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGOVS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRLAW\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSIZE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCAP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eROA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLTD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNII\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCONC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePIB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eINF\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNPLs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCCORR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.0913*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGOVS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.1516*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7920*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRLAW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.1132*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8756*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8666*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSIZE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.1554*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2152*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1247*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1217*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.1971*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2670*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2418*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2723*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3864*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eROA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.1673*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.0041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.0019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1784*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1897*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLTD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.0495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.0185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.0864*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.0752*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.0516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0944*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.1580*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1140*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1241*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1118*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3082*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2494*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.0595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1403*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCONC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.1518*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3078*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6165*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6026*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.0683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3430*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.1439*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePIB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.0628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1254*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0756*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3077*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1250*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.1685*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eINF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.3531*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.3427*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.4371*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.0630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1216*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0869*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.1697*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.4352*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\"\u003e\u003cem\u003e*, indicates the level of significance at 5%\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e: \u003cstrong\u003eCorrelation matrix\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2. The Impact of institutional quality indicators on NPLs in the MENA region\u003c/h2\u003e\n \u003cp\u003eGiven the results in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, the Sargan overidentification and autocorrelation tests do not reject the null hypothesis of the validity of the overidentification restrictions and the absence of correlation. The AR (2) and Sargan tests had p-values above 5%.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe Impact of institutional quality indicators on NPLs in the MENA region\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNPLs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eM1\u0026nbsp;: CCORR\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eM2\u0026nbsp;: GOVS\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eM3\u0026nbsp;: RLAW\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCoef.\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCoef.\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCoef.\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNPLs(-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.122\u003c/p\u003e\n \u003cp\u003e-0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSIZE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.498\u003c/p\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.241\u003c/p\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eROA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.575\u003c/p\u003e\n \u003cp\u003e-1.85*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.445\u003c/p\u003e\n \u003cp\u003e-1.660*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.354\u003c/p\u003e\n \u003cp\u003e-1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLTD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003cp\u003e5.82***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003cp\u003e4.830***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003cp\u003e6.190***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCONC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003cp\u003e-3.39***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003cp\u003e-3.720***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.012\u003c/p\u003e\n \u003cp\u003e-3.850***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePIB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003cp\u003e2.36**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003cp\u003e1.840*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003cp\u003e1.930*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eINF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003cp\u003e2.92***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003cp\u003e2.500**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003cp\u003e2.