e-ISSN 2300-3065 p-ISSN 2300-12402025, volume 14, issue 1 Copernican Journal of Finance & Accounting Date of submission: January 8, 2025; date of acceptance: May 27, 2025 * Contact information: philominamnarh@gmail.com, University of Cape Coast, Cape Coast, Ghana, phone: +233 543835107; ORCID ID: https://orcid.org/0009-0002- 3275-1231. ** Contact information: collinsdodzi.cd@gmail.com, Renmin University of Chi- na, Beijing, China, phone: +86 15506159515; ORCID ID: https://orcid.org/0000-0002- 5710-9686. Narh, P.M., & Dzitse, C.D. (2025). Financial Inclusion Towards Financial Stability in Ghana: In- sights from Quantile Regression Analysis. Copernican Journal of Finance & Accounting, 14(1), 47– 69. http://dx.doi.org/10.12775/CJFA.2025.003 PHiloMina MaKu narH* University of Cape Coast collins dodzi dzitse** Renmin University of China FINANCIAL INCLUSION TOWARDS FINANCIAL STABILITY IN GHANA: INSIGHTS FROM QUANTILE REGRESSION ANALYSIS Keywords: financial inclusion, financial stability, cointegration quantile regression, Ghana. Jel Classifications: G28, O16, O55. Abstract: In Ghana, only 52.7% of the population is fully financially included in the for- mal financial system. However, while financial inclusion is often linked to economic re- silience and stability, as it can broaden access to financial services and reduce vulner- ability, its direct, quantifiable impact, particularly at different quantile levels, remains largely untested in the sub-Saharan African context. This study addresses this gap by employing quantile regression analysis to examine the impact of financial inclusion on Ghana’s financial stability, using time-series data from 2005 to 2021. The results show significant cointegration between the variables, indicating that the independent vari- Philomina Maku Narh, Collins Dodzi Dzitse4848 ables act as long-run forcing factors for financial stability. The long-run analysis shows that financial inclusion positively affects financial stability, especially at higher quan- tiles. These findings highlight the need to consider various quantile levels in national financial assessments. The study suggests prioritizing financial inclusion during pe- riods of low financial stability because of its positive correlation with strengthening a country’s financial landscape, which can aid in economic resilience and sustainability in Ghana and many sub-Saharan African countries.  Introduction Introduction Financial inclusion is essential for financial stability, as 1.7 billion people re- main unbanked globally, with Ghana among seven countries representing near- ly 50% of the unbanked population (Global Financial Index Report, 2017). Fi- nancial inclusion can reduce financial fragility and enhance stability (Camara & Diallo, 2020). However, Ghana’s significant financial exclusion, with substan- tial amounts of money outside the banking system, has resulted in pronounced financial instability. Financial instability has been a recurring issue for over 20 years. In Ghana, this instability has worsened due to the lingering effects of the COVID-19 pandemic, influencing inflation and debt sustainability. Addition- ally, the domestic debt exchange program (DDEP) of the Government of Ghana (GoG) has failed to achieve financial stability, leading to a bailout request from the IMF in 2023. Financial inclusion is crucial for achieving financial stability (Koudalo & Toure, 2023). Policymakers consider various factors, such as inflation, reces- sion, policy changes, political situations, global markets, fiscal deficits, and fi- nancial inclusion, to address financial stability. Financial inclusion enhances the financial sector and provides access to services such as credit, thus strengthen- ing stability (Moeti & Sin Yu, 2024). While numerous studies have investigated the factors influencing financial stability (Frimpong, Yusuf, Boateng, Ankomah & Abeka, 2023), including financial inclusion (Babatunde, 2024), research on the financial inclusion-stability relationship in developing countries such as Ghana is limited. As of June 2022, only 52.76% of Ghana’s population had ac- cess to formal financial services (Sackitey, 2024). Moreover, existing studies in this field have rarely used the quantile regression approach to analyze this re- lationship. Feghali, Mora and Nassif (2021) further suggest that a general meas- ure of inclusion is insufficient to assess financial inclusion-stability effects. Quantile regression is a crucial tool for studying financial inclusion and stability, because it enables the measurement and analysis of these concepts FINANCIAL INCLUSION TOWARDS FINANCIAL STABILITY IN GHANA… 4949 at various quantiles. Specifically, it allows for a comprehensive assessment of financial stability across different levels of financial inclusion (Olusegun, Ev- buomwan & Belonwu, 2021). Furthermore, quantile regression can facilitate the examination of financial inclusion in Ghana at diverse income-level quan- tiles. By employing quantile regression, policymakers and stakeholders can gain insights into the relationships, dynamics, and potential disparities in fi- nancial inclusion and stability across various income segments in Ghana (Ibra- him, Mazlina, Az-Saini & Zakaria, 2016). This approach provides a different un- derstanding of the effect of income levels on financial inclusion, and allows for targeted interventions that can help promote inclusive and stable financial sys- tems in Ghana. Thus, a