The impact of remittances on financial development in selected asian countries

INTRODUCTION

Problem statement

In recent decades, migration flows have significantly increased, leading to a rapid rise in remittances—funds sent by migrants to their home countries These remittances have now surpassed traditional financial channels, such as official development assistance (ODA) and private capital According to World Bank data, personal remittances in 2014 accounted for one-third of foreign direct investment (FDI) and were more than three times larger than the total of ODA and official aid, growing from US$ 67 billion in 1990 to over US$ 500 billion.

Between 2000 and 2014, foreign direct investment (FDI) experienced significant fluctuations, while official development assistance (ODA) and other external sources either remained stable or decreased Although remittances saw a slight decline of nearly 5 percent in 2009 due to the global financial recession, this was minimal compared to the staggering 45 percent drop in FDI during the same year Following this period, remittances fully recovered and continued to rise, whereas FDI faced further declines of nearly 10 percent in 2012 and 20 percent in 2014.

Recent decades have seen a surge in research focusing on the impact of remittance inflows on economic development, with most studies examining their effects on growth, poverty, and education However, there has been less emphasis on exploring the relationship between remittance inflows and financial development, despite existing literature highlighting that financial development can significantly promote economic growth and alleviate poverty.

Kunt and Levine, 2004), across recipient countries despite their important contribution in total external financial sources.

Various perspectives highlight the complex relationship between remittance flows and financial sector development, with empirical studies showing inconsistent results across different countries On one hand, remittances can promote financial development when formal channels are used for transactions, enabling banks to engage with unbanked recipients and fostering the creation of banking products for saving surplus income Additionally, remittance flows can enhance banks' willingness to lend, as stable remittances may serve as collateral, potentially increasing overall credit availability in recipient communities due to the additional loanable funds generated from deposited remittances.

Remittances can potentially hinder the development of financial sectors by easing recipients' budget constraints, leading to reduced demand for external credit (Martínez, Mascaró, and Moizeszowicz, 2008; Brown et al., 2011) Many recipients may view remittances as extra income, primarily using them for consumption, while a lack of trust in financial institutions may deter them from depositing money in banks This results in minimal demand for financial products and stagnant bank deposits (Chami et al., 2009) Consequently, the relationship between remittances and financial development in recipient countries remains complex, highlighting the necessity for further research on this topic.

In an effort to address the relationship between remittances and the development of financial sector in Asia, this study utilized a panel data from

From 1990 to 2014, a study analyzed the relationship between personal remittances and financial development indicators—specifically, domestic credit to the private sector and broad money—across thirty-seven Asian countries using various econometric methods, including fixed effects, random effects, and the system Generalized Method of Moments Additionally, the research assessed the differing impacts of remittance inflows on high, middle, and low-income countries, as classified by the World Bank.

Research objectives

This study is conducted in an attempt to:

 Analyze the trend of remittance inflows and the situations of domestic credit to private sector provided by banks and broad money in Asia region,

 Access the impact of remittances on financial development in Asia in general,

 Evaluate different impacts of remittance flows on financial development in different income-groups of countries in Asia.

From the findings, this study will propose recommendations in order to foster the effects of remittance inflows on financial development in Asian remittance recipient countries.

Scope and data of the study

This research explores the significant impact of remittances on financial development in Asian countries Over the past decade, remittance inflows to Asia have surged, accounting for nearly 50% of global remittances Many middle and low-income countries in this region have seen substantial increases in remittances, both in numerical value and as a percentage of their Gross Domestic Product (GDP) However, the effects of these remittances on the financial sectors in these countries have not been adequately analyzed.

Research on the impact of remittances on financial development has primarily focused on individual countries or specific regions in Asia For instance, Chowdhury (2011) identifies a direct positive correlation between remittance flows and the financial breadth and depth in Bangladesh In contrast, Noman and Uddin (2012) present evidence of an indirect effect of remittances on the banking sector and economic growth in selected South Asian countries.

Previous studies have often categorized countries into developing and developed groups, which can skew results due to varying income tolerances within these categories Therefore, it is essential to explore the relationship between remittances and financial development in this context Specifically, examining the effects of remittance flows across different income groups will offer valuable insights for policymakers.

Remittances play a significant role in financial development in Asia, particularly in Vietnam, where inflows surged from US$1.34 million in 2000 to over US$13 million by 2015, representing nearly 7% of the country's GDP This trend highlights the importance of optimizing remittance channels to enhance economic growth and financial stability in middle-income nations.

This study investigates the impact of remittances across various income groups in Asia, utilizing data from selected Asian countries spanning 1990 to 2014 The research incorporates the Chinn-Ito Index to measure financial openness, while other data is sourced from the World Bank database.

Structure of the study

This thesis comprises five chapters, beginning with an exploration of the relationship between remittances and financial development in Asian countries It defines remittances and reviews key literature on their impact on financial sectors, alongside empirical studies examining their effects on financial development The research methodology and data used are detailed in Chapter 3 Chapter 4 analyzes remittances and financial development in Asia from 1990 to 2014, presenting empirical results from regression analysis The final chapter summarizes the main findings, offers policy recommendations, and discusses limitations and directions for future research.

LITERATURE REVIEWS

Theory of remittances and financial development

2.1.1 The concepts and channels of remittances

Remittances refer to the cross-border money that migrants send back to their home countries, though the term is often used without a clear definition These funds can be transferred through official channels, such as banks and money transfer organizations, or unofficial channels, which typically involve cash transactions facilitated by friends, family, or traditional methods like hawala In hawala, money is deposited with unlicensed organizations in one country and can be withdrawn by recipients in another, highlighting the diverse mechanisms through which remittances are sent (Freund and Spatafora, 2008; Nyamongo, 2012; Giuliano and Ruiz-Arranz, 2005; Aggarwal et al., 2011).

Informal channels are widely used for remittances globally due to their accessibility, anonymity, low cost, and reliability Unlike official channels, remittance senders and receivers do not need bank accounts or complex procedures, making the process simpler Additionally, transaction details remain confidential, enhancing privacy These informal transfers are also more cost-effective compared to traditional banking methods Furthermore, the reliance on networks of family and friends for delivering remittances fosters trust, particularly among those who rarely engage with formal financial institutions.

Using informal channels for remittances can lead to several negative consequences Official organizations may struggle to accurately collect data, resulting in miscalculations of remittance volumes Additionally, these informal methods can facilitate money laundering by criminal organizations or individuals Ultimately, this undermines the intended financial development benefits of remittances, as highlighted by Nyamongo (2012).

The financial sector, as defined by the World Bank, encompasses institutions, instruments, markets, and the legal and regulatory frameworks that facilitate credit transactions Developing this sector aims to mitigate the costs associated with the financial system By reducing costs related to information acquisition, contract enforcement, and transaction execution, financial contracts, markets, and intermediaries emerge Various combinations of information, enforcement, and transaction costs, alongside differing legal, regulatory, and tax systems, have led to the creation of unique financial contracts, markets, and intermediaries throughout different countries and historical contexts.

Financial development occurs through enhancements in financial instruments, markets, and intermediaries, though these advancements may not necessarily alleviate issues related to information, enforcement, and transaction costs (Levine, 2005).

2.1.3 The role of remittances in financial development

Numerous studies have explored the theoretical impact of remittances on financial development in recipient countries, highlighting various perspectives One key argument is that remittance inflows enhance the financial sector by influencing both demand and supply On the demand side, remittances are believed to improve financial literacy in recipient communities as migrants and their families utilize formal transfer channels This increased awareness encourages the search for banking products and services, ultimately driving demand for financial offerings Furthermore, even when remittances are not processed through banks, the surplus income generated can lead individuals to seek additional banking services, as they require secure means to store or invest their excess funds.

Remittances play a crucial role in enhancing the development of financial sectors by prompting banks to expand their product offerings and branch networks to meet the needs of remittance recipients As these recipients become more attractive customers, banks may increase credit availability for those with stable and significant remittance inflows Additionally, the rise in deposits from remittance recipients, coupled with transaction costs associated with remittances, can lead to increased bank credit within communities This dynamic suggests that remittances contribute to the broadening and deepening of financial systems in recipient economies, highlighting the importance of examining their impact on financial development at the national level.

