Tài chính toàn diện và hiệu quả của chính sách tiền tệ tại các thị trường mới nổi châu Á

Nội dung text: Tài chính toàn diện và hiệu quả của chính sách tiền tệ tại các thị trường mới nổi châu Á

1. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 FINANCIAL INCLUSION AND MONETARY POLICY EFFECTIVENESS IN ASIA EMERGING MARKETS TÀI CHÍNH TOÀN DIỆN VÀ HIỆU QUẢ CỦA CHÍNH SÁCH TIỀN TỆ TẠI CÁC THỊ TRƯỜNG MỚI NỔI CHÂU Á Do Song Huong, Pham Hoang Cam Huong University of Economics, Hue University phchuong@hce.edu.vn ABSTRACT Financial inclusion and its impact on monetary policy effectiveness have become challenging issues for policymakers. However, there is limited knowledge about the current financial inclusion level and its influence on the monetary policy in Asia Emerging Markets. Therefore, using Principal Component Analysis (PCA) to construct a Financial Inclusion Index that serves as a proxy variable for the accessibility of financial inclusion in Asia Emerging markets, this study aims to analyze the impact of financial inclusion on the monetary policy effectiveness in these economies from 2007 to 2018. Adding to it, three different models including the Fixed Effect model, Random Effect model, and Driscoll and Kraay regression are employed. The results show that the increase in financial inclusion reduces the monetary policy rate, hence enhancing macroeconomic stability in Asia emerging economies. This study adds to the limited number of studies on the relationship between financial inclusion and monetary policy effectiveness in these countries. Keywords: Monetary policy, financial inclusion, inflation rate, Asia Emerging Markets. TÓM TẮT Vấn đề về tài chính toàn diện và ảnh hưởng của nó đến hiệu quả của chính sách tiền tệ đã và đang đặt ra nhiều thách thức cho các nhà hoạch định chính sách. Tuy vậy, vẫn chưa có nhiều nghiên cứu về mức độ triển khai tài chính toàn diện hiện nay và ảnh hưởng của nó đến chính sách tiền tệ tại các thị trường mới nổi châu Á. Do đó, nghiên cứu này nhằm mục đích phân tích ảnh hưởng của tài chính toàn diện đến hiệu quả của chính sách tiền tệ tại các thị trường này bằng việc áp dụng phương pháp phân tích thành phần chính (Principal Component Analysis - PCA) để xây dựng chỉ số tài chính toàn diện cho các nước này từ năm 2007 đến năm 2018. Thêm vào đó, ba mô hình được sử dụng trong nghiên cứu này bao gồm mô hình tác động cố định (FEM), mô hình tác động ngẫu nhiên (REM) và mô hình Driscoll và Kraay. Kết quả nghiên cứu cho thấy việc phát triển tài chính toàn diện giúp kiềm chế lạm phát và góp phần ổn định nền kinh tế vĩ mô tại các nền kinh tế mới nổi ở khu vực châu Á. Nghiên cứu này góp phần đưa ra bằng chứng thực nghiệm về mối quan hệ giữa tài chính toàn diện và hiệu quả của chính sách tiền tệ tại các quốc gia này. Từ khoá: Chính sách tiền tệ, tài chính toàn diện, tỷ lệ lạm phát, thị trường mới nổi châu Á. 1. Introduction The issue of financial inclusion has attracted the attention of many scholars, researchers, and policymakers all over the world. Financial inclusion, a necessary condition for sustaining equitable growth, has a pivotal role in helping people access comfortably to financial services and providing them opportunities to build savings, make investments and avail credit. Financial inclusion generally specified as ensuring access to formal financial services at an affordable cost in a fair and transparent manner (De Koker & Jentzsch, 2013). According to Sarma (2015), ‘Financial Inclusion’ is a process that ensures the ease of access, availability, and usage of the formal financial system for all members of an economy. In the global economy, financial inclusion also has become a central issue for monetary policy effectiveness. Evidence suggests that financial inclusion is among the most important factors affecting monetary policy transmission (Anarfo, Abor, Osei, & Gyeke-Dako, 2019). In other words, the increases in financial inclusion can have implications on monetary policy. More recently, the degree of financial inclusion has been argued to matter for optimal monetary policy (Mehrotra & Yetman, 2014). Also, there 59
2. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 is a growing body of literature reviewing the implications of financial inclusion for the effectiveness of monetary policy (i.e. Di Bartolomeo & Rossi, 2011; Lenka & Bairwa, 2016; Mbutor, 2013). Coined by Antoine Van Agtmael in 1981, emerging countries is a set of promising stock markets, lifted from obscurity, thereby attracting the investment they needed to thrive. According to the business dictionary, emerging economies defined as rapidly growing and volatile economies of certain Asian and Lain American countries, they promise huge potential for growth but also pose significant political, monetary, and social risks. Emerging Asia was forecasted to lead the change for premium growth, expanding by three times the world average over the next two years (Re, 2019). Based on Morgan Stanley Capital International’s (MSCI) classification, Asia emerging markets include China, India, Indonesia, Korea, Malaysia, Pakistan, Philippines, Taiwan, Thailand, Bangladesh, Sri Lanka, and Vietnam. However, The