Banking Sector Stability and Economic Growth in Ethiopia: The Two-Step System GMM Analysis

Abstract

This study investigates the relationship between banking sector stability and economic growth in Ethiopia, employing a dynamic panel data approach with the Two-Step System Generalized Method of Moments (GMM). The analysis uses a balanced dataset from 13 Ethiopian commercial banks covering 2014 to 2023, gathered from the World Bank database, the National Bank of Ethiopia, and audited financial statements. Banking sector stability is assessed using indicators such as Z-score, non-performing loan (NPL) ratio, capital adequacy ratio (CAR), liquidity ratio (LR), return on assets (ROA), and loan-to-deposit ratio (LDR), along with key macroeconomic and institutional factors. The results show that banking stability, as indicated by Z-score, liquidity ratios, and profitability, has a positive and significant effect on economic growth, confirming the sector’s role in promoting development. Surprisingly, a positive correlation between NPLs and economic growth suggests unique structural features in the Ethiopian banking system that warrant further investigation. Other variables, such as inflation rates, government expenditure, and gross domestic savings, positively influence economic growth, whereas foreign direct investment is negatively associated with it. The study highlights the importance of enhancing the stability of the banking sector by implementing robust regulatory frameworks, prudent risk management practices, and improved profitability to support sustainable economic development in Ethiopia, while calling for additional research into the unexpected effects of NPLs and FDI amid ongoing financial reforms.

1. Introduction

The stability and profitability of the banking sector are essential prerequisites for economic development in any nation. A robust banking system helps to mitigate the effects of financial shocks, facilitates investment activities, and fosters an environment conducive to sustained economic growth, given that the broader economy depends significantly on the resilience of this sector (Butola et al., 2022). This relationship is particularly evident in developing economies like Ethiopia, where commercial banks play a dominant role within the financial system and act as a primary source for investment financing (Muhammed et al., 2024). As such, bank performance and operational efficiency are pivotal in influencing economic growth while ensuring financial stability. To maintain this stability, banks must implement practices aimed at promoting sustainable development alongside enhancing their operational efficiency (Saksonova, 2014).

A stable financial system is crucial for fostering a robust and resilient economy. Economic performance is often evaluated through the lens of economic growth, with gross domestic product (GDP) serving as a primary indicator of economic development. Increasing GDP levels typically signify enhanced business activity and favorable labor market conditions, while declining GDP indicates potential economic decline (Ahulu et al., 2021). Strong financial structures enable efficient resource allocation, effective risk management, and the capacity to absorb shocks, thereby diminishing both the likelihood and severity of financial crises. In this regard, financial stability functions as a protective measure against systemic risks and economic downturns.

Empirical evidence supports the positive link between banking sector stability and economic growth. For instance, (Ijaz et al., 2020) examined cross-sectional data from 38 European countries over the period 2001–2017 and demonstrated that higher financial stability significantly enhances economic growth, particularly during crisis periods. Their findings further suggest that reduced banking competition can contribute to improved financial stability and, in turn, foster economic growth. This approach offers important benefits for both banks and regulators by strengthening financial resilience and supporting long-term economic expansion (Ijaz et al., 2020).

The global financial crisis revealed significant flaws within financial systems across the globe, prompting regulators and policymakers to prioritize financial stability on the international policy agenda. This shift resulted in extensive reforms within the financial sector aimed at bolstering resilience and sustainability. Numerous studies underscore that economic growth and sustainability are intricately linked to banking sector performance. For instance, (Ntarmah et al., 2019) demonstrate that real GDP growth is typically higher in financially stable environments, whereas instability correlates with economic contraction. Instability within the banking system has immediate repercussions for governments, businesses, and households alike, leading to both short-term disruptions and long-term structural challenges. Furthermore, (Hamal et al., 2025) establish a positive relationship between bank stability and stock market growth, underscoring the banking sector’s interconnectedness with other components of the financial system.

Despite the growing body of research examining the link between banking sector stability and economic growth, evidence from low-income countries remains scarce. Most current studies focus on developed or emerging economies with more diverse financial systems and mature capital markets (Ijaz et al., 2020). However, Ethiopia’s financial structure is quite different, as the banking sector dominates the financial system and capital markets remain largely underdeveloped. Consequently, the stability of commercial banks becomes even more crucial in shaping macroeconomic performance.

Furthermore, previous studies in Ethiopia have primarily focused on macroeconomic factors influencing growth, such as inflation, government spending, and foreign direct investment, while largely neglecting the importance of banking sector stability indicators, including the bank Z-score, non-performing loans, and capital adequacy (Sijabat, 2023; Yitayaw et al., 2022). Additionally, studies using dynamic panel estimation techniques such as the Two-Step System Generalized Method of Moments (System GMM), which effectively address endogeneity and unobserved heterogeneity in financial sector data, are limited in the Ethiopian context.

Therefore, this research seeks to address this gap by investigating how banking sector stability affects economic growth in Ethiopia using the Two-Step System GMM method. By combining bank-specific stability measures with macroeconomic variables, the study offers new empirical evidence on the significance of financial stability in fostering sustainable economic growth in bank-centric developing economies.

This study adds several important contributions to the existing literature. First, unlike previous studies that mainly focus on developed or diverse financial systems, this paper provides empirical evidence from Ethiopia, a bank-dominated economy where the banking sector is central to financial intermediation. Second, the study includes bank-level stability indicators, such as the Z-score, non-performing loans, and capital adequacy, within a macroeconomic growth framework, offering a more comprehensive look at financial stability beyond traditional macroeconomic factors. Third, the study uses the Two-Step System GMM estimation technique to address potential endogeneity, dynamic effects, and unobserved heterogeneity, which have been largely neglected in earlier Ethiopian research. Finally, by combining both bank-specific and macroeconomic variables, this research offers a more robust and policy-relevant understanding of how banking sector stability impacts economic growth in bank-centric developing economies.

2. Literature Review

Research in finance on the relationship between bank stability and economic growth has been discussed for decades. In this section, an understanding of these theoretical propositions and the empirical research that elucidates this complex relationship is presented. In many economies around the globe, existing research has documented a link between financial development on one side and economic growth on the other. Three notable results from these studies indicate that the empirical data have not been able to reach a definitive conclusion regarding this relationship: it either has a positive, negative, or no impact (Masoud & Hardaker, 2012; Ntarmah et al., 2019; Tripathy, 2019).

A stable banking system is a key factor for sustainable financial growth. Strong performance in the banking sector facilitates effective capital mobilization and remobilization, builds investor confidence, and supports economic activities (Muhammed et al., 2026). This section assumes that the stability of the banking sector greatly benefits Ethiopia’s economic expansion. Therefore, this study focuses on banking sector stability indicators as the primary variables; these include bank size, non-performing loans, capital adequacy ratio, liquidity ratio, return on assets, and loan-to-deposit ratio. The control variables are inflation, government consumption expenditure, foreign direct investment, gross domestic savings, and regulatory quality. This research also aims to highlight the role that a healthy and stable banking sector plays in growth and development, to inform relevant policies for fostering sustainable banking and economic progress (Mothobi & Kebotsamang, 2024; Uzoma et al., 2020).

For many years, the field of economics has been involved in discussions about financial stability and economic growth. Financial stability, which refers to the capacity of a financial system to survive financial shocks without significant disruption, has been a key focus of research. Some writers have shown evidence that financial stability promotes economic growth. On the other hand, certain economic theories contend that financial stability follows economic development. The study by (Wijethunga et al., 2023) suggests a solid foundation for claims that financial development promotes economic growth. The paper argues that an effective and stable financial system is essential for capital resources, risk distribution, fund mobilization, and technical advancement.

The low policy rates and prolonged financial stability can hinder economic growth by accumulating systemic risks and credit leverage. Despite the ongoing debate about the nature of the connection between financial stability and economic development, all researchers agree on one thing: the preservation of both factors is essential (Barajas et al., 2021). Overall, the current literature emphasizes the conditions for economic development on the one hand and financial stability on the other. Although the literature supports the notion that establishing a certain degree of financial stability is healthy for growth in the long run since it minimizes the possibilities of disruptions or crises, a stable financial system may further ease credit flow to productive sectors, thus boosting growth. However, as it is known, long-term financial stability can also support a proliferation in the level of leverage and systemic risks, which can hurt the economic development rate.

