The Relationship between Financial Development and ... · Chapter 1 INTRODUCTION 1.1 Introduction...

The Relationship between Financial Development

and Economic Growth: Singapore 1978 - 2006

Seet Min Kok

Thesis submitted in fulfillment of the requirement of Doctor of Philosophy

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ACKNOWLEDGEMENTS

I wish to record my utmost appreciation and gratitude to my supervisors Winthrop Professor

David Plowman and Professor Nicolaas Groenewold of the University of Western Australia

for their astute guidance, insightful advice and keen assistance in this dissertation.

I also want to express my sincere thanks to my wife, Marilyn, for her consistent support and

kind understanding throughout my PhD journey.

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ABSTRACT

The study of the causal relationship between financial development and economic growth has

been a topic of keen interests and controversy. Past studies provide mixed evidence on the

direction of causality in the finance-growth nexus. This thesis examines the relationship

between financial development and economic growth in Singapore. As a longitudinal study,

it also examines whether the finance-growth relationship changes over time, particularly

when subjected to major shocks. The research employs vector auto-regression analysis on

time-series data over the period 1978-2006, with in-depth analyses in the sub-periods 1978-

1996 and 1998-2006 which are separated by the 1997 Asian financial crisis. From the

perspective of banking sector development, the study found negative bi-directional causality

between banking activities and economic growth in Singapore, with the finance-growth nexus

becoming more volatile after being subjected to major shocks such as the 1997 Asian

financial crisis. From the perspective of stock-market development, the study indicated

positive bi-directional causality between stock-market activities and economic growth in

Singapore, with the mutually beneficial linkages between the stock-market and the real

economy becoming less persistent after the 1997 Asian financial crisis. The implications of

the results for theory and policy are discussed and areas for further research are also

highlighted.

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TABLE OF CONTENTS

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Chapter 1 INTRODUCTION

1.1 Introduction ………………………………………………………………………...

1.2 Questions and Method …………………….………………….……………………

1.3 Thesis structure ……..………………………………………………………………..

1.4 Conclusion …………….…………………………………………………………...

Chapter 2 LITERATURE REVIEW

2.1 Introduction …………………………………………………………………….

2.2 Theoretical Framework …………………………………………………………

2.2.1 McKinnon-Shaw model …………………………………………….

2.2.2 Neo-Keynesian model ………………………………………………

2.2.3 Neo-structuralist model ……………………………………………..

2.2.4 Endogenous growth model …....…………………………………….

2.2.5 Two-sector externalities model ……………………………………..

2.3 Direction of Causality in the Relationship between Financial Development and

Economic Growth ………….…….……………………..…………………………...

2.3.1 Unidirectional causality from financial development to economic growth ……………………………………………………………….. 2.3.2 Unidirectional causality from economic growth to financial

development ………………………………………………………….. 2.3.3 Bi-directional causality between financial development and economic

growth ………………………………………………........................... 2.3.4 No causality between financial development and economic growth…. 2.3.5 Negative impact of financial development on economic growth …….

2.4 Conclusion ………. ….………………………………………………….................

Chapter 3 OVERVIEW OF FINANCIAL AND ECONOMIC DEVELOPMENT

IN SINGAPORE, 1978-2006

3.1 Introduction …………………………………………………………………………

3.2 Economic Development in Singapore ……….……………………………………..

3.2.1 1978-1996 …………………………………………………………...

3.2.2 1997-1998 …………………………………………………….…......

3.2.3 1999-2006……………………………………………………..….….

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3.3 Financial Development of Singapore……………………………………………......

3.3.1 1978-1996 …………………………………………………….…......

3.2.2 1997-1998 …………………………………………………….…......

3.2.3 1999-2006………………………………………………………........

3.4 Conclusion ……………………………………………………………………........

Chapter 4 METHODOLOGY

4.1 Introduction ………………………………………………………………….….......

4.2 Theory underlying the finance-growth relationship………………………….….......

4.3 Methods, constructs and indicators used in past studies

4.3.1 Historical case studies …………………………………………........

4.3.2 Cross-section regression analysis ……….…..……………….….......

4.3.3 Panel data studies …………………………………………….….......

4.3.4 Time series analysis.…………………………………………...….....

4.4 Vector Auto-regression (VAR) Model …………………………………………......

4.5 Variables ……………………………. ……….…………………………………….......

4.5.1 Real per capita GDP ..……………………………………….….......

4.5.2 Financial loans over nominal GDP …………………………….........

4.5.3 Stock-market turnover over nominal GDP …………………….........

4.6 Data Sources, Study Period and Statistical Tools ………………………………......

4.7 Testing Procedures ……………………………………………………………........

4.7.1 Unit root test for data non-stationarity …………………………........

4.5.2 Determine the order of integration of time series ……………….......

4.7.3 Cointegration test ………………………………………………........

4.7.4 Model for causality test ………………..…………………….….......

4.7.5 Impulse response function ……………………………………......…

4.7.6 Break point analysis …………………………………………….......

4.7.7 Checking result robustness …………………………………......…..

4.8 Conclusion ….………………………………………………………………......…

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Chapter 5 DATA ANALYSIS I

5.1 Introduction ……………………………………………………………………........

5.2 Main variables and framework of analysis ……………………………………........

5.2.1 Main variables ..……………………………………………..…........

5.5.2 Framework of analysis ……………………..………………..….......

5.3 Unit root test and order of integration ………………………………………….......

5.3.1 Unit root test ………....……………………………………….….......

5.3.2 Determining the order of integration of the variables ………..….......

5.4 Cointegration test …………………………………………………………….…......

5.4.1 Engle-Grange test ……………………………………………..…......

5.4.2 Johansen test …………………………………………………..….....

5.5 Conclusion ……….…………………..…………………………………………......

Chapter 6 DATA ANALYSIS II

6.1 Introduction …………………………………………………………………………

6.2 Granger Causality Tests …………………………………………………………….

6.2.1 Lag length selection for Granger causality test in VAR model...........

6.2.2 Results of Granger causality tests ….………......................................

6.3 Model specification and estimation of results ………………………………….......

6.3.1 Model specification and estimation of the full-sample, 1978(1)-

2006(4) ………………………………………………………………

6.3.2 Test for structural break ……………………………………………..

6.3.3 Sub-sample analysis………………………………………………….

6.4 Generalised impulse response functions (GIRFs) ..………………………………

6.4.1 VAR model on Y and L …………………………………………….

6.4.2 VAR model on G and T …………………………………………….

6.4.3 VAR model on Y and T …………………………………………….

6.4.4 VAR model on G and L .…………………………………………….

6.5 Cumulative impulse response functions .…………………………………………

6.5.1 VAR model on Y and L ……………………………………………..

6.5.2 VAR model on G and T ……………………………………………..

6.5.3 VAR model on Y and T ……………………………………………..

6.5.4 VAR model on G and L ……………………………………………..

6.6 Conclusion .……………………………………...………………………………

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Chapter 7 DATA ANALYSIS III

7.1 Introduction …………………………………………………………………………

7.2 Generalised Impulse Response Functions for different VAR lags …………………

7.2.1 VAR model (with different lag length) on Y and L ………................

7.2.2 VAR model (with different lag length) on G and T ……………........

7.2.3 VAR model (with different lag length) on Y and T ……………........

7.2.4 VAR model (with different lag length) on G and L …………….......

7.3 Choleski impulse response functions ……………………………………………….

7.3.1 VAR model on Y and L .....………………………………………….

7.3.2 VAR model on G and T .....………………………………………….

7.3.3 VAR model on Y and T .....………………………………………….

7.3.4 VAR model on G and L .....………………………………………….

7.4 Generalised Impulse Response Functions (GIRFs) generated from

cointegrated systems(VECM) ………………………………………………………

7.4.1 GIRFs from VECM involving Y and L .…………………………….

7.4.2 GIRFs from VECM involving G and L .…………………………….

7.5 Summary of robustness test results …………………………………………………

7.6 Conclusion ………………………………………………………………………….

Chapter 8 SUMMARY AND CONCLUSION

8.1 Introduction ………………………………………………………………….….......

8.2 Summary of research ………………………………………………………..…........

8.3 Summary of findings ……………..………………………………………………........

8.4 Limitations of the study ……………………………………………………..…........

8.5 Areas of further research ……………………………………………………….........

8.6 Conclusion ………………………………………………………………………......

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APPENDICES

Appendix 1 Activities of Key Financial Institutions Operating in Singapore ………

Appendix 2 Foreign Full Banks in Singapore: Listed by Levels of Activity …….....

Appendix 3 Major Financial Sector Policies and Developments in Singapore...........

Appendix 4A Unit Root Testing: 1978Q1-2006Q4 (Full Sample)………………….....

Appendix 4B Unit Root Testing: 1978Q1-1996Q4 (Pre-Asian Financial Crisis)……..

Appendix 4C Unit Root Testing: 1998Q1-2006Q4 (Post-Asian Financial Crisis)….....

Appendix 5A Determining the Order of Integration: 1978Q1-2006Q4 (Full Sample)..

Appendix 5B Determining the Order of Integration: 1978Q1-1996Q4 (Pre-Asian

Financial Crisis) …………………………………………………….....

Appendix 5B Determining the Order of Integration: 1998Q1-2006Q4 (Post-Asian

Financial Crisis) ………………………………………………………..

Appendix 6A Engle-Granger Cointegration Test: 1978Q1-2006Q4 (Full Sample) …..

Appendix 6B Engle-Granger Cointegration Test: 1978Q1-1996Q4 (Pre-Asian


Appendix 6C Engle-Granger Cointegration Test: 1998Q1-2006Q4 (Post-Asian


Appendix 7A Johansen Cointegration Test: 1978Q1-2006Q4 (Full Sample) ………...

Appendix 7B Johansen Cointegration Test: 1978Q1-1996Q4 (Pre-Asian Financial

Crisis) ………………………………………………………………….

Appendix 7C Johansen Cointegration Test: 1998Q1-2006Q4 (Post-Asian Financial

Crisis) ……………………………………………………………….....

Appendices 8 Selection of Optimal Lag Length of VAR Model ……………….........

REFERENCES ……………………………………………………………………..…......

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Chapter 1

INTRODUCTION

1.1 Introduction

This thesis examines the relationship between financial development and economic

growth in Singapore. As a longitudinal study, it also examines whether the finance-

growth relationship changes over time, particularly when subjected to major shocks.

1.2 Questions and Methods

The study explores the following research questions:

Research Question 1

What is the causal relationship between financial development and economic growth

in Singapore?

Research Question 2a

Does the finance-growth relationship remain constant or change over time?

Research Question 2b

What is the effect of major shocks, such as the 1997 Asian financial crisis, on the

finance-growth relationship?

The study of the causal relationship between financial development and economic

growth has been a topic of keen interests and controversy among academics,

particularly since the publication of the seminal works of McKinnon (1973) and Shaw

(1973). As Levine (1997) indicated, economists hold “startlingly different” views

concerning the role of the financial system in economic development. Schumpeter

(1912) was among the earliest to suggest the importance of an efficient banking

system in successfully identifying firms and providing adequate funding for the firms

to engage in technological innovation, which is critical for maintaining economic

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growth. Thus, the Schumpeterian (1912) view is that financial development causes

economic growth to take place. On the other hand, Robinson (1952, p. 86) asserted

that “where enterprise leads, finance follows”, as economic development merely

generates demand for various types of financial services, which the financial system

provides. Hence, Robinson‟s (1952) view is that economic growth causes financial

development. Moreover, there are economists who are either skeptical of the finance-

growth nexus (Lucas, 1988) or choose to ignore the importance of the financial

system in development economics (Stern, 1999; Chandavarkar, 1992).

