Financial inclusion and the performance of banking sector in Palestine

Khatib, Saleh F. A.; Hendrawaty, Ernie; Bazhair, Ayman Hassan; Rahma, Ibraheem A. Abu; Al Amosh, Hamzeh Article Financial inclusion and the performance of banking sector in Palestine Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Khatib, Saleh F. A.; Hendrawaty, Ernie; Bazhair, Ayman Hassan; Rahma, Ibraheem A. Abu; Al Amosh, Hamzeh (2022) : Financial inclusion and the performance of banking sector in Palestine, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 10, Iss. 10, pp. 1-15, https://doi.org/10.3390/economies10100247 This Version is available at: https://hdl.handle.net/10419/328547 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Citation: Khatib, Saleh F. A., Ernie Hendrawaty, Ayman Hassan Bazhair, Ibraheem A. Abu Rahma, and Hamzeh Al Amosh. 2022. Financial Inclusion and the Performance of Banking Sector in Palestine. Economies 10: 247. https://doi.org/ 10.3390/economies10100247 Academic Editor: Robert Czudaj Received: 17 August 2022 Accepted: 26 September 2022 Published: 9 October 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). economies Article Financial Inclusion and the Performance of Banking Sector in Palestine Saleh F. A. Khatib 1,* , Ernie Hendrawaty 2, Ayman Hassan Bazhair 3, Ibraheem A. Abu Rahma 4 and Hamzeh Al Amosh 5,* 1Azman Hashim International Business School, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia 2Department of Management in Economic and Business Faculty, University of Lampung, Bandar Lampung 35141, Indonesia 3Department of Economic and Finance, Faculty of Business Administration College, Taif University, Taif 21944, Saudi Arabia 4Business Administration Department, Faculty of Administrative and Financial Sciences, Gaza University, P.O. Box 1113, Gaza P920, Palestine 5Ministry of Education and Higher Education Qatar, Doha 35111, Qatar *Correspondence: [email protected] (S.F.A.K.); [email protected] (H.A.A.); Tel.: +60-111-773-6582 (S.F.A.K.) Abstract: Despite evidence on the social and economic importance of financial inclusion (FI), the relationship between FI and bank profitability remains unclear. In this research, we evaluated the association between financial inclusion and the performance of banks in Palestine using dynamic panel analysis applied to a sample of 11 banks, with two econometric models representing profitability indicators over a nine-year period (2012–2020). In addition to linear regression models, the generalized method of moments estimator was utilized. The results showed that access to financial services (e.g., the number of automated teller machines (ATMs) and the number of bank branches), service delivery (including the average costs to maintain a current account), and the quality of the products improve banks’ profitability. However, point-of-sale terminals have no impact on profitability. Additionally, financial service utilization reflected in bank account number sand credit to small and medium-sized enterprises do not affect bank profitability, and among bank-specific variables, the nonperforming loan ratios, the cost-to-income ratios, and liquidity were found to be the main drivers of profitability. Policymakers in Palestine must prioritize FI by adopting rules that encourage lending to practices of financial institutions. Keywords: bank performance; Palestine; ATMs; branches; financial inclusion 1. Introduction Financial inclusion refers to the availability and utilization of affordable and useful financial products and services provided by businesses or individuals to meet needs, such as transactions, credit payments, and savings, and the delivery of these services to them in a responsible and sustainable manner. It has been suggested that better inclusion improves the effectiveness and availability of financial services while being a safe, convenient, secure, and cost-effective approach (Vo and Nguyen 2021;Ikram and Lohdi 2015). There is growing evidence that progress in financial inclusion enhances financial stability and contributes to banks’ economic growth, financial efficiency, and performance. The government also immensely benefits from increased local economic activities that decrease economic inequality at the macroeconomic level (Chikalipah 2017). Despite an increased focus on the development of initiatives to promote FI in Asian