South Asia’s sustained financial development and the role of remittance: An empirical analysis

doi:10.21203/rs.3.rs-1715873/v1

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South Asia’s sustained financial development and the role of remittance: An empirical analysis

https://doi.org/10.21203/rs.3.rs-1715873/v1

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Banks are crucial to the financial system and the economy. In this context, the objective of this study is to examine the effects of overseas remittances on banks' lending capacity to the private sector as an indicator of financial development in order to ensure that the South Asian region's goal of sustainable financial development is accomplished. An ARDL method is used to analyze the time series data from 1975 to 2020. It has been found that remittances have a strong long-term influence on financial development, with each 1% increase in remittance inflow enhances the financial development by 0.42 percent. As a result, the study concludes that remittance inflows are critical to South Asia's financial growth. The findings of this study will assist economic and financial policymakers in channeling remittances in such a manner that it can be used to improve banks' financial capabilities, which will in turn improve the private sector's capacities.

Jel Code – H81, G21, F40

Remittances

Financial development

Sustainable Growth

Domestic credit

South Asia

ARDL

Agenda 2030, with its 17 Sustainable Development Goals (SDGs), serves as the framework for achieving the 17 Sustainable Development Goals (SDGs) to which all 193 United Nations (UN) member states have committed (United Nations, 2015). In contrast to previous development agendas that emphasized economic growth, the SDGs are a global framework that encompasses a diverse range of potentially conflicting policy objectives in the economic, social, and environmental realms, while also acknowledging that some objectives are highly compatible. The agenda's success is predicted by our ability to capitalize on these benefits and resolve existing trade-offs. To obtain insight into this vital subject, scholars are beginning to examine the relationships between the seventeen goals. (Breuer et al., 2019; Lu et al., 2015; Pradhan, 2019; Schmidt et al., 2015). Before the SDGs, prior research examined the connections between, among other things, adapting to global warming (SMITH & OLESEN, 2010); achieving the Millennium Development Goals (Lo Bue & Klasen, 2013); poverty reduction (Mathy & Blanchard, 2016), and adjusting economic growth, environmental protection, and social integration for personal well-being (Ibisch et al., 2016; Lo Bue & Klasen, 2013; Sachs, 2012). However, the SDGs have introduced a new degree of possibility for categorizing relationships, enabling future systematic examination of these concerns (Costanza et al., 2016; Nilsson et al., 2016; Rickels et al., 2016).

Consistent performance measurement of progress toward achieving the SDGs at various tiers, such as provincial, national, and state levels, over time can especially assist countries in identifying critical issues regarding their sustainable development progress and the gaps between that progress so that countries can better match their reform agenda toward achieving these ambitious SDGs (Schmidt-Traub et al., 2017; Xu et al., 2020). By 2016, assessments and analyses of SDGs at the regional and global levels have primarily included an assessment of progress toward achieving SDGs (Allen et al., 2017; Campagnolo et al., 2018; Clark et al., 2018; Jeffrey Sachs, 2018; Lior et al., 2018; Lozano et al., 2018; Mateusz et al., 2018; Nicolai et al., 2015; OECD, 2018; Prof. Charles M.A. Clark & Dr. Catherine Kavanagh, 2017). From this, it has been found that some countries and regions are on track while others are lagging. Similarly, some sustainable development goals are on track while others are not.

One such area of SDG is financial development, and it is addressed in SDG 8, specifically SDG 8.10, "Strengthen the domestic financial institutions' capability." Given the vital role financial institutions play in influencing a country's economic progress, A well-developed and efficient financial sector ensures long-term economic growth (Demirguc-Kunt et al., 2003) and contributes to capital accumulation (Al-Fayoumi & Abuzayed, 2014). Moreover, a thriving financial sector makes risk management easier by minimizing an economy's sensitivity to financial shocks. Considering the critical role of financial development, current studies have concentrated on the variables that contribute to developing nations' lack of financial development compared to affluent countries (Baltagi et al., 2009).