Despite extensive research on the finance-growth relationship ranging from cross-

sectional studies, panel data studies, time-series analyses and historical analyses in the

developed and developing countries, there is inconclusive evidence on the direction of

causality between financial development and economic growth. While Levine (2005,

p.868) has alluded to the “burgeoning empirical literature on finance and growth”,

there have been relatively few single-country time-series studies.

The empirical literature on the finance-growth nexus indicates that there were only

three single-country time-series studies which specifically focused on the relationship

between financial development and economic growth in Singapore. In a study by

Murinde and Eng (1994), it was found that financial institutions enabled savings to be

channeled to productive investments thus stimulating economic growth during the

1980s. A later study by Ariff and Khalid (2000) found that while financial

liberalization in Singapore over the period 1975 to 1998 benefited the real economy,

the 1997 Asian financial crisis had “diminished” Singapore‟s role as a financial

centre. In a more recent study by Khalid and Tyabji (2002), it was found that

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financial development caused economic growth over the period 1975-1997 but

economic growth did not cause financial development over the same period.

Nonetheless, over the slightly longer period from 1975-1999, the same study found no

evidence of any causal relationship between financial development and economic

growth in Singapore. The study conjectured that this puzzling result could be

attributable to the adverse impact of the 1997 Asian financial crisis on Singapore‟s

economic and financial development.

This research examines the causal relationship between financial development and

economic growth over the period 1978-2006. It further examines the significance of

any differences in the relationship over the two major periods of Singapore‟s

economic development: 1978-1996 and 1998-2006. The two periods are clustered

around the Asian financial crisis of 1997. This examination is important in the light

of Patrick‟s (1966) hypothesis that countries in the initial stages of economic

development tend to exhibit “supply-leading” finance, in which the creation of

financial institutions provides liquidity for spurring economic growth; while countries

in later stages of economic development tend to exhibit “demand-following” finance,

whereby economic development generates demand for financial services which leads

to the creation of financial institutions.

The proposed study will use a vector autoregression (VAR) analysis employing

quarterly time-series data for Singapore. The main features of the research design

including the data sources and period of study, constructs employed, investigative

method and approach to data analysis are summarized below.

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As stated, the period of study will be from 1978 to 2006. The first sub-period 1978-

1996 is chosen as 1978 marked the start of “complete liberalization of the foreign

exchange market in Singapore” (Murinde and Eng, 1994, p.396) and early

commencement of Singapore as an international financial centre. The year 1997 was

a watershed as it witnessed the Asian financial crisis which halted Singapore‟s stellar

economic performance. The second sub-period 1998-2006 is selected as 1998

marked the start of many new reforms and restructuring measures in the financial

sector which opened up the domestic banking sector to international competition and

brought about more transparency in the disclosure of banking assets (Peebles and

Wilson, 2002; Tan, 2006).

There are two main constructs in the study: economic growth and financial

development. Two different indicators are employed for measuring economic growth:

real GDP per capita and real GDP. The real GDP per capita of Singapore, which

measures the ratio of real GDP to total population in the domestic economy, was

employed as an indicator for economic growth in the study by Khalid and Tyabji

(2000) as well as other similar studies (Jung, 1986; King and Levine, 1993,

Demetriades and Luintel, 1996; Levine and Zervos, 1998, Ram, 1999). Real GDP

was also separately employed as an indicator for economic growth in the study by

Murinde and Eng (1994).

The construction of financial development indicators is more difficult because of the

diversity of financial services and wide array of institutions associated with financial

intermediation (Thangavelu and Ang, 2004). Following Levine‟s (1997)

classification of the financial sector into financial intermediaries (banks) and stock

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markets, two separate indicators reflecting the two main sectors are used to measure

financial sector development. To measure banking sector development within the

financial system, the ratio of bank loans to nominal GDP is used as it reflects the role

of financial intermediaries, mainly the banks, in channeling funds to the private sector

(Levine and Zervos, 1998). To measure stock-market development, the ratio of

stock market turnover to nominal GDP is used as an indicator as it reflects the level of

liquidity in the stock market, which in turn, influences the efficient functioning of the

stock market in terms of acquisition of information, savings mobilization, corporate

control and risk diversification among firms (Levine and Zervos, 1998; Thangevelu

and Ang, 2004; Tang, 2006).

Quarterly time-series data are used to analyze the relationship between financial

development and economic growth. Economic data on nominal GDP and real GDP

are obtained from the Economic Survey of Singapore while population statistics are

obtained from the Yearbook of Statistics. Financial data on stock market turnover and

loans of financial intermediaries are obtained from the Monthly Statistical Bulletin

published by the Monetary Authority of Singapore. The E-views statistical software

package is employed for various statistical tests, including determining the optimal

lag length, testing for cointegration, developing the vector auto-regression (VAR)

models, testing for causality and generating impulse response functions (IRFs) from

the VAR models.

1.3 Thesis structure

This introductory chapter is followed by a comprehensive literature review in Chapter

2. The literature examines the theory underlying the causality between financial

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development and economic growth and reviews past empirical studies on the finance-

growth nexus along the lines of the directions of causality. Chapter 3 provides an

overview of the economic and financial development of Singapore over the period

1978-2006, focusing particularly on the developments before and after the 1997 Asian

financial crisis. Chapter 4 examines the statistical methodologies adopted in past

studies and provides a description of the appropriate methodology, constructs and data

employed in the research. Chapter 5 reports on the results of a battery of tests

performed on the time-series data to determine the stationarity, order of integration

and cointegration of the variables employed. Chapter 6 provides the findings on the

relationship between financial development and economic growth in Singapore using

the Granger causality tests and impulse response analyses. Chapter 7 looks at the

results of the robustness tests which are further undertaken to assess the finance-

growth nexus in Singapore. The final chapter provides a summary of the research and

main findings of the study along with suggestions for areas of further research.

1.4 Conclusion

This thesis explores the relationship between financial development and economic

growth in Singapore. It also examines whether the finance-growth nexus is stable

over time, particularly when subjected to great shocks such as the 1997 Asian

financial crisis. In doing so, the research employs a number of statistical techniques

including vector auto-regression, Augmented Dickey Fuller test, Granger causality

test, impulse response functions and robustness tests. The next chapter will review

the finance-growth relationship that is provided in the literature.

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Chapter 2

LITERATURE REVIEW

2.1 Introduction

The relationship between financial sector development and economic growth is an

important and on-going debate in both developed and developing economies. Interest

in this topic has arguably gone back as far as the nineteenth century, when the

industrial revolution in England was facilitated by the financial system which

provided a critical source of capital financing (Bagehot, 1873; Hicks, 1969). After

more than 200 years, the age-old issue concerning the causality between financial

development and economic growth remains relevant and important for world

economies. For the developing world, this topic relates to the contentious issue of

whether financial restructuring programmes need to be part of the overall economic

plan to stimulate and maintain economic growth. International agencies, such as the

International Monetary Fund and the World Bank, have often advocated that financial

liberalization be the central aspect of government policy for developing economies to

integrate into the world economy. At the same time, the topic is also important for

developed economies which have embarked on policies to deregulate and liberalize

their financial sectors in recent years in order to sustain growth prospects in the

longer-term.

In the context of Singapore, government-initiated policies to develop a strong

international financial centre had been an important hallmark of Singapore‟s

economic progress over the last 40 years. In the after-math of the Asian Financial

Crisis in 1997, the Singapore government adopted policies of liberalizing the

domestic financial sector. The critical issue is to what extent are such financial

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policies necessary for maintaining Singapore‟s economic growth. More importantly,

could Singapore‟s continued prosperity be assured without the sustained development

of its financial sector?

This chapter initially reviews the literature on the theoretical frameworks for

understanding the linkages between financial development and economic growth.

Following this, empirical studies are examined to explore the nexus between financial

development and economic growth from the perspective of the direction of causality.

The final part of the chapter concludes in summarizing the theoretical and empirical

relationship between financial development and economic growth. The literature

concerning methodology will be developed in Chapter 4 (Methodology) of the thesis.

2.2 Theoretical Framework

The literature suggests a wide variety of economic models for analyzing the finance-

growth nexus. This section begins with a discussion of the McKinnon-Shaw (1973)

model, which provides one of the early theoretical foundations for assessing the

importance of financial development to economic growth. Opposing schools of

thought associated with the Neo-Keynesian model (Taylor, 1983; Beckerman, 1988;

Burkett and Dutt, 1991; Gibson and Tsakalotos, 1994; Stiglitz, 1994, Hellman,

Murdock and Stiglitz, 2000) and the Neo-Structuralist (Ghatak, 1975; van

Wijenbergen, 1983) model are further examined in light of the McKinnon-Shaw view

concerning financial repression and growth. Since the 1980s, various types of

endogenous growth models (Greenwood and Jovanovic, 1990; Bencivenga and

Simth,1991; Murinde and Eng,1994) have been put forward to explain the interaction

and direction of causality between financial and economic development. More

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recently, the two-sector externalities model (Wang, 2000) has also been identified to

analyze the reciprocity between the financial and real sectors in the economy. Taken

together, these different models provide a comprehensive theoretical framework for

understanding the underlying relationship between financial development and

economic growth.

2.2.1 McKinnon-Shaw Model

The McKinnon-Shaw model provides an insightful analytical framework which

brings together the separate strands of research by McKinnon (1973) and Shaw (1973)

concerning the linkages between finance and growth. The primary focus of the model

is the negative effects of financial repression on savings, investment and growth in the

economy, particularly in less developed countries. Nonetheless, the transmission

mechanisms through which the negative financial effects adversely influence the real

economy are differently accounted for in the two separate studies.

In the study by McKinnon (1973), it was noted that Keynesian and monetarist theories

tended to “assume that capital markets are essentially „perfect‟, with a single

governing rate of interest or a term structure of interest rates” (p3). As a result, real

money balances and physical capital were conventionally treated in past theories as

substitutes for each other. However, McKinnon argued that “the brute fact of

underdevelopment is overwhelming fragmentation in real interest rates” (McKinnon,

1973, p3). As the economy is “fragmented”, it isolates households from firms thus

resulting in different relative prices that they face for various factors of production

such as labour and capital. He pointed to examples of countries in Asia, Latin

America and Africa in which the mass population tended to operate outside of the

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market economy. Consequently, “indigenous entrepreneurs had limited access to

capital” which meant that investors had to accumulate their own money balances

before investment could occur (McKinnon, 1973, p6). Hence, McKinnon argued that

the demand for money balances and physical capital are complementary rather than

substitutes for each other. This “basic complementarity between money and physical

capital” is based on two important assumptions: (i) all investment is self-financed;

(ii) investment expenditure is lumpy and less divisible than consumption expenditure

(McKinnon, 1973, p59). Hence, unlike consumption, investment cannot take place

until sufficiently large pools of savings are accumulated by individual households to

finance the capital expenditures. Under such circumstances, higher real interest rates

would increase the accumulation of money balances (i.e. attract more savings) and

encourage more investments as the supply of funds rises.

Shaw (1973) pointed to the importance of the banking system in financial

intermediation which, in turn, facilitates economic growth. He asserted that the

degree of “financial deepening” would influence the extent of financial intermediation

between savers and investors, thereby ultimately affecting the per capita income of a

country. Shaw (1973) suggested that a number of indicators could reflect “financial

deepening” like the “stocks of financial assets aggregatively grow relative to income”

or “a lengthening of maturities, and a wider variety of debtors gains access to

financial markets” (p7).

He maintained that financial liberalization, which tended to lead to higher real

institutional interest rates, would increase the incentives to save and invest, and to

raise the overall efficiency of investment. Thus, financial liberalization “permits the

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process of mobilizing and allocating savings” and “opens the way to superior

allocations of savings by widening and diversifying the financial markets on which

investment opportunities compete for the savings flow” (Shaw, 1973, p10).

Consequently, financial liberalization facilitates economic growth in two ways. On

the one hand, it increases real deposit rates thereby increasing financial savings and

enhancing the capacity of the banking system as a loans provider. On the other hand,

it also induces investment and growth by reducing the real costs to investors in

providing liquidity, reducing risk through diversification, reaping economies of scale

in lending, increasing operational efficiency and lowering information costs through

specialization and division of labour.

