Do profitable banks make a positive contribution to the economy?

Kumar, Vijay; Bird, Ron Article Do profitable banks make a positive contribution to the economy? Journal of Risk and Financial Management Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Kumar, Vijay; Bird, Ron (2020) : Do profitable banks make a positive contribution to the economy?, Journal of Risk and Financial Management, ISSN 1911-8074, MDPI, Basel, Vol. 13, Iss. 8, pp. 1-18, https://doi.org/10.3390/jrfm13080159 This Version is available at: https://hdl.handle.net/10419/239272 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. 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Vijay Kumar 1,* and Ron Bird 2 1New Zealand Institute for Business Research, The University of Waikato, Hamilton 3240, New Zealand 2School of Accounting, Finance and Economics, The University of Waikato, Hamilton 3240, New Zealand; ron.bir[email protected] *Correspondence: vijay[email protected] Received: 30 May 2020; Accepted: 22 July 2020; Published: 24 July 2020   Abstract: Anumberofstudieshaveinvestigatedtherelationship between financial sector development and economic growth; however, the impact of bank profitability on economic growth is still unclear. We investigate the link between bank profitability and economic growth in the Asia-Pacific region over the period 2004–2014. Using the system GMM estimator, our findings suggest that a profitable banking sector is a prerequisite for economic growth in the Asia-Pacific region and that the impact of bank profitability on economic growth is more prominent in small banking sectors. Perhaps surprisingly, we found that the bank size has a negative impact on GDP growth, with the influence of bank profitability on economic growth reducing as the size of the banking sector increases. Our results also show that the impact of profitability on economic growth is much larger in developed economies compared to small emerging and large emerging economies. Keywords: economic growth; financial sector development; global financial crisis; bank profitability 1. Introduction The banking sector is an important component of the financial system (Dia et al. 2020). Banks create additional wealth in the economy by attracting funds from depositors and channel these funds to investors. Dietrich and Wanzenried (2011) argued that the smooth running of a country’s economic activities depends on the efficient banking system. Levine and Zervos (1998) suggested that banks foster economic growth by funding productive projects and are a prerequisite for economic growth (Levine et al. 2000). Given the importance of the banking sector in economies, it is not surprising that it has been the subject of much academic interest, or that there is still much disagreement as to the extent of the contribution that it makes. Most of the previous studies have focused on different measures of bank size in order to explain the contributions of the banking system to the economic development. Only a few studies have investigated the impacts of bank profitability on economic growth. A profitable banking sector plays an important role in overcoming the economic shocks (Athanasoglou et al. 2008). European Central Bank (2016) suggested that profitable banks are able to attract capital from investors and are also likely to generate capital through their retained earnings. Trujillo-Ponce (2013) argued that the profitability of banks is also essential for the sustainability of the banking system and that profitable banks are able to inject funds into the economy by providing loans. There is also empirical evidence suggesting that profitable banks are less likely to fail (Claeys and Schoors 2007). Hence, bank profitability is considered one of the key measures for predicting bank failures, using measures such as the Z-Score and the CAMELS rating system. A number of studies have indicated a direct link between financial stability and economic growth. Creel et al. (2015) found that financial instability resulted in negative economic growth in the EU. There are also other studies that support this notion. For example, studies by Levine (1997) and Wachtel (2001) indicated that the J. Risk Financial Manag. 2020,13, 159; doi:10.3390/jrfm13080159 www.mdpi.com/journal/jrfm J. Risk Financial Manag. 