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCCORR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.097\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e-1.91*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026mdash;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026mdash;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGOVS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026mdash;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.035\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.56\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026mdash;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRLAW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026mdash;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026mdash;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.021\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e_cons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.041\u003c/p\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAR(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66912\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAR(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.32453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.42561\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7546\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSargan test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.4511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.7821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.9199\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2585\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e682\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cem\u003e***,** and * indicate the level of significance at 1%, 5% and 10%\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eEmpirical results show that only control of corruption (CCORR) reduces credit risk in the MENA region. Specifically, an improvement of 10% in control of corruption significantly lowers the NPLs by 9.7%. A corrupt system undermines financial control, allowing insolvent debtors to obtain loans through personal relationships with bankers, favoritism, or bribes. Corruption frequently encourages bankers to manipulate loan applications and approve high-risk loans for personal benefit. Furthermore, corruption encourages irresponsible behavior, especially when corrupt officials protect bankers and borrowers from punishment, increasing NPLs. Conversely, a strong anti-corruption framework enhances financial supervision by implementing rigorous and transparent procedures. This system mitigates fraudulent behaviors while ensuring more efficient resource allocation. Furthermore, strict sanctions against acts of corruption deter illicit behaviors, reducing financial risks and contributing to the stability and reliability of the banking system. Thus, our findings confirm those of Murshed and Saadet (2018) and Boudriga et al. (\u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e). The results confirm the first hypothesis (H1).\u003c/p\u003e\n \u003cp\u003eThe findings reveal that bank performance, as measured by ROA, significantly decreases the volume of NPLs by 10% in MENA banks (M1 and M2). Highly profitable banks tend to minimize non-performing loans by establishing solid systems for loan monitoring and rigorous borrower credibility assessments, thereby mitigating credit risk. Additionally, these banks often maintain a diversified loan portfolio, which helps reduce overall risk exposure. They also typically hold sufficient provisioning reserves to cover potential losses in the event of defaults. These results are consistent with the studies of Jabbouri et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Jenkins et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eUnlike the effect of ROA, the loan-to-deposit ratio (LTD) has a positive and significant association with NPLs at 1%. Credit risk and liquidity risk are recognized as reciprocal risks, with an increase in liquidity risk increasing credit risk, and vice versa (Hakimi and Khemiri, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Banks often solve a lack of liquidity by raising credit interest rates to generate higher income and mitigate liquidity risk. However, this strategy may jeopardize borrowers\u0026apos; repayment capacity, increasing the NPL ratio. This result confirms those of Boussaada et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Anastasion et al. (2019).\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e demonstrates that concentration (CONC) has a significant negative impact on non-performing loans (NPLs) in the MENA region. In a concentrated banking sector, banks enhance their risk management strategies, reduce expenses, provide competitive interest rates, and strengthen sector monitoring and supervision. Dominant banks contribute to greater stability by selecting highly creditworthy borrowers, thereby reducing credit risk. These findings align with those of Hakimi and Khemiri (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eRegarding macroeconomic variables, GDP and inflation rate are positively and significantly correlated with NPLs in the MENA region. During periods of strong economic growth, the increase in credit demand pushes banks to relax their lending conditions. However, corruption can exacerbate credit risk by encouraging borrowers to provide inaccurate information, complicating decision-making and leading to a rise in non-performing loans. These findings are consistent with those of Radivojevic et al. (2019). Furthermore, during an inflationary period, central banks typically raise interest rates to counter inflationary pressures. However, this can negatively impact loan costs and borrowers\u0026apos; ability to repay loans, making them vulnerable to rising financial charges. As a result, higher inflation rates contribute to an increase in non-performing loans. These observations are similar to those of Polat (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e: \u003cstrong\u003eThe Impact of institutional quality indicators on NPLs in the MENA region\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e5. The Impact of institutional quality indicators on NPLs\u003c/strong\u003e: \u003cstrong\u003eA sensitivity analysis between GCC and non-GCC countries\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eAs sensitivity analysis to evaluate whether institutional quality indicators influence credit risk, we intend to look into this relationship in the MENA sub-region: GCC and non-GCC countries. Our sample of 70 banks was divided into 42 banks from the six GCC countries and 28 banks belonging to six non-GCC countries.\u003c/p\u003e\n \u003cp\u003eSargan overidentification and autocorrelation tests had p-values greater than 5%, thus do not reject the null hypothesis of the validity of the overidentification restrictions and the absence of correlation. Similar to the findings of the MENA region, we found that the control of corruption and bank performance (ROA) significantly decreased the NPLs ratio in both GCC and non-GCC countries.\u003c/p\u003e\n \u003cp\u003eIn contrast, results displayed in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e show that government stability significantly decreases the level of NPLs for GCC banks, while it has the opposite effect in non-GCC countries. A period of strong political stability encourages all economic actors, including financial institutions, to adopt more opportunistic strategies towards the economy. Stability creates an excess of confidence and encourages banks to relax credit agreement conditions by reducing requirements related to borrowers\u0026apos; solvency and guarantees, increasing NPLs. Political stability can distort economic reality and encourage risky investments by promoting excessive access to credit, making them vulnerable to subsequent economic shocks. Even in a politically stable environment, the economy might experience periods of stagnation that deteriorate borrowers\u0026apos; financial situations, accumulating NPLs. Our findings corroborate those of Alnabulsi et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). The second hypothesis (H2) has been confirmed only in the GCC countries.