The McKinnon-Shaw model concludes that financial repression adversely influences

savings, investment and economic growth while financial liberalization, which

removes the ceiling on interest rate, would be beneficial for investment and growth.

Fry (1995; 1997) identified six different ways that ceilings on lending and borrowing

rates could lead to inefficient allocation of resources within the economy thus

ultimately hampering economic growth. First, artificially suppressed low deposit

rates tend to encourage households to increase present consumption, hence reducing

savings below the socially optimum level. Second, to ensure their own liquidity,

individual savers tend to invest directly in low-yielding projects rather than depositing

with banks, which would otherwise have been able to pool together the resources

from small savers to fund more profitable but less liquid projects. Third, as lower

interest rate implies a lower cost of capital funds, entrepreneurs might tend to choose

more capital-intensive projects due to the relatively lower funding costs compared to

labour-intensive projects. Fourth, the low interest rate would encourage entrepreneurs

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to take up low-yielding projects which would not have been undertaken at the market

equilibrium rate of interest. Fifth, with low income accruing from the low lending

rates, financial institutions have little incentive to expend resources on collecting risk-

related information on projects and borrowers which could alleviate the adverse

selection problems associated with information asymmetry between borrowers and

lenders. Lastly, as financial institutions are forced to charge low interest rates and

are prevented from charging the high risk premium associated with high yielding

investments, many projects are likely to reap returns which are below the optimal

levels in the economy.

Financial repression within the McKinnon-Shaw model can also take the form of

excessive monetary growth to finance imprudent government spending (Roubini and

Sala-i-Martin, 1992; Shreft and Smith, 1997). The resultant low interest rate

associated with excessive monetary growth would reduce investment efficiency hence

retarding economic growth. Roubini and Sala-i-Martin (1992) argued that the main

objective of financial repression in many less developed countries is to raise

government revenue. With a high rate of tax evasion in many less developed

countries, the governments in these countries tend to repress the financial sector by

increasing monetary growth to pay for their large budget deficits. As a result of the

excessive monetary expansion, inflation accelerates which ultimately undermines the

efficiency of investment and growth in the economy.

2.2.2 Neo-Keynesian Model

In contrast to the McKinnon-Shaw model, which advocates financial development as

a means to spur economic growth, the neo-Keynesian model suggests that financial

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development can stymie aggregate demand, reduce economic growth and increase

instability in the financial system (Taylor, 1983; Beckerman, 1988; Burkett and Dutt,

1991; Stiglitz, 1994). The neo-Keynesian approach is skeptical about the assumption

in the McKinnon-Shaw model concerning the equality between savings and

investments. This is because the neo-Keynesian model posits that investments

depend on a variety of factors such as expectations about future demand or “animal

spirits” (Keynes, 1936), while savings (derived as a residual of disposable income not

consumed) are a function of income rather than interest rates. Consequently, higher

accumulated savings do not necessarily lead to increased investments, as both

components are determined by completely different factors. Moreover, contrary to the

McKinnon-Shaw postulate that higher interest rates would attract more savings for

investment, it is arguable that “the offsetting income and substitution effects of

increased interest rates imply that the net impact on savings must be ambiguous”

(Dornbusch and Reynoso, 1989, p205). Additionally, the neo-Keynesian model also

suggests the presence of information asymmetries, externalities and economies of

scale in the lending process causes market failures which could lead to financial

instability if financial markets are not carefully managed and well-regulated.

Burkett and Dutt (1991), in their neo-Keynesian model, predict that financial

liberalization adversely influences economic growth because of the resultant negative

effects of interest rate deregulation This is because the increase in savings which

results from higher interest rate brought on by financial de-regulation would imply a

fall in consumer spending (as savings equals income less consumption). The lower

spending by households, in turn, depresses aggregate demand thus leading to a decline

in aggregate output and profits. Moreover, the resultant uncertainty in economic

22

prospects and future profits could also have negative accelerator effects on

investment. In the longer run, the higher borrowing cost associated with higher

lending rate tends to lead to higher prices, thereby cutting back real wages which

further reduces household consumption, aggregate demand and output (Dutt, 1991).

Gibson and Tsakalotos (1994) further discussed the negative consequences of

financial liberalization and interest rate deregulation in an open economy. They

argued that the higher interest rate tends to lead to an appreciation in the real

exchange rate, thereby hampering exports and increasing imports thus leading to a

deterioration of the trade balance. Moreover, as a large proportion of the fiscal

deficits in Less Developed Countries are financed from bank borrowing, the higher

borrowing cost worsens the budget deficits in these economies. Additionally,

financial liberalization in terms of a reduction in reserve requirements also reduces tax

revenues, further aggravating the budget deficits. In the longer term, faced with large

fiscal imbalances, the governments in these countries are likely to cut back on

investments in education and infrastructural projects, thus further reducing output and

economic growth.

Hellman, Murdock and Stiglitz (2000) argued that financial liberalization, which

boosts competition among financial institutions, could encourage banks to invest in

more risky assets thus leading to a moral hazard problem which could ultimately

undermine the stability of the financial system. This argument is based on the

premise that financial liberalization erodes banking profitability and forces the banks

to compete more aggressively for deposits by offering higher interest rates.

Consequently, to restore profits, banks invest in more risky projects which carry

23

higher risks of failure thus generating a moral hazard problem. Hence, Hellman,

Murdock and Stiglitz (2000) maintained that financial liberalization could lead to less

prudent bank behaviour which is systematically related to financial crises.

2.2.3 Neo-Structuralist Model

The neo-Structuralist model constitutes another opposing school of thought to the

McKinnon-Shaw model. The neo-Structuralist model emphasizes the importance of

the unorganized money markets, commonly called the curb markets, in Less

Developed Countries. Arguably, in many under-developed economies such as India,

a large proportion of the rural poor are unable to gain access to the formal banking

system (Ghatak, 1975). This is primarily because poor peasants in these countries are

unable to furnish the required collaterals on their loans. Hence, these poor peasants

would normally borrow in the curb markets which do not require significant

collaterals.

Neo-Structuralists argue that the “non-institutional” lendings, primarily by village

money-lenders and landlords to small borrowers in the rural or peasant sector,

constitute a large proportion of total lending activities in the Less Developed

Countries (van Wijenbergen, 1983). When interest rates rise with financial

liberalization, neo-Structuralists argue that lenders in the curb markets would tend to

shift their funds out of the relatively riskier curb markets to time deposits at the

formal financial institutions which now pay higher deposit rates. This exerts a

dampening effect on the amount of overall credit in the economy as time deposits at

formal financial institutions are subject to reserve requirements by the central bank

whilst funds in the curb markets are not constrained by similar reserve needs.

24

Hence, financial liberalization leads to a “credit squeeze” which is contractionary on

the economy. Furthermore, the rise in interest rate raises borrowing cost, which

causes the price level to spiral upwards. Consequently, neo-Structuralists maintain

that financial liberalization which leads to interest rate deregulation results in

stagflation rather than enhancing economic growth as postulated by the McKinnon-

Shaw School.

2.2.4 Endogenous Growth Models (EGMs)

The endogenous growth model (EGM) provides a mathematical template to analyze

the inter-relationship between the financial sector and the rest of the economy. There

are various types of EGMs which essentially identify the role of financial

intermediaries in the collection and analysis of information, pooling of risks, and

provision of liquidity in the financial markets. The impact of financial intermediation

on long-run economic growth is analyzed within a framework which posits that

financial development can influence economic growth and/or vice versa.

Nonetheless, in the EGM developed by Murinde and Eng (1994), it is asserted that the

relationship between financial and economic development is ambiguous.

Greenwood and Jovanovic (1990) developed an EGM in which financial

intermediation and economic growth are endogenously determined and independent

of technological improvement. Financial intermediation promotes economic growth

because financial institutions can efficiently collect and analyze information on

potential investment projects and enable pooled personal savings to be channeled to

high-yielding investments. At the same time, economic growth also facilitates

financial development by increasing the demand for financial services and providing

the requisite funds for financing and implementing financial structures. Using this

25

EGM, Greenwood and Jovanovic (1990) maintained that “growth and financial

structure were inextricably interlinked” (p1099). They argued that the model yields a

development process consisting of three stages. In the early stages of development,

slow economic growth is associated with a largely unorganized exchange. As income

increases, economic growth accelerates along the development of a more extensive

financial structure. As the economy matures with a fully developed financial

structure, economic growth slows but still remains above the rate in the early stages of

development.

Bencivenga and Simth (1991) developed an EGM drawing on the contributions of

endogenous growth literature by Romer (1986), Prescott and Boyd (1987) and Lucas

(1988). They made four important assumptions in the model. First, it is assumed that

the state of development of financial markets is “exogenously determined” by

government regulation. Second, in less developed economies, banks are assumed to

form the bulk of organized financial market. The third assumption is that there are

long delays between investment expenditure and profit receipts which tend to deter

investments in less liquid but highly profitable projects. Finally, it is further assumed

that most investments will be self-financed in the absence of banks. Using these four

assumptions, an EGM is developed which explains how financial intermediation

shifts the composition of savings, instead of raising the savings rate, to facilitate

capital accumulation thereby promoting economic growth. Importantly, financial

institutions in the EGM are better able than individuals to efficiently allocate funds to

productive investments that would promote economic growth. Bencivenga and Simth

(1991) argued that the EGM provides a “rigorous theoretical construct” (p208) for

examining government regulations on the financial system such as reserve

26

requirements or interest rate ceilings. Nonetheless, they admitted that their model

only focus on banks and would be inappropriate for an economy characterized by a

small number of banks which could jointly influence the aggregate capital stock.

A different endogenous growth model developed by Saint-Paul (1996) explains how

financial development could be triggered by a rise in demand for financial services.

The increased demand for financial services could arise from a higher level of public

debt or from technological innovations. Following his own earlier research (Saint-

Paul, 1992) and using Blanchard‟s (1985) overlapping generations model, Saint-Paul

(1996) argued that technological improvements relating to large scale investments in

infrastructural projects such as dams and railroads could only be funded by pooling

together smaller amounts of savings by individuals. Thus, increased opportunities for

investments in large projects, which are typically associated with technological

innovations, increase the demand for financial intermediation. Citing evidence by

Cameron (1967), Saint-Paul maintained that demand-driven financial development

occurred in the growth of the French financial sector in the nineteenth century, when

“large investment projects not only triggered the development of banking, but also of

stock-markets‟ (Saint-Paul, 1996, p40). In the case of England, Saint-Paul (1996)

argued that financial development was triggered by the founding of the Bank of

England in 1694 which was established to finance the large budget deficits generated

by the extended war with France. Hence, “an increase in the government‟s borrowing

requirements may exert positive spillovers on the country‟s financial infrastructure”

leading to the development of private banks and capital markets (Saint-Paul, 1996,

p39).

27

Using two contrasting endogenous growth models, Murinde and Eng (1994) applied

economic theory to explain the two competing hypotheses concerning the relationship

between financial development and economic growth. In the first endogenous

growth model, the production function is assumed to exhibit constant returns to factor

(Lucas, 1988 ; Romer, 1989 ; Pagano, 1993). Using this assumption, Murinde and

Eng (1994, p393) derived the equation for steady state growth (g) in the economy

which could be expressed as:

g = A Φ β – δ …………….. Equation (1)

where A reflects the social productivity of capital in the economy

Φ reflects the extent of the financial intermediation in the economy

β reflects the savings rate in the economy

δ reflects the depreciation rate of capital in the economy

Equation (1) suggests that financial development, which increases either A or Φ or β

(or all three variables), would increase steady state growth (g) in the economy.