missing explanation in empirical literature is that finan- cial inclusion affects stability at various levels of financial inclusivity. Conse- quently, this study analyzed the following: (1) short-run financial inclusion – financial stability relationship; (2) long-run financial inclusion – financial stability relationship; and (3) effect of financial inclusion on financial stability at different quantiles. Literature ReviewLiterature Review Financial Inclusion (FI)Financial Inclusion (FI) Financial inclusion, defined as the percentage of individuals and firms using fi- nancial services, has garnered the interests of stakeholders, scholars, and poli- cymakers. A World Bank global payment system survey in 2021 indicated that international forums, such as G-20, emphasize its importance for social and economic development (World Bank, 2021). Fifty nations have recently estab- lished financial inclusion goals, recognizing their role in reducing poverty, fos- tering prosperity, and promoting sustainable development. In 2016, the World Bank noted that over 2.5 billion people lacked formal financial accounts, with half the adult population without financial services, revealing significant ac- cess gaps (World Bank, 2021). Consequently, poor families often rely on infor- mal sources such as friends, relatives, savings plans, moneylenders, and cash. Financial inclusion, often associated with access to credit from official insti- tutions, has various dimensions that influence financial attitudes (Babatunde, 2024; Kamal, Hussain & Khan, 2021). Formal accounts, including deposits and Philomina Maku Narh, Collins Dodzi Dzitse5050 loans, can be assessed on the basis of purpose, access type, and usage frequen- cy. Alternatives include mobile money and insurance services in agriculture and health (Demirgüç-Kunt & Klapper, 2012). Financial exclusion imposes high costs on those in need (Cull, Ehrbeck & Holle, 2014). In regions such as Gha- na and sub-Saharan Africa (SSA), financial inclusion is crucial for reducing de- pendence on informal loans and enabling savings (Kamara, 2024), with relia- ble rural financial services essential for economic growth and supporting rural livelihoods. Financial Stability (FS)Financial Stability (FS) The World Bank defines financial stability as the absence of a systemic failure in the financial system, signifying resilience to stress. It involves smooth op- eration of the system, with effective monitoring and management of financial risks to prevent crises (Babar, Latief, Ashraf & Nawaz, 2019). To grow their fi- nancial and economic sectors, countries enhance their financial inclusion and provide access to financial services (Anthony-Orji, Orji, Ogbuabor, Mba & Onwe, 2021). Le, Chuc and Taghizadeh-Hesary (2019) highlight that financial inclu- sion offers affordable, need-based financial services. Interest in financial sta- bility intensified among academics and policymakers during the 2007–2009 economic crisis, and resurfaced during the COVID-19 pandemic (2019–2021), causing another global financial crisis. Research shows that financial stabil- ity is vital for a country’s development and sustainability (Kamal et al., 2021). Financial Inclusion and Financial StabilityFinancial Inclusion and Financial Stability Frequent inquiries suggest that financial inclusion enhances financial stability. Anthony-Orji et al. (2021) assert that diversifying bank assets reduces risk and bolsters resilience. Atellu and Sule (2019) find that greater access to financial services strengthens the deposit base and stabilizes financial institutions. Fi- nancial inclusion improves monetary policy transmission, reduces credit risk and default likelihood, promotes economic activities, enhances risk manage- ment, and positively impacts socioeconomic well-being in developing coun- tries (Antwi, Kong & Gyimah, 2024; Yangdol & Sarma, 2019). Financial inclu- sion plays a vital role in economic growth (Ahamed & Mallick, 2019). However, FINANCIAL INCLUSION TOWARDS FINANCIAL STABILITY IN GHANA… 5151 some argue that it may lower lending criteria, contributing to financial crises (Camara & Diallo, 2020). Neaime and Gaysset (2018) found a positive correlation between financial stability and the number of banks, challenging the notion that increased finan- cial inclusion is risky. Effective regulatory frameworks are crucial because in- adequate regulation can lead to negative outcomes (Kamal et al., 2021). Bal- anced regulations are essential to maximize the benefits of financial inclusion while mitigating risks. Feghali et al. (2021) support the findings of Ahamed and Mallick (2019) and Canlas, Ravalo and Remolona (2025), emphasizing the im- portance of nuanced measures in assessing the impact of financial inclusions, especially through increased deposits. Regional studies indicate that financial inclusion positively impacts financial stability in sub-Saharan African countries (Djoufouet & Pondie, 2022; Moeti & Sin Yu, 2024), the Asian banking sector (Vo, Van, Dinh & Ho, 2021), and the European Union (Danisman & Tarazi, 2020). Existing literature indicates a complex relationship between financial in- clusion and stability, highlighting both positive and negative effects. Although empirical evidence typically indicates a positive correlation, these findings em- phasize the need for well-designed regulatory frameworks and targeted meas- ures. Understanding the dynamics between financial inclusion and