Remittances can negatively impact financial development by easing budget constraints for recipients, leading to reduced demand for credit from financial institutions (Caceres and Saca, 2006; Martínez, Mascaró, and Moizeszowicz, 2008; Aggarwal et al., 2011) This effect is exacerbated when recipients adopt a pattern of conspicuous consumption, which hinders the cultivation of saving habits essential for fostering investments and driving economic growth.

Remittance inflows may not stimulate financial development in countries with underdeveloped financial systems, as migrants often prefer informal transfer methods over formal banking channels (Brown et al., 2011) This preference leads to a lack of awareness and utilization of banking services among recipients, hindering financial literacy within communities Consequently, unbanked individuals may hoard cash at home or use funds for immediate consumption due to distrust in banks, resulting in minimal demand for savings products or formal financial services As a result, the behaviors of remittance recipients can contribute to stagnation in the financial sector rather than fostering growth.

The relationship between remittances and financial development is complex, with evidence suggesting that underdeveloped financial systems in recipient countries can lead to distrust among unbanked individuals, pushing them towards informal transaction channels Conversely, a robust financial sector can build trust and increase demand for formal financial products and services Additionally, improved financial development can lower remittance transfer costs, encouraging both senders and receivers to utilize these services more effectively.

Empirical studies

In recent decades, various methods and datasets have been employed to examine the impact of steadily increasing remittance inflows on global financial development Research on this relationship can be divided into two main approaches: indirect and direct The indirect approach focuses on whether remittances contribute to economic growth at specific levels of financial development Conversely, the direct approach assesses the effects of remittances on financial development itself, evaluating how they enhance the performance and depth of the financial sector.

Studies examining the relationship between remittances and economic growth highlight two contrasting perspectives based on a country's level of financial development In nations with underdeveloped financial systems, remittances are crucial as they provide vital capital for investment, fostering economic growth For example, an analysis of over 100 countries incorporating financial development alongside remittances in growth equations demonstrates this significant impact.

From 1975 to 2002, Giuliano and Ruiz-Arranz (2009) utilized the GMM approach to demonstrate that remittance inflows significantly contribute to economic growth by serving as an alternative financial resource for investment and alleviating credit constraints in nations with underdeveloped financial systems Their findings remain consistent across various measures of financial system development, even after addressing endogeneity issues related to the causal relationship between remittances and financial development The study also indicates that remittances can enhance economic growth through an investment channel, particularly when the financial sector's credit supply does not meet public demand Conversely, in countries with well-developed financial systems, the impact of remittances on investment is diminished, as the availability of credit reduces the reliance on remittances for investment purposes.

Ramirez and Sharma (2008) found similar results to Giuliano and Ruiz-Arranz (2009), suggesting that remittances can enhance economic growth more significantly in countries with underdeveloped financial systems Their analysis, which utilized unit root tests, cointegration tests, and fully modified ordinary least squares methods, examined data from twenty-three upper and lower-income countries in Latin America and the Caribbean from 1990 to 2005 Notably, the study revealed that the impact of remittances on economic growth is more pronounced in upper-income countries compared to lower-income ones Additionally, it highlighted other factors through which remittances positively affect economic growth, including education levels and economic liberalization.

Remittances have a more significant impact on economic growth in countries with developed financial systems, as these systems facilitate the effective utilization and investment of remittance funds Mundaca (2009) analyzed data from Latin America and the Caribbean (1970-2002) using First Difference GMM to address endogeneity issues, concluding that remittances can enhance growth by directing funds towards technology and capital investments Additionally, the study found that incorporating remittance proxies into growth equations substantially increases the impact of per capita investment on economic growth Supporting this, research by Ojeda (2003), Terry and Wilson (2005), and the World Bank (2006) indicates that channeling remittances through formal financial sectors boosts their developmental effects.

Bettin and Zazzaro (2011) found comparable results in their study on the connection between remittance inflows and financial development levels, using a panel dataset from sixty-six developing countries over a specified period.

From 1970 to 2005, a study utilizing system GMM revealed that remittance inflows significantly enhance economic growth in countries with well-functioning banking systems This research highlights the importance of efficient banking performance in remittance-receiving nations, emphasizing the positive impact of financial system development on economic progress.

This study emphasizes the significant role of remittance inflows in shaping financial development within recipient economies While previous research has focused on the overall financial development level, this analysis directly investigates how remittances impact the growth of financial sectors The findings highlight the increasingly vital influence of remittances on economic development, underscoring their importance in enhancing financial stability and growth.

Recent empirical studies have shifted focus to the direct impact of remittances on financial development in recipient countries, yielding mixed results However, a common finding is that remittances positively influence financial development across many nations For example, Gupta et al (2009) analyzed the relationship between remittances, financial development, and poverty in 44 Sub-Saharan African countries from 1975 to 2004 Using advanced regression models, the study revealed that while remittances are relatively small compared to other aid flows, their stable and increasing volume during the period has a significant positive effect on financial development and aids in poverty alleviation Notably, these results hold true even after accounting for potential reverse causality in the remittances-financial development and poverty relationship.

Demirguc-Kunt et al (2010) demonstrate a significant positive relationship between remittance inflows and the development of the banking sector in Mexico, based on county-level data from 2000 Their analysis reveals that counties with a higher percentage of the population receiving remittances boast more bank branches, increased bank accounts, and greater bank deposits Specifically, for every one percentage increase in households receiving remittances, there is a corresponding rise of approximately 0.16 to 0.19 percentage points in the utilization of financial products and services.

Aggarwal et al (2011) investigate the relationship between remittance inflows and financial system development in 109 developing countries from 1975 to 2007, employing various research methods such as fixed effects estimations, dynamic system GMM, and instrumental variables Their findings reveal that worker remittances significantly and positively impact bank credit and deposits in the private sector, measured as a proportion of GDP, while accounting for variable omission, reverse causality, and measurement errors.

Cooray (2012) investigates the relationship between remittances and the financial sector by analyzing data from 94 non-OECD countries between 1990 and 2010 using pooled OLS and system GMM The study reveals that remittance inflows positively influence both the size and efficiency of financial systems Additionally, it explores how the interaction between remittances and government ownership of banks affects these financial metrics The findings suggest that lower government ownership in banks correlates with a greater impact of migrant remittances on the expansion of the financial sector, while increased efficiency in financial sectors necessitates higher government ownership.

Chowdhury (2011) examines the impact of remittance flows on the financial sector's development in Bangladesh, utilizing annual data from 1971 to 2008 and employing Cointegration and Vector Error Correction Model methods The study reveals that remittance inflows significantly enhance the expansion and depth of the financial system in Bangladesh Furthermore, the findings suggest that there is no reverse causation between remittance inflows and financial development indicators, as confirmed by tests for endogeneity bias.

While many studies suggest a positive link between remittances and financial development, some research indicates otherwise Brown et al (2011) provide evidence that remittances may have no effect or even hinder financial development, analyzing data from 138 countries between 1970 and 2005 using fixed effects and Probit models At the micro level, data from 3,899 households in Azerbaijan and 3,995 households in Kyrgyzstan reveal that remittances can negatively impact financial development in Azerbaijan, while in Kyrgyzstan, they show a positive correlation with both household and community financial growth.

Other determinants of financial development

In addition to remittances, previous studies have highlighted key macroeconomic factors and openness variables that influence financial development, including country size, GDP per capita, inflation, financial openness, and trade openness Research by Goldsmith (1969) and Gurley and Shaw (1967) indicates that economic growth increases the demand for financial products and services, prompting the financial sector to adapt to these evolving requirements This rising demand encourages the emergence of more sophisticated financial intermediaries to address the new needs for their offerings (Yartey, 2008).

