Natural Resource Rent and Bank Stability in the MENA Region: Does Institutional Quality Matter?

Abstract

In natural resource-dependent economies, global resource price volatility makes financial systems more vulnerable to economic shocks. The relationship between natural resource rent and bank stability lies in how fluctuations in resource revenues can affect financial institutions’ stability. The purpose of this paper is twofold. First, it explores the effect of natural resource rent (NRR) on bank stability (BS) in the Middle East and North Africa (MENA) region. Second, it examines whether institutional quality (IQ) moderates the association between BS and NRR. To achieve these goals, we used a sample of 68 conventional banks located in the MENA region between 2005 and 2020 and performed the System Generalized Method of Moments (SGMM) as an econometric approach. The empirical findings show that NRR is negatively and significantly associated with BS, while IQ significantly enhances BS in the MENA region. Additionally, the outcomes support evidence that the MENA banks benefit from an interaction between IQ and NRR. This result was confirmed for both the Z-ROA and Z-ROE as measures of BS. The results of this paper could have several useful applications for policymakers and bankers. Policymakers should prioritize strengthening institutional frameworks to mitigate the adverse effects of resource dependence on financial stability. In addition, bankers are invited to focus on improving institutional quality by fostering an institutional environment, including compliance with anti-corruption standards and coordination with regulatory bodies to boost financial resilience.

1. Introduction

Banks play a crucial role in the economy’s financing. By enabling a simpler and more extensive transfer of capital within a nation, they serve as a financial intermediary between depositors and borrowers. McLeay et al. (2014) expound on how the fractional reserve mechanism of the banks transforms initial deposits into larger money, facilitating economic growth and development. In addition, banks make credit and loans available to households, firms wishing to expand their businesses, and governments financing infrastructure. They also provide various other financial products, such as risk management, investment guidance, and payment processing, which are necessary to maintain economic stability (Allen et al. 2012, 2019; Kahn et al. 2003). Since banks are the primary source of finance for economies, they are important in ensuring economic development. The banking system contributes to the acceleration of economic growth by facilitating investment and business activity. In some cases, the stability of a country with a highly indebted economy often depends on the stability of its financial sector. For this reason, it is very useful to identify factors that increase profitability and enhance bank stability.

The World Bank defines bank stability as follows: there are numerous definitions of bank stability. What most of them have in common is that financial stability is characterized by the absence of system-wide episodes in which the bank system fails to function (crises). It is also about the resilience of the banking system to stress. Schinasi (2004), from the International Monetary Fund (IMF), defines bank stability as the ability of the bank to facilitate and enhance economic processes, manage risks, and absorb shocks. Moreover, bank stability is considered a continuum: changeable over time and consistent with multiple combinations of the constituent elements of finance.

Several prior studies have reported that natural resources are crucial for economic growth and bank stability, as they drive industrial output, trade, and financial resilience. According to the definition of the World Bank, “total natural resource rents are the sum of oil rents, natural gas rents, coal rents (hard and soft), mineral rents, and forest rents. The estimates of natural resource rents are calculated as the difference between the price of a commodity and the average cost of producing it”.

In regions such as the MENA area, where natural resources are considered a significant portion of gross domestic product (GDP), the interplay between resource dependence and financial stability warrants closer examination. According to the statistics of the World Development Indicators (WDI) database, natural resource rents (NRR) represent 24.67% of GDP in 2000, 24.87% in 2010, and 18.36% in 2021. Compared to the MENA countries, other regions recorded low percentages. For example, in 2021, NRR in % of GDP represented only 1.43% in OECD members, 1.27% in the United States, and 10.43% in Europe and Central Asia. While NRRs can provide critical economic support, their volatility often poses risks, including economic instability and governance challenges, which can have far-reaching implications for the banking sector.

Similarly to NRR, institutional quality is vital for economic stability and bank resilience, as it ensures effective governance, legal frameworks, and trust in financial systems. High-quality institutions can make the implementation of effective policies and reforms possible, attract foreign investments, and achieve economic growth. Kaufmann and Kraay (2024) define institutional quality as “indicators of governance” divided below into six governance dimensions, based on a definition of governance as “the traditions and institutions by which authority in a country is exercised. This includes (a) the process by which governments are selected, monitored, and replaced; (b) the capacity of the government to effectively formulate and implement sound policies; and (c) the respect of citizens and the state for the institutions that govern economic and social interactions among them”. They consider six dimensions: Voice and Accountability (VA), Political Stability and Absence of Violence/Terrorism (PV), Government Effectiveness (GE), Regulatory Quality (RQ), Rule of Law (RL), and Control of Corruption (CC).

Good institutional quality seems to be a determinant of greater bank stability, as indicated in several studies (Dutta and Saha 2021; Bermpei et al. 2018). It is for this reason that governments should create and implement policies directing economic activity and reducing the effects of financial shocks to ensure financial stability (Fazio et al. 2018). Strong institutional quality also mitigates the negative effects of market competition on bank stability (Muizzuddin et al. 2021). Additionally, IQ has a significant impact on how resource rents affect economic and financial stability. Strong institutions are critical for managing resource wealth effectively, mitigating the risks of corruption, and ensuring a stable financial environment. Conversely, weak institutions can exacerbate the resource curse, undermining economic diversification and banking sector resilience. Despite growing recognition of these dynamics, the linkages between natural resource rents, institutional quality, and bank stability remain underexplored, particularly in the MENA region.

The question of whether the effect of natural resource rent is beneficial or harmful to bank stability is closely related to the quality of institutions (Torvik 2002; Robinson et al. 2006; Mehlum et al. 2006). Abundant natural resources are specifically negative for those resource-rich countries characterized by poor governance. Indeed, weak governance is characterized by high levels of corruption and the absence of democratic institutions and press freedom, and where the rules of law and property rights are weak. This study aims to address the following question: Does IQ moderate the NRR and BS relationship for a sample of MENA banks?

To answer the two following questions, we suggest the following hypotheses:

H1. 

Natural resource rent negatively affects bank stability.

H2. 

IQ moderates the relationship between the NRR and bank stability.

The purpose of this paper is twofold. First, it examines the impact of NRR on BS. Second, it examines whether institutional quality moderates the association between BS and NRR. To achieve these goals, this study uses a sample of 68 conventional banks from 10 MENA nations between 2005 and 2020. The System Generalized Method of Moments (SGMM) is performed as an econometric technique that solves endogeneity and unobserved heterogeneity.

Investigating the relationship between natural resource rents and bank stability, with a focus on the moderating role of institutional quality, could be an interesting study for the MENA region. The MENA region could be considered an appropriate case study in several ways. First, the MENA region provides a compelling context for such an analysis due to its high dependence on natural resource revenues and the considerable variation in institutional frameworks across its countries. Secondly, the MENA region is the most reliant on resource rents globally. While the typical country in the globe receives about 5% of its GDP from total resource rents, the MENA and Arab countries receive roughly 31% and 34% of their GDP from this source, respectively. Many MENA economies are heavily reliant on oil, gas, or other natural resources for revenue, which can lead to economic instability if global prices drop. This volatility can cause large swings in national income, affecting the creditworthiness of borrowers and the ability of governments to meet fiscal obligations. Banks in such economies may face higher non-performing loans, reduced profitability, and pressure on liquidity if resource revenues decline sharply. Additionally, the overreliance on resource rent may stifle diversification, making the banking sector vulnerable to external shocks tied to global commodity markets. As a consequence, it is essential to explore these dynamic connections to implement reforms and policies that improve financial stability in economies with abundant resources.

