Saving behaviour of some developing countries in southeast asia

VIETNAM ECONOMIC GROWTH AND SAVING ANALYSIS IN THE PERIOD 1989 – 2012 Abstract This paper employs Autoregressive-Distributed Lag model to detect the cointegrating vectors amongst three

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES

VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

VIETNAM ECONOMIC GROWTH AND SAVING

ANALYSIS IN THE PERIOD 1989 - 2012

BY

LE DUC ANH

MASTER OF ARTS IN DEVELOPMENT ECONOMICS

HO CHI MINH CITY, DECEMBER 2014

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES

VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

VIETNAM ECONOMIC GROWTH AND SAVING

ANALYSIS IN THE PERIOD 1989 - 2012

A thesis submitted in partial fulfilment of the requirements for the degree of

MASTER OF ARTS IN DEVELOPMENT ECONOMICS

By

Le Duc Anh

Academic Supervisor:

Dr Dinh Cong Khai

HO CHI MINH CITY, DECEMBER 2014

Acknowledgement

Foremost, I would like to express my sincere gratitude to my supervisor Dr Dinh Cong Khai for the continuous support of my M.A study and research, for his patience, motivation, enthusiasm and immense knowledge His guidance helped me in all the time of research and writing of this thesis

Besides my supervisor, I would like to thank VNP teaching staff for their encouragement, insightful comments, and hard questions

Last but not the least, I would like to thank my family: my parents, my wife and my brothers for supporting me spiritually throughout my life

OUTLINE

Abstract 1

Chapter 1: Introduction 2

1.1 Problem statement 2

1.2 Research objectives 5

1.3 Research questions 5

1.4 Methodology 6

1.5 Research scope 6

1.6 Structure of the study 6

Chapter 2: Literature Review 7

2.1 Theoretical literature 7

2.2 Empirical literature 10

2.3 Conceptual framework 18

Chapter 3: Research Methodology 20

3.1 Data collection 20

3.2 Unit root test 21

3.3 Cointegration analysis 23

3.4 Granger causality analysis 24

Chapter 4: Empirical Analysis 26

4.1 Empirical evidence 26

4.1.1 Unit root test results 26

4.1.2 Cointegration test results 28

4.1.3 Granger causality test results 30

4.2 Discussion of findings 33

Chapter 5: Concluding Remarks 40 References

Appendix

LIST OF TABLES AND FIGURES

Figures

Figure 1: Relationship between saving and economic growth stated by Solow (1956) 8

Figure 2: The grapth of LGDP, LGDS and LFDI series 26

Tables Table 1: The summary of the main empirical studies 13

Table 2: Unit root test of variables at level value 27

Table 3: Unit root test of variables at the first difference 28

Table 4: Results of ARDL bound test 29

Table 5: Estimated long run coefficients using the conditional ARDL(2, 0, 0) 30

Table 6: Results of VECM based on error terms taken from the conditional ARDL 31

Table 7: Results of diagnostic tests for equation 11 32

Table 8: Results of short-run Granger causality 33

Table 9: Average values of FDI, GDP and GDS in three stages 34

Table 10: Commercial bank network density 36

Table 11: Average percentage of rural population over total population in three stages 36 Table 12: Vietnam stock market in the period 2006 – 2012 37

LIST OF ABBREVIATIONS

Abbreviation Meaning

ASEAN Association of Southeast Asian Nations

LFDI Logarithm of Foreign Direct Investment

VIETNAM ECONOMIC GROWTH AND SAVING ANALYSIS IN THE

PERIOD 1989 – 2012 Abstract

This paper employs Autoregressive-Distributed Lag model to detect the cointegrating vectors amongst three variables that are gross domestic saving per capita, gross domestic product per capita and foreign direct investment being stationary at the mixture of I(0) and I(1) in the period 1989 – 2012 The results do not support for the hypothesis as Solow (1956) states in which domestic saving and foreign direct investment are sources for domestic investments, then push economic growth The long run relationship amongst these indicators is consistent with some recent empirical studies for the case of developing countries That is, the long run causal direction running from economic growth to domestic saving Additionally, foreign direct investment does not cause economic growth in both short and long run, but tends to reduce domestic saving in long run

Furthermore, combining the estimated results with some statistical measurements of demographic change in total population and financial market development gives to us evidences in which financial market in Vietnam is still weak, thus does not strengthen the channel of domestic private saving accumulation, especially from huge amount of rural population FDI has increased annually, but it only takes a small percentage in GDP As a result, there is no evidence to support for the nexus from saving to growth GDS increasing annually is explained by a rise of GDP, and perhaps by some positive demographic change in total population Last but not the least, in long run FDI tends to reduce domestic saving due to ineffective channel of domestic capital accumulation, especially for Vietnam stock market Then, it casts doubts on an assumption of Solow about the positive nexus running from saving to growth, especially for developing countries

Chapter 1: Introduction

In this chapter, the study presents the reason of choosing this kind of research for the specific case of Vietnam to analyze the interaction amongst domestic saving, foreign direct investment and economic growth in the period 1989 – 2012

1.1 Problem statement

The central assumption of the Solow’s (1956) growth model is a positive nexus between saving rate and economic growth in which saving rate plays a role of conditional factor to push growth This model implies some policy implications for a country to concentrate on increasing saving, thus economic growth will increase in response However, inversely if there is a possibility of negative impact, a country shall focus on removing barriers to growth instead of accumulating saving