comprehensive Financial report G20 (Kloke-lesch, 2015) shows that in most emerging countries, only 20% to 50% of the population can access to formal financial services. Therefore, financial inclusion is considered to be the core objective of many developing nations, and play a catalytic role for economic and social development (Sharma, Sumita Kukreja, & Professor, 2013). Though the importance of financial inclusion on monetary policy is widely recognized, the literature on financial inclusion lacks comprehensive measures to evaluate the extent of financial inclusion in an economy. The main objective of this study is thus to explore the question: whether and to what extent financial inclusion can foster or hinder monetary policy effectiveness in Asian emerging countries? Our contributions are two-fold. Firstly, to the best of our knowledge, this is one of the limited studies investigating the relationship between financial inclusion and monetary policy in Asia Emerging Markets. Secondly, Asia Emerging Markets include all developing nations and have a large scope of financial inclusion, to some extent, this paper fills the gap of literature by introducing more recent and detailed empirical evidence on the relationship between financial inclusion and monetary policy. The remainder of the paper proceeds as follows: section 2 reviews the relevant literature, section 3 describes the dataset and econometric model, section 4 presents the main empirical results, section 5 details the discussion and conclusion. 2. Literature Review 2.1. Theoretical framework First, with regard to the definition of financial inclusion, in the context of a larger issue of social inclusion, we will reflect the term financial inclusion and its alternative, financial exclusion. One of the early explications of financial exclusion has been suggested by Leyshon and Thrift (1995), referring to a process that aims to restrict certain segments of the population to use formal financial services. According to Sinclair (2001), financial exclusion means the inability of certain social groups such as the poor and the advantaged to access financial facilities. Sarma (2008) defines financial inclusion as a process built on three dimensions including the ease of access, availability, and usage of formal financial service for all members of an economy. In the same spirit, Amidžić, Massara, and Mialou (2014) stated that financial inclusion is an economic condition in which individuals and firms are able to use saving and borrowing instruments via formal financial institutions. Thus, most definitions perceive financial inclusion based on three dimensions which are the availability, the accessibility and the usage of the financial system. Second, most of the previous studies focus on the nexus of financial structure and monetary policy through transmission mechanisms. Specifically, these studies investigate how the balance-sheet positions of banks, governments, households, and enterprises change in the response to monetary policy. However, little attention has been paid on the dynamic and causal linkage between monetary policy and the access to formal financial services such as credit, saving, and remittance which are defined as financial inclusion (Hariharan & Marktanner, 2012). Misati et al. (2010) assert that the development of financial services stemming from financial innovation could have an impact on the pace and magnitude of transmission 60
3. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 mechanisms to monetary policy, in turn influencing the whole economy. Additionally, in the pursuit of price stability teamed with more liquid and complete financial markets, monetary authorities need to monitor more closely movements in asset prices which can eventually influence macroeconomic variables. In other words, financial inclusion is conceived as one of the leading indicators of monetary policy effectiveness and thus, financial stability. It is therefore important to well understand the impact of financial inclusion on monetary policy regarding the operation of transmission channels in the changing financial environment. This part will highlight the monetary policy transmission channels that are affected by the level of financial inclusion. According to de Bondt (1999), monetary policy is transmitted through two main mechanisms including the interest rate and the money supply. As an alternative, monetary instruments can be gauged by price-based or quantity-based. One of the major theories that underline the nexus between monetary policy and financial inclusion is the interest rate channel. There are multiple channels that could explain for a more effective interest rate tool stemming from the development of financial inclusion. It has been conceded that changes in interest rates, either up or down, would result in the variation of capital cost, driving them to spend or save in the light of the change and thus influencing the macroeconomic situation. These economic forces as a result of interest rate changes were suggested in Keynesian economic theory, causing shifts in aggregate demand. Particularly, while lower interest rates stimulate