stability is essential for policymakers, regulators, and financial institutions in fostering inclusive and resilient systems. MethodsMethods Research DesignResearch Design This explanatory study employed a quantitative method to identify cause-and- effect relationships between variables (Saunders, Lewis & Thomhill, 2012). It investigates the link between financial inclusion and stability using second- ary data (Koudalo & Toure, 2023). The analysis uses time series data from 2005 to 2021, including the Bank’s Z-score, the volume of outstanding depos- its with commercial banks (% of GDP), outstanding loans from commercial banks (% of GDP), the Overall Consumer Price Index, and the Central Bank pol- icy rate in Ghana, sourced from the World Bank, the International Monetary Fund, and the Bank of Ghana website. Philomina Maku Narh, Collins Dodzi Dzitse5252 Measurement of VariablesMeasurement of Variables Measurement selection for this study’s variables was based on their preva- lent use in existing literature. Financial inclusion, the independent variable, is measured by the volume of outstanding deposits and loans as a proportion of GDP, representing the ratio of total funds in deposits and loans within a coun- try’s banking system relative to GDP. Financial stability, the dependent vari- able, is measured using the Z-scores of various financial institutions in Ghana. The Z-score, created by Edward Altman in the late 1960s as a bankruptcy pre- diction model, indicates an institution’s financial health and risk. Although ini- tially used to predict corporate bankruptcy, it is now used to assess financial institutions’ creditworthiness and stability. Additionally, four macroeconomic indicators – inflation, recessions, interest rates, and policy changes – are in- cluded to provide a broader context and to assess their impact on the relation- ship between financial inclusion and stability in Ghana’s financial sector. The variables are defined as follows: FI = financial inclusion; FS = financial stability; INF = inflation; RE = recession; R = interest rate; PC = policy changes. Table 1. Variables and Measurement Variable Measurement Source FI Usage of Financial Service (Using the volume of outstanding deposits with commercial banks (% of GDP) and outstanding loans from commercial banks (% of GDP)) Financial Access Survey (FAS) FS Bank’s Z Score Global Financial Development INF Overall Consumer Price Index (Non-food and Food) World Development Indicators R Central Bank policy rate World Development Indicators S o u r c e : fieldwork 2022. Estimation and Analysis TechniqueEstimation and Analysis Technique Quantile regression (QRM) is employed to investigate the association between FI and financial stability FS, offering a unique approach compared to tradition- al methods, such as linear regression (LRM), autoregressive distributed lag FINANCIAL INCLUSION TOWARDS FINANCIAL STABILITY IN GHANA… 5353 (ARDL), and vector error correction models (VECM). The QRM overcomes the limitations of these models, which often yield biased results and inaccurate hy- pothesis testing when data are not normally distributed, by avoiding normal- ity assumptions (Koenker & Hallock, 2001) and minimizing asymmetrically weighted absolute residuals (Koenker & Bassett, 1978), thus providing more reliable country-specific policy recommendations during the different stages of FI and FS. ARDL and VECM require strict integration conditions (I(0) or I(1) for ARDL and I(1) for VECM, respectively), limiting their use if the variables exceed these orders (Anokye & Peterson, 2017). In contrast, QRM can be used regardless of integration order or variable differentiability (Koenker & Hallock, 2001). This study emphasizes the assessment of variable stationarity before choosing a time-series estimation method. Stationarity was tested using the augmented Dickey-Fuller (ADF) and Phillips–Perron (PP) tests, differing only in the auto- correlation correction method for residuals. Both tests compare the alterna- tive hypothesis of no unit root with the null hypothesis that the variables have a unit root. The ADF and PP formulations are given in models 4 and 5 as follows: = financial stability; INF = inflation; RE = recession; R = interest rate; PC = policy changes. Table 1. Variables and Measurement Variable Measurement Source FI Usage of Financial Service (Using the volume of outstanding deposits with commercial banks (% of GDP) and outstanding loans from commercial banks (% of GDP)) Financial Access Survey (FAS) FS Bank’s Z Score Global Financial Development INF Overall Consumer Price Index (Non-food and Food) World Development Indicators R Central Bank policy rate World Development Indicators Source: fieldwork 2022. Estimation and Analysis Technique Quantile regression (QRM) is employed to investigate the association between FI and financial stability FS, offering a unique approach compared to traditional methods, such as linear regression (LRM), autoregressive distributed lag (ARDL), and vector error correction models (VECM). The QRM overcomes the limitations of these models, which often yield biased results and inaccurate hypothesis testing when data are not normally distributed, by avoiding normality assumptions (Koenker & Hallock, 2001) and minimizing asymmetrically weighted absolute residuals (Koenker & Bassett, 1978), thus providing more reliable country-specific policy recommendations during the different stages of FI and FS. ARDL and VECM require strict integration conditions (I(0) or I(1) for ARDL and I(1) for VECM, respectively), limiting their use if the variables exceed these orders (Anokye & Peterson, 2017). In contrast, QRM can be used regardless of integration order or variable differentiability (Koenker & Hallock, 2001). This study emphasizes the assessment of variable stationarity before choosing a time-series estimation method. Stationarity was tested using the augmented Dickey-Fuller (ADF) and Phillips–Perron (PP) tests, differing only in the autocorrelation correction method for residuals. Both tests compare the alternative hypothesis of no unit root with the null hypothesis that the variables have a unit root. The ADF and PP formulations are given in Models 4 and 5 as follows: ΔYt = µ + δt + ρYt-1 + ∑ ψ� � ��� ΔY��� + εt ΔYt = µ + δt + ρYt-1 + ѱ�Y��� + εt Yt represents the series at time t; Δ is the difference operator; µ, δ, ρ, and ψ are the parameters to be estimated; and ε is the error term. The hypotheses test was as follows: H0: ρ = 0 (series contains unit root, non-stationary) H1: ρ ≠ 0 (series has no unit root, indicating stationarity). The null hypothesis (Ho) is accepted if the estimated t-values are less negative in absolute terms than the critical Dickey-Fuller (DF) values, indicating that the series has a unit root or is integrated beyond order one, or I(1). If the t-values are more negative than the critical DF values, the null hypothesis (Ho) is reject- ed in favor of the alternative hypothesis (H1), suggesting that the series does not have a unit root or is not integrated beyond order one or I(1). Philomina Maku Narh, Collins Dodzi Dzitse5454 Quantile Regression AnalysisQuantile Regression Analysis The primary analysis of the financial stability and inclusion relationship was conducted using QRM. QRM is more suitable than classical LRM in assessing the impact of financial inclusion on financial stability across various stability stages. This is significant because differing financial development policies may be necessary based on the severity of economic growth. This study investigates this relationship at varying levels of financial stability and enhances the quan- tile regression model proposed by Koenker and Bassett (1978). The quantile re- gression function is as follows: 𝑄𝜏(FS𝑖|X𝑖) = 𝛽𝜏X𝑖 FS denotes financial stability and X is a vector of independent variables. This equation represents the conditional mean equation. By solving the following optimization problem, the QRM parameters at the 𝜏th quantiles are estimated: Yt represents the series at time t; Δ is the difference operator; µ, δ, ρ, and ψ are the parameters to be estimated; and ε is the error term. The hypotheses test was as follows: H0: ρ = 0 (series contains unit root, non-stationary) H1: ρ ≠ 0 (series has no unit root, indicating stationarity). The null hypothesis (Ho) is accepted if the estimated t-values are less negative in absolute terms than the critical Dickey-Fuller (DF) values, indicating that the series has a unit root or is integrated beyond order one, or I(1). If the t-values are more negative than the critical DF values, the null hypothesis (Ho) is rejected in favor of the alternative hypothesis (H1), suggesting that the series does not have a unit root or is not integrated beyond order one or I(1). Quantile Regression Analysis The primary analysis of the financial stability and inclusion relationship was conducted using QRM. QRM is more suitable than classical LRM in assessing the impact of financial inclusion on financial stability across various stability stages. This is significant because differing financial development policies may be necessary based on the severity of economic growth. This study investigates this relationship at varying levels of financial stability and enhances the quantile regression model proposed by Koenker and Bassett (1978). The quantile regression function is as follows: 𝑄𝑄𝜏𝜏(FS𝑖𝑖|X𝑖𝑖) = 𝛽𝛽𝜏𝜏X𝑖𝑖 FS denotes financial stability and X is a vector of independent variables. This equation represents the conditional mean equation. By solving the following optimization problem, the QRM parameters at the 𝜏𝜏th quantiles are estimated: min � τ ����:��� � ����� | 𝐹𝐹𝐹𝐹� � �τ𝑋𝑋i │ � � �1 � τ� | 𝐹𝐹𝐹𝐹� � �τ𝑋𝑋i│ ����:��� � ����� Auto Regressive Distributed Lag Analysis This study employs quantile regression analysis and the ARDL technique to analyze the relationship between financial stability and financial inclusion in Ghana. This method is effective regardless of whether the variables are I (0), I (1), or their integration sequence. The initial step in ARDL modeling involves estimating the following equation: ARDL is defined using a time-series approach. Auto Regressive Distributed Lag AnalysisAuto Regressive Distributed Lag Analysis This study employs quantile regression analysis and the ARDL technique to ana- lyze the relationship between financial stability and financial inclusion in Gha- na. This method is effective regardless of whether the variables are I (0), I (1), or their integration sequence. The initial step in ARDL modeling involves estimat- ing the following equation: ARDL is defined using a time-series approach. ∆𝐹𝐹𝐹𝐹� � 𝛼𝛼� ���� � ��� ∆𝐹𝐹𝐹𝐹��� �� ø� � ��� ∆𝑂𝑂𝑂𝑂��� ���� � ��� ∆𝐹𝐹𝐹𝐹��� �� �� � ��� ∆𝐹𝐹𝐼𝐼𝐹𝐹��� ���� � ��� ∆𝑂𝑂𝑂𝑂��� � 𝜕𝜕�𝐹𝐹𝐹𝐹��� � 𝜕𝜕�𝑂𝑂𝑂𝑂��� � 𝜕𝜕�𝐹𝐹𝐹𝐹��� � 𝜕𝜕�𝐹𝐹𝐼𝐼𝐹𝐹 ��� � 𝜕𝜕�𝑂𝑂𝑂𝑂 ��� � �� Where FI represents financial inclusion, FS represents financial stability, INF