There are some robust empirical findings about the positive association between saving and growth Nevertheless, this correlation does not imply a causal direction amongst them, and then this controversy is still unsolved Moreover, the debate concerning with the priority of policy implication for these indicators is an important issue raised at the current macroeconomics, as stated by Schmidt-Hebbel et al (1996) Hence, determining the causal direction of saving-growth linkage is crucial, and has many implications for policy makers in developing countries

At the notion of growth to saving, it has been supported by some empirical evidences

of several recent studies analyzing in developing countries For instance, in the research by Ramesh (2006), he finds that in the group of low-middle income countries there is mostly the causal direction from growth to saving Furthermore, the study by Pradeep, Pravakar, and Ranjan (2008) also shows the empirical evidences from 5 countries – Bangladesh, India, Pakistan, Srilanka and Nepal – there is a causal

direction from growth to saving, however, only Bangladesh with bi-directional causality

In an open economy, two capital sources contributing to domestic investment are domestic and foreign saving With foreign saving, it is represented by some forms of international capital inflows Theoretically, these inflows are assumed to be additive, or supplement domestic saving to increase an amount of investment, hence to push economic growth This hypothesis is seemingly relevant in some certain capital inflows, especially to foreign direct investment (noted as FDI) because this inflow is more stable than the other components of foreign saving in term of predictability and volatility (Taylor, 1997 and Lipsey, 1999 cited in Maite, Ana and Vicente, 2004) In the research composed by Matie, Ana and Vicente (2004), they analyze the role of FDI

to the nexus between economic growth and domestic saving in Mexico and find that FDI causes both GDP and GDS positively, hence to reinforce the relationship between economic growth and domestic saving However, this hypothesis does not hold in the study by Ahmad, Marwan and Salim (2002) for the case of Malaysia, Thailand and Philippines They find foreign savings affecting domestic saving negatively in both short and long run Moreover, in long run the causal direction runs from foreign to domestic saving, but the inverse direction does not exist

In the history, Latin America crisis happened in Mexico after the peso was devaluated

by the authorities in 1994, then quickly spreading to several other countries in this region Consequently, a huge amount of capital outflow left these countries into shambles However, on the contrary to that of Latin America countries, East Asian economies were not seriously affected by the crisis Then, based on this fact, some economists conclude that Latin America countries were more sensitive to the shift of investor sentiments and the fluctuation of international capital outflows than Asian countries This difference relates to domestic saving rate in which the higher the

domestic saving in a country is, therefore can reduce the international capital outflows’ influence through a crisis Furthermore, in this period, an increase of foreign investment in East Asian countries correlated with a huge decrease of domestic saving rate in Latin America countries

In the mid of year 1997, the Asian crisis started from Thailand, then influenced to several other countries Based on this fact, it casts doubts on the economic notion held earlier through Latin America crisis by some economists The Asian economies’ currencies were devaluated sharply, then leading to a massive outflow of foreign capital at the end of year 1997 On the other hand, the experience was similar with the case of Latin America crisis The retreat of foreign capital emphasizes the necessity of more domestic financing seen as a complement to that decline in capital As dramatized by Latin America and Asian crisis, therefore, it is important to analyze the nexus between domestic and foreign saving because foreign capital could be withdrawn as easily as it enters, consequently a domestic economy could be left into shambles through a crisis

In recent years, Vietnam’s economy has succeeded in the new stage of development after opening the economy in 1988 Especially, Vietnam has been a member of ASEAN since 1995, and WTO since 2007 Then, its economic growth has increased rapidly in the period 1989 – 2012 Tariffs and trade barriers have been reduced step by step amongst members in ASEAN and WTO, therefore, Vietnam’s trade balance has been improved as well From a poor country with per capita income only equals to 290 U.S dollars a year in 1989, Vietnam has reached to nearly 1000 U.S dollars a year in

2012 Moreover, domestic saving rate and foreign direct investment in Vietnam have also increased annually through this period In detail, its gross domestic saving has changed from 0.837 to 26.9 billion U.S dollars, and FDI has also moved from 0.01 to 4.7 billion U.S dollars, respectively in 1989 and 2012 (World Bank Indicators, 2012)

Recently, almost empirical studies for developing countries show the results that the causal direction runs from economic growth to domestic saving, however, the role of FDI to the relationship is still ambiguous Additionally, there is a lack of empirical evidences for the specific case of Vietnam, thus we choose this research topic to answer what kind of relationship stands behind the upward movement amongst these indicators in the period, 1989 – 2012

1.2 Research objectives

As some issues mentioned on the problem statement above, they cast an empirical research objective to analyze whether or not the hypothesis of saving – growth nexus composed by Solow (1956) holds for the case of Vietnam Moreover, in an open economy foreign capital inflows play an important role to increase income Therefore,

we want to measure the effect of domestic saving to economic growth with an addition

of foreign direct investment seen as a component of foreign saving On the other hand,

it means that the study will analyze whether or not domestic and foreign saving are sources to affect income positively as Solow (1956)’s hypothesis mentions Finally based on this estimated result, the study will suggest some policy implications for the specific case of Vietnam that relate to the saving and growth relationship

1.4 Methodology

Due to the specification of Vietnam’s data set in the period 1989 – 2012 (discussed in detail at Chapter 4), thus ARDL cointegration bound test is employed to detect a long-term cointegration amongst GDS, GDP and FDI Based on the results of the bound test,

we will employ VECM to estimate short term and long term nexus between these variables Finally, to make the findings more robust, we must combine the estimated results with the statistical measurement of some relevant indicators in the discussion 1.5 Research scope

The research aims at interpreting an interaction amongst the three Vietnam economic indicators that are GDP, GDS and FDI in the period 1989 – 2012 The analysis concentrates on dealing with the research questions raised above Furthermore, because the time series span of Vietnam data is limited, then it is important to note that some more relevant variables could not be added into the estimation to analyze the interaction between economic growth and domestic saving, but only FDI (Modigliani, 1970; McKinnon and Shaw, 1973)

1.6 Structure of the study

The study includes five chapters in which: The first chapter presents the problem statement, research objectives and research questions The scope of study is also stressed at this chapter The second chapter presents the relevant theoretical and empirical literatures, and the conceptual framework is built on this background The third chapter is the research methodology that includes the necessary econometric techniques could be employed to deal with the research question corresponding to the specification of data set In the fourth chapter, the estimated results will be presented and discussed The fifth chapter, the study will summarize the empirical findings Finally, we will suggest some further research based on the study’s limitations

Chapter 2: Literature Review

In this chapter, the study summarizes the relevant theoretical and empirical studies that support for the research objectives raised above The empirical literatures are presented below that concentrate on analyzing the relationship amongst the three indicators – domestic saving, economic growth and foreign direct investment – in developing countries to give some empirical evidences that are expected to contribute for the case

of Vietnam

2.1 Theoretical literature

There are two relations that link between economic growth and saving: the first relation

is represented by aggregate production function in which a higher capital stock leads to

an increase of output The second relation is based on a condition of saving and investment equilibrium, thereby a higher saving rate will definitely increase investment

or capital accumulation, then output level will be higher

At the first relation, the Solow growth model has a linearly homogeneous production function of the form 𝑌 = 𝐹(𝐿, 𝐾), where Y is output, K is capital and L is labor Moreover, the production function in form of labor intensive that is written as 𝑦 =

𝑓(𝑘) in which k is the capital-labor ratio and equals to 𝑘 = 𝐾/𝐿 There is an

assumption which marginal product of capital is positive but decreasing, written as

𝑓′(𝑘) > 0, 𝑓′′(𝑘) < 0 Furthermore, the labor force is also assumed to grow at a constant rate gL As the model above, the condition for the steady state or equilibrium

to occur that is 𝑓(𝑘) = (𝑔𝐿

𝑠) 𝑘, where s is known as the saving rate While 𝑓(𝑘) measures actual per capita output produced for any capital-labour ratio k, and (𝑔𝐿

𝑠) 𝑘 represents for the amount of output is necessary to maintain the corresponding capital-labour ratio If the saving rate increases, as a result it will increase the steady state

capital stock per capita, eventually per capita output In detail, at the point the saving

rate equals to s 0 , the equilibrium is assumed to be at e 0 Then an increase in the saving

rate makes the equilibrium e 0 will shift to the new equilibrium, denoted as e 0 ’ (see at

Figure 1 below) Hence, we could see that a saving increase definitely leads to a higher capital stock per capita, and then to output in steady state

On the other hand, saving rate increasing will create more per unit investment of output, in turn leading to expand capital per worker However, at a given growth rate of labor, this process comes to a halt because a proportion of investment increase needs to

be kept for maintaining the capital-labor ratio Thereby, according to the Solow (1956) growth model, saving rate change will lead to a change in an economy’s balanced growth path, then to output per capita in steady state, but will not affect the growth rate

of output per worker on the balanced growth path There is only technological change that could create a further increase in Y/L at a steady state

Figure 1: Relationship between saving and economic growth stated by Solow (1956)

e0 ’

y0 ’

y0

e0

At the second relation, in an open economy the condition of saving and investment equilibrium seemingly does not exist As usually, a country can invest more than an amount of national saving by borrowing from the rest of the world, on the other hand it means countries running current account deficit Therefore, this process is written formally as 𝐼𝑡 = 𝑆𝑡− 𝐶𝐴𝑡, where domestic saving and investment are denoted

respectively as S t and I t , finally CA t is the current account balance

Moreover, domestic saving is normally a main capital source for domestic investment, however, foreign saving also plays an important role itself to be a second capital source

of investment in a country Nevertheless, 𝐼𝑡 = 𝑆𝑡− 𝐶𝐴𝑡, does not state any causal nexus amongst this identity’s components An exogenous movement of international capital inflows may affect either of variables in the identity above that depends on these capital flows’ types such as foreign direct investment, portfolio investment and other financial flows, then leading to the differences in nature amongst them Additionally, there are some empirical studies that prove an existence of different effects of these types of inflows As Reinhart (1999) states “there are important behavioral differences amongst the various types of capital flows, then their effects on economic activity, such as saving and investment, are also likely to differ”

In the empirical studies (Bosworth and Collins, 1999; Hausman and Arias, 2000), they show that FDI affects positively on domestic investment, hence a general belief occurs

to expect that FDI is the most important one On the other hand, it has been considered

as “good cholesterol” for developing economies Moreover, in term of volatility and predictability, FDI is more stable than the other foreign capital inflows, and usually associates with the managerial and technological know-how transfers For instance, the case study of Mexico composed by Matie, Ana and Vicente (2004) shows that FDI could create the positive spillover for domestic firms to increase their labor productivity

2.2 Empirical literature

There are some main recent empirical studies that relate to this kind of research and give the robust empirical findings to support for the analysis All of these papers chosen that relate to the research objectives mentioned at Chapter 1 and have analyzed for the case of developing countries Especially, there are some evidences taken from South-East Asian countries having quite the same economic conditions and relations with Vietnam such as Malaysia, Indonesia, the Philippines and Thailand that could support the study’s findings

In the paper by Rubiana, Sunder and Phaninda (2000), a Cobb-Douglas production function is employed to test whether foreign machinery is more productive than domestic machinery or not This study applies a panel model analyzing for seven countries that are Hong Kong, Singapore, South Korea, Malaysia, Indonesia, the Philippines and India in the period 1975 – 1990 The empirical evidences show there is

a positive impact of foreign and domestic capital to the national manufacturing productivity, then to economic growth Especially in developing countries, the results show that foreign capital imports go far beyond the effects of foreign capital investment because they associate with the positive spillover of technology transfers and high educated worker training on national productivity

In the paper by Admad, Marwan and Salim (2002), it analyzes the factors that have influenced the saving behavior in Singapore, South Korea, Malaysia, Thailand and the Philippines for years 1960 to 1997 Granger causality based on VECM regression for annual time series was applied in this study for each country to detect both short and long run effects between relevant explanatory indicators on the saving movement The main results include: Firstly, the negative effects from foreign to domestic saving both

in the short and long run Secondly, Granger causality from saving to economic growth does not exist, however Singapore is an exception Thirdly, the mixed effect of interest

rate on saving might be due to the differences of financial liberalization adopted differently across these countries Finally, there is not to have the long run causality running domestic saving to foreign saving

Furthermore, in the study by Dipendra (2002), it analyzes the nexus between saving and investment rates for Japan and 10 other Asian countries by employing Johansen cointegration test with the consideration of structural break existence for the specific data set of each country The results show the information that saving rate causes the growth of investment rate for Malaysia, Singapore, Sri Lanka and Thailand However, the reverse causality holds for Hong Kong, Malaysia, Myanmar and Singapore Finally, the rest of analyzed countries show no evidence of the relationship between saving and investment rate

The research composed by Admad and Marwan (2003) identifies the major determinants of gross national saving ratio in Malaysia This article applies the Johansen multivariate cointegration approach followed by the error-correction model to investigate the saving behavior in a dynamic framework The paper uses a time span of

1960 – 2000 to analyze the movement of saving and its determinants in Malaysia The explanatory variables include economic growth, interest rate, tax rate, export rate, dependency ratio and foreign direct investment that simultaneously affect to gross national saving in the analytical model The statistical evidence reveals that the long run relationship between saving and its determinants is different from that of short run dynamics Specifically, exports and taxes have only temporary effects on the national saving rate In addition, evidences found support for the long run effect of interest rate and demographic ratio on saving that can differ from its short-run Additionally, there

is a bidirectional nexus between saving rate and economic growth

The other research composed by Matie et al (2004), employs Granger-non causality test procedure developed by Toda and Yamamoto (1995) to analyze the saving-growth

nexus in Mexico through the period of 1970 – 2000 Empirical results support for the hypothesis stated by Solow (1956) growth model which higher saving rate leads to an increase of income Furthermore, foreign direct investment seen as a main component

of foreign saving seems to be a relevant indicator of the saving-growth nexus when empirically affecting both domestic saving and economic growth in short and long run

As a result, an addition of FDI in analytical model confirms and reinforces the relationship between domestic saving and economic growth

Additionally, the research composed by Aylit (2005) applies the Johansen cointegration test followed by VECM estimation technique to analyze the causal direction between saving and economic growth in South Africa over the period 1946 –

1992 Aggregate private saving is examined to interact with investment and economic growth Empirical results show that private saving rate known as a proxy of domestic saving affects direct, and indirect on economic growth through the private investment channel Moreover, economic growth, in turn also positively affects to private saving rate On the other hand, there is a cycle of positive interaction between saving rate and economic growth empirically found for some countries in South Africa

In the paper by Ramesh (2006), it aims at interpreting the causal relationship between saving and economic growth in countries with different income levels This study uses annual data of GDS and GDP from 25 countries for years 1960 to 2001, and also divides the countries of interest into 4 groups including low income, low middle, upper middle and high income In low income countries, the empirical results are mixed However, in low middle income group, almost countries show that the causal direction runs from economic growth to saving In all high income countries (Singapore is an exception), results support for causal relationship moving from economic growth to saving And it is very interesting for the empirical results of upper middle income countries, bi-directional causality for almost countries

The other research by Pradeep, Pravakar and Ranjan (2008) gives some empirical evidences of the saving behavior in South Asia This research uses annual data of saving rate and real income per capita from 5 countries (Bangladesh, India, Pakistan, Sri Lanka and Nepal) for years 1960 to 2004 by employing VECM and ARDL methods to detect the interaction between gross domestic saving and its determinants including economic growth, dependency ratio, the percentage of agriculture to GDP, inflation rate, M2 per GDP, real interest rate and bank branch density The results show that saving movement in South Asian countries are determined by economic growth, accessibility to banking system, dependency ratio and rate of foreign saving Furthermore, the impacts of interest rate on saving are ambiguous and minor when compared to that of the other components At last, the causal direction from economic growth or income to saving is found for mostly analyzed countries, only Bangladesh is

an exception with bi-directional causality

The last paper presented here is the research by Nurudeen (2010) that empirically examines the relationship between gross national saving and gross domestic product in Nigeria for years 1970 to 2007 The estimated result shows these two variables are co-integrated, and exist a long run equilibrium after employing the Johansen co-integration test followed by VECM In addition, there is the causal direction running from economic growth to saving

Table 1: The summary of the main empirical studies

1 Rubiana

et al

(2000)

Pooled sectional time-series model

cross-Domestic capital investment, foreign capital

The period

1975 – 1990

Hong Kong, Singapore,

Effective impact of foreign and domestic capital on manufacturing productivity and then output

investment, and labor are explanatory variables

Dependent is GDP

South Korea, Malaysia, Indonesia, the

Philippines and India

GNS, GNP, real interest rate,

dependency ratio and current account

Annual time-series

in period 1970-1997 Malaysia, Thailand, the

Philippines, Singapore and South Korea

(1) Foreign saving affects domestic saving negatively in long and short run

(2) saving does not Granger cause economic growth, except

Singapore (3) the effect of interest rate is mixed

(4) In long run the causal direction runs from foreign to domestic saving

3 Dipendra

(2003)

Johansen cointegration test and VECM

The ratio of gross

domestic investment to gross

domestic

Annual time series data

Different length of data for each country

(1) Two rates are cointegrated for Myanmar and Thailand (2) Saving rate causes growth of the investment rate for Malaysia,

product and the ratio of gross domestic saving to gross domestic product

Malaysia, Singapore, Sri Lanka, Thailand, Hong Kong, Myanmar and Singapore

Singapore, Sri Lanka and Thailand

4 Ahmad,

Marwan

(2003)

Multivariate cointegration approach

GNS ratio, GDP ratio, dependency ratio, tax rate, export rate, dependency ratio, foreign direct

investment

Period 1960-2000

in Malaysia

(1) Bidirectional nexus between saving and economic growth

(2) Long run effect of interest rate and demographic ratio on saving but not in short-run

(3) saving-interest rate relation hypothesis is hold in long run

5 Matie and

Ana

(2004)

Multivariate causality test, Granger non-causality test

Annual series include GDP, GDS and FDI

a period of 1970-2000

in Mexico

(1) GDS and GDP relation as Solow model suggests

(2)FDI causes both GDP and GDS then reinforces these two relations

6 Aylit

(2005)

VECM cointegration

Per capita GDP, GDS are

represented

by private investment rate, ratio of private saving

to GDP, ratio

of government consumption expenditure

to GDP, real interest rate

Annual time series South Africa countries The period

1946 – 1992

(1) Private saving rate has a direct, as well as, indirect effect on growth

(2) Growth positively affecting on private saving rate

(3) Bi-directional nexus between growth and saving

7 Ramesh

(2006)

Granger causality test

time-series

in period 1960-2001

25 countries divided into

4 groups based on their income level

(1) low income group results are mixed (2) low middle income group results are mostly causal direction from growth to saving (3) upper middle income group results : bi-

directional causality (4) high income group results: almost cases exist causal direction

from growth to saving, except Singapore

GDS, Real per capita GDP, inflation rate, dependency ratio, foreign saving, interest rate, financial sector development, and bank branch density

A period of 1960-2004 Bangladesh, India,

Pakistan, Sri Lanka and Nepal

(1) Mixed results about the effect of a

determinant on saving function

(2) Causal direction from growth to saving, except Bangladesh with bi-directional causality

9 Nurudeen

(2010)

Johansen integration test followed

co-by VECM, Granger causality

GNS and GDP

Annual data

in Nigeria for years

1970 to

2007

(1) Existing long run equilibrium between gross national saving and gross domestic product

(2) Causal direction runs from growth to saving

2.3 Conceptual framework

In the Solow (1956) model, it assumes that higher saving rate will definitely lead to higher domestic investment financed by two capital resources known as domestic and foreign saving Furthermore, in this study, foreign saving is assumed to complement domestic saving, then will push economic growth through the channel of domestic investment These components are expected to positively affect economic growth, hence the conceptual framework is constructed as below

As stated by Solow (1956) we should measure domestic saving and economic growth

in a form of per capita unit to capture the population growth effect Moreover, FDI seems to be a permanent component of foreign saving when comparing with the other inflows in terms of volatility and predictability Therefore, an addition of foreign direct investment seen as a component of foreign saving, this, then contributes to domestic investment, in turn pushes economic growth

Last but not the least, because the nature of cointegration analysis, then with each pair amongst three variables of interest, the analytical model will measure two possible causal directions In the study, with three variables as mentioned above – GDS, FDI and GDP – we have three pairs of variables, thus conceptual framework is built as

Gross Domestic Product (GDP)

Gross Domestic

Saving (GDS)

Foreign Saving represented by FDI

shown above Once more, we should note that even the econometrical method step by step will estimate all possible cases of causality amongst variables, however, the main objectives of the study are to focus on interpreting the causal direction from GDS and FDI to GDP in long run described by dashed lines in the conceptual framework, and their effects are expected to positively cause GDP

Finally, it is important to note that in the context of three variable cointegration analysis, the study will only measure the independent effect of each explanatory variable on economic growth, according to the research objectives raised above Furthermore, the econometric technique employed in this study can’t deal with the mixed effects of independent variables to the dependent variable

Chapter 3: Research Methodology

In this chapter, the study will present the relevant time-series econometric techniques employed to analyze the specific data set of Vietnam for years 1989 to 2012 The following sections will discuss in detail each method necessarily to measure Granger causality amongst three variables of interest (GDS, GDP and FDI)

3.1 Data collection

In the study, the variables are gathered from World Bank Indicators publicly released

in 2012 Three variables used in the econometric model are collected from year 1989 to

2012 because of the limited data and historical milestone in Vietnam Per capita GDP, per capita GDS (simply denoted as GDP and GDS respectively) and FDI are measured

in real term at year 2005 (U.S dollar unit) Then transforming the original series into logarithmic values for each variable, hence the first difference represents for the growth rate Moreover, because LGDP and LGDS are stationary at level 1, but LFDI is stationary at level 0 (results of unit root test shown in Chapter 4) As a result, ARDL cointegration bound test will be employed for the mixture of I(0) and I(1)

Furthermore, in the data set of World Bank 2012, the indicators of interest are calculated respectively as followings: (1) Foreign direct investment is measured by the net capital inflows to invest into a domestic enterprise for acquiring a lasting management interest with an amount of at least 10 per cent of voting stock In detail, FDI is the sum of equity capital, reinvestment of earnings, and other short-term and long-term capital as shown in the balance of payments (2) GDP is calculated as the sum of gross value added by all resident producers plus any product taxes, and minus any subsidies not included in the value of the products However, this calculation does not include deductions for depreciation of capital assets, or for depletion and

degradation of natural resources (3) Gross domestic savings are calculated as GDP less final consumption expenditure or total consumption

Finally, as the calculating methods mentioned above, foreign direct investment and gross domestic saving are measured as a percentage of GDP, therefore, we multiply these series’ values with GDP measured in billions of U.S dollars constant at year

2005 Furthermore, two series, GDS and GDP, should be measured in term of per capita as suggested by Solow (1956), then we need to divide these two series’ values with total population per year in the period 1989 – 2012, to receive the measure unit of interest In sum, GDS and GDP are measured at U.S dollar per capita a year, but FDI is measured at U.S billion dollars a year

3.2 Unit root test

One of the most important issues in time-series analysis that is a spurious regression, then affects the reliability of estimated coefficient meanings Moreover, most of macroeconomic time-series variables have trend, hence they usually are not stationary Then, the OLS regression on these variables is not applied There are many methods could be employed to solve this problem, and usually are to transform original series into values of logarithm, or to detrend the series Additionally, taking the difference of series is mostly used to obtain a stationary series According to Gujarati (2003), “a time series is said to be stationary if its mean and variance are constant over time and the value of the covariance between the two periods depends only on the distance or gap or lag between the two time periods and not the actual time at which the covariance is computed” Furthermore, Asteriou (2007) states that logarithm transformation and taking the difference of original series are relevant, usually finds these kind of series are stationary at the first difference denoted as I(1) There are two most-frequently-employed techniques to test a problem of stationary known as Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) test

In Augmented Fuller method, the test is less restricted than Simple Fuller because the error term is unlikely white noise, therefore, ADF test should be employed rather than Simple Dickey-Fuller test Three possible forms of ADF test are described by the following equations:

Dickey-𝛥𝑌𝑡 = 𝛿𝑌𝑡−1+ ∑𝑝𝑖=1𝛽𝑖𝛥𝑌𝑡−1 + 𝑢𝑡 (eq.1)

𝛥𝑌𝑡 = 𝛼 + 𝛿𝑌𝑡−1+ ∑𝑝𝑖=1𝛽𝑖𝛥𝑌𝑡−1 + 𝑢𝑡 (eq.2)

𝛥𝑌𝑡 = 𝛼 + 𝛶𝑇 + 𝛿𝑌𝑡−1+ ∑𝑝𝑖=1𝛽𝑖𝛥𝑌𝑡−1+ 𝑢𝑡 (ep.3) Where equations 1, 2 and 3 present for three cases that are without intercept and trend, with intercept and no trend, at last with drift and trend, respectively However, which equation should be applied that depends on a specific case of data It is important to note that equation 2 and 3 are more important than equation 1 because the intercept coefficient just only plays a role of rescaling On the other hand, we should check whether or not Y series has a trend

In Phillips-Perron test, it allows for a fairly mild assumption about the error term distribution compared with that of ADF – iid(0, σ2) And the PP test equation is written as below:

Last but not the least, in empirical analysis, researchers usually employ both of ADF and PP test to conclude about a problem of non-stationary It is often more reliable when two tests show a same result If |δ| < 1 in both ADF and PP test, it is concluded there is no unit root existence or series is said to be stationary

3.3 Cointegration analysis

In order to empirically analyze the long run nexus and short run dynamic amongst the variables, we apply autoregressive distributed lag (ARDL) cointegration technique as a general vector autoregressive (VAR) model of order p in the vector of these variables (with p is the maximum lag length of dependent variable) The ARDL bound testing methodology composed by Pesaran et al (2001) has some advantages compared to Johansen cointegration test that include: The first is that ARDL does not need all variables of interest must be integrated of the same order, on the other hand it can be applied when the variables of interest are integrated at the mixture of I(0) and I(1) The second advantage is that ARDL test is still relatively effective in the case of finite small sample size The last advantage is that ARDL regression gives unbiased estimations of long run model (Harris and Sollis, 2003 cited in Anshul, 2013)

The ARDL models used in this study are expressed as followings:

The bound test is based on the joint F-statistic coefficient test which its asymptotic distribution is non-standard under the null hypothesis of no cointegration The first step

in the ARDL bound test is to estimate separately three equations (5, 6 and 7) by ordinary least squares regression (OLS) The estimation of these equations detects for

an existence of the long run relationship by conducting an F-test for the joint significance of the lagged levels of the variables, on the other hand it means 𝐻0: 𝑏1𝑡 =

𝑏2𝑡 = 𝑏3𝑡 = 0 For a given siginificance level, lower-bound and upper-bound critical values, can be determined and given in the research by Pesaran (2001) In the three equations (5, 6 and 7) above, the lagged level terms are calculated on the assumption that all variables included in the ARDL model are integrated of order zero, while the latter lagged first-difference terms are calculated on the assumption that the variables are integrated of order one The null hypothesis of no cointegration is rejected when the statistical value of the test is higher the upper critical bound value, inversely it is accepted if the F-statistic is lower than the lower bound value Other cases, the cointegration test is inconclusive The employment of this approach is guided by the short time span of the data set, and the case of the mixture of I(0) and I(1) amongst variables Furthermore, the determination of optimal lag order is based on Akaike information criterion (AIC) while estimating ARDL bound test equation

3.4 Granger causality analysis

If cointegration exists amongst variables, the conditional ARDL (p, q1, q2) long run model for LGDS or LGDP or LFDI, could be estimated as forms below:

As the suggestion by Anshul (2013), we should estimate the short-run dynamic coefficients by adding error correction terms taken from the long run equation The long run cointegration between the variables implies that Granger causality exists in at least one direction which is determined by the F-statistic of the lagged first-difference terms and the error-correction term as shown in equations 11 to 13 In detail, the short-run causal effect is determined by F-statistic on the coefficients of lagged first-difference terms of explanatory variables while the F-statistic on the coefficient of the lagged error-correction term (denoted as ECT) represents for the long run causal relationship However, there are only equations where the null hypothesis of no cointegration rejected would be estimated with an error-correction term

𝛥𝐿𝐺𝐷𝑆𝑡 = 𝑎0+ ∑𝑝𝑖=1𝑎1𝑖𝛥𝐿𝐺𝐷𝑃𝑡−𝑖 + ∑𝑝𝑖=1𝑎2𝑖𝛥𝐿𝐹𝐷𝐼𝑡−𝑖 + 𝛼𝐸𝐶𝑇1,𝑡−1+ 𝑢1𝑡 (eq.11) 𝛥𝐿𝐺𝐷𝑃𝑡 = 𝑎0 + ∑𝑝𝑖=0𝑎1𝑖𝛥𝐿𝐺𝐷𝑆𝑡−𝑖 + ∑𝑝𝑖=0𝑎2𝑖𝛥𝐿𝐹𝐷𝐼𝑡−𝑖 + 𝛼𝐸𝐶𝑇2,𝑡−1+ 𝑢2𝑡 (eq.12) 𝛥𝐿𝐹𝐷𝐼𝑡 = 𝑎0+ ∑𝑝𝑖=0𝑎1𝑖𝛥𝐿𝐺𝐷𝑆𝑡−𝑖 + ∑𝑝𝑖=0𝑎2𝑖𝛥𝐿𝐺𝐷𝑃𝑡−𝑖 + 𝛼𝐸𝐶𝑇3,𝑡−1+ 𝑢3𝑡 (eq.13) Where a1i and a2i are the short run coefficients of the model being convergence to equilibrium and α is the coefficient of the speed adjustment Furthermore, ECT1, ECT2 and ECT3 are error-term series taken from long run equations 8, 9 and 10, respectively The equations (11) to (13) are estimated by OLS regression separately to obtain short-run and long run effects amongst these variables Once again it is important to note that only an equation with the null hypothesis of no cointegration rejected could be estimated with error term

Chapter 4: Empirical Analysis

In Chapter 4, the study presents estimated results for the interaction between economic growth and saving with the confirmation of FDI in Vietnam Step by step while

analyzing the 1989-2012 data, specific problems are solved and discussed as the followings

4.1 Empirical evidence

In this part, the study will employ the econometric techniques mentioned briefly at Chapter 3 in which results of unit root test, ARDL cointegration bound test, long-run coefficient estimation and VECM conditional on ARDL are presented respectively

4.1.1 Unit root test results

The three variables are plotted on the graph as shown on Figure 1 below There exists a deterministic trend inside each series Therefore, ADF and PP tests employed to detect

a problem of unit root existence should have a trend factor in unit-root test equations

Figure 2: The graph of LGDP, LGDS and LFDI series

2 3 4 5 6 7

16 18 20 22 24

In detail, we could see that LFDI is said to be stationary at level by both unit root test methods as shown on Table 2 Inversely, LGDP and LGDS are not stationary at level, even the significance level is 10 per cent Detecting orders of stationary in time series analysis is the most important step because it will influence which method should be employed to estimate short-run and long run effects amongst variables of interest Then, the next step is to check whether LGDP and LGDS are stationary at level 1 or not

Table 2: Unit root test of variables at level value

Notes: the subscript τ in the model allows for a drift and deterministic trend These (*),

(**) and (***) indicate the rejection of null hypothesis at 10 per cent, 5 per cent and 1 per cent respectively Additionally, critical value obtains from MacKinnon (1995) After taking the first difference of LGDP and LGDS, applying ADF and PP test, the results in Table 3 show information as expected that two series are stationary at the first difference As Asteriou (2007) states most of economic indicators are trended, therefore, rarely they are stationary at level data and usually stationary after taking the first difference, and our research data is the case as he mentioned For the case of LGDS, the null hypothesis of non-stationary is rejected by two methods, thus this series is said to be stationary at the first difference

Table 3: Unit root test of variables at the first difference

Notes: the subscript τ in the model allows for a drift and deterministic trend while μ

allowing for a drift These (*), (**) and (***) indicate the rejection of null hypothesis

at 10 per cent, 5 per cent and 1 per cent respectively Additionally, critical value obtains from MacKinnon (1991)

Last but not the least, for the case of unit root tests of LGDP, PP test seems to fail to reject the null hypothesis of non-stationary at the significance level 5 per cent Then,

we must test on an assumption of ADF in which error term series of this test whether follows the normal distribution or not As shown at Appendix A.1, this error term series follows a normal distribution, therefore, the results of ADF test for LGDP are robust Then, we could conclude that LGDS and LGDP are stationary at level 1, and LFDI is stationary at level 0 Finally, ARDL method should be applied to test a

cointegration existence amongst these variables

4.1.2 Cointegration test results

Based on the results of unit-root test, Johansen cointegration test for multivariate causality could not be applied because this method only works if all relevant variables are stationary at the same level Therefore, ARDL bound test should be employed instead of Johansen method As mentioned in Chapter 3, ARDL is also effective in the case of finite short time span This is really important when analyzing Vietnam’s data because the time span could not be longer the period 1989 – 2012 for three variables of interest

Table 4: Results of ARDL bound test Dependent variable Independent

Lower-bound critical value for “without intercept and trend” at 1% = 3.88

Upper-bound critical value for “without intercept and trend” at 1% = 5.30

Applying the ARDL cointegration tests, we estimate three cointegration equations 5 –

7 as shown in Chapter 3 The maximum of lag length is 2 because the time span of Vietnam data set is short, only 24 years, or on the other hand we have only 24 observations Reducing the lag length of the first-difference terms corresponding to each variable in each equation step by step, then we receive the optimum lag length is 1 based on Akaike information criterion for equations 5 to 6, but equation 7 with the optimum lag length is 2 (see at Appendix A.2) the F statistical values are calculated for the joint hypothesis of coefficients of lagged level terms in each equation respectively given in Table 4 Comparing these statistical values with critical ones given in the research by Pesaran (2001), we could only reject the null hypothesis of no cointegration in equation 5 That means there is an existence of cointegration direction from LGDP and LFDI to LGDS Nevertheless, in equation 6 to 7, statistical values are less than the lower-bound critical value, thus we accept the null hypothesis of no

cointegration

4.1.3 Granger causality test results

The next step of ARDL bound test is to estimate a long run coefficient equation in the form of equation 8 Detecting the optimum lag length based on Akaike information criterion as the same way when estimating ARDL cointegration equations 5 – 7 (see at Appendix A.3), finally ARDL (2, 0, 0) is employed to estimate long run coefficients between LGDS, LGDP and LFDI The results of long run coefficient equation are shown on Table 5 As we could see, in long run LGDP really causes and pushes LGDS represented by the coefficient value equals to 1.06 and is significant at 1 per cent Inversely LFDI causes, but tends to reduce LGDS represented by the coefficient value equals to -0.12 and is significant at 5 percent

Table 5: Estimated long run coefficients using the conditional ARDL(2, 0, 0) Equation 8: LGDS is dependent variable

coefficients combined with error terms Then the VECM is applied in form of equation

11 because only this equation has the cointegration vector The optimum lag length is detected through estimating Unrestricted VAR amongst ΔLGDP, ΔLGDS and ΔLFDI series Then the optimum lag length 0 is selected by AIC (see more at Appendix A.3) With the VECM results shown on Table 6, the long run adjustment coefficient (α) equals to -0.706 and is significant at 5 per cent It means there is an existence of long run causal direction running from LGDP and LFDI to LGDS as early proved by the ARDL bound test results above The α value is -0.706 means the speed of adjustment

to equilibrium after an economic shock is high Approximately 70.6% of disequilibrium from the previous year’s shock could converge back to the long run equilibrium in the current year (Asteriou, 2007)

Table 6: Results of VECM based on error terms taken from the conditional ARDL VECM in a form of Equation 11: ΔLGDS is dependent variable

of CUMSUM and CUMSUM-Squared tests (see more at Appendix A.3) show the estimated coefficients and variance of VECM are stable in the period 1989 – 2012, on

the other hand it means no structural break found Therefore, the results taken from this regression are robust

Table 7: Results of diagnostic tests for equation 11

Test statistic Probability

Breusch-Godfrey Serial Correlation

test

However, in the causal direction running from LGDP and LFDI to LGDS, the short-run coefficients are not statistical significant in the form of equation 11, even at 10 per cent

as shown on Table 8 Furthermore, in the causal direction from LGDS and LFDI to LGDP or from LGDS and LGDP to LFDI, short-run coefficients are estimated through VAR model with the optimum lag length 0 detected from Unrestricted VAR mentioned above On the other hand, these short-run coefficients are estimated in form of equations 12 to 13, but without Error Correction Term (ECT) And the results of short-run coefficients estimated as shown on Table 8, then we could conclude that in short run LGDP, LGDS and LFDI do not cause each other

Finally, the analyzed results show the variables of interest that are integrated at the mixture of I(0) and I(1) Then ARDL bound test is employed to detect long run equilibrium between these variables As the analysis above, we could conclude in long run these variables are co-integrated The long run causality direction runs from LGDP and LFDI to LGDS only, furthermore, there is no short-run effect received amongst them