Higher income levels can lead to increased savings rates, which in turn foster the development of supplementary financial instruments that channel funds into more effective investments (Kamar and Ben Naceur, 2007; Yartey, 2008) Conversely, poor legal institutions significantly hinder economic development, as countries with weak institutions face challenges in financial development due to decreased stability in savings and investments Research by La Porta et al (1997) highlights that differences in the protection of investor and creditor rights, as well as the enforcement of laws and regulations, can explain the varying levels of advancement in financial systems across different nations.

High inflation negatively impacts financial properties, leading investors to seek opportunities outside the financial sector rather than depositing their money This trend can hinder the growth of financial sectors, as evidenced by recent studies, including those by Boyd et al (2001) and Naceur and Ghazouani (2008), which highlight the adverse relationship between inflation and financial sector performance.

Regarding financial openness, according to the findings of Chinn and Ito

(2002) and Baltagi et al (2009), the capital account openness enhances financial sector accountability and transparency, hence augmenting the access and utilization of financial products and services.

Countries with a higher degree of trade openness are more likely to develop advanced financial systems that facilitate trade transactions Research by Rajan and Zingales (2003) suggests that in nations with low trade openness, established industries may obstruct the growth of the financial sector.

Despite previous studies highlighting the positive effects of remittances on financial development, inconsistent results arise from varying datasets and methodologies Additionally, while factors such as country size, GDP per capita, financial openness, and trade openness generally show a positive influence on financial development, and inflation has a negative impact, the specific effects of these determinants in Asia and among different income groups remain unclear Therefore, it is essential to separately analyze the roles of remittances and other factors in the financial sector's development across selected Asian countries and income groups.

MODEL SPECIFICATION AND DATA

Model specification

When analyzing the relationship between remittances and financial development, potential endogeneity due to measurement error, variable omissions and reverse causality should be taken in to account (Aggarwal et al.,

Remittances often suffer from measurement errors due to informal transfer channels like friends, relatives, and Hawala-type organizations Understanding the causal relationship between remittances and financial development is crucial, as increased financial development may lead to higher recorded remittances This can occur either because financial advancements stimulate remittance inflows or because a greater share of remittances is documented when sent through banks and formal financial institutions Additionally, financial development can reduce the costs associated with transferring remittances, further boosting inflows However, neglecting key factors that influence either remittance growth or financial sector performance can result in biased estimates of the impact of remittances on financial sector development.

This study aims to build a model to explore the effects of remittances on financial development, drawing on prior research to identify suitable variables and model specifications Utilizing the dynamic panel data model as outlined by Aggarwal et al (2006) and Gupta et al (2009), the study addresses existing challenges in the analysis of this relationship The general structure of the dynamic panel data model is presented below.

The equation Y it = α + γY i,t-1 + β 1 X it + n i + ε t + u it illustrates the dynamics of financial development indicators over time, where 'i' denotes individual entities and 't' signifies time periods In this model, Y it represents the current indicators of financial development, while Y it-1 captures their lagged values The explanatory variables, X it, include remittances and other factors influencing financial development Additionally, n i accounts for unobserved country-specific effects, ε t reflects time-specific influences, and u it represents the error term.

Based on previous studies on financial development, remittance indicators serve as a key explanatory variable; however, other influential factors are also included in the model.

FDi,t = β1FDi,t-1 + β2REMITi,t + β3GDPPCi,t + β4LNGDPi,t + β5INFi,t + β6TRADEOPENNESSi,t + β7FINANCIALOPPENNESSi,t + β0 + ui,t (3.2) where i represents for country 1,2,…,37 and t represents for year from 1990 to 2014;

Financial development (FD) is primarily indicated by two key metrics: domestic credit to the private sector by banks as a share of GDP (CREDIT) and the ratio of broad money (M2) to GDP The CREDIT metric assesses the banking sector's efficiency in resource allocation, with a higher ratio signifying increased financial support to the private sector, which in turn fosters greater domestic investment and enhances financial sector development.

Beck et al (1993) and Beck et al (2000) employed the ratio of broad money to GDP to assess the financial system's size Aggarwal et al (2006) argued that the growth of private financial assets, measured through broad money (M2 or M3), reflects the liquidity position of the financial system and indicates the level of monetization and financial market development Additionally, the percentage of broad money relative to GDP (M2/GDP) has been recognized as a key indicator of financial deepening (Brown et al., 2013).

REMIT refers to personal remittances as a percentage of GDP, encompassing personal transfers and compensation of employees, as defined in the IMF's BPM6 Personal remittances include personal transfers, which are broader than traditional worker’s remittances and are not tied to migrants' earnings or recipient relationships Additionally, compensation of employees consists of wages and salaries in cash and kind, as well as social contributions from employers, covering border, seasonal, and short-term workers in non-resident economies and residents employed by non-resident entities (World Bank, 2016).

This study incorporates macroeconomic and openness variables that literature suggests influence financial development Key indicators include GDP per capita (GDPPC) in thousands of constant 2010 US dollars, which reflects economic development and institutional quality Country size is represented by the logarithm of GDP (LNGDP), with GDP data also expressed in constant 2010 US dollars Inflation (INF) is captured by the annual growth rate of the GDP implicit deflator, indicating overall price changes in the economy Trade openness (TRADEOPENNESS) is measured by the sum of exports and imports of goods and services as a percentage of gross domestic product Financial openness (FINANCIALOPENNESS) is assessed using the KAOPEN index, developed by Chinn and Ito (2006), which is derived from four binary dummy variables that reflect restrictions on cross-border financial transactions reported in the IMF's Annual Report on Exchange Arrangements and Exchange Restrictions.

 the existence of multiple exchange rates;

 restrictions on current account transactions;

 restrictions on capital account transactions; and

 the requirement of the surrender of export proceeds.

Chinn and Ito invert binary variables to assign a value of one when there are no capital account restrictions They then calculate the first principal component, which serves as their summary measure known as KAOPEN.

Data sources

This study examines the impact of remittances on financial development by analyzing eight key variables, including two measures of financial development, personal remittances, GDP in constant 2010 US dollars, GDP per capita in constant 2010 US dollars, trade openness, and financial openness.

Most of data except for financial openness were gathered from World Development Indicators (WDI) Financial openness is presented by KAOPEN index, which is calculated by Chinn and Ito (2006).

This study aimed to analyze data from all Asian countries; however, due to the lack of comprehensive data for various variables, it ultimately focuses on an unbalanced dataset As a result, the research includes data from thirty-seven Asian countries spanning the years 1990 to 2014, based on data availability.

Table 3.1: Definition and expected sign of variables

CREDIT Domestic credit to private sector by banks expressed as a percentage of GDP

M2 Broad money measured as a proportion of GDP

Remittances REMIT Personal remittances comprising personal transfers and compensation of employees expressed as a percentage of GDP

Country size LNGDP Log of GDP in constant 2005

The economic development and quality of country legal institutions

GDPPC GDP per capita in thousands of constant 2010 US$

Inflation INF GDP deflator (annual %) Negative

Sum of exports and imports of goods and services expressed as a share of GDP

KAOPEN Index calculated by Chinn and Ito

To assess the impact of remittance inflows across different income groups, countries are categorized as high, middle, or low income based on the World Bank's annual income classifications This study employs two dummy variables, HIGH and MIDDLE, to facilitate the analysis.

 HIGH equal 1 if that country is classified as high income country while HIGH equal 0 if that country is classified as middle or low income country.

 MIDDLE equal 1 if that country is classified as middle income country while MIDDLE equal 0 if that country is classified as high or low income country.

In total of 704 observations, 121 observations are classified as high income country, 365 observations are classified as middle income country and the remaining are classified as low income country.

Estimation methods

This study employs various empirical models to explore the relationship between remittances and financial development Initially, the Pooled OLS model, fixed effects model (FEM), and random effects model (REM) are used to analyze panel data, providing insights into the dynamics between remittances and financial growth Additionally, the System General Method of Moments (GMM) is utilized to address issues related to causal relationships in this context.