2020,13, 159 2 of 18 financial sector development promotes economic growth. Similarly, a number of studies have indicated that bank failures reduce economic growth (see Bernanke 1983;Calomiris and Mason 2003;Anari et al. 2005). Since existing research shows that bank profitability leads to financial stability and reduces bank failures, and there is evidence that financial stability and reduced bank failures have a positive impact on economic growth 1 , our study aims to investigate the dynamic relationship between profitable banks and economic growth across ten economies in the Asia-Pacific region2over the period 2004–2014. A number of studies have investigated determinants of the profitability of banks ( Athanasoglou et al. 2008 ;Dietrich and Wanzenried 2011;Kumar et al. 2020); however, there is limited research on the consequences of bank profitability. The literature on the determinants of the profitability of banks suggests that bank size, credit risk management, bank liquidity, and cost management are key drivers of bank profitability. In addition, a number of studies have empirically investigated the relationship between financial sector development and economic growth. Most of these studies have used the bank size to measure financial sector development. Therefore, the impact of bank profitability on economic growth is still unclear. To our knowledge, only a few studies have explored this relationship. Using data from 133 countries, Klein and Weill (2017) suggested that profitable banks make a significant contribution to economic growth, and Cole et al. (2008) found a positive relationship between the stock returns of banks and economic growth. Our study differs from these previous investigations. Cole et al. (2008) focused on the link between bank stock returns and economic growth while we use return on bank assets (ROA) as an independent variable to investigate the impacts of bank profitability on economic growth. Klein and Weill (2017) used global data to investigate the impact of bank profitability on economic growth. Given that banks around the world operate under different policies and regulations, the findings of their study cannot be generalized to the Asia-Pacific region. We also investigate the causal relationship between bank profitability and economic growth and identify how the impact of bank profitability varies across different economies. Our study makes significant contributions by investigating the impacts of bank profitability on economic growth and adds a new strand to the literature on the relationship between financial sector development and economic growth. This research makes three important contributions to the existing literature. First, this is the first study that investigates the impact of bank profitability on economic growth across a range of countries in the Asia-Pacific region that are at different stages of economic development but are operating within a similar regulatory setting. 3 Second, this study identifies how the impact of bank profitability varies across economies in our sample: Small emerging, large emerging, and developed economies. Third, this study investigates the causal relationship between bank profitability and economic growth. One of the major objectives of policy makers is to achieve targeted economic growth. Knowledge of whether or not bank profitability promotes economic growth would help policy makers make important decisions related to the structure of the banking sector. We found that the profitable banks are the key drivers of economic growth. Our results suggest a positive relationship between bank profitability in period (t–1) and economic growth in period (t). Our findings suggest that an increase in bank profitability increases economic growth, while an increase in the banking sector size decreases economic growth, indicating that bank profitability is more important than the banking sector size in order to drive economic growth. We also found that the impact of bank profitability on economic growth reduces with the increase in banking sector size. In terms of macroeconomic variables, our findings confirm a negative relationship between inflation and economic growth, and a positive relationship between government expenditure and economic growth. The remainder of this paper is structured in the following manner: In Section 2, we provide a brief overview of the banking sectors of the ten countries in our study. Section 3discusses the existing 1Refer to the conceptual nexus between bank profitability and economic growth in Figure A1 (Appendix A). 2The countries are Australia, Bangladesh, China, Hong Kong, India, Indonesia, Japan, Malaysia, Pakistan, and Singapore. 3 For example, most of the central banks in these countries require banks to maintain capital adequacy ratios and a certain percentage of deposits as cash reserves. J. Risk Financial Manag. 2020,13, 159 3 of 18 literature. Section 4discusses dependent and independent variables. Section 5highlights data sources and methods. In Section 6, we present and discuss our empirical results. Section 7presents a summary of findings. 2. Overview of the Banking Sectors This study focuses on commercial banks, which are the most important part of any financial system, being the key suppliers of credit in the economy (World Bank 2005). The exchange of domestic and international payments between different parties are done through banking channel; therefore, an efficient banking system is necessary for smooth running of economic activities. This study focuses on ten countries in the Asia-Pacific region, which are at different stages of economicdevelopment. However, the bankingregulations are similaracross thecountries. We classified these countries in three categories based on the state of their economy; i.e., small emerging economies, large emerging economies, and developed economies. In this section, we briefly discuss some institutional and regulatory characteristics of the banking sectors of the nations in our study. 2.1. Small Emerging Economies Bangladesh, Indonesia, Malaysia, and Pakistan are small emerging economies in our study. All these countries have also Islamic banks which operate in parallel with the conventional banks. Table 1highlights the regulatory and institutional characteristics of the banking system in these four countries. The table shows that Malaysia has the largest banking sector in terms of total assets, while there are more banks in Indonesia compared to other countries. The table demonstrates that financial inclusion is very low in these countries, ranging from eight branches per 100,000 adults in Bangladesh to eleven branches per 100,000 adults in Malaysia. The bank assets to GDP ratio is highest (193%) in Malaysia and lowest in Indonesia (42%). Table 1. Regulatory and institutional characteristics of banking systems in small emerging economies. Country Name Bangladesh Indonesia Malaysia Pakistan Total assets (USD) 107 billion 440 billion 602 billion 100 billion Number of conventional banks 48 109 37 28 * Number of Islamic banks 25 34 16 20 Minimum capital adequacy ratio requirement410% 8% 8% 10% Cash reserve requirement 5% 6% 4% 5% Non-performing loan (NPL) criteria +90 days +365 days +90 days +90 days Financial inclusion (branches/100,000 adults) 8 9.6 11 9 Bank assets to GDP ratio 80% 42% 193% 43% * Includes full-fledged Islamic banks and Islamic windows of conventional banks. Source: Data related to total assets and number of banks, capital adequacy ratio requirement, cash reserve requirement, and non-performing loan criteria were collected from the websites of central banks. Data related to financial inclusion and bank assets to GDP ratio were collected from the World Bank database. 2.2. Large Emerging Economies China and India are the large emerging economies in our sample. The banking sectors in both countries have experienced a number of reforms. The purpose of these reforms was to improve the performance of banks and to bring their operations more into line with international standards. The regulatory and institutional characteristics of Chinese and Indian banks are reported in Table 2. The Chinese banking sector is larger than India’s, with total assets of US 24.3 trillion compared to US 1.8 trillion for the Indian banking sector. Financial inclusion in both countries is low, with eight branches per 100,000 adults in China and twelve branches per 100,000 adults in India. The bank assets to GDP ratio is higher in China (292%), compared to 95% in India. 4Capital adequacy ratio is the amount of capital maintained by banks to cover unexpected losses (Anandarajan et al. 2007). J. Risk Financial Manag. 2020,13, 159 4 of 18 Table 2. Regulatory and institutional characteristics of banking systems in large emerging economies. Country Name China India Total assets (USD) 24.5 trillion 1.8 trillion Number of banks 672 89 Minimum capital adequacy ratio requirement 8.50% 9.00% Cash reserve requirement 19% 4% Non-performing loan (NPL) criteria +90 days +90 days Financial inclusion (branches/100,000 adults) 8 12 Bank assets to GDP ratio 292% 95% Source: Data related to total assets and number of banks in India were obtained from the Reserve Bank of India. Data related to total assets and number of banks in China were obtained from the annual reports of the Chinese Banking Regulation Commission. Information about capital adequacy ratio requirements and cash reserve requirements was collected from the websites of central banks of India and China. Data related to financial inclusion and bank assets to GDP ratio were collected from the World Bank database. 