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Financial inclusion and Financial Performance: The interplay role of capital adequacy requirements in Egyptian Banks

Future Business Journal volume 10, Article number: 96 (2024) Cite this article

Abstract

Purpose

Financial inclusion aims to provide affordable financial services, including banking, loans, equity, and insurance products, to underserved populations. This study aims to examine the moderating effect of a bank’s capital adequacy ratio (CAR) on the nexus between financial inclusion (FI) and a bank’s financial performance (FP) in the Egyptian setting.

Design/methodology/approach

The study uses two empirical linear mixed models (LMM) to test the moderation effect of a bank’s CAR on the association between FI and FP. The study sample comprises 360 bank quarter-observations of 10 listed banks in the Egyptian Stock Exchange (EGX) from 2013 to 2021.

Findings

The findings show that the bank’s CAR strengthens the association between FI dimensions, namely, deposit growth, loan growth, and the number of employees, and the bank’s FP with contradicted directions.

Research limitations/implications

This study provides policymakers insights into the crucial role of complying with banking regulation, namely, the capital adequacy ratio (CAR) and expanding financial inclusion practices to enhance and improve the bank’s FP. Thus, encouraging more strategies and facilities toward financial inclusion.

Originality/value

Due to the scarcity of financial inclusion literature in emerging economies, this paper extends FI literature by highlighting the moderation impact of a bank’s CAR on the relationship between FI dimensions and FP in the Egyptian banking sector. Consequently, this study clarifies this beneficial relationship, which may have significant implications for restoring the challenges faced by the Egyptian economy following the critical events it went through, which, in turn, impacted the country’s poor and vulnerable.

Introduction

In emerging nations, substantial issues such as human rights abuses, poverty, and poor working conditions are a concern of different international associations [35]. Thus, on a global scale, the financial services sector greatly enhances society as a whole and the daily lives of individuals by offering financial services to the least fortunate and unbanked people, which, in turn, mitigates these significant issues. As a result, the banking industry contributes to economic growth by facilitating safe and efficient financial transactions [41].

Inclusion in financial markets has increasingly become a key component of long-term growth because it allows people to focus on their educational and business goals, and to manage financial risks effectively. In addition, it has helped those less fortunate overcome poverty [27, 64].

In Egypt, the Central Bank of Egypt (CBE) leads the nation’s FI strategy procedure. In 2013, it joined the Alliance for Financial Inclusion (AFI) and became a member of the network’s data and FI working groups. Consequently, according to the CBE report [24], the FI indicators reflect a significant increase, achieving a growth rate of 147%, and bringing the total number of Egyptians who own transactional accounts to 42.3 million consumers, which represents 64.8% of the population (16 years +).

Practitioners and academics have been paying close attention to FI throughout the past few decades [31, 39]. On December 18, 2012, the board of directors of the CBE revealed a cutting-edge plan. It released regulations determining the minimum allowable capital adequacy ratio (CAR) for all Egyptian banks, excluding foreign banks, under the framework of Basel II implementation. This suggests that banks must preserve a minimum capital requirement of 10% between the capital base and CAR to fulfill operational, market, and credit risks.

One of the primary methods used by regulators to protect banks against credit risk is the CAR. Banks that fail to comply with CAR regulations may face a variety of regulatory penalties. According to Abdel Karim [1], banks that do not comply may face severe dividend limits, rejection requests to combine or establish new branches, or be required to present detailed plans outlining how capital would be increased. According to Moyer [58], certain bank managers tend to modify profitability measures to lower the regulatory costs imposed by CAR authorities.

Furthermore, this motivated a significant number of academics to develop a variety of finance theories on the ideal capital structure after Modigliani and Miller’s theory [56, 57]. Trade-off and the pecking order theories are two of these conventional theories that tackled the capital structure dilemma. The trade-off theory states that if the financial benefits of debt financing are balanced with bankruptcy filing expenses, an ideal capital structure can be achieved [47, 66]. On the other hand, according to pecking order theory, organizations utilize retained earnings as their first option for funding new investment opportunities, followed by debt financing, and then equity financing [32, 33]. In other words, considering agency issues retained earnings override debt in new investment financing and debt over equity [33].

The previous research based on the aforementioned theories suggested that banks should constantly assess the minimum capital requirements to determine how to gain access to less expensive funding sources and increase their profit margins. This would significantly contribute to the public’s continued trust in banks to meet consumer credit demands and protect depositor money. Banks in Egypt may benefit greatly from focusing on FI to comply with CAR requirements, as more FI transactions may increase FP and reduce the amount of capital needed.