Hence, the first endogenous growth model supports the hypothesis that financial

development induces economic growth. In the second endogenous growth model,

Murinde and Eng (1994, p393) adopted the “continuous-time, representative-agent,

perfect-foresight specification” as suggested by Wang and Yip (1992). In this

model, money constitutes an input in the production process, while physical

machinery and human capital are endogenously determined. Using the Cobb-Douglas

production function which exhibits diminishing returns to factor (rather than constant

returns to factor in the production process as assumed in the first endogenous model)

and assuming “Hick-neutral production technology” (Murinde and Eng, 1994, p394),

the second endogenous growth model suggests that macroeconomic aggregates are

independent of monetary variables. Hence, the second model supports an alternative

28

competing hypothesis that financial development has no impact on economic growth.

Consequently, Murinde and Eng (1994) maintained that endogenous growth theory

does not provide clear theoretical conclusions regarding the relationship between

financial development and economic growth. Moreover, as Arestis and Demetriades

(1997) pointed out, the institutional structure underlying the financial system, the

government policy stance as well as the extent of financial and monetary control are

likely to influence the causality patterns. As a result of these diverse influences in

different economies, the causality relationship between financial development and

economic growth could vary from one country to another.

2.2.5 Two-Sector Externalities Model

A more recent model used to examine the relationship between financial development

and economic growth is the two-sector externalities model. The inter-sectoral

externalities model (Wang, 2000) involves analyzing an economy with two inter-

related sectors, namely a real sector and a financial sector, to derive and estimate the

externalities or spillover effects of each sector on another. The model posits that

financial sector output depends on labour and capital inputs, while real sector output

hinges on labour and capital inputs as well as expectations of financial output.

Causality between financial and economic development is thus examined and

analyzed in terms of the magnitude of the externalities between the financial and real

sectors in the economy.

The two-sector model was first proposed by Feder (1983) to assess the impact of

export expansion on economic growth. In dichotomizing economic activities into

export and non-export sectors, Ram (1987) used the two-sector model to evaluate the

29

effects of export promotion policy in eighty-eight countries. Odedokun (1996)

modified the two-sector framework to analyze the relationship between financial

development and economic growth for seventy-one developing economies. Wang

(1999) extended the analysis by using a static two-sector econometric model for

examining the inter-sectoral externalities between the financial sector and the rest of

the economy. In further developing the static model into a dynamic two-sector

framework, Wang (2000) found that the inter-sectoral externality of Patrick‟s (1966)

supply-leading version was greater than that of the demand-following version in

Taiwan over the over the period 1961-1996.

2.3 Direction of Causality Identified in Empirical Studies

Having examined the theoretical framework for understanding the finance-growth

nexus, it is useful to examine the direction of causality identified in past studies.

ECONOMIC GROWTH

Dependent Variable

Independent Variable

FINANCIAL

DEVELOPMENT

Independent

Variable

Unidirectional Causality from Financial

Development to Economic Growth (a) Studies on positive effects of financial

development on economic growth are analyzed in Section 2.3.1.

♦ The studies are consistent with the

predictions of the McKinnon-Shaw (1973) model and the EGM developed by

Bencivenga and Smith (1991)

(b) Studies on negative effects of financial development on economic

growth are analyzed in Section 2.3.5. ♦ The studies are consistent with the

predictions of the Neo-Keynesian model

and the Neo-Structuralist model.

No relationship between Financial

Development and Economic Growth (Section 2.3.4).


predictions of the EGM developed by

Murinde and Eng (1994)

Dependent

Variable

Bi-directional Causality between Financial Development and Economic

Growth (Section 2.3.3).


predictions of the EGM developed by Greenwood and Jovanic (1990) and the

Two-Sector Externalities model developed by Wang (2000)

Unidirectional Causality from Economic Growth to Financial

Development (Section 2.3.2)


predictions of the EGM developed by Saint-Paul (1996)

30

Past empirical studies on the direction of causality between financial development and

economic growth can be broadly classified under five major categories. These five

different categories of studies, which are summarized in the above template, are

explained in the following sections.

2.3.1 Unidirectional Causality from Financial Development to Economic

Growth

A large number of studies attested to the unidirectional impact of financial

development on economic growth. In the 1970s, several policy-related studies

pointed to the hypothesis that financial development enhances economic growth.

McKinnon (1973) proposed a positive relationship between financial and economic

development. He argued that as investments are lumpy, potential investors need to

accumulate money balances before they can invest. Consequently, he maintained that

the aggregate demand for money would vary directly with the proportion of

investment in gross domestic expenditure. This implies that higher real interest rate,

which results from financial liberalization, would attract more savings and encourage

more investments concurrently. McKinnon (1973) suggested that the levels of

development of financial markets, along with financial repression and liberalization,

are exogenously determined by government legislation and policies. Shaw (1973), in

his studies of various developing countries like Korea, Taiwan, Malaysia and Ghana

in the 1960s, further asserted that financial liberalization would benefit economic

growth as increased financial intermediation increases savings and investments which

would, in turn, stimulate economic growth. A major conclusion of the McKinnon-

Shaw view is that government intervention in the financial system by way of interest

rate ceiling, restrictive reserve requirements and directed credit programmes would

impede financial development thereby retarding economic growth in the longer-term.

31

Several later studies supported the McKinnon-Shaw view regarding the unidirectional

causality from financial development to economic growth. In a two-sector model of

financial intermediation and growth, Galbis (1977) suggested that the process of

financial intermediation could re-allocate resources from traditional low-yielding

investments in the “backward” sector to investments in “technologically advanced”

sectors, thereby accelerating economic growth. A study by Fry (1978) also noted that

interest rate ceilings tended to discourage risk-taking by financial institutions while

the removal of such ceilings tended to positively influence savings and economic

growth in the Asian Less Developed Countries (LDCs). This finding was further

corroborated by a later study of Mathieson (1980), which maintained that financial

reform should be integrated within the growth policies of developing countries.

Similarly, a separate study by the World Bank (1989) concluded that financial

development is integral to economic growth by bringing about the efficient

mobilization, allocation and utilization of scarce resources within the economy.

Various studies undertaken in the 1990s continued to underline the importance of the

financial sector in fostering economic growth. In an empirical study of 80 countries

over the period 1960-1989, King and Levine (1993) found that the financial system

can promote economic growth. The study showed evidence indicating that higher

levels of financial development were positively and strongly correlated with faster

rates of economic growth, increased rate of physical capital accumulation, and

economic efficiency improvements. This was confirmed in a later study by Levine

and Zervos (1998) which indicated that stock market liquidity and banking

development could be jointly used to positively predict economic growth, capital

32

accumulation, and productivity improvements, after controlling for economic and

political factors. Interestingly, in the same year, a separate study by Odedokun (1998)

also arrived at the same conclusion regarding the positive effects of financial

intermediation on economic growth in 90 developing countries. Using Feder‟s (1983)

two-sector framework, Odedokun‟s (1998) research showed that there are two main

channels through which financial intermediation can promote economic growth. The

first channel operates through the enhancement of productivity in the financial sector

vis-à-vis the non-financial sector whilst the second channel operates through the

creation of positive effects (i.e. positive externalities) by the financial sector on the

non-financial sector.

The nexus between financial development and economic growth continued to attract

keen research interests in recent years. In a study by Xu (2000) to examine the effects

of financial development on investment and output in 41 countries, it was found that

investment constitutes an important channel through which financial development

positively influences economic growth. Using data from five developed countries,

Arestis, Demetriades and Luintel (2001) also found that banks were “more powerful”

than stock-markets in promoting economic growth. In a country-specific study on

India, Bhattacharya and Sivasubramanian (2003) found evidence to suggest that

“financial development led growth and not the other way around” (p929). This

unidirectional causality running from financial to economic development was

supported in a similar study on Taiwan by Chang and Caudill (2005). Using the

modified growth model to examine the impact of financial development on economic

growth in Asia-Pacific Economic Cooperation (APEC) countries, Tang (2006) also

33

found that stock-markets exhibited strong “growth-enhancing” effects, particularly

among developed APEC countries.

2.3.2 Unidirectional Causality from Economic Growth to Financial

Development

A contrary view to the assertion that financial development precedes economic

growth is the claim that the causality runs in the opposite direction, namely, from

economic growth to financial development. Arguably, economic progress results in

increased demand for financial services among investors and borrowers. The rising

demand for banking and financial services, in turn, leads to the creation of financial

institutions to meet the rising investor needs in the economy. As Robinson (1952)

succinctly put it, “where enterprise leads, finance follows”. Thus, this view maintains

that financial development is a passive outcome of, rather than a stimulus for,

economic growth.

Some studies tended to support Robinson‟s (1952) view regarding the unidirectional

causality from economic growth to financial development. In cross-country analyses

by Kuznets (1971), it was found that financial markets only became important during

the “structural transformation” of the economy in the intermediate stage, not the early

stage, of the growth process. Similarly, Lucas‟ (1988) model of economic

development tended to emphasize the role of human capital accumulation through

schooling and learning-by-doing, rather than financial sector development, as the

source of economic growth. In another study by Singh and Weisse (1998) to examine

the components of financial development, portfolio capital flows, and stock-market

development in lesser developed economies, it was maintained that economic growth

34

creates increased demand for financial services, thereby leading to a more developed

financial sector. Additionally, a survey of developing countries by Stern (1999)

suggested that appropriate agricultural policies and involvement in world trade

constitute the key factors for economic success ahead of financial development.

2.3.3 Bi-directional Causality between Financial Development and Economic

Growth

Interestingly, some studies pointed to a two-way causation between financial

development and economic growth. Lewis (1955) was one of the earliest to suggest

that while economic growth tends to spur the development of financial markets, the

consequent financial development also tends to add impetus to economic growth.

Patrick (1966) characterized the bi-directional causality between financial

development and economic growth in terms of two hypotheses, namely, the “demand-

following” and “supply-leading” hypotheses. The “demand following” phenomenon

refers to the situation where the creation of financial institutions and related financial

services is a “passive” response to increased demand for financial services as real

output rises. The development of the financial system is thus shaped by “the

economic environment, the institutional framework, and by changes in the subjective

responses – individual motivations, attitudes, tastes, preferences” (Patrick, 1966,

p174). On the other hand, the “supply-leading” phenomenon relates to financial

development as a determinant of economic growth. The “supply-leading”

phenomenon suggests that the financial sector actively undertakes two important

functions in the economy: (a) transfer resources from traditional low-growth sectors

to modern high-growth sectors and (b) encourage entrepreneurial enterprise by

35

facilitating access to funding and enabling entrepreneurs to “think big”. According to

Patrick, the “supply-leading” and “demand-following” phenomena are likely to

interact in actual practice. In the early stage of economic development, the “supply-

leading” phenomenon tends to dominate in inducing investment and growth.

However, in the later stage of economic development, the “demand-following”

phenomenon tends to emerge as the “supply-leading” impetus slows down.

Using long-term data over several decades, Goldsmith (1969) found bi-directional

relationship between financial and economic development in a number of economies.

However, Goldsmith indicated that the results should be interpreted with caution as

the direction of the causal mechanism was unclear in the study. To overcome this

problem, Gupta (1984) used time-series analysis to examine the causality issue.

Similarly, Jung (1986) investigated international evidence on the causal relationship

between financial development and economic growth by applying the Granger

causality test on 56 countries, taking one country at a time. Jung‟s study found that

less developed countries were characterized by the causal direction running from

financial to economic development, while developed countries showed the reverse

causal direction. This was in line with Patrick‟s (1966) “supply-leading” and

“demand-following” hypotheses.

Separate studies by Demetriades and Hussein (1996) and Demetriades and Luintel

(1996) also pointed to the bi-directional relationship between economic and financial

development. Similar conclusions were found by Luintel and Khan (1999) using

multi-variate vector autoregression (VAR) on ten countries.