2.3. Developed Economies The developed economies in our study include Australia, Hong Kong, Japan, and Singapore. Table 3highlights the regulatory and institutional characteristics of the banking system in these four countries. The total assets of Japanese banking sector are US 8 trillion which makes it the largest banking sector among developed economies in the sample. Financial inclusion is higher in Japan and Australia compared to Hong Kong and Singapore. Japan has 34 branches per 100,000 adults and Australia has 30 branches per 100,000 adults. On the other hand, Hong Kong has 23 branches per 100,000 adults and Singapore has 9.5 branches per 100,000 adults. The bank assets to GDP ratio is highest (700%) in Hong Kong and lowest in Japan (163%). Table 3. Regulatory and institutional characteristics of banking systems in developed economies. Country Name Australia Hong Kong Japan Singapore Size (USD) 2.8 trillion 2.1 trillion 8 trillion 779 billion Number of banks 70 56 198 124 Minimum capital adequacy ratio requirement 8% 8% 8% 10% Cash reserve requirement 0% 0% 0.1–1.3% * 3% Non-performing loan (NPL) criteria +90 days +90 days +90 days +90 days Financial inclusion (branches/100,000 adults) 30 23 34 9.5 Bank assets to GDP ratio 179% 700% 163% 261% * Reserve requirements vary by type of financial institution and by size of deposits. Sources: Data related to total assets and number of banks, capital adequacy ratio requirement, cash reserve requirement, and non-performing loan criteria were collected from the websites of central banks. Data related to financial inclusion and bank assets to GDP ratio were collected from the World Bank database. 3. Literature Review There is extensive empirical literature on the relationship between financial sector development and economic growth. Different researchers have used different proxies to measure financial sector development. Bank credit to the private sector, loans, total assets, money supply, deposits, and bank claims are some of the most common proxies used in the literature. A large number of studies have suggested that financial sector development promotes economic growth (Levine 1997) while some studies have also found a negative impact of financial sector development on economic growth (Buffie 1984;Van Wijnbergen 1983). The study by Goldsmith (1969) is one of the earliest studies that investigated the relationship between financial sector development and economic growth. The study used financial institution assets to GDP ratio to measure financial sector development and found that financial sector development promotes economic growth. After Goldsmith, extensive work in this area occurred in the 1990s. Studies by King and Levine (1993a,1993b) are considered to be benchmark studies. They used various proxies to measure financial inclusion, including current liabilities of the financial sector to GDP ratio, and non-financial private sector liabilities to GDP ratio and non-financial J. Risk Financial Manag. 2020,13, 159 5 of 18 private sector liabilities to total credit ratio. They reported that the financial sector promotes economic growth largely as a result of the role played by financial institutions in evaluating promising projects and financing those that are productive and innovative. Levine and Zervos (1998) used the ratio of credit to private sector to GDP as a measure of bank development and found a positive relationship between bank development and long-term economic growth. Levine et al. (2000) used liquid liabilities to GDP ratio, central bank ratio, credit to private sector to GDP ratio, and bank assets to total assets of banking industry ratio to measure financial sector development. They also found a positive relationship between financial sector development on economic growth. Using credit to GDP ratio, Botev et al. (2019) also found a positive relationship between financial sector development and economic growth in developing, emerging, and advanced economies. Studies that suggest a negative impact of financial sector development on economic growth include those by De Gregorio and Guidotti (1995), La Porta et al. (2002), and Prochniak and Wasiak (2017). Using the ratio of domestic credit to the private sector to GDP as a proxy for financial sector development, De Gregorio and Guidotti (1995) found a negative relationship between financial sector development and economic growth. Similarly, La Porta et al. (2002) also used the ratio of private credit to GDP to measure financial development, and found a negative relationship between financial sector development and economic growth. A study by Prochniak and Wasiak (2017) also found a negative impact of financial sector development (domestic credit as a percentage of GDP) on economic growth. A number of studies investigated a causal relationship between financial sector development and economic growth. A large number of studies have confirmed that the causal relationship exits but there is still ambiguity on the direction of the causality. Four hypotheses related to the causal relationship between financial sector development and economic growth are supply-leading causality, demand-following causality, bidirectional causality, and no causality. Supply-leading hypothesis indicates that the increase in financial sector development leads to an increase in economic growth (Ahmed and Ansari 1998). According to the demand-following hypothesis, increase in economic growth leads to an increase in financial sector development (Robinson 1952). Bi-directional causality hypothesis suggests that financial sector development