According to Beck et al. [9] and Kumar [48], FI significantly affects banks’ financial performance through a variety of parameters at the country level. Prior research does not adequately address whether FI has a positive (e.g., [17, 43, 46, 65, 76]) or negative (e.g., [5, 13]) influence on bank financial performance (FP) based on bank-level factors. Despite this, bank-level attributes can play a crucial role in enhancing country-level financial indicators. Thus, this study aims to answer this question: Do capital adequacy requirements moderate the association between FI and the Egyptian banks’ FP?

This research contributes to accounting literature in several ways to fill this gap. Firstly, this study adopts a single approach that offers a thorough analysis of how financial inclusion affects bank financial performance in Egypt since conclusions from country studies [36, 67, 70, 72] cannot be generalized. This complements the few research that have examined the relationship between financial performance and financial inclusion in the Egyptian context. As far as the researchers are aware, the only study that has examined the influence of financial inclusion on Egyptian bank-level features is that of Al-Adwey [4]. Secondly, although the previous studies used full sample analysis, this study goes in a different direction by focusing on the critical years 2013–2021, which mark a recovery from the 2011 revolution’s instability, the 2015 expansion of the Suez Canal that improves international trade, the 2016 currency devaluation that was conquered, and the COVID pandemic recovery to investigate the effect of financial inclusion on bank performance in Egypt.

Thirdly, this study examines how CAR moderates the effect of FI on banks’ FP in Egypt. This study will only focus on Egypt since it has declared FI as a national priority and an integral part of its Vision 2030. Since 2013, Egypt has implemented several encouraging strategies, initiatives, and procedures to attain an increase in the rates of FI to ease the achievement of the Sustainable Development Goals (SDGs) [61]. Furthermore, FI would assist Egypt achieve inclusive progress, improving people’s lives especially those who are less fortunate, and empowering women more [23].

This study presents several contributions. First, the study examined the association between specific FI measures such as deposit growth, loan growth, and the number of employees and the bank’s FP quarterly. Second, the study investigated mandated CAR as a vital moderator, and the results suggest that bank executives cope with FI pillars and CAR with caution. Banks’ FP is increased due to higher loans along with increased CAR requirements. Third, prior studies focused mainly on CAR [30] and its impact on banks’ performance, though, this study is the primary to provide an empirical analysis that proves that mandated CAR requirement has a moderator role that affects the relationship between FI and the banks’ FP. Finally, this study included micro and macro-economic variables in the econometric model to capture the interaction impact of FI and CAR, on FP of Egyptian banks.

The rest of this paper proceeds as follows: Section “Theory, literature, and hypotheses development” covers the literature review and the development of hypotheses, whereas Section “Research method” outlines the sample and statistical technique employed in this research. In Section “Empirical findings and discussion of results,” the empirical findings are explained, and the results are discussed. Section “Robustness tests” reports the robustness tests. Section “Conclusion, limitations, and suggestions for future research” includes the conclusion, practical implications, limitations, and proposals for future research.

Theory, literature, and hypotheses development

Theoretical review

This section discusses and integrates two theories—financial asymmetry theory and financial intermediation theory—to establish a coherent theoretical framework that will support the current research analysis of the effect of FI on banks’ performance. Pecking order theory and trade-off theory are two theories that are discussed and integrated to investigate the second effect of FI on banks’ performance through the moderating effect of bank regulation.

FI and bank’s FP theories

The banking sector is essential to promote FI in emerging nations. FI has contributed to increasing the concern from various global organizations, including the World Bank and the governments in the context that less fortunate people still lack sufficient access to the financial services that they require. FI is described as “the ability of people and businesses to obtain practical, cost-effective financial products and services that satisfy their transactional, payment, savings, credit, and insurance needs ethically and sustainably” [78].

According to Hannig and Jansen [38], through FI, unbanked people are empowered to access all forms of financial services under the formal financial system, which they would not be able to access otherwise. FI promotes domestic savings, supports household resilience, promotes entrepreneurship, and preserves the financial sector’s stability [74]. Furthermore, FI will help vulnerable communities have access to equivalent opportunities, and support them in decreasing poverty, which will lead to a decline in social inequality and, eventually, the accomplishment of sustainable development. Since FI may improve the capacity of vulnerable population families to withstand financial shocks, improve investments in human capital, or undertake a moderate asset accumulation to gain access to promising investments, which result in the accomplishment of sustainable development [78].

The association between FI and banks’ FP has been established by employing Diamond’s [29] theory of financial intermediation, where banks serve as an intermediary between savers and borrowers. Banks acquire access, financial diversity, and financial utilization through their role as financial intermediaries. The degree of inclusion influences the level of banks’ FP. Diamond [29] also documented that banks can monitor borrowers effectively, and thus can play the role of a delegated monitoring agent. Competitive advantage is significantly impacted by lowering the monitoring expenses. Financial intermediation, according to Ndebbio [62], is the degree to which financial institutions combine surplus and deficit spending units. It is, therefore, suggested that FI improves bank’s performance.