36

In a study by Shan, Morris and Sun (2001) on nine OECD countries and China, it was

found that half of the countries showed two-way causality between financial

development and economic growth. Additionally, a more recent study on Australia

by Thangavelu and Ang (2004) also found evidence of bi-directional causality in

financial and economic development, with the causality running from economic

growth to financial development for the Australian banking sector and causality

operating in the reverse direction for its stock-market.

2.3.4 No Clear Relationship between Financial Development and Economic

Growth

While many studies have suggested some relationship between financial and

economic development, a number of studies pointed out that the causality between

financial development and economic growth could be negligible or even spurious.

In a cross-sectional study of 84 developing countries, Dornbush and Reynoso (1989)

found no significant relationship between financial deepening and economic growth

as “by judicious choice of sample, any partial correlation can be generated” (p.205).

Moreover, their study also suggested little empirical support for the McKinnon-Shaw

view that the removal of interest rate ceilings in repressed financial systems would

stimulate savings and investments.

In a later study by Ram (1999) it was found that the results relating to the finance-

growth nexus were ambiguous and weak. Similarly, using empirical evidence from

cross-country analysis over the period 1970-1990, Graff (2002) argued that the

relationship between financial and economic development was not stable.

Additionally, Bloch and Tang (2003), who conducted statistical tests on 75 countries

37

over the sample period 1960-1990, concluded the “existence of spurious relationship

between financial development and economic growth” (p246).

Khan and Senhadji (2003) demonstrated that financial development, as proxied by

various banking development indicators, was statistically insignificant in explaining

economic growth. They suggested that this could be because the banking sector tends

to develop slowly whereas economic growth is much more volatile.

Using the VAR techniques of variance decomposition and impulse response analysis,

Shan (2005) found little support for the hypothesis that financial development “leads”

economic growth. In response to Levine‟s (1997) claim that investment and

productivity growth are two important “channels” to facilitate economic development,

Shan (2005) argued that other factors such as industry policy, taxation policy, factor

endowments, foreign investment policy and business confidence are equally important

in determining investment growth. Moreover, though finance facilitates productivity

growth associated with new technology and physical capital, productivity

improvements arising from human capital development are often linked to education

and training policy. Hence, according to Shan (2005), the relationship between

financial and economic development is “not obvious”.

2.3.5 Negative Impact of Financial Development on Economic Growth

Some studies suggested that financial development could be an impediment rather

than a stimulant for growth. While the causality still runs from finance to real

activities, the focus is on the potentially destabilizing effects of “financial excesses”

which ultimately lead to economic crises. Arguably, this view perceives financial

38

markets to be inherently unstable (Keynes, 1936; Diamond and Dybvig, 1983;

Krugman, 1996; Singh 1997). The sources of financial distress could originate from

commercial banks, stock-markets, or international capital flows. Such financial

distress associated with excessive speculation, over-trading, or over-leveraging could

precipitate sharp falls in financial markets, with adverse consequences on economic

activities such as investment and consumption. The studies identified in this section

are illustrative of the negative impact that financial development has on economic

growth.

In an important study by Shleifer and Summers (1988), it was argued that stock-

market development could hamper economic growth by facilitating

“counterproductive” corporate takeovers. Similarly, De Long et al. (1989) found that

“excessive” trading of stocks tends to introduce “noise” into the financial market,

thereby leading to inefficient allocation of resources which subsequently retards

economic growth. In the same perspective, Bhide (1993) maintained that

“excessive” liquidity, which enables equity holders to sell their stakes easily in the

stock-market, tends to undermine the efficient monitoring of managers. Devereux

and Smith (1994) showed that greater risk sharing in the stock-market could reduce

the savings rate, thus slowing economic growth.

Levine (2002) proposed three reasons to explain possible negative effects of banking

development on economic growth. First, large banks with huge influence over firms

could extract more from the firms‟ profits, thereby reducing the firms‟ funding for

future investment. Second, banks‟ inherent bias towards financial prudence tends to

impede corporate innovation and growth. Third, influential banks could collude with

39

the firms‟ management to prevent minority shareholders from removing inefficient

managers (Black and Moersch, 1998), thus adversely affecting corporate governance

and economic growth. In a recent study by Zhang (2003) using time-series data for

eight Asian economies, it was found that there was a significant negative relationship

between banking development and economic growth over the period 1960-1999.

2.4 Conclusion

This chapter has reviewed the various theories underlying the relationship between

financial development and economic growth. Empirical studies on the reciprocal

interactions between the financial and real sectors in the economy were subsequently

analyzed from the perspective of the direction of causality. The next chapter will

provide an overview of the economic and financial development in Singapore over the

last three decades.

41

Chapter 3

OVERVIEW OF ECONOMIC AND FINANCIAL

DEVELOPMENT IN SINGAPORE, 1978-2006

3.1 Introduction

In the preceding chapter a comprehensive literature review was undertaken

concerning the relationship between financial development and economic growth.

This chapter examines the economic and financial development of Singapore over the

period 1978-2006. The chapter serves to provide the contextual background for the

thesis. The analysis is undertaken over two distinct development phases: 1978-1996

and 1999-2006. The watershed period 1997-1998, which divides the two periods, is

also examined. In reviewing the financial development of Singapore underpinning

the various phases of economic growth, the chapter also examines the various types of

financial institutions and activities which emerged over the years. The process of

financial intermediation is also discussed in the context of facilitating financial

development and economic growth.

3.2 Economic Development of Singapore There is extensive literature documenting the economic and financial development of

Singapore. The literature suggests that the Singaporean economy and its financial

sector have undergone different phases of development over the last three decades.

The different phases can be broadly categorized into two major periods: 1978-1996

and 1999-2006.

The first phase of development (1978-1996) corresponds with the initial phase of

deregulation in the Singapore financial markets and its emerging status as a newly

industrializing economy (NIE). The year 1978 is chosen as it marked the start of

42

“complete liberalization of the foreign exchange market in Singapore” (Murinde and

Eng, 1994, p.396) and the commencement of Singapore as an international financial

centre. Over this twenty-year period, the Singaporean economy advanced rapidly to

become one of the four renowned Asian NIEs, alongside Hong Kong, South Korea

and Taiwan.

The watershed period 1997-1998 reflects the onset of Asian financial crisis in July

1997 and the subsequent adjustment process in the economic and financial sectors in

the following year. The Asian financial turmoil abruptly halted the stellar economic

performance of Singapore in the preceding two decades. This brought about

significant structural changes in the Singaporean economy and financial system.

The second phase of development (1999-2006) corresponds with another phase of

financial deregulation and liberalization. It also reflects a period of economic

restructuring as the Singaporean economy attempted to grapple with the challenges of

globalization, re-alignment of exchange rates in the region, and the emergence of new

players such as China and India in the global marketplace. The year 1999 marked the

start of many new reforms and restructuring measures in the Singaporean financial

sector (S. Tan, 2006). As Peebles and Wilson (2002, p.117) indicated, since the Asian

financial crisis in 1997, “the government considered reforms of the banking sector to

introduce more competition into commercial banking” and to ensure more

transparency in the disclosure of assets among banks. Many of these financial reform

measures to strengthen the domestic banking sector were implemented in 1999. From

the economic perspective, this second development phase also witnessed a

transformation in the Singaporean economy as it progressed from an NIE in the 1980s

43

and 1990s to become a First World economy in the new millennium (Lee, 2000). In

2001, Singapore‟s per capita income in US$ terms was US$20,700, which was

comparable to that of developed countries such as France (US$21,500), Italy

(US$18,900) and Australia (US$18,500).

3.2.1 1978-1996

The Singaporean economy grew steadily over the period 1978-1996, interrupted

briefly by a short recession in 1985. This is shown in Chart 1 below.

Chart 1: Real GDP Growth of Singapore, 1978-2006

G DP G rowth

1978-2006

-4

-2

0

2

4

6

8

10

12

1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006

GDP (Real) Growth

Recession

Asian Financial

Crisis, Jul 07

Recession

Source : Department of Statistics

Real GDP growth averaged 7.8% per annum over the period 1978-1996, with most

major sectors in the economy performing well. Manufacturing and construction

sectors registered 8% growth per annum, while the transport and communications as

well as financial sectors recorded double-digit annual growth rates (Table 1).

Table 1 : Growth rates of real GDP and major sectors, 1978-1996

Real

GDP

Manufacturing

Construction

Transport &

Communications

Finance

Average Growth

(p.a.)(percent)

7.8

8.0

8.0

10.1

11.2

44

S

Source: Department of Statistics

The broad-based economic growth enabled personal income to increase steadily over

the period. Real per capita GNP rose more than four-fold from S$7,463 in 1978 to

S$35,454 in 1996. This is shown in Chart 2 below.

Chart 2 : Real per capita GNP of Singapore, 1978-2006 (1978=100)

R eal P er C apita G NP

1978-2006

0.0

5000.0

10000.0

15000.0

20000.0

25000.0

30000.0

1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006

Real Per Capita GNP

Recession

Asian Financial

Crisis, Jul 07

Recession


For the period until 1985, the unemployment rate hovered at around 3 percent (Chart

3). This rate jumped sharply to 6.5 percent during the 1985-86 recession and fell to

an even lower rate of below 2 percent after the recession.

Chart 3: Unemployment Rate in Singapore, 1978-2006

45

Unemployment R ate Yearly

1978-2006

0

1

2

3

4

5

6

7

1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006

Unemploy Rate

Recession

Asian Financial

Crisis, Jul 07

Recession Recession


As Singapore has a small domestic market, manufacturing growth was primarily

export-led. Moreover, the lack of technological and financial resources meant that

Singapore‟s industrialization drive had to be achieved by attracting global multi-

national corporations (MNCs) to set up operations domestically. Kwong et al. (2001,

p.5) observed that “the foreign companies manufactured goods mainly for exports”.

Importantly, the manufacturing process moved increasingly into higher value added

production over the years. A 1994 study by the Ministry of Trade and Industry

(MTI), summarized in Table 2 below, suggested that while the lower-end products

such as crude rubber, natural gums, oil bunkers and radio-broadcast receivers formed

the top five exports in 1980, these products were replaced by the higher-end

manufactures such as data processing machines and telecommunications equipment

by 1994. At the same time, the high-end production of electronic valves became

increasingly important, with its share of total exports rising from 6.1 percent in 1980

to 12.4 percent in 1994 (MTI, 1994).

Table 2 : Singapore’s Top 5 Exports, 1980 and 1994

1980

1994

46

Top 5 Exports Value Added

per worker ($)

% share of

total

exports

Top 5 Exports Value Added

per worker

($)

% share of

total

exports

Petroleum

products

64,200 28.2 Data processing

machines

65,800 15.1

Crude rubber,

natural gums

19,100 7.9 Electronic valves 104,400 12.4

Oil bunkers 18,600 6.2 Parts for office machines 63,300 7.6

Electronic valves 25,700 6.1 Petroleum products 486,300 7.5

Radio-broadcast

receivers

16,500 3.3 Telecommunications

equipment

56,900 6.3

Others 29,900

54.6 Others 56,200 58.1

Total exports

($41.5 b)

100% Total exports

($147.3 b)

100%

Sources: Department of Statistics/ Trade Development Board

Over the period 1978-96, S. Tan (2006) suggested that the strong 11.2 percent annual

growth of the financial sector was underpinned by policies aimed at encouraging

financial institutions to be outward-looking in securing international rather merely

domestic businesses. S. Tan (2006, p.249) observed that since the 1970s, “the

[Singapore] government moved aggressively to attract international financial

institutions and broaden the range of financial services” to service the booming

offshore market. Murinde and Eng (1994) further suggested that the removal of

exchange controls in 1978 provided an impetus to the growth of international

financial services, thereby contributing to overall domestic growth. Over this

period, Singapore evolved from being a regional offshore banking centre to becoming

an international financial centre (Tan, 1999). Fee-based and financial trading

activities, such as underwriting and treasury activities, grew rapidly. Tan (1999)

noted that the Singapore International Monetary Exchange (SIMEX) started trading

financial futures as early as September 1984. SIMEX was the first financial futures

exchange in Asia. It had a direct linkage with the Chicago Mercantile Exchange

(CME) and the capacity to trade in futures contracts over the full 24 hours in any day.