At the same time, in emerging nations, when the financial system evolves, it provides many benefits to the economy, while at the same time, it also highlights the importance of those factors that contribute to financial development. Several factors can influence financial prosperity. Of these, remittance inflows are one of them, and researchers in the past have highlighted that remittance can affect an economy in many ways, as it can lead to a skills shortage in the home country (Bollard et al., 2011; Faini, 2007), can affect the human resources and supply of labor (S. S. Azizi, 2018; Lopez et al., 2007; Mckenzie & Sasin, 2007), can affect poverty and injustice (Adams & Page, 2005; Barham & Boucher, 1998), can impact human resources and supply of labor (S. S. Azizi, 2018; Lopez et al., 2007; Mckenzie & Sasin, 2007).

One such emerging region that receives one of the highest remittances in the world is South Asia. As per World Bank estimates, the expatriate worker’s remittances has increased from 10 billion USD in the year 1975 to 646 billion USD by the end of the year 2020, while during the same time the remittance to South Asia has increased from 438 million USD to 147 billion (World Bank, 2021). By the end of the year 2020 the remittance to South Asia stand at a level of 22.7% out of total global remittance, while in the year 1975, South Asia’s share in total global remittance was only 4.30% (See Table-1). With such rapid growth, South Asia has established itself as a strategic region in terms of remittance inflows.

Insert Table-1 here (South Asia Remittance Growth)

Even though remittance inflows have grown manifold in the past decade for the South Asian region, The authors have previously stated that it is unclear whether and how remittances contribute to financial development (Aggarwal et al., 2011).On the one hand, recipients of remittances may require financial solutions that enable the safekeeping of these funds, even if the majority of these payments are not transferred through banks. Families who receive their remittances through banks have a significantly higher proclivity to educate themselves about and seek out more bank products. Simultaneously, offering remittance transfer services enables banks to connect with recipients who have little financial intermediation. Nevertheless, on the other hand, since remittances can help individuals overcome funding constraints, they may result in decreased demand for credit and have a dampening influence on the development of credit markets. Numerous scholars have researched the effect of remittances on financial development over the last few years and come to widely varied conclusions. However, for the South Asian region, remittance inflows flow in parallel to domestic credit to the private sector by banks, as illustrated in Fig. 1. This demonstrates that remittance inflows have a direct impact on the region's financial development; as each time remitted inflows increase, financial development improves.

Insert Figure-1 here (Domestic Credit & Remittance Inflow Growth)

In addition to remittances, other macroeconomic factors such as inflation, GDP growth, and trade openness all contribute significantly to financial development, particularly in emerging economies (SeyedSoroosh Azizi, 2020). In Table 2 mentioned below, it highlights the data from World Bank data for the period 1975–2020 (World Bank, 2021c), in which it can be noticed that the correlation between GDP growth, trade openness, and remittance has a positive relationship with financial development, while inflation has an inverse relationship with financial development, which means these factors play a very important role in the financial development of South Asia.

Insert Table-2 here (Correlation Matrix)

Finally, South Asia used to be known around the world for its simmering internal turmoil, high poverty rates, low income, bad economic performance, and negative social features. Nonetheless, the region's outlook has drastically changed. Expectations of an economic comeback in South Asia are getting back to normal as growth is anticipated to climb by 7.2% and 4.4% in the years 2021 and 2022, respectively (world bank, 2021b). With a population of 1.96 billion people, South Asia is home to approximately 26.16% of the world's population (world population review, 2021). While the Indian economy outperforms the rest of the area, Bangladesh and Pakistan are also thriving (Ahmed, 2006). To attain the goal of sustainable development, we should assess the region's financial development in connection to remittance inflows, given its geography, population, and impact on the global economy. As a result, the research question is as follows:

Does remittance inflow affect the financial development of South Asia?

There are five sections in this research study. The second section contains a thorough overview of the literature. Section 3 discusses data and technique, whereas Section 4 discusses empirical findings. In the last section, there is a summary and a conclusion.