In dynamic panel data models, incorporating a lagged dependent variable as an explanatory factor can introduce bias in the results of Pooled OLS, Fixed Effects Model (FEM), and Random Effects Model (REM) To address this issue, models that exclude the lagged dependent variable are utilized for regression analysis Additionally, to evaluate the impact of remittance inflows across various income groups, the model includes interactive terms between remittances and income group dummy variables The analysis presents separate equations for two indicators of financial development.

CREDITi,t = β1REMITi,t + β2GDPPCi,t + β3LNGDPi,t + β4INFi,t + β5TRADEOPENNESSi,t + β6FINANCIALOPPENNESSi,t + β0 + ui,t (1)

CREDITi,t = β1REMITi,t + β2REMIT_HIGHi,t + β3REMIT_MIDDLEi,t + β4GDPPCi,t + β5LNGDPi,t + β6INFi,t + β7TRADEOPENNESSi,t + β8FINANCIALOPPENNESSi,t + β0 + ui,t (2)

M2i,t = β1REMITi,t + β2GDPPCi,t + β3LNGDPi,t + β4INFi,t + β5TRADEOPENNESSi,t + β6FINANCIALOPPENNESSi,t + β0 + ui,t (3)

M2i,t = β1REMITi,t + β2REMIT_HIGHi,t + β3REMIT_MIDDLEi,t + β4GDPPCi,t + β5LNGDPi,t + β6INFi,t + β7TRADEOPENNESSi,t + β8FINANCIALOPPENNESSi,t + β0 + ui,t (4)

Pooled OLS model is one of the most common employed methods with general form as below:

The equation Y it = β 0 + β 1 X it + u it represents a model where Y is the dependent variable, X is the independent variable, β 1 is the coefficient for X, β 0 is the constant intercept, and u it is the error term In this model, 'i' denotes individual observations (1, 2, …, n) and 't' indicates time periods (1, 2, …, T) Assuming no differences among individuals in the estimated cross-section, the Pooled OLS method estimates a common constant β for all individuals, providing a unified approach to analyzing the data across the sample.

To successfully implement this method, several stringent assumptions must be met: the model parameters must be linear, and the error term $ u_{it} $ should follow an independently and identically distributed (iid) pattern, specifically $ u \sim iid(0, \sigma^2) $ Additionally, it is crucial that the error term remains uncorrelated with the explanatory variables over time for each individual Furthermore, the variance of $ u_{it} $ must exhibit homoscedasticity, and there should be no autocorrelation present between $ u_i $ and $ u_j $ (where $ i \neq j $).

In comparison with FEM and REM, the results derived from the regression of the Pool OLS the constant intercept and slope coefficients

Fixed effects regression is a statistical method used to assess the impact of explanatory variables on a dependent variable by analyzing changes in these variables over time This approach allows for a clearer understanding of the relationships by controlling for unobserved variables that may influence the results, making it a valuable tool in longitudinal data analysis.

Where i represents for individual 1,2,…,N and t represents for time 1,2, ,T; α i is the specific intercept for individual i; Y is explained variable; � is explanatory variable; β is coefficient for X it ,; ε it is error component.

The Fixed Effects Model (FEM) asserts that the coefficients for explanatory variables remain constant, with individual-specific effects varying across individuals but remaining unchanged over time By incorporating individual-specific effects through the intercept, FEM allows for a correlation between these time-invariant effects and the explanatory variables, while ensuring that the idiosyncratic error (ε it) does not correlate with the explanatory variables.

Fixed Effects Models (FEM) effectively eliminate the influence of individual time-invariant characteristics, allowing for an accurate assessment of the impact of predictor variables on the dependent variable However, it's crucial that these characteristics are unique to each individual and not correlated with those of others, as well as ensuring that the error terms among individuals are uncorrelated If these conditions are not met, the use of FEM may be inappropriate, and alternative models, such as Random Effects Models, should be considered.

Unlike the Fixed Effects Model (FEM), the Random Effects Model (REM) accommodates variability in individual characteristics, treating these traits as random factors that do not influence the model's input or output variables.

The common form of REM is:

Y it =β 0 +β 1 X it +u it where u it =ε it +v it (3.5)

In this analysis, we denote individual observations by $ i $ (where $ i = 1, 2, \ldots, N $) and time periods by $ t $ (where $ t = 1, 2, \ldots, T $) The dependent variable is represented as $ Y $, while $ X $ signifies the independent variable The coefficients for the independent variable $ X $ are denoted by $ \beta_0 $ and $ \beta_1 $, which represent the intercepts The error term $ u_{it} $ consists of two components: $ \epsilon_{it} $, which captures the cross-sectional error, and $ v_{it} $, which accounts for the combined error from both cross-section and time series data.

In this model, the country-specific effect is treated as a random variable, assumed to be uncorrelated with the regressors Unlike the fixed effect model, the random effect model allows for the inclusion of invariant variables However, if any omitted invariant variables are correlated with the regressors, it can lead to biased and inconsistent estimators.

3.3.4 Tests for choosing sufficient model

This study employs three tests—F-test, Breusch–Pagan LM test, and Hausman Specification test—to determine the most suitable model among the three options The F-test is used to compare the Fixed Effects Model (FEM) and Pooled Ordinary Least Squares (OLS) model, while the Breusch–Pagan LM test assesses the appropriate model between the Random Effects Model (REM) and Pooled OLS Finally, the Hausman Specification test is utilized to evaluate and select the more effective model between FEM and REM.

Basing on goodness of fit, the F-test is applied to choose an appropriate technique between fixed effects method and Pooled OLS The regression

2 model of fix effect method is given as Y it =α+ μ i +βX it +ε it to test the hypothesis

H0: μ 1 = μ 2 =… = μ n-1 = 0 F-test is calculated as follows:

(RSS-URSS)/(N-1) F= URSS/(NT-N-K) ~ FN-1,N(T-1)-K

Where RSS is the restricted residual sum of squares obtained from the Pooled OLS model; URSS is the unrestricted residual sum of squares of FEM.

If the hypothesis H0 is rejected, this means that at least one μ i is different from zero Consequently we can conclude that fixed effect model is favored over the pooled OLS.

The Breusch-Pagan LM test is essential for deciding whether to use Random Effects Model (REM) or pooled Ordinary Least Squares (OLS) in this study The regression model for the random effects method is expressed as Y_it = β_0 + β_1 X_it + u_it, where u_it is the sum of the error terms ε_it and v_it, facilitating hypothesis testing.

H0: var(u)=0 The LM test is computed as:

If the hypothesis H0 is rejected, this means that at least one variance component is different from zero or REM is better than the pool OLS.

The Hausman Specification Test is utilized to determine the appropriate model between Fixed Effects Model (FEM) and Random Effects Model (REM) by assessing the correlation between time-invariant individual effects (ui) and the regressors (xit) When ui is correlated with xit, FEM proves to be consistent and efficient, whereas REM lacks consistency Conversely, if ui is uncorrelated with xit, REM becomes consistent and efficient, while FEM is deemed inefficient.

(𝛽 �� − 𝛽 𝑅 )′[𝑉𝑎𝑟(𝛽 �� ) − 𝑉𝑎𝑟(𝛽 𝑅 )] -1 (𝛽 �� − 𝛽 𝑅 )~χ 2 The null hypothesis Ho: ui is not correlated with the xit or the REM should be employed.

H1: ui is correlated with xit or the FEM should be employed.

To determine the most suitable model, this study employed multiple statistical tests: the F-test, LM test, and Hausman test The F-test was utilized to differentiate between Fixed Effects Model (FEM) and pooled Ordinary Least Squares (OLS) Subsequently, the LM test was performed to select between Random Effects Model (REM) and pooled OLS Lastly, the Hausman test was applied to ascertain the more appropriate model between FEM and REM.

3.3.5 The system generalized method of moment estimation

To explore the relationship between remittance inflows and financial development using a panel dataset, it is essential to consider the positive influence of previous financial development on current financial growth (Chinn and Ito, 2006; Baltagi et al., 2009) This implies that incorporating lagged values of financial development into the model is necessary, or alternatively, employing a dynamic model may be required.

Y it =α+γY i,t-1 +β 1 X it +n i + ε t +u it (3.1) where i represents for individual 1,2,…,n and t represents for time 1,2, ,T;

THE IMPACT OF REMITTANCES ON FINANCIAL

Overview of remittance inflows and financial development in Asia

4.1.1 Overview of remittance inflows to Asia from 1990 to 2014

In recent decades, official remittance inflows have significantly increased, particularly in Asia, where from 1990 to 2014, these inflows consistently rose, representing nearly 50% of global remittances.

Figure 4.1: Remittances received by areas in the world from 1990 to 2014 (US$ billion)

According to World Bank data, the top three remittance recipients in 2014 were Asian countries, with India receiving $70.39 billion, followed by China at $29.91 billion, and the Philippines at $28.69 billion Additionally, Pakistan and Bangladesh also featured prominently, receiving remittances of $17.24 billion and $14.99 billion, respectively.

Figure 4.2: Top 10 remittance recipient countries in 2014 (US$ billion)

In 2014, several Asian countries topped the list of remittance recipients as a share of GDP, with Tajikistan receiving remittances that contributed 43% to its GDP, followed by the Kyrgyz Republic at 30.29% and Nepal at 29.18% Additionally, other middle-income nations such as Lebanon and Armenia also saw significant impacts from remittances, which accounted for over 15% of their GDP.

Despite rankings based on official data, Asia is recognized as the largest recipient of global remittance flows This discrepancy suggests that government institutions may not fully capture the total amount of remittances, indicating that actual flows are likely significantly higher.

Figure 4.3: Top 10 remittance recipient countries in 2014 (% GDP)

South Asia and East Asia are the two largest destinations for remittance inflows in Asia Over the past decade, South Asia has experienced a significant and continuous increase in remittances, accounting for nearly half of the total remittance flows to the region While the volume of remittances may fluctuate, with occasional decreases, East Asia still receives approximately 35% of Asia's total remittances Overall, both regions exhibit a general upward trend in remittance inflows.

From 2000 to 2014, South Asian countries, including India, Pakistan, Bangladesh, and Sri Lanka, saw a substantial increase in remittances, with India receiving nearly US$ 58 billion, followed by Pakistan with over US$ 16 billion and Bangladesh with more than US$ 13 billion In East Asia, China and the Philippines ranked highest in remittance inflows, each exceeding US$ 20 billion, despite a declining percentage of remittances to GDP in China In the Middle East, Lebanon stands out with a notable rise in remittances, contributing significantly to the region's economy.

In Asia, particularly in South Asia and the Middle East, remittance inflows have shown varied trends, with Jordan and Yemen experiencing an increase in volume but a significant decrease in their GDP share Central Asia stands out, as Tajikistan, Kyrgyz Republic, and Armenia have the highest remittance-to-GDP ratios in the region, even though the total remittance amounts in these countries are lower compared to other areas.

Figure 4.4: Remittances to areas in Asia from 1990 to 2014 (US$ billion)

Middle-income countries are the largest recipients of global remittances, as many migrant workers move to high-income areas for better living and working conditions, thereby increasing the funds sent back to their home countries High-income and low-income countries rank second and third in remittance reception, respectively This trend can be attributed to a decline in the number of low-income countries over time and challenges in tracking remittance flows in nations with underdeveloped financial systems Notably, middle-income countries receive approximately 70% of global remittances, with nearly 30% distributed among other income groups.

0 remittances flowing to high income countries while just 1% or 2% is recorded belong to low income countries.

Figure 4.5: Remittances received by income groups in the world from

In Asia, middle-income countries receive the highest volume of remittances, accounting for approximately 50% of total remittances during the specified period Interestingly, low-income countries in the region receive more remittances than high-income countries, contrasting with global trends.

Between 1990 and 2006, remittances increased significantly, with over 75% growth in later years and reaching as high as 90% in 2014 This surge can be attributed to the participation of India and Bangladesh, which are among the top recipients of remittances globally Both countries were classified as middle-income economies starting in 2007 and 2014, respectively, contributing to this remarkable trend.

The absence of India and Bangladesh, coupled with a decline in the number of low-income countries, has significantly reduced remittance volumes to these nations, which accounted for only about 2% of total remittance flows in Asia by 2014.

0 countries, high income economies have been receving more remittances in the period from 2007 to 2014 with around 8% since 2012.

Figure 4.6: Remittances received by income groups in Asia from 1990 to

Asia is the leading region in the world for remittance inflows, both in absolute terms and as a percentage of GDP Over the past two decades, there has been a notable upward trend in these remittance flows, particularly in South and East Asia, as well as among middle-income countries.

4.1.2 Overview of financial development in Asia from 1990 to 2014

Since the 1990s, Asia has experienced significant financial development, evidenced by rising domestic credit to the private sector by banks and an increase in broad money relative to GDP Most regions in Asia showed a clear upward trend in these indicators until the onset of the financial crisis in 1997-1998.

The Asian financial crisis was primarily triggered by the debt crisis in East Asia, while other regions such as South Asia, the Middle East, and Central Asia experienced little change in their financial ratios during that period, except for a significant decline in East Asia.

Figure 4.7: Domestic credit to private sector by banks (% of GDP) in Asia from 1990 to 2014

Between 1998 and 2002, East Asia experienced a continued decline in the ratio of domestic credit to the private sector by banks relative to GDP However, this trend reversed post-2002, with East Asia successfully increasing this ratio, while other regions began to see similar growth During the financial crisis of 2008-2009, significant disparities emerged across Asia, as East Asia's domestic credit to private sector ratio surged, in stark contrast to the declines observed in the Middle East and South Asia.

Empirical results

Table 4.1 summarizes the descriptive statistics of the variables used in this study, revealing high standard deviations and significant differences between minimum and maximum values The average ratio of domestic credit to the private sector by banks to GDP (CREDIT) is approximately 51.8%, with a standard deviation exceeding 46.6% In comparison, the values for broad money (M2) are 65.5% and 53.5%, respectively These figures indicate substantial variability in financial development indicators relative to their mean values Furthermore, the wide range in both domestic credit to the private sector and broad money to GDP across countries highlights the inequality in financial sector development within the Asia region.

Remittances constitute an average of 4.7% of GDP, with a significant standard deviation of over 7.6%, indicating vast disparities among countries; while some nations see remittances contribute nearly 50% to their GDP, others report figures close to zero High-income countries, such as Japan and Saudi Arabia, tend to attract fewer migrants, resulting in lower remittance inflows Additionally, other economic indicators like inflation and GDP per capita also exhibit standard deviations that are more than double their mean values, highlighting the pronounced economic inequalities across Asia.

Table 4.1: The summary statistics of variables

Variable Obs Mean Std Dev Min Max

Figures 4.1 and 4.2 illustrate the relationship between domestic bank credits to the private sector, broad money as a percentage of GDP, and personal remittances Notably, total remittance inflows show a negative linear correlation with domestic bank credit to the private sector, while its correlation with broad money remains ambiguous.

REMIT_MIDDLE CREDIT Fitted values

Figure 4.11: Correlation between domestic credit to private sector by banks (%GDP) and remittance inflows (%GDP) and other controlling variables

Figure 4.12: Correlation between broad money (%GDP) with remittance inflows (%GDP) and other controlling variables

REDIT M2 REMIT GDPPC LNGDP INF TRADE- FINANCIAL- REMIT REMIT

Remittances exhibit a positive correlation with domestic bank credit to the private sector in remittance-receiving countries, despite an unclear relationship within the middle-income group In contrast, both remittances and broad money show a positive correlation in middle-income countries, similar to that observed in high-income nations Other variables, excluding inflation, demonstrate similar fluctuation patterns with indicators of financial development, although these connections are less pronounced regarding trade and financial openness Notably, inflation displays a clear correlation with credit from local banks to the private sector and broad money These correlations are further illustrated in the correlation matrix presented in Table 4.2.

Table 4.2: The correlation between variables

Except for the high correlation value between domestic banks credit to private

F weak connections while the inflows of remittances surprisingly have completely different way in linking with these both indicators as above discussions.