The findings of this research are expected to advance our understanding of the complex dynamics between resource rents, institutional quality, and bank stability, offering actionable recommendations for policymakers and financial institutions in resource-dependent economies. Overall, the empirical results show that natural resource rent negatively affects bank stability, while institutional quality enhances bank stability for MENA banks. Furthermore, findings support evidence that MENA banks benefit from an interaction between natural resource rent and institutional quality. This result was confirmed for the two measures of bank stability (Z-ROA and Z-ROE).

This study adds several worthwhile contributions to the existing literature. First, to the best of our knowledge, it is the first study that examines the relationship between bank stability, NRR, and IQ in the MENA area. Few studies have looked at the micro and macro-economic effects of natural resource dependency in this area (Bilgili et al. 2023; Ragmoun 2023). Second, no studies investigate how institutional quality affects financial stability in the presence of resource rents, especially in the MENA region. By examining the dynamic relationship between NRR and bank stability, taking into account IQ’s moderating role, the current study closes this gap. Third, this study contributes to the expanding corpus of the resource curse hypothesis and its effects on financial systems, especially in resource-rich economies. Incorporating institutional quality as a moderating factor provides a more nuanced understanding of how institutions shape the impact of natural resource rents on banking sector performance. This offers both a theoretical contribution by extending the resource curse literature and a practical contribution by offering insights into the role of governance structures in fostering banking sector stability. Finally, this study provides region-specific insights by focusing on the MENA region, where countries exhibit significant variations in their reliance on natural resources and institutional quality. The findings of this paper could offer useful policy implications for MENA countries, helping to implement strategies to strengthen bank stability and manage the challenges associated with natural resource wealth.

The rest of this paper is organized as follows. Section 2 provides an overview of the literature and the development of hypotheses. Section 3 describes the sample and the empirical approach. Section 4 discusses the empirical findings. Section 5 concludes and addresses policy recommendations.

2. The Relevant Literature and Hypotheses Development

2.1. Natural Resource Rent and Bank Stability

The resource curse refers to the phenomenon where countries rich in natural resources tend to experience slower economic growth, poor governance, and greater social inequality compared to those with fewer natural resources (Auty 1993; Sachs and Warner 1995; Saeed 2021). One key mechanism is economic overdependence on a single sector, such as oil or minerals, which makes the country vulnerable to commodity price volatility. When resource prices fluctuate, they can cause large swings in national income, leading to instability, inflation, and economic mismanagement. This overreliance on resources often crowds out other industries, stifling diversification and innovation, which are essential for sustainable growth.

Another mechanism involves governance and institutional challenges. In resource-rich countries, large rents from natural resources can foster corruption and poor governance (Sharma and Mishra 2022; Dong et al. 2019). The windfall of resource revenues may lead to a concentration of wealth and power among elites, undermining democratic processes and fueling political instability. Moreover, the abundance of resources can reduce the need for effective tax systems and public accountability, as governments rely on resource rents rather than broad-based taxation. This lack of institutional development, combined with the temptation for rent-seeking behavior, often results in weak rule of law, ineffective public services, and social unrest, further hindering long-term economic and political stability.

Defined as the economic gains derived from the extraction of natural resources, natural resource rents have complex implications for financial stability and development. The relationship between NRR and BS is complex and influenced by governance, economic conditions, and the management of resource wealth. While natural resource rents can provide significant economic benefits, they may also lead to instability if it is not properly managed.

The linkage between NRR and BS is complex and has been the subject of considerable debate in economic theory. The theory of the “resource curse” suggests that countries rich in natural resources may experience negative economic and financial outcomes, including instability in their banking systems. The resource curse theory also suggests that developing countries with abundant natural resources may face slower economic growth (Adabor and Mishra 2023). This paradox occurs because natural resource wealth often leads to economic distortions, such as excessive reliance on resource exports, neglect of other sectors, and greater exposure to global commodity price volatility. Such volatility can result in economic instability, which can, in turn, destabilize the banking sector by increasing default risks and reducing credit availability. Empirically, prior research indicates that natural resource rents can negatively affect financial development. For example, a study analyzing data from 20 African countries found a robust negative relationship between natural resource rents and both stock market capitalization and private credit availability (Nurmakhanova et al. 2023).

The volatility of natural resource rents is another important factor that affects bank stability. Resource-rich economies often experience significant fluctuations in the prices of natural resources, which can lead to macroeconomic instability. These fluctuations can influence interest rates, exchange rates, inflation, and overall economic growth. Banks operating in such environments face higher risks, as the volatility can affect borrowers’ ability to repay loans, lead to asset price bubbles, and create uncertainty in financial markets. Prior works have examined the direct effects of NRR on banking sector stability, focusing on credit growth, non-performing loans (NPLs), and overall financial system risk. For instance, studies have shown that in countries with high dependence on natural resources, banking systems are often more vulnerable to external shocks, such as commodity price fluctuations. Resource price volatility can lead to sharp declines in government revenue, impacting the liquidity and capital adequacy of banks. For example, the study by Arezki and Brückner (2011) found that countries with higher natural resource rents tend to experience higher levels of banking instability due to their vulnerability to price shocks.

The theoretical and empirical literature suggests that while natural resource rents can provide significant economic benefits, they also present risks to bank stability. The volatility associated with resource rents, along with the economic distortions caused by excessive dependence on natural resources, can undermine financial stability. Based on previous studies that provide evidence of the negative effects of natural resource rent on bank stability, we suggest the following hypothesis: H1: natural resources’ price volatility that affects natural resource rents negatively affects bank stability.

Figure 1 summarizes the transmission channels on how natural resource rent can affect bank stability.

2.2. The Moderating Role of IQ in the NRR–Bank Stability Relationship

IQ is important in shaping the stability of financial systems, as it influences how resources are allocated, regulated, and monitored within the economy. The stability of the banking sector is significantly affected by the institutional environment in which banks operate. Theoretical frameworks in institutional economics emphasize those high-quality institutions defined by factors such as the rule of law, transparency, effective governance, and low corruption. These factors can create a conducive environment for economic stability and growth. According to North (1990), institutions reduce transaction costs and mitigate risks in the financial system. Institutional quality provides a framework for enforcing contracts, protecting property rights, and ensuring the proper functioning of markets. Strong institutions, therefore, reduce uncertainty and contribute to financial system stability by promoting confidence in the banking sector.

A key theoretical contribution comes from the governance and regulatory quality literature, which links the effectiveness of financial regulation with institutional quality. In environments where regulatory frameworks are weak or poorly enforced, banks face greater risks, including asset mismanagement, credit risk, and fraud. In contrast, strong regulatory frameworks supported by transparent and efficient institutions can improve financial stability by reducing information asymmetries, ensuring financial discipline, and preventing excessive risk-taking by financial institutions. In this regard, La Porta and Lopez-de-Silanes (1998) argue that stronger legal and regulatory institutions are associated with better protection for investors, which contributes to lower financial instability. Conversely, Rajan and Zingales (2003) state that poor institutional quality can lead to the misallocation of capital, higher levels of corruption, and weaker enforcement of financial contracts. These factors can contribute to the accumulation of systemic risks in the banking sector. When institutions are weak, banks may engage in riskier lending practices, reducing their resilience to financial shocks and increasing the likelihood of financial crises. However, strong institutions can help mitigate these risks by providing checks and balances, encouraging prudent banking practices, and enabling effective crisis management when problems arise.

Empirical research on the linkage between IQ and bank stability has confirmed the theoretical understanding that strong institutions contribute to the stability of the banking sector. Empirical results reveal that IQ directly affects the performance and stability of banks. For example, Laeven and Levine (2009) highlighted the importance of governance quality in determining the stability of financial institutions. They found that strong corporate governance practices, which are a key component of institutional quality, are associated with lower levels of risk and more stable banking systems. In contrast, weak governance and regulatory oversight create environments conducive to excessive risk-taking, which increases the likelihood of banking sector instability. Moreover, countries with weak legal systems, characterized by inefficiencies in contract enforcement and protection of property rights, tend to experience higher levels of bank failures and financial instability.

Research on how resilient the banking industry is to outside shocks highlights the importance of institutional quality in improving banks’ capacity to control and manage risks. According to Kim and Choi (2020), banks in countries with strong institutional quality are more resilient during the global financial crisis of 2007–2008. These banks had stronger regulatory oversight, more effective risk management frameworks, and more transparent operations. Conversely, in countries with weaker institutions, banks were more vulnerable to the crisis, suffering from higher levels of non-performing loans and liquidity shortages.

The theoretical and empirical literature confirms that institutional quality is a key determinant of bank stability. Strong institutions, including effective governance, regulatory frameworks, and transparent legal systems, create a conducive environment for banking stability by reducing financial system risks, improving crisis management, and encouraging responsible banking practices. Conversely, weak institutions are associated with higher levels of corruption, mismanagement, and financial instability, which increase the likelihood of banking crises. These insights suggest that enhancing institutional quality should be a key focus for policymakers seeking to promote financial stability, particularly in developing and emerging economies.

The relationship between natural resource rents and bank stability is considered a complex one. Indeed, institutional quality could play a crucial moderating role in this relationship. Strong institutions play a critical role in mitigating the negative effects of natural resource rents on bank stability. Good governance, transparent fiscal management, and sound regulatory frameworks can help manage resource wealth in ways that promote long-term economic stability. Conversely, weak institutions are often associated with corruption, mismanagement of resource rents, and inefficient allocation of financial resources, all of which can undermine the stability of the banking sector.

Several empirical studies have highlighted the importance of institutional quality in moderating the impact of natural resource rents on bank stability. Mehlum et al. (2006) found that strong institutions can mitigate the negative effects of resource dependence by promoting economic diversification, ensuring effective management of resource revenues, and creating a stable regulatory environment for the banking sector. In contrast, weak institutions can exacerbate the resource curse by encouraging rent-seeking behavior and poor governance, which can destabilize financial systems. For example, Sachs and Warner (2001) demonstrated that countries with weak institutional frameworks tend to experience higher levels of corruption and mismanagement, leading to less stable banking sectors. Additionally, some empirical studies that compare different regions have found significant changes in the relationship between natural resource rents and bank stability, often driven by differences in institutional quality. In the MENA region, Davoodi and Abed (2003) found that countries with better governance structures, such as the UAE and Qatar, experienced greater banking stability despite their reliance on natural resources. On the other hand, countries with weaker institutions, such as Algeria and Iraq, showed higher levels of financial instability, which could be linked to the mismanagement of resource rents and poor regulatory frameworks.

Recently, Niftiyev (2022a) compared institutional quality among Georgia, Azerbaijan, and Armenia, highlighting Azerbaijan’s reliance on resources from its oil resources. It concludes that institutional factors in Azerbaijan, such as government effectiveness and control of corruption, have a negative relationship with oil-related factors, supporting the natural resource curse hypothesis. This suggests that while Azerbaijan is oil-rich, its institutional quality may be deteriorating, leading to economic growth that is lower than Georgia’s, with better institutional development but lower economic indicators. In another study, Niftiyev (2022b) analyzed NRC using principal component and regression techniques (dynamic and ordinary least squares) in Azerbaijan between 1996 and 2019. The author found that the oil industry had a negative impact on institutional quality. More recently, Sedighi and Niftiyev (2024) examined the interaction between rent flows, financial development, and institutional quality within the MENA region during 1990–2020. They found that institutional quality not only moderates the effect of resource rents on economic performance but also interacts with financial development to determine long-run growth dynamics.

The theoretical and empirical literature suggests that while natural resource rents can provide significant economic benefits, they also present risks to bank stability, particularly when combined with institutional weaknesses. The volatility associated with resource rents, along with the economic distortions caused by excessive dependence on natural resources, can undermine financial stability. However, strong institutional quality can play a crucial role in mitigating these risks, highlighting the importance of sound governance and regulatory frameworks in ensuring the stability of the banking sector in resource-rich economies. This paper examines the moderating effect of institutional quality on the relationship between NRR and bank stability in the MENA area, building on the theoretical and empirical insights produced by the relevant literature.

3. Sample, Empirical Strategy, and Model Specification

3.1. Description of the Sample and Variable Selection

Using a sample of conventional banks belonging to ten (10) MENA countries between 2005 and 2020, we investigated the effect of natural resource rent on bank stability and checked whether institutional quality could moderate the relationship between natural resource rent and bank stability. The initial sample covers 109 banks. However, several banks have been excluded due to discontinuity or unavailability of some bank information. As a result, only 68 conventional banks made up the final sample. There are three main sources of the variables used in this study. Accounting and financial variables of bank specifics are collected from the annual reports of individual banks and the Refinitiv Eikon database (2005–2020). Variables relative to the institutional quality and macroeconomic factors are collected from the World Bank Database (2005–2020). More precisely, institutional variables are derived from the World Governance Indicators (WGI 2005–2020) database. Macroeconomic variables are collected from the World Development Indicators (WDI 2005–2020) database. For more details on the distribution of banks by country, see

Table 1. Number of banks per country and summary statistics on bank stability and NRR.

Middle East North Africa Countries			Bank Stability	Natural Resource Rents
Countries	Number of Banks	%	Bank Z-Score (*1)	NRR in % of GDP (*2)
Jordan	13	19.11%	52.464	1.59
Kuwait	5	7.35%	16.607	46.55
Oman	3	4.41%	17.583	33.13
Lebanon	4	5.88%	17.853	0.001
Qatar	4	5.88%	24.979	29.31
Saudi Arabia	8	11.76%	19.639	37.90
United Arab Emirates	13	19.11%	25.538	21.58
Egypt	4	5.88%	17.745	9.24
Morocco	4	5.88%	38.995	3.30
Tunisia	10	14.70%	32.663	4.86
Number of banks	68	100%	Average = 26.40	Average = 18.74%

Note: (*1) Statistics relative to bank Z-score are collected for the Global Financial Development (2005–2020) Database; (*2) Statistics relative to NRR in % of GDP are collected for the World Development Indicators (2005–2020) Database.

.

3.1.1. Dependent Variable: Bank Stability

In this paper, we extend the existing literature by investigating the effect of natural resource rent and institutional quality on banking stability. To capture this relationship, the dependent variable used in this study is the bank stability measured by both Z-score (ROA) and Z-score (ROE). Referring to Zaghdoudi (2019) and Hakimi and Zaghdoudi (2017), we employ these two metrics that represent different dimensions of bank stability. The Z-score (ROA) is equal to the return on assets plus the capital adequacy ratio divided by the standard deviation of the return on assets. The Z-score (ROE) is equal to the return on equity plus the capital adequacy ratio divided by the standard deviation of the return on equity. The bank’s efforts to mitigate risks and absorb losses were reflected in the Z-score. The bank was steady when the Z-score number was high, and vice versa.

3.1.2. Main Explanatory Variable: Natural Resource Rent

To examine the impact of natural resource rents on bank stability, we employ natural resource rents as the main explanatory variable. This is measured as the total natural resource rents comprising oil rents, mineral rents, natural gas rents, coal rents, and forest rents expressed as a percentage of GDP, a metric commonly utilized in the comparative literature. (Aljarallah 2019).

3.1.3. Other Explanatory Variable: Institutional Quality

The quality of governance institutions in the MENA region is assessed using the World Governance Indicators (WGIs). Six policy variables, sourced from the World Bank’s global governance dataset and analyzed by Kaufmann et al. (2011), are included. These indicators include control of corruption, government effectiveness, political stability, absence of violence/terrorism, regulatory quality, rule of law, and voice and accountability. As outlined by Kaufmann et al. (2011), institutional quality is measured as the average of these six indicators, with values ranging from −2.5 (indicating poor governance) to 2.5 (indicating strong governance).

3.1.4. Control Variables

The econometric models of this paper incorporate several control variables. The first category is relative to bank-specific factors, including bank size (BS), which is used to explain variations in bank stability (Ghenimi et al. 2017), and capital adequacy ratio (CAR), a key determinant of bank stability (Anginer et al. 2014). We also included the profitability measured by the ROA, the loan-to-deposit (LTD) ratio to measure the liquidity risk, and the non-performing loans (NPLs) ratio to measure credit risk (Zaghdoudi 2019). The second category covers industry-specific variables, such as bank concentration (CONC) and bank competition (LERN), both recognized as significant drivers of bank stability (Mercieca et al. 2007). The third group is devoted to variables that reflect institutional quality. In this study, we built an index of institutional quality using the six indicators of governance, including control of corruption, government effectiveness, political stability and absence of violence/terrorism, regulatory quality, rule of law, and voice and accountability. The fourth category encompasses macroeconomic conditions and the financial environment represented by the GDP growth rate (GDPG), inflation rate (INF), the 2008 global financial crisis (CRISIS), and the unemployment rate (UNEM) (Djebali and Zaghdoudi 2017).

3.2. Empirical Strategy and Model Specification

The SGMM technique was performed as the empirical approach in this investigation. The SGMM technique is performed to solve endogeneity, one of the main issues in corporate and banking finance. Furthermore, OLS and fixed- and random-effect (FE and RE) models frequently encounter two issues: measurement errors and omitted variable bias. In order to do this, we employed the SGMM technique initially proposed by Blundell and Bond (1998). According to Zhou (2014), Teixeira and Queirós (2016), Danisman and Tarazi (2020), and Hakimi et al. (2023), the SGMM approach yields more dependable and practical results.

In this paper, two steps are followed as an empirical strategy. We investigated the association between bank stability and natural resource rent in the first stage. Equation (1) provides the econometric model to be tested in this step:

$$\begin{gathered} {\mathrm{B}\mathrm{S}\mathrm{T}\mathrm{A}\mathrm{B}}_{\mathrm{i},\mathrm{t}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1{\mathrm{B}\mathrm{S}\mathrm{T}\mathrm{A}\mathrm{B}}_{\mathrm{i},\mathrm{t}-1}+{\mathsf{\beta }}_2{\mathrm{N}\mathrm{R}\mathrm{R}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_3{\mathrm{B}\mathrm{S}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_4{\mathrm{C}\mathrm{A}\mathrm{R}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_5{\mathrm{R}\mathrm{O}\mathrm{A}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_6{\mathrm{L}\mathrm{T}\mathrm{D}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_7{\mathrm{N}\mathrm{P}\mathrm{L}\mathrm{s}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_8{\mathrm{C}\mathrm{O}\mathrm{N}\mathrm{C}}_{\mathrm{i},\mathrm{t}} \\ +{\mathsf{\beta }}_9{\mathrm{L}\mathrm{E}\mathrm{R}\mathrm{N}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_{10}{\mathrm{G}\mathrm{D}\mathrm{P}\mathrm{G}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_{11}{\mathrm{I}\mathrm{N}\mathrm{F}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_{12}{\mathrm{C}\mathrm{R}\mathrm{I}\mathrm{S}\mathrm{I}\mathrm{S}}_{\mathrm{i},\mathrm{t}}{+{\mathsf{\beta }}_{13}{\mathrm{U}\mathrm{N}\mathrm{E}\mathrm{M}}_{\mathrm{i},\mathrm{t}}+\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}} \end{gathered}$$

(1)

In the second step, we examine whether the relationship between natural resource rent and bank stability could be moderated by institutional quality. Therefore, an interactional variable reflecting the relationship between natural resource rent and institutional quality (NRR*IQ) is incorporated into the econometric model. Equation (2) provides the econometric model to be tested:

$$\begin{array}{cc}\hfill {\mathrm{B}\mathrm{S}\mathrm{T}\mathrm{A}\mathrm{B}}_{\mathrm{i},\mathrm{t}}={\mathsf{\beta }}_0+& {\mathsf{\beta }}_1{\mathrm{B}\mathrm{S}\mathrm{T}\mathrm{A}\mathrm{B}}_{\mathrm{i},\mathrm{t}-1}+{\mathsf{\beta }}_2{\mathrm{N}\mathrm{R}\mathrm{R}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_3{\mathrm{I}\mathrm{Q}}_{\mathrm{i},\mathrm{t}}{+\mathsf{\beta }}_4{\mathrm{N}\mathrm{R}\mathrm{R}\ast \mathrm{I}\mathrm{Q}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_5{\mathrm{B}\mathrm{S}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_6{\mathrm{C}\mathrm{A}\mathrm{R}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_7{\mathrm{R}\mathrm{O}\mathrm{A}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_8{\mathrm{L}\mathrm{T}\mathrm{D}}_{\mathrm{i},\mathrm{t}}\hfill \\ & +{\mathsf{\beta }}_9{\mathrm{N}\mathrm{P}\mathrm{L}\mathrm{s}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_{10}{\mathrm{C}\mathrm{O}\mathrm{N}\mathrm{C}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_{11}{\mathrm{L}\mathrm{E}\mathrm{R}\mathrm{N}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_{12}{\mathrm{G}\mathrm{D}\mathrm{P}\mathrm{G}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_{13}{\mathrm{I}\mathrm{N}\mathrm{F}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_{14}{\mathrm{C}\mathrm{R}\mathrm{I}\mathrm{S}\mathrm{I}\mathrm{S}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_{15}{\mathrm{U}\mathrm{N}\mathrm{E}\mathrm{M}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}\hfill \end{array}$$

(2)

All variables’ definitions are given in

Table 2. Definition and measurement of variables.

Variables	Definitions	Measures
Dependent variables (BSTAB)
Z-score (ROA)	Bank stability	The ratio of the sum of the averaged ROA and the CAP to the standard deviations of ROA.
Z-score (ROE)	Bank stability	The mean of return on equities plus the capital adequacy ratio divided by the standard deviation of return on equities
Natural Resource Rent and Institutional Quality
NRR	Natural Resource Rent	The total natural resource rents expressed as a percentage of GDP
IQ	Institutional quality	An index of IQ (see Kaufmann et al. 2011)
NRR*IQ	Interactional variable	The interaction between NRR and IQ
Bank specifics
BS	Bank size	Natural logarithm of total assets
CAR	Capital adequacy ratio	Bank capital to total assets (%)
ROA	Return on assets	Net income after tax to total assets
LTD	Liquidity risk	Loan-to-deposit ratio (%)
NPLs	Non-performing loans	Bank non-performing loans to gross loans (%)
Industry specifics
CONC	Bank Concentration	Bank concentration (%)
LERN	Bank competition	The Lerner index
Financial environment and macroeconomic conditions
GDPG	The growth rate of GDP	Annual growth rate of GDP (%)
INF	The inflation rate	Consumer price index (%)
CRISIS	Global financial crisis of 2008	Dummy variable that takes 0 before the crisis of 2008 and 1 after.
UNEM	The unemployment rate	The unemployment rate (%)

.

4. Analysis and Results

4.1. Summary Statistics and Correlation Matrix

Descriptive statistics highlight the main characteristics of the data used in this investigation. For each variable, we present the mean, standard deviation, minimum, and maximum values. In line with the descriptive data presented in

Table 3. Descriptive statistics.

Variable	Mean	Std. Dev.	Min	Max
LNZROA	2.657	0.860	−2.798	4.431
LNZROE	1.303	0.655	−1.269	3.229
NRR	16.925	16.743	0.001	59.069
IQ	−0.038	0.417	−1.008	0.724
BS	9.887	2.660	5.045	18.080
CAR	14.869	4.941	1.256	40.350
ROA	1.954	3.505	−10.304	101.432
LTD	82.676	27.869	1.438	215.322
NPLs	8.267	7.692	0.010	58.130
CONC	67.906	19.267	40.218	100.000
LERN	0.423	0.109	0.098	0.615
GDPG	3.225	4.465	−21.464	26.170
INF	3.955	6.403	−4.863	84.864
UNEM	1.954	3.505	−10.304	101.432
CRISIS	0.812	0.390	0	1

, the average value of bank stability, as measured by (LnZ-ROA), stands at 2.657, while the maximum is 4.431 and the lowest value is −2.798. Correspondingly, the average (LnZ-ROE) stands at 1.303 and has fluctuated between its high of 3.229 and low of −1.269. The natural resource rent has a minimum value of 0.017, a highest value of 0.878, and an average value of 0.239. In the MENA area, the best institutional quality score was 0.724, while the lowest was −1.008.

Regarding bank characteristics, the average size of banks equals 9.887. Its values vary from a minimum of 2.660 to a maximum of 18.080. The Capital Adequacy Ratio (CAR) has a minimum of 1.256% and a maximum of 40.35%, with an average of 14.869%. The mean value of bank profitability, measured by ROA, stands at 1.954%. In the meantime, bank profitability registers a higher value of 101.432% and a lower value of −10.304%. According to the loan-to-deposit ratio, the mean level of liquidity risk reaches about 82.676% from a minimum value of 1.438% and a maximum value of 215.322%. The average credit risk as proxied by NPLs is 8.267%, with a high value of 58.130% and a minimum value of 0.010%.

Results in Table 3 show that, as an industry-specific measure, bank concentration (CONC) has an average value of 67.906, with the maximum and minimum values equaling 100,000 and 40.218, respectively. Bank competitions (LERN) have an average value of 0.423, while the maximum and minimum are 0.615 and 0.098. For the macroeconomic condition, GDP growth in the MENA area ranges from a maximum of 26.17% to a minimum of −21.46%, while inflation ranges from a minimum of −4.9% to a maximum of 84.86% with an average of 3.95%. UNEM ranges from a minimum of 0.11 to a maximum of 18.5, with an average of 7.999

By determining the coefficients of linear correlations between the independent variables, the correlation matrix provides details on the strength and type of those associations.

Table 4. Correlation matrix.

	NRR	IQ	BS	CAR	ROA	LTD	NPLs	CONC	LERN	GDPG	INF	CRISIS	UNEM
NRR	1.0000
IQ	0.2951 *	1.0000
	0.0000
BS	−0.0086	−0.2628 *	1.0000
	0.7776	0.0000
CAR	0.1849 *	0.2309 *	0.0073	1.0000
	0.0000	0.0021	0.8091
ROA	0.0185	−0.0031	−0.0398	0.2474 *	1.0000
	0.5415	0.9178	0.1902	0.0000
LTD	0.0873 *	0.2992 *	−0.3316 *	−0.2009 *	−0.0567	1.0000
	0.0086	0.0000	0.0000	0.0000	0.0885
NPLs	−0.2634 *	−0.1236 *	−0.2635 *	−0.2331 *	−0.0349	0.1971 *	1.0000
	0.0000	0.0010	0.0000	0.0000	0.3536	0.0000
CONC	0.0404	0.1250 *	−0.1930 *	0.0556	0.0659 *	−0.0866 *	−0.0205	1.0000
	0.1827	0.0000	0.0000	0.0668	0.0297	0.0092	0.5862
LERN	0.7070 *	0.5357 *	−0.2379 *	0.2359 *	0.1091 *	0.1673 *	−0.2673 *	0.1586 *	1.0000
	0.0000	0.0000	0.0000	0.0000	0.0054	0.0003	0.0000	0.0000
GDPG	0.1836 *	0.1372 *	−0.0843 *	0.0304	0.1135 *	−0.0611	−0.0590	0.0101	−0.0022	1.0000
	0.0000	0.0000	0.0054	0.3168	0.0002	0.0666	0.1178	0.7398	0.9562
INF	−0.0465	−0.2962 *	0.0820 *	−0.0904 *	0.0068	−0.2014 *	0.1552 *	0.1356 *	−0.2422 *	−0.1157	1.0000
	0.1350	0.0000	0.0083	0.0037	0.8270	0.0000	0.0000	0.0000	0.0000	0.0002
CRISIS	−0.1057 *	−0.0232	0.1239 *	−0.0648 *	−0.1090 *	0.0357	−0.1439 *	0.0733 *	0.1573 *	−0.3571 *	−0.0322	1.0000
	0.0005	0.4450	0.0000	0.0327	0.0003	0.2838	0.0001	0.0155	0.0001	0.0000	0.3009
UNEM	−0.7084 *	−0.5206 *	−0.3580 *	−0.2391 *	0.0302	0.0262	0.2607 *	−0.0581	−0.4822 *	−0.1384 *	0.0817 *	0.0150	1.0000
	0.0000	0.0000	0.0000	0.0000	0.3199	0.4314	0.0000	0.0555	0.0000	0.0000	0.0086	0.6217

*, indicate level of significance at 5%.

below displays the correlation matrix for each variable used in this investigation.

Graphs of Summary Statistics for the Main Variables

As additional support for the results reported in Table 4, we conducted a Variance Inflation Factors (VIFs) used to check for multicollinearity among the regressors. Values of the mean VIFs range between 1 and 5. A level of 1 indicates no correlation. Values between 2 and 5 refer to a moderate correlation. However, values above 5 indicate potential high multicollinearity.

Results from

Table 5. Variance Inflation Factor (VIF).

Model 1			Model 2
Variable	VIF	1/VIF	Variable	VIF	1/VIF
NRR	5.13	0.194	UNEM	5.14	0.194
LERN	4.16	0.240	LERN	3.10	0.322
UNEM	4.07	0.245	BS	2.91	0.344
BS	2.60	0.384	NRR*IQ	2.18	0.458
CONC	2.05	0.486	LTD	2.09	0.477
LTD	1.90	0.525	CONC	2.06	0.485
CAR	1.75	0.570	CAR	1.72	0.580
NPLS	1.65	0.604	NPLS	1.65	0.607
CRISIS	1.51	0.664	CRISIS	1.57	0.635
ROA	1.49	0.672	NRR	1.51	0.725
INF	1.37	0.730	ROA	1.48	0.674
GDPG	1.18	0.846	IQ	1.29	0.620
			INF	1.26	0.792
			GDPG	1.22	0.822
Mean VIF	2.41		Mean VIF	2.08

, show that the mean VIF for the first model that investigates the effect of NRR on bank stability is around 2.41, hence indicating no severe multicollinearity among the variables and thus showing a good moderate correlation across all values. The second model that explores the moderating effect of natural resource rent and institutional quality on bank stability presents a mean VIF of 2.08. Thus, like the first model, no strong multicollinearity would be expected. Based on the results displayed in Table 4 and Table 5, we conclude that there is no issue with the multicollinearity problem.

Before estimating the model, we proceed to check the stationarity of the studied variables. To do this, we use three Panel Unit Root tests by Levin et al. (2002), Im et al. (2003), and the ADF–Fisher developed by Maddala and Wu (1999). The results of

Table 6. Panel unit root tests.

	LLC (2002) t *	IPS (2003) W-Stat	ADF-Fisher (MW, 1999) Chi-Square
LNZROA	−2.50379 * (0.06928)	2.10789 (0.9825)	48.755 ** (0.0214)
LNZROE	−1.52762 * (0.0633)	−0.73301 (0.2318)	186.419 *** (0.0027)
NRR	−2.64772 *** (0.0041)	66.7149 * (0.0762)	96.3090 (0.9960)
IQ	−2.17113 ** (0.0150)	5.95364 * (0.0829)	103.431 (0.9829)
BS	−12.3291 *** (0.0000)	−4.37312 *** (0.0000)	248.237 *** (0.0000)
CAR	−2.33264 *** (0.0098)	−0.51402 (0.3036)	179.061 *** (0.0057)
ROA	−11.7177 *** (0.0000)	−4.81784 *** (0.0000)	239.340 *** (0.0000)
LTD	−21.6178 *** (0.0000)	−5.89156 *** (0.0000)	217.333 *** (0.0000)
NPLs	−43.0370 *** (0.0000)	−4.09924 *** (0.0000)	136.210 ** (0.0345)
CONC	−2.3712 * (0.0991)	6.01301 * (0.07850)	58.5298 (0.76321)
LERN	−9.96446 *** (0.0000)	−3.90457 *** (0.0000)	174.629 *** (0.0039)
CDPG	−3.06114 ** (0.0489)	20.4640 * (0.0993)	100.481 (0.9902)
INF	−2.56089 * (0.0694)	−0.44988 (0.3264)	115.829 * (0.0894)
CRISIS	−18.4457 *** (0.0000)	−12.3550 *** (0.0000)	394.194 *** (0.0000)
UNEM	−7.03310 ** (0.0449)	−4.32586 * (0.0887)	107.495 (0.9660)

*, **, and *** indicate statistical significance at the 1%, 5%, and 10% level.

show that two tests reject the Panel Unit Root hypothesis, and consequently, we can conclude that all the variables of the model are stationary in terms of level.

4.2. Discussion of the Empirical Findings

4.2.1. NRR and Bank Stability

In the first step of the empirical strategy, we explore the effect of NRR on bank stability in the MENA region using the Z-ROA and Z-ROE. The empirical results are shown in

Table 7. Results of the effect of Natural Resource Rent on bank stability.

	Z-Score (ROA)				Z-Score (ROE)
	Coef.	Std. Err.	Z	P > z	Coef.	Std. Err.	Z	P > z
LnZROA(-1)	0.764	0.013	56.23	0.000 ***	-	-	-	-
LnZROE(-1)	-	-	-	-	0.477	0.001	40.87	0.000 ***
NRR	−0.002	0.0004	−4.16	0.000 ***	−0.003	0.001	−3.33	0.001 ***
BS	0.034	0.100	3.46	0.001 ***	0.152	0.012	12.02	0.000 ***
CAR	0.038	0.001	35.67	0.000 ***	0.023	0.001	18.41	0.000 ***
ROA	0.099	0.003	29.65	0.000 ***	0.097	0.004	19.93	0.000 ***
LTD	−0.001	0.0001	−6.22	0.000 ***	−0.002	0.0002	−6.68	0.000 ***
NPLs	−0.006	0.0007	−7.64	0.000 ***	−0.002	0.0008	−2.39	0.017 **
CONC	−0.001	0.001	−0.89	0.373	0.010	0.0008	11.59	0.000 ***
LERN	0.031	0.138	0.23	0.818	0.865	0.108	7.97	0.000 ***
GDPG	0.003	0.000	3.59	0.000 ***	0.006	0.001	5.18	0.000 ***
INF	0.0009	0.001	0.93	0.353	0.0005	0.001	0.52	0.601
CRISIS	−0.077	0.007	−11.05	0.000 ***	−0.227	0.010	−21.47	0.000 ***
UNEM	−0.022	0.002	−7.83	0.000 ***	−0.011	0.001	−6.54	0.000 ***
_cons	0.417	0.136	3.06	0.002 ***	1.905	0.160	11.86	0.000 ***
AR(1)	−1.1277				−2.2102
Prob	0.2594				0.0271
AR(2)	0.7835				−1.2872
Prob	0.3824				0.1980
Sargan test	46.757				46.784
Prob	0.3208				0.3198
Obs	968				968

Note: ***, and ** indicate the rejection of null hypothesis at 1%, and 5% significance levels, respectively.

. These results imply that Sargan and serial correlation diagnostic tests are unable to rule out the null hypothesis of over-identifying restrictions and no correlation. Specifically, the Sargan test and the Arellano and Bond AR (2) test both had p-values above 5%.

The findings in Table 7 show that the lagged dependent variable exerts a positive and significant effect. This means that the bank stability of the previous year exerts a positive and significant impact on the bank stability measured in the current year. This result is confirmed for the two measures of bank stability, Z-ROA and Z-ROE. Concerning the effect of other variables on bank stability, we found that bank size, capital, profitability, concentration, competition, and GDP growth significantly increase bank stability. However, natural resource rent, liquidity risk, credit risk, unemployment, and crisis significantly decrease bank stability.

Results in Table 6 show that the coefficient of natural resource rent is negatively and significantly associated with bank stability. This result is confirmed for the two measures of bank stability, Z-ROA and Z-ROE. This implies that in the MENA region, natural resource rents exert a harmful effect on bank stability as a result of over-dependence on the proceeds of oil and gas resources, hence leaving banks open to volatility in commodity prices and economic shocks. In addition, the lack of economic diversification exposes banks in this region to resource-driven sectors, and procyclical fiscal policy, together with political instability, undermines financial resilience. These factors collectively increase non-performing loans, reduce profitability, and weaken banking systems. This outcome confirms the resource curse hypothesis. Countries that heavily depend on natural resource rents tend to suffer from economic volatility, corruption, and political instability. This can perhaps lead to greater risks for banks operating in such resource-dependent economies, as uncertainties in government policies and revenues affect loan repayment rates and overall financial stability. Furthermore, bank lending and liquidity may be affected by changes in bank lending behavior induced by boom-and-bust cycles in natural resource prices. In short, overdependence on NRR may pose significant risks to bank stability. This finding is consistent with the study of Arezki and Brückner (2011). Thus, we accept the hypothesis H1.

Specifically, we found that bank size is positively and significantly associated with banking stability at a 1% significance level. This means that large banks experienced a high level of bank stability. This is realized through economies of scale, which allow for cost efficiency and profitability, and their diversified portfolios and operations result in less exposure to certain risks. Their access to stable funding sources, capital markets, and advanced risk management systems strengthens the liquidity and resilience of banks. Furthermore, stricter regulatory oversight for larger banks promotes better risk management practices, and their stronger reputations enhance market confidence, reducing the likelihood of bank runs. These factors collectively improve their ability to withstand economic and financial shocks, enhancing overall bank stability. This result is in line with the work of Imbierowicz and Rauch (2014).

The capital adequacy ratio exerts a positive and significant effect on banking stability. Specifically, capital serves as a sort of financial buffer against losses and strengthens the resistance of banks in times of economic downturn. The increase in the level of capital builds a buffer to absorb surprise losses and, therefore, decreases the chance of insolvency or impairment of continuous operation under stress. Well-capitalized banks inspire greater confidence among depositors, investors, and regulators, minimizing the risk of bank runs and improving funding stability. Moreover, higher capital reduces moral hazard by encouraging prudent risk-taking because banks with more equity have more to lose in case of failure. Overall, stronger capital positions improve the ability of banks to withstand shocks, maintain liquidity, and ensure long-term stability. This finding is consistent with the works of Bourke (1989) and Molyneux and Thornton (1992).

Bank profitability, measured as ROA, significantly improves bank stability by enhancing the financial position and resilience to shocks of the bank. The higher the ROA, the more efficiently the bank generates profits, which enhances its ability and generates capital buffers internally. These could be retained earnings to absorb unexpected losses, reduce dependence on external funding, and enhance liquidity. Moreover, profitable banks are better positioned to manage risks, meet regulatory capital requirements, and withstand economic downturns. Higher profitability also boosts investor and depositor confidence, reducing the likelihood of bank runs and supporting financial stability. In essence, strong ROA enhances a bank’s ability to remain solvent, resilient, and operational during periods of stress. This result also contradicts the one found by Srairi (2013) and Imbierowicz and Rauch (2014), who investigated the negative effect of ROA on banking stability.

We also found that liquidity risk hurts bank stability. Banks that experience a lack of liquidity are forced into liquidating assets, in most cases at low prices, leading to losses in their financial books. The erosion of the capital base worsens the financial positioning of the bank. Additionally, liquidity risk undermines depositor and investor confidence, where large numbers of depositors withdraw funds simultaneously, further straining liquidity. If unresolved, liquidity crises can escalate into solvency issues, as the bank may struggle to meet its operational and financial commitments, leading to potential failure. Overall, high liquidity risk amplifies vulnerability to shocks, reducing bank stability and threatening the broader financial system. This negative relation between liquidity risk and the stability of banks, therefore, confirms findings by Hakimi and Zaghdoudi (2017) and Djebali and Zaghdoudi (2020).

As for the effect of credit risk, it was found to be negatively and significantly linked to bank stability. Credit risk increases the probability of loan defaults and deteriorates the quality of a bank’s assets. If borrowers default on their loans, banks are confronted with higher NPLs, which in turn reduce interest income and require large loan loss provisions. This reduces the profitability of banks and thus weakens the capital base of the bank, making it more vulnerable to shocks. Prolonged credit risk tends to weaken the confidence of both investors and depositors, along with liquidity. Moreover, higher credit risk compels banks to utilize more inputs to monitor defaulted loans at the cost of growth and stability. If left unchecked, excessively high levels of credit risk lead to solvency problems and, hence, negatively affect not only the bank’s financial health but also overall stability. This goes in line with the work of Katuka et al. (2023).

For the effect of industry specifics, findings in Table 7 also indicate that a highly concentrated banking sector is associated with higher levels of stability. The coefficient for bank concentration is positive and significant at the 1% level only for the Z-ROE. There are several channels through which a more concentrated banking sector may positively influence bank stability. First, bigger banks operating in more concentrated markets realize greater economies of scale and efficiency in their activities, which contributes to greater profitability and bank stability. These results are in line with the findings of Berti et al. (2017). The LERN index variable can also positively affect bank stability as a competitiveness measure that contributes to economic growth, risk diversification, higher investment, and an improvement in the regulatory and institutional framework. All these reasons enhance the banking sector’s resilience to economic and financial shocks. This finding aligns with the work of Moudud-Ul-Huq et al. (2023).

We also found that the effect of either bank concentration or bank competition is only significant with Z-score (ROE). However, no significant effect was registered with Z-score (ROA). Bank competition and concentration have a significant effect on the Z-score (ROE) because they influence profit variability and leverage, both of which are more directly tied to return on equity (ROE) than return on assets (ROA). In highly concentrated markets, banks tend to have greater pricing power and stable profit margins, which can result in lower earnings volatility and higher average ROE. Consequently, when used in the Z-score formula, this reduces the likelihood of equity being eroded by negative shocks. In contrast, the Z-score (ROA) tends to be less sensitive to changes in competition and concentration because ROA reflects the overall profitability of a bank’s assets, which is more stable and less influenced by capital structure decisions or strategic competition. ROA does not directly incorporate leverage, so the impact of capital adequacy or earnings retention decisions, often shaped by market structure, does not significantly shift the Z-score when calculated with ROA. Moreover, ROA tends to be less volatile than ROE, particularly for larger, well-capitalized banks, making it a less reactive measure to market power or rivalry changes.

Concerning the macroeconomic and financial environment effect, we found that the impact of GDP growth is positive and significant for bank stability based on Z-ROA and Z-ROE. GDP growth enhances bank stability by increasing loan demand and increasing the capacity to repay credit. It also improves asset quality, boosts bank profits and deposits, and reduces credit risk. All these positive effects lead to a more resilient banking sector. This result corroborates the findings of Espinoza and Prasad (2010) and Klein (2013). Findings also show that unemployment is inversely related to bank stability. In fact, high unemployment rates negatively affect bank stability through higher credit risk, reduced loan demand and profitability, strained liquidity, and eroded consumer and business confidence. These findings are in agreement with the results obtained by Ghenimi et al. (2017). The global financial crisis of 2008 had a significant and negative effect on bank stability. In periods of crisis, borrowers reduce their capacity to service their debt, which impairs the quality of the loan portfolio and increases non-performing loans, a critical issue that threatens bank stability. Moreover, banks go to more conservative lending practices, which decrease loan issuance and lower interest income, and therefore, stability decreases. The findings confirm those found by Ghenimi et al. (2017).

4.2.2. The Moderating Role of IQ in the NRR and Bank Stability Relationship

The findings given in

Table 8. Results of the moderating role of institutional quality in the NRR and bank stability relationship.

	Z-Score (ROA)				Z-Score (ROE)
	Coef.	Std. Err.	Z	P > z	Coef.	Std. Err.	Z	P > z
LnZROA(-1)	0.776	0.013	58.11	0.000 ***	-	-	-	-
LnZROE(-1)	-	-	-	-	0.468	0.013	33.90	0.000 ***
NRR	−0.001	0.0003	−3.21	0.000 ***	−0.002	0.001	−4.71	0.001 ***
IQ	0.074	0.019	3.83	0.000 ***	0.048	4.818	8.48	0.000 ***
NRR*IQ	0.001	0.0002	4.16	0.000 ***	0.005	0.0007	6.34	0.000 ***
BS	0.035	0.007	4.85	0.000 ***	0.149	0.012	11.52	0.000 ***
CAR	0.037	0.001	31.25	0.000 ***	0.022	0.001	14.07	0.000 ***
ROA	0.100	0.002	34.99	0.000 ***	0.093	0.004	20.37	0.000 ***
LTD	−0.001	0.0001	−5.71	0.000 ***	−0.001	0.0001	−7.62	0.000 ***
NPLs	−0.007	0.001	−7.18	0.000 ***	−0.001	0.0007	−1.39	0.163
CONC	−0.001	0.001	−0.98	0.329	0.008	0.0008	9.43	0.000 ***
LERN	−0.089	0.086	−1.03	0.303	0.704	0.105	6.66	0.000 ***
GDPG	0.005	0.0006	7.74	0.000 ***	0.005	0.001	4.99	0.000 ***
INF	0.001	0.017	0.29	0.768	−0.0001	0.001	−0.17	0.864
CRISIS	−0.072	0.003	−19.14	0.000 ***	−0.226	0.013	−17.30	0.000 ***
UNEM	−0.016	0.002	−6.94	0.000 ***	−0.003	0.002	−1.43	0.153
_cons	0.352	0.125	2.80	0.005 ***	1.713	0.158	10.84	0.000 ***
AR(1)	−1.0806				−2.3212
Prob	0.2799				0.0203
AR(2)	0.8471				−1.2698
Prob	0.3969				0.2042
Sargan test	46.766				50.222
Prob	0.3205				0.2090
Obs	968				968

Note: ***, indicates the rejection of null hypothes is at 1% significance level.

are relative to the moderating role of institutional quality in the relationship between bank stability and natural resource rent. We found that institutional quality enhances bank stability in the MENA region. In addition, banks in this area benefit from an interaction between natural resource rent and institutional quality (NRR*IQ). From Table 8, it is also evident that the effects of the control variables on bank stability remain largely consistent with the results presented in Table 6. Among the bank-specific factors, bank size, capital adequacy ratio, and return on assets continue to exhibit a positive and significant influence on bank stability, while liquidity risk and credit risk have a negative and significant impact. Regarding industry characteristics, both bank concentration and competition show a positive and significant relationship with bank stability. Finally, for the international financial and macroeconomic environment, the analysis reveals that financial crises and unemployment are negatively associated with bank stability, whereas GDP growth positively and significantly enhances it.

The results show that the institutional quality index MENA enhances bank stability in the MENA region. High institutional quality influences bank stability positively by ensuring effective regulation, good governance, and a strong legal framework that supports prudent risk management and transparency. Strong institutional quality enforces prudent financial standards, protects depositors, and promotes investor confidence. In addition, effective legal systems and low levels of corruption promote accountability and reduce the possibility of high-risk or fraudulent practices, further enhancing the soundness of the financial system. Institutional quality provides a stable environment within which banks can manage their risks, build capital, and maintain resilience in periods of financial stress. This finding is in line with the works of Laeven and Levine (2009) and Kim and Choi (2020).

We also found that the interaction variable between natural resource rent and institutional quality (NRR*IQ) significantly increases the level of stability for the MENA banks. This means that IQ moderates this relationship. The role of institutional quality is pivotal in ensuring that the richness of natural resources is well harnessed. Resource rents are used in strongly institutionalized countries to foster long-term economic stability, dispel the resource curse, and diversify the economy, thereby strengthening the banking sector. Good governance and regulatory frameworks lead to better credit risk management, investor confidence, and stability of the financial system, while weaker institutions engender mismanagement and instability and undermine bank stability. For instance, high institutional quality harnesses the positive impact of resource rents while maintaining its risks at a lower level. The result lends credence to the study conducted by Davoodi and Abed (2003) and Mehlum et al. (2006). Thus, the hypothesis H2 is accepted.

5. Conclusions

The main research question of this study was the following: Does IQ moderate the NRR and BS relationship for a sample of MENA banks? Hence, this paper tries to answer this question by investigating the relationship between NRR and bank stability and to check whether institutional quality can moderate this relationship.

Studying the moderating role of IQ in the relationship between NRR and bank stability can contribute to the existing body of the literature. This study contributes to the expanding corpus of the resource curse hypothesis and its effects on financial systems, especially in resource-rich economies. Second, less abundant studies investigate how institutional quality affects financial stability in the presence of resource rents, especially in the MENA nations. Third, incorporating institutional quality as a moderating factor provides a more nuanced understanding of how institutions shape the impact of natural resource rents on banking sector performance.

To this end, we have conducted a panel analysis on the sample of MENA banks from 2005 to 2020. The empirical results of this study reveal three main conclusions: (i) natural resource rent significantly decreases bank stability, (ii) institutional quality enhances bank stability, and (iii) institutional quality moderates the natural resource rent–bank stability relationship. These results are confirmed for the two metrics of bank stability.

These findings may have several useful applications for policymakers and bankers. First, policymakers should prioritize strengthening institutional frameworks to mitigate the adverse effects of resource dependence on financial stability. Transparent governance, robust legal systems, and effective regulatory mechanisms can help channel resource rents into productive investments while minimizing volatility and corruption risks that undermine bank stability. Second, to ensure more bank stability in the MENA countries, bankers are invited to focus on improving institutional quality. Institutional quality provides a buffer against economic shocks by enhancing risk management and fostering a stable operating environment. Financial institutions should be more actively supportive and aligned with policies that foster institutional development, such as compliance with anti-corruption standards and coordination with regulatory bodies to boost financial resilience. By improving institutional quality, the MENA region will achieve more sustainable banking systems that guarantee long-term economic stability coupled with inclusive growth. Third, policymakers in the MENA region should give priority to the strengthening of institutional frameworks in order to maximize the benefits of natural resource rent on bank stability.

Although the results of this paper have useful policy implications for policymakers, this study has some limitations. MENA banks have been handled as a single category in this analysis. Nonetheless, there are a number of financial, social, and economic distinctions among the nations in this region. This limitation could be overcome in future studies by providing comparative analyses of regression results for these two distinct groups of countries, such as GCC and non-GCC countries. Moreover, other future studies may focus on the impact of financial technology and climate risks on the interrelationship between natural resource rents, institutional quality, and bank stability. Sectoral differences in resource rents and their financial implications might also be investigated for an in-depth understanding of such dynamics.