This led to a lowering of transactions cost and an increase in market efficiency.

47

The strong performance of the transport and communications sector (averaging 10.1

percent annual growth) was supported by the application of new technologies. An

example of this was the implementation of state-of-the-art information technology and

artificial intelligence by the Port of Singapore Authority for loading and unloading

cargoes. Moreover, integrated services digital network was also implemented in the

telecommunications industry to support more sophisticated activities in the economy.

The growth of the transport and communications sector was further boosted by

Singapore‟s development into a warehousing and distribution hub which attracted

many international companies to set up regional logistics and distribution

headquarters in the country (Huff, 1994).

Notwithstanding a recession in 1985-86, the Singapore economy managed to grow an

annual rate of around 7.8 percent over the period 1978-1996. The unemployment rate

fell from a high of around 6 percent in the mid-1980s to between 1.5 and 2 percent in the

mid-1990s (Chart 3). The Singaporean economy successfully moved away from low-

end manufacturing and entrepot trading activities in the 1960s and 1970s to a well-

diversified newly industrializing economy (NIE) by the mid-1990s. Entrepot trade,

which comprised nearly one-third of GDP in 1970, accounted for less than one-fifth of

GDP by 1996. In contrast, manufacturing, finance and transport sectors together made up

66.5 percent of GDP in 1996 compared to their combined share of merely 44.4 percent

of GDP in 1970 (Table 3).

Table 3 : Industry Share of GDP, 1960-1996

48


3.2.2 1997-1998

The Asian financial crisis which commenced in July 1997 marked an important

turning point in Singapore‟s economic development. The financial turmoil led to a

downward spiral in regional currencies and stock-markets over the period 1997-98.

Thailand and Indonesia were among the regional countries worst hit by the currency

falls and stock-market declines. With Singapore‟s proximity and close linkages to the

region, the Singapore dollar and domestic stock-market also suffered significant

double-digit declines.

Table 4: Changes in exchange rates and stock-markets, July 97-May 98

Country

Cumulative percentage change in

exchange rate (vis-a-vis the US dollar)

from July 1997-May 1998

Cumulative percentage change in stock-

market (local stock index) from July 1997-

May 1998

Thailand -36 -27

Malaysia -34 -46

Indonesia -74 -40

Philippines -33 -21

Hong Kong 0 -34

South Korea -36 -50

Taiwan -14 -9

Singapore -12 -29

Source: Bloomberg

The financial turmoil in Southeast Asian countries adversely affected the Singaporean

economy, with real GDP contracting by 0.1 percent and the unemployment rate

climbing to 2.4 percent in 1998. The performance of the various sectors over 1997-

98 were also substantially weaker compared to the exhilarating growth rates

registered in these sectors in the preceding two decades. This is shown in Chart 4.

SECTOR

Current GDP ($m)

Share of GDP (%)

1960 1970 1978 1996 1960 1970 1978 1996

Manufacturing

235.6 1047.9 4256.3 33164.2 11.9%

19.7% 26.1% 25.4%

Construction 71.7 386.1 1089.6 11140.2 3.6%

7.3% 6.7% 8.5%

Trade/Commerce 712.5 1608.3 4541.1 24937.4 35.9% 30.2% 27.9% 19.1%

(entrepot

trading)

Transport 282.8 595.0 2352.1 14671.3 14.2% 11.1% 14.4% 11.2%

Finance 224.5 757.4 2210.9 39051.9 11.3% 13.6% 13.6% 29.9%

Total 1985.3 5319.9 16299.3 130775.3 100% 100% 100% 100%

49

Chart 4: Average Growth Rates of GDP and Sectors Over Three Periods

The slowdown in the manufacturing industry over 1997-98 affected all sub-sectors of

the industry (Table 5). The fall in regional demand dampened the production of

petroleum products. Moreover, the pessimistic economic outlook in Asia, which

weakened investment demand in the region, also led to significant fall in the

production of machinery and equipment, fabricated metal products and electrical

machinery.

Table 5: Production in selected manufacturing industries, 1997-1998

Manufacturing

Industry

Percentage change in production

(over the previous year)

1997 1998

Electronic products 3.4 -3.1

Machinery and equipment 6.4 -8.6

Fabricated metal products -3.0 -4.9

Petroleum products 3.9 -1.5

Electrical machinery 1.4 -12.7

Total Manufacturing 4.6 -0.5

Source: Economic Development Board

Though there was a nascent upturn in the global electronics cycle in early 1998, the

subsequent over-supply in the global electronics industry, coupled with the decline in

regional demand, caused production of electronic products to fall 3.1 percent over the

year.

-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0

8.0

10.0

12.0 Average Growth:1978-96

Average Growth:1997-98

Average Growth:1999-06

Manufacturing

Finance Transport

GDP

construction

50

The transportation sector was also adversely affected. This was largely because of

Singapore‟s role as a distribution hub in facilitating the movement of goods within the

region. The slowdown in regional trade led to decline in the handling of air cargo and

sea cargo in 1998 (Table 6).

Table 6: Growth in air cargo and sea cargo handled, 1997-1998

Type of cargo

Percentage change

(over the previous year)

1997 1998

Total Sea Cargo

General and bulk cargo

Mineral oil-in-bulk

4.2

7.3

-0.1

-4.6

-6.4

-2.0

Total air cargo 12.4 -3.8

Source: PSA Corporation Ltd

Growth of the financial sector decelerated sharply from 11.2 percent per annum in the

period 1978-1996 to a mere 2 percent per annum over 1997-98. The substantial

slowdown in financial activities was largely attributable to the scaling down of

offshore lending activities in the Asian Dollar Market (ADM). The ADM, which

helped to mobilize funds from around the world for on-lending to the region, slowed

substantially in 1998. The assets of Asian Currency Units (ACUs) in the ADM,

which reflected the size of offshore lending activities, contracted by 9.6 percent in

1998. This was partly a result of the withdrawal or holding back of turnkey projects

in Asia as regional countries attempted to reduce their current account deficits.

Moreover, on-going currency volatilities and economic uncertainties also encouraged

banks to adopt a “wait-and-see” approach before extending new loans to customers.

Overall, the Asian financial crisis and economic adjustment process over 1997-98 was

critical in highlighting Singapore‟s connectedness with the region in terms of trade

and financial linkages. It also pointed to Singapore‟s dependence on healthy regional

developments to sustain its own economic development and growth. Importantly, the

51

1997 Asian financial crisis witnessed the first significant fall in per capita GNP from

S$39,394 in 1997 to S$37,193 in 1998 (Chart 2). Arguably, this financial crisis

served as a “wake-up” call for policymakers and provided an impetus for them to

implement new measures to reinvigorate the economy and elevate Singapore‟s

economic development.

3.2.3 1999-2006

Tan (2002) noted that economic recovery in the affected Southeast Asian countries

started towards the end of 1998. By 1999, the current account imbalances, which

were partly responsible for triggering the Asian financial crisis in July 1997, had

largely unwound to turn positive in the countries adversely affected by the financial

turmoil (Table 7). Along with the turnaround in the current account balances, real

GDP in Thailand, Malaysia and Indonesia also turned positive by the second quarter

of 1999, leading to a stabilization of exchange rates and gradual recovery of stock-

markets in these countries.

Table 7: Current account balances (% of GDP)

Country 1996 1997 1998 1999

Thailand -7.9 -2.0 11.4 8.4

Indonesia -3.3 -1.8 3.0 2.0

Malaysia -4.9 -4.2 11.0 9.2

S.Korea -4.7 -1.8 13.2 8.7

Philippines -4.7 -5.2 1.2 0.6

Singapore 15.9 15.4 19.2 18.4

Hong Kong -1.1 -3.1 0.0 1.2

Source : IMF

The upturn in regional growth, coupled with continued expansion in the USA, enabled

the Singapore economy to rebound in 1999 and 2000, with real GDP growth of 6.9

percent and 10.1 percent in the two years respectively. Notwithstanding the economic

rebound, per capita GNP only stood at $39,226 in 2000 (Chart 2) which was still below

the pre-crisis level.

52

In 2001, the Singaporean economy plunged into another recession, brought on by the

USA slowdown after September 11 (2001) and the downturn in the global electronics

cycle. Real GDP contracted by 2 percent in 2001, dragging real per capita GNP down

to around $22,000 (Chart 2) which was roughly around the level prevailing in the

aftermath of the Asian financial crisis in 1998. Notably, the subsequent economic

recovery over 2002-03 was significantly lacklustre, with real GDP growth hovering at

around 3 percent (Chart 1). As H. Tan (2006) pointed out, the average GDP growth of

Singapore between 2001-2003 was even less than that of Indonesia, a record

unprecedented in Singapore‟s history since its independence in 1965. At the same

time, unemployment in the domestic economy also worsened from 2.7 percent in 2001

to 4 percent in 2003. After three consecutive years of sub-par growth over 2001-2003,

it took another three consecutive years of stronger GDP growth of between 6.6-8.8

percent over 2004-2006 to bring down the unemployment rate to 2.7 percent in 2006

(Chart 3). Even though real per capita GNP reached a new peak of $26,000 in 2006, it

was only 9.1 percent higher than the 1997 level. Importantly, real per-capita GNP had

risen at a significantly slower annual rate of 2.4 percent over the period 1999-2006

compared to the exhilarating growth rate of 6.2 percent per annum over 1978-1996

(Table 8).

Table 8: Growth rates of real per-capita GNP over different periods

1978-1996

1997-1998

1999-2006

Growth rate of per-capita

GNP (per annum)

6.2%

-5.3%

2.4%


The materially slower growth in real per-capita GNP over 1999-2006 would suggest

that Singapore has entered into a new phase of economic development following the

1997 Asian financial crisis. This view is supported by factors such as:

53

(a) slower average growth and increased volatility in growth;

(b) different exchange rates in the region;

(c) emergence of China and India as competitors in the world economy;

(d) emergence of new industries and structural changes in the economy.

(a) Slower average growth and increased volatility in growth

Table 9 shows that real GDP growth slowed from an average of 7.8 percent per annum

over the period 1978-96 to 5.6 percent per annum over the period 1999-2006. While

manufacturing growth remained at roughly 8 percent per annum in the two periods, the

average growth rates in the transport and communications and financial sectors were

nearly halved.

Table 9: Average growth rates and standard deviation in growth rates over 1978-1996 and 1999-2006

(percent)

GDP Manufacturing

Transport &

Communications Finance

Average annual growth rate

(1978-96)

7.8

8.0

10.1

11.2

Standard deviation

of growth rate (1978-96)

3.1

6.7

3.0

4.4

Average annual growth rate

(1999-06)

5.6

7.9

5.4

5.0

Standard deviation

of growth rate (1999-06)

4.0

8.8

3.7

4.3


Importantly, the volatility of the growth appears to have increased over the two

periods. This is shown in Table 9 where the standard deviations in growth rates of

overall GDP, manufacturing and transport and communications sectors have all

increased over the two periods (1978-1996 and 1999-2006). Thus, it is arguable that

the Singapore economy has become more volatile since the 1997 Asian financial crisis.

(b) Exchange rate re-alignment among countries in the region

54

The Asian financial crisis, which resulted in substantial exchange rate movements in

regional economies over 1997-98, has significantly altered the exchange rates among

countries in the region. The exchange rates of regional currencies vis-à-vis the US

dollar before and after the July 1997 Asian financial crisis are shown in Table 10. The

table shows that the exchange rates of these currencies had weakened substantially

during the 1997 Asian financial crisis. More importantly, as at end-2006, the exchange

rates of regional currencies remained at around the same levels prevailing in December

1998 without showing any signs of recovery to the pre-crisis exchange rates.