Irrespective of the excess of literature focused on exploring the correlation between financial stability and economic growth, the results of the empirical work are inconclusive and even inconsistent. To provide an example, some studies claim that financial stability increases economic growth by means of better resource allocation, or stronger financial intermediation, and easier activities in the area of investments (Masoud & Hardaker, 2012). On the same note, numerous economies have indicated through empirical data that a robust banking system enhances investor confidence and leads to long-term economic growth (Ntarmah et al., 2019).

Nevertheless, other literature streams point to the possible negative outcomes of the long-term financial stability, especially assuming that it causes the excesses in credit growth, accumulation of debt, and the systemic risk that might eventually erode the economic growth (Ahulu et al., 2021; S. Ullah et al., 2024). These contradictory results indicate that the dependence of the banking sector stability on economic growth could be conditional on the country-specific financial structure, the quality of the institutions, and the macroeconomic conditions. In other emerging economies like Ethiopia, which dominate the banking sector, the financial system and capital markets are still underdeveloped, and hence banking stability is potentially crucial to economic performance (S. Ullah et al., 2024; Yitayaw et al., 2022). However, limited empirical evidence exists in such contexts. Thus, the current research adds to the current literature in terms of empirically testing the impact of banking sector stability indicators on economic growth in Ethiopia, taking into consideration the presence of some macroeconomic variables.

The literature available can be divided into three conflicting views on the importance of the stability of the banking sector to economic growth. The first one, commonly known as the finance-led growth hypothesis, is that a stable banking system fosters economic growth through the process of financial intermediation, better resource allocation, and easier investment. The second view proposes that there is a reverse causality and therefore economic growth itself is a cause of increased stability of the banking sector in terms of increased borrower capacity and decreased credit risk. The third school of thought emphasizes a non-linear or conditional relationship indicating that too much stability can lead to risk-taking behavior, credit expansion and accumulation of systemic risk that can eventually impede growth. These contradictory results show that the relationship is situation-specific and depends on the institutional quality, financial structure, and macroeconomic conditions.

There are also a number of important transmission channels in the relationship between the stability of the banking sector and the development of the economy. To begin with, stable banks improve financial intermediation by effectively mobilizing savings and providing credit to productive investments. Second, stability enhances supply of credit and management of risks, clearing non-performing loans, and ensuring that there is lending even when the economy is down. Third, banks with sufficient capital and liquidity serve as shock absorbers to reduce the negative impacts of financial crisis on the real economy. Fourth, banking stability enhances the confidence of the investors, which facilitates domestic and foreign investment. But too much stability can be a source of risk build-up and credit overextension, which could undermine growth over the long run. Thus, the net impact of banking stability in economic growth lies in the equilibrium of these positive and negative conduits.

Hence, policymakers can only support both the soundness of money and the growth of the economy simultaneously. This can be possible through forming rigorous policies that foster the superficial growth of the financial institutions with little threat to the system, hence future growth. The following section examines how different factors influence economic growth.

2.1. Lagged Value of Real GDP

Empirical evidence from dynamic panel data models supports the hypothesis that the previous year’s GDP growth is positively associated with current economic growth. Economic growth usually remains persistent, as earlier growth contributes to current performance due to high capital, steady policies, and trust among investors. Recent studies utilizing Generalized Method of Moments (GMM) methodologies affirm that in developing economies, including Ethiopia, current growth dynamics are firmly rooted in historical GDP performance. For example, Asmare’s (2022) study, employing a dynamic panel framework, illustrates that lagged GDP plays an essential role in influencing contemporaneous economic growth within Ethiopia. This persistence suggests that prior economic advancements contribute to macroeconomic stability while fostering conditions conducive to sustained investment and long-term development.

Similarly, Ali (2024) finds that past levels of GDP significantly enhance current growth rates by reflecting the cumulative effects of ongoing economic expansion alongside gradual improvements in policy over time. Evidence from system GMM analyses across Sub-Saharan Africa further substantiates this perspective; specifically, Sore et al. (2024) demonstrate that historical GDP serves as a critical determinant for present-day economic output among East African nations. They attribute this persistent relationship to factors such as increased globalization, improved macroeconomic policies, and effective integration of new knowledge along with technological innovations.

H1. 

The lagged value of real GDP has a significant and positive impact on economic growth in Ethiopia.

2.2. Bank Stability (Z-Score)

The Z-score model is one of the most popular instruments used to evaluate the stability of banks. Since banks that have high Z-scores would be less vulnerable to shocks in the economy, they may help generate a good environment for the economy. (Owen & Temesvary, 2018) observed that development is supported by more substantial banks that possess fairly constant Z-scores, therefore promoting lending throughout the busts. In the same regard, (Stewart & Chowdhury, 2021) suggest that increased Z-score highlights that well-funded bank have found that these institutions are capable of reducing the emergence of financial risks, thus promoting the attainment of steady economic growth. As other scholars have pointed out in the literature on developing economies, advances in bank stability, measured by Z-score, play a role in moderating the fluctuations of the financial services, which are crucial for sustained economic growth (Athari et al., 2023; Yitayaw et al., 2022).

H2. 

A higher Z-score significantly and positively affects economic growth.

2.3. Non-Performing Loan (NPL)

A significant volume of non-performing loans (NPL) indicates low-quality assets and hampers economic growth by undermining the banking sector’s ability to expand credit. Hor (2025) confirmed that a high NPL ratio adversely impacts credit supply, investment, and consumption, all critical components for fostering growth. Similarly, Bayar (2019) found that banks burdened with elevated levels of NPLs face liquidity constraints that restrict their economic activities. Furthermore, Pradhan et al. (2024) highlighted that high NPL ratios pose negative risks to the banking system while diminishing investor confidence and contributing to lower rates of economic growth.

H3. 

A higher NPL ratios have a negative and significant effect on economic growth.

2.4. Capital Adequacy Ratio (CAR)

The Capital Adequacy Ratio (CAR) is a crucial factor for the stability of banks, as higher CAR values provide loss protection buffers that enhance institutional resilience and foster economic growth (Muhammed et al., 2023). Research indicates that improved CAR levels enable banks to maintain stronger lending capabilities during economic downturns, thereby facilitating enhanced economic expansion (Leykun, 2016). Moreover, Stewart and Chowdhury (2021) noted that in developing countries with favorable conditions, a robust CAR positively influences economic resilience. Additionally, Martynova (2015) supports this assertion by demonstrating how the Capital Adequacy Ratio contributes to GDP growth through the reinforcement of financial system stability, which results in more consistent credit availability.

H4. 

The higher CAR has a positive and significant effect on the economic growth of the country.

2.5. Liquidity Ratio (LR)

The liquidity ratio reflects a bank’s ability to meet short-term liabilities, which is essential for maintaining ongoing credit operations. Tran and McMillan (2020) found that banking liquidity ratios significantly impact a bank’s productivity in extending credit to productive sectors, thereby enhancing its capacity to support economic growth. According to Porcellacchia and Sheedy (2024), a higher ratio of liquid assets to total assets bolsters this capability by enabling banks to fund additional credits, which becomes increasingly advantageous during periods of business cycle contraction. However, they also caution that excessive liquidity may present challenges; banks might hold onto potentially inactive assets instead of actively extending credit.

H5. 

The optimal level of liquidity has a positive and significant effect on economic development.

2.6. Return on Asset (ROA)

ROA, a key metric for value investors, is a powerful tool for assessing a company’s profitability. In the banking sector, profitable banks play a crucial role in supporting the economy by reallocating funds to profitable economic sectors (Isayas, 2022). This aligns with the findings from Bhari (2023), which demonstrate that increased returns on assets can enhance banks’ credit facilities for the economic growth of the country. Additionally, Ozili and Arun (2023) point out that the profit/liability ratio is a key factor in increasing banks’ resilience in the face of financial shocks, enabling them to continue lending and further support the economy. So, most of the findings underscore that a significant and positive correlation exists between ROA and economic growth. This correlation was consistently observed across different models, further validating the crucial role of ROA in inspiring economic advancement.

H6. 

The ROA has a positive and significant effect on economic development.