Conversely, Akerlof’s financial asymmetry theory [3] has also been used to explain how FI affects a bank’s FP. According to Boffondi and Gobbi [16], when loan contracts are restricted, adverse selection happens when a lender is unable to distinguish between borrowers who constitute different risks. Due to this, borrowers return their debts when they can afford to, which causes a rise in non-performing loans [16]. As defined by Hanning and Jansen [38], FI is “the inflow of numerous, inexperienced, and new customers into commercial banks and other financial institutions”. Due to the difficulty of determining a credit rating for each customer and the possibility of default, banks’ financial performance suffers due to this barrier. Therefore, banks have difficulty in determining if FI represents an appropriate and low-risk investment.

Richard [69] asserted that moral hazard and adverse selection brought on by the information asymmetry between lenders and borrowers might cause credit to contract, which will have an impact on banks’ performance. When a customer gives false information regarding its assets, liabilities, or credit capacity, a moral hazard emerges. It has been recognized that this is a reason for non-performing loans. Thus, it proposed that FI undermines banks’ performance.

FI, bank FP, and capital regulation theories

According to the pecking order theory, capital used to fund an organization’s operations is prioritized according to a specific preference order [60]. In the long run, managers behave in the best interests of shareholders since they possess more insider knowledge than investors due to the concept of information asymmetry and the occurrence of transaction costs. Organizations are going to prioritize using internal earnings for funding new investments, followed by loan financing and equity financing [34, 53]. Therefore, organizations with higher earnings should have a lower debt-to-capital structure ratio, assuming all other variables remain unchanged.

According to the trade-off theory, the organization evaluates the advantages and drawbacks of debt financing to establish the optimal ratio of debt financing to equity financing. One of the benefits of debt is tax savings, namely, tax shielding. According to Myers [59], the cost of debt is the price of being in a state of financial distress. Subsequently, the lesser costs (including bankruptcy costs and tax shield) are required to burden, the additional debt in capital structure should be predictable, and vice versa [52]. According to the trade-off theory, an organization should assess the benefits and drawbacks of debt before deciding on the right level of leverage.

In summary, trade-off theory confirms the idea that a reduced cost of debt increases the usage of leverage. Pecking order theory, on the other hand, indicates an opposition to leverage based on reflection of information asymmetry. Though these theories shed light on the factors influencing capital leverage from many angles, it is still unclear how FI affects a bank’s FP when it comes to the moderating influence of the CAR. Yet FI has a significant influence on the usage of capital leverage from a sustainability perspective.

Empirical review

FI and bank’s FP empirical review

Khatib et al. [46] investigated how FI affected commercial banks’ performance. The authors concluded that there was a significant and positive relationship between FI and bank performance. They also documented that implementing innovation by starting new, improved services increases banks’ profitability and that installing more ATMs lowers the cost of transactions for depositors. Additionally, Nizam et al. [65] revealed that FI (access to credit) significantly and positively affected banks’ growth up to a specific level but negatively impacted it after that. In the same vein, Bolaji’s et al. [17] results documented a significant positive effect of digital financial inclusion on the bank’s efficiency.

Jajah et al. [43] investigated the impact of FI on banks’ performance, and the results showed that the government initiatives to boost FI are likely to have a positive effect on bank profitability. Bose et al. [18] investigated the relationship between the banks’ market-based performance and the disclosure of banks’ FI activities in emerging markets. The results revealed a positive and significant relationship between FI and bank performance. Furthermore, government ownership and market competition have a significant impact on the prior interaction.

Vo et al. [76] demonstrated a relationship between a higher degree of FI and increased bank resilience. FI allows banks to increase their market share, reduce costs, and enhance revenue. According to Ahamed and Mallick [2], banks can gain from FI by strengthening the currency market, lowering marginal borrowing costs, and introducing stability to their financial operations. The reductions in credit and borrowing are less likely to have an impact on banks with greater levels of FI.

Therefore, during periods of crisis, banks that have higher financial integration benefit from more flexible financial markets [51]. Therefore, financial services must be readily accessible to all segments of society, with a focus on those who are less fortunate. Furthermore, Neaime and Gaysset [63] proposed that the voluntary availability of current financial resources helps lower-income communities to overcome their difficult economic circumstances more effectively. Dabla-Norris et al. [25] indicated that country-specific characteristics affect the settlement of the trade-offs between FI and financial performance. The limitations on distance can be mitigated by making financial services more easily accessible through advancements in mobile banking and improving the financial infrastructure. As a result, nations with insufficient physical and financial infrastructure can depend on technological advancement to provide less fortunate consumers with financial literacy.