investment spending, higher interest rates reduce it. Changes in interest rates are likely to drive investment and consumption which are major components of aggregate demand. This elucidation denotes that given a low level of financial inclusion, the interest rate would not act as an effective tool of monetary policy. In terms of the direct monetary channel or the money supply channel, monetary policies can be expansionary or contractionary. Providing that recession threatens, the central bank increases the money supply by employing expansionary monetary policy to increase the number of loans, shifting aggregate demand downward. On the contrary, contractionary monetary policy is to serve the purpose of curbing inflation, causing loan quantity to decrease and aggregate demand to shift upward. On top of that, the level of financial inclusion will determine how effective is the monetary policy in affecting the quantity of loan, thus boosting economic growth or mitigating the threat of inflation. Furthermore, in a literature summarization, Yetman (2018) points out that the quantity mechanism is less effective than the interest rate channel regarding the influence of financial inclusion on monetary policy choices. Beyond the traditional monetary channels, credit channel refers to the influence of monetary policy regarding the informational asymmetry between the creditor and the debtor (Miskhin, 1996). In this respect, monetary policy has been transmitted through two channels, which are bank lending and balance sheet of economic agents (Bernanke & Gertler, 1995). Bernanke and Gertler (1995) also emphasize the financial accelerator effect, stating that the magnitude of monetary policy shocks would increase in an imperfect financial market. Specifically, while the bank lending channel is supposed to completely tackle information failure by depository institutions, balance sheet transmission signifies the impact of monetary policy on the net value of firms and their collaterals (Simatele, 2004). According to Chileshe (2017), the first channel is expected to be more effective given strict restrictions to credit markets. Providing that central bank engages contractionary monetary policy, a decrease in money supply causes banks to decline the number of loans, leaving a higher lending rate. The rise in borrowing cost would lead to a decreasing level of financial inclusion, which in turn affects the activities of debtors (Anarfo et al., 2019). Also, Loutskina and Strahan (2009) evidence that the bank-lending channel is less effective on the condition that mortgages are securitized. It is also noticeable that the increasing arrival of non-bank lenders also negatively influences the bank lending channel (Misati et al., 2010). On the other hand, the balance-sheet transmission denotes the changes in borrowers’ balance sheets and income statements under the impact of monetary policy. When expansionary monetary policy pushes interest rates down to a low level, economic agents are better off by the rise in stock prices, sales, and 61
4. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 lower debt servicing costs, thus reducing the risk of informational asymmetries. This situation hence facilitates the access to loans of bank borrowers, stimulating spending and investment. In this regard, the development of financial inclusion will enhance the effectiveness of monetary policy transmissions, consequently boosting economic activity. Regarding the face of financial securitization, Ashcraft and Campello (2007) have proven that the impact of the balance sheet channel is strengthened under this situation. 2.2. Empirical research Existing literature mainly focused on the relationship between financial inclusion and macroeconomic variables such as growth, poverty, and income inequality. In this regard, we would focus on two aspects of earlier research. First, we would review how financial inclusion is measured in previous studies. Second, we desire to discuss its effects, with a focus on monetary policy. Furthermore, we also examine which econometric method had been applied and what conclusion had been drawn in previous studies. Although there is an agreement in defining financial inclusion, there is still no consensus on the measurement method of financial inclusion (Park & Mercado, 2015). Some studies simply measure financial inclusion by the proportion of the population who are able to use formal financial services (i.e. owning formal bank accounts). However, this approach experiences numerous disadvantages that result in an inconsistent and incomparable measurement across countries. Particularly, this type of data can be attained within country and only in a limited number of countries. Also, such primary surveys would exaggerate discrepancies in survey dates, survey units, and methodologies. Thus, most scholars employ the World Bank’s Global Findex Database in their studies (i.e. Demirguc-Kunt & Klapper, 2012; Demirguc-Kunt et al, 2015; Mehrotra & Nadhanael, 2016). Such method can remove the inconsistencies stemming from the countrywide primary surveys (Sarma, 2016). Other studies develop financial inclusion index or