Although empirical data on the relationship between remittances and financial development is still in its early stages of development, the bulk of studies demonstrates that remittances have a positive impact on financial development. Remittances help the economy thrive in two ways (Giuliano & Ruiz-Arranz, 2009). For developed economies, remittances can be directed to high-yielding projects that can help enhance the economic growth of the country and its financial system. While in developing or emerging economies, remittances aid in the evolution of the financial system by providing investors with alternative sources of investment and protecting financial institutions from cash/liquidity crunch scenarios. In this regard, (Aggarwal et al. 2011) have taken the data of 109 countries from 1975 to 2007, to find out the relationship between financial development and remittance inflow. They used bank deposits and the credit-to-GDP ratio as indicators of financial development and discovered a positive relationship between remittances and financial sector development. (Basnet et al., 2019) have taken the data of five South Asian countries for the period 1975–2014 and applied panel cointegration and FMOLS techniques to find out the impact of remittance on exchange rates and linked them with the domestic financial products that denoted the financial development. They find out that the remittance inflow leads to an appreciation in the exchange rate, which further deteriorates the competitiveness of financial products in international markets. (Fromentin, 2018) has taken a data set of 32 countries related to Latin America and the Caribbean region from 1970 to 2013, to find out the relationship between remittances and financial development. They have applied causality tests and the GMM method to a panel data set. The results of their study indicated that the inflow of remittances deepens the financial sector. It uses panel data from 66 developing countries for the period 1991–2005 and has introduced a quality-based indicator of financial development. They find that remittance flows help support the financial system from a liquidity crisis, but their benefit can be fully reaped only if the banking channels appropriately utilize them. According to Chowdhury (2011), remittances have a positive and significant impact on financial development in Bangladesh when compared to other South Asian countries. (Coulibaly, 2015) has taken annual data from 1980–to 2010 for 19 SSA countries. They have used liabilities and credit as a proxy for financial sector development. They find out that the impact of remittance is different for different countries and different proxies. Overall, they find that remittance has no strong influence on financial development. (Brown et al., 2013) has analyzed the relationship between remittance and domestic credit to the private sector. In two low-income countries, Kyrgyzstan and Azerbaijan, they discovered that remittances do not enhance domestic credit to the private sector. (Khan et al., 2019) have analyzed the relationship between remittance inflow and investment in five South Asian countries by using panel data for the years 1990 to 2016. They find that private investment is positively affected by remittance inflows. Since most of the past research has highlighted that remittance has a positive effect on financial development, Hence, we have come up with the following hypothesis:

H1. Remittances positively affect South Asia's financial development.

In principle, trade and financial development are thought to be complementary. As (Law, 2008) points out in his analysis of the impact of trade openness on the development of the Malaysian financial industry, According to the study, the openness of Malaysia's trade and financial sectors could greatly benefit the country's financial development. (Kim et al., 2010) applied the Pooled Mean Group method to analyze the effects of trade openness on financial development. They have taken data from 88 countries from 1960 to 2005. Their findings revealed that trade openness and financial development have a positive long-run association, but trade openness has a detrimental short-run impact on financial development. (Rajan & Zingales, 2003) provide critical analysis in this area. They looked at the extent of financial development for a group of nations from 1913 to 1980 and found that parallel openness of trade and the financial sector has a favorable impact on a country's financial development. In their investigation, (Baltagi et al., 2009) somewhat supported this notion. They believed that financial openness had a detrimental impact on trade openness and vice versa. As a result, they concluded that both sectors should not be liberalized at the same time. (Acikgoz et al., 2012) used four financial indicators to investigate the impact of trade and financial openness on the financial development of Turkey for the period 1989–2007. They have applied the ARDL model and also used the (Rajan & Zingales, 2003) hypothesis to find out whether trade and financial development should be liberalized simultaneously or not. They found out that trade openness positively affects financial development. At the same time, they confirmed that both Turkey and the EU can benefit from liberalizing both sectors together. (Arif & Rawat, 2019) investigated the impact of trade and financial sector openness on the financial development of South Asia. They used panel data for the years 1996 to 2015. They find that there is a positive impact of trade openness on the financial development of South Asia. However, their results did not support the simultaneous openness of trade and the financial sector in the studied region. As a result, while the bulk of research points to a positive relationship between trade and financial development, some studies emphasize the importance of both areas growing at the same time. Our hypothesis is as follows:

H2. Trade openness positively affects South Asia's financial development.