Moreover, Sedera et al. [71] investigated financial inclusion’s impact on bank profitability in Indonesia. The results reveal a positive association between FI and a bank’s profitability over the three dimensions of FI: access, availability, and usage. Furthermore, Bhatter and Chhatoi [14] assessed the moderating effect of CSR on the relationship between FI and FP. According to the results, CSR moderates the positive relationship between FI indicators and the FP of the bank in a significant and positive way.

In contradiction to the aforementioned research, Bhattacharyya et al. [13] showed an insignificant relationship between FI and banks’ FP accounting metrics. The authors claimed that governmental banks prioritized serving society’s needs over their financial interests by engaging in more inclusive transactions. Additionally, the results showed that FI harmed banks’ market performance. In the same vein, Al-Eitan et al. [5] found that the number of loans and deposits accounts are negatively impacted the profitability of the commercial banks in Jordan. However, the number of branches and ATMs had no significant effect on the bank’s profitability.

Similarly, Yakubu and Musah [77] examined the impact of FI on bank profitability sub-Saharan Africa (SSA) over the period 2000–2017. The authors found that FI impacts negatively on the bank’s profitability.

Further, Kumar et al. [49] documented mixed results regarding the association between FI and the bank’s profitability. The authors found that the number of branches positively and significantly impacts the bank’s profitability. However, the numbers of loan accounts and ATMs have no significant impact on the bank’s profitability.

Based on the abovementioned discussion, there is an inconsistent association between FI and bank FP. Increased FI enables individuals and unbanked businesses to access the official banking system. It facilitates the diversification of loan portfolios and deposit bases for financial institutions. Financial institutions may be more resilient if they have more diversification [55]. According to Beck et al. [8], inclusive finance could lessen information asymmetries and agency issues between lenders and borrowers by increasing access to financial instruments and broadening financial sector accessibility. Since banks can collect deposits from a wide range of customers, low information asymmetries assist in reducing any sort of financing instability [37]. As a result, banks depend less on expensive and riskier money market funds, which lowers return volatility [10, 28, 45, 68]. Since inclusive banking offers several options for funding customer deposits, it could reduce the return volatility of banks.

On the other hand, in an inclusive financial sector, banks open branches in rural and underbanked areas. Senior managers find it harder to monitor remote branches as the distance between them and headquarters grows [20]. In this instance, senior managers’ efficiency is challenged as they have to shift to other branches to improve the overall operating efficiency of these further branches from the headquarters [11]. Furthermore, banks must continue to offer a variety of products targeting different categories of customers to increase access to financial services for people from diverse economic levels. Thus, FI may thereby increase agency problems due to the wide range of products and complex organizational structure.

The contradictory impacts discussed above suggest that there is a need for empirical analysis to determine whether FI increases or decreases bank performance. However, an overall positive relationship between FI and the bank’s FP would be expected if the advantages of FI exceed the disadvantages. Therefore, the first hypothesis is developed as follows:

H1

There is a positive nexus between FI and the Egyptian banks’ FP.

FI, bank FP, and capital regulation empirical review

Regulators from all around the world are still searching for appropriate financial regulations that encourage bank resilience while simultaneously supporting FI strategies. As a result, it is critical to evaluate how regulatory frameworks, in particular the Basel implementation of the minimum CAR can moderate the association between FI and bank’s FP. Barth et al. [6] argued that high regulatory limits on banking operations may limit banks’ prospects for diversification. Consequently, less diversification will lead to a reduction in income, which will lead to ineffective financial intermediation [7, 50].

Additionally, more restrictions on banks’ operation may increase the holding of safe assets while decreasing the expected return of hazardous assets, this might increase bank performance. On the other hand, more regulations on banking operations would make it more difficult to use (invest) the additional funds when deposits rise because of FI, which would decrease banks’ performance. Therefore, given the negative effects of regulation constraints, it is hypothesized that the net effect of FI could have an undesirable impact on a bank’s FP for the banks functioning in a setting with rigid restrictions on banking operations [54].

In contrast, unregulated financial operations could exaggerate moral hazard issues and encourage banks to accept excessive risk investments [19]; this might decrease bank performance. Additionally, since managers’ action is constrained by greater debt repayment requirements, higher capital regulations raise agency costs between insiders and outsiders [21]. As a result, a crucial role for regulatory capital requirements is based on the CAR in the relationship between FI and bank performance. Basel II capital regulations have the potential to increase bank FP, although FI might raise the upper bound on hazardous investments, it can also increase the expected return and mitigate any potential negative impact on bank performance.