composite financial access indicators. For instance, Honohan (2008) constructed a composite indicator that reveals the proportion of adults/households using formal financial services in a given economy out of 160 countries. Nevertheless, this method only delivers a one-time measure of financial inclusion, which failed to capture the changes over time and across economies (Park & Mercado, 2015; Sarma 2016). On this reckoning, Amidžić, Massara, and Mialou (2014) develop a composite indicator that captures various dimensions of financial inclusion including outreach, usage, and quality. Each dimension is then aggregated by assigning different statistical weights. However, this measurement appears to be biased when treating the dimensions unequally. Previous studies have also looked into the impact of financial inclusion to the macroeconomic and country characteristics. Having access to financial instruments has been documented to positively influence the economic situation in numerous studies (i.e. Ashraf, Karlan & Yin, 2006; Chibba, 2009; Dupas & Robinson, 2011; Sarma, 2016). Burgess and Pande (2005) evidenced that poverty reduction in India is closely linked with a state-lead expansion of the banking sector. Park and Mercado (2015) found evidence that financial inclusion contributes to poverty eradication and lower-income disparities, hence forcing economic growth. Nonetheless, there has been less attention paid to the relationship between financial inclusion and the effectiveness of monetary policy like Mehotra and Yetman (2014), Lenka and Bairwa (2016) and Mbutor and Uba (2013). Mehrotra and Yetman (2014) indicate that greater financial inclusion would help to stabilize the inflation rate. Mbutor and Uba (2013) document that the effectiveness of monetary policy is associated with the development of financial inclusion in Nigeria. Employing data spanning the period from 2004 to 2013 in the South Asian Association for Regional Cooperation (SAARC) countries, Lenka and Bairwa (2016) propose a Financial Inclusion Index by performing the principal component analysis. They conclude that increases in financial inclusion curb inflation rates in SAARC countries. However, according to Anarfo et al. (2019), previous studies have proxied financial inclusion and monetary policy inadequately. Specifically, inflation rate is used to gauge monetary policy, which should be considered as a policy outcome rather than a policy instrument. In this 62
5. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 regard, Anarfo et al. (2019) suggest the central bank monetary policy rate as a more reliable and appropriate proxy of monetary policy. Additionally, they also signify two dimensions in measuring financial inclusion instead of using single-variable proxies, which are demand-side indicators and supply- side factors. In our study, we would follow the assertion of Anarfo et al. (2019) to proxy financial inclusion which should be proxied by the usage and the ability to access financial systems. Moreover, most empirical evidence has been found in developed countries, but few studies dealt with financial inclusion in less developed countries. Specifically, earlier studies usually investigate in Africa or Latin America but rarely in Asia. To the best of our knowledge, this is the first study investigating in Asian Emerging and Frontier Markets with the focus on the impact of financial inclusion and monetary policy effectiveness, with a more comprehensive approach to financial inclusion. 3. Data and methodology 3.1 . Data source and variables description Data on monetary policy and all financial inclusion variables was originated from the international financial statistics (IFS) and World Bank development indicators (WDI). The sample consists of 11 emerging Asia countries (China, India, Indonesia, South Korea, Malaysia, Pakistan, Philippines, Thailand, Bangladesh, Sri Lanka, and Vietnam) for the 2007 – 2018 period. The financial inclusion index (FII) is a multidimensional index. It can be gauged by the demand side and supply side, for instance the number of people using financial services and the availability of the financial system. Generally, the most common indicators, used by financial regulators, are number of bank accounts, number of bank branches, number of automated teller machines (ATMs), amount of bank credit, and amount of bank deposits (i.e. Lenka & Bairwa, 2016; Sarma, 2008; Sarma, 2016). These indicators do provide useful information on the inclusiveness of a financial system and cover a wide dimension of financial inclusion. In this study, FII includes four financial accessibility variables such as Commercial bank branches per 100,000 adults (CBB), number of ATM per 100,000 adults (NA), outstanding loans from commercial banks (OL), and outstanding deposits with commercial banks (OD). This choice of indicators is in line with previou studies (i.e. Anarfo et al., 2019; Mbutor, 2013). Besides, the lending rate (LR) and GDP growth (GG) are used as the control variable. Furthermore, according to Lenka and Bairwa (2016), the main objective of an effective monetary policy is to curb inflation and stabilize the price