promotes economic growth and economic growth promotes financial sector development (Harrison et al. 1999;Patrick 1966). According to the no causality hypothesis, as the name suggests, no relationship exists between financial sector development and economic growth (Lucas 1988). Pradhan et al. (2014) concluded that causality ran from banking sector development to economic growth in most of the countries in the ASEAN region. Jun (2012) investigated a causal relationship between financial sector development and economic growth in 27 Asian countries using different measures of financial sector development such as liquid liabilities to GDP ratio and domestic credit to GDP ratio. They reported that there is a two-way causal relationship between financial sector development and economic growth. Using different measures of financial sector development, Kar et al. (2011) explored the causality between financial sector development and economic growth. They found evidence for both supply-leading and demand-following hypotheses in Middle East and North African (MENA) countries. The literature provides evidence of the impact of financial sector development and economic growth and casual relationship between financial sector development and economic growth. Most of these studies have used bank size to measure financial sector development. Therefore, it is unclear whether or not bank profitability promotes economic growth. This study fills the gap by investigating both the impact of bank profitability on economic growth and the direction of the relationship and adds a new strand to the literature on the relationship between financial sector development and economic growth. This study identifies how the impact of bank profitability varies across economies in our sample: Small emerging, large emerging, and developed economies. J. Risk Financial Manag. 2020,13, 159 6 of 18 4. Dependent and Independent Variables 4.1. Dependent Variables In order to determine the relationship between profitable banks and economic growth, we used yearly GDP growth (%) as a measure of economic growth. GDP is one of the most widely used indicators of economic growth in previous studies. King and Levine (1993a), Demetriades and Hussein (1996) and Levine et al. (2000) have used GDP growth to establish a link between financial sector development and economic growth. 4.2. Independent Variables We classified explanatory variables into two categories: Key independent variables and control variables. Key independent variables include the lagged value of GDP growth, profitability, and size of the banking sector while control variables include macroeconomic variables and one variable related to the stock market. The variables were selected from a wider number of variables available in the literature. The following section provides the reasons for using these variables and the rationale behind their expected effect. 4.2.1. Key Independent Variables Lagged Gross Domestic Product Growth (Lag GDP) (+): We used lagged GDP growth as a potential determinant of economic growth. Lagged GDP growth has been used in several research studies. Lucas (1988) suggested that GDP growth in period (t–1) had a positive and significant effect on GDP growth in period (t) in developed and emerging markets. On the other hand, Van Wijnbergen (1983) showed that GDP growth in period (t–1) had a negative and significant effect on GDP growth in period (t) in Turkey. Given that our study focuses on developed and developing countries, in line with the findings of Cole et al. (2008), we hypothesized that lagged GDP growth will have a positive impact on economic growth. Return on Assets (ROA) (+): We used Return on Assets (ROA) in period (t) and ROA in period (t–1) as measures of profitability. Lagged ROA was used because the profitability of banks may not immediately translate into better economic growth. For standardization purposes, we transformed ROA and lagged ROA into (1 +ROA) and lagged (1 +ROA). Supporting the view of Athanasoglou et al. (2008) that a profitable banking sector is necessary to drive economic growth, we hypothesized that profitability indicators will have a positive impact on economic growth. Banking Sector size (SIZE) (+): The most common measure of banking sector size used in previous studies is credit to the private sector; however, some researchers have also used bank loans and deposits as a measure of size. Önder and Özyıldırım (2013) used bank credit as a measure of bank size and found a positive effect of bank credit on economic growth. Shaw (1973) used bank loans and bank deposits as measures of size to investigate their impact on economic growth in China. In both cases, there was a positive impact of size on economic growth in high-income provinces and a negative impact on economic growth in low-income provinces. Taking a more novel approach, Stern (1989) used an interaction variable (R&D intensity and bank assets) as a proxy for size and concluded that higher growth in the financial sector had a negative impact on productivity growth. We investigated different measures of size but found that the impact of bank assets on economic growth was more significant than the other measures of size. By weight of numbers, previous research has found that bank size has a positive effect on economic growth; therefore, we also hypothesized a positive relationship between bank size and economic growth. 