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- 1. UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL STUDIES THE HAGUE THE NETHERLANDS VIETNAM-NETHERLANDS PROGRAMME FOR MASTER IN DEVELOPMENT ECONOMICS THE RELATIONSHIP BETWEEN FINANCIAL DEVELOPMENT AND HOUSEHOLD WELFARE: CASE STUDY IN FIVE ASIAN COUNTRIES By PHAN THI KHANH VAN This paper was submitted in partial fulfillment of the requirements for Master’s degree in Development Economics Ho Chi Minh City, July 2013
- 2. UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HAGUE VIETNAM THE NETHERLANDS VIETNAM-NETHERLANDS PROGRAMME FOR MASTER IN DEVELOPMENT ECONOMICS THE RELATIONSHIP BETWEEN FINANCIAL DEVELOPMENT AND HOUSEHOLD WELFARE: CASE STUDY IN FIVE ASIAN COUNTRIES By PHAN THI KHANH VAN Academic supervisor Dr. DUONG NHU HUNG Thispaper was submitted in partial fulfillment of the requirements for Master’s degree in Development Economics Ho Chi Minh City, July 2013
- 3. [i] CONTENTS ACKNOWLEDGEMENT .....................................................................................................iii ABSTRACT ............................................................................................................................iv ABBREVIATION.................................................................................................................... v LIST OF FIGURES................................................................................................................vi LIST OF TABLES..................................................................................................................vi CHAPTER I: INTRODUCTION........................................................................................... 1 1. Problem statement...............................................................................................................1 2. Research objectives.............................................................................................................3 3. Research questions..............................................................................................................4 4. Justification of the study.....................................................................................................4 5. Scope of the study...............................................................................................................4 6. Structure of the study..........................................................................................................4 CHAPTER II: LITERATURE REVIEW............................................................................. 5 1. Definitions of key concepts ................................................................................................5 1.1. Financial development.................................................................................................5 1.2. Household welfare and Poverty...................................................................................5 2. Theoretical literature...........................................................................................................6 2.1. Direct relationship........................................................................................................7 2.2. Indirect relationship .....................................................................................................8 3. Empirical studies.................................................................................................................9 CHAPTER III: ECONOMETRICS REVIEW................................................................... 12 1. Stochastic Process, Stationarity and Random Walks........................................................12 2. Unit Root Test...................................................................................................................13 3. Cointegration.....................................................................................................................13 4. Granger Causality Test .....................................................................................................14 5. Panel Unit Root Test.........................................................................................................15 6. Panel Cointegration...........................................................................................................16
- 4. [ii] 7. Instrumental Variables Regression (IV) ...........................................................................18 8. Generalized method of moments (GMM).........................................................................19 CHAPTER IV: DATA AND RESEARCH METHODOLOGY ....................................... 22 1. Data...................................................................................................................................22 2. Research methodology......................................................................................................23 CHAPTER V: ANALYSIS RESULTS................................................................................ 26 1. Data descriptions...............................................................................................................26 2. Empirical results ...............................................................................................................31 CHAPTER VI: CONCLUSIONS AND POLICY IMPLICATIONS............................... 36 1. Conclusions.......................................................................................................................36 2. Policy implications............................................................................................................37 3. Limitations and directions for further studies...................................................................38 3.1. Limitations.................................................................................................................38 3.2. Directions for further studies .....................................................................................39 REFERENCES ...................................................................................................................... 40 APPENDICES.......................................................................................................................... a Appendix 1: Description of FD and PR variables (1998-2011) ............................................. a Appendix 2: Panel Unit Root Test of variables ......................................................................b Appendix 3: Pedroni Cointegration Test .................................................................................j Appendix 4: GMM.................................................................................................................m
- 5. [iii] ACKNOWLEDGEMENT The study would be done successfully thank to the beautiful assistance and guidance of everybody who are always with me during the research period. First, I would like to express my deepest appreciation to The Board of Management and Theoretical Supervision of this program. In fact, I would like to send sincere thanks to Dr. Nguyen Trong Hoai, Dr. Pham Khanh Nam who give me a very first step guidance doing this study (theoretical review and techniques understanding) and give me a piece of advice when I got in stuck with my study. Besides that, I also thank to Dr. Duong Dang Thuy, who has given me theoretical advice and introduced me to Dr. Le Van Chon for intensive support. Moreover, I would like to express my gratitude to Dr. Le Van Chon and Dr. Phung Thanh Binh who supported my study and my mind when I lost inspiration during research period. Not only the teaching staff but also the class MDE 17 classmates are those I really appreciate. Thanks to their care, their understanding and their sharing, I know exactly what I should do. The more importantly, I would like to express my appreciation to my direct supervisor Dr. Duong Nhu Hung. He is a very kind teacher who always cares me and encourages me during the research period. He is kind to me with his scientific guidance, soft but invaluable advice till the final stage of the study. Last but not least, during the time doing this study, I encountered both mental and fiscal problems. At that time, my family, especially my mother, my grandmother and my aunts who always advise me to try my best and give me spiritual assistance. I also send my sincere thanks to my husband who is always with me. I am proud of his patience and his sympathy. He has given me a chance to concentrate on my studying instead of housework.