level in an economy. Adding to this, since the main focus of monetary policy in emerging nations is to control inflation and to stabilize the price level, therefore inflation rate is used in our study as a proxy variable to access the efficiency of the monetary policy. The choice of monetary policy effectiveness is also akin to the study of Mbutor (2013). Table 1: Variables selection Variable Notation Description Data source Inflation rate IFR CPI, annual variation in % IFS Index of four variables of Financial inclusion index FII financial inclusion 1. Commercial bank branches per 100,000 adults (CBB) 2. Number of ATM per 100,000 adults (NA) 3. Outstanding loans from commercial banks (%GDP) (OL) IFS 4. Outstanding deposits with commercial banks (%GDP) (OD) Lending rate LR Lending interest rate WDI GDP growth GG Economic growth WDI 63
6. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 3.2. Methodology and the regression model Principal component analysis (PCA) To construct a financial inclusion index (FII), the study applied the principal component analysis (PCA) technique. This is a standard technique to simplify data by extracting hidden features and eliminating excessive information in the dataset. In previous studies, the PCA technique has been rarely involved to quantify the accessibility to financial products and services (Le, Chuc & Taghizadeh-Hesary, 2019). Nonetheless, this method was applied in various researches that analyze phenomena influenced by a set of financial variables (i.e. Ang & McKibbin, 2007; Adu, Marbuah & Mensah, 2013; Le, Kim & Lee, 2016). For instance, Ang and McKibbin (2007) constructed the financial depth index and financial repression index for Malaysia by applying the PCA method. Engaging the same method, Adu et al. (2013) also derived a composite index to study the long-run growth effects of financial development in Ghana. Additionally, many researchers have recognized that there are at least two main dimensions of financial inclusion (Anarfo et al., 2019) including demand-side factors and supply-side factors. Following that, this study builds up a composite index for financial inclusion from a panel principal component analysis. Here, FII made up of two dimensions where each dimension consists of two factors. (1) Supply-side factors: includes two indicators namely Commercial bank branches per 100,000 adults (CBB) and Number of ATM per 100,000 adults (NA). (2) Demand-side factors: presents data on the level of outstanding deposits (OD) and outstanding loan (OL) of commercial banks. The FII can be specified as FII = WJ1CBB + WJ2NA + WJ3OD + WJ4OL which WJ is the weight of the coefficient of the factor score. The regression model This research firstly uses standard panel econometrics such as FEM and REM, then Driscoll and Kraay regression to tackle the problem of heteroskedasticity, autocorrelation, and cross-sectional independence. The equation modeling the relationship between Financial inclusion index and Monetary policy effectiveness are specified below: lnIFRit = + lnFII i,t-1 + lnLR i,t-1 + GG i,t-1 + eit In which, the index i and t denote for country and time, respectively (t = 2007, , 2018). The authors also specified the lags of financial inclusion index, and the lags of the controlled variables while controlling for time and country. To control for the possible endogeneity, we use the lags of independent variables. Additionally, it might take time for financial inclusion strategy to have an impact on inflation rate. 4. Results Descriptive statistics The descriptive statistics of the Asia emerging countries are shown in Table 2. The median of the inflation rate is 5.25%. The number of ATMs per 100,000 adults (NA) has a median value of 21.88 which much higher comparing to Sub-Saharan Africa. The median value of CBB is 10.04 which quite low in general. OL and OD have median values at 8,423,702.00 and 9,887,711.00 respectively. Table 2: Descriptive statistics CBB NA OL OD GG LR IFR Mean 10.795 52.798 749,000,000 821,000,000 5.327 8.869 5.254 Median 10.043 21.881 8,423,702 9,887,711 5.617 8.616 7.581 Maximum 18.699 288.632 7,120,000,000 7,980,000,000 9.145 18.892 23.116 Minimum 3.107 0.513 606,234.800 674249.900 -1.514 3.368 -0.900 Std. Dev. 4.218 74.052 1,490,000,000 1,670,000,000 1.992 3.754 4.271 N 122 122 122 122 122 122 122 64
7. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 Result of PCA Using PCA method, we calculated eigenvalues of all factors. However, the value contains more than one component, then we have to consider another principal component during the analysis. Based on the factor score (weights) of PCA, we then multiply it with the respective variable and add them together for getting the final Financial inclusion index. It is worth noting that South Korea and India have quite high overall financial inclusion, whereas Malaysia got the lowest index for financial inclusion. The countries like China, India, and Vietnam are in the middle segment representing the medium level of financial inclusion. Results of panel unit root test This study employed the unit root test to test for stationarity of variables. To prevent spurious regression, a