4.2.2. Control Variables Inflation (INF) (–): We measured inflation as the yearly percentage increase in the consumer price index. Most previous studies have found that inflation has a negative impact on economic growth. For example, studies by Koivu (2002), Ndlovu (2013), and Buffie(1984) showed a negative J. Risk Financial Manag. 2020,13, 159 7 of 18 and significant impact of inflation on economic growth. Based on these findings, we also hypothesized that inflation will have a negative effect on economic growth. Government Expenditure (EXP) (+/ − ): Government expenditure is also referred to as public expenditure. We used annual percentage change in government expenditure as a potential determinant of economic growth. Van Wijnbergen (1983) found that an increase in public expenditure led to an increase in economic growth in Turkey. However, Buffie(1984) suggested that government expenditure had a negative impact on economic growth in 87 developed and developing countries. Taking account of the existing literature, we were unable to predict the sign of the relationship between EXP and economic growth. Openness of Economy (TRADE) (+): This is measured as the sum of exports and imports of goods and services (Andersen and Babula 2009). A high degree of regulation imposed by a country restricts the degree of openness (Rodriguez and Rodrik 2001). We used annual percentage change in the sum of exports and imports as a potential determinant of economic growth. Based on the study by Buffie(1984), which suggests a positive relationship between trade and economic growth, we also hypothesized that TRADE will have a positive impact on bank profitability. Stock Market Capitalization (MKTCAP) (+): We used annual percentage change in market capitalization as a potential determinant of economic growth. MKTCAP has been used in a number of studies as a control variable. For example, Ndlovu (2013) used stock market capitalization to determine the causal relationship between the financial sector and economic growth and concluded that MKTCAP does not drive economic growth. In contrast, Goldsmith (1969) suggested that stock market capitalization had a positive and significant impact on economic growth in Taiwan and Korea. Asteriou and Spanos (2019) also found a positive relationship between stock market capitalization and economic growth in EU. Based on the findings of Goldsmith (1969) and Asteriou and Spanos (2019), we hypothesized that MKTCAP will have a positive effect on economic growth. 5. Data and Methods 5.1. Description and Sources of Data This study used annual data from ten countries in the Asia-Pacific region, covering the period 2004–2014. The countries were divided into three groups. The first group consisted of small emerging economies: Bangladesh, Indonesia, Malaysia, and Pakistan. The second group comprised large emerging economies: China and India. The third group consisted of developed economies: Australia, Hong Kong, Japan, and Singapore. In this study, we used the lagged value of GDP growth, profitability, and bank size as key independent variables. We also used three macroeconomic variables: Inflation, government consumption and openness to the economy (trade), and one variable related to the stock market. Data were collected from two sources: The Bureau van Dijk’s Bankscope 5 database and the World Bank database. We collected data for return on assets and bank size from the Bankscope database while World Bank database was used to gather data for other variables such as GDP growth, inflation, government consumption, trade, and market capitalization. Our dataset consisted of all active commercial banks in the ten previously described countries in the Asia-Pacific region. In some cases, there was duplicate information on a bank where both consolidated and unconsolidated statements were maintained in the database. In these cases, we included only the consolidated statements to avoid duplication. There were some instances where we found statements covering only part of a year (three months or six months), all those observations were excluded. 5 It is a comprehensive database with over 12,000 banks around the world and covers around 90% of the banks in every country. J. Risk Financial Manag. 2020,13, 159 8 of 18 5.2. Methods In most of the existing literature, Ordinary Least Squares (OLS) regression was applied to fixed-effects or random-effects models to deal with simultaneous causality and unobserved heterogeneity. The fixed-effects model estimates parameters for each unit, which not only reduces the power of the model but also results in an increase in the standard errors of the coefficient estimates. It creates more problems when the sample size is small because variation in the dependent variable may be caused by these unit effects (Patrick 1966). On the other hand, a random-effects model lowers the variability within the sample by partially pooling the data. However, fixed-effects and random-effects models do not resolve issues related to endogeneity; therefore, we used the System Generalized Method of Moments (GMM) estimator suggested by Arellano and Bond (1991) to address the endogeneity concerns, including dynamic endogeneity, simultaneity, and time-invariant unobserved heterogeneity across banks. For robustness purposes, we also ran regressions using a pooled OLS estimator. The results were largely consistent with the GMM estimator.6 The regression equation that we used is: GDPit =α+β1GDPi(t−1)+β2(1+ROA)it +β3(1+ROA)i(t−1)+β4SIZEit +β5(1+ROA)i(t−1)∗SIZEit +β6INFit +β7MKTCAPit +β8EXPit +β9TRADEit +GFCDummy +EconomyDummies (1) where subscript i refers to the country and t refers to the time period. GDP is the GDP growth for a country i , (1 +ROA) is the measure of profitability of the banks in country i , SIZE refers to the percentage change in the size of the banking sector in country i , INF refers to inflation in country i , MKTCAP refers to the percentage change in stock market capitalization of country i , EXP refers to the percentage change in government expenditure of country i , TRADE refers to the percentage change in the sum of exports and imports of country i , and GFCdummy is a dummy variable for Global Financial Crisis (GFC). We ran the regression on the combined countries using a dummy variable for GFC. The dummy variable took a value of 1 for the years 2008 and 2009 and 0 otherwise. We selected years 2008 and 2009 as the GFC period because these were the years when the GFC had a negative impact on the economic growth of the ten countries. In order to investigate whether the impact of profitability of banks is conditional on the size of the banking sector, we divided banking sectors into large and small, based on the 11-year median result (2004–2014) of the total assets to population ratio for every country. Based on the median results, the large banking sectors were Australia, Japan, Hong Kong, and Singapore and the small banking sectors were Bangladesh, China, India, Indonesia, Malaysia, and Pakistan. The relationship was investigated using the following equation: GDPit =α+β1GDPi(t−1)+ J X j β1Xj it + J X j=1 β2D1XJ it + L X l=1 βlXl it+∈it (2) where Xj it refers to bank key independent variables, and Xl it refers to variables related to macroeconomic and stock market capitalization. D 1 .X it is the difference between the coefficient values for small banking sectors and large banking sectors. D 1 takes a value of 1 when the banking sector is large and 0 when the banking sector is small. The sum of X it and D 1 .X it is the coefficient of the explanatory variables for large banking sectors. In order to find the joint significance of the variables, Wald tests were performed. 6The results of pooled OLS are not reported but are available on request from the corresponding author. J. Risk Financial Manag. 2020,13, 159 15 of 18 a large extent in the Asia-Pacific region. This is also consistent with the results of contemporaneous relationships between bank profitability and GDP growth as shown in Table 6. 7. Conclusions This study investigated the relationship between the profitability of banks and economic growth in ten countries across the Asia-Pacific region in the period from 2004 to 2014. We started with the proposition that a national economy cannot run smoothly without a well-functioning and profitable banking sector. Our results showed that there was a positive and statistically significant relationship between the profitability of banks and economic growth. However, the impact that bank profitability had on economic growth was slow to take effect. In relation to bank size, our findings are interesting. Our results showed that increases in bank size had a negative impact on economic growth, which was not consistent with our expectations. Overall, our results suggest that an increase in the profitability of the banking sector leads to an increase in economic growth, while an increase in the size of the banking sector leads to a decrease in economic growth. The causality results suggest that bank profitability fosters economic growth, and that GDP growth has a delayed feedback effect on bank profitability. Furthermore, our results suggest that the impact of bank profitability