- 6. [iv] ABSTRACT The study presents the empirical result of the relationship between financial development and household welfare, which has been the hotly debated issue recently. To detect the nexus of financial development and household welfare, the Pedroni cointegration test is run to find out the long-run relationship between financial development and household welfare. In empirical study, it is affirmed that there exists the long-run relationship between financial development and household welfare. However, the impact of financial development on household welfare cannot be shown through Pedroni cointegration test. Thus, 2SLS GMM is deployed to identify the impact of financial development on household welfare. Keywords: financial development, household welfare, cointegration, two stage least squares,
- 7. [v] ABBREVIATION 2SLS: Two Stage Least Squares ADF: Augmented Dickey Fuller ADRL: Autoregressive Distributed Lag Model AIC: Akaike information criterion AR: Auto-regressive DCBS: Domestic credit provided by banking sector as a percentage of GDP DCP/GDP: Domestic credit to the private sector as a ratio of gross domestic product DCPS: Domestic credit to the private sector as a percentage of GDP DF: Dickey Fuller DMBA: Domestic money bank assets EG: Economic growth FD: Financial development GDP: Gross Domestic Product GMM: Generalized method of moments HLSS: Household Living Standards Survey IMF: International Monetary Fund IV: Instrumental Variable M2/GDP: money and quasi money as percentage of GDP M3: the broadest definition of money OECD: Organization for Economic Co-operation and Development OLS: Ordinary Least Square PP: Phillips-Perron PR: Poverty reduction SBC: Schwarz’s Bayesian criterion SME: Small, medium-sized enterprise VAR: Vector Auto-regressive VECM: Vector Error Correction Model WB: World Bank WDI: World Development Indicator WEF: World Economic Forum
- 8. [vi] LIST OF FIGURES Figure 1: Money and quasi money (M2) as percentage of GDP ................................................. 2 Figure 2: Household per capita consumption (constant 2000 US$) ............................................ 3 Figure 3: Financial Sector Development and Poverty Reduction................................................ 6 Figure 4: Line graph of proxies of FD and household welfare from 1960 to 2011in five Asian countries..................................................................................................................................... 28 Figure 5: Relationship between FD and per capita consumption in five Asian countries (1960- 2011) .......................................................................................................................................... 30 LIST OF TABLES Table 1: Empirical studies about the causal nexus of FD and PR ............................................. 10 Table 2: Proxy variables ............................................................................................................ 23 Table 3: Description of FD and household welfare variables (1960-2011)............................... 27 Table 4: t-statistics panel unit root tests..................................................................................... 32 Table 5: t-statistics panel unit root test: Variables at the first difference .................................. 32 Table 6: Pedroni cointegration tests: Variables from 1960 to 2011 .......................................... 33 Table 7: Pedroni cointegration tests: Variables from 1998 to 2011 .......................................... 33 Table 8: Two stage least squares estimator between FD and PR .............................................. 34
- 9. [1] CHAPTER I: INTRODUCTION 1. Problem statement It is undeniable that the relationship between financial development (FD) and economic growth (EG) has been one of the most attractive areas of research in the field of economic development over recent decades. Some related studies have been employed, yet this relation remains controversial issues. In fact, there have been some conflicts on the relationship between finance and growth in earlier literature. In fact, Robinson (1952) and Lucas (1988) dismiss the role of finance in understanding EG while McKinnon (1973) and Miller (1988) insist on this relation between FD and EG. In recent researches, the harmony of vital roles of finance in enhancing growth has been reached. For example, Kirkpatrik (2000) states that good financial system that mobilizes savings and allocates resources to more productivity contributes to growth by supporting capital accumulation, promoting investment efficiency, and improving technology. Furthermore, many people believe that EG reduces absolute poverty because the more growth the economy reaches, the more jobs would be generated for the poor or the fewer differentials in wage between the skilled and unskilled labor at a later stage of development (Galor and Tsiddon, 1996) benefits the poor. Then, a consensus emerged recently is that EG overall leads to poverty reduction (PR) through the improvement of household welfare. However, these close relationships between FD and EG or between EG and household welfare do not mean that FD contributes to PR (Beck et al, 2007) through the improvement of household’s welfare. The explanation follows that the goal of EG in most developing countries is linked with both PR and income distribution. In other words, if FD stimulates EG by increasing income of the rich, which results in worsening income equality, FD will not benefit the poor. This debate appeals many researchers to conduct studies on relationship between FD and household welfare. In addition, this paper aims to examine the relationship between FD and household welfare in five Asian countries including Indonesia, Malaysia, Philippines, Thailand, and Vietnam. The reason why the research focuses on a set of these five Asian countries is that there is little research on FD and PR in Asia. Due to the limit of short time series data, the research will identify the relationship between FD and PR in panel data, especially panel five Asian countries with the assumption that these countries are nearly at the same foundation of development.
- 10. [2] In the context of these countries including Vietnam, it can be seen that finance sector has a rapid development in both quantity and quality, especially in the global economy. According to Odhiambo (2008), the ratio M2/GDP indicates the real size of financial sector of a developing country. Figure 1 provides some statistics of financial depth of five Asian countries in the period 1990-2010. Most of them (except Indonesia which has a slightly decrease in M2/GDP and Philippines which is nearly stable) have an increase in finance sector. In fact, the slightly increasing financial sector in such three countries – Malaysia and Thailand – has been seen while it is noted that Vietnam is the country, which has a dramatic improvement in financial sector, particularly the ratio M2/GDP has been moving from around 21 percent in 1992 to nearly 110 percent in 2011. Figure 1: Money and quasi money (M2) as percentage of GDP (Source: World Development Indicator – World Bank) Moreover, household consumption per capita in five Asian countries has also gained as the illustration of figure 2. In detail, only in Malaysia, it can be seen the dramatic increase in household welfare which is expressed by the per capita consumption from around 1,800 US$ in 1997 to 2,800 US$ in 2011 even though there is a steep reduction to over 1,500 US$ in 1998. It reaches nearly to the double in per capita consumption. Similarly, household per 0 20 40 60 80 100 120 140 160 M2/GDP (%) Year Indonesia Malaysia Philippines Thailand Vietnam
- 11. [3] capita consumption in four countries is increasing. The slope of this line is not so steep as Malaysia’s. In sum, it can be seen that finance sector improves, household per capita consumption increases. Hence, the household welfare can be said to be improved. In other words, FD and household welfare have a positive relationship. Figure 2: Household per capita consumption (constant 2000 US$) (Source: World Development Indicator – World Bank) Thus, it is suggested to raise the question of whether a well-functioning financial system will help to enhance the welfare of household or not. For answering this question, as well as providing policy implications, the paper particularly focus on investigating the causal relationship between FD and household welfare in the five Asian countries. 2. Research objectives In the paper, the specific research objectives are to: i. To examine whether there is any relationship between FD and household welfare in these five Asian countries. ii. How does FD affect household welfare? 0 500 1000 1500 2000 2500 3000 3500 Household per capita consumption (constant 2000 US$) Year Indonesia Malaysia Philippines Thailand Vietnam
- 12. [4] iii. To draw some policy implications in order to help authorities have a general view and then propose some necessary/appropriate interventions. 3. Research questions This paper attempts to answer following research questions: - Main question: What is the relationship between FD and household welfare in these five Asian countries? How does FD influence household welfare? 4. Justification of the study This paper attempts to identify the relationship between FD and household welfare, which has been one of the hottest issues in research field recently. In academics, most of researches focus on the nexus of finance-growth, while this paper tries to make an effort to go further to the relationship between FD and household welfare. In addition, this paper attempts to update this relationship in these five Asian countries, which should be taken a significant consideration because researches on this field mainly have been done in African regions, some Europe countries especially in Turkey and some Asian countries such as India, China. Hence, this research will establish a new foundation for further study and for local authorities to propose some necessary and appropriate policies to improve the standard living of household. 5. Scope of the study The study will examine the causal nexus of development finance and household welfare in five Asian countries including Malaysia, Indonesia, Philippines, Thailand and Vietnam with the data time series spanning from 1960 to 2011. 6. Structure of the study The rest of the paper will be organized in four more sections. Section 2 presents the thereotical review of the relationship between FD and household welfare; some empirical studies are also mentioned in this section. Section 3 presents a brief discussion about econometrics review. Next, section 4 describes data and research methodology. Section 5 dicusses the findings and discussions. Finally, Section 6 concludes and suggests some practical policy implications; limitation and direction for futher studies are considered at the end.