Table 10: Exchange Rates of Regional Currencies, June 1997-December 2006

Country

Exchange rate (vis-à-vis the US dollar)

June 1997 December 1998 December 2006

Thailand 24.7 Baht 36.7 Baht 35.5 Baht

Malaysia 2.5 Ringgit 3.8 Ringgit 3.5 Ringgit

Indonesia 2,432 Rupiah 8,000 Rupiah 8,994 Rupiah

Philippines 26.4 Pesos 38.8 Pesos 49.0 Pesos

Singapore 1.42 Dollars 1.65 Dollars 1.53 Dollars

Source: Bloomberg

Table 4 also shows that between July 1997-May 1998, measured against the US dollar,

the Indonesian rupiah fell 74 percent, the Thai baht fell 36 percent, the Malaysian

ringgit fell 34 percent, the Philippines peso fell 33 percent while the Singapore dollar

fell only 12 percent. Using data provided by the Asian Development Bank, H. Tan

(2006) found that the real effective exchange rate movements led to a “re-alignment”

of exchange rates among regional economies following the financial crisis.

As the Singapore dollar weakened by significantly less than the other regional

currencies (Table 10), Singapore suffered a substantial loss in exchange rate

competitiveness vis-à-vis the South-east Asian countries like Malaysia, Thailand,

Indonesia and the Philippines. This implied a less competitive environment for

Singapore‟s manufactured exports and re-exports to the region after 1997.

55

(c) Emergence of China and India as competitors in world economy

Apart from the more adverse competitive environment facing the Singapore economy

after 1997 due to changes in exchange rate parities in the region, the emergence of

China and India in the world economy further intensified competition. After opening

up to the world economy in 1978, China‟s economy has been transformed from a small

exporter in the 1980s to become a major exporter in world markets in the 2000s.

China‟s exports have risen more than twenty-fold from US$10 billion in 1985 to

US$226 billion in 2001.

A 2003 study by the Ministry of Trade and Industry (MTI) indicated that the export

penetration of China in the US, Japan and EU markets had also increased substantially

over the period (Table 11). For example, while China‟s exports accounted for only 1

percent of US import market in 1985, this share had risen nine-fold by 2001. Over the

same period, China‟s share of imports by Japan and EU had also risen significantly.

Table 11: Share of China and ASEAN imports by major markets, 1985 and 2001 (percent)

US

Japan

EU

China‟s share of imports in the

market in 1985

1.0

5.1

0.3

China‟s share of imports in the

market in 2001

9.1

16.4

2.8

ASEAN‟s share of imports in the

market in 1985

5.3

15.8

1.2

ASEAN‟s share of imports in the

market in 2001

8.6

15.2

2.3

Sources : US Census Bureau, Ministry of Finance, Japan, International Monetary Fund

In contrast, over the same period, ASEAN‟s (including Singapore‟s) share of imports

in the three major markets only rose marginally in the US and EU and fell in Japan

(Table 11). Importantly, the MTI (2003) study suggested that by 2001, China‟s share

56

of imports in the three major markets (US, Japan and EU) had overtaken that of the

ASEAN countries (as a whole).

Additionally, China had become a global centre for hardware manufacturing, moving

into knowledge-intensive areas like software development in the 2000s, compared to

low-end manufacturing activities in the 1980s. Following the footsteps of China, India

also began liberalizing its economy in recent years. Arguably, while reforms in India

were not as vigorous as they were in China, some export sectors such as information

technology (IT) have taken off. In 2002, India exported some US$7.6 billion of

software and related services. Moreover, H. Tan (2006, p.43) noted that India has

become a key destination for outsourcing in recent years, and its “IT prowess in

Bangalore and other centres demonstrate world-class capability”.

The emergence of China and India reflected a different world environment in the 2000s

which small economies such as Singapore. Importantly, the changed environment

implies increased competition for export-oriented industries in Singapore, which would

likely result in a slower phase of economic growth and development in the 2000s

compared to the 1980s and 1990s. As H. Tan (2006, p.43-44) succinctly writes,

“clearly, for Singapore, the halcyon days of harnessing itself to MNCs of the world to

get rapid growth are over… Singapore has to discover a new paradigm of growth and

new niches … or else adjust painfully to a new era of much slower growth”.

(d) Emergence of new industries and structural changes in economy

Another important factor underlying the view that there is a change in the phase of

economic development after the 1997 Asian financial crisis is the emergence of new

57

niche industries and structural changes in the economy in recent years. These new

niche industries and structural changes, which arose amidst a more competitive

environment in the 2000s, were particularly evident in the manufacturing and financial

sectors of the Singapore economy.

Within the manufacturing sector, specific high value-added niche industries such as

biomedical manufacturing (pharmaceuticals and medical technology) and precision

engineering (machinery and systems and precision modules) emerged in the 2000s.

Table 12: Industry share of value-added in the manufacturing sector, 1978, 1988, 1998 and 2006 (percent)

1978

1988

1998

2006

Food and beverages

Textiles and wearing apparel

Wood products and furniture

Printing

Electronics & electronic

appliances

Chemicals and petroleum

products

Machinery and transport

equipment

Biomedical engineering

Precision engineering

Others

TOTAL

6.4

6.7

4.3

4.7

20.1

20.9

16.8

---

---

20.1

100

5.6

3.0

0.3

3.9

35.4

20.3

12.8

---

---

18.7

100

2.5

---

---

4.0

43.4

22.5

10.7

---

---

16.9

100

2.4

---

---

2.4

28.8

13.9

11.2

24.6

12.6

4.1

100

Source: Department of Statistics/ Economic Development Board

58

Table 12 shows the changing industrial structure of the manufacturing sector in

Singapore over the last three decades. The newly emerging industries such as

biomedical and precision engineering displaced the lower value-added industries like

textiles, wearing apparel, wood products and consumer electronics, which moved out

of Singapore to lower-cost countries in the region. Importantly, Table 12 suggests that

the Singapore manufacturing sector has diversified away from electronics during the

period between 1998-2006. This contrasted sharply with the development in the

preceding two decades when the share of electronics value-added in manufacturing

more than doubled from 20.1 percent in 1978 to 43.4 percent in 1998. As Chua (2007,

p.13) noted, the “diversification from electronics will reduce the volatility to the

volatile tech cycles”.

Besides manufacturing, the financial sector also went through significant structural

changes in the 2000s. Table 13 shows the rising importance of fund management,

private banking and investment advisory services over 2001-06 vis-à-vis other financial

activities such as insurance.

Table 13: Industry share of value-added in the financial sector, 2001 - 2006

2001

2003

2006

Banking

Stocks, futures and commodity brokers

Fund management and investment advisory activities

Insurance

Others

TOTAL

51.8

6.5

2.8

20.5

18.4

100

50.4

6.4

4.5

13.6

25.1

100

51.1

7.8

8.8

12.8

19.5

100

Source : Department of Statistics

59

In recent years, fund management and investment advisory activities have been a key

driver of financial sector growth, reflecting the buoyant Asian investment climate on

the back of renewed investor confidence since the 1997 financial fallout. Increased

fund management activities also reflected the development of Singapore‟s wealth

management industry, as relatively low interest rate and a weak US dollar prompted

fund managers to explore higher yielding alternatives in Asian markets. In contrast to

the expansion of the fund management industry, insurance activities fell significantly

as a share of financial sector value-added from 20.5 percent in 2001 to 12.8 percent in

2006. This was largely attributable to the decline in the life insurance business.

Arguably, the 1997 Asian financial crisis had witnessed a “turning point” in

Singapore‟s economic development (MTI, 2003). Since the 1997 Asian financial

crisis, the government has targeted the growth of new industries like education,

healthcare and creative services. As Koh (2006, p.13) succinctly put it, “as Singapore

approaches the technological frontier and enters a new phase of development, its

economic future will depend increasingly on its ability to engage in technological

creation and develop internal engines of growth [by adopting] innovation-driven

growth strategy”.

3.3 Financial Development of Singapore

The previous section reviews the economic development of Singapore. This section

examines the financial development of Singapore which is closely intertwined with

the country‟s economic development.

60

Singapore‟s financial development is characterized by the emergence of a variety of

institutions which provide a broadening range of financial instruments over the last

three decades.

Table 14: Number & Types of Financial Institutions in Singapore, 1978-2006

Institutions

1978

1985

1990

1996

1998

2002

2006

Commercial Banks

81

135

141

143

154

120

108 Local* 13 13 13 12 12 6 5

Foreign 68 122 128 128 140 114 103 Full banks 24 24 22 22 22 22 24

Wholesale banks** 13 14 14 14 13 33 34

Offshore banks 31 84 92 92 105 59 45

Asian Currency Units Banks

Merchant banks

Others

85 64

20

1

179 123

54

2

199 131

67

1

209 132

77

0

224 144

80

0

169 115

54

0

151 104

47

0

Finance Companies

34 34 28 23 19 7 3

Merchant Banks

39 138 68 77 80 55 48

Insurance Companies

70 84 124 141 154 144 149

Stockbroking

Companies

18

32

57 81 95 95 110

Investment

Advisers

- - 60 136 162 167 185

* All local banks are full banks Source : MAS Annual Reports for the various years

** Previously known as restricted banks

Table 14 (above) shows a breakdown of the various types of financial institutions and

their numerical presence in Singapore over the years. The financial institutions offer

a wide array of financial products ranging from basic banking activities and foreign

exchange/securities trading to loan syndication and underwriting as well as asset

management and investment advisory services in more recent years. A description of

the services provided by the major financial institutions is given in Appendix 1.

More importantly, the assets of all the major financial institutions have increased

substantially over the last three decades. Table 15 suggests that the assets of

commercial banks and Asian Currency Units (ACUs) comprised the bulk of total

assets in the banking sector. These assets had risen by more than twenty-fold over

1978-2006, representing an exhilarating growth rate of more than 12 percent annually.

61

Table 15: Assets of Financial Institutions in Singapore, 1978-2006 S$m

Institutions

1978

1985

1990

1996

1998

2002

2006

Commercial Banks

21218

70618

13400

252732

300974

353115

508614

Asian Currency Units

27040

155371

390395

506870

503609

482612

698668

Finance Companies

2017 6936 11424 21189 21189 13722 10067

Merchant Banks

3747 28207 32336 53581 60545 52564 78029

Insurance Companies

1161 3534 7057 22507 28896 63768 105909

Source : MAS Annual Reports for the various years

While the assets of other financial institutions like finance companies, merchant banks

and insurance companies had also risen significantly over the years, they remained

relatively small compared to the assets of ACUs and commercial banks. This

underlines the pivotal role played by ACUs in offshore banking activities which will

be further discussed in section 3.3.1. Additionally, it also underscores the crucial

function of commercial banks in domestic banking activities.

Commercial banks have flourished in Singapore since colonial times. Foreign banks

were the earliest banks to be established. Beginning with the Union Bank of Calcutta,

which was the first bank to set up operations in Singapore in 1840, many other foreign

banks soon followed. These included the Mercantile Bank (1856), the Chartered

Bank (1861) (now Standard Chartered Bank), Hongkong and Shanghai Bank (1877),

Nederlandsche Handel-Maatschappij (1883) (now ABN Amro Bank) and the First

National City Bank of New York (1902) (now Citibank). Huff (1994) suggested that

banking and financial services initially developed in the early years to provide trade

financing in support of entrepot trading activities. Notably, Jones (1994) indicated

that prior to Singapore‟s independence in 1965, entrepot activities provided demand

for international banking and insurance services, which constituted the core business

for Singapore-based banks. S. Tan (2006, p.248) further asserted that the pre-

62

independence “colonial banking sector in Singapore was confined to financing

international trade and serving primarily British trade interests, which did not support

the broader economic development of Singapore state”.

The first local bank, the Kwong Yik Bank, was established in 1903 primarily to

provide banking services to the Cantonese-speaking business community. Other

dialect groups promptly followed in setting up their own “dialect group” banks. In

1906, the Sze Hai Tong (Four Seas Communications Bank) was established to serve

the Teochew community. Subsequently, the Chinese Commercial Bank (1912), the

Ho Hong Bank (1917) and the Oversea-Chinese Bank (1919) also emerged to cater to

the banking needs of the Hokkien business community. These three banks merged in

1932 to form the present Oversea-Chinese Banking Corporation (OCBC).

Additionally, the United Chinese Bank (now United Overseas Bank, UOB) was

formed in 1935 to largely serve the Hokkien community as well. While providing the

basic banking services which complemented those of the foreign banks, the local

banks served specific niches in the corporate community by catering to the “dialect-

speaking” preferences of the local businessmen. This “clan-based” development of

local banks provided propitious conditions for certain family groups affiliated with

the various clans to perpetuate their control over the local banks for many years.

Despite a series of mergers among local banks since the 1960s, the local banks

continued to operate largely under “tight family-control” and “lack of transparency”

up till the 1997 Asian financial crisis (Tan, 2002). This constituted a major weakness

in Singapore‟s banking sector development which will be further assessed in Section

3.3.2.

63

Another important characteristic of the local banks is that almost all of them were

privately owned, except for the Post Office Savings Bank (POSB) and the

Development Bank of Singapore (DBS). This could be attributable to the initial

motivation underlying the establishment of local banks in the early years, which was

primarily to serve the banking needs of private businesses in the various dialect clans.

As such, it is perhaps unsurprising that the majority of local banks were privately

owned by businessmen who were ethnically connected to the various clans. The

government-owned DBS bank was established by the Singapore Economic

Development Board (EDB) in June 1968 for the purpose of providing funds to

businesses in the manufacturing industries as the EDB embarked on an aggressive

industrialization plan following Singapore‟s secession from Malaya in 1965. S. Tan

(2006) noted that the DBS served to provide long-term credit to selected industries

within the government‟s industrialization strategy. The POSB was established as a

statutory board in 1972 with the twin objectives “to provide means for deposit of

savings and to encourage thrift” and “to mobilize domestic savings for the purpose of

public development” (Schulze, 1990). However, following the 1997 Asian financial

turmoil, the DBS acquired the POSB for $1.6 billion in 1998 thereby consolidating

DBS‟s position as the largest bank in Southeast Asia. By end 2007, there were three

local banks (DBS, UOB and OCBC) and 23 foreign banks in Singapore. The detailed

list of foreign full banks along with their corresponding countries of origin and

financial activities in Singapore are summarized in Appendix 2.

The funds for commercial banks come from a variety of sources. These sources are

shown in Table 16.

Table 16: Sources of Funding for Commercial Banks, 1978-2006 S$m

64

Source of funding

1978 1985 1990 1996 1998 2002 2006

Deposits of non-bank customers

10045

28744

63379

118201

162310

180138

272462

Amount due to banks

6624

29531

52697

101576

105301

106060

146643

In Singapore 1718 5724 14512 34328 29769 18218 19879

ACUs 1944 9688 15309 31293 43034 61777 79015 Outside Singapore

2961 14118 22874 35954 31304 26064 47748

Others (Reserves, bills payable)

4549 12343 17326 32946 35954 66917 89536

TOTAL Liabilities/Assets

21218

70618

13400

252732

300974

353115

508614


Table 16 suggests that over the period 1978-2006, nearly half of the commercial

banks‟ funds were from deposits of non-bank customers. Additionally, around one-

third of commercial banks‟ funds were borrowed from the inter-bank market, which

included borrowings from banks within and outside of Singapore as well banks

operating in the offshore ACU market.

Significantly, banks became an increasingly important source of financing for the

domestic economy over the period 1978-2006. This is reflected in the steady uptrend

in loans to non-bank customers (Table 17). Moreover, bank loans (to other banks) in

the inter-bank market also accelerated sharply, rising nearly fifty-fold over the same

period. At the same time, domestic financing from the stock market also rose

substantially as indicated by the persistent increase in stock market capitalization

from $22.7 billion in 1978 to $589.6 billion in 2006 (Table 17).

Table 17: Breakdown of Domestic Financing from Banks, Bonds and Stocks, 1978-2006 S$m

Type of domestic financing

1978

1985

1990

1996

1998

2002

2006

BANKING SECTOR

Bank loans to non-bank customers

12226

37043

57696

126987

151640

161283

194597

Bank loans to other banks

3712

20768

55205

86112

105151

96807

184163

In Singapore 1264 5341 18438 33555 38216 17435 51554

ACUs 1068 6820 16923 24852 27994 41871 63650 Outside Singapore

1380 8607 19844 27705 38941 37501 68959

BOND MARKET

New funds raised by issuance of

1348

4611

5117

10096

11491

7014

2200

65

government bonds

New funds raised by issuance of

corporate bonds

- 230 1632 2309 1606 3838 10310

STOCK MARKET

Stock market capitalization

22708

70619

134500

301600

420000

407501

589611


However, the domestic bond market appeared to lag significantly behind the banking

sector and the stock market in raising funds. In 2006, new funds raised through the

issuance of government bonds and corporate bonds constituted less than six percent of

bank loans to non-bank customers and less than two percent of stock market

capitalization (Table 17). This critical issue concerning the purported “under-

development” of the domestic bond market vis-à-vis the banking sector and the stock

market will be discussed in Section 3.3.2 in the context of comparative data for other

developed economies such as US and Europe.

Apart from the development of private sector financial institutions, past studies

suggest that the establishment of the Monetary Authority of Singapore (MAS) had

played a critical role in Singapore‟s financial development (Murinde and Eng, 1994;

Tan, 1999; Khalid and Tyabji, 2002; S. Tan, 2006). MAS was established by the

Singapore government in 1971 to implement monetary policy and to provide “a sound

regulatory and supervisory framework” for developing the domestic financial sector

(Tan, 1999, p.345). Between 1971 and 1981, the monetary policy adopted by MAS,

which sought to achieve sustainable long-term growth with low inflation, was guided

by the intermediate targets of money stock and interest rates. However, after 1981,

MAS decided to use the exchange rate as the key instrument for conducting monetary

policy as the “exchange rate is a relatively more important anti-inflation instrument in

the context of the small open Singapore economy” (MAS, 1981/82, p.4).

66

Over the period 1978-1996, money supply as measured by M2, grew at double-digit

rates almost every year except for the brief period during the 1985-86 recession

(Chart 5). Interest rate, as measured by the 3-month interbank rate, peaked at 13.6

percent in 1980 after the oil price shock in the late 1970s before falling gradually over

the years to a low of 6.3 percent by 1996 (Chart 6).

Chart 5: Growth of Money Supply (M2), 1978-2006 Chart 6: Interest Rate, 1978-2006

Money S upply G rowth (M2 G rowth)

1978-2006

-5

0

5

10

15

20

25

30

35

1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006

M2 Growth

Recession

Asian Financial

Crisis, Jul 07 Recession

Interes t R ate

1978-2006

0

2

4

6

8

10

12

14

16

1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006

Interest Rate

Recession

Asian Financial

Crisis, Jul 07

Recession

Source: Monetary Authority of Singapore Source : Monetary Authority of Singapore

Up to the 1985 recession, the exchange rate was relatively steady with US$/S$ rate

constrained within the narrow range of 2.11-2.27 (Chart 7). However, after 1985, the

exchange rate showed a steady trend of strengthening from 2.17 Singapore dollar per

US$ in 1986 to 1.41 Singapore dollars per US$ in 1996 (Chart 7). The strengthening

Singapore dollar (vis-à-vis the US dollar) in the late 1980s and 1990s helped to hold

down inflation to between 2 to 4 percent. This represented a marked improvement

from the 8 to 8.5 percent inflation rates registered in the early 1980s (Chart 8).

Chart 7: Exchange Rate of US$/S$, 1978-2006 Chart 8: Inflation Rate, 1978-2006

67

E xc hang e R ate (US $/S $)

1978-2006

0

0.5

1

1.5

2

2.5

1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006

Exchange Rate of S$ (per US$)

Recession

Asian Financial

Crisis, Jul 07

Recession

Inflation R ate

1978-2006

-2

0

2

4

6

8

10

1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006

Consumer Price Index (YOY%)

Recession

Asian Financial

Crisis, Jul 07

Recession

Source: Monetary Authority of Singapore Source: Monetary Authority of Singapore

With regard to financial sector policy, MAS began liberalizing the foreign exchange

market in the 1970s. In 1972, MAS abolished the cartel system of foreign exchange

quotations by commercial banks. This was followed shortly by the “managed

floatation” of the Singapore dollar in mid-1973 after the collapse of the Smithsonian

Agreement (Khalid and Tyabji, 2002). By 1978, all exchange controls were abolished

and “the complete liberalization of the foreign exchange market in Singapore began”

(Murinde and Eng, 1994, p.396). This boosted the foreign exchange market by

allowing merchant banks and offshore banks to deal directly with resident non-bank

customers (Khalid and Tyabji, 2002).

Besides the deregulation of the foreign exchange market, the establishment of the

Stock Exchange of Singapore (SES) in 1973 also facilitated the development of the

stock market. This provided an important alternative source of finance outside of

banks for corporations to raise funds for business investments (C. Tan, 1999).

Additionally, the Singapore government also removed the “cartel system” of interest

rate setting by commercial banks in 1975, thereby liberalizing the loanable funds

market in facilitating financial development.

68

3.3.1 1978-1996

Financial deregulation, which began in the early 1970s, allowed financial institutions

to expand their operations in Singapore (Bryant, 1989). Over the period 1978-1996,

the total number of banks in Singapore increased rapidly, led by a near doubling of

foreign banks whilst the number of local banks remained relatively constant (Table

14). Total bank assets rose significantly from $21 billion in 1978 to $253 billion in

1996, reflecting a high annual growth rate of 14.8 percent (Chart 9).

Chart 9 : Total Banking Assets and Banking Loans in Singapore, 1978-2006

0

100000

200000

300000

400000

500000

600000

19781980

19821984

19861988

19901992

19941996

19982000

20022004

2006

Total Bank

Loans

Total Bank

Assets

Total Bank Assets

and

Total Bank Loans

Asian Financial

Crisis, Jul 07

Source: Monetary Authority of Singapore

The phenomenal rise in banking assets between 1978 and 1996 was largely

attributable to the rapid growth in bank loans, which comprised some 50 to 60 percent

of the total assets of banks (Chart 9).

Chart 10: Real GDP Growth and Banking Loans Growth, 1978-2006

69

-10

-5

0

5

10

15

20

25

30

35

19781980

19821984

19861988

19901992

19941996

19982000

20022004

2006

Real GDP

Growth

Total Bank Loan

Growth

Real GDP Growth

vs

Total Bank Loan Growth

Asian Financial

Crisis, Jul 07


Importantly, Chart 10 shows that the growth in banking sector loans appeared to

follow real GDP growth1. For example, during the 1985 recession, bank loans

seemed to lag behind real GDP growth; banking sector loans turned around in 1987

and 1988 only after the economy started to rebound in 1986, as reflected by the pick-

up in real GDP growth over 1986-1988 (Table 18).

Table 18: Growth in Bank Loans and Real GDP Growth, 1985-1988

Year Growth in Bank Loans (%) Real GDP Growth (%)

1985 1.5 - 1.8

1986 - 4.3 1.8

1987 5.8 8.8

1988 10.5 11.1


The above analysis suggests that banking development, as reflected in bank assets

growth which is underpinned by bank loans growth, could be driven by economic

growth. Importantly, this seems to support the view that economic growth could play

a catalytic role in Singapore‟s financial development. This view, however, tends to

undermine the oft-held view in past studