




































99 

 

Finance, Accounting and Business Analysis 
Volume 7 Issue 1, 2025 

http://faba.bg/       
ISSN  2603-5324 

DOI: https://doi.org/10.37075/FABA.2025.1.08 

 

Between Finance and Growth: The Role of Financial Development in 

Promoting Economic growth in Africa 
 

Boulenouar Ilias Zakaria Mennad 1* , Anes Meskini 2 , Amina Benhaddou 3  

  
Laboratory of Strategies for Development of the Agricultural and Tourism Sector, University of Ain 

Temouchent, Algeria1 

Laboratory of Strategies for Development of the Agricultural and Tourism Sector, University of Ain 

Temouchent, Algeria2 

Laboratory of Markets, Employment, Simulation and Legislation in the Maghreb Countries, University of 

Ain Temouchent, Algeria3 

* Corresponding author 

 

Info Articles   Abstract 
 

History Article: 

Submitted 28 February 2025 

Revised  4 May 2025 

Accepted 14 May 2025 

 Purpose: This study investigates the impact of financial development on 

economic growth, with a particular focus on the roles of financial 

institutions' access, depth, and efficiency. It aims to provide a nuanced 

analysis of the finance-growth nexus and evaluate whether financial 

development acts as a catalyst for or a constraint on economic growth 

within the African context. 

Design/Methodology/Approach: The study employs the Panel ARDL 

(Autoregressive Distributed Lag) approach to analyze the short-run and 

long-run effects of financial development on GDP. The analysis is 

conducted on a panel of 31 African countries over the period 1990–2021, 

capturing both cross-country variations and dynamic relationships 

between financial development indicators and economic growth. 

Findings: The findings emphasize that financial access and depth are 

key drivers of long-term economic growth, whereas financial efficiency 

exerts a negative impact. In the short run, financial access significantly 

enhances GDP, while financial depth may impede growth due to 

transitional costs or structural imbalances. Moreover, the heterogeneous 

short-run effects across countries underscore the pivotal role of 

institutional and economic factors in shaping the financial development 

and economic growth nexus. 

Practical Implications: Policymakers should prioritize financial 

inclusion and sector depth while addressing inefficiencies that may 

hinder economic performance. Strengthening regulatory frameworks 

and improving financial institutions’ operational effectiveness can foster 

sustainable economic growth. 

Originality/Value: This study provides new empirical evidence on the 

finance-growth nexus in African economies, considering multiple 

dimensions of financial development and utilizing a robust econometric 

approach (Panel ARDL). It contributes to the debate on financial 

development by distinguishing between access, depth, and efficiency. 

Paper Type:  Research Paper. 

 

Keywords:  

Financial development, 

Economic growth, 

Financial institutions, Panel 

ARDL, African countries 
 

 

JEL: G21, O16, C33, O55  

* Address Correspondence:   

E-mail:  ilias.mennad@univ-temouchent.edu.dz1 

anes.meskini@univ-temouchent.edu.dz2 

amina.benhaddou@univ-temouchent.edu.dz3 

 

  

http://faba.bg/
https://doi.org/10.37075/FABA.2025.1.08
mailto:ilias.mennad@univ-temouchent.edu.dz
mailto:anes.meskini@univ-temouchent.edu.dz
mailto:amina.benhaddou@univ-temouchent.edu.dz
https://orcid.org/0000-0001-5521-7950
https://orcid.org/0009-0004-1008-6108
https://orcid.org/0009-0003-7841-5986


B.I.Z..Mennad, A. Meskini, A. Benhaddou / Finance, Accounting and Business Analysis, Volume 7, Issue 1, 2025 

100 

 

INTRODUCTION 
 

Financial development is widely recognized as a key driver of economic growth, a view supported by 

numerous empirical studies, including (Levine 1999). However, the precise nature of the relationship 

between financial development and economic growth remains a topic of extensive research and ongoing 

debate. While some studies highlight the positive impact of financial development in stimulating economic 

expansion, others emphasize the complexities introduced by institutional quality, income inequality, and 

regional disparities (Asante et al. 2023).  

A well-functioning financial system plays a crucial role in mobilizing savings, efficiently allocating 

resources to the most productive investments, reducing transaction costs, distributing risks, and fostering 

innovation and technological advancement (Mengesha and Berde 2023). Expanding financial services and 

transactions within an economy enhances overall productivity, contributing to job creation, poverty 

reduction, and income distribution, particularly in developing nations (Abbas et al. 2022). Moreover, 

financial development broadens the scope of financial transactions, increasing capital availability and 

improving economic performance (Omri et al. 2015). At the same time, some scholars argue that economic 

growth itself drives financial development, as rising incomes increase the demand for financial services (Song 

et al. 2021).  
Despite extensive research, empirical findings on the causal direction of this relationship remain 

inconclusive. Some studies assert that financial development precedes and accelerates economic growth, 

while others suggest that economic expansion stimulates the financial sector. The role of institutional 

quality, financial regulations, and political stability further complicates this nexus (Asante et al. 2023). Given 

these ambiguities, further research is essential to provide policymakers with insights into the optimal 

strategies for fostering sustainable economic growth through financial development.  
This study contributes to the continuing discourse on the relationship between financial development 

and economic growth by addressing the following question: Does financial development from the 

perspective of financial institutions stimulate economic growth in the African countries under study? 

LITERATURE REVIEW 

 Multiple studies have examined the relationship between financial development and economic 

growth, utilizing diverse objectives, methodologies, and findings. Given the theoretical challenges in 

determining the direction of this relationship, recent research has largely focused on empirical analysis. The 

literature indicates that the impact of financial development varies, manifesting as positive, negative, or 

neutral. The following section provides a critical review of key prior studies, highlighting existing research 

gaps. 

Extensive research has examined the relationship between financial development and economic 

growth, with meta-analytical studies offering a comprehensive synthesis of empirical findings. Bijlsma et al. 

(2018) conduct a meta-analysis of 551 estimates from 68 studies, highlighting significant publication bias 

that has likely overstated the impact of financial development measured by private credit to GDP on 

economic growth. After accounting for this bias, the results indicate a small yet positive effect in logarithmic 

models, where a 10% increase in private credit leads to a 0.09 percentage point rise in growth, while linear 

models show no significant relationship. These findings align with the “too much finance” hypothesis, 

suggesting that excessive financial development may hinder rather than enhance growth. Similarly, 

Valickova et al. (2014) analyze 1,334 estimates from 67 studies, reporting a statistically significant positive 

effect of financial development on growth. However, due to methodological transformations, the study does 

not provide an interpretable economic magnitude of this effect. Their FAT-PET analysis finds no evidence 

of publication bias, although their full meta-regression analysis reveals a negative and significant association 

between standard errors and estimates, which is counterintuitive as it suggests a bias against large significant 

results. In contrast, Arestis et al. (2014) using 1,151 observations from 69 studies, also find a significant 

positive impact of financial development on growth but detect a positive publication bias in their FAT-PET 

analysis. Moreover, their full MRA reveals instances of negative and significant bias, further complicating 

the interpretation of results. These meta-analyses underscore the complexity of the finance-growth nexus, 

highlighting the influence of methodological choices, data transformations, and potential biases in shaping 

empirical findings. 
This relationship is further influenced by institutional quality, sectoral variations, and regional 

disparities, rendering it a dynamic and multidimensional phenomenon. Several studies provide critical 

insights into this nexus, highlighting both the enabling and constraining factors influencing growth across 

different economies. 

Financial development plays a crucial role in enhancing the effects of foreign direct investment on 

economic growth. An et al. (2025) show that financial institutions, rather than financial markets, are key in 



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mediating FDI’s impact on growth. However, excessive financial development can diminish these benefits, 

suggesting an inverted-U relationship. Similarly, El Menyari (2019) finds that foreign bank entry stimulates 

growth in North and Southern Africa due to stronger financial institutions, while structural deficiencies in 

other regions limit its positive impact. These findings emphasize the importance of strengthening financial 

institutions to maximize growth benefits.  