Thus, this study supposes the legislative CAR to affect the relationship between FI and FP of Egyptian banks:

H2

Compliance with CAR has a vital impact on the association between FI and the Egyptian banks’ FP.

Research method

This study attempts to examine the influence of FI on the bank FP. Further, to investigate whether this association is affected by the bank regulation (CAR). Thus, this research uses the linear mixed model (LMM) to test the two empirical models. Since LMM modeling the autocorrelated error terms resulting from the repeated measures of the bank’s FP over time. Furthermore, time can be included and treated as a fixed or random effect by using the LMM. As a result, randomized effect modeling is improved, and biases and type I errors are less likely to occur [26].

The data were statistically analyzed using STATA 14 for descriptive and univariate analysis and SPSS software for applying the LMM technique. At the 1% and 99% levels, winsorizing was applied to all variables.

Data sources and the study sample

The population of the research is all the Egyptian banks registered with the Central Bank of Egypt (37 banks). The initial sample comprises all listed banks in the Egyptian Stock Exchange (EGX) (12 banks) from 2013 to 2021. We focused on listed banks since they are obliged to more restrictions and rules regarding the capital adequacy requirements. The bank to be included in the sample must meet the following criteria:

  1. (i)

    It is a commercial bank listed in the EGX.

  2. (ii)

    It is capital adequacy data available in the DataStream database over the study timescale from the year 2013–2022.

  3. (iii)

    Has available financial data in the DataStream database over the study timescale from the year 2013 to 2022.

It was noted that not all listed banks meet these criteria. Banque Du Caire and Faisal Islamic Bank were omitted due to their unavailability data of FI dimensions, CAR, and other financial data; hence, our final sample is composed of (10 banks) with 360 quarter-observations. Table 1 shows details of the sample selection.

Table 1 The study sample
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FI dimensions, CAR, and other financial data related to banks’ financial performance, size, leverage, intellectual capital, and efficiency ratio are collected from the DataStream database. The macroeconomic indicators, namely, inflation rate and GDP, are collected from the World Bank database.

Study variables and measurements

This study aims to investigate the nexus between FI and the bank’s FP as well as the moderating role that CAR plays in this relationship.

Measurement of bank’s FP This study follows prior studies and measures the bank’s FP through the bank’s return on assets (ROA) (e.g., [14, 22, 46, 49, 73, 75]).

Measurement of FI Prior related studies have utilized the following proxies for measuring FI, namely: the number of loan accounts, the number of bank branches, the number of ATMs, the number of credit cards, the number of debit cards, and the total deposits accepted (e.g., Kumar 2022; [14, 22, 40, 64]). This study uses the growth rate of the bank’s loans, deposit accounts, and the number of employees as proxies for FI following [5, 14, 64], these data were collected from the DataStream database. Other proxies, such as the number of branches and ATMs, were unavailable as the Egyptian banks considered them confidential and did not publish them on their websites over the study timescale.

CAR A bank’s capital adequacy is measured by dividing its core capital by its supplemental capital over its risk-weighted assets [12, 44]. This study measured CAR (Tier1) as the sum of common stockholder’s equity, retained earnings, accumulated other comprehensive income, and minority interest as stated on the DataStream database.

Measures of control variables This study controls some of the bank-specific characteristics that could influence the bank’s FP and some macroeconomic control variables. Referring to previous studies (e.g., [5, 14, 26, 49, 73]), the bank-specific control variables are: bank size (Size), leverage (Lev), intellectual capital (IC), efficiency ratio (ER), inflation rate (INF), and gross domestic product (GDP). All control variables’ measurements are as referred to in Table 2.

Table 2 Variable definitions, notation, measures, and source
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Empirical models

This study investigates the link between FI and bank FP. In addition, to test whether the bank CAR has a moderating effect on this association. Therefore, we run the following two main models. The first regression model is as follows:

$$\begin{aligned} {\text{ROA}}_{{{\text{it}}}} =\, & \alpha_{0} + \, \beta_{{1}} {\text{DG}}_{{{\text{it}}}} + \, \beta_{{2}} {\text{LG}}_{{{\text{it}}}} + \, \beta_{{3}} {\text{NE}}_{{{\text{i}}\;{\text{t}}}} + \beta_{{4}} {\text{Size}}_{{{\text{it}}}} + \beta_{{5}} {\text{Lev}}_{{{\text{it}}}} \\ & + \beta_{{6}} {\text{IC}}_{{{\text{it}}}} + \beta_{{7}} {\text{ER}}_{{{\text{it}}}} + \beta_{{8}} {\text{INF}}_{{{\text{it}}}} + \, \beta_{{9}} {\text{GDP}}_{{{\text{it}}}} + \, \varepsilon_{{{\text{it}}}} \\ \end{aligned}$$
(1)

where ROAit is the bank (i) financial performance in the quarter (t), DGit is the bank (i) total deposit growth in the quarter (t), LGit is the bank (i) loan growth ratio in the quarter (t), NEit is the bank (i) number of employees in the quarter (t), Sizeit is the bank (i) size in the quarter (t), Levit is the bank (i) leverage in the quarter (t), ICit is the bank (i) intellectual capital in the quarter (t), ERit is the bank (i) efficiency ratio in the quarter (t), INFit is the yearly inflation rate, GDPit is the yearly GDP growth rate, and Ɛit is the random error.

To test the interaction effect of both FI and bank’s CAR on its FP, the second regression model is formulated as follows:

$$\begin{aligned} {\text{ROA}}_{{{\text{it}}}} = & \alpha_{0} + \beta_{{1}} {\text{DG}}_{{{\text{it}}}} + \beta_{{2}} {\text{LG}}_{{{\text{it}}}} + \beta_{{3}} {\text{NE}}_{{{\text{it}}}} + \beta_{{4}} {\text{CAR}}_{{{\text{it}}}} + \beta_{{5}} \left( {{\text{DG}}_{{{\text{it}}}} \times {\text{CAR}}_{{{\text{it}}}} } \right) + \beta_{{6}} \left( {{\text{LG}}_{{{\text{it}}}} \times {\text{ CAR}}_{{{\text{it}}}} } \right) \\ & + \beta_{{7}} \left( {{\text{NE}}_{{{\text{it}}}} \times {\text{ CAR}}_{{{\text{it}}}} } \right) + \beta_{{8}} {\text{Size}}_{{{\text{it}}}} + \beta_{{9}} {\text{Lev}}_{{{\text{it}}}} + \beta_{{{1}0}} {\text{IC}}_{{{\text{it}}}} + \beta_{{{11}}} {\text{ER}}_{{{\text{it}}}} + \beta_{{{12}}} {\text{INF}}_{{{\text{it}}}} + \beta_{{{13}}} {\text{GDP}}_{{{\text{it}}}} + \varepsilon_{{{\text{it}}}} \\ \end{aligned}$$
(2)

where CARit is the bank (i) capital adequacy ratio in the quarter (t), (GDit × CARit), (LGit × CARit), and (NEit × CARit) are the interactive impact of both FI and CAR on the bank’s FP. Other variables are as defined in the multiple regression Model (1).

Empirical findings and discussion of results

Descriptive statistics and univariant analysis

Table 3 illustrates the descriptive statistics of the study’s variables over the years 2013–2021. As shown in Table 3, the overall average bank’s FP is 5.3% (Std. = 0.24), ranging from − 0.005 to 2.544. This implies that there was a relatively low variability in the banks’ FP over the period from 2013 to 2021.

Table 3 Descriptive statistics
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Regarding the FI, the results reveal almost the same mean value for deposit growth and loan growth, 4.7% and 4.5%, respectively. However, the loan growth standard deviation is higher than the deposit growth with 54–8.1%, respectively. This implies that banks provided more loan services than deposit services over the years from 2013 to 2021. In addition, the results indicate a mean value of 14.5% for the bank capital adequacy, with a standard deviation of 6.1%, ranging from 0.057 to 0.983.

Table 4 reports Pearson correlation analysis for the tested variables. The bank’s FP is not correlated with its deposit growth or loan growth. Further, the results show a low negative correlation between the bank’s FP and the number of employees, size, and the GDP level at 10.3%, 10.4%, and 23.2%, respectively. Though the correlation coefficient of the number of employees and bank size is high (87.4%), none of the correlation coefficients among independent variables exceeds the critical value of 70%. Further, the variance inflation factor (VIF) analysis for all independent variables shows values less than 10, which implies no multicollinearity issues in our models.

Table 4 Pearson coefficient correlation matrix
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Multivariate analysis

This section deliberates the empirical results of the two linear mixed models that were formulated to test: (i) the impact of FI on the bank’s performance and (ii) the impact of the bank’s CAR on the association between FI and bank FP. Table 5 illustrates the results of these two liner mixed models.

Table 5 Linear mixed model results
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Model’s (1) results reveal a significant negative relationship between a bank’s deposit growth and FP at a significant level of 10%. However, the other two FI proxies, namely: Loan growth and number of employees have a significant positive association with the bank’s FP at a significant level of 5%. This implies that exaggerating in offering deposit services, which increases the bank’s liabilities, could harm the bank’s FP. Meanwhile, expanding loan services and increasing workers could increase the bank’s operating FP. This result aligns with financial intermediation theory, extending loans and increasing the availability of financial services will improve the bank’s FP. Thus, the first null hypothesis is rejected, and Model’s (1) results support accepting H1.

After considering the bank’s CAR as a moderating variable in Model (2), the results indicate some slight changes. First, the moderation effect of CAR has strengthened the negative association between deposit growth and the bank’s FP from a significant level of 10% to a significant level of 1% (highly significant). Further, the relationship between loan growth and the bank’s FP remains constant at significant level of 5%. However, the relationship between the number of employees and the bank’s performance has turned into an insignificant and negative relationship. Since Egyptian banks tend to increase lending rather than depositing, this result fits well with pecking order and trade-off theories. Consequently, the results support the acceptance of H2 and reject the second null hypothesis.

Furthermore, the association between the bank’s financial performance and other control variables, namely, size, lev, IC, and ER, were insignificant in Model (1) and turned into a highly significant relationship after adding the interrelation effect of the bank’s CAR with FI components at a significant level of 1%. Size and IC have a significant positive relationship with the bank's FP; however; Lev and ER have a negative association with the bank's FP. Regarding the Akaike’s Information Criterion (AIC) values, Model (2) has a better fit than Model (1) since it has a lower AIC value.

Discussion

From the statistical analysis of our linear mixed models that examined the interplay effect of CAR and FI on the bank’s FP, we find a slight change between the two models. Our empirical results for Model (1) reveal a negative association between a bank’s deposit growth and its FP. This suggests that providing deposit services to low-income groups excessively will raise bank liabilities and negatively impact the bank’s FP through elevated transaction costs. This, in turn, will increase the information asymmetry among depositors and the bank. Our finding is consistent with the results of Bhattacharyya et al. [13], Al-Eitan et al. [5], Yakubu and Musah [77], and Bhatter and Chhatoi [14]. However, this result contradicts some prior studies in the FI and bank FP nexus (e.g., [46, 65, 75, 76]).

However, our results reveal that the Egyptian bank’s FP rises as a result of over-lending and recruiting more staff. This implies that banks’ expansion in credit services would attract low-income groups to be involved in FI, which, in turn, would increase the bank’s cash inflow. As well as recruiting more employees to provide financial services to borrowers and depositors would enhance the bank’s FP. Our results contradict Al-Eitan et al.’s [5] results, the authors found no effect on the bank’s number of employees and a negative effect on credit services on the bank’s profitability while our results show a positive association for both dimensions of FI on the bank’s FP. Similarly, Kumar et al. [49] found no effect of bank loan growth on its FP, while our results report a positive association between the two variables. On the other hand, this positive association between FI dimensions and FP is in line with the results of Bose et al. [18], Ahmed and Mallick [2], Nizam et al. [65], Vo et al. [76], Jajah et al. [43], and Khatib et al. [46].

Regarding the empirical findings of Model (2), which represents the moderation effect of the bank’s CAR on the nexus between FI dimensions and the bank’s FP. To the best of our knowledge, no previous studies explored the moderating effect of CAR on the relationship between FI and bank FP. Further, there is no consensus on the impact of CAR on bank FP, since Bitar et al. [15] and Khatib et al. [46] reported a negative relationship, while Islam and Nishiyama [42] documented a positive relationship. Though our results reveal a negative impact on the interaction between CAR and DG on the bank’s FP, the bank’s CAR strengthened the positive association between LG and the bank’ FP. This result signifies that balancing the bank loan services and regulations together could mitigate potential risks [46]. This result is consistent with the trade-off theory since the Egyptian banks would swap the costs and benefits involved in offering deposits and loan services to maintain their FP.

Concerning the control variables in Model (1), none of the model controls is significantly associated with bank FP. However, Model’s (2) results illustrate a positive association between bank size, intellectual capital, and FP. This result is rational due to the size effect assumption. However, our results are inconsistent with those of the related prior studies (e.g., [5, 14, 49]). Similarly, Model’s (2) results reveal a negative association between both the bank leverage and efficiency ratio and FP. However, Al-Eitan et al.’s [5] and Bhatter and Chhatoi’s [14] results document a positive association between bank leverage and its FP. These contradictory results might be due to the different variable proxies we used in our models.

Robustness tests

To assess the precision of our mixed models’ results, and whether the bank CAR has a moderating impact on the nexus between FI dimensions and the bank FP, the two Models (1) and (2) were run after constructing an FI index using a factorial analysis of the principal components of the three determined FI dimensions.

Table 6 confirms the same conclusions of our original mixed models since Model (2) is a better fit than Model (1) with a lower AIC value. As shown in Table 6, Model’s (2) results report a significant negative relationship between the FI and the bank’s FP after considering the moderating impact of the bank’s CAR at a significant level of 10%. However, Model’s (1) results reveal no association between the composite FI and the bank’s FP. These contradictory results might be due to the differences in the relationship direction among the FI dimensions and the bank’s FP. In addition, the association between the bank's FP and other control variables remains constant significant (but at a significant level of 5%) and with the same directions.

Table 6 Additional analysis: composite FI score
Full size table

Conclusion, limitations, and suggestions for future research

The banking sector seeks to improve its financial services over the globe to people in rural and remote area to alleviate pressing issues such as poverty. Consequently, the economy will grow faster. Thus, this study aims to explore the moderation impact of capital adequacy requirements on the association between FI and the bank’s FP in the Egyptian banking sector.

This study utilized a linear mixed model statistical technique for a sample comprised of 10 listed banks in the EGX over the period 2013–2021. The measures used to proxy FI are the bank’s deposit growth, loan growth, and the number of bank employees. To gauge the risk and performance of banks, both macro and micro-control factors are employed. Our results reveal a significant negative association between a bank’s deposit growth and FP and a positive relationship between a bank’s loan growth and the number of employees and a bank’s FP. However, after incorporating the CAR in our mixed model, the negative relationship between deposit growth and the bank’s FP has been strengthened. Further, the relationship between loan growth and the bank’s FP remained constant, and the relationship between the number of employees and the bank’s FP has changed into an insignificant relationship. Further, among the control variables, our results in Model (2) indicate that bank size, leverage, intellectual capital, and efficiency ratio are the main drivers of a bank’s FP.

Our results suggest some practical implications. This study provides insights for policymakers toward a deeper understanding of the crucial role of complying with banking sector regulations (CAR) to enhance the offered financial services (FI) and improve the bank’s FP. Further, banks should maintain their asset management to promote banking loans, which, in turn, will boost the bank’s FP. Even though the results demonstrated that the FI activities affect the bank’s performance. The low participation rate can be attributed to two factors: Either banks are not aware of the significance of disclosing financial inclusion activities, or there are additional activities related to financial inclusion that are unique to the Egyptian environment and are not included in the FI parameters used. This suggests that in addition to passing legislation and laws requiring all listed banks to disclose their financial inclusion activities as part of their social responsibility, the Central Bank of Egypt needs to work on creating an index of financial inclusion related to Egypt’s environmental conditions.

This study has some limitations. First, this study focused on some of the FI proxies, not all of them. Hence, future research could examine the impact of FI on a bank’s FP using other FI proxies such as the number of ATMs, the number of branches, and internet banking and mobile financial services. Second, our models use only one proxy for bank FP. Thus, future research might examine the association between FI and bank FI utilizing other bank’s FP proxies such as return on equity and Tobin’s q. Further, future research could investigate the impact of FI on a bank’s cost of capital since FI can increase the quality of banks’ profits and reduce banks’ cost of capital. Moreover, the research sample was limited to listed banks on the Egyptian Stock Exchange only, which led to a limited sample size. Future research in this field can study listed and unlisted banks because financial inclusion activities are equally applicable to both types of banks. Finally, the LMM was used without checking for endogeneity issues, so our models might suffer from endogeneity problems.

Availability of data and materials

Secondary sources of data as DataStream database, the World Bank database, and banks’ financial statements are used to complete this study.

Abbreviations

AIC:

Akaike’s information criterion

AFI:

Alliance for Financial Inclusion

CAR:

Capital adequacy ratio

CBE:

The Central Bank of Egypt

EGX:

The Egyptian Stock Exchange

FI:

Financial inclusion

FP:

Financial performance

LMM:

Linear mixed model

SDGs:

Sustainable Development Goals

VIF:

Variance inflation factor

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  1. Faculty of Commerce, Sohag University, Sohag, Egypt

    Yousra R. Obiedallah

  2. Faculty of Management Sciences, Modern Sciences and Arts University (MSA), Giza, Egypt

    Asmaa H. Abdelaziz

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AA developing the original draft, helped in methodology, collecting data, and reviewing the theoretical and empirical literature. YO collected the data, analyzed the results, and concludes the draft as well as edited and reviewed the original draft. All authors have read and approved the manuscript.

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Obiedallah, Y.R., Abdelaziz, A.H. Financial inclusion and Financial Performance: The interplay role of capital adequacy requirements in Egyptian Banks. Futur Bus J 10, 96 (2024). https://doi.org/10.1186/s43093-024-00383-9

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