countries, fostering it continues to present challenges in the region (Marcelin et al. 2022;Le et al. 2019). Policymakers have recently focused on attracting the “unbanked” populations into existing financial systems. A lack of access to finance has been found to have adverse effects on economic growth Economies 2022,10, 247. https://doi.org/10.3390/economies10100247 https://www.mdpi.com/journal/economies Economies 2022,10, 247 2 of 15 and the reduction of poverty. In low-development markets, populations face difficulties accumulating savings, investing in a project generating income, and building their assessment approach to minimize risk. Khatib et al. (2021b) highlighted several advantages of low financial constraints, as they encourage entrepreneurial individuals to positively contribute to growth by investing more and taking risks. Their study showed that entrepreneurship has a large influence on per capita GDP (Neaime and Gaysset 2018). In Middle East countries, including Palestine, there has not been success in decreasing the presence of financial exclusion and poverty despite the expansion of bank branches and the increasing presence of microfinance institutions. Limited accessibility to essential financial services remains a significant barrier for populations in these countries. Policymakers are increasingly recognizing the limitations that exist for banks to reach underprivileged populations despite significant growth in profitability and efficiency. Therefore, we examined the interrelationship between FI and bank performance in this study. Palestine is a developing nation with an emerging economy that exhibits certain characteristics that set it apart from other economies (Awad and Al Karaki 2019). Among these characteristics is a lack of local currency and a reliance on the three main currencies of the Israeli shekel, Jordanian dinar, and the US dollar. Additionally, no effective strategy has been developed by Palestinian officials to consistently redirect resources from unproductive to productive sectors, thus lowering unemployment and increasing aggregate demand and GDP growth in the long term. In Palestine, small and medium-sized enterprises (SMEs) are essential for enhancing the GDP, lowering unemployment, and encouraging domestic investment. These firms account for nearly 99% of Palestinian businesses and 82% of all jobs. According to the 2018 Establishment Census, there were 151,066 enterprises in Palestine, with 102,344 of them located in the West Bank and 48,722 located in the Gaza Strip (Ahmad and Ramadan 2018). Furthermore, the Palestinian economy is reliant on international assistance and is classified as a service economy. However, the banking sector appears to be relatively sound (Awwad and El Khoury 2021). Palestine’s financial system is still in its infancy, with 17 banks and 232 branches and offices; one is a foreign bank, nine are Arab banks, and seven are domestic banks (Abusharbeh 2020;Awwad and El Khoury 2021). The bank industry in Palestine dominates the financial system; the collective balance sheet of banks exceeds the stock capitalization by more than twofold. In the last five years, however, the performance of the Palestinian banks has experienced instability. It is therefore critical to evaluate factors that enhance the performance of this sector for better management and policy implementations that eventually promote the country’s market development. Empirically, the majority of research on FI has focused on the definition, measurements, and antecedents of financial inclusion (Allen et al. 2014;Patwardhan 2018;Jegede 2014; Kumar 2013). Bank-specific determinants include nonperforming loans, liquidity, bank capital, bank efficiency, cost management, and bank size. Nevertheless, FI’s influence on bank performance is currently unknown (Kumar et al. 2021;Le et al. 2019). There have been a few studies on it in some countries (Chikalipah 2017), especially Palestine. However, contemporary developments have the potential to nurture or, at the very least, change Palestine’s financial inclusion position, including the rise of mobile banking and increased economic growth. Additionally, there have been relatively few studies on the association between FI and the performance of banks (Shihadeh et al. 2018;Bhattacharyya et al. 2021; Kumar et al. 2021;Vo and Nguyen 2021). It has been reported that financial inclusion significantly influences the performance of banks. However, prior studies have applied few proxies of financial inclusion. For instance, Shihadeh (2021) only used two out of three dimensions of FI (utilization and access to financial service) with four FI indicators. Kumar et al. (2021) only utilized four proxies to measure this variable. Nevertheless, the Global Partnership for Financial Inclusion (GPFI) has developed and identified three significant dimensions—service delivery and quality of products, the usage of financial services, and access to financial services (GPFI 2012)—with several measurements. Economies 2022,10, 247 3 of 15 Unlike prior studies, we used all three attributes of financial inclusion—quality, access, and usage of financial services and delivery (Goel and Sharma 2017;Sarma and Pais 2008; Ajefu et al. 2020)—with six indicators for a sample of eleven banks during the years of 2012–2020 in the current research. The indicators are: the average cost of maintaining a primary bank current account, the number of point-of-sale (POS) terminals per 100,000 inhabitants, the number of automated teller machines (ATMs) per 100,000 adults, the number of branches per 100,000 adults, the percentage of SMEs with outstanding loans or lines of credit, the number of individuals with deposit accounts per 1000, and the number of adults with deposit accounts per 1000. This study contributes to the body of knowledge on the subject matter since there has been no previous examination of the impact of the variables employed here (the previously discussed financial inclusion indicators). Understanding the role of all indicators would encourage financial institutions to offer superior financial services at a reasonable price and contribute to the objective of FI and the country’s financial development. Our study adds to the continuing discussion in favor of FI as a viable tool for poverty reduction in developing and impoverished nations. Furthermore, in this study, we used a GMM dynamic panel technique model following the work of Kumar et al. (2021). Finally, Palestine, a developing country, is a suitable case to study because of the important role financial inclusion can play in addressing urgent problems and issues such as high levels of poverty, unemployment, low economic growth rates, and economic and social differences between individuals. In addition, the contribution of FI can enhance the levels of financial stability. 2. Literature Review and Hypotheses Development FI, defined as monetary services, significantly impacts economic development. Individuals not impacted by financial exclusion have the ability to create businesses and invest in education, which is attributed to reducing poverty and increasing economic growth. This occurs as a result of providing individuals with an opportunity to have a secure place to save, thereby encouraging financial stability resulting from high levels of bank deposits being utilized to contribute to a stable deposit base for banks (Fungáˇcová and Weill 2015). According to the financial intermediation theory, the financial service institutions offer is seen as a means to connect surplus spenders to deficit units within an economic space. As Diamond (1984) argued, financial mediators serve as designated agents of savers and can attain economies of scale. As a result, those who save entrust their funds to these intermediaries to be invested in whatever ventures they deem viable, such as digital credit, with investors possessing the ability to fund withdrawals at any time via predetermined circumstances. As mentioned earlier, few empirical studies have been devoted to the investigation of financial inclusion performance outcomes. Nevertheless, attention toward this topic has increased in recent years, with emerging literature focusing on understanding the impact of FI on financial stability (Ramzan et al. 2021;Ozili 2018), the determinants of FI ( Abel et al. 2018; Kumar 2013; Chikalipah 2017), and FI’s development (Arun and Kamath 2015) . It has been reported that the reduced access to banking services in underprivileged populations has been displayed in countries where the belief is that FI can only increase GDP by growing the income of the privileged and leaving the underprivileged behind. The authors of different studies have used different measures and indicators of financial inclusion. Most previous studies have applied few proxies of financial inclusion. To measure this variable, Kumar et al. (2021) only utilized four proxies. Nevertheless, the Global Partnership for Financial Inclusion (GPFI) designed and identified three significant attributes—quality, access, and usage of services (GPFI 2012)—with several measurements. Additionally, Shihadeh (2021) only used two out of three attributes of FI (utilization and access to funds services), with four financial inclusion indicators. Generally, financial inclusion can be measured by three attributes: access, usage, and quality of financial services and delivery (Goel and Sharma 2017;Sarma and Pais 2008;Ajefu et al. 2020), and it was considered here in terms of seven indicators for a panel of 11 banks from 2012 to 2020. Economies 2022,10, 247 4 of 15 The number of bank branches is an indicator of access to finance ( Kumar et al. 2021) . This variable is suggested to positively and significantly influence bank profitability ( Kumar et al. 2021) and CSR activities (Ramzan et al. 2021). Goel and Sharma (2017) argued that ignoring the absence of nearby branches or a dearth of items fit the weaker part’s requirements. Bank branches are critical for underserved segments of society, and eliminating branches limits the amount of credit available to small businesses. It should be noted that increased branch presence results in a rise in client base, boosting deposit and loan portfolios and diversifying risk. Regarding the number of ATMs, Ramzan et al. (2021) suggested that the more a bank invests in CSR activities, the greater its inclusion in the form of additional banks. Banks must create extensive branch and ATM networks in order to reach and service a greater number of consumers and to consequently increase their performance (Shihadeh 2021). Byukusenge (2021) argued that adding ATMs is a critical and very successful strategy since they enable clients to access their accounts to withdraw or deposit money, just as digital banking, debit cards, and smart cards do. However, the authors of some studies have reported that ATMs do not influence bank performance (Kumar et al. 2021), suggesting that transaction and direct expenses may be sufficient to counterbalance the increased revenue from extra loan accounts. Banks grow their customers via branch locations, ATMs, point-of-sale (POS) locations, and other electronic terminals (Shihadeh and Liu 2019). This expansion is expected to enhance the performance of banks as it leads to more customers, although point-of-sale terminals might increase the capital expenditure and profit might not be significantly high as a result of cost increment (Shihadeh et al. 2018), banks use branches, ATMs, and POS locations to increase customer reach, attract deposits, and offer services (Shihadeh et al. 2018). Almaleeh (2020) argued that bank goals might be accomplished by enabling SMEs’ access to funds resources. Despite the large percentage of SMEs in the Palestinian economy, comprising 97% of total businesses, they suffer from financing problems. According to the World Bank, the share of SMEs in Palestine’s total banking credit portfolio does not exceed 10% because banks are conservative in lending to them and consider them high-risk projects. Regarding the number of adult deposit accounts in Palestine, the percentage is still low, at no more than 24% of the adult population. Shihadeh et al. (2018) argued that Jordanian banks are uninterested in lending to small and medium-sized businesses. Deposit accounts are important sources of funds that financial institutions use for loans and generating profit. Ozili (2021) suggested that more financial sector concentration is connected with increased access to deposit accounts and loans, and nations with regulatory frameworks that let banks participate in a larger range of activities have a higher level of FI. In India, however, while deposit accounts have improved over time, their penetration has not kept pace with population increase over the research period (Kumar 2013). Although it is expected that the greater the number of deposit accounts, the greater the bank performance, some studies have failed to support this association, such as those by Akhisar et al. (2015) and Almaleeh (2020). Finally, the average cost of maintaining a basic current bank account significantly positively impacts profitability (Trujillo-Ponce 2013). Based on the assumption of financial intermediation theory, where financial intermediation is a process in which financial institutions collect deposits and provide loan information to support investment in the economy (Ratnawati 2020), as well as the abovementioned arguments, the following hypotheses were tested: H1. Banks’ performance is positively affected by the number of bank branches. H2. Banks’ performance is positively affected by the number of ATMs. H3. Banks’ performance is positively affected by the number of POS. H4. Banks’ performance is positively affected by credit for SMEs. H5. Banks’ performance is positively affected by the number of customer deposit accounts. Economies 2022,10, 247 5 of 15 H6. Banks’ performance is positively affected by the average cost of maintaining a basic current bank account. 3. Research Methodology 3.1. Sample Population In this study, 13 banks operating in the Palestinian market were initially included in the analysis: 10 conventional banks and three Islamic banks. The study sample comprised the entire Palestinian banking sector. Two conventional small banks were excluded due to their newness and insufficient data. Using the financial statements of banks, the reports of the Palestinian Monetary Authority, and the Association of banks in Palestine, the data were collected during 2012–2020 after Palestine joined the Alliance for Financial Inclusion (AFI) in 2010. All data sources used in the current study were considered reliable, and the time period of the research sample was selected due to data availability. 3.2. Variable Measurements 3.2.1. Dependent Variable In line with prior studies (Angori et al. 2019;Banda 2021;Khatib et al. 2022a,2022b; Gupta and Mahakud 2020;Mbekomize and Mapharing 2017), we used return on assets (ROA) and net interest margin (NIM) as measures of bank profitability. ROA is an indicator of a bank’s efficiency in utilizing assets and producing net income, while NIM is a measure of the net return on a bank’s earning assets. These measures effectively indicate a bank’s efficiency in using its total assets (Hay et al. 2018). Table 1provides a summary of the research variables. Table 1. Summary of the variables employed in the research. Variable Measurement Symbol Dimension Dependent variables Return on assets Net income/total assets ROA Net interest margin Net interest income/earning assets NIM Return on equity Net income/total equity ROE Independent variables Number of branches Number of branches per 100,000 adults FIN_BRANCH Access Number of ATMs Number of ATMs per 100,000 adults FIN_ATM Access Number of POS terminals Number of POS terminals per 100,000 inhabitants FIN_POS Access Credit for SMEs Credit for SMEs/total outstanding loans FIN_SME Usage Number of customer deposit accounts Number of customer accounts per 1000 adults FIN_DEPOSIT Usage Average cost of maintaining a basic current bank account (annual fees) Logarithm of the amount of annual fees FIN_COST Quality Control variables Nonperforming loan ratio Nonperforming loans/total loans NPLR Liquidity ratio Liquid assets/total assets LAT Cost efficiency ratio Cost-to-income ratio CIR Capital adequacy ratio Tier 1 capital and tier 2 capital/risk-weighted assets CAR Note: ROA, Branch, ATMs, POS, SMEs, CAR, Quality, and CAR were directly taken from the Bankscope database, while NIM, NPLR, LAT, and CIR were self-calculated with the help of data taken from Bankscope. 3.2.2. Independent Variables Following the Global Partnership for Financial Inclusion (GPFI) recommendations of the basic set of financial inclusion indicators, which represent the three central dimensions of financial inclusion (access, usage, and quality of financial services and delivery), we employed six independent variables that represent the financial inclusion dimensions in this study. The number of branches per 100,000 adults, the number of ATMs per 100,000 adults, and the number of POS terminals per 100,000 inhabitants represent the access to financial services dimension, while the number of customer deposit accounts per 1000 Economies 2022,10, 247 6 of 15 adults and credit for SMEs represent the usage of financial services dimension. Finally, the average cost of maintaining a primary bank current account (annual fees) represents the quality of products and service delivery dimension. It should be noted that the authors of some previous studies only used four indicators of financial inclusion (Kumar et al. 2021). 3.2.3. Control Variables We used a set of bank-specific variables that have robust effects on profitability, namely, nonperforming loan ratio (NPLR), liquidity ratio (LAT), cost-to-income ratio (CIR), and capital adequacy ratio (CAR). Based on the assumption that increased exposure of a bank to credit risk is associated with decreased profitability, CAR stands for capital adequacy ratio, which is defined as a proportion of tier 1 and tier 2 capital to risk-weighted assets ( Forcadell et al. 2020; Kumar et al. 2021). Though previous studies have indicated that CAR has an effect on bank profitability, the direction of this association is unknown ( Kumar et al. 2021). Researchers often use NPLR as a metric to evaluate credit risk management. Y.-K. Chen et al. (2018) and Tran and Nguyen (2020) found that NPLR reduced bank profitability. The liquid-assets-to-total-assets ratio (LAT) is commonly used as a liquidity indicator. There have been mixed findings regarding the impact of liquidity on bank profitability. Alshatti (2015) found a negative effect on profitability, while Islam and Nishiyama (2016) found a positive relationship between both factors. CIR is often used to measure cost efficiency. The authors of many studies have employed this ratio as a determinant of bank profitability (Neves et al. 2021;Ch 2017), and most suggest that it has a negative effect. Finally, CAR is often used as a credit risk management indicator. CAR is the ratio of a bank’s capital in relation to its risk-weighted assets and is determined by the country’s central bank. Capital adequacy is an indicator of a bank’s ability to absorb potential shocks and losses before becoming insolvent. Again, there is no consensus regarding the impact of CAR on bank profitability. Islam and Nishiyama (2016) found a positive impact on profitability measured with ROA, while Bitar et al. (2018) found a negative relationship. 3.3. Model Specification The regression models used in this study are specified below. The models test the association between FI and bank performance. ROAit =β0 + β1FIN_BRANCHit +β2FIN_ATMit +β3FIN_POSit + β4FIN_SMEit +β5FIN_DEPOSITit +β6FIN_COSTit +β7NPLRit + β8LATit + β9CIRit +β10CARit +µit (1) NIMit = β0 + β1FIN_BRANCHit +β2FIN_ATMit +β3FIN_POSit + β4FIN_SMEit +β5FIN_DEPOSITit +β6FIN_COSTit +β7NPLRit +β8LATit + β9CIRit +β10CARit +µit (2) where i indicates an individual bank; t refers to the time period (year); β 0 is the constant intercept; ROA and NIM are the profitability proxies of the bank; FIN_BRANCH, FIN_ATM, FIN_POS, FIN_SME, FIN_DEPOSIT, and FIN_COST measure the FI indicators; NPLR, LAT, CIR, and CAR are the bank-specific control variables; β 1– β 10 are the coefficients of the function; and µit is the error term. The research models were developed based on relevant prior studies (Chauvet and Jacolin 2017;Shihadeh 2020;Al-Eitan et al. 2022;Almaleeh 2020; Kumar et al. 2021). When the sample size is small and data consist of a mixture of time series, pooled panel data analysis is the most appropriate tool to utilize. In this study, we tested three models to choose that which fit the analysis: the fixed-effect model, the random effect model, and pooled ordinary least square estimation. Hausman and Breusch–Pagan tests were run to validate the model specification. The results of the Hausman test indicated a significant p-value of 0.008, which led to the conclusion that the fixed-effect model would be more appropriate. The results showed a Economies 2022,10, 247 7 of 15 p-value of 0.014, which was less than the significance level of 5%. Thus, the null hypotheses were rejected, which meant that the fixed model best fit the sample data. Furthermore, endogeneity issues, including dynamic endogeneity, simultaneity, and time-invariant unobserved heterogeneity among banks, plague panel regression studies. The two-step system GMM estimator is commonly utilized to mitigate the endogeneity problem in banking datasets (Kabara et al. 2022). Several researchers have suggested that GMM is a good analytical tool to overcome the endogeneity problem ( Zamil et al. 2021 ; Hazaea et al. 2022;Khatib et al. 2021a,2022a). The idea is to find an instrument that is simultaneously correlated with its corresponding endogenous variables (financial inclusion) and uncorrelated with bank performance. Here, we used the strategy provided by Schultz et al. (2010) , in which chosen lags are associated with the regressors but uncorrelated with contemporaneous error. As a result, we used the instrumental variable estimate approach to circumvent the endogeneity issue. More precisely, we utilized the age of the bank as an instrumental variable and the first lag of the independent variable to control for potential endogeneity issues. The age of organization has been commonly utilized by prior studies to control for this problem (Zhang et al. 2020;Alsaifi et al. 2020; Bhattacharyya et al. 2021). The Sargan test, which evaluates instruments’ overall validity by assessing the sample analogue of the moment circumstances employed in the estimate procedure, was used in this study to test for over-identification issues ( Lee and Chang 2009) . Both the Sargan test and the Hansen test of over-identification of restrictions were not found to be statistically significant (p-value > 0.10), implying the chosen instruments’ validity. Additionally, in all models, the coefficient of delayed bank performance was substantial, confirming the study’s use of a dynamic model. The AR (2) had p-values less than 10%, suggesting that the instruments were valid and the findings were dependable. The regression results showed that the null hypothesis for AR (1) was rejected because of the presence of first-order autocorrelation, and the null hypothesis for AR (2) was not rejected because of the absence of second-order correlation. 4. Empirical Results and Discussion 4.1. Descriptive Statistics Table 2summarizes the descriptive statistics for the variables used in the research; it contains precise information about the independent and dependent variables in the form of standard deviation, mean, minimum, and maximum. The findings suggested that there were differences in the mean and standard deviation of ROA and NIM. The results indicated a variation between the mean values and standard deviation of ROA and NIM as profitability measures from 2012 to 2020 among Palestinian banks. It was revealed that the average loan to SMEs was 11%, which was extremely low compared with the total credit. This implied that Palestinian banks were conservative in extending credit to SMEs, which negatively affected the economy. Palestinian banks had a varied number of ATMs, ranging between 0.38 and 5.90. The ATM average was 2.1 for every 100,000 adults. Similarly, there was one bank branch for every 100,000 adults, with a standard deviation of only 0.635. Regarding the average cost of maintaining a basic current bank account (annual fees), the mean was 13.9, which was relatively high compared with the total revenue. Table 2. Descriptive statistics. Variable Obs. Min Max Mean St. Dev. Profitability measurements (dependent variables) ROA 99 –0.01 0.02 0.0089 0.00494 NIM 99 0.03 0.09 0.0506 0.01577 Independent variables FIN_BRANCH 99 0.23 3.21 1.0245 0.63561 FIN_ATM 99 0.38 5.90 2.1138 1.39870 Economies 2022,10, 247 8 of 15 Table 2. Cont. Variable Obs. Min Max Mean St. Dev. FIN_POS 99 199.00 232.00 219.6667 14.63330 FIN_SME 99 0.00 0.25 0.1176 0.04858 FIN_DEPOSIT 99 1104.00 1234.00 1177.5000 49.24765 FIN_COST 99 11.51 15.73 13.9068 1.14972 Control variables NPLR 99 0.01 0.08 0.0365 0.01846 LAT 99 0.21 0.57 0.3781 0.06809 CIR 99 0.49 0.99 0.7238 0.10526 CAR 99 0.11 0.34 0.1829 0.06580 Valid N (listwise) 99 4.2. Correlation Matrix Multicollinearity diagnostics were conducted with both VIF and tolerance tests to provide a more reliable and accurate study. The correlation between the variables was quite good in the absence of a high correlation between the variables and, hence, the lack of a multicollinearity issue, as shown in Table 3. Additionally, the Durbin–Watson test was conducted to test for autocorrelation. As seen in Table 4, no autocorrelation was detected. Table 3. Correlation matrix and multicollinearity diagnostics. Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) ROA (1) 1 ROE (2) 0.523 1 FIN_BRANCH (3) 0.489 0.398 1 FIN_ATM (4) −0.331 −0.423 −0.416 1 FIN_POS (5) 0.065 −0.142 0.089 0.160 1 FIN_SME (6) −0.417 −0.579 −0.375 0.244 0.216 1 FIN_DEPOSIT (7) 0.021 −0.058 −0.041 0.135 0.014 −0.026 1 FIN_COST (8) 0.473 0.464 0.394 −0.298 0.138 −0.407 −0.061 1 NPLR (9) −0.309 −0.507 −0.411 0.392 0.146 0.513 0.065 −0.302 1 LAT (10) 0.063 0.044 0.066 −0.114 0.039 −0.116 −0.033 0.158 −0.013 1 CIR (11) −0.216 0.096 −0.083 0.147 −0.126 0.217 −0.096 −0.088 −0.066 −0.201 1 CAR (12) −0.381 0.258 0.094 *** −0.122 ** 0.213 * −0.191 ** −0.313 ** −0.057 ** 0.196 ** 0.065 −0.126 1 Diagnostics of multicollinearity VIF - - 1.26 1.29 1.16 1.62 1.02 1.39 1.53 1.04 1.87 1.63 Tolerance - - 1.51 0.77 0.86 0.61 0.97 0.71 0.65 0.95 0.67 0.66 Note: *, **, and *** indicate statistical significance at 10%, 5%, and 1%, respectively. Table 4. Full sample regression results. Variables Panel A Panel B ROA NIM (1) (2) (1) (2) Fixed Effect GMM Fixed Effect GMM Constant 0.000 *** 0.021 ** 0.009 *** 0.083 * (2.205) (3.244) (1.598) (4.347) L_ROA 0.002 *** 0.089 * (1.067) (2.087) FIN_BRANCH 0.003 *** 0.049 ** 0.024 ** 0.081 * (0.1429) (0.1273) (0.1921) (0.7691) FIN_ATM 0.002 *** 0.001 *** 0.081 * 0.1563 (0.1573) (0.1231) (0.1343) (0.2823) FIN_POS 0.140 0.132 0.4398 0.2367 (0.07314) (0.0482) (0.1096) (0.4398) FIN_SME 0.341 0.154 0.3671 0.9521 (0.1031) (0.1598) (0.0156) (0.2671) FIN_DEPOSIT 0.642 0.3273 0.3761 0.6341 (0.1173) (0.2854) (0.1041) (0.4129) Economies 2022,10, 247 15 of 15 Patwardhan, Anju. 2018. Financial Inclusion in the Digital Age. In Handbook of Blockchain, Digital Finance, and Inclusion. Amsterdam: Elsevier, vol. 1, pp. 57–89. [CrossRef] PMA. 2020. Palestine Monetary Authority, Annual Report 2020. Ramallah—Palestine. Available online: https://www.pma.ps/en/ Publications//AnnualReports (accessed on 20 May 2022). Ramzan, Muhammad, Muhammad Amin, and Muhammad Abbas. 2021. How Does Corporate Social Responsibility Affect Financial Performance, Financial Stability, and Financial Inclusion in the Banking Sector? Evidence from Pakistan. Research in International Business and Finance 55: 101314. [CrossRef] Ratnawati, Kusuma. 2020. The Influence of Financial Inclusion on MSMEs’ Performance Through Financial Intermediation and Access to Capital. Journal of Asian Finance, Economics and Business 7: 205–18. [CrossRef] Sarma, Mandira, and Jesim Pais. 2008. Financial Inclusion and Development: A Cross Country Analysis. In Annual Conference of the Human Development and Capability Association, New Delhi 168: 1–30. [CrossRef] Schultz, Emma L., David T. Tan, and Kathleen D. Walsh. 2010. Endogeneity and the Corporate Governance-Performance Relation. Australian Journal of Management 35: 145–63. [CrossRef] Shihadeh, Fadi. 2020. The Influence of Financial Inclusion on Banks’ Performance and Risk: New Evidence from MENAP. Banks and Bank Systems 15: 59–71. [CrossRef] Shihadeh, Fadi. 2021. Financial Inclusion and Banks’ Performance: Evidence from Palestine. Investment Management and Financial Innovations 18: 126–38. [CrossRef] Shihadeh, Fadi, and Bo Liu. 2019. Does Financial Inclusion Influence the Banks Risk and Performance? Evidence from Global Prospects. Academy of Accounting and Financial Studies Journal 23: 1–12. Shihadeh, Fadi Hassan, Azzam Mohammed Tayseer Hannon, Jian Guan, Ihtisham Ul Haq, and Xiuhua Wang. 2018. Does Financial Inclusion Improve the Banks’ Performance? Evidence from Jordan. Research in Finance 34: 117–38. [CrossRef] Tran, Son Hung, and Liem Thanh Nguyen. 2020. Financial Development, Business Cycle and Bank Risk in Southeast Asian Countries. The Journal of Asian Finance, Economics and Business 7: 127–35. [CrossRef] Tran, Vuong Thao, Chien-Ting Lin, and Hoa Nguyen. 2016. Liquidity Creation, Regulatory Capital, and Bank Profitability. International Review of Financial Analysis 48: 98–109. [CrossRef] Trujillo-Ponce, Antonio. 2013. What Determines the Profitability of Banks? Evidence from Spain. Accounting & Finance 53: 561–86. [CrossRef] Vo, Duc H., and Nhan T. Nguyen. 2021. Does Financial Inclusion Improve Bank Performance in the Asian Region? Asian-Pacific Economic Literature 35: 123–35. [CrossRef] Zamil, Izdihar Abdullah, Suresh Ramakrishnan, Noriza Mohd Jamal, Majeed Abdulhussein Hatif, and Saleh F.A. Khatib. 2021. Drivers of Corporate Voluntary Disclosure: A Systematic Review. Journal of Financial Reporting and Accounting,ahead of print. [CrossRef] Zhang, Lu, Yuan George Shan, and Millicent Chang. 2020. Can CSR Disclosure Protect Firm Reputation During Financial Restatements? Journal of Business Ethics 173: 157–84. [CrossRef]