on economic growth decreases when the size of the banking sector increases. In line with our expectations, we found that economic growth was hampered during the Global Financial Crisis. Our results indicate that inflation has a negative effect on economic growth, and that increases in government expenditure on health, education, and infrastructure lead to an increase in economic growth. One other question of interest is: Do the explanatory variables impact differently on different types of economies? Our results show that the impact of lagged value on profitability was larger for developed economies than for small emerging and large emerging economies. In addition, our results for the interaction term (lagged value of ROA × SIZE) suggest that an increase in profitability leads to an increase in economic growth, while an increase in banking sector size leads to a decrease in economic growth in small emerging and large emerging economies. In the case of developed economies, the coefficient is also negative but statistically insignificant. Overall, our results support the view of Athanasoglou et al. (2008) that bank profitability is a prerequisite for economic growth. Policy makers should be aware of the impact that policies and regulations will have on bank profitability because of the possible knock-on impact they may have on the economy. Author Contributions: Conceptualization, V.K. and R.B.; methodology, V.K.; software, V.K.; validation, V.K. and R.B.; formal analysis, V.K.; investigation, V.K. and R.B.; resources, V.K.; data curation, V.K.; writing—original draft preparation, V.K.; writing—review and editing, R.B.; visualization, V.K.; supervision, R.B.; project administration, V.K. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Conflicts of Interest: The authors declare no conflict of interest. J. Risk Financial Manag. 2020,13, 159 16 of 18 Appendix A J. Risk Financial Manag. 2020, 13, x FOR PEER REVIEW 17 of 19 Appendix A Figure A1. Conceptual nexus between bank profitability and economic growth. Notes: The figure shows that bank profitability leads to financial stability and results in low bank failures. Both financial stability and low bank failures promote economic growth. Appendix B Table A1. Correlation matrix for variables. Correlation Matrix GDP (1 + ROA) SIZE INF EXP TRADE MKTCAP GDP 1 (1 + ROA) 0.49 1 SIZE −0.19 −0.04 1 INF 0.15 0.33 −0.23 1 EXP 0.48 0.35 −0.33 0.21 1 TRADE 0.02 −0.02 −0.13 −0.03 −0.07 1 MKTCAP 0.18 0.03 0.17 −0.09 −0.08 −0.09 1 Source: Authors’ calculations. Table A2. Vector inflation factor. Variable VIF 1/VIF (1 + ROA) 4.81 0.20 INF 1.95 0.51 SIZE 1.84 0.54 EXP 1.24 0.81 PSC 1.2 0.83 NPLS 1.14 0.88 TRADE 1.11 0.90 MKTCAP 1.09 0.92 Mean VIF 2.06 Source: Authors’ calculations. References (Ahmed and Ansari 1998) Ahmed, Syed M., and Mohammed I. Ansari. 1998. Financial sector development and economic growth: The South-Asian experience. Journal of Asian Economics 9: 503–17. (Anandarajan et al. 2007) Anandarajan, Asokan, Iftekhar Hasan, and Cornelia McCarthy. 2007. Use of loan loss provisions for capital, earnings management and signalling by Australian banks. Accounting & Finance 47: 357–79. (Anari et al. 2005) Anari, Ali, James Kolari, and Joseph Mason. 2005. Bank asset liquidation and the propagation of the US Great Depression. Journal of Money, Credit and Banking 37: 753–73. (Andersen and Babula 2009) Andersen, Lill, and Ronald Babula. 2009. The link between openness and long-run economic growth. Journal of International Commerce Economics 2: 31–50. Figure A1. Conceptual nexus between bank profitability and economic growth. Notes: The figure shows that bank profitability leads to financial stability and results in low bank failures. Both financial stability and low bank failures promote economic growth. Appendix B Table A1. Correlation matrix for variables. Correlation Matrix GDP (1 +ROA) SIZE INF EXP TRADE MKTCAP GDP 1 (1 +ROA) 0.49 1 SIZE −0.19 −0.04 1 INF 0.15 0.33 −0.23 1 EXP 0.48 0.35 −0.33 0.21 1 TRADE 0.02 −0.02 −0.13 −0.03 −0.07 1 MKTCAP 0.18 0.03 0.17 −0.09 −0.08 −0.09 1 Source: Authors’ calculations. Table A2. Vector inflation factor. Variable VIF 1/VIF (1 +ROA) 4.81 0.20 INF 1.95 0.51 SIZE 1.84 0.54 EXP 1.24 0.81 PSC 1.2 0.83 NPLS 1.14 0.88 TRADE 1.11 0.90 MKTCAP 1.09 0.92 Mean VIF 2.06 Source: Authors’ calculations. References Ahmed, Syed M., and Mohammed I. Ansari. 1998. Financial sector development and economic growth: The South-Asian experience. Journal of Asian Economics 9: 503–17. [CrossRef] Anandarajan, Asokan, Iftekhar Hasan, and Cornelia McCarthy. 2007. Use of loan loss provisions for capital, earnings management and signalling by Australian banks. Accounting & Finance 47: 357–79. Anari, Ali, James Kolari, and Joseph Mason. 2005. Bank asset liquidation and the propagation of the US Great Depression. 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