In low-income and developing economies, financial development’s impact varies significantly. Bist 

and Read (2018) confirm a long-term positive relationship between financial development and growth in 16 

low-income African nations, stressing the need to improve credit access to the private sector. An et al. (2021) 

further highlight income-based disparities, showing that financial development hinders growth in low- and 

middle-income countries due to inefficient credit allocation and weak regulatory environments, whereas in 

upper-income nations, financial development fosters growth. This underscores the need for tailored financial 

policies that address specific economic conditions.  

Institutional quality emerges as a critical determinant in the finance-growth relationship. Asante et 

al. (2023) demonstrate that financial development significantly enhances growth, with its impact amplified 

in countries with strong institutional frameworks, particularly where governance, political stability, and 

regulatory quality are robust. Aluko and Ibrahim (2020) similarly find that while financial development is 

more effective in countries with strong institutions, even weaker institutional settings can benefit through 

informal mechanisms and external financial inflows. Fengju and Wubishet (2024) reinforce this by showing 

that governance factors such as corruption control and political stability significantly enhance the benefits 

of financial development, particularly in East Africa.  

The sectoral impact of financial development is another crucial consideration. Ustarz et al. (2021) 

reveal that while financial development positively influences the service and agricultural sectors in sub-

Saharan Africa, its benefits in the industrial sector only materialize after reaching a certain threshold. This 

aligns with Mlambo (2024), who finds that financial development in low-income SADC nations not only 

results from economic growth but also actively contributes to it by improving resource allocation. These 

studies emphasize the need for policies that foster financial sector development to support broader economic 

transformation.  

The causal relationship between financial development and economic growth also varies across 

regions. Akinlo and Egbetunde (2010) find a long-run link between the two in ten sub-Saharan African 

countries, with financial development driving growth in some nations, economic growth leading financial 

development in Zambia, and a bidirectional relationship in others. 

In contrast, Zimu and Godspower-Akpomiemie (2024) challenge conventional assumptions, showing 

that in South Africa, financial development and economic growth progress independently. This calls for 

policies that strengthen financial institutions and financial markets to improve firms’ access to external 

finance and enhance economic performance.  

Structural conditions further shape financial development’s effectiveness. Ibrahim and Alagidede 

(2018) warn against excessive financial expansion, which can finance high-risk investments and undermine 

economic performance, aligning with the “too much finance” hypothesis. However, they stress that when 

effectively channeled, financial development supports productive investments and sustainable growth. 

Collectively, these studies underscore that financial development’s impact on economic growth 

depends on institutional quality, financial regulation, economic structure, and regional factors. 

Policymakers must adopt context-specific strategies, strengthen financial institutions, and ensure a balanced 

approach to financial expansion to maximize its benefits for economic growth. 

 DATA AND METHODS 

 

This study employs the Panel Autoregressive Distributed Lag (ARDL) approach to analyze the 

impact of financial development on economic growth across 31 African economies. The analysis spans a 

32-year period (1990–2021), with the temporal scope determined by data availability constraints for key 

financial development indicators across all sample countries. The panel framework is particularly suited for 

this investigation as it accommodates both heterogeneous dynamics across countries and long-run 

equilibrium relationships critical features when examining African economies with diverse financial systems 

and growth trajectories.  

Data sources   

This study relies on panel data drawn from two main sources, the World Bank's World Development 

Indicators (2022), which provide data on GDP as the dependent variable, and the International Monetary 

Fund's Financial Development Index Database (2022), which offers measures for three keys independent 

variables representing different dimensions of financial development. The table 1 below provides a detailed 

description of the variables used in the analysis, including their definitions, measurement methods, and data 



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sources. 

 

Table 1. Variables and data collection sources 

Variable Source Measurement 

Dependent Variable 

Gross Domestic Product 

(GDP) 

(World Bank 2022) Constant 2015 USD. 

Independent Variables 

Financial Institutions Access 

Index (FDA) 

(IMF 2022) Measures financial inclusion through 

physical infrastructure density (bank 

branches and ATMs per 100,000 adults); 

A composite index ranging from 0 to 1, 

where 1 indicates the highest level of access. 