In empirical research, the relationship between financial development and economic growth is a widely discussed topic. The link between financial development and economic growth may be traced back to Joseph Schumpeter's foundational work (1912). However, in the recent past, plenty of research has been done to understand the relationship between economic growth and financial development by applying different methodologies, periods, and different regions. However, most of them find out that economic growth has a positive relationship with financial development. As (Sobiech, 2019) has taken data from 61 countries, including both developed and developing nations, from 1970 to 2010. Using this dataset, they have built and employed a financial index that can measure the size, depth, and efficiency of the financial sector. Their results indicated that the more a country is financially developed, the less the impact of remittances on economic growth and vice-versa. (Opoku et al., 2019) have taken data from 47 countries in the African region from 1980 to 2016. They have applied a frequency domain causality technique. By using this technique, they were able to capture the impact of economic growth on financial development at different time durations. Their results indicated that although economic growth positively impacts financial development, they found strong evidence of neutrality, which means both these variables grow independently. (Ibrahim & Alagidede, 2018) have used panel data for 29 sub-Saharan African countries spanning 1980–2014, to find out the impact of economic growth on financial development by using the GMM method. They found that economic growth positively affects financial development, however, they highlighted that financial development depends on the simultaneous growth of financial development and economic growth. (Batuo et al., 2018) studied the links between financial instability, financial liberalization, financial development, and economic growth in 41 African nations for the period between 1985 and 2010. They find that financial growth and liberalization have a favorable impact on financial instability, while economic growth decreases financial instability. They (KUMAR & PARAMANIK, 2020) analyzed the relationship between financial development and economic growth in India throughout 1996-2018. They have employed time-series data for economic growth and financial development. They find out that, in the long run, financial development does impact economic growth positively. The hypothesis that was formed is as follows:

H3. GDP growth positively affects South Asia's financial development.

A few studies have looked at the relationship between inflation and financial development from an empirical standpoint. (Boyd et al., 2001) applied the GMM method to 100 countries from 1960-1995. To find the relationship between inflation and banking sector development, which they link with financial development. They find out that for economies that have inflation of above 15 percent, it adversely affects the financial sector's development. (Batayneh et al., 2021) analyzed the effect of inflation on the financial development of Jordan for the period 1993–2018. They have applied the ARDL model to find out the short and long-term relationships between these selected variables. As per their results, they find that inflation negatively affects financial development in both the short and long run. (Hami, 2017) examined the impact of inflation on the financial development of Iran. by using seasonal time-series data from 2000 to 2015. They have applied the VECM model and used five different financial development indicators. They find out that inflation negatively affects financial depth.

(Bittencourt, 2011) used disaggregated data from 10 economically varied regions in Brazil between 1985 and 2002 to study the influence of inflation on financial development. The timeframe featured inflation under many periods. The study concludes that inflation is harmful to financial development. (Kim D. & Lin S., 2010) has taken an unbalanced panel data sample of 87 countries from 1960–2005. They have applied the Pooled Mean Group estimator to find out the short and long-run impacts of inflation on financial development. Their results indicated that there is inflation that negatively impacts financial development. Inflation has a positive impact on financial development if the data is split into different income and inflation groups. Finally, the theoretical literature suggests that inflation and financial sector development have a negative association. The empirical literature, on the other hand, yields mixed findings.

H4. Inflation negatively affects South Asia's financial development.

The study examines the effect of remittances on financial development using annual time series data from 1975 to 2020. The World Bank Development Indicators Database was used to extract data on remittance inflows (RE), inflation (IN), bank credit to the private sector (DC), trade openness (TR), and gross domestic product per capita (GD). The data was analyzed using the Eviews10 software. The description of the selected variable is mentioned in Table 3.