- 13. [5] CHAPTER II: LITERATURE REVIEW In this chapter, some theories and studies of financial development and household welfare are reviewed. In addition, this chapter also covers some empirical study on the relationship between financial development and household welfare. In general, this chapter comprises three main parts: definitions of key concepts; theoretical review of the relationship between financial development and household welfare; and some empirical studies. 1. Definitions of key concepts 1.1. Financial development There are several definitions of FD in many researches. FD is a concept related to activities of the stock market (Chinn and Ito, 2007), which financial contracts are enforceable (Mendoza, Quadrini and Rios-Rull, 2007) and the process of innovations and improvements of financial institutions or organizations in the financial market (Hartmann et al., 2007). In 2011, Noureen Adna addresses at one international conference that all the factors such as policies, factors and the institutions that make a contribution to the efficiency of financial intermediaries and the efficiency of financial market are related to FD. Its definition is quite consistent with that of the report of World Economic Forum (WEF) in the same year. Similarly, in the new research of Imran and Khalil (2012), “financial development can be defined as a process of improving the quantity, quality and efficiency of financial intermediary services”. 1.2. Household welfare and Poverty As mentioned in Merriam-Webster dictionary, welfare is a concept which refers to the state of doing well especially in respect to good fortune, happiness, well-being, or prosperity. Consequently, household welfare simply refers to household well-being or household prosperity. The failure for achieving a minimal capabilities or doing primarily important functions means poverty. The concept of poverty has been raised for a long time. Actually, there are numbers of definitions of poverty. Poverty is referred, on basics, to the fact that needs of individuals or households might be satisfied in a range of limited resources. According to World Development Report of WB in 2000, poverty is defined as the state of that human suffers the physiological deprivation and social deprivation as well in their life. Moreover, based on citation of United Nation’s Economic and Social Council in
- 14. [6] Weisfeld-Adams et al. (2008), poverty is fundamentally referred to an offense of human dignity. It means that there is limited access to take park in social activities and limited resources to meet some certain basic needs such as clothes, food, water, education, credit. Then, poverty reduction is ultimately aimed at encouraging the pro-poor growth in main sectors such as infrastructure, agriculture. 2. Theoretical literature Several studies have tried to examine the impact of FD on household welfare. There are two main ways that are relevant (see Figure 3). First, the direct approach is to examine the link between FD and PR without other intermediary concepts, then sticks to household welfare. Second, the indirect approach when investigating the connection between FD and PR also considers several concepts such as savings, growth. It is believed that FD helps resource allocation efficiently, improves corporate governance effectively, mobilizes more savings and facilitates the exchange of goods and services. Then, it leads to the improvements of the poor because FD creates more opportunities for them to be employed, and consequently their consumption is becoming smoothing, which enhances their well-being. The detailed flow of process of financial sector development and PR or welfare is demonstrated as follows: Figure 3: Financial Sector Development and Poverty Reduction (Source: Adapted from Claessens and Feijen, 2006, as cited in Zhuang et al, 2009)
- 15. [7] 2.1. Direct relationship FD directly reduces poverty mainly by widening accessibility of credit for the poor and for SMEs. First, FD can increase the probabilities of formal financial service access of the poor; therefore, it enhances PR (Stiglitz, 1998; Jalilian and Kirkpatrick, 2001), so it also improve their welfare. FD could strengthen the productiveness of the poor households’ assets, therefore, enhance their productivity. Credit accesses and other financial service give low income households a chance to switch from low-risk, low-return assets for preventive purposes (like jewelry), to higher risk and higher return assets, (for instance education, or an agricultural instrument), with generally long-term income improving effects (Dehejia & Gatti, 2002). Savings facilities’ supplying can allow the poor to build up of reserves securely over time to finance comparatively large, incoming investments or expenditures. For instance, the credit’s accessibility can reinforce the poor’s productive assets by allowing them to invest in productivity like new and higher technological implements, or to invest in their education and health. Moreover, the poor can use their savings to facilitate smooth consumption for unexpected changes in their life (Holden and Prokopenko, 2001; Odhiambo, 2009). Furthermore, they can occasionally attain their savings’ return. In all, those features can be principally essential for the poor to improve their condition (DFID, 2004). FD increases the possibility for accomplishing sustainable livelihoods. Credit’s accesses can decrease the susceptibility of the low income households in the case of no savings or insurance when shocks come . As discussed above, savings facilities can allow the poor to accumulate their funds for unexpected thing like diseases or unemployment. Hence, the shocks might be avoided, and the probability of being poor, as a result, might be minimized significantly (Zhuang et al., 2009). Second, the FD allows the poor households to build up reserves or to borrow money to establish micro-enterprises (DFID, 2004). Credit access can be a determinant in the construction or development of small and medium businesses. Therefore, it generates employment and raises incomes (DFID, 2004). In developing countries, the small, medium and micro-sized enterprises are the most important instrument for PR or welfare improvement. It is due to the fact that creating job is the principal channel to improve prosperity whilst SMEs are obviously employment-intensive (Zhuang et al., 2009). However, Zhuang et al. (2009) also maintained that the accessibility of credit for SMEs is lower
- 16. [8] compared with large enterprises. Similarly, cost of credit for SMEs is higher. In fact, there are good explanations for those observations. From a lender’s viewpoint, it is desirable to supply credit to large enterprises. In addition, SMEs do not have ability to offer collateral to make loans. In all, enhancing SME credit has a significant role of PR. 2.2. Indirect relationship Several researches established the indirect relationship between the financial FD and the household welfare. This indirect relationship is considered by examining the role of FD to the EG and investigating the contribution that growth leads to improvement of household welfare. First, the contribution of FD to growth is examined by many recent studies. Theoretically, EG is affected by some certain financial variables in the way of increasing savings of financial assets. It results in the accumulation of the capital formation. Indeed, several empirical researches such as Odhiambo (2008), Liang and Teng (2006), and Kar et al. (2011) have supported that concern. Second, the linkage between growth and household welfare (or it can be said to be poverty reduction) is also focused with attention in recent years. Dollar and Kraay (2001) used the data related to the lowest income quintiles, claimed that growth benefits the poor more than other income quintiles and therefore reduce income inequalities as well as PR. Klasen (2008) used both income and non-income indicator to support that growth could reduce income inequalities. Donaldson (2008) also claimed that growth is good for the poor. However, Holden and Prokopenko (2001) indicated EG does not have any relationship with poverty alleviation or household welfare in some situation. They argued that in high growth countries, the beneficiaries may not be the poor. In those cases, the issues about the income inequality increases. This means that the rich are richer while almost the poor become poorer. Likewise, Basu and Mallick (2008), when examining the rural Indian case, they found that grow reduced poverty does not appear in that location. Indeed, the associations between concepts in indirect methods are currently debated (Kar et al., 2010). Therefore in this research, the causal relationship between FD and PR are examined by applying the direct method.