Financial Institutions Depth 

Index (FDD) 

Captures financial intermediation intensity 

using private sector credit and institutional 

investments (as % GDP); 

A composite index ranging from 0 to 1, 

where 1 indicates the highest level of depth. 

Financial Institutions Efficiency 

Index (FDE) 

Evaluates institutional performance via 

profitability metrics (ROA, ROE) and cost 

ratios (net interest margin, overhead costs); 

A composite index ranging from 0 to 1, 

where 1 signifies the highest level of 

efficiency.  

Source: Prepared by authors (2025) 

Methodology  

The study utilizes a Panel ARDL approach to examine the relationship between financial 

development and economic growth. This methodology is selected as it enables the estimation of both short- 

and long-run relationships, even when variables have different orders of integration (Pesaran et al. 1999). 

Additionally, the selection of this approach is particularly appropriate as the data series exhibit stationarity 

either at level I(0) or at first difference I(1), ensuring the validity of the estimation. 

The analysis begins by addressing non-stationarity in the data through Panel Unit Root Tests. 

Specifically, the Levin-Lin-Chiu (LLC) Test, suited for panels with common unit roots, and the Im-Pesaran-

Shin (IPS) Test, which accommodates individual unit roots, are applied. Next, Cointegration Analysis is 

conducted to investigate long-run relationships using the Kao Residual Cointegration Test and the Pedroni 

Heterogeneous Panel Cointegration Test.  

Following this, the Panel ARDL model is applied, employing the Pooled Mean Group (PMG), Mean 

Group (MG), and Dynamic Fixed Effects (DFE) estimators. These estimators help capture both long-run 

coefficients reflecting the relationship between financial development indices and GDP and short-run 

dynamics, including the speed of adjustment through the Error Correction Term (ECT).  

The Hausman Test is then used to determine the most appropriate estimator between PMG, MG and 

DFE estimation. Finally, a country-specific short-run analysis is performed by extracting individual nation 

results from the ECM coefficients to identify heterogeneity in short-run impacts. 

RESULT AND DISCUSSION 
 

This section presents the findings of the study and provides an in-depth discussion of the results. The 

analysis focuses on examining the relationship between financial development and economic growth using 

the Panel ARDL approach. The results of the unit root test, cointegration analysis, and estimated model 

parameters are discussed to highlight both short-run and long-run dynamics. Additionally, the implications 

of these findings are explored in the context of financial policy and economic growth in the selected African 

countries. 

  



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Panel unit root tests 
Before estimating the model, it is crucial to assess the stability of the variables to prevent misleading 

regression results. This study employs two commonly used unit root tests for panel data. The first is the 

Levin-Lin-Chiu (LLC) test, which assumes a common unit root process across all sub-sectors (Levin, Lin 

and Chu 2002). This test is particularly suitable for panel data with a small number of countries and a long 

time series.  

The second test is the Im-Pesaran-Shin (IPS) test, which allows for individual unit root processes 

across sub-sectors, making it more adaptable to heterogeneous panel data (Im et al. 2003). In both tests, the 

null hypothesis states that the data series contains a unit root, while the alternative hypothesis indicates 

stationarity. If the variables are found to be stationary at level I(0) or at the first difference I(1), the Panel 

ARDL methodology can be applied (Pesaran et al. 1999). The following table illustrates these results: 

Table 2. Results of the panel unit root 

Variable Levin-Lin-Chiu (LLC) Im-Pesaran-Shin (IPS) 

Level 1st Difference  Level 1st Difference  

GDP 11.4450 

(1.0000) 

-5.3075 

(0.0000) 

21.3451 

 (1.0000) 

-10.8945 

(0.0000) 

FDA 4.3293  

(1.0000) 

-3.2827 

(0.0005) 

11.6107 

 (1.0000) 

-11.3173 

(0.0000) 

FDD -1.3985 

 (0.0810) 

-15.7023 

 (0.0000) 

2.3359 

(0.9903) 

-16.6874 

(0.0000) 

FDE -5.6881 

 (0.0000) 

-17.0526 

 (0.0000) 

-6.1146 

 (0.0000) 

-18.8499 

(0.0000) 

Note: those in ( ) are p-Value 

Source: Stata 15 software output (2025) 

The panel unit root tests results indicate that most variables, including GDP, FDA, and FDD, are 

non-stationary at the level but become stationary at the first difference, as confirmed by both the Levin-Lin-

Chu and Im-Pesaran-Shin tests. Specifically, the p-values for these variables exceed 5% at the level, 

indicating non-stationarity, while at the first difference, the p-values fall below 5%, confirming stationarity. 

Conversely, FDE is stationary at both the level and first difference, as evidenced by the p-values in both 

tests. These findings suggest that the variables are either I(0) or I(1), making the Panel ARDL model suitable 

for analyzing both short-run and long-run relationships (Pesaran et al. 1999). 

Panel cointegration tests 
To examine the presence of a long-run equilibrium relationship between financial development 

indicators and economic growth, this study employs the Kao and Pedroni cointegration tests. The Pedroni 

test considers cross-sectional dependence and heterogeneity (Pedroni 2004), while the Kao test follows a 

similar approach but assumes homogeneity (Kao 1999). Rejection of the null hypothesis in either test 

indicates the presence of a cointegrated relationship. The following table presents the results: 

Table 3. Results of the panel cointegration 

Test Statistics p-value Decision 

Kao Test 

Modified Dickey-Fuller 4.7783 (0.0000) 

Alternative Hypothesis: 

Cointegration exists 

Dickey-Fuller 6.3774 (0.0000) 

Augmented Dickey-Fuller 5.5256 (0.0000) 

Unadjusted modified Dickey-Fuller 5.1538 (0.0000) 

Unadjusted Dickey-Fuller 7.3816 (0.0000) 

Pedroni Test 

Modified Phillips-Perron 3.1447 (0.0008) 
Alternative Hypothesis: 

Cointegration exists 
Phillips-Perron 1.8057 (0.0355) 

Augmented Dickey-Fuller 2.3224 (0.0101) 

Source: Stata 15 software output (2025) 



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Table 3 presents the results of panel cointegration tests, specifically the Kao and Pedroni tests, to 

examine whether a long-run relationship exists among the variables. The Kao test results show highly 

significant p-values (0.0000) across all statistics, confirming strong evidence in favor of cointegration, 

meaning the variables move together over time. Similarly, the Pedroni test results, including the Modified 

Phillips-Perron (p = 0.0008), Augmented Dickey-Fuller (p = 0.0101), and Phillips-Perron (p = 0.0355), all 

indicate p-values below 0.05, further supporting the presence of a stable long-term relationship. Overall, both 

tests confirm that financial development and economic growth are cointegrated, suggesting that changes in 

financial development indicators have a lasting impact on GDP. 

Panel ARDL approach 

The Panel ARDL model is used due to its ability to handle variables with different integration orders 

while capturing both short-run dynamics and long-run relationships. The main model is formulated as 

follows: 

 

∆𝐺𝐷𝑃𝑖𝑡 =  𝛼𝑖 + ∑ 𝛽𝑝∆𝐺𝐷𝑃𝑖,𝑡−𝑝

𝑝

𝑝=1

+ ∑ 𝛾1𝑞∆𝐹𝐷𝐴𝑖,𝑡−𝑞

𝑞

𝑞=0

+ ∑ 𝛾2𝑞∆𝐹𝐷𝐷𝑖,𝑡−𝑞

𝑞

𝑞=0

+ ∑ 𝛾3𝑞∆𝐹𝐷𝐸𝑖,𝑡−𝑞

𝑞

𝑞=0

+ 𝛿𝑖𝐸𝐶𝑖,𝑡−1 + 휀𝑖𝑡 

 

(1) 

Where: 

 GDPit: GDP for country i at time t. 

 FDAit, FDDit, FDEit: Financial development indicators (access, depth, efficiency). 

 ECi,t−1: Error correction term. 

 εit: Error term. 