This article explores the relationship between remittance inflows and various financial development indicators, highlighting the complex interactions that exist, particularly across different income groups While the scatter diagram and correlation matrix suggest only minor correlations, the study indicates that these relationships may be more nuanced than they appear Further analysis using economic methods will provide a clearer understanding of these connections in the subsequent section.

This study examines the relationship between remittances and domestic credit to the private sector, as well as the broad money to GDP ratio, by estimating equations using Pool OLS, REM, and FEM methodologies The analysis includes appropriate tests to identify the most suitable model among the three, with the findings detailed in Table 4.3.

Table 4.3: The results of tests for choosing models

F value P-value Chi-square P-value Chi-square P-valueEquation (1) 64.61 0.0000 2976.63 0.0000 31.69 0.0000Equation (2) 55.82 0.0000 2476.28 0.0000 41.94 0.0000Equation (3) 230.23 0.0000 3114.32 0.0000 33.60 0.0000Equation (4) 215.35 0.0000 3128.74 0.0000 44.01 0.0000

In Chapter 3, we analyzed tests for selecting optimal models, revealing that the F-Test and Breusch-Pagan LM Test yielded p-values of 0.0000 for four equations, indicating that both fixed effects and random effects models are preferable to the Pool OLS model The Hausman test, also showing a p-value of 0.0000 across all equations, suggests that fixed effects models are more suitable than random effects models for the dataset used in this study.

Table 4.4 presents the results of fixed effect models with robust standards, illustrating the overall impact of remittances on the ratios of bank credit to the private sector and broad money to GDP in Asia, as outlined in equations (1) and (3) Additionally, the effects across different income groups in the region are detailed in the results of equation (2).

The results of equations (2) and (4) are analyzed to confirm that the interaction terms between remittances and both the high-income and middle-income groups are not simultaneously equal to zero.

Remittance inflows show positive trends in financial development indicators; however, their coefficients lack statistical significance, indicating no relationship with financial growth in the Asia region In contrast, remittances positively correlate with the domestic credit to the private sector by banks to GDP in high-income countries at a 5% significance level, while in middle-income countries, they negatively associate with the broad money to GDP ratio at a 10% significance level.

Research by Singh et al (2011) and Caceres and Saca (2006) highlights the varying effects of remittances on financial development indicators, despite many earlier studies showing similar impacts on these two measures.

Table 4.4: The results of FEM with robust

Notes: *, **, *** denote statistical coefficients at 10%, 5% and 1% significance levels, respectively R-square (within) is reported.

Remittances have varying impacts across income groups, particularly highlighting a trend in middle and low-income countries where the majority of remittance funds are allocated to consumption rather than savings or investment In contrast, high-income countries tend to utilize remittances more productively, with households often depositing surplus funds into banks, thereby enhancing bank credit to the private sector (Brown et al., 2013).

The analysis reveals that, aside from the significant positive correlation between country size (LNGDP) and financial development, and the negative correlation with inflation (albeit insignificant), other variables exhibit varying levels of statistical significance Notably, the openness of the financial sector shows a significant positive correlation with the percentage of domestic credit to the private sector by banks to GDP at the 5% and 1% levels, while having an insignificant association with the proportion of broad money to GDP Conversely, GDP per capita and trade openness are positively correlated with the ratio of bank credit to the private sector to GDP, though insignificantly, while they show significant positive associations with the portion of broad money to GDP at the 5% and 1% levels, respectively These findings suggest that the development of the financial sector is influenced by economic growth, country size, and the openness of financial and trade sectors, but is hindered by rising inflation, as reflected in various measures of financial development.

The findings from the FEM analysis may be skewed due to the bidirectional relationship between financial sector development and remittances, along with other variables considered in the study Therefore, system GMM will be employed to more effectively address endogeneity and assess the effects of remittance inflows on financial development.

Table 4.5: The results of system GMM

Equation (5) Equation (6) Equation (7) Equation (8) LAG 1 OF

Notes: *, **, *** denote statistical coefficients at 10%, 5% and 1% significance levels, respectively R-square (within) is reported.

Table 4.5 presents the findings from the system GMM analysis for equations (5), (6), (7), and (8), illustrating the overall impact of remittances on the ratios of bank credit to the private sector and broad money to GDP in Asia, as detailed in equations (5) and (7) Additionally, the effects of remittances across various income groups within the region are highlighted in the results of equation (6).

CONCLUSIONS AND POLICY IMLICATIONS

Conclusions

Remittance inflows have become a significant source of external finance, ranking as the second largest over recent decades and garnering substantial attention from researchers and policymakers Asia, capturing nearly half of global remittance inflows, stands out as the largest recipient of these funds While much research has focused on the link between remittances, economic growth, and poverty alleviation, there has been a notable lack of investigation into their impact on financial development in the region Additionally, previous studies have produced inconsistent results regarding this relationship, reflecting the diverse methodologies and datasets employed.

This thesis analyzes the impact of increasing remittance inflows to the Asia region on financial development, revealing mixed effects on the financial sector from a panel dataset spanning 1990 to 2014 Despite Asia receiving the largest share of global remittances, the study finds that these inflows do not significantly influence financial development indicators when assessed through Fixed Effects Model (FEM) and System Generalized Method of Moments (GMM) Notably, remittances have distinct effects across income groups: while middle and low-income countries show a positive correlation between remittance inflows and domestic credit to the private sector, high-income countries experience an insignificant impact on broad money to GDP The findings suggest that remittances in lower-income nations are primarily used for consumption, whereas in higher-income areas, they foster trust in formal financial institutions, leading to increased bank deposits and credit access for small businesses Overall, remittances serve as a stable financial resource, potentially acting as collateral for loans and enhancing credit opportunities for small enterprises.

Policy implications

From empirical outcomes, several policies are recommended for taking advantage of remittances in boosting the development of financial sectors.

Improving access to bank products and services is crucial for households in middle and low-income countries, as it raises awareness of formal remittance transfer channels This initial step is essential for familiarizing remittance recipients with formal financial institutions Additionally, diversifying savings can promote the flow of remittances into banks, enabling more productive uses and supporting overall financial development.

Reducing remittance costs and enhancing the quality of banking products and services will motivate migrants and their recipients to use formal banking channels, making transactions cheaper and safer This shift will increase the fees collected by banks and boost household deposits available for lending to the private sector However, high fees associated with formal transfer methods often lead migrants and low-income recipients to rely on unofficial channels, despite their access to banks Additionally, poor service quality deters remittance recipients from further engaging with banking institutions.

Utilizing remittances as collateral for small investments or business loans can benefit recipients with stable and consistent remittance flows This approach can unlock opportunities for remittance-receiving households or individuals who face challenges in accessing capital for their business endeavors.

Limitations and further researches 56 REFERENCES APPENDIX I APPENDIX II: THE REGRESSION RESULTS

Despite significant efforts in this thesis, several limitations remain Firstly, the study only utilizes two indicators to represent financial development due to data shortages, whereas previous research employed multiple measurements Secondly, reliance on World Bank data has resulted in gaps for several variables across different years, particularly in the percentage of broad money to GDP, which may contribute to varying outcomes compared to the domestic credit to the private sector by banks Lastly, the use of an unbalanced panel dataset poses an additional drawback to the research.

To address the limitations in current research on the impact of remittances on financial development, several strategies should be employed Firstly, diverse measurements of financial development must be applied to assess the varying effects of remittance inflows across different dimensions Secondly, data collection should primarily rely on a single source while integrating additional sources to enhance accuracy and comprehensiveness Thirdly, the relationship between remittances and financial development is influenced by the economic methods used, indicating a need for a broader range of methodologies in future studies Lastly, a micro-level analysis is essential to understand how remittance flows are utilized, which will inform the development of relevant programs and policies.

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Table I.1 List of countries and region

10 Iran, Islamic Rep Middle East

FDI ODA & Official Aid Personal remittances

Figure I.1 Remittances, FDI, ODA & other official aids in the world from

Figure I.2: Remittances, FDI, ODA & other official aids in Asia from

APPENDIX II: THE REGRESSION RESULTS

1 Result of equation (1) by Pool OLS method

reg CREDIT REMIT GDPPC LNGDP INF FINANCIALOPENNESS TRADEOPENNESS

Source | SS df MS Number of obs = 699

-+ - F( 6, 692) = 95.10 Model | 684997.648 6 114166.275 Prob > F = 0.0000 Residual | 830716.061 692 1200.45673 R-squared = 0.4519 -+ - Adj R-squared = 0.4472 Total | 1515713.71 698 2171.50961 Root MSE = 34.648

CREDIT | Coef Std Err t P>|t| [95% Conf Interval] -+ -

REMIT | 1368913 1896032 0.72 0.471 -.2353752 5091579 GDPPC | 0012036 0001384 8.70 0.000 0009319 0014754 LNGDP | 10.309 7867851 13.10 0.000 8.76423 11.85377 INF | -.6383684 0979327 -6.52 0.000 -.8306493 -.4460875 FINANCIALOPENNESS | -4.327701 4.783017 -0.90 0.366 -13.71867 5.063265 TRADEOPENNESS | 4753293 0379894 12.51 0.000 4007409 5499176

reg CREDIT REMIT REMIT_HIGH REMIT_MIDDLE GDPPC LNGDP INF FINANCIALOPENNESS

REMIT | 1166973 2121297 0.55 0.582 -.2997999 5331944 REMIT_HIGH | 54.53668 5.495241 9.92 0.000 43.74728 65.32608 REMIT_MIDDLE | 4220063 2902355 1.45 0.146 -.1478444 991857 GDPPC | 0007343 0001402 5.24 0.000 0004591 0010096 LNGDP | 10.9108 7459716 14.63 0.000 9.446154 12.37545 INF | -.5943789 0920438 -6.46 0.000 -.7750983 -.4136595 FINANCIALOPENNESS | 1.875316 4.651326 0.40 0.687 -7.257135 11.00777 TRADEOPENNESS | 4433518 0358946 12.35 0.000 3728761 5138275

REMIT | 1.863729 2113521 8.82 0.000 1.448733 2.278724 GDPPC | 0009946 0001607 6.19 0.000 000679 0013102 LNGDP | 15.81706 8923633 17.72 0.000 14.06488 17.56924 INF | -.747123 1087341 -6.87 0.000 -.9606252 -.5336207 FINANCIALOPENNESS | 12.4839 5.455497 2.29 0.022 1.771881 23.19591 TRADEOPENNESS | 3630693 0426034 8.52 0.000 2794163 4467223

REMIT | 925867 2430129 3.81 0.000 4487019 1.403032 REMIT_HIGH | -1.002219 7.684238 -0.13 0.896 -16.09051 14.08607 REMIT_MIDDLE | 2.354881 3317965 7.10 0.000 1.703386 3.006376

GDPPC | 0011605 0001628 7.13 0.000 0008408 0014802 LNGDP | 15.06913 8681118 17.36 0.000 13.36456 16.7737 INF | -.6870088 1053382 -6.52 0.000 -.8938443 -.4801733 FINANCIALOPENNESS | 4.015715 5.423139 0.74 0.459 -6.632822 14.66425 TRADEOPENNESS | 3308575 0414108 7.99 0.000 2495458 4121693

reg M2 REMIT GDPPC LNGDP INF FINANCIALOPENNESS TRADEOPENNESS

M2 | Coef Std Err t P>|t| [95% Conf Interval] -+ -

reg M2 REMIT REMIT_HIGH REMIT_MIDDLE GDPPC LNGDP INF FINANCIALOPENNESS

REMIT | 3734473 1739304 2.15 0.032 0319208 7149738 GDPPC | 0016842 0002496 6.75 0.000 001194 0021743 LNGDP | 11.88314 2.453112 4.84 0.000 7.066252 16.70002 INF | -.0237079 0594917 -0.40 0.690 -.1405246 0931089 FINANCIALOPENNESS | 22.27592 4.106088 5.43 0.000 14.21329 30.33856 TRADEOPENNESS | 0941891 0442767 2.13 0.034 0072482 1811301

_cons | -277.6653 59.53601 -4.66 0.000 -394.569 -160.7615 sigma_u | 45.438543 sigma_e | 16.871812 rho | 87883382 (fraction of variance due to u_i)

REMIT | 3249448 1866753 1.74 0.082 -.0416094 6914991 REMIT_HIGH | 18.68919 4.528686 4.13 0.000 9.796702 27.58169 REMIT_MIDDLE | 1648921 2689052 0.61 0.540 -.363128 6929122

GDPPC | 0018311 0002497 7.33 0.000 0013408 0023215 LNGDP | 11.14733 2.480295 4.49 0.000 6.277039 16.01762 INF | -.024802 0588158 -0.42 0.673 -.1402923 0906884 FINANCIALOPENNESS | 24.58052 4.100262 5.99 0.000 16.52928 32.63176 TRADEOPENNESS | 0871429 043806 1.99 0.047 0011258 17316

5 Result of equation (1) by FEM

xtreg CREDIT REMIT GDPPC LNGDP INF FINANCIALOPENNESS TRADEOPENNESS, fe

Fixed-effects (within) regression Number of obs = 699

Group variable: code Number of groups = 36

R-sq: within = 0.3200 Obs per group: min = 4 between = 0.1791 avg = 19.4 overall = 0.3097 max = 25 corr(u_i, Xb) = -0.2915

xtreg CREDIT REMIT REMIT_HIGH REMIT_MIDDLE GDPPC LNGDP INF FINANCIALOPENNESS TRADEOPENNESS, fe

REMIT | 4304403 1200561 3.59 0.000 1946824 6661981 REMIT_HIGH | 1.975491 3.243214 0.61 0.543 -4.393308 8.344291 REMIT_MIDDLE | -.5078442 1729705 -2.94 0.003 -.8475118 -.1681767

GDPPC | 0018115 000177 10.24 0.000 001464 0021591 LNGDP | 20.27213 1.623084 12.49 0.000 17.08483 23.45943 INF | -.0308403 0379141 -0.81 0.416 -.1052934 0436129 FINANCIALOPENNESS | 6.176824 2.760999 2.24 0.026 754966 11.59868 TRADEOPENNESS | 2407214 0287956 8.36 0.000 1841746 2972682

xtreg M2 REMIT REMIT_HIGH REMIT_MIDDLE GDPPC LNGDP INF FINANCIALOPENNESS

9 Result of equation (1) by REM

xtreg CREDIT REMIT GDPPC LNGDP INF FINANCIALOPENNESS TRADEOPENNESS

Random-effects GLS regression Number of obs = 699

R-sq: within = 0.3147 Obs per group: min = 4 between = 0.2064 avg = 19.4 overall = 0.3284 max = 25

Wald chi2(6) = 290.51 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000

CREDIT | Coef Std Err z P>|z| [95% Conf Interval] -+ -

xtreg CREDIT REMIT REMIT_HIGH REMIT_MIDDLE GDPPC LNGDP INF FINANCIALOPENNESS TRADEOPENNESS

CREDIT | Coef Std Err z P>|z| [95% Conf Interval] -+ -

REMIT | 4741564 1219161 3.89 0.000 2352053 7131076 REMIT_HIGH | 851582 3.317811 0.26 0.797 -5.651209 7.354373 REMIT_MIDDLE | -.4131369 176143 -2.35 0.019 -.7583708 -.0679031

xtreg M2 REMIT GDPPC LNGDP INF FINANCIALOPENNESS TRADEOPENNESS

M2 | Coef Std Err z P>|z| [95% Conf Interval] -+ -

13 Breuschs – Pagan LM test of equation (1)

Breusch and Pagan Lagrangian multiplier test for random effects

CREDIT[code,t] = Xb + u[code] + e[code,t]

When analyzing the differenced variance matrix, it's important to note that its rank (5) does not match the number of coefficients being tested (6), which could indicate potential issues with the test computation It's advisable to review the output of your estimators for any anomalies and consider scaling your variables to ensure that the coefficients are comparable in scale.