- 17. [9] 3. Empirical studies FD is able to induce PR theoretically in different ways (Obhiambo, 2009).By cutting down cost of activities in lending procedures, FD is able to facilitate the poor lending money in formal financial institutions (Stiglitz, 1998). Moreover, FD can help the poor achieve a sustainable livelihood by enabling them to access financial services.By somehow FD may indirectly impact the poor by influencing economic growth. Recent studies have attempted to study the causal nexus of FD and household welfare as following. Quartey (2005) used VECM and descriptive statistics to investigate the relationship between FD and PR in Ghana by using annual data from 1970 to 2001. He tested the causal direction between financial sector development and domestic resource mobilization; financial sector development and poverty reduction; and domestic resource mobilization and poverty reduction. So, the answers for this main research question are that: even though there are no linkages between FD and mobilized savings in Ghana, he found that financial sector development seems to cause PR. Besides that, his findings are also included that: First, the impact of FD on per capita consumption statistically is insignificant although the sign is positive; second, there seems to exist on a long-run cointegration relationship between FD and per capita consumption. Similarly, Odhiambo (2009) studied the causal nexus of FD and PR in Zambia from 1969 - 2006, but he used different method which is ARDL model. In this research, he used three proxies of FD, which are broad money supply (M2/GDP), domestic credit to the private sector as a ratio of gross domestic product (DCP/GDP) and domestic money bank assets (DMBA) and use per capita consumption as a proxy of PR. When M2/GDP is used as a proxy of FD, he found that PR might cause per capita consumption. However, when the DMBA and the DCP are used as proxy of FD, FD tend to cause per capita consumption respectively. Moreover, in the research of Odhiambo & Ho (2011), ARDL method is used to find out the relationship between FD and PR in China from 1978 to 2008. When using DCP/GDP ratio as a proxy for FD, they found the distinct causal direction in short run that FD causes per capita consumption. Whilst using M2/GDP ratio for proxy of FD, there still have bidirectional causality from FD to per capita consumption in short run; but inversely per capita consumption induces FD in long run. Kar et al (2010) used the annual data of IMF and OCED online database spanning from 1970-2005, and using VECM model toexamine the causal nexus of FD and economic growth. Three proxies of FD were used to investigate respectively were M2/GDP ratio,
- 18. [10] DCP/GDP ratio and private credit/GDP ratio. Some findings that the influence of FD on the per capita consumption could be found in the case of short run and weak; and they concluded that the relation of FD and per capita consumption is too blurry in short run once comparing to the causality in long run. Finally, Inoue and Hamori (2010) investigated how financial deepening affected poverty reduction in India using state-level panel data in India. However, they used a different method that is a dynamic generalized method of moments (GMM) estimation. Ultimately, they found the evidence supporting for the relationship between FD and PR; EG and PR. Moreover, they concluded that the higher inflation and the more international openness affect negatively on the poor. Table 1: Empirical studies about the causal nexus of FD and PR No Authors Methodology Data Findings 1 Quartey (2005) Descriptive statistics and Granger causality Annual data of Ghana from 1970- 2001 -FD does not cause savings mobilization. - FD induces per capita consumption. - Saving mobilization causes per capita consumption. 2 Odhiambo (2009) ARDL Annual data of Zambia from 1969- 2006 - PR may cause per capita consumption when M2/GDP is used as a proxy of FD. - Nevertheless, when DMBA and DCP are used, FD tends to cause per capita consumption respectively. 3 Kar et al (2010) VECM Annual data of Turkey from 1970- 2007 -FD causes EG and EG induces per capita consumption - The direct nexus from FD to per capita consumption is unclear in the short-run. 4 Inoue and Hamori (2010) Dynamic generalized method of moments (GMM) State-level panel data in India (28 states in India) -FD and EG induce PR. - The higher inflation and the more international openness affect negatively on the poor.
- 19. [11] 5 Ho and Odhiambo (2011) ADRL Annual data of China from 1978 to 2008 -When using DCP/GDP ratio as a proxy for FD, they found a distinct causal direction in short run that FD causes PR. - Whilst using M2/GDP ratio for proxy of FD, there still have bidirectional causality from FD to PR in short run; but inversely PR induces FD in long run. In sum, this chapter has captured a general picture of the nexus between FD and household welfare as well as PR in theoretical and empirical aspects as well. In practice, there is a variety of methodologies employed to detect this relationship. Most of them, which used VECM method to find the causal relationship between FD and per capita consumption, found the same finding that FD induces per capita consumption.
- 20. [12] CHAPTER III: ECONOMETRICS REVIEW In this chapter, the econometric issues related to my study will be presented. In particular, this chapter will describe basic concepts on stochastic process, stationarity, randon walk in time series and some advanced methodologies and concepts such as panel cointegration, generalised method of moments. 1. Stochastic Process, Stationarity and Random Walks In time series econometrics, it is equally important that the analysts should clearly understand the term “stochastic process”. “Stochastic process is a collection of random variables ordered in time” (Gujarati, 2003). All basic assumptions in time series models are related to the stochastic process. In the context of time series regression, the idea that historical relationships can be generalized to the future is formalized by the concept of stationary. According to Gujarati (2003), a key concept underlying stochastic process that attracts many analysts’ attention is named stationary stochastic process. In general, when there exist a constant mean value and a constant variance over time, a stochastic process can be called stationary. Otherwise, it is called a nonstationary time series. Stationarity is very important in the context of time series models because if the series is nonstationary, all the typical results then are invalid. Regressions with nonstationary time series may have no meaning and are therefore called spurious (Asteriou, 2007). The simplest model of a variable with a stochastic trend is the random walk. There are two kinds of random walks: (1) random walk without drift, (2) random walk with drift, which are defined as below: 𝑌𝑡 = 𝑌𝑡−1 + 𝑢𝑡 (1) 𝑌𝑡 = 𝛼 + 𝑌𝑡−1 + 𝑢𝑡 (2) Where 𝑌𝑡 : is the random variable at the year t 𝑢𝑡 : is a white noise error term 𝛼 : is the drift parameter
- 21. [13] 2. Unit Root Test Most macroeconomic time series are trended and therefore in most cases are nonstationary. Consequently, a test for nonstationarity is a need. Dickey and Fuller (1981) devised a procedure to formally test (named DF test). However, the error term is unlikely to be white noise, so Dickey and Fuller extended their test procedure suggesting an augmented version of the test (named ADF test), which includes extra lagged terms of the dependent variable in order to eliminate autocorrelation. The three possible forms of the ADF test are given by the following equations: ∆𝑌𝑡 = 𝛿𝑌𝑡−1 + ∑ 𝛽𝑖∆𝑌𝑡−𝑖 + 𝑢𝑖 (3) 𝑝 𝑖=1 ∆𝑌𝑡 = 𝛼 + 𝛿𝑌𝑡−1 + ∑ 𝛽𝑖∆𝑌𝑡−𝑖 + 𝑢𝑖 (4) 𝑝 𝑖=1 ∆𝑌𝑡 = 𝛼 + 𝛾𝑇 + 𝛿𝑌𝑡−1 + ∑ 𝛽𝑖∆𝑌𝑡−𝑖 + 𝑢𝑖 (5) 𝑝 𝑖=1 The difference between the three regressions (3), (4) and (5) concerns the presence of the deterministic elements 𝛼 and 𝛾𝑇. Moreover, there is another way to test the stationarity of the data, and it is called Phillips-Perron (PP) Unit Root Test. Unlike ADF, PP test tries to fix the error term's autocorrellation without adding lagged terms by using the Newey-West (1987) heteroskedasticity. It is also an advantage of PP test. It is robust to generate forms of heteroskedasticity in the error term. Thanks to the unit root test for stationarity of time serties data, the result becomes more reliable and less spurious. 3. Cointegration According to Asteriou (2007), most of macroeconomic variables are stationary at the first difference. When two variables are nonstationary, then stochastic trends can be represented. However, in the case that two variable are related, it is expected that these two variables move together and when two stochastic trends are combined, it should be possible
- 22. [14] to find a combination of them which eliminates the nonstationarity. At that time, it is said that two variables are cointegrated. The definition of two variables integrated of order one is that two variables are cointegrated if there exists a parameter 𝛼 that is followed by the equation 𝑢𝑡 = 𝑦𝑡 − 𝛼𝑥𝑡 stationary process. In general, according to the brief summary of Binh (2010), there are three cases as belows: (i) if two nonstationary variables are integrated of the same order, but not co- integrated, we should apply Vector Autoregressive model (VAR model) for the differenced series. These models just provide short-run relationships between them. (ii) If two nonstationary variables are integrated of the same order, and co-integrated, which suggests that there must be Granger causality in at least one direction. To determine the direction of causation, it may be determined by using the error correction mechanism (ECM) model. The ECMenables us to distinguish between short-run and long-run Granger causality. (iii) If two nonstationary variables are integrated of the different orders, or non- cointegrated or cointegrated of an arbitrary order, it is often suggested to employ the Toda and Yamamoto version of Granger causality or Bounds Test for Cointegration within ARDL. 4. Granger Causality Test The Granger causality test of two stationary variables is expressed as followings 𝑌𝑡 =∝ + ∑ 𝛽𝑖 𝑛 𝑖=1 𝑌𝑡−𝑖 + ∑ 𝛾𝑗𝑋𝑡−𝑗 + 𝑢𝑦𝑡 (6) 𝑚 𝑗=1 𝑋𝑡 =∝ + ∑ 𝜃𝑖 𝑛 𝑖=1 𝑋𝑡−𝑖 + ∑ 𝛿𝑗𝑌𝑡−𝑗 + 𝑢𝑥𝑡 (7) 𝑚 𝑗=1 Where 𝑢𝑦𝑡 and 𝑢𝑥𝑡 are uncollerated white-noise error terms. The optimal lag length is popularly determined by using the Akaike’s information criterion (AIC) and Schwarz’s Bayesian criterion (SBC). The two equation (6) and (7) is run
- 23. [15] by OLS and then the F Wald test is applied to test the importance of the coefficients on the lagged terms in the unrestricted models as described in the following null hypothesis (𝑎)𝐻0: ∑ 𝛾𝑗 = 0 𝑚 𝑗=1 𝑜𝑟 𝑋 𝑑𝑜𝑒𝑠 𝑛𝑜𝑡 𝐺𝑟𝑎𝑛𝑔𝑒𝑟 𝑐𝑎𝑢𝑠𝑒 𝑌 (𝑏)𝐻0: ∑ 𝛿𝑗 = 0 𝑚 𝑗=1 𝑜𝑟 𝑌 𝑑𝑜𝑒𝑠 𝑛𝑜𝑡 𝐺𝑟𝑎𝑛𝑔𝑒𝑟 𝑐𝑎𝑢𝑠𝑒 𝑋 5. Panel Unit Root Test As mentioned above, the augmented Dickey-Fuller (ADF) is very well-known to test the unit root. However, it has the drawback of low power in rejecting the null hypothesis of non stationarity test due to the short-spanned data. In recent years, some researchers such as Levin, Lin and Chu (LLC) (2002), Im et al. (IPS) (2003), Maddala and Wu (1999) and Hardi (2000) developed panel-based unit root test to overcome the problem of traditional ADF because they have higher power than the tradition one. In fact, they take advantage of the additional information by pooled cross-section time series. According to Al-Iriani (2006), among different kinds of unit root test, LLC and IPS are proposed to be the most popular because both are based on ADF principle. Their model takes the following form: ∆𝑌𝑖,𝑡 = 𝛼𝑖 + 𝛽𝑖𝑌𝑖,𝑡−1 + ∑ ∅𝑘∆𝑌𝑖,𝑡−𝑘 + 𝑛 𝑘=1 𝑢𝑖𝑡 LLC assumes homogeneity in the dynamics of the autoregressive coefficients for all panel members. Therefore, LLC tests the null hypothesis that 𝐻0: 𝛽𝑖 = 𝛽, ∀𝑖 Against the alternative hypothesis 𝐻1: 𝛽𝑖 = 𝛽 < 0, ∀𝑖 In contrast, the IPS is said to allow for the heterogeneity in these dynamics. Hence, the null hypothesis to be test is 𝐻0: 𝛽𝑖 = 0, ∀𝑖
- 24. [16] Against the alternative hypothesis 𝐻1: 𝛽𝑖 < 0 for at least one 𝑖 6. Panel Cointegration There are many types of tests for panel cointegration such as Kao (1999), Pedroni (1997, 1999, 2000) and Larsson et al. (2001). According to Asterious (2007), Kao’s test imposes homogenous cointegrating vector and AR coefficients. However, this test does not allow for multiple exogenous variables in the cointegrating vector and it does not address the problem of identifying the cointegration vectors and the cases where more than one cointegrating vector exists. Then, Pedroni’s test was arised to fix these drawbacks. This approach differs from Kao’s in assuming trends for the cross-sections and considering as the null hypothesis that of no cointegration. He proposed seven statistics: four of them (panel v- statistic, p-statistic, t-statistic non-parametric, t-statistic parametric) are based on pooling along the “within” dimension; the rest (group p-statistic parametric, t-statistic non-parametric, t-statistic parametric) are based on pooling the “between” dimension. Here is the general equation for cointegration regression 𝑦𝑖,𝑡 = 𝛼𝑖 + 𝛽1𝑖𝑥1𝑖,𝑡 + 𝛽2𝑖𝑥2𝑖,𝑡 + ⋯ + 𝛽𝑀𝑖𝑥𝑀𝑖,𝑡 + 𝑒𝑖,𝑡 𝑡 = 1, … , 𝑇; 𝑖 = 1, … , 𝑁; 𝑚 = 1, … , 𝑀 Where T: the number of observations over time N: the number of individual members in the panel (cross-section) M: the number of independent variables The first-difference of the original series are taken to compute the panel-p and panel-t and then the residuals of the following regression is estimated ∆𝑦𝑖,𝑡 = 𝑏1𝑖∆𝑥1𝑖,𝑡 + 𝑏2𝑖∆𝑥2𝑖,𝑡 + ⋯ + 𝑏𝑀𝑖∆𝑥𝑀𝑖,𝑡 + 𝜋𝑖,𝑡 The long-run variance of 𝜋𝑖,𝑡 ̂ (symbolized as 𝐿11𝑖 2 ̂ ) is formulated as follows 𝐿 ̂11𝑖 2 = 1 𝑇 ∑ 𝜋 ̂𝑖,𝑡 2 + 2 𝑇 ∑(1 − 𝑠 𝑘𝑖 + 1 𝑘𝑖 𝑠=1 ) ∑ 𝜋 ̂𝑖,𝑡𝜋 ̂𝑖,𝑡−𝑠 𝑇 𝑡=𝑠+1 𝑇 𝑡=1 Moreover, for the panel-p and group-p statistics, the long-run variance and the contemporaneous variance is calculated as a following formula: 𝑠̂𝑖 2 = 1 𝑇 ∑ 𝑢 ̂𝑖,𝑡 𝑇 𝑖=1
- 25. [17] 𝜎 ̂𝑖 2 = 1 𝑇 ∑ 𝑢 ̂𝑖,𝑡 + 2 𝑇 ∑(1 − 𝑠 𝑘𝑖 + 1 ) ∑ 𝑢 ̂𝑖,𝑡𝑢 ̂𝑖,𝑡−𝑠 𝑇 𝑡=𝑠+1 𝑘𝑖 𝑠=1 𝑇 𝑡=1 While the panel-t and group-t statistics, the long-run variance and the contemporaneous is defined as: 𝑠̂𝑖 ∗2 = 1 𝑇 ∑ 𝑢 ̂𝑖,𝑡 ∗2 𝑇 𝑡=1 𝑠̂𝑁,𝑇 ∗2 ≡ 1 𝑁 ∑ 𝑠̂𝑖 ∗2 𝑁 𝑖=1 Then, the calculation of the seven statistics is applied as follows: 1. The panel v statistic 1 1 2 1 , 2 11 1 2 / 3 2 , ˆ 2 / 3 2 ˆ ˆ T t t i i N i T N V e L N T Z N T 2. The panel p statistic T t i t i t i i N i T t t i i N i T N e e L e L N T Z N T 1 , 1 , 2 11 1 1 1 2 1 , 2 11 1 , ˆ ˆ ˆ ˆ ˆ ˆ ˆ 3. The panel t statistic (Non-parametric) T t i t i t i i N i T t t i i N i T N T tN e e L e L Z 1 , 1 , 2 11 1 2 / 1 1 2 1 , 2 11 1 2 , , ˆ ˆ ˆ ˆ ˆ ˆ ~ 4. The panel t statistic (parametric) T t t i t i i N i T t t i i N i T N T tN e e L e L S Z 1 * , * 1 , 2 11 1 2 / 1 1 2 * 1 , 2 11 1 2 * , * , ˆ ˆ ˆ ˆ ˆ ~ 5. The group p statistic(parametric) T t i t i t i T t t i N i T N e e e TN Z TN 1 , 1 , 1 1 2 1 , 1 2 / 1 1 , ~ 2 / 1 ˆ ˆ ˆ ˆ ~ 6. The group t statistic (non-parametric) T t i t i t i T t t i i N i T tN e e e N Z N 1 , 1 , 2 / 1 1 2 1 , 2 1 2 / 1 1 , 2 / 1 ˆ ˆ ˆ ˆ ˆ ~
- 26. [18] 7. The group t statistic (parametric) T t t i t i T t t i i N i T tN e e e S N Z N 1 * , * 1 , 2 / 1 1 2 * 1 , 2 * 1 2 / 1 * , 2 / 1 ˆ ˆ ˆ ˆ ~ In which, the first four statistics based on pooling data across the within-dimension of the panel are constructed by “summing both the numerator and the denominator terms over the N dimension separately” whereas the last three statistics based on pooling data along the between dimension of the panel are constructed by “dividing the numerator by the denominator prior to summing over the N dimension” (Pedroni, 1999). Thus, the former are based on estimators that effectively pool the autoregressive coefficient across different members for the unit root tests on the estimated residuals, while the latter are based on estimators that simply average the individually estimated coefficients for each member. 7. Instrumental Variables Regression (IV) Instrumental Variables (IV) estimation is used when the model has endogenous X’s. It is also used to address some threats: (1) omitted variable bias from a variable that is correlated with X but is unobserved, so cannot be included in the regression, (2) simultaneous causality bias, (3) errors-in-variable bias. Let consider the following model: 𝑦 =∝ +𝛽1𝑥1 + 𝛽2𝑥2 + ⋯ + 𝛽𝑘𝑥𝑘 + 𝑢 Where 𝐸(𝑢) = 0 and 𝑐𝑜𝑣(𝑥𝑗, 𝑢) = 0 for 𝑗 = 1,2, … , 𝐾 − 1 If the residual is correlated with 𝑥𝑘, there exists the potentially endogenous problem, which causes OLS inconsistent. This reason leads to the IV estimator solution. The instrument 𝑧1 is chosen to satisfy two conditions: (1) the instrument must be exogenous or it can be said that 𝑐𝑜𝑣(𝑧𝑗, 𝑢) = 0, (2) the instrument must be informative or relevant. This means that the instrument must be correlated with the endogenous regressor 𝑥𝑘 and conditional on all exogenous variables in the model. The considered IV regression above is simple with one endogenous explanatory variable and one instrument. Similarly, if the regression with two endogenous explanatory variables and two instruments is deployed, the model is identified. The model is considered
- 27. [19] under-identified when there are fewer instruments than endogenous regressors and vice versa. In practice, it is a good idea to have more instruments than what is really needed. Wooldridge (2005) assure that the two stage least squares (2SLS) obtained by using all instruments simultaneously in the first stage regression is the most efficient IV estimator. 8. Generalized method of moments (GMM) According to Gujarati (2004), there are three methods of parameter estimation: (1) least squares, (2) maximum likelihood, and (3) method of moments and its extension, the generalized method of moments (GMM). Assume the following model: 𝑌𝑖𝑡 =∝ 𝑋𝑖𝑡 + 𝛿𝑌𝑖,𝑡−1 + 𝜇𝑖 + 𝑢𝑖𝑡 in which N countries are observed over T period, 𝜇𝑖 is country-specific effects and the disturbances 𝑢𝑖𝑡 are assumed to be independently distributed across countries with a zero mean. Since 𝑌𝑖𝑡 is a function of 𝜇𝑖, this implies that 𝑌𝑖,𝑡−1 is also a function of 𝜇𝑖. Therefore, 𝑌𝑖,𝑡−1 is correlated with the error term through the presence of 𝜇𝑖. As a result. OLS is biased and inconsistent. To fix this problem, Anderson and Hsiao (1981, 1982) suggested first differencing the model to get rid of the residual 𝜇𝑖, and then using an instrument variable method (IV method). This proposed method leads to consistent but not necessarily efficient estimates because it does not make use of all available moment conditions and not take into account the differenced structure on the residual disturbances. It is also a reason that Arellano and Bond (1991) proposed a more efficient estimation procedure. His idea is to take the first differences to get rid of the individual effects and use all the past information of dependent variables as instruments. GMM is introduced to overcome this problem. The reason lies in the possible correlation between the lagged dependent variable and the unobserved country specific effect. However, Blundell and Bond (1998) found that it has some drawbacks that this has poor finite sample properties in terms of bias and precision. Arellano and Bover (1995) and Blundell and Bond (1998) proposed a system based approach to overcome these limitations. The combination of the standard set of equations in first difference with suitably lagged levels as instruments and with an additional set of equations in the levels with lagged first differences as instruments forms the system GMM. The Arellano and Bover test is used to
- 28. [20] detect the autocorrelation, in which there is a null hypothesis that no autocorrelation exists. It is also applied to the differenced residuals. The more important thing is that the test of AR(2) in first difference since autocorrelation in levels will be detected. As discussed above in previous sections, conventional IV estimators such as two stage least squares (2SLS) is considered special cases of GMM estimator. In fact, the method is nearly the same. Due to the fact that the finding of a compatible and reasonable instrument variables encounter with problems, it is suggested that the lagged variables be used as instrument variables. The basic model: 𝑦 = 𝛽𝑋 + 𝑢, 𝑢~(0, Ω), X ( N x k ) Let define Z ( N x l ) where l ≥ k the Generalized Method of Moments IV (IV-GMM) estimator. The l instruments give rise to a set of l moments: 𝑔𝑖(𝛽) = 𝑍𝑖 ′ 𝑢𝑖 = 𝑍𝑖 ′ (𝑦𝑖 − 𝑥𝑖𝛽), 𝑖 = 1, 𝑁 The averaging over N of g is estimated by 𝑔̅(𝛽) = 1 𝑁 ∑ 𝑧𝑖(𝑦𝑖 − 𝑥𝑖𝛽 𝑁 𝑖=1 ) = 1 𝑁 𝑍′ 𝑢 In the case of over-identification (l > k) a set of k instruments are defined 𝑋 ̂ = 𝑍(𝑍′ 𝑍)−1 𝑍′ 𝑋 = 𝑃𝑍𝑋 2SLS estimator is calculated by 𝛽2𝑆𝐿𝑆 ̂ = (𝑋′ ̂𝑋)−1 𝑋′ ̂𝑦 = (𝑋′ 𝑃𝑍𝑋)−1 𝑋′ 𝑃𝑍𝑦 If and only if an equation is over-identified, with more excluded instruments than included endogenous variables, we may test whether the excluded instruments are appropriately independent of the error process. That test should always be performed when it is possible to do so, as it allows us to evaluate the validity of the instruments. A test of over-identifying restrictions does a regression the residuals from an IV or 2SLS regression on all instruments in Z. Under the null hypothesis that all instruments are uncorrelated with u. the test has a large sample chi square distribution where r is the number of over-identifying restrictions.
- 29. [21] Besides that, according to Hansen (1982), there is Hansen test which is tested the validity of the instruments or it can be said that Hansen test is a test whether the instruments are uncorrelated with the error term 𝑢𝑖𝑡. Hansen test has the null hypothesis that over- identifying restrictions are valid. If the p-value of the test is less than our critical significance level, we reject the null hypothesis that the over-identifying assumptions are valid. Hansen test displays outside as J-statistic. It is referred to the value of the GMM objective function evaluated using an efficient GMM estimator. This statistic acts as an omnibus test statistic for model mis-specification. A large value of J-statistic indicates a mis-specified model. The p- value of this test is calculated by the chi square function of the J statistic and the degree of freedom. In general, the degree of freedom is formulated equally to the number of moment conditions minus the number of coefficients. It can be expressed by the formula 𝑝_𝑣𝑎𝑙𝑢𝑒 = 𝑐ℎ𝑖𝑠𝑞(𝐽_𝑠𝑡𝑎𝑡𝑖𝑠𝑡𝑖𝑐, 𝑑𝑒𝑔𝑟𝑒𝑒 𝑜𝑓 𝑓𝑟𝑒𝑒𝑑𝑜𝑚) In sum, all related econometric concepts and methods are presented in this chapter. This chapter gives us more clear vision about econometrics.
- 30. [22] CHAPTER IV: DATA AND RESEARCH METHODOLOGY The literature review of the nexus between FD and household welfare and econometrics review have been described in the previous chapter. This chapter continues to present an overview of data and research methodology used for this study. 1. Data There have been various measurements to address the level of FD. In several recent studies, the ways to measure the level of FD are varied. According to Hassan, Sanchez, & Yu (2011), they used some popular proxies for FD such as domestic credit provided by banking sector as a percentage of GDP (DCBS), domestic credit to the private sector as a percentage of GDP (DCPS), the broadest definition of money (M3) as a proportion of GDP or the ratio of gross domestic savings to GDP. For this study, DCBS, DCPS and the broad money supply ratio (M2/GDP) are proxies for FD. Hassan et al. (2011) suggested choosing M3/GDP because it is more likely to reflect the ability of the financial systems to provide transaction services than to the ability to channel funds from savers to borrowers (Khan & Senhadji, 2003, p.ii93). However, due to the lack of data, in this study, I select the ratio M2/GDP even although it is not a good demonstration of the level of FD. The support of my selection is the study of measuring banking sector development of WB, which assume the ratio M2/GDP one of traditional indicators of financial sector development. This ratio captures the degree of monetization in the system. Additionally, two more proxies DCBS and DCPS are used to show the size of the financial market. Based on the study of Levine (1997), banks are assumed to provide five main functions in the market; hence, the higher DCBS means the higher degree of dependence on the banking sector for financing. Similarly, a high DCPS indicates a higher level of domestic investment and a higher development of FD as well. In this research paper, per capita consumption is used as a proxy for welfare. In the previous research, the authors have used GDP per capita as a proxy for welfare; however, it does not account for the observed connection between poverty and growth. Meanwhile, as suggested by WB, it had better to use consumption as a measurement of PR than income. First, consumption is said to be a better indicator because actual consumption has a close link with ones’ well-being to meet their basic needs while income is only one among other elements that allow goods consumption. Second, it is also reliable because it measures
- 31. [23] poverty more exactly. In reality, the income flows of the poor may fluctuate during a year whereas their consumption pattern seems to be certain and steady for a year. Therefore, it is realized that per capita consumption is the suitable proxy for welfare. Consequently, table 2 demonstrates clearly the details of all proxies of FD and welfare of the five Asia countries (including Indonesia, Philippines, Malaysia, Thailand and Vietnam) which will be collectedannually from World Development Indicator (World Bank) in the period of time from 1960 to 2011. Table 2: Proxy variables Variable Proxy Label Time Data sources FD The broad money supply1 as a percentage of GDP M2GDP Annual World Bank Domestic credit provided by banking sector2 as a percentage of GDP DCBS Annual World Bank Domestic credit to the private sector3 as a percentage of GDP DCPS Annual World Bank Household welfare Household final consumption expenditure per capita (constant 2,000 US$) POV Annual World Bank 2. Research methodology In respective to the objective of this study, the question whether FD is causally related to household welfare might be investigated. The general form to show the relationship between FD and household welfare is: 1 “Money and quasi money comprise the sum of currency outside banks, demand deposits other than those of the central government, and the time, savings, and foreign currency deposits of resident sectors other than the central government. This definition of money supply is frequently called M2.” (World Bank) 2 “Domestic credit provided by the banking sector includes all credit to various sectors on a gross basis, with the exception of credit to the central government, which is net. The banking sector includes monetary authorities and deposit money banks, as well as other banking institutions where data are available (including institutions that do not accept transferable deposits but do incur such liabilities as time and savings deposits).”(World Bank) 3 “Domestic credit to private sector refers to financial resources provided to the private sector, such as through loans, purchases of nonequity securities, and trade credits and other accounts receivable, that establish a claim for repayment. For some countries these claims include credit to public enterprises.” (World Bank)
- 32. [24] POV = f (FD+ ) Or 𝑃𝑂𝑉𝑖𝑡 = 𝛼 + 𝛽𝐹𝐷𝑖𝑡 + 𝜀𝑖𝑡 Where POV is the symbol of household welfare, in particular it calculated by per capita consumption. FD is the symbol of financial development, in which there are three measurements: DCBS (domestic credit provided by banking sector), DCPS (domestic credit to private sector), M2GDP (money and quasi money as a percentage of GDP) In particular, the three equations are formed below: 𝑃𝑂𝑉𝑖𝑡 = 𝛼 + 𝛽𝐷𝐶𝐵𝑆𝑖𝑡 + 𝜀𝑖𝑡 𝑃𝑂𝑉𝑖𝑡 = 𝛼 + 𝛽𝐷𝐶𝑃𝑆𝑖𝑡 + 𝜀𝑖𝑡 𝑃𝑂𝑉𝑖𝑡 = 𝛼 + 𝛽𝑀2𝐺𝐷𝑃𝑖𝑡 + 𝜀𝑖𝑡 When the panel cointegration is detected through the Pedroni’s cointegration test, this means that there exists a long-run relationship. As previewed in previous section, Pedroni proposed seven statistics of residual-based panel cointegration test composed of two main groups: within-dimension-based (panel-p and panel-t) and between-dimension-based (group- p and group-t). He also assumed that the rho-statistics is the most powerful one to test the cointegration in the large sample size (Pedroni, 1999) whilethe panel-t statistics and the group-t statistics are more reliable in a small sample (Pedroni, 2004). Before doing the Pedroni’s, the panel unit root test is run. In this study, we use LLC and IPS panel unit root test because both are based on ADF principle. The difference between LLC and IPS lies on that LLC assumes homogeneity in the dynamics of the autoregressive coefficients for all panel members while IPS is said to allow for the heterogeneity in these dynamics. In reality, recent studies have applied a variety of techniques to estimate systems of linear equation simultaneously. The well-known technique commonly used is the class of instrument variables (IV) methods, of which 2SLS is the most important special case. In this study, 2SLS is employed to detect the relationship between FD and household welfare.
- 33. [25] The instrumental variables chosen are the lagged variables of POV. It is sufficient because it meets two requirements that it is highly correlated with DCBS, DCPS, or M2GDP and uncorrelated with itself of the t time. When using 2SLS, we should pay attention to the J-statistics because if the J-statistic value is too high, it also displays the mis-specification of the model or if the J-statistic is identically zero it will be positive for an over-identified equation. The J-statistics is the test of over-identifying restrictions. The null hypothesis of this test is that over-identifying restrictions are valid. The p-value of J-statistics is calculated mentioned in previous section. If the specification is correct, we expect is the failure of rejection of the null hypothesis. This chapter has presented the data and research methodology. As for the data, the study collects the macroecnomic data from WDI - WB. As for the research methodology, the study employs the GMM two-stage least square model. Tải bản FULL (66 trang): https://bit.ly/3GY20UE Dự phòng: fb.com/TaiHo123doc.net
- 34. [26] CHAPTER V: ANALYSIS RESULTS In this chapter, the general picture of financial development and consumption per capita is fully and statistically described to enhance as the strong evidences for further analyses. The GMM 2SLS model is used to test the hypotheses; and the discussion of results is also shown in the end of this chapter. 1. Data descriptions This study investigates the relationship between FD and household welfare in five Asian countries (Indonesia, Malaysia, Philippine, Thailand and Vietnam) through four variables including DCBS (Domestic credit provided by banking sector), DCPS (Domestic credit to private sector), M2GDP (The broad money supply as a percentage of GDP) and POV (Household final consumption expenditure per capita. The brief statistical summary of FD and household welfare variables is presented in table 3. In general, Indonesia and Philippines always get lower scores on FD and household welfare while Vietnam stands in the middle and on the top are Malaysia and Thailand. In particular, DCBS of Indonesia is on average 30.78, which means that domestic credit banking sector has provided takes a proportion 30.78 percent of GDP. It is the lowest score among other figures in panel 1. While the fourth position is Philippines with its DCBS mean 39.66, Thailand and Malaysia are on the top. In fact, compared to Philippines’s, their DCBS mean nearly double and reach to approximately 84 percent of GDP. In the middle is Vietnam with the figure 57.94. Similarly, we can apply this trend to two other proxies of FD. However, the high values of FD do not mean to be good. Therefore, their standard deviation should be taken into consideration. The high standard deviation of their DCBS, DCPS and M2GDP shows us the high uncertainty due to the sensitive fluctuation. For example, table 3 and figure 4 give us evidence that Malaysia and Thailand are those which have a large range between the maximum values and the minimum one, which leads to the high standard deviation (Malaysia has the DCBS standard deviation around 51, and Thailand around 50). The high standard deviation shows us the high dispersion in data and then the high uncertainty may happen. In contradiction with Malaysia and Thailand’s, the standard deviation in Indonesia and Philippines is lower, which indicates the low dispersion in data. Besides that, POV values in all countries are increasing. The highest mean POV with the value 1310.92 belongs to Malaysia. This figure shows that consumption per capita in Malaysia is, on average, 1310.92 USD. The runner-up is Thailand with the value 760.84. The three last positions belong to Philippines, Vietnam and Indonesia. It is a surprise that Tải bản FULL (66 trang): https://bit.ly/3GY20UE Dự phòng: fb.com/TaiHo123doc.net
- 35. [27] Philippine gets over Vietnam in POV to get the third position. In addition, the good explanation may be due to missing data in the early period (1960-1997) (see figure 4). As a result, these figures may not indicate the true status of the economy. Hence, there is another table (see the appendix 1) showing the summary of these statistics from 1998 to 2011 in order to have an inside look. Table 3: Description of FD and household welfare variables (1960-2011) IDN: Indonesia, MYS: Malaysia, PHL: Philippines, THA: Thailand, VNM: Vietnam DCBS: Domestic credit provided by banking sector, DCPS: Domestic credit to private sector, M2GDP: The broad money supply as a percentage of GDP. POV: Household final consumption expenditure per capita Mean Median Maximum Minimum Std. Dev. DCBS_IDN 30.78 26.41 60.85 8.99 14.53 DCBS_MYS 84.94 98.95 163.35 5.59 50.85 DCBS_PHL 39.66 37.38 78.54 19.54 14.07 DCBS_THA 83.07 84.31 177.58 15.67 49.24 DCBS_VNM 57.94 44.79 135.79 15.71 39.44 DCPS_IDN 33.00 36.45 62.07 8.20 17.66 DCPS_MYS 71.85 73.65 158.51 7.00 46.96 DCPS_PHL 26.84 25.93 56.46 11.98 9.00 DCPS_THA 68.02 57.60 165.72 10.12 46.58 DCPS_VNM 54.22 41.14 124.97 13.66 36.79 M2GDP_IDN 32.34 38.17 59.86 8.02 15.66 M2GDP_MYS 87.84 99.17 139.17 22.14 41.64 M2GDP_PHL 37.27 28.47 62.11 19.56 15.73 M2GDP_THA 67.29 63.93 128.27 23.34 34.83 M2GDP_VNM 61.66 53.04 125.11 19.57 37.08 POV_IDN 306.27 269.67 671.24 104.09 179.42 POV_MYS 1310.92 1144.87 2897.37 593.10 646.12 POV_PHL 658.78 630.75 1000.67 459.96 141.13 POV_THA 760.84 592.33 1460.08 277.54 395.01 POV_VNM 332.34 302.76 511.50 213.74 95.58 6677336