2.7. Loan to Deposit Ratio (LDR)

The loan-to-deposit ratio (LDR) reflects the liquidity status of the banking industry and its standards for credit extension (Arebo et al., 2024). Ying and District (2020) have demonstrated that a moderate LDR enables banks to support economic growth by providing credit while maintaining adequate liquidity. Conversely, Arnanto and Lutfi (2025) concur that an increase in LDR can facilitate growth through expanded lending; however, they caution against excessively high ratios, which may lead to liquidity challenges. This perspective is further supported by Bunga et al. (2020), who found that a well-managed LDR enhances capital resource management and boosts overall economic productivity.

H7. 

The optimal loan-to-deposit ratio has a positive impact on economic growth.

2.8. Inflation Rate (InfR)

An implication of a high inflation rate affects growth by increasing uncertainty, aggressiveness in sales, and low purchasing power (Akinsola & Odhiambo, 2017). S. Ullah et al. (2024) believe that high inflation reduces the real value of savings, hence decreasing the level of investments and economic activity. Similarly, Konstantakopoulou (2023; Mashamba & Chikutuma, 2023) determines that inflation hurts growth in developing countries, where inflation interrupts financial markets. On the other hand, moderate inflation also has a neutral and positive impact in the sense that inflation encourages demand when it is expected.

H8. 

A high inflation rate has significant and negative effects on economic growth in Ethiopia.

2.9. Government Expenditure (GE)

Government expenditure, a key driver of economic development, creates social overhead capital and welfare. The study by Adenutsi et al. (2024) and Yitayaw et al. (2022) suggests that increased government spending stimulates demand for goods and services, thereby boosting growth in the short run. This is further supported by the work of (Takele & Demissie, 2023; Bayar, 2019; Sore et al., 2024), which highlights the positive impact of government spending on essential services for the economy. However, it is crucial to heed the perspective of Hajamini and Falahi (2014), who emphasize the need for efficient government expenditure to prevent crowding out of private investment, underlining the urgency and importance of this issue.

H9. 

Expenditure by the government has a significant and positive effect on economic development in Ethiopia.

2.10. Foreign Direct Investment (FDI)

FDI is considered imperative for the development of the economy in developing nations, as it provides capital, technology, and know-how (Kithandi, 2025). The literature on FDI indicates that it increases efficiency, generates employment, and serves as a catalyst for growth (Alfaro, 2016). Similarly, (Takele & Demissie, 2023; Ali, 2024; Wijethunga et al., 2023) found that FDI has a positive impact on economic growth through better capital formation and technology transfer. Furthermore, Ali (2024) also confirmed that FDI acts as the catalyst for the economic diversification required for emerging economies to thrive.

H10. 

The foreign direct investment has a significant and positive effect on economic development in Ethiopia.

2.11. Gross Domestic Saving (GDS)

Savings enable an economy to finance investments and serve as an indicator of financial position when conducting business, thereby demonstrating the generated income. Bhari (2023) established that higher domestic savings provide more capital to fund investment that, in turn, enhances economic growth. (Talbi & Bougatef, 2018) agree that savings help build up economic strength because they offer an amount on which the banks can lend money. Supporting these, Ntarmah et al. (2019) and Wanzala and Obokoh (2024) noted that savings augment growth since they minimize reliance on foreigners’ capital and nurture equilibrium.

H11. 

An increase in gross domestic savings significantly enhances the economic growth of the country.

2.12. Regulatory Quality (RQ)

Regulatory quality opens the way for more stable financial developments and helps to minimize risks, which can be truly achieved through the improvement of the quality of regulation. Studies by Awan (2016) and S. Ullah et al. (2024) affirm that the healthy regulation of the financial sector boosts the confidence of members in the financial system, thus boosting economic development. In support of this notion, Athari et al. (2023) affirm this result because regulatory quality strengthens the stability of the financial sector, which is important for economic growth. Furthermore, (Arnanto & Lutfi, 2025; Bhari, 2023; Hor, 2025) claim that the regulation established in the main post improves systematic risk and thus improves the economic performance of banking services.

H12. 

Regulatory quality has a positive and significant impact on economic growth.

In summary, research consistently demonstrates that the stability of the banking sector and prevailing macroeconomic conditions play a crucial role in influencing economic growth. Indicators such as a high Z-score, low levels of non-performing loans, and robust capital adequacy and liquidity ratios signify that the banking sector is well-capitalized. These factors enhance lending capabilities and investment potential, thereby establishing a solid financial foundation for sustainable growth. Moreover, increasing rates of return alongside higher loan-to-deposit ratios reflect the banking sector’s capacity to finance economic development while simultaneously boosting profitability. Macroeconomic elements also significantly impact this dynamic; effective government spending can channel resources into productive sectors while foreign direct investment injects vital capital into the economy. Conversely, high inflation poses challenges by eroding purchasing power and stifling growth prospects. Domestic savings further contribute to strengthening the economy by fostering self-reliance and facilitating investments within local markets. Additionally, there exists a strong correlation between institutional quality, particularly regulatory effectiveness, and financial stability; sound institutions safeguard financial systems while promoting investor confidence in bank operations. Collectively, these insights underscore how bank-specific factors interact with broader economic conditions and institutional quality to influence overall growth trajectories, especially given that an efficient banking sector is pivotal for driving economic development. This framework offers valuable perspectives on understanding the intricate connections between financial stability and sustained economic progress.

2.13. Theoretical Perspectives

Embedded within the broader theory of financial development is a framework for understanding the determinants of banking stability and its relationship with economic growth. The banking system plays a crucial role in facilitating transactions, mobilizing savings, and extending credit while prudently investing available resources (Hamal et al., 2025). Konstantakopoulou (2023) posits that economic growth and financial development are inherently linked, particularly through a well-functioning banking system. The authors contend that robust financial systems enhance market efficiency for funds, thereby promoting both growth and productivity.

Recent theoretical advancements on the relationship between banking sector stability and economic growth have emerged from various strands of economic research. Among these theories, two prominent ones include the financial intermediation hypothesis, which asserts that banks serve as intermediaries channeling savings from savers to borrowers to stimulate investment, and arguments supporting high regulatory capital requirements (Kithandi, 2025). Stewart and Chowdhury (2021) argue that stringent capital regulations, such as those outlined in Basel III, contribute positively to financial stability, which in turn fosters economic growth. They conclude that banks with substantial capital reserves act as more effective shock absorbers, thus enhancing their capacity to maintain stability essential for sustained economic advancement.

Another theoretical framework relevant to this discussion is the financial liberalization hypothesis, which posits that a series of financial market liberalizations can positively impact economic growth by enhancing efficiency in financial intermediation. However, several studies (Ahulu et al., 2021; Konstantakopoulou, 2023; Masoud & Hardaker, 2012; Yitayaw et al., 2022) highlight potential weaknesses associated with this approach. They demonstrate that the liberalization of financial systems may also precipitate deeper financial crises and increase banking risks. This underscores the necessity for politically neutral regulatory strategies aimed at fostering expansion while mitigating associated risks.

Variations in institutional quality further enrich this discourse, as robust institutions amplify the positive effects of banking stability on economic development. Consequently, any country must establish stable and growth-oriented banking regulations alongside strong governance frameworks, a notion supported by recent research (Marjit et al., 2020; S. Ullah et al., 2024). This perspective aligns with empirical findings indicating that nations possessing solid institutional structures tend to experience more stable and sustained economic growth over time.

Financial stability, a critical component of financial development, refers to the ability of financial institutions to withstand economic shocks. The maintenance of trust in the financial system, essential for sustaining economic activity, is heavily reliant on stable banks. Common indicators used to assess bank stability include the Z-score, which measures the likelihood of a bank’s insolvency, and the ratio of non-performing loans (NPLs) to total loans, which reflects the quality of a bank’s loan portfolio (Kong et al., 2020).

Moreover, recent theoretical discussions have increasingly focused on external shocks such as those posed by the COVID-19 pandemic. Research indicates that this pandemic significantly diminished both bank performance and overall stability within financial systems, highlighting how vulnerabilities in banking sectors can be exacerbated by disturbances in global economies (Firdaus et al., 2022).

Building on these theoretical perspectives, the explanatory variables used in this study are grounded in the broader framework of financial development and banking stability theory. Indicators such as the bank Z-score, capital adequacy ratio (CAR), liquidity ratio (LR), loan-to-deposit ratio (LDR), non-performing loans (NPLs), and others capture different dimensions of banking sector soundness and financial intermediation. According to financial intermediation theory, stable and well-capitalized banks are better able to mobilize savings and allocate credit efficiently, thereby promoting investment and economic growth (Masoud & Hardaker, 2012). The Z-score reflects the probability of bank insolvency, while CAR measures the capacity of banks to absorb unexpected losses, both of which are central to banking stability and the resilience of financial institutions (Kong et al., 2020). Similarly, NPLs indicate asset quality and credit risk within the banking system, which may influence banks’ lending capacity and ultimately economic performance (Ahulu et al., 2021). In addition to banking indicators, macroeconomic and institutional variables such as inflation, government expenditure, foreign direct investment, gross domestic savings, and regulatory quality are included to reflect the broader economic and institutional environment that shapes the functioning of financial systems and their contribution to economic development (Ahulu et al., 2021). Therefore, integrating these variables within the empirical model provides a comprehensive framework for examining how banking sector stability and the surrounding macroeconomic environment jointly influence economic growth in developing economies such as Ethiopia.

In light of these challenges presented by external shocks like pandemics or economic downturns, there is an urgent call for more resilient banking institutions capable not only of withstanding such disruptions but also supporting sustained economic growth during turbulent times.

2.14. Empirical Evidence

Empirical evidence regarding the relationship between banking sector stability and economic growth is mixed, yet a growing body of literature suggests a generally favorable association. Athari et al. (2023) examined global banking sector instability, highlighting how financial, political, and economic risk factors affect banking institutions. Their findings indicate that improvements in regulatory capital enhance both banking sector stability and economic growth. This suggests that, contrary to standard growth model assumptions of a trade-off, banking stability and economic growth can operate as mutually reinforcing processes.

Similarly, studies in the framework of new classical economics underscore the importance of financial development for economic growth. Tieguhong and Mandiefe (2017) employed time-series data from Cameroon to analyze the long-run relationship between financial development and economic growth. Their results reveal cointegration among variables, with unidirectional causality from financial development to economic growth. The study demonstrates that increases in money supply, improvements in the efficiency of financial intermediaries, and a more favorable investment climate significantly contribute to economic development. Such findings highlight the critical role of a stable and efficient banking sector in sustaining long-term economic prosperity.

Macroeconomic stability is closely linked to the health of the banking sector (Dawood, 2016), which demonstrates that financial stress within banks, such as during credit crunches, reduces funding availability and constrains economic growth. Regulatory capital reforms, including Basel III, are shown to strengthen banking sector stability, allowing banks to maintain lending during economic downturns and act as effective shock absorbers (Stewart & Chowdhury, 2021). These results collectively support the view that a sound banking sector fosters economic resilience and stability.

Focusing specifically on Ethiopia, the literature provides growing empirical support for the connection between banking sector stability and economic growth, particularly through dynamic panel approaches such as the Two-Step System Generalized Method of Moments (System GMM). Much of the research has concentrated on determinants of banking stability, often measured by Z-scores, rather than directly linking stability indicators to GDP. Nonetheless, these studies reveal strong bidirectional relationships: higher economic growth enhances banking stability, while a stable banking sector is essential for sustaining growth. This is achieved through improved credit allocation, reduced systemic risk, and more efficient resource mobilization within Ethiopia’s predominantly state-influenced banking system.

Yitayaw et al. (2022) employed a Two-Step System GMM using Ethiopian commercial bank-level data and found that GDP growth has a significant positive impact on bank stability, with increases in GDP correlating with stronger financial resilience. Higher GDP improves borrower creditworthiness, reduces default risks, strengthens bank balance sheets, and decreases non-performing loans, thereby reinforcing financial sector stability. The study further notes the persistence of bank stability, highlighting that maintaining soundness over time is critical for long-term sector health.

Arebo et al. (2024) similarly demonstrate that macroeconomic expansion stabilizes banks, with Z-scores and other indicators such as asset tangibility, lending rates, and institutional quality measures (e.g., rule of law, control of corruption) playing a significant role. These findings point to a virtuous cycle: economic growth strengthens banking stability, which in turn promotes further economic development through improved intermediation and risk management practices.

Related Ethiopian research reinforces these conclusions. Studies examining bank profitability, efficiency, and financial inclusion reveal that stable and well-managed banks underpin favorable macroeconomic outcomes (Berhe, 2024). While direct applications of two-step System GMM regressions linking Ethiopian GDP to bank stability measures remain limited, often due to data constraints, existing evidence supports the notion that banking sector stability positively contributes to economic growth. These findings highlight the importance of sound regulatory frameworks, effective risk management, and robust profitability strategies for leveraging the banking sector’s role in national development.

In summary, the body of empirical work substantiates that banking sector stability is fundamental to economic growth in Ethiopia. Policymakers are encouraged to prioritize regulatory reforms, strengthen risk management practices, and enhance operational efficiency within banks to harness this relationship for sustainable long-term development.

3. Materials and Methods

This study requires a dynamic panel model because the data used in the study are dynamic, and the actual result is determined by the previous one. Therefore, the fact that the model is dynamic means that econometrics models such as the pooled ordinary least squares (POLS) and fixed effect estimator could be biased and inconsistent since the unobserved panel-level effects are correlated with the lagged dependent variable (Ahulu et al., 2021; Firdaus et al., 2022). Hence, the econometric problems observed may occur since the dynamic models are not addressed by the fixed/random-effect models that can be used with the panel data. Endogeneity leads to biased estimates, while heterogeneity between banks, which is unobservable, leads to inefficiency. Thus, (Sarafidis et al., 2009) developed a new GMM estimator for the dynamic panel model. They proposed using different transformations and including more instruments in the dynamic panel model. After that, Arellano and Bover (1995) and Blundell and Bond (1998), cited in (Hamal et al., 2025; Ntarmah et al., 2019), proposed a modification of the Arellano and Bond estimator, which offered more limitations to the starting conditions for deriving greater efficiency and the possibility of including more instruments. It estimates the original and modified versions of the System GMM, a system of two equations.

Besides dealing with dynamic panel bias, the Two-Step System GMM estimator fits this study especially due to a number of reasons. First, the model explicitly takes into consideration the endogeneity, which is often a problem in growth models because of reverse causality between financial sector indicators and economic growth. To exemplify, although the stability of the banking sector can affect economic growth, economic growth can also affect the banking performance. This simultaneous problem could not be properly handled by traditional estimators like pooled OLS, fixed effects, and random effects to produce biased and inconsistent estimates (Ahulu et al., 2021). Second, the System GMM estimator can be useful in controlling the unobserved heterogeneity between cross-sectional units since it removes fixed effects by making suitable transformations. Third, the two-step form of the estimator is more efficient and relies on an optimal weighting matrix that corrects two factors: heteroskedasticity and autocorrelation in the error terms, and the estimates are more reliable (Ntarmah et al., 2019). Moreover, System GMM is specifically applicable to panel data where the time dimension is relatively small, whereas cross-sectional units are larger, as is the case in the present study. The endogeneity issues are minimized effectively because the method uses internal instruments based on lagged values of the explanatory variables, and the method produces better estimation performance relative to the difference GMM estimator.

The Two-Step System GMM estimator is robust to econometric problems such as endogeneity, heteroskedasticity, autocorrelation, and measurement errors, which commonly arise in dynamic panel models (Kiviet, 2011). The GMM difference estimator is sensitive to the unit root characteristic, whereas the system GMM provides the least-squares error estimates. The differenced GMM approach deletes fixed effects and begins by first differencing each regressor to handle endogeneity. However, the first difference transformation has a disadvantage resulting from the characteristics of the response variable, because it widens data loss gaps by subtracting the current observation from the previous one (Ntarmah et al., 2019). As a result, it affects the expected impact to some extent. The difference between the System GMM technique lies in changing the instruments to be uncorrelated with both fixed effects and providing new instruments for lagged dependent variables and any other endogenous variable to raise efficiency, which corrects the problem of endogeneity. Moreover, System GMM regresses and takes away the average of all future accessible variables’ observations compared to differenced GMM, which regresses the present observation minus the previous one. By considering all the issues raised earlier, the two-step system GMM was used in the study.

Therefore, it is appropriate to employ the system GMM to remove and manage the endogeneity problem. This research uses the panel data collected from 13 selected banks over ten years (2014–2023). These banks were chosen on the basis of the access and the uniformity of financial data during the study time. This sample consists of established and fairly new banks in terms of age, meaning that the sample includes both long-established and newer commercial banks, both privately and publicly operated in Ethiopia. The data on the non-performing loans, capital adequacy ratio, liquidity ratio, return on assets, loan-to-deposit ratio, and the Z-score indicators of the respective banks were gathered in the audited annual report of the respective banks and the annual financial supervision report of the National Bank of Ethiopia. The international databases, like the World Bank and the International Monetary Fund (IMF), provided the macroeconomic variables like inflation, government expenditure, foreign direct investment, and gross domestic savings. The data on regulatory quality were obtained in the World Governance Indicators database.

The sample of this study makes a balanced panel of 13 banks that were observed during a period of ten years, which made it 130 bank-year observations. A balanced panel design is used to make sure that all banks provide their observations on all years of interest in the research, which increases the accuracy of the estimation findings. Nevertheless, data availability has some limitations, as experienced especially in cases where the banks were relatively young and lacked comprehensive financial records in preceding years. The sample used to conduct the study did not include banks that had no complete data in the period of the study to ensure consistency and comparability. This is used to make sure that the final dataset is consistent and can be estimated using the System GMM methodology in dynamic panel estimation.

The dependent variable is economic growth, the independent variables are separated into groups: macroeconomic variables such as inflation, government general expenditure, foreign direct investment, gross domestic saving, institutional variables, and regulation quality. On the other hand, the banking sector stability variables include bank size (Z-score), non-performing loans, capital adequacy ratio, liquidity ratio, return on assets, and loan-to-deposit ratio. Data analysis is a step-by-step process. Firstly, basic measures of description are computed to get an understanding of the basic features of the data. Next, correlation analysis was undertaken to look at relationships between the variables. Finally, the two-step system GMM that handles the endogeneity problem and the dynamic panel bias is the core of the estimation that has been conducted. The model employs its first lags as the instrument to correct for endogeneity and omitted variables by controlling for individual heterogeneity. For model validity, diagnostic tests are carried out, including the test of serial correlation using the Arellano–Bond variables and the overidentifying restrictions test using the Hansen tests. The model specification for this study is as follows:

$$\begin{array}{ll}{realGDP}_{it}=\alpha & + {\beta }_{1}real{GDP}_{it-1}+{\beta }_2{Zscore}_{it}+{\beta }_{3}NP{L}_{it}+{\beta }_{4}CA{R}_{it}+{\beta }_5{LR}_{it}+{\beta }_6{ROA}_{it}\\ & + {\beta }_7{LDR}_{it}+{\beta }_{8}In{fR}_{it}+{\beta }_{9}GG{E}_{it}+{\beta }_{10}FD{I}_{it}+{\beta }_{11}GD{S}_{it}+{\beta }_{12}{RQ}_{it}+{ϵ}_{it}\end{array}$$

where (${realGDP}_{it}$) represents a proxy of economic growth (i) at time (t), $real{GDP}_{it-1}$ is the lagged value of the dependent variable, (${Zscore}_{it}$) is the bank stability, ($NP{L}_{it}$) is the non-performing loans ratio, ($CA{R}_{it}$) is the capital adequacy ratio, (${LR}_{it}$) is the liquidity ratio, (${ROA}_{it}$) is the return on assets, (${LDR}_{it}$) is a loan to deposit ratio, ($In{fR}_{it}$) is the inflation rate, ($GG{E}_{it}$) is general government expenditures as a percentage of GDP, ($FD{I}_{it}$) is foreign direct investment, and ($GD{S}_{it}$) is the gross domestic saving. The error term (${ϵ}_{it}$) captures the unobserved factors affecting economic growth. This model allows the examination of the direct and indirect effects of banking sector stability on economic growth.

The list of dependent variables and independent variables is stated in

Table 1. Variables, formulas, and expected signs.

Variables	Indicators	Symbols	Formulas/Source	Expected Sign
Dependent variables
Economic Growth	Real Gross Domestic Product	GDP	Nominal GDP/deflator
Independent variables
Bank-specific variables	Bank size (Z-score)	Z-score	${\displaystyle \frac{\mathrm{R}\mathrm{O}\mathrm{A}+\mathrm{E}\mathrm{q}\mathrm{u}\mathrm{i}\mathrm{t}\mathrm{y}/\mathrm{A}\mathrm{s}\mathrm{s}\mathrm{e}\mathrm{t}}{\mathrm{S}\mathrm{d}\left(\mathrm{R}\mathrm{O}\mathrm{A}\right)}}$	+
	Non-performing loans	NPL	Non-performing loan/Total loan	−
	Capital adequacy ratio	CAR	Total Capital/Total Assets	+
	Liquidity Ratio	LR	Liquid asset/Total assets	+
	Return on Assets	ROA	Net income/Total assets	+
	Loan-to-Deposit Ratio	LDR	Total loan/Total deposit	+(balanced) *
Macroeconomic variables	Inflation	InfR	From the World Bank database	−
	General Government expenditure	GGE		+
	Foreign Direct Investment	FDI		+
	Gross Domestic Savings	GDS		+
Institutional Variable	Regulatory quality	RQ	From Worldwide Governance Indicators (WGI)	+
Another variable	Lag of real GDP	L.GDP	The lag value of GDP	+

Source: Computed by authors. * Balanced: The balanced amount of loan to deposit ratio refers to the ideal proportion of a bank’s total loans to its total deposits, typically ranging between 80% and 90 balance ensures that the bank maintains enough liquidity to meet withdrawal demands while maximizing its lending capacity to generate income.

below, with their formula and stated sign.

Statistical tests and model selection

To have a sound econometric evaluation of the study area, a set of preliminary tests and model estimations was undertaken. These steps involved heteroscedasticity and serial correlation tests, as well as regression analyses using pooled Ordinary Least Squares (OLS), fixed effects (FE), difference GMM, One-Step System GMM, and Two-Step System GMM.

Statistical Tests

Serial Correlation: As shown in

Table 2. Serial correlation.

Test	F-Statistic	Prob > F	Decision	Conclusion
Wooldridge test for autocorrelation	1983.999	0.0000	Reject H0	Presence of first-order serial correlation

Source: Computed by authors.

below, the Wooldridge test indicates that p = 0.000 is significantly less than commonly used alpha levels (e.g., 0.05, 0.01, or 0.001), so we reject the null hypothesis. This indicates first-order autocorrelation, as the residuals (or errors) from one period are related to those from the previous period; if not controlled for in panel data analysis, this can lead to inefficient estimation.

Heteroscedasticity: The result in

Table 3. Heteroscedasticity test.

Test	Chi2 (df)	Prob > Chi2	Decision	Conclusion
Modified Wald test for Groupwise heteroskedasticity	5158.41 (13)	0.0000	Reject H0	Presence of heteroskedasticity

Source: Computed by authors.

presents the Modified Wald test for groupwise heteroskedasticity in a fixed effect model regression. It comes to p = 0.000, which is less than customary alpha levels (0.05, 0.01, or 0.001). Hence, we fail to reject the null hypothesis that the standard error is constant across all levels of predictor variable values. This entails results that depict the existence of heteroskedasticity in the study.

Model Selection

Based on the regression result using pooled Ordinary Least Squares (POLS), fixed effects (FE), difference GMM, One-Step System GMM, and the Two-Step System GMM presented in

Table 4. The set of models’ regression results for model selection.

	Regression Outputs
Variables		Model 1 POLS	Model 2 FE	Model 3 DGMM	Model 4 One−Step System GMM	Model 5 Two−Step System GMM
L1.GDP	Coefficient	0.9805699	0.9850316	0.9933273	0.9845462	0.7330655
	Std. err.	0.0248064	0.0264725	0.0330752	0.0311485	0.0951193
	p > |t|	0.000	0.000	0.000	0.000	0.000
Z−score	Coefficient	−0.0126579	−0.0129224	−0.0135301	−0.0131163	0.1837628
	Std. err.	0.0023974	0.0025944	0.0033256	0.0030839	0.0624532
	p > |t|	0.000	0.000	0.000	0.000	0.003
NPL	Coefficient	0.0001622	0.0003039	0.000364	0.0000715	0.0312362
	Std. err.	0.00026680	0.000562	0.0009609	0.000744	0.0113583
	p > |t|	0.573	0.590	0.705	0.923	0.006
CAR	Coefficient	−0.0001519	−0.0005114	−0.0011645	−0.0004703	0.0068996
	Std. err.	0.0002201	0.0004305	0.0007806	0.0005576	0.0044227
	p > |t|	0.492	0.238	0.136	0.399	0.119
LR	Coefficient	−0.000265	−0.0004514	−0.0007784	−0.000389	0.0278558
	Std. err.	0.0003106	0.0004409	0.0007044	0.0004933	0.0098545
	p > |t|	0.396	0.309	0.269	0.430	0.005
ROA	Coefficient	0.0015341	0.001368	0.0014464	0.0012051	0.4502703
	Std. err.	0.0025161	0.0037308	0.0059265	0.0041874	0.1347563
	p > |t|	0.543	0.715	0.807	0.774	0.001
LDR	Coefficient	−0.0000924	−0.0002435	−0.000488	−0.0001317	−0.001646
	Std. err.	0.0001634	0.0002508	0.0004753	0.0002895	0.0005829
	p > |t|	0.573	0.334	0.304	0.649	0.005
InfR	Coefficient	0.0050288	0.0048373	0.0044666	0.0048564	0.0699947
	Std. err.	0.0010354	0.0011078	0.0013902	0.0013044	0.0205943
	p > |t|	0.000	0.000	0.001	0.000	0.001
GGE	Coefficient	0.024493	0.0251586	0.0260659	0.0248204	0.225764
	Std. err.	0.0035569	0.0038833	0.0049497	0.0045507	0.0622066
	p > |t|	0.000	0.000	0.000	0.000	0.000
FDI	Coefficient	−0.0584159	−0.0587236	−0.0597497	−0.0593361	−0.6997874
	Std. err.	0.0176503	0.018636	0.0230261	0.0219368	0.1913343
	p > |t|	0.001	0.002	0.009	0.007	0.000
GDS	Coefficient	0.000761	0.0002237	−0.0005997	0.0002885	0.1484913
	Std. err.	0.0025798	0.0027421	0.0034068	0.003232	0.0459629
	p > |t|	0.782	0.935	0.860	0.929	0.001
RQ	Coefficient	−0.0266506	−0.0267299	−0.0270283	−0.0270606	−0.0621607
	Std. err.	0.0026683	0.0028518	0.0035893	0.00338	0.010946
	p > |t|	0.000	0.000	0.000	0.000	0.000
_cons	Coefficient	0.8308279	0.8468967	0.8829584	0.8550972	−2.674965
	Std. err.	0.1137041	0.1298571	0.1733249	0.1525144	1.296291
	p > |t|	0.000	0.000	0.000	0.000	0.039

Source: Computed by authors.

, the model selection criteria were discussed with brief justifications.

POLS: Pooled OLS does not consider any other unobserved characteristics of the banks, which hold a constant and unchanging value over time. This can cause bias in the estimation process whenever such differences are related to the independent variables. In addition, random errors cannot be eliminated because POLS do not consider various problems like variation in variance or heteroskedasticity and autocorrelation, which have been managed in this study.

Fixed Effect model: Though fixed effect manages the time-invariant differences in the variable across the cross-sectional units in the sample, it performs less well when applied for use in dynamic panel data models where the lagged dependent variable is involved, such as L1.GDP. This is why the FE estimator may be biased in such cases, as it does not account for the endogenous character of the lagged dependent variable. Moreover, it does not account for problems such as heteroscedasticity and autocorrelation shown in earlier tests.

Difference GMM (DGMM): This changes the model by differencing and can cause weak instruments, especially where the variables are persistent across time (as is the case for GDP). This weak instrumentation can lead to estimation biases.

One-Step System GMM: This shapes the DGMM by solving the weak instrument problem by using the first difference equation and the level equation. However, the estimation using this method does not necessarily correct for heteroskedasticity to the fullest extent. However, the One-Step System GMM employs standard errors that are not corrected for heteroskedasticity; hence, this model also may suffer from inefficiency, as foreseen in the tests that showed that there is evidence of heteroskedasticity.

Two-Step System GMM: This is a suitable model since it addresses the endogeneity problem by including lagged dependent variables like L.GDP. It estimates the model using the differenced and level equations to come up with more valid instruments. It gives reliable standard errors that are consistent with heteroskedasticity since the analysis showed heteroskedasticity via the Modified Wald test, and this method is ready to address the autocorrelation problem that is evident in this study based on the Wooldridge test at first order. This is found to be the case with the two-step approach because, in the second step, residuals from the first step are used to form the improved weighting matrix, thus providing better parameter estimates than the one-step approach.

4. Result and Discussion

Descriptive Analysis

Table 5. Descriptive statistics.

Variable	Obs	Mean	Std. dev.	Min	Max
Dependent Variable
realGDP	130	4.291907	0.31433	3.7684	4.71197
Independent Variables
L.realDGP	117	4.245233	0.296486	3.7684	4.678957
Z-score	130	10.409	1.12142	8.47	12.38
NPL	130	10.74595	6.427561	1.445	32
CAR	130	25.29445	7.582546	0.179	49
LR	130	18.21608	6.629235	0.1396	45.89
ROA	130	2.716679	0.740071	1.0087	5.126
LDR	130	66.61189	10.37712	39.722	91.45
InfR	130	17.465	9.427116	6.62	33.88
GGE	130	9.1492	1.43152	6.31	11.13
FDI	130	7.760849	0.408914	7.053901	8.32916
GDS	130	20.1	2.916102	14.77	24.08
RQ	130	14.134	1.694673	11.43	16.98

Source: Computed by authors.

provides a snapshot of the key variables used in the research regarding the effect of the Banking Sector’s Stability on Economic Growth in Ethiopia using the Two-System GMM Approach. The dependent variable, economic growth proxied by real GDP, has a mean of 4.2919 and a standard deviation of 3.1433, meaning moderate fluctuation of economic growth rates around the sample mean. On the other hand, the mean values of independent variables give us an idea of the average conditions of the banking sector and macroeconomic environment in Ethiopia. For instance, the mean of the Z-score, which was 10.409, relatively indicates stable banking conditions. While there is not a universally fixed standard amount, a Z-score above 10 is generally considered indicative of a stable bank (Li et al., 2020). It is possible to conclude that, on average, the banks are stable. However, the standard deviation of 1.121 shows that there is a bit of variation in stability among the banks. It is also observed that the mean of the NPL ratio is 0.7459, which depicts the proportion average of non-performing loans, and the standard deviation of 0.4275 shows volatilities in loan performance. CAR, LR, and ROA analyzed the banking industry’s financial health and its performance; the CAR is 25.294, LR is 18.216, and ROA is 2.716 to moderate levels of capital adequacy, liquidity, and profitability, respectively. The total values of the distributed LDR are 66.611, InfR is 17.465, GGE is 9.149, FDI is 7.760, GDS is 20.12, and RQ is 14.134, which provide the vision of the overall economic and regulatory criteria of the concerned country. These descriptive statistics form the basis for elaborating on the nature of the relationships between banking sector stability and economic growth, as contemplated in the study. From the fluctuation of these variables, it is therefore apparent that, besides the overall average conditions in the banking sub-sector and the macroeconomic environment, each of the above factors can vary in a manner that constrains economic growth.

Two-Step System GMM estimation result and discussion

Diagnostic Tests

Table 6. Two-Step System GMM estimation result.

Explanatory Variables	Coefficient	Standard Error	p > [t]
L1.GDP	0.733 ***	0.095	0.000
Zscore	0.184 ***	0.062	0.003
NPL	0.031 ***	0.011	0.006
CAR	0.007	0.004	0.119
LR	0.028 ***	0.01	0.005
ROA	0.45 ***	0.135	0.001
LDR	−0.002 ***	0.001	0.005
InfR	0.07 ***	0.021	0.001
GGE	0.226 ***	0.062	0.000
FDI	−0.7 ***	0.191	0.000
GDS	0.148 ***	0.046	0.001
RQ	−0.062 ***	0.011	0.000
Number of observations	117	A-Bond AR (2) test	0.069
F statistics	853.07 ***	Sargan test	0.005
Group/Instruments	13/52	Hansen test	0.78
A-Bond AR (1) test	0.029

Source: Author’s computation. Note: *** p < 0.01, show statistical significance at 1%. Source: Computed by authors.

below presents the diagnostic tests used in the study, unequivocally confirming the robustness and superiority of the two-step System GMM model as applied in the analysis. This robustness instills confidence in the validity of our research methodology.

Wald Chi-Square Test

The Wald chi-square test, a cornerstone of our analysis, evaluates the joint significance of all the coefficients in the model. The statistical evidence is compelling, with the Wald chi-square statistics reaching an extraordinarily high value of 8.39 × 10⁷ and a p-value of 0.000. This significant result underscores the weight of the independent variables’ collective impact on the dependent variable, confirming the overall significance of the model.

Arellano–Bond Tests: As shown in Table 6, the results concerning the Arellano–Bond test for autocorrelation show the degree of serial correlation in the error terms. The results of the GMM estimator include autocorrelation in each equation at each level, which is a general feature of dynamic panel data models; the value of AR (1) equals 0.029 for a p-value with a z-value of −2.18, which is expected in the dynamic panel data model (Arellano & Bond, 1991). In addition, the AR (2) test result has a p-value of 0.069. The insignificant value of AR (2) validates the fact that the model is specified correctly and the instruments applied in the estimation of the System GMM are valid (Arellano & Bover, 1995).

Sargan and Hansen tests: Looking at the validity of the instruments, Sargan and Hansen’s tests were employed to determine whether or not the selected instruments were exactly orthogonal to the error term. Using the Sargan test, given the value of p = 0.005, suggests that there may be overidentifying restrictions. However, the Hansen test, which is preferred in the two-step GMM model, has a 0.78 p-value, which indicates no problem with the choice of instruments. Therefore, this gap implies that more reliance should be placed on the Hansen test to eradicate any doubt about using instruments.

Overall, the diagnostic tests for the two-step system GMM model confirm its robustness and validity for analysis. The Wald chi-square test indicates that, collectively, the independent variables are related to the dependent variable, demonstrating overall model significance. The Arellano–Bond test results, after analyzing the time series data, show that first-order autocorrelation exists but second-order autocorrelation does not, confirming the validity of the instrument. However, the Sargan test points to a problem of over-identification, while the Hansen test, which is robust to heteroscedasticity, supports the validity of the instruments used. Additionally, an analysis of Hansen tests confirms that the instrument subsets are homogeneous in capacity, establishing their reliability. These findings underscore the appropriateness of the two-step system GMM model in addressing endogeneity and yielding efficient, credible estimates for our study on the impact of digital financial inclusion on banking sector stability and economic growth in Ethiopia.

Hence, Table 6 presents system GMM results used to assess the effect of banking sector stability on economic growth in Ethiopia, with each variable interpreted accordingly.

The lagged real GDP is positively and statistically significant for current economic growth. The coefficient of 0.733 indicates that economic growth is highly persistent, meaning that a one-unit increase in real GDP in the past is associated with a 0.733-unit increase in current real GDP, holding all else constant. This aligns with (Aghion et al., 2005; Beck et al., 2023; Tieguhong & Mandiefe, 2017), who note that past GDP influences future economic performance. This observation aligns with the dynamic growth theory, which posits that economic performance tends to be dynamic over time due to capital accumulation, institutional development, and the continuity of policies. These findings are found in some other empirical research, such as Aghion et al. (2005), which states that historical economic performance provides a basis for future growth based on the diffusion of technology and increased productivity. This finding has been consistent with previous studies. It therefore confirms that economic growth in Ethiopia is dynamic in nature, with the previous economic success playing a significant role in the present economic performance.

Regarding the Z-score, the coefficient of 0.184, with a significance level of 0.003, suggests a positive and significant impact of bank stability on economic growth. Supporting this, recent studies by Adenutsi et al. (2024) that a higher Bank Z-score signifies financial stability, which promotes economic growth by reducing insolvency risk. Additionally, Jokipii and Monnin (2013) confirm that bank stability encourages investor confidence, leading to increased investment and economic development. This finding is similar to the empirical evidence provided by Jokipii and Monnin (2013), who indicate that the stability of the banking industry increases investor confidence and enriches financial intermediation. The fact that the current result is similar to the past research findings implies that a stable banking system is important in facilitating economic growth, especially in bank-based financial systems like Ethiopia, where the banking industry is the main source of investment funding.

Concerning NPL, the coefficient of 0.031 at a p-value of 0.006 reveals that NPL has a significant positive effect on economic growth, which is indeed counterintuitive.

Traditionally, higher levels of NPLs should be expected to harm economic growth because of higher credit risk and constrained credit availability. This may call for more empirical analysis of the conditions in which Ethiopian banks operate. NPLs indicate declining asset quality, which affects a bank’s livelihood and constrains the development of credit and, thereby, economic growth (Nkusu, 2011). Nkusu focuses more on this negative relation, while proving that high NPLs impair the efficiency of the banking sector and, therefore, the rates of economic growth slow down. However, the experimental evidence in certain developing economies at times takes the opposite variant of the traditional hypothesis. In the context of Ethiopia, there is potential for a positive relationship between the non-performing loans and economic growth as a result of an expansionary stage of credit growth in the banking sector (Adenutsi, 2025; Yitayaw et al., 2022). When the economy grows at a very high rate, the banks are likely to give more loans to enhance investment and consumption, and in the process, this is likely to escalate the level of non-performing loans since there is also an amplification of the credit risk. Under these conditions, the growth in NPLs can indicate more financial intermediation, as opposed to the direct worsening of the quality of assets. Furthermore, using bank-based financial systems such as those in Ethiopia, where other funding options are few, credit growth, despite an increase in credit risk, is still able to boost economic activities in the short term. Hence, the positive relationship that is observed could be because when lending is higher, growth periods assist in growth, even though credit risk is also rising (AIshumoos, 2025; Mashamba & Chikutuma, 2023).

Concerning CAR, the coefficient is equal to 0.007 (t = 2.145, p = 0.119) and thus indicates a positive relationship, even though it is not significant at the 5% level. This suggests that though higher capital adequacy may lead to economic development, the effect is not strong in this model. A higher CAR level ensures that banks have adequate capital that will enable them to meet losses, hence enhancing financial stability and growth (AIshumoos, 2025). Moreover, Aghion et al. (2005) and Masoud and Hardaker (2012) stress that greater capitalization and economic contours more effectively mitigate financial risks.

The increasing significance of the statistic, which is significant at 0.028 (p = 0.005), in LR indicates that banks with high liquidity ratios can better support economic growth. The liquidity ratio helps banks meet their short-term obligations and boosts confidence in the banking system, thereby supporting economic activities (Beck et al., 2023). Additionally, Abdel Megeid (2017) also emphasizes the importance of liquidity management in responding to aspects of financial crises that can weaken the economy. For ROA, the coefficient is 0.045 with a p-value of 0.001, showing a positive trend that greater bank profitability helps facilitate economic development. By increasing bank returns, ROA, which measures bank profitability, promotes credit growth for efficient sectors to support economic growth (Rakshit & Bardhan, 2022). Rakshit and Bardhan also identify that profit efficiency enhances banks’ ability to pursue economic growth through lending.

When we came to LDR, the result showed a negative effect by the coefficient of −0.002 at p = 0.005. Healthy LDR ensures the extension of credit while, at the same time, protecting the stability of a given organization, which proves that a high LDR qualifies as over-leveraging. Tran and McMillan (2020) argue that maintaining a stable LDR supports a sustainable credit supply for long-run economic growth. Taking the different arguments regarding LDR, most scholars agree that the balanced loan-to-deposit ratio has a positive effect on economic growth. Concerning inflation, the estimated coefficient of −0.07 (p = 0.001) suggests that inflation harms growth. However, some scholars also suggested that high inflation harms growth, but moderate inflation brings investment through the favorable distortion of price signals. Akinsola and Odhiambo (2017) further appreciated that inflation above a certain level reduces the growth average for developing countries. Concerning GGE, the coefficient = 0.226, p = 0.000 indicates that government expenditure has a strong positive effect on the economic growth of the country, thus supporting the role of fiscal policy in the development of any country. This means that government expenditure has a positive effect on growth due to the provision of infrastructure services, but spending too much has a negative impact since it crowds out private investment. Ijaz et al. (2020), and Kaur (2018) hypothesize that productive use of government expenditure promotes growth and that reckless use of expenditure hampers growth. When we come to FDI, although the FDI has a negative beta coefficient of −0.7 and is significant (p = 0.000), it is normally assumed to have a positive effect on economic development. This result may call for more investigation into some of the key drivers of FDI in the country. FDI also contributes to capital and efficiency, which increases productivity and hence growth. According to Sijabat (2023), FDI has a positive impact on African economies; Sijabat also found that FDI improves technological transfer and boosts economic growth. Though it is quite common to agree that foreign direct investment should facilitate economic growth by bringing in capital, the transfer of technology, and even creating employment, it has been found that FDI does not necessarily have the expected positive impact on an economy’s growth in some cases, as observed in some of the developing economies. The advantages of FDI might not fully apply to the overall economic growth in countries with rather immature financial systems and institutional limitations. The negative correlation between FDI and economic growth in Ethiopia might be linked to the factors of inadequate linkages between foreign companies and the local industry, profit repatriation, and FDI concentration on sectors with few spillover impacts. Moreover, structural obstacles, including regulatory limits, infrastructural imbalances, and a low level of technological uptake potential, can minimize the growth-promoting impact of foreign investments (Aghion et al., 2005; Awan, 2016; Kong et al., 2020).

Furthermore, GDS obtained a coefficient value of 0.148 and a significance level of 0.001, which highlights the significance of domestic savings in supporting economic growth and development. Retentions furnish the needed capital to push investment and, thus, development. (Wanzala & Obokoh, 2024) have observed that higher domestic savings rates enhance capital accumulation and result in an improved rate of economic growth in the developing world.

The regression result about RQ further confirms the earlier findings of a negative but significant effect of regulatory quality on economic growth, with a coefficient of −0.062 (p = 0.000); this might mean that over-accentuation on the quality of regulations could have a negative effect in the sense that it slows the rate of economic activities. Good regulations are key to better financial stability and improved growth by making a portfolio safer and more attractive to investors (W. Ullah et al., 2024). In addition, according to Awan (2016), high regulatory quality also discourages foreign investment, which would also help enhance economic growth.

In general, the empirical results of this research show some agreements and contradictions with the past empirical sources. The overall similarity between the positive impacts that the bank stability (Z-score), liquidity ratio, profitability (ROA), government expenditure, and gross domestic savings have on economic growth and the previous research works that highlight the significance of a good and effective banking system in ensuring economic growth can be largely attributed to the importance of having a good and efficient banking system (Adenutsi et al., 2024). These findings support the hypothesis that a stable and profitable banking industry improves financial intermediation, investment, and macroeconomic outcomes. Nonetheless, not all the results are in line with the traditional theoretical assumptions and past research results. To illustrate, the positive correlation between non-performing loans and economic growth is not the same as that of (Firdaus et al., 2022; Kong et al., 2020), which indicates that high levels of NPLs tend to lower the effectiveness of the banking sector and prevent growth. Likewise, the negative association between foreign direct investment and economic growth is inconsistent with other works, like Sijabat (2023), which reported positive growth impacts of FDI in developing economies. The differences could represent country-related structural features of the Ethiopian financial system, such as the lack of financial diversification, the lack of connection between foreign investors and national industries, and institutional restrictions that inhibit the successful transfer of financial flows into productive economic processes. Hence, the findings demonstrate that the relationship between banking sector stability and economic growth in developing economies should be analyzed in terms of structural and institutional context.

5. Conclusions, Recommendations, and Policy Implications

Conclusions

The study examines how banking sector stability influences economic growth in Ethiopia using the two-step system GMM model. Data analysis through regression indicates that banking stability indicators are significant factors for economic development. Specifically, the coefficient of Z-score, a measure of bank stability, has a positive impact on economic growth, supporting the idea that the banking sector contributes to development. Other variables, such as NPLs and LR, also positively influence economic growth. Surprisingly, high NPLs show a positive correlation with economic growth, which calls for further analysis of the Ethiopian banking sector’s nature. Although an acyclic relationship exists, LDR and CAR have minimal effects on economic growth, suggesting that, despite their importance for banking stability, they may not influence short-term economic growth. Additionally, the study finds that both inflation and government expenditure significantly and positively affect economic growth. Conversely, FDI, typically viewed as a positive growth driver, shows an inverse relationship with overall performance. A key conclusion is that banking sector stability, achieved through proper regulation, sound practices, liquidity, and increased profitability, contributes to economic development. The study recommends further empirical research to explain the unexpected effects of NPLs and FDI, and urges policymakers to continue reforming institutional frameworks as a pathway to sustained growth.

This study, despite offering valuable information about the relationship between the stability of the banking sector and economic growth in Ethiopia, has a number of limitations, which must be understood. To begin with, the analysis will be based on the data of a small sample of commercial banks during a decade, which might not be representative of reflecting the long-term structural changes in the Ethiopian financial market. Second, the research is aimed primarily at the indicators of the banking sector and the choice of macroeconomic variables, whereas other possible and influential institutional and financial development indicators can also affect economic growth. Third, as much as the System GMM approach attempts to overcome the problem of endogeneity, the findings can be prone to instrument selection and model specification. Consequently, future studies may expand the analysis by employing longer time series analysis, more financial development indicators, and conducting comparative research to other developing economies in Africa to have a better picture of the external role of banking sector stability in facilitating sustainable economic development.

6. Recommendation and Policy Implications

The analysis of the Ethiopian banking sector stability and economic growth highlights some key recommendations and policy implications. To strengthen the regulatory framework, the Ethiopian government and financial regulators should prioritize compliance with international banking standards, focusing on the reduction in non-performing loans and improving the capital adequacy ratio to enhance resilience. Banks should also prioritize increasing profitability, as indicated by a high return on assets, and maintaining a strong liquidity ratio, both of which contribute positively to economic growth. Additionally, gross domestic savings can create a more stable funding base, promoting sustained economic development.

Given the unexpected positive effect of NPL on economic growth, a deeper analysis is necessary to understand its unique implications within Ethiopia’s context, and targeted policy should aim to reduce default rates while exploring the drivers of this effect. In terms of foreign direct investment, although it currently exhibits an inverse relationship with growth, aligning foreign direct investment with sectors that promote financial stability should better harness its potential. Maintaining a moderate inflation level is also critical as inflation showed a positive impact on economic growth; however, excessive inflation undermines economic stability in the long run. Banking sector reforms are essential, with an emphasis on regulatory quality and institutional stability, both of which contribute to sustainable economic growth. Strengthening institutional quality alongside robust risk management practices will prepare Ethiopian banks to withstand economic shocks better, providing a safeguard for growth. Policy initiatives should also emphasize the importance of domestic investment in growth-driving sectors to provide a sustainable economic foundation for economic progress. Besides this, policymakers and other financial regulators in Ethiopia ought to contemplate enhancing supervisory frameworks that facilitate sound credit risk management in the banking industry. The National Bank of Ethiopia might also improve the quality of loans monitored and spur the banks towards better risk evaluation procedures so that the accumulation of non-performing loans can be minimized in the long run. Moreover, the policies that are meant to enhance the connection between foreign direct investment and local financial institutions can be used in order to maximize the growth advantage of foreign capital inflows. It is also possible to promote the beneficial spillover effects of FDI on economic development by encouraging investment in productive sectors, including manufacturing, infrastructure, and technology. Lastly, the financial stability of the financial sector through increased regulatory transparency and institutional quality can be improved, which will provide a more favorable environment for sustainable economic growth in Ethiopia.