Panel ARDL model estimators and the Hausman test 
This section provides an analysis of the Panel ARDL model results, utilizing three estimation 

methods: Mean Group (MG), Pooled Mean Group (PMG), and Dynamic Fixed Effects (DFE). It examines 

both short-run and long-run relationships between financial development indicators (FDA, FDD, FDE) and 

economic growth (GDP) in the selected African countries. The results are summarized in the following table. 

Table 4. Results of MG, PMG and DFE estimation 

Estimator 
Mean Group 

(MG) 

Pooled Mean Group 

(PMG) 

Dynamic Fixed Effects 

(DFE) 

Variable Coefficient p-Value Coefficient p-Value Coefficient p-Value 

Long Run 

FDA 314.1063 0.378 190.7591 0.000 352.2051 0.017 

FDD -352.3448 0.685 159.2675 0.000 -239.3409 0.154 

FDE -62.05083 0.724 -10.75505 0.000 -48.62717 0.494 

Short Run 

ECT -0.049262 0.068 -0.014218 0.171 -0. 018615 0.000 

FDA 65.11428 0.033 87.14563 0.009 37.96531 0.001 

FDD -56.39753 0.017 -79.36094 0.040 -28.41883 0.000 

FDE -1.526893 0.562 2.578938 0.289 2.034714 0.441 

Cons_ 1.533818 0.633 1.113648 0.000 0.305934 0.000 

Source: Stata 15 software output (2025) 

To determine the appropriate model for estimating the relationship between financial development 

and economic growth, the Hausman Test is conducted. This test compares different estimators, including 

Pooled Mean Group (PMG), Mean Group (MG), and Dynamic Fixed Effects (DFE), to identify the most 

efficient and consistent model. It evaluates whether there are systematic differences between the coefficients 

of the models. If the p-value exceeds 5%, the null hypothesis is not rejected, favoring the PMG estimator. 

Conversely, if the p-value is below 5%, the alternative hypothesis is accepted, indicating that the MG or 

DFE estimator is more suitable (Hausman 1978). The test results are presented in the following table. 

 
 



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Table 5. Results of the Hausman test 

Trade-off  Statistics p-Value Decision 

MG/PMG 0.23 0.9719 PMG is more efficient than the MG 

PMG/DFE 1.53 0.6750 PMG is more efficient than the DFE 

Source: Stata 15 software output (2025) 

The Hausman Test results (0.9719 for MG/PMG and 0.6750 for PMG/DFE) indicate that the 

Pooled Mean Group (PMG) estimator is more efficient than both the Mean Group (MG) and Dynamic 

Fixed Effects (DFE) estimators. The high p-values (above 5%) suggest that there are no significant 

differences between the coefficients, leading to the acceptance of the null hypothesis. Therefore, PMG is the 

preferred model for analyzing the relationship between financial development and economic growth. 

Interpretation of PMG Estimation Results 
The pooled mean group (PMG) estimation reveals distinct patterns in both long-run and short-run 

dynamics, as presented below: 

 

Long-Run Relationships 
The results indicate that the Financial Institutions Access (FDA) has a highly significant positive 

effect on GDP, with (β = 190.76, p < 0.001), suggesting that enhanced accessibility to financial institutions 

plays a crucial role in stimulating long-term economic growth. This finding is consistent with previous 

studies such as Bist and Read (2018) and Asante et al. (2023), which emphasize the positive relationship 

between financial development and growth, particularly in low-income African countries with strong 

institutional frameworks.  
 Financial Institutions Depth (FDD) shows significant positive elasticity (β = 159.27, p < 0.001), 

confirming that financial intermediation intensity (private credit/GDP ratio) sustains growth. This aligns 

with Valickova et al. (2014) 's meta-analysis and Aluko and Ibrahim (2020) 's African study, where deeper 

systems: 

- Enhance capital accumulation; 

- Improve risk diversification; 

- Foster technological adoption.  