- b = consistent under Ho and Ha; obtained from xtreg

B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic chi2(5) = (b-B)'[(V_b-V_B)^(-1)](b-B)

When analyzing the differenced variance matrix, it's important to note that its rank (7) may not match the number of coefficients being tested (8) Ensure this discrepancy aligns with your expectations to avoid potential issues in test computation Additionally, review the output from your estimators for any anomalies, and consider scaling your variables to ensure that the coefficients are on a comparable scale.

When conducting tests involving a differenced variance matrix, it's important to note that the rank of the matrix may not match the number of coefficients being tested In this case, the rank is 5 while the number of coefficients is 6, which could indicate potential issues with the computation of the test It's advisable to review the output of your estimators for any anomalies and consider scaling your variables to ensure that the coefficients are comparable.

Coefficients b = consistent under Ho and Ha; obtained from xtreg

REMIT | 3734473 2300381 1.62 0.113 -.0935549 8404496 GDPPC | 0016842 0011945 1.41 0.167 -.0007409 0041092 LNGDP | 11.88314 4.985376 2.38 0.023 1.762286 22.00399 INF | -.0237079 0383598 -0.62 0.541 -.1015823 0541666 FINANCIALOPENNESS | 22.27592 8.334838 2.67 0.011 5.355303 39.19655 TRADEOPENNESS | 0941891 1094212 0.86 0.395 -.1279477 316326

When analyzing the differenced variance matrix, it's important to note that its rank (7) may not match the number of coefficients being tested (8) This discrepancy should align with your expectations, as any mismatch could lead to complications in test computations Additionally, review the output of your estimators for any anomalies and consider scaling your variables to ensure that the coefficients are comparable.

21 Result of equation (1) by FEM with robust

xtreg CREDIT REMIT GDPPC LNGDP INF FINANCIALOPENNESS TRADEOPENNESS, fe r

(Std Err adjusted for 36 clusters in code) -

GDPPC | 0018311 0011578 1.58 0.123 -.0005193 0041816 LNGDP | 11.14733 5.130455 2.17 0.037 7319498 21.56271 INF | -.024802 0394337 -0.63 0.533 -.1048567 0552528 FINANCIALOPENNESS | 24.58052 7.429819 3.31 0.002 9.497182 39.66385 TRADEOPENNESS | 0871429 1031763 0.84 0.404 -.1223161 2966019

xtreg CREDIT REMIT REMIT_HIGH REMIT_MIDDLE GDPPC LNGDP INF FINANCIALOPENNESS TRADEOPENNESS, fe r

- test REMIT REMIT_HIGH REMIT_MIDDLE

REMIT | 2938719 2568168 1.14 0.260 -.2280427 8157865 GDPPC | 001841 0008173 2.25 0.031 0001801 0035018 LNGDP | 19.3266 5.833566 3.31 0.002 7.471372 31.18184 INF | -.031237 0492367 -0.63 0.530 -.131298 0688241 FINANCIALOPENNESS | 6.38807 6.509756 0.98 0.333 -6.841347 19.61749 TRADEOPENNESS | 2420957 0708248 3.42 0.002 0981623 386029

xtreg M2 REMIT GDPPC LNGDP INF FINANCIALOPENNESS TRADEOPENNESS, fe r

REMIT | 4304403 2722296 1.58 0.123 -.1227968 9836773 REMIT_HIGH | 1.975491 13.51297 0.15 0.885 -25.48617 29.43715 REMIT_MIDDLE | -.5078442 2845456 -1.78 0.083 -1.08611 0704219

GDPPC | 0018115 0008505 2.13 0.040 0000831 00354 LNGDP | 20.27213 5.857076 3.46 0.001 8.369117 32.17514 INF | -.0308403 0522309 -0.59 0.559 -.1369862 0753057 FINANCIALOPENNESS | 6.176824 6.748893 0.92 0.367 -7.538576 19.89222 TRADEOPENNESS | 2407214 0660723 3.64 0.001 1064464 3749965

Group variable: code Number of obs = 625

Time variable : year Number of groups = 36

Number of instruments = 8 Obs per group: min = 2

L1 | 9838308 0066582 147.76 0.000 9707553 9969064 REMIT | -.0277577 | 0323711 -0.86 0.392 -.0913285 0358131 GDPPC | 000049 0000277 1.77 0.077 -5.39e-06 0001034 LNGDP | -.1095696 1520272 -0.72 0.471 -.4081231 1889839 INF | -.077817 0169666 -4.59 0.000 -.1111363 -.0444976 FINANCIALOPENNESS | -1.193222 8314943 -1.44 0.152 -2.826124 4396799 TRADEOPENNESS | 0122349 0071549 1.71 0.088 -.0018159 0262858

25 Result of equation (5) by system GMM

Dynamic panel-data estimation, one-step system GMM

GDPPC LNGDP INF FINANCIALOPENNESS TRADEOPENNESS L.REMIT L2.CREDIT

L1 | 9771188 0072232 135.28 0.000 9629336 9913039 REMIT | -.0077992 | 0392346 -0.20 0.842 -.0848491 0692508 REMIT_HIGH | 3.30484 1.307392 2.53 0.012 7373462 5.872334 REMIT_MIDDLE | -.0238959 0550876 -0.43 0.665 -.1320785 0842868

GDPPC | 0000238 0000293 0.81 0.416 -.0000337 0000813 LNGDP | 0192888 1597831 0.12 0.904 -.2944977 3330754 INF | -.0800792 0169113 -4.74 0.000 -.1132902 -.0468682 FINANCIALOPENNESS | -.7029093 8702677 -0.81 0.420 -2.411966 1.006148 TRADEOPENNESS | 014597 0071926 2.03 0.043 0004719 028722

xtabond2 CREDIT l1.CREDIT REMIT REMIT_HIGH REMIT_MIDDLE GDPPC LNGDP INF

FINANCIALOPENNESS TRADEOPENNESS, iv( GDPPC LNGDP INF FINANCIALO

> PENNESS TRADEOPENNESS l1.REMIT l1.REMIT_HIGH l1.REMIT_MIDDLE l2.CREDIT,eq(level)) small arlevels

Favoring speed over space To switch, type or click on mata: mata set matafavor space, perm.

GDPPC LNGDP INF FINANCIALOPENNESS TRADEOPENNESS L.REMIT L.REMIT_HIGH

L1 | M2 | 9936289 0061673 161.11 0.000 9815165 1.005741 REMIT | -.0113328 | 0350881 -0.32 0.747 -.0802447 0575791 GDPPC | 0000247 0000288 0.86 0.391 -.0000318 0000813 LNGDP | 2303206 1745863 1.32 0.188 -.112561 5732022 INF | -.0896665 0172437 -5.20 0.000 -.1235325 -.0558005 FINANCIALOPENNESS | -.0810879 8715184 -0.09 0.926 -1.79272 1.630544 TRADEOPENNESS | 0019923 0069409 0.29 0.774 -.0116394 0156241

xtabond2 M2 l1.M2 REMIT GDPPC LNGDP INF FINANCIALOPENNESS TRADEOPENNESS, iv( GDPPC LNGDP INF FINANCIALOPENNESS TRADEOPENNESS l1.REMIT l

Favoring speed over space To switch, type or click on mata: mata set matafavor space, perm.

GDPPC LNGDP INF FINANCIALOPENNESS TRADEOPENNESS L.REMIT L2.M2

Tiêu đề	The Impact Of Remittances On Financial Development In Selected Asian Countries
Tác giả	Huynh Thi My Chi
Người hướng dẫn	Dr. Nguyen Van Ngai
Trường học	University of Economics Ho Chi Minh City
Chuyên ngành	Development Economics
Thể loại	Thesis
Năm xuất bản	2016
Thành phố	Ho Chi Minh City

Định dạng
Số trang	94
Dung lượng	1,17 MB