represents inflation, RE represents recession, and R represents the interest rate. The ARDL bounds test is used to evaluate the potential existence of a long-term relationship. The alternative hypothesis indicates a long-run relationship, while the null hypothesis suggests no integration. The null hypothesis was rejected if the estimated F-statistic exceeded the upper critical value. The optimal lag length for the long-run model was selected using the Hannan-Quin (HQ) and Akaike Information Criterion (AIC). EMPIRICAL ANALYSIS AND RESULTS Table 2. Descriptive Analysis D_R_ Z_SCORE OL OD INF_D Mean 11.7889 12.8085 15.3294 22.3571 20.8105 Median 11.5729 13.1927 15.3317 22.3733 15.115 Maximum 17.4752 14.7706 22.3622 26.945 89.0766 Minimum 8.52025 9.52205 11.53 16.4438 -41.434 Std. Dev. 2.1167 1.42511 2.59483 2.7292 21.5054 Skewness 0.53887 -0.8345 0.40634 -0.1868 1.51229 Kurtosis 3.33449 2.71352 2.48025 2.50573 6.52602 Jarque-Bera 3.60801 8.1249 2.63664 1.0875 61.1458 Probability 0.16464 0.01721 0.26759 0.58057 0 Sum 801.642 870.976 1042.4 1520.28 1415.11 Sum Sq. Dev. 300.188 136.073 451.121 499.051 30986.4 Observations 68 68 68 68 68 Source: field survey 2022: analyzed with EViews 12. Looking at the descriptive analysis above, based on the Jaque-Bera test of normality, financial inclusion, financial stability, interest rate, and inflation are not normally FINANCIAL INCLUSION TOWARDS FINANCIAL STABILITY IN GHANA… 5555 Where FI represents financial inclusion, FS represents financial stability, INF represents inflation, RE represents recession, and R represents the inter- est rate. The ARDL bounds test is used to evaluate the potential existence of a long-term relationship. The alternative hypothesis indicates a long-run rela- tionship, while the null hypothesis suggests no integration. The null hypothesis was rejected if the estimated F-statistic exceeded the upper critical value. The optimal lag length for the long-run model was selected using the Hannan-Quin (HQ) and Akaike Information Criterion (AIC). Empirical Analysis and ResultsEmpirical Analysis and Results Table 2. Descriptive Analysis D_R_ Z_SCORE OL OD INF_D Mean 11.7889 12.8085 15.3294 22.3571 20.8105 Median 11.5729 13.1927 15.3317 22.3733 15.115 Maximum 17.4752 14.7706 22.3622 26.945 89.0766 Minimum 8.52025 9.52205 11.53 16.4438 -41.434 Std. Dev. 2.1167 1.42511 2.59483 2.7292 21.5054 Skewness 0.53887 -0.8345 0.40634 -0.1868 1.51229 Kurtosis 3.33449 2.71352 2.48025 2.50573 6.52602 Jarque-Bera 3.60801 8.1249 2.63664 1.0875 61.1458 Probability 0.16464 0.01721 0.26759 0.58057 0 Sum 801.642 870.976 1042.4 1520.28 1415.11 Sum Sq. Dev. 300.188 136.073 451.121 499.051 30986.4 Observations 68 68 68 68 68 S o u r c e : field survey 2022: analyzed with EViews 12. Philomina Maku Narh, Collins Dodzi Dzitse5656 Looking at the descriptive analysis above, based on the Jaque-Bera test of nor- mality, financial inclusion, financial stability, interest rate, and inflation are not normally distributed. The average values are listed in table 2. This means that not all the variables are normally distributed. Correlation AnalysisCorrelation Analysis Table 3. Correlation Matrix D_R_ Z_SCORE OL OD INF_D Mean 11.7889 12.8085 15.3294 22.3571 20.8105 Median 11.5729 13.1927 15.3317 22.3733 15.115 Maximum 17.4752 14.7706 22.3622 26.945 89.0766 Minimum 8.52025 9.52205 11.53 16.4438 -41.434 Std. Dev. 2.1167 1.42511 2.59483 2.7292 21.5054 Skewness 0.53887 -0.8345 0.40634 -0.1868 1.51229 Kurtosis 3.33449 2.71352 2.48025 2.50573 6.52602 Jarque-Bera 3.60801 8.1249 2.63664 1.0875 61.1458 Probability 0.16464 0.01721 0.26759 0.58057 0 Sum 801.642 870.976 1042.4 1520.28 1415.11 Sum Sq. Dev. 300.188 136.073 451.121 499.051 30986.4 Observations 68 68 68 68 68 S o u r c e : field survey 2022; analyzed with EViews 12. A thorough examination of table 3 reveals that none of the pairwise correla- tions between the independent variables exceeds 0.9, indicating no multicollin- earity among the regressors (Daoud, 2017). FINANCIAL INCLUSION TOWARDS FINANCIAL STABILITY IN GHANA… 5757 Unit root testUnit root test The ARDL cointegration estimation requires variables to be cointegrated at I(0) or I(1). Philip–Perron and Augmented Dickey-Fuller tests were used for all vari- ables at levels and first differences. The results are presented in tables 4 and 5. Table 4. ADF Unit Root Estimation based on trend and intercept D_R_ Z_SCORE OL OD INF_D Mean 11.7889 12.8085 15.3294 22.3571 20.8105 Median 11.5729 13.1927 15.3317 22.3733 15.115 Maximum 17.4752 14.7706 22.3622 26.945 89.0766 Minimum 8.52025 9.52205 11.53 16.4438 -41.434 Std. Dev. 2.1167 1.42511 2.59483 2.7292 21.5054 Skewness 0.53887 -0.8345 0.40634 -0.1868 1.51229 Kurtosis 3.33449 2.71352 2.48025 2.50573 6.52602 Jarque-Bera 3.60801 8.1249 2.63664 1.0875 61.1458 Probability 0.16464 0.01721 0.26759 0.58057 0 Sum 801.642 870.976 1042.4 1520.28 1415.11 Sum Sq. Dev. 300.188 136.073 451.121 499.051 30986.4 Observations 68 68 68 68 68 S o u r c e : field survey 2022; analyzed with EViews 12. The results show that financial stability, financial inclusion, interest rate, and inflation are stationary at the first difference but not at the level (table 4), indi- cating that all the variables are integrated of order 1. Philomina Maku Narh, Collins Dodzi Dzitse5858 Table 5. PP Unit Root Estimation based on trend and intercept Variables Level First Difference T Statistic P Value T Statistic P Value LNZSCORE -3.4783 0.4878 -3.4794 0.0094 LNOL -3.4783 0.1038 -3.4794 0.0066 LNOD -3.4783 0.0377 -3.4794 0.008 LNDR -3.4783 0.3504 -3.4794 0.0283 LNINFD -3.4794 0 -3.4805 0 S o u r c e : field survey 2022; analyzed with EViews 12. The results of the Philip–Perron tests in table 5 show that inflation is stationary at order 0, indicating its level of stationarity. In contrast, financial stability, finan- cial inclusion, and interest rates are stationary at order 1, indicating their first- difference stationarity. These findings support the use of the ARDL estimation method, as confirmed by the Philip–Perron (PP) and Augmented Dickey-Fuller (ADF) tests. This study assessed the co-integration of variables using Pesaran, Shin and Smith’s (2001) approach following unit root tests. The appropriate lag length, determined using the Hannan-Quinn Criterion, Schwartz Bayesian Crite- rion (SBC), and Akaike Information Criterion (AIC), is presented in table 6. Table 6. VAR Lag Order Selection Criteria Lag LogL LR FPE AIC SC HQ 0 157.157 NA 4.31E-09 -5.0719 -4.8974 -5.0036 1 550.488 707.996 2.01E-14 -17.35 -16.302 -16.94 2 602.114 84.3225 8.44E-15 -18.237 -16.317 -17.486 3 612.87 15.7749 1.42E-14 -17.762 -14.97 -16.67 4 621.252 10.8963 2.70E-14 -17.208 -13.543 -15.775 5 753.635 150.034 8.76E-16 -20.788 -16.25 -19.013 6 815.268 59.5784 3.30E-16 -22.009 -16.599 -19.893 7 852.781 30.0109 3.18E-16 -22.426 -16.143 -19.968 8 918.012 41.31312* 1.50e-16* -23.76708* -16.61140* -20.96810* S o u r c e : field survey 2022; analyzed with EViews 12. FINANCIAL INCLUSION TOWARDS FINANCIAL STABILITY IN GHANA… 5959 Ardl resultsArdl results Bounds TestBounds Test Table 7. F-Bounds Test F-Bounds Test Null Hypothesis: No levels relationship Test Statistic Value Signif. I(0) I(1) F-statistic 127.169 10% 3.03 4.06 k 4 5% 3.47 4.57 2.50% 3.89 5.07 1% 4.4 5.72 S o u r c e : field survey 2022; analyzed with EViews 12. Table 7 shows that the F-static value of 127.1689 exceeds the lower and up- per bounds, indicating long-run cointegration between financial inclusion and stability when stability is the dependent variable. Consequently, a quantile re- gression model can be estimated at these levels. Table 8 provides the Error Correction Model (ECM), or short-run model, to further confirm the long-term relationship. Table 8. Error Correction Model Dependent Variable: D(LNZSCORE) Selected Model: ARDL(8, 8, 8, 8, 8) ECM Regression Variable Coefficient Std. Error t-Statistic Prob. C -15.2905 0.5351 -28.575 0 @TREND -0.05985 0.00208 -28.757 0 D(LNZSCORE(-1)) -3.79122 0.1528 -24.811 0 D(LNZSCORE(-2)) -3.77278 0.14347 -26.297 0 D(LNZSCORE(-3)) -3.73469 0.13908 -26.853 0 Philomina Maku Narh, Collins Dodzi Dzitse6060 Dependent Variable: D(LNZSCORE) Selected Model: ARDL(8, 8, 8, 8, 8) ECM Regression Variable Coefficient Std. Error t-Statistic Prob. D(LNZSCORE(-4)) -1.41717 0.04046 -35.028 0 D(LNZSCORE(-5)) -1.22872 0.08151 -15.074 0 D(LNZSCORE(-6)) -1.14021 0.073 -15.62 0 +-++D(LNZSCORE(-7)) -1.06234 0.06752 -15.733 0 D(LNOL) 0.249419 0.04017 6.20971 0 D(LNOL(-1)) 2.77801 0.09795 28.3622 0 D(LNOL(-2)) 2.853596 0.10489 27.2054 0 D(LNOL(-3)) 2.91193 0.10819 26.9144 0 D(LNOL(-4)) 1.529315 0.06246 24.4838 0 D(LNOL(-5)) 1.59672 0.06717 23.7717 0 D(LNOL(-6)) 1.64266 0.06839 24.0205 0 D(LNOL(-7)) 1.684078 0.06959 24.2002 0 D(LNOD) -0.70393 0.03524 -19.978 0 D(LNOD(-1)) -4.78586 0.17022 -28.115 0 D(LNOD(-2)) -4.84162 0.17258 -28.055 0 D(LNOD(-3)) -4.89825 0.17574 -27.872 0 D(LNOD(-4)) -2.45459 0.09623 -25.507 0 D(LNOD(-5)) -2.47608 0.0876 -28.266 0 D(LNOD(-6)) -2.50395 0.09111 -27.482 0 D(LNOD(-7)) -2.55957 0.09445 -27.1 0 D(LNINFD) -0.51316 0.01838 -27.922 0 D(LNINFD(-1)) 0.429901 0.01557 27.6091 0 D(LNINFD(-2)) 0.419701 0.01538 27.2961 0 D(LNINFD(-3)) 0.413438 0.01532 26.9873 0 D(LNINFD(-4)) 0.017625 0.00743 2.37379 0.0325 D(LNINFD(-5)) 0.003447 0.00754 0.45702 0.6547 Table 8. Error… FINANCIAL INCLUSION TOWARDS FINANCIAL STABILITY IN GHANA… 6161 Dependent Variable: D(LNZSCORE) Selected Model: ARDL(8, 8, 8, 8, 8) ECM Regression Variable Coefficient Std. Error t-Statistic Prob. D(LNINFD(-6)) -0.00394 0.00734 -0.5364 0.6001 D(LNINFD(-7)) -0.01116 0.0065 -1.7181 0.1078 D(LNDR) 1.611059 0.06726 23.9534 0 D(LNDR(-1)) -1.27612 0.05346 -23.87 0 D(LNDR(-2)) -1.26206 0.05179 -24.37 0 D(LNDR(-3)) -1.25022 0.05136 -24.34 0 D(LNDR(-4)) -0.22192 0.0169 -13.13 0 D(LNDR(-5)) -0.19538 0.01455 -13.432 0 D(LNDR(-6)) -0.18437 0.01494 -12.34 0 D(LNDR(-7)) -0.18986 0.014 -13.562 0 CointEq(-1)* 2.843136 0.09944 28.5922 0 R-squared 0.998072 Mean dependent var. 0.00162 Adjusted R-squared 0.993681 S.D. dependent var. 0.02945 S.E. of regression 0.002341 Akaike info criterion. -9.0805 Sum squared resid 9.86E-05 Schwarz criterion. -7.6145 Log likelihood 314.4147 Hannan-Quinn criter. -8.507 F-statistic 227.2781 Durbin-Watson stat. 2.02629 Prob(F-statistic) 0 S o u r c e : field survey 2022; analyzed with EViews 12. Table 8 illustrates the expected negative sign of the error-correction term lagged by one period (ECTt-1) at the 1% significance level. ECT, with a coeffi- cient of 2.843136, indicates the adjustment rate required to restore stability af- ter disruption. This substantial ECT suggests that following a short-run shock, approximately 2.84% of the deviation from the long-run equilibrium is correct- ed for each quarter. Thus, when variables are shocked, they stabilize over time, with higher absolute ECT, reinforcing evidence for long-term relationships. Table 8. Error… Philomina Maku Narh, Collins Dodzi Dzitse6262 Diagnostics of ARDLDiagnostics of ARDL The functional form specification (Ramsey Reset Test), heteroskedasticity, model stability, and serial correlation LM tests are among these diagnostic tests. The p-values (0.112) and F-statistic (0.0718) of the BGLM test were great- er than 0.05, respectively (Appendix A). Consequently, the study concludes that the model has no serial correlation, and thereby accepts the null hypothesis that there is no serial correlation. This ensured the dependability of the mod- el. Furthermore, the p-value of the heteroskedasticity test in Appendix A was 0.8624, exceeding 0.05, thus accepting the null hypothesis of no heteroske- dasticity and indicating the model’s reliability. The CUSUM plot in Appendix B shows the CUSUM line within the 5% bound for ARDL, thus confirming the long-term connection between financial stability and inclusion. Table 9. Quantile Process Estimates Quantile Process Estimates Specification: LOG(LNZSCORE) LNOL LNOD LNINFD LNDR C Quantile Coefficient Std. Error t-Statistic Prob. LNOL 0.1 -0.2939 0.03284 -8.9499 0 0.2 -0.2556 0.03203 -7.9787 0 0.3 -0.2411 0.04106 -5.8711 0 0.4 -0.2438 0.04644 -5.2504 0 0.5 -0.1769 0.05439 -3.2518 0.0018 0.6 -0.1185 0.03267 -3.6286 0.0006 0.7 -0.1374 0.0366 -3.7549 0.0004 0.8 -0.1423 0.0369 -3.8559 0.0003 0.9 -0.1393 0.03119 -4.4667 0 LNOD 0.1 -0.0112 0.0884 -0.1264 0.8998 0.2 0.03203 0.06761 0.47381 0.6373 0.3 0.05098 0.07509 0.67899 0.4996 0.4 0.03495 0.0806 0.43369 0.666 0.5 0.08518 0.06493 1.312 0.1943 FINANCIAL INCLUSION TOWARDS FINANCIAL STABILITY IN GHANA… 6363 Quantile Process Estimates Specification: LOG(LNZSCORE) LNOL LNOD LNINFD LNDR C Quantile Coefficient Std. Error t-Statistic Prob. 0.6 0.12181 0.04846 2.51364 0.0145 0.7 0.13323 0.05511 2.41736 0.0185 0.8 0.16134 0.05179 3.11506 0.0028 0.9 0.18235 0.04492 4.05972 0.0001 LNINFD 0.1 0.00886 0.0187 0.47396 0.6372 0.2 0.00581 0.0121 0.48046 0.6326 0.3 0.00964 0.01276 0.75556 0.4527 0.4 0.00488 0.0134 0.3639 0.7172 0.5 0.01873 0.01153 1.62439 0.1093 0.6 0.02271 0.0099 2.29321 0.0252 0.7 0.01966 0.0094 2.09139 0.0405 0.8 0.02828 0.00778 3.63354 0.0006 0.9 0.03157 0.00661 4.77709 0 LNDR 0.1 0.05821 0.0416 1.39931 0.1666 0.2 0.014 0.02661 0.5262 0.6006 0.3 0.01049 0.03022 0.34717 0.7296 0.4 0.0118 0.03414 0.34561 0.7308 0.5 0.05685 0.04077 1.39441 0.1681 0.6 0.08702 0.03615 2.40717 0.019 0.7 0.08889 0.03607 2.46454 0.0165 0.8 0.0931 0.03382 2.75277 0.0077 0.9 0.0876 0.03037 2.88422 0.0054 C 0.1 1.56165 0.34808 4.48649 0 0.2 1.45126 0.26742 5.42692 0 0.3 1.3569 0.30472 4.45289 0 0.4 1.42826 0.32547 4.38831 0 Table 9. Quantile… Philomina Maku Narh, Collins Dodzi Dzitse6464 Quantile Process Estimates Specification: LOG(LNZSCORE) LNOL LNOD LNINFD LNDR C Quantile Coefficient Std. Error t-Statistic Prob. 0.5 0.95829 0.33509 2.8598 0.0057 0.6 0.61539 0.24752 2.48622 0.0156 0.7 0.64106 0.23802 2.69327 0.0091 0.8 0.53784 0.20521 2.62089 0.011 0.9 0.47233 0.17618 2.68098 0.0094 S o u r c e : field survey 2022; analyzed with EViews 12. The results of the long-run regression analysis demonstrate a significant pos- itive impact of financial inclusion on financial stability, especially at higher quantile levels. For instance, LNOL showed a significant effect from quantile 5 upward, and LNOD, LNDR, and LNINFD all showed significant effects from quantile 6 upward. This indicates that financial inclusion has a stronger sta- bilizing effect on economic actors in the upper quantiles, and fosters stabil- ity over time. This suggests that the financial inclusion mechanism becomes influential as financial engagement increases among the people. Thus, the fi- nancial sector can benefit significantly from expanding accessibility, which en- hances inclusion at all levels of financial engagement in Ghana. These results correspond with findings from broader sub-Saharan Africa, where financial in- clusion tends to have a more pronounced impact on economic stability in econ- omies with higher financial penetration (Djoufouet & Pondie, 2022). For in- stance, studies in Nigeria show that financial inclusion significantly enhances financial stability, particularly through mobile banking and fintech adoption, which offer more accessibility and inclusion for financial engagement (Oluse- gun et al., 2021; Anthony-Orji et al., 2021). This strongly mirrors Ghana’s trend toward stronger effects at higher quantiles, as the results indicate. Similarly, research across West Africa suggests that financial inclusion fosters econom- ic resilience at increasing quantile levels (Babatunde, 2024; Camara & Diallo, 2020), However, country-level disparities in access, especially in rural areas, can limit its effectiveness. Notwithstanding, since most sub-Saharan African countries exhibit similar economic characteristics (World Bank, 2021), the re- Table 9. Quantile… FINANCIAL INCLUSION TOWARDS FINANCIAL STABILITY IN GHANA… 6565 sults suggest that, as the financial system in Ghana and these countries be- comes more inclusive, it will also become more stable, particularly at higher quantiles. Comparatively, international studies have indicated that the impact of financial inclusion varies by region. In developed economies, financial inclu- sion stabilizes markets through diversified financial instruments (Danisman & Tarazi, 2020), whereas in sub-Saharan Africa, its role is more fundamental, providing basic financial access and reducing economic volatility (Moeti & Sin Yu, 2024; Kamara, 