stationarity test is necessary. This study employed two panel unit root tests for the unbalanced panel: the Im – Pesaran - Shin (IPS) test and Fisher – type tests. The null hypothesis is that the variable contains unit-root. All variables are checked to be stationary exception to the financial inclusion index. The results show that the financial inclusion variable is stationary on log-level. We then take the natural logarithm of all other variables except the GDP growth rate to stabilize the spread or remove skewness. Regression results The results, as shown in Table 3, indicate that the regression model contained three models, including the Random effect model (REM), Fixed effect model (FEM), and regression with Driscoll – Kraay standard errors. The REM shows evidence of a negative but not significant effect of the lag of financial inclusion and the lag of lending rate on inflation rate. Also, a positive correlation was found between the lag of GDP growth and monetary policy effectiveness proxied by inflation rate. However, the coefficients are not statistically significant. In the same vein, FEM reveals a negative impact of the lag of financial inclusion, the lag of lending rate on the monetary policy. Nevertheless, they are not statistically significant. Table 3: Estimation of regression results Model Variables Coef. Std. Err T Dependent variable lIFR lFII_1 -0.649 0.099 -0.66 REM lLR_1 -0.047 0.091 -0.52 2 (R = 9.52%) GG_1 0.019 0.033 0.60 _cons 3.154 0.089 3.54 lFII_1 -0.146 0.104 -1.40 lLR_1 -0.062 0.090 -0.69 FEM GG_1 0.013 0.033 0.40 (R2 = 9.96%) _cons 4.879 1.122 4.35 Variables Coef. Drisc/Kraay T Driscoll – Kraay Std. Err estimation lFII_1 -0.146* 0.080 -1.82 (R2 = 9.96%) lLR_1 -0.062 0.007 -7.92 GG_1 0.013 0.012 1.05 _cons 4.879 1.364 3.58 *, and denote that coefficients are significant at the 10%, 5%, and 1% level respectively. 65
8. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 To compare the two results, we used the Hausman test which shows that REM is the best-fitted model. Following, to address the problem of heteroskedasticity, autocorrelation, and cross-sectional independence in panel data, we used the Modified Wald test, Wooldridge test, and Pesaran’s test respectively. The results indicate that heteroskedasticity, autocorrelation, and cross-sectional independence presented in the data. From the above data analysis, it can be said that the standard FEM and REM estimators are consistent, although not efficient, and the estimated standard errors are biased. Therefore, the authors corrected the standard errors of coefficients using Driscoll and Kraay regression. This is a nonparametric technique of estimating standard errors, suitable for both balanced or unbalanced panels, and capable to handle missing values. In particular, with Fixed effects regression with Driscoll and Kraay standard errors, the respective fixed-effects estimator is implemented in two steps (Hoechle, 2007), thus this approach yields standard errors that are robust to very general forms of cross-sectional and temporal dependence. After using Driscoll and Kraay regression to overcome the problem of heteroskedasticity, autocorrelation, and cross-sectional independence, the results show that the lag of the financial inclusion index and the lag of lending rate are statistically significant and negatively associated with inflation rate. In further detail, 1% increase in the lag of financial inclusion index and the lag the lending rate decreases the inflation rate by 0.146 and 0.062 % respectively. Though GDP growth has a positive influence on the inflation rate, the result is not significant. 5. Discussion and implication Monetary policy and financial inclusion play key roles in Asian emerging economies, carrying greater implications for macroeconomic stability. The study evaluated the effect of financial on the ultimate objective of monetary policy in Asian markets by engaging a multidimensional measure of financial inclusion index. Using the data for Asian emerging countries, we documented that a higher level of financial inclusion is associated with a lower inflation rate. This result of the study supports the notion that growing financial inclusion would improve the effectiveness of monetary policy (Anarfo et al., 2019; Lenka & Bairwa, 2016; Mbutor, 2013). In other words, an improve of accessibility to financial products such as loans or deposits will reduce the inflation rate, which helps to stabilize the price level in Asian emerging markets. Moreover, Yetman (2018) also emphasizes that the effectiveness of interest rate channel will improve given a higher level of financial inclusion, hence orienting monetary authorities appropriately in ensuring price stability and general trust in the national currency. However, according to Di Bartolomeo and Rossi (2007), a fall of financial inclusion level will not hinder the effectiveness of monetary policy as the excluded households are more income-sensitive then the included ones, which is considered as the indirect policy channel. Specifically, the monetary policy first influences the consumption demand of financially included consumers, then affecting the