Insert Table-3 (Variables Description)

The general model formulation is given in Eq. 1. The following function has been incorporated:

$${DC}_{t}=f({RE}_{t },{TR}_{t}, {GD}_{t}, {IN}_{t})$$

In equation-1, t is representing the time trend. While the empirical model of the study is as follows:

$${DC}_{t}={\alpha }_{0}+{\alpha }_{1}{RE}_{t }+{{\alpha }_{2}TR}_{t}+{\alpha }_{3}{GD}_{t}+{\alpha }_{4} {IN}_{t}+{\epsilon }_{t}$$

Where are the coefficients of remittance inflow, trade openness, GDP per capita growth, inflation respectively, and ${\epsilon }_{t}$ is the error term.

By taking the log for our variables, we can reduce the influence of outliers. As a result, the structural form of the basic estimated model is as follows:

$${lnDC}_{t}={\beta }_{0}+{\beta }_{1}{lnRE}_{t }+{{\beta }_{2}lnTR}_{t}+{\beta }_{3}{lnGD}_{t}+{\beta }_{4} {lnIN}_{t}+ {\epsilon }_{t}$$

Where the endogenous variables include the log of financial development (${lnDC}_{t})$, the log of remittance inflow (${lnRE}_{t }$), the log of trade openness (${lnTR}_{t}$), the log of GDP per capita growth (${lnGD}_{t}$) and the log of inflation (${lnIN}_{t}$).

The main objective of this research is to find out the impact of remittance inflows on the financial development of south Asia. To achieve this objective we have applied the Autoregressive distributed Lag model, which involves the steps beginning from unit root testing, optimal lag length formation, selection of appropriate ARDL model, cointegration test, and finally diagnostic testing.

3.1 Unit Root Test

The unit root test is used to check the stationarity of the time-series data. As in time series modeling, we often come across data that shows trends with time. However, the time series data must not be of such a time-trending nature. As has been highlighted by (Gujarati, 2003), it is critical to avoid "spurious" regression findings that make accurate forecasting difficult. The hypothesis of the unit root test is as follows:

H0 Series is nonstationary and has a unit root (p-value higher than 5%) H1 Series is stationary and do not have a unit root (p-value less than 5%)

3.2 Optimum lag length Selection

The next step is to select the optimal lag length for our stationary data series. In empirical research that is based on an autoregressive model, it is important to select the appropriate lag length for each series. Including too many lags while specifying the model can reduce the degree of freedom and consequently lead to multicollinearity. (Jeffrey M. Wooldridge, 2012) mentioned that we should keep the small lag length up to 2 lags in order not to lose the degree of freedom. There are many criteria, like AIC, BIC, SIC, and HQ, which can be used to find out the appropriate lag length. By using these criteria, we are required to select the model that gives the lowest values of these criteria.

3.3 ARDL model formation and bound testing

Once the optimum lag length has been obtained, then we have to find the appropriate ARDL model among many models which may contain different lag lengths for different variables. The model is to be selected based on the lowest value of the information criteria that has been obtained based on optimal lag length.

An autoregressive distributed lag (ARDL) bounds testing approach is used to assess the influence of remittance on financial development in South Asia. The functionality of the ARDL model works in a way that it takes the lagged values of the dependent variable, and the current and lagged values of the independent variables and then regresses them with the dependent variable to find out the results.

In comparison to conventional cointegration procedures, the ARDL bounds cointegration methodology has several advantages (Pesaran et al., 2001). The first advantage is that it accepts variables of different orders, as this model,can accept some variables at I(0), other variables at I(1), but not I(2), or higher-order variables (MUHAMMADSHAHBAZ & FARIDUL ISLAM, 2011; Ozturk et al., 2012). Secondly, irrespective of sample size, which can be small or finite and comprise up to 80 observations, the autoregressive distributed lag model is preferable (Ghatak & Siddiki, 2001). Thirdly, the ARDL bounds cointegration method considers fractionally integrated variables. The key aspect is that the ARDL bounds technique is capable of addressing omitted variables and serial correlation concerns, as well as any endogeneity issue; because it offers, unbiased estimates in the long-run model. Finally, the ARDL bounds cointegration technique has the advantage of simultaneously estimating long-run and short-run dynamics in a single reduced form of the equation (Richard I HarrisR. Sollis, 2003). The ARDL model is as follows:

$${\varDelta lnDC}_{t}={\beta }_{0}+\sum _{i=0}^{p}{\beta }_{1i}{\varDelta lnDC}_{t-i}+ \sum _{i=0}^{p1}{\beta }_{2i}{\varDelta lnRE}_{t-i}+ \sum _{i=0}^{p2}{\beta }_{3i}{\varDelta lnTR}_{t-i}+$$

$$\sum _{i=0}^{p3}{\beta }_{4i}{\varDelta lnGD}_{t-i}+ \sum _{i=0}^{p4}{\beta }_{5i}{\varDelta lnIN}_{t-i}+ {\delta }_{7}{lnDC}_{t-1}+$$

$${\delta }_{8}{lnRE}_{t-1}+{\delta }_{9}{lnTR}_{t-1}+{\delta }_{10}{lnGD}_{t-1}+{\delta }_{11}{lnIN}_{t-1}+{\epsilon }_{t}$$

In which Δ depicts the first difference; while p, p1, p2, p3, p4, and p5 are denoting the optimal lag length; the error term is represented by${\epsilon }_{t}$; constant is denoted by${\beta }_{0}$; ${\beta }_{1i}- {\beta }_{6i }$ represents the short-run dynamic coefficients, while ${\delta }_{7}- {\delta }_{11}$represent the long run phenomenon. In the unrestricted error correction model (ECM) specified in Eq. (4), the hypothesis is as follows-

$[{H}_{0} :{\delta }_{7}={\delta }_{8}={\delta }_{9}={\delta }_{10}{=\delta }_{11}=0$ ] The long-run relationship does not exist.

$[{H}_{1} : {\delta }_{7}\ne {\delta }_{8}\ne {\delta }_{9}\ne {\delta }_{10}\ne {\delta }_{11}\ne 0$ ] The long-run relationship exist

By comparing the obtained bound testing-F-test statistic values to the critical values given by (Pesaran et al., 2001), the long-run link between the variables used in this investigation was validated. (Pesaran et al., 2001) analyzed the long-run relationship between the variables by comparing two sets of alternative critical values at each significance level. One set of critical values reflects the critical value at the lower bound when all regressors are I(0). In contrast, another set represents the critical value at the upper bound when all regressors are I(1). The null hypothesis of no long-term relationship can be rejected if the computed F-statistic is greater than the critical value, irrespective of the time series integration orders. However, if the F statistic is smaller than the crucial value, the null hypothesis cannot be rejected. But, if the test statistics fall between the lower and higher critical levels, no conclusion on cointegration can be drawn. If cointegration occurs among the variables in this investigation, the following ECM will be utilized to approximate the variables' short-run dynamics. The ECM requirements for this investigation is as follows:

$${\varDelta lnDC}_{t}={\beta }_{0}+\sum _{i=0}^{p}{\beta }_{1i}{\varDelta lnDC}_{t-i}+ \sum _{i=0}^{p1}{\beta }_{2i}{\varDelta lnRE}_{t-i}+ \sum _{i=0}^{p2}{\beta }_{3i}{\varDelta lnTR}_{t-i}+$$

$\sum _{i=0}^{p3}{\beta }_{4i}{\varDelta lnGD}_{t-i}+ \sum _{i=0}^{p4}{\beta }_{5i}{\varDelta lnIN}_{t-i}+{\lambda lnECT}_{t-1}$ +${\epsilon }_{t}$ (5)

In the above mention equation, the coefficient of error correction term is represented by 𝜆. It is required that the value of 𝜆 should be negative and statistically significant to achieve the long-run equilibrium.

3.4 Diagnostic Tests

Lastly, the diagnostic tests have been applied to ensure that the regression model is correctly specified for the included regressors. We have applied the Jarque Bera test for normality to confirm that the data is normally distributed. The Breusch–Godfrey serial correlation LM test has been applied to check for autocorrelation in the errors in a regression model. The Breusch-Pagan test is used to determine whether or not heteroscedasticity is present in a regression model to confirm that the residuals are distributed with equal variance. For model stability and to detect the structural change in the series, the CUSUM and the CUSUM of the square test have been applied.

The Phillips-Perron unit tests (Phillips & Perron, 1988) were used to examine the integration of the variables, and the results are shown in Table-4. At level, GDP per capita growth becomes stationary, whereas other variables such as domestic credit (${lnDC}_{t})$, inflation (${lnIN}_{t})$, remittance inflows ${(lnRE}_{t}$), and trade openness ${(lnTR}_{t})$ become stationary at first difference. Thus, is found to be I (0), and the other variables are found to be integration order one, I (1). As a result, the variables employed in this study have mixed stationarity attributes, comprising stationary at level and first difference.

Insert Table-4 here (Unit Root Test)

Table-5 and Figure-2 summarize the test results for determining the optimal lag length which will be used for the analysis. Like other studies analyzing the data using the ARDL approach, this study also uses the Akaike information criterion to choose an optimal lag length. Model 255 was chosen as the best model for ARDL analysis since its AIC value is the lowest among the 20 available models. Hence, we have selected it for further analysis. Additionally, as seen in Fig. 3, the ARDL (4, 2, 4, 4, 0) model has a lower lag than the other models. As a result, this study employs the ARDL (4, 2, 4, 4, 0).

Insert Table-5 here (Model Selection Criteria Table)

Insert Figure-2 here (ARDL Model Selection Graph)

The ARDL bounds test is used to find out the existence of long-run relationships amongst the selected variables. To confirm this, it is required that the F-statistics value be higher than the upper bound critical value. As per the results which are mentioned in Table 6, the value of F-statistics is 6.73, which is higher than the upper bound critical value of 3.49 at a 5% level of significance. Hence, it has been confirmed that there exists a long-run relationship between financial development and remittance inflows in South Asia throughout the research study.

Insert Table-6 here (ARDL Bound Test)

Table 7 illustrates the computed long-run coefficients of the variables employed in this study. Remittance inflow positively affects financial development, which is statistically significant. The estimated coefficient of remittances indicates that with every 1% rise in remittance inflow, financial development increases by 0.42%, which is an indicator that when the remittance inflow increases, the people put more money in the bank. Later on, the bank utilizes the same money for providing credit to the private sector. While with every 1% rise in trade activity, financial development increases by 0.40% and it is also statistically significant, which is an indicator that trade and financial development complement each other as financial development leads to more credit available for business owners, and consequently, business owners use the bank facility for financial transactions and saving in the bank. As far as another variable like inflation is concerned, it has a negative and significant impact on financial development as with every 1% increase in inflation, financial development decreases by 0.29%. It can be interpreted that when the price of the product increases, people tend to withdraw more cash from the bank as more cash is required to buy the product. However, it is also an indicator that avoids investing via the bank and follows other routes of investment in the hope of getting more returns to cover the inflationary effects. Finally, GDP per capita growth has a negative impact on financial development since every 1% rise in GDP per capita growth results in a 0.20 percent decline in financial development. However, this effect is insignificant in the long run.

Insert Table-7 here (Long Run Results)

Table 8 summarizes the short-run dynamics test results. The estimated error correction coefficient (${ECT}_{t-1}$) is negative and statistically significant. The negative result demonstrates that financial development (DC) is approaching long-run equilibrium. Furthermore, the error correction coefficient (${ECT}_{t-1}$) is 0.2115, indicating that 21.15 percent of the equilibrium error is corrected each year. Additionally, the one-year lag of variable financial development has a positive influence on its present value, whereas GDP per capita growth has a significant but negative effect on same-year financial development. Inflation, on the other hand, does not appear to have a significant short-run effect on financial development. Similarly, remittances have no significant effect on financial development in the short run, as only the three-year lag value of remittances has a positive and significant effect on financial development. Finally, trade has a positive and significant short-run effect on the development of the financial system in the same year.

Insert Table-8 here (Short Run Results)

The normality test results for the variables used in this study are shown in Table-9. Hence, since the relevant p-value for the Jarque–Bera test is larger than the 5% level of significance, the null hypothesis that the residuals are normally distributed is not rejected. As a result, the residuals of the ARDL (4, 2, 4, 4, 0) model generated in this study are normally distributed.

The outcomes of the Breusch Godfrey serial correlation LM test and the heteroskedasticity test are presented in Tables 10 and 11, respectively. The estimated model in this study does not demonstrate autocorrelation since the corresponding p-value for the Breusch–Godfrey serial correlation LM test in Table-10 is greater than the 5% level of significance. Furthermore, the calculated ARDL model is not heteroskedastic because the related p-value for the heteroskedasticity Breusch Pagan Godfrey test is greater than the 5% significance level.

Figures 3 and 4 show the CUSUM and CUSUMSQ, which were utilized in this investigation for stability diagnosis. If the blue line lines lie inside the limits of two red lines, the parameters employed in the study are stable, and likewise, according to the CUSUM and CUSUMSQ plots (Tanizaki, 1995). The CUSUM and CUSUMQ results show that the model employed in this study cleared the stability tests since the blue line in both tests fits inside the red line borders.

Insert Table-9 here (Normality Test Results)

Insert Table-10 here (Serial Correlation Test Results)

Insert Table-11 here (Heteroskedasticity Test Results)

Insert Figure-3 here (CUSUM Test for model stability)

Insert Figure-4 here (CUSUM of square test for model stability)

The impact of remittances on financial development is examined in this study, utilizing annual time series data for South Asia from 1975 to 2020. The relationship was investigated using the ARDL bounds testing technique. The study has specifically targeted sustainability development SDG 8.10, which focuses on strengthening domestic financial institutions' capability, and for that, we have used domestic credit to the private sector by banks as a proxy for financial development.

Apart from that, the study has also included remittance inflow, GDP per capita growth, inflation, and trade openness as independent variables. It has been found that remittances assist financial development in the long run, but have a negligible effect in the short run in South Asia. It is therefore recommended that South Asia maintain policies that promote remittance inflows as a means of financial development, in addition to being considered as a fund received for the family maintenance of migrant workers. The current epidemic also allows South Asian countries to work together with service providers and host governments to achieve this goal.

Moreover, the region's financial policymakers and central bank decision-makers can act to make a policy that enhances the remittance inflow to the region. It can be in the form of providing remittance inflow incentives to the remitters so that it motivates them to remit more funds to the home country. By giving remitters investment options, they can use idle cash in their bank accounts to invest through banking channels. By making it easier to use the multicurrency account, so that remitters keep more money in their account. We provide preferential exchange rates during currency conversion so that remitters do not lose much of the amount. It is also critical that both the public and private sectors lend the necessary technological support to help cut the average cost. The central banks in the home countries should consider reducing know-your-customer (KYC) and regulatory procedures for both traditional service providers and fintech enterprises. Aside from that, economies will need to develop an investment infrastructure to attract migrants to send more money home to invest.

Declaration of Interest

The author hereby states that they have no potential conflicts of interest with this research publication.

Availability of data and materials

Data can be provided as per the request of the readers.

Competing Interest

The policy on declaration of interest has been properly read by the author and declare that they have no competing interest

Funding

The author obtained no funding for this article's research, writing, or publication

Authors Contribution

The author is the sole contributor of this research article.

Acknowledgements

Authors have no acknowledgment to be provided.

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South Asia’s sustained financial development and the role of remittance: An empirical analysis

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1. Introduction

2. Literature Review

3. Data And Methodology

3.1 Unit Root Test

3.2 Optimum lag length Selection

3.3 ARDL model formation and bound testing

3.4 Diagnostic Tests

4. Empirical Results And Discussion

5. Conclusion And Recommendations

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