2024). The findings of this study therefore reinforce the ar- gument that higher levels of financial inclusion amplify stability, particularly in emerging economies (Antwi et al., 2024). However, regional disparities in infrastructure and policy frameworks can influence the extent of this relation- ship. Hence, strengthening financial literacy and digital banking that enhance inclusion could further enhance stability, particularly in the sub-Saharan Af- rican region. The study results generally suggest that at the international, re- gional, or national level, when examining the relationship between financial in- clusion and stability, especially policy actions, it is crucial to consider varying quantile levels rather than relying solely on the average effects, as this can pro- vide valuable insights for policymaking and financial evaluation.  Conclusion and policy implications Conclusion and policy implications This study uses a quantile regression approach to assess the impact of finan- cial inclusion on Ghanaian financial stability. Secondary data from the World Bank, IMF, and Bank of Ghana (2005–2021) were analyzed. The stationarity of the data was verified using the ADF and PP tests. The ADRL bound test for coin- tegration reveals a significant long-term relationship between the variables, indicating that the independent variables explain financial stability. A long-run regression analysis demonstrates the positive impact of financial inclusion on financial stability, especially at higher quantile levels. The findings of this study have significant policy implications for Ghana, West Africa, and the broader sub-Saharan African region, particularly concern- ing financial inclusion and economic stability. In Ghana, the noticeable impact of financial inclusion at higher quantile levels indicates that policies should aim to enhance financial access for lower-income and underserved populations. While financial inclusion bolsters stability at higher quantiles, policymakers must address disparities in banking accessibility, digital financial literacy, and regulatory support for fintech innovations at individual, firm, and household Philomina Maku Narh, Collins Dodzi Dzitse6666 levels. Strengthening mobile banking frameworks, expanding microfinance institutions, and ensuring robust consumer protection laws can help lower quantiles benefit equally from financial inclusion (World Bank, 2021). Further- more, targeted interventions for women, rural populations, and informal sec- tor workers are crucial for bridging the financial gaps and maximizing inclu- sion-driven stability. These interventions should address different segments of the financial system where the impact of financial inclusion on stability is most significant. A similar policy focus is necessary across West and sub-Saharan Africa. Countries such as Nigeria, Kenya, and South Africa have witnessed how financial inclusion can bolster macroeconomic resilience; however, disparities and access to formal banking persist. To reinforce economic stability, region- al governments should collaborate on harmonized digital financial policies, cross-border fintech regulations, and mobile money interoperability. Addition- ally, aligning financial inclusion with monetary policies, such as interest rate stabilization and inflation control, can further enhance financial security. Poli- cymakers should start to leverage AI-driven financial tools to improve credit accessibility and risk management, particularly for SMEs (Djoufouet & Pondie, 2022; Olusegun et al., 2021). Finally, a financial inclusion strategy that incorpo- rates best practices among the economies in each regional block would also fa- cilitate sustainable and inclusive economic growth. Future research should explore the mechanisms underlying the increased impact of financial inclusion in higher quantiles and its applicability in differ- ent economic contexts. Understanding these quantile-specific relationships can help to develop nuanced strategies to promote financial inclusion and sta- bility in Ghana and other developing economies.  References References Ahamed, M.M., & Mallick, S.K. (2019). 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International Studies, 56(2–3), 163–185. https://doi. org/10.1177/0020881719849246. http://dx.doi.org/10.1257/jep.15.4.143 https://doi.org/10.1016/j.bir.2019.07.002 https://doi.org/10.1016/j.frl.2017.09.007 https://doi.org/10.1016/j.frl.2017.09.007 https://doi.org/10.1002/jae.616 https://doi.org/10.1002/jae.616 FINANCIAL INCLUSION TOWARDS FINANCIAL STABILITY IN GHANA… 6969 Appendix (A)Appendix (A) Heteroskedasticity and Serial Correlation Heteroskedasticity Test: Breusch-Pagan-Godfrey Null hypothesis: Homoskedasticity F-statistic 0.61412 Prob. F(45,14) 0.86249 Obs*R-squared 37.4116 Prob. Chi-Square(45) 0.7261 Scaled explained SS 1.64211 Prob. Chi-Square(45) 1 Breusch-Godfrey Serial Correlation LM Test: Null hypothesis: No serial correlation at up to 8 lags F-statistic 223.917 Prob. F(8,6) 0.112 Obs*R-squared 59.7997 Prob. Chi-Square(8) 0.0718 S o u r c e : field survey 2022. Appendix (B)Appendix (B) The CUSUM plot of long-term relationship F-statistic 223.917 Prob. F(8,6) 0.112 Obs*R-squared 59.7997 Prob. Chi-Square(8) 0.0718 Source: field survey 2022. APPENDIX (B) The CUSUM plot of long-term relationship -8 -6 -4 -2 0 2 4 6 8 II III IV I II III IV 2020 2021 CUSUM 5% Significance Source: field survey 2022. S o u r c e : field survey 2022.