incomes of excluded consumers and still boosting policy effectiveness. In analyzing the impact of control variables on monetary policy, our study found that there is a significant inverse relationship between the rate of inflation and the lending rates in Asian emerging economies. The sign of interest rate clearly supports the conventional arguments, stating that a rise in the interest rate causes the opportunity cost of holding money to increase. Investment and GDP then decline as a result of the increasing interest rate, teamed with a reduction in aggregate demand regarding the consumption angle. This empirical result is also akin to the findings of Lenka and Bairwa (2016) conducting in SAARC countries and Mbutor and Uba (2013) in Nigeria. For the success of the monetary policy, Mbutor and Uba (2013) also specify that financial inclusion additionally deepens the effect of interest rate on aggregate demand. 66
9. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 Furthermore, it is expected that the growth rate has a positive impact on the inflation rate, but the results are less supportive. The empirical results indicate that economic growth cause the inflation rate to increase but the size of the coefficient is found to be insignificant in our study. However, for further research, we still consider the argument of Mehrotra and Yetman (2014) regarding the trade-off between output and inflation. According to Mehrotra and Yetman (2014), given a high development of financial inclusion, the central banks will choose an optimal monetary policy in which controlling inflation is served as the main focus, aiming at balancing output volatility and inflation volatility. In short, financial inclusion is found to be of benefit to maintaining stable inflation in Asian Emerging Markets, with the considerable support of the lending rates. With these considerations in mind, we next draw some implications for Asian emerging economies. Financial inclusion and monetary policy play an essential role in Asia emerging countries and have greater implications for macroeconomic stability. The result shows that financial inclusion does help to stabilize the price level and controls the inflation rate in Asian emergin markets. Thus, it may be said that the foremost important task of the government in Asia emerging countries is to improve the efficiency of the domestic financial sector. In other words, to some degree financial inclusion can perform a similar function as monetary policy. It suggests, therefore, governments of Asia emerging countries also need to focus on developing financial inclusion, which strongly linked to economic development and economic structure of a region. This implies that to broaden financial access, it needs to strengthen the rule of law including enforcement of financial contracts and financial regulatory oversight. The index presented in our study has certain limitations, mainly due to the lack of adequate and appropriate data. The main problem of a macro index is the loss of country-specific information on account of the aggregative nature of the data. Therefore, the financial inclusion index is still not comprehensive enough, resulting in a low R-squared. Further research might also take into account the channels through which financial inclusion influences monetary policy effectiveness. REFERENCES [1] Adu, G., Marbuah, G., & Mensah, J. T. (2013). Financial development and economic growth in Ghana: Does the measure of financial development matter?. Review of Development Finance, 3(4), 192-203. [2] Anarfo, E. B., Abor, J. Y., Osei, K. A., & Gyeke-Dako, A. (2019). Monetary Policy and Financial Inclusion in Sub-Sahara Africa: A Panel VAR Approach. Journal of African Business, 1-24. [3] Ang, J. B., & McKibbin, W. J. (2007). Financial liberalization, financial sector development, and growth: evidence from Malaysia. Journal of development economics, 84(1), 215-233. [4] Ashcraft, A. B., & Campello, M. (2007). Firm balance sheets and monetary policy transmission. Journal of Monetary Economics, 54(6), 1515-1528. [5] Ashraf, N., Karlan, D., & Yin, W. (2006). Tying Odysseus to the mast: Evidence from a commitment savings product in the Philippines. The Quarterly Journal of Economics, 121(2), 635-672. [6] Bernanke, B. S., & Gertler, M. (1995). Inside the black box: the credit channel of monetary policy transmission. Journal of Economic Perspectives, 9(4), 27-48. [7] Chibba, M. (2009). Financial inclusion, poverty reduction, and the millennium development goals. The European Journal of Development Research, 21(2), 213-230. [8] Chileshe, P. M. (2017). Banking structure and the bank lending channel of monetary policy transmission: evidence from panel data methods. Retrieved from: 82757/1/MPRA_paper_82757.pdf. [9] De Koker, L., & Jentzsch, N. (2013). Financial Inclusion and Financial Integrity: Aligned Incentives? World Development, 44(July 2011), 267–280. 2012.11.002. [10] Di Bartolomeo, G., & Rossi, L. (2011). Efficacy of Monetary Policy and Limited Asset Market Participation: Neoclassical vs. Keynesian Effects. SSRN Electronic Journal, (1991), 1–9. 67
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