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe Impact of institutional quality indicators on NPLs in the GCC/non-GCC countries\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eNPLs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eGCC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eNon-GCC\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eM1\u0026nbsp;: CCORR\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eM2\u0026nbsp;: GOVS\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eM3\u0026nbsp;: RLAW\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eM1\u0026nbsp;: CCORR\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eM2\u0026nbsp;: GOVS\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eM3\u0026nbsp;: RLAW\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCoef.\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCoef.\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCoef.\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCoef.\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCoef.\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCoef.\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNPLs (-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.530\u003c/p\u003e\n \u003cp\u003e2.580***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.688\u003c/p\u003e\n \u003cp\u003e3.800***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003cp\u003e4.880***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003cp\u003e2.59***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003cp\u003e3.37***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSIZE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003cp\u003e2.220**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003cp\u003e3.670***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003cp\u003e2.630***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.023\u003c/p\u003e\n \u003cp\u003e-3.35***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.030\u003c/p\u003e\n \u003cp\u003e-2.98***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.022\u003c/p\u003e\n \u003cp\u003e4.21***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.244\u003c/p\u003e\n \u003cp\u003e-0.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.332\u003c/p\u003e\n \u003cp\u003e-0.930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.677\u003c/p\u003e\n \u003cp\u003e-2.070**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003cp\u003e-1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.028\u003c/p\u003e\n \u003cp\u003e-2.78***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.024\u003c/p\u003e\n \u003cp\u003e-2.02**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eROA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.262\u003c/p\u003e\n \u003cp\u003e-1.650*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.544\u003c/p\u003e\n \u003cp\u003e-1.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.462\u003c/p\u003e\n \u003cp\u003e-0.870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.812\u003c/p\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;3.58***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.051\u003c/p\u003e\n \u003cp\u003e-2.84***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.749\u003c/p\u003e\n \u003cp\u003e-3.26***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLTD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003cp\u003e-0.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003cp\u003e1.98**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0002\u003c/p\u003e\n \u003cp\u003e-1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00007\u003c/p\u003e\n \u003cp\u003e-0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003cp\u003e-0.320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003cp\u003e0.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003cp\u003e0.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003cp\u003e-4.37***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003cp\u003e-6.66***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003cp\u003e-5.36***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCONC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003cp\u003e1.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003cp\u003e1.670*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003cp\u003e2.110**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003cp\u003e-2.17**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0002\u003c/p\u003e\n \u003cp\u003e-0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePIB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003cp\u003e-1.930*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.009\u003c/p\u003e\n \u003cp\u003e-1.520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003cp\u003e-3.130***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003cp\u003e-15.40***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003cp\u003e-7.28***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003cp\u003e-11.79***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eINF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003cp\u003e-1.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.009\u003c/p\u003e\n \u003cp\u003e-0.710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.029\u003c/p\u003e\n \u003cp\u003e-2.870***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003cp\u003e7.71***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003cp\u003e3.09***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCCORR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.145\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e-2.350**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026mdash;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026mdash;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.258\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e-6.41***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026mdash;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026mdash;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGOVS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026mdash;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.079\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e-3.080***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026mdash;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026mdash;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.047\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e5.09***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026mdash;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRLAW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026mdash;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026mdash;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.353\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e-3.310***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026mdash;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026mdash;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.195\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e10.99***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e_cons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.841\u003c/p\u003e\n \u003cp\u003e2.750***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.621\u003c/p\u003e\n \u003cp\u003e3.350***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.375\u003c/p\u003e\n \u003cp\u003e-2.270**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003cp\u003e4.48***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.987\u003c/p\u003e\n \u003cp\u003e4.04***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.768\u003c/p\u003e\n \u003cp\u003e6.25***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAR(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.1194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.2147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.1775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.0615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.8076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.77409\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4389\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAR(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.32472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.3964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.50667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.0556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.3757\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1689\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSargan test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.2127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.9446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.0325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.629\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e292\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cem\u003e***,** and * indicate the level of significance at 1%, 5% and 10%\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eContrary to the results of the whole sample, we found that the role of low negatively and significantly affects the NPLs ratio in the GCC countries. Whereas this effect is the opposite in the non-GCC countries. An ineffective regulatory framework leads bankers and borrowers to disregard contracts, fostering unclear or insufficient standards and regulations for provisioning requirements and borrower creditworthiness assessments. Additionally, a poor legal system complicates the swift enforcement of guarantees and impedes efficient decision-making. In a corrupt environment, poor adherence to the rule of law fosters unethical practices and weakens the fair enforcement of regulations. This dynamic motivates lenders and borrowers to engage in excessive risk-taking, confident that they will face any penalties. These findings contradicted those of Murshed and Saadet (2018) and Alnabulsi et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). The results confirm the third hypothesis (H3) in GCC countries, but it is rejected in non-GCC countries.\u003c/p\u003e\n \u003cp\u003eWe also found that the more the lag of NPLs (-1) increases, the more the current NPLs increase across the two sub-regions. Furthermore, the large bank size increases significantly the level of NPLs in the GCC countries. In contrast, it was found to have a negative effect on NPLs in the non-GCC countries. Large banks may attempt to improve the profitability of their assets by expanding the volume of loans and loosening lending standards, frequently without paying adequate attention to default risks. Furthermore, these banks may confront managerial challenges arising from the complexities of communication and coordination between managers and administrators. However, as stated by Jabbouri et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), large banks tend to maintain low levels of non-performing loans, attributed to their advanced risk management processes and tools. These conclusions correspond with the results of Hakimi and Khemiri (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Chaibi and Ftiti (\u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn the GCC and non-GCC countries, the higher the capital adequacy ratio, the lower the credit risk. Better-capitalized banks are generally exposed to less credit risk and are more resilient to unexpected losses. Overcapitalized banks employ highly efficient credit management strategies to address information asymmetry issues. They implement more effective measures to prevent non-productive loans and preserve their capital reserves, which are used in case of risk. These findings are similar to those of Naili and Lahrichi (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Alnabulsi et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eBank diversification has a negative and significant impact on the amount of NPLs. Diversified banks generate a large portion of income from non-lending activities, allowing them to better manage their credit risk exposure. Moreover, diversifying banking activities promotes a distribution of risks across different sectors, thereby reducing the accumulation of non-performing loans (NPLs). Furthermore, a diverse source of income enhances banks\u0026apos; resilience in the face of financial crises, allowing them to maintain strict and prudent credit standards when providing loans, thus contributing to NPL reduction. These findings align with the conclusions of Saliba et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn contrast to the effect of bank concentration on NPLs in the MENA region, it significantly increases the level of NPLs in GCC countries. Banking concentration increases NPLs by reducing competition, weakening market discipline, and encouraging dominant banks to engage in riskier lending. These banks often focus their operations on specific sectors, making them particularly vulnerable to economic downturns in those industries. Additionally, the \u0026quot;too big to fail\u0026quot; phenomenon fosters excessive risk-taking, as banks expect government intervention during crises. A lack of innovation in risk management and lending to high-risk borrowers further exacerbates the likelihood of loan defaults. As a result, banking concentration heightens financial risks and accelerates the accumulation of non-performing assets. These results support those of Canh et al. (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eConcerning the macroeconomic factors, findings indicate that the GDP significantly decreases the NPLs ratio in both GCC and non-GCC countries, while the inflation rate reduces the NPLs ratio only in the GCC banks. During economic expansion, rising household incomes enhance their purchasing power, boosting corporate profitability. This cycle improves borrowers\u0026apos; financial stability, thereby lowering default risks. Conversely, during periods of high inflation, diminished purchasing power weakens the financial capacity of both households and businesses. In response, banks implement stricter credit policies and tighten loan approval criteria. Additionally, reduced income levels lead to a decline in credit demand, indirectly curbing the accumulation of non-performing loans while promoting stricter risk management practices. Our results confirm those of Hakimi et al. (2023) and Jabbouri et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e: \u003cstrong\u003eThe Impact of institutional quality indicators on NPLs in the GCC/non-GCC countries\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Heading\"\u003e6. Robustness check: \u003cem\u003eThe use of an institutional quality index\u003c/em\u003e\u003c/div\u003e\n\u003cp\u003eAlthough the importance of the quality of the institutional environment has been recognized, which can influence the stability, regulation, and supervision of the banking system and thus improve the behavior of banks, there is little evidence that the Worldwide Governance Indicators (WGI) as a whole are directly linked to a reduction in NPLs. Based on the results obtained in the MENA region, the GCC, and non-GCC countries, we have built an institutional quality index (IQ index) from the following six WGI: control of corruption (CCORR), political stability (GOVS), rule of law (RLAW), Voice and Accountability (VA), Government Effectiveness (EG), and regulatory Quality (QR).\u003c/p\u003e\n\u003cp\u003eThis study takes a more holistic approach using the Principal Component Analysis (PCA) method. Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e shows a strong correlation below 70% between all the indicators in the QI_index. As a result, we will employ the PCA method of Hotelling and Thurstone (1934), which has its main advantage of isolating the common component (Goyal et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, PCA aids in the resolution of multicollinearity concerns that build when numerous highly correlated variables are included separately in the same regression (Wooldridge, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelation matrix of Institutional quality indicators\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCCORR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEG\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGOVS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRLAW\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVA\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCCORR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9248*\u003c/p\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGOVS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8014*\u003c/p\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6814*\u003c/p\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8216*\u003c/p\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8955*\u003c/p\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6541*\u003c/p\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRLAW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9041*\u003c/p\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8413*\u003c/p\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8371*\u003c/p\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8325*\u003c/p\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.2886* 0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2760*\u003c/p\u003e\n \u003cp\u003e0.0005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.2442* 0.0021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.3279* 0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.3297*\u003c/p\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cem\u003e*, indicates the level of significance at 5%\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e: \u003cstrong\u003eCorrelation matrix of Institutional quality indicators\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PCA technique consists of two main steps. The first uses the Min-Max approach to normalize each institutional quality indicator (Hakimi et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). This recently transforms data values within a given range, usually between 0 and 1. This phase minimizes the data\u0026apos;s dimensionality while keeping the most important information. VN\u003csub\u003ek\u003c/sub\u003e is the normalized vector of each indicator during period t, with min (QI) and max (QI) representing the minimum and maximum values of each indicator, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe second step is to ponderate each normalized indicator equally, using coefficients equal to 1/N, to ensure that all variables have uniform influence. This approach is easier, more practical, and more equitable, allowing for a consistent analysis of the variables\u0026apos; relevance without bias and avoiding weighting biases. Once the QI_index has been created, the new values will range from 0 to 1, with 0 representing the poorest institutional quality and 1 being the best. Here, N is the number of WGI used to build the QI_index.\u003c/p\u003e\n\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e shows the effect of the institutional quality index on NPLs in the MENA region, GCC, and non-GCC countries. The findings reveal that the QI_index significantly decreases NPLs in both GCC and non-GCC countries. By maintaining stable and transparent governance, institutional quality minimizes the volume of non-performing loans (NPLs), hence mitigating political and economic instability. In the case of a default, strong institutions enhance the chances of debt recovery by ensuring an efficient judiciary and protecting creditors\u0026apos; rights. Additionally, strict banking regulations and effective risk management promote diligent credit monitoring, which in turn minimizes excessive risk-taking. Ultimately, a stable economy supported by strong institutions enhances borrowers\u0026apos; ability to repay their debts, thereby lowering the likelihood of default. Our findings align with those of Goyal et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab7\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe Impact of Institutional Quality Index on NPLs in the MENA, GCC and non-GCC countries\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNPLs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMENA\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eGCC\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003enon-GCC\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eQI_index\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eCoef.\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eQI_index\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eCoef.\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eQI_index\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eCoef.\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNPLs(-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.026\u003c/p\u003e\n \u003cp\u003e-0.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.736\u003c/p\u003e\n \u003cp\u003e4.470***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSIZE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003cp\u003e1.410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003cp\u003e1.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.041\u003c/p\u003e\n \u003cp\u003e-6.32***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.220\u003c/p\u003e\n \u003cp\u003e0.860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.916\u003c/p\u003e\n \u003cp\u003e-3.090***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eROA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.208\u003c/p\u003e\n \u003cp\u003e-1.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.326\u003c/p\u003e\n \u003cp\u003e-0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.794\u003c/p\u003e\n \u003cp\u003e-3.38***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLTD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003cp\u003e6.060***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003cp\u003e-0.410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003cp\u003e-2.62***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003cp\u003e-0.240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003cp\u003e-0.610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003cp\u003e-5.57***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCONC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.009\u003c/p\u003e\n \u003cp\u003e-4.200***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003cp\u003e4.070***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003cp\u003e-4.74***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePIB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003cp\u003e1.630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.020\u003c/p\u003e\n \u003cp\u003e-4.290***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003cp\u003e-4.87***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eINF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003cp\u003e2.560**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.041\u003c/p\u003e\n \u003cp\u003e-4.310***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003cp\u003e5.90***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQI_INDEX\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.195\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.200\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.901\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e-3.580***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.087\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e12.65***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e_cons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.032\u003c/p\u003e\n \u003cp\u003e-0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.896\u003c/p\u003e\n \u003cp\u003e-3.640***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003cp\u003e3.84***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAR(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.98762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.0794\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2804\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAR(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.2557\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2092\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSargan Test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.8847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.3309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.825\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e292\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cem\u003e*** and ** indicate the level of significance at 1% and 5%\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eRegarding the effect of control variables, no significant effect has been noticed compared to the results displayed in Tables\u0026nbsp;(4) and (5).\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e: \u003cstrong\u003eThe Impact of Institutional Quality Index on NPLs in the MENA, GCC and non-GCC countries\u003c/strong\u003e\u003c/p\u003e"},{"header":"Conclusion and Policy Recommendations","content":"\u003cp\u003eTo visualize the influence of institutional quality on NPLs in the MENA region, we estimated a dynamic panel model using the SGMM approach. To conduct a comparative study, we divided our sample into GCC and non-CCG countries using the same empirical approach.\u003c/p\u003e \u003cp\u003eComparing the results for all the regions in our sample, we observe that the impact of institutional quality on credit risk differs with the region, although there are some similarities. The control of corruption has a significant negative impact on NPLs in all our regions. Government stability has a negative and significant effect on NPLs in GCC countries, whereas it positively and significantly influences NPLs in non-GCC nations. Similarly, the rule of law significantly reduces NPLs in GCC countries but increases them in non-GCC countries. The institutional quality index has a negative and significant impact on NPLs in both GCC and NGCC countries but shows no significant effect on NPLs in the MENA region.\u003c/p\u003e \u003cp\u003eThe disparities in the results indicate that each region has unique institutional characteristics and responds differently to banking credit risk. Nevertheless, all regions place significant emphasis on combating corruption. In conclusion, the findings highlight that enhancing institutional quality is essential for mitigating credit risk across the MENA region and its sub-regions (GCC and non-GCC countries).\u003c/p\u003e \u003cp\u003eTo ensure the stability of the financial sector, maintain the continuity of its activities, and effectively manage credit risks, it is crucial to establish a strong and stable institutional environment. This environment must emphasize combating corruption by reducing uncertainty and ensuring the consistent enforcement of rules. Additionally, it is vital to develop an institutional framework that fosters sustainable financial and economic development in both GCC and non-GCC countries. This can be achieved by enhancing political and governmental stability, reinforcing legal stability, and ensuring sufficient legal protection for creditors' rights, particularly concerning the guarantees required.\u003c/p\u003e \u003cp\u003eThe current study has a few limitations. First, some MENA countries were excluded due to the unavailability of institutional quality data. Second, a comparative analysis of MENA countries based on the nature of institutional quality indicators (whether poor or good) was not explored. Lastly, the potential moderating role of corporate social responsibility in the relationship between institutional quality and credit risk was not examined. These limitations present opportunities for future research.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSS and AH conceptualized the study, obtained the data, conducted the data analysis and drafted the paper. AH and HS contributed to the model development and results interpretation. SS contributed to the literature review, the formation and compilation of conclusion. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdem, M. (2022). Determinants of Credit Risk in Ethiopian Banking Industry: Does Political Stability Matter? Sage journals. https://doi.org/10.1177/09721509221104244\u003c/li\u003e\n\u003cli\u003eAhmed, S., Majeed, M. E., Thalassinos, E., \u0026amp; Thalassinos, Y. (2021). The Impact of Bank Specific and Macro-Economic Factors on Non-Performing Loans in the Banking Sector : Evidence from an Emerging Economy. \u003cem\u003eJournal of Risk and Financial Management, \u003c/em\u003e14: 217, 1‑14. https://doi.org/10.3390/jrfm14050217\u003c/li\u003e\n\u003cli\u003eAlnabulsi, K., Kozarevic, E., \u0026amp; Hakimi, A. (2022). 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Econometric Analysis of Cross Section and Panel Data. https://www.jstor.org/stable/j.ctt5hhcfr\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Institutional Quality, Corruption, Government Stability, Rule of Law, Non-performing Loans, MENA Region, SGMM","lastPublishedDoi":"10.21203/rs.3.rs-6153859/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6153859/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eInstitutional quality plays a critical role in controlling nonperforming loans (NPLs) by ensuring strong legal frameworks, effective regulations, and good governance in the banking sector. The primary objective of this article is to evaluate how institutional quality indicators influence NPLs in the MENA region. To explore this relationship, we employed the System Generalized Method of Moments (SGMM) approach to estimate a dynamic panel model based on data from 70 banks across 12 MENA countries, observed from 2010 to 2022. The findings indicate that controlling corruption is a crucial factor in reducing NPLs in both the MENA region and its sub-regions. In terms of government stability and the rule of law, these factors significantly lower NPLs in the Gulf Cooperation Council countries (GCC), however, they appear to have the opposite effect in the non-Gulf Cooperation Council countries (non-GCC). The results of the sensitive analysis proved that banks faced a lower level of NPLs when the institutional quality improved in all the regions studied. The results of this paper have substantial implications. Strengthening institutional frameworks can enhance banking stability by reducing default risks and ensuring efficient loan recovery mechanisms. Additionally, Policymakers should prioritize regulatory reforms and anti-corruption measures to mitigate the adverse effects of weak institutions on nonperforming loan accumulation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL codes : \u003c/strong\u003eD73, G18, G21, G28\u003c/p\u003e","manuscriptTitle":"Does institutional quality affect non-performing loans in the MENA countries? 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