Contrary to expectations, Financial Institutions Efficiency (FDE) exhibits a significant negative 

coefficient (β = -10.76, p < 0.001), indicating that improvements in financial efficiency may have a 

detrimental effect on long-term economic growth. This result suggests that inefficiencies within financial 

institutions can hinder economic performance, aligning with the "too much finance" hypothesis proposed 

by Ibrahim and Alagidede (2018) and supported by the findings of An et al. (2021), which emphasize the 

adverse effects of inefficient financial systems in low and middle-income economies. The negative 

relationship may be attributed to the structural and transitional costs that financial institutions face during 

the adjustment process, as efforts to enhance efficiency can incur substantial short-term costs. These 

adjustments may not yield immediate long-term economic benefits unless the expected gains materialize 

over a longer time horizon than that considered in the present study. 
 

Short-Run results  
The estimated error correction term (ect) shows a coefficient of -0.014 (p = 0.171), indicating it is not 

statistically significant, suggesting that in the short run, the economy may not adjust immediately to the 

long-term equilibrium following a shock to financial development. This aligns with the dynamic nature of 

the relationship between financial development and economic growth observed in various studies.  

In contrast, the Financial Institutions Access (FDA) shows a statistically significant positive short-

run effect on GDP, with a coefficient of 87.14563 and a p-value of 0.009, indicating that improvements in 

financial accessibility have an immediate positive impact on economic growth, in line with findings by 

Fengju and Wubishet (2024) on the role of financial accessibility in East Africa.  

However, the Financial Institutions Depth (FDD) presents a significant negative short-run effect on 

GDP, with a coefficient of -79.36094 and a p-value of 0.040, suggesting that deeper financial systems may 

have a detrimental effect on growth in the short run. This contrasts with the long-term result and may reflect 

short-term adjustment costs or inefficiencies in a developing financial system, as noted by Akinlo and 

Egbetunde (2010).  

Finally, the Financial Institutions Efficiency (FDE) shows no significant short-run effect on GDP, 

with a coefficient of 2.578938 and a p-value of 0.289, supporting the notion that financial efficiency may 



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106 

 

take time to influence growth, consistent with Ibrahim and Alagidede (2018) findings on the delayed impact 

of financial efficiency. 
 

Comparison of the study results with key previous studies 

The results of the study align with previous research on the link between financial development and 

economic growth, focusing on the role of financial institutions. The positive long-term effects of financial 

access and depth on growth are consistent with studies by Bist and Read (2018) and Asante et al. (2023), 

highlighting the importance of improving financial access. However, the negative impact of financial 

efficiency on growth supports the "too much finance" hypothesis by Bijlsma et al. (2018) and Ibrahim and 

Alagidede (2018), suggesting excessive financial development may hinder growth. The study also shows 

contrasting short-term effects, with financial access positively affecting growth and financial depth negatively 

impacting it. This indicates that the impact of financial development may vary by region and time frame. 

Overall, the study emphasizes the need for balanced, context-specific financial policies to optimize the 

benefits of financial development for economic growth. 

Short run dynamics: country specific PMG estimations  
Table 6 presents the Pooled Mean Group (PMG) estimation results for short-run financial 

development impacts across 31 African economies, revealing substantial cross-country variation in 

adjustment patterns. 

Table 6. Results of PMG estimation - country specific short run coefficients 

Nation ect D(FDA) D(FDD) D(FDE) 

Algeria 0.0123324 132.2984 -155.5122 -4.84566 

Angola -0.0152894 183.9158*** -10.04702 1.976293 

Botswana 0.0026572 -1.810661 -14.01898*** -2.989185 

Burkina Faso -0.0651529*** 3.960603 -4.52069 0.8840683* 

Cameroon -0.0695837*** -17.90698 38.78154*** -0.5215656 

Rep.Congo 0.0403555*** -3.959476 0.4567818 1.133537 

Ivory Coast -0.09228*** 56.7254 -100.6129* 10.61079** 

Egypt 0.008888 437.5956*** -163.5638** 38.56473* 

Equatorial Guinea -0.0643633*** -86.4732*** 0.5253728 9.841948*** 

Ethiopia -0.0864774*** 152.7495* -43.65883 -5.277885 

Gabon -0.012033 9.461511 8.886967 0.6300535 

Ghana -0.0869553** 42.06831*** -88.14726* 6.705531 

Guinea -0.1110889*** -33.32236 -28.45098 1.047506** 

Kenya -0.0886626 45.38455 19.22988 6.461953* 

Libya -0.1121235 184.566 -374.8736*** -50.54235* 

Madagascar 0.026808 112.8404** -34.48675** 0.0973368 

Malawi -0.0208035** 2.150411 -7.426631 -0.9681273 

Morocco -0.0305794 16.42375 -178.3692*** 15.26909 

Mozambique -0.0112739 34.50571*** -6.697442 -0.185186 

Namibia 0.0010966 0.9604781 -1.279954 2.286843 

Niger 0.0994694 148.6162*** -14.85066 -0.13474 

Nigeria -0.0222853* 942.000*** -1151.879*** -22.7828 

Rwanda -0.0180366*** 5.445791 -4.537254 1.455988* 

Senegal -0.0501282*** -10.61204 15.60657 1.77174 

South Africa -0.0008568 5.129907 -44.89754 22.74186 

Sudan -0.0494035*** 113.0074 -16.19166* -0.7644594 

Tanzania 0.0780717 53.18275 18.02849 -1.654024 

Togo -0.0135456*** -5.292597 -4.853441* -0.0025687 

Tunisia 0.0372121*** 51.73695*** -101.5233*** 3.161519 

Uganda 0.0779395*** 74.36759*** 4.391212 -0.1257642 

Zambia 0.055656* 51.79884*** -15.42691 -0.5086009 

Source: Stata 15 software output (2025) 



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The individual countries short-run results highlight significant differences in the impact of financial 

development across African countries. The error correction term (ECT) is negative and significant in several 

countries, including Burkina Faso, Ivory Coast, Ethiopia, Ghana, and Guinea, indicating a tendency to 

return to long-run equilibrium after financial shocks. In contrast, its positive significance in the Republic of 

Congo and Tunisia suggests divergence from equilibrium. The Financial Institutions Access (FDA) has a 

strong positive short-run effect in Angola, Egypt, Ethiopia, Nigeria, and Uganda, demonstrating the 

immediate benefits of improved financial accessibility on economic growth, whereas Equatorial Guinea 

shows a significant negative effect, possibly due to inefficiencies.  

The Financial Institutions Depth (FDD) negatively affects GDP in Botswana, Ivory Coast, Libya, 

and Nigeria, indicating that deeper financial markets may initially disrupt economic stability due to credit 

misallocation or structural weaknesses, while Cameroon benefits from financial depth.  

The Financial Institutions Efficiency (FDE) has a positive impact in Ivory Coast, Equatorial Guinea, 

Ghana, and Tunisia, reflecting how improved efficiency enhances economic performance, while its negative 

effect in Libya, Nigeria, and Sudan highlights inefficiencies that hinder growth. The remaining countries 

exhibit statistically insignificant results, suggesting that financial development does not exert a meaningful 

short-term effect in those economies. 

CONCLUSION 
 

This study provides empirical evidence on the impact of financial development on economic growth 

in 31 African countries over the period 1990–2021 using the Panel ARDL approach. The findings reveal 

that financial institutions’ access and depth significantly contribute to long-run economic growth, 

underscoring the critical role of an inclusive and well-developed financial sector. However, the negative 

impact of financial institutions' efficiency suggests that inefficiencies within financial systems may 

counteract growth benefits, aligning with the "too much finance" hypothesis. In the short run, financial 

accessibility exerts a strong positive influence on GDP, while financial depth demonstrates a negative effect, 

possibly reflecting transitional costs or structural imbalances in financial systems. Moreover, heterogeneous 

short-run results across countries highlight the complexity of financial development’s impact, influenced by 

country-specific institutional and economic conditions. 

These findings have important policy implications. Policymakers should prioritize enhancing 

financial accessibility and depth while addressing inefficiencies that may hinder economic performance. 

Strengthening regulatory frameworks and improving financial institutions’ operational effectiveness can 

foster sustainable economic growth. Future research could further explore the role of institutional quality 

and financial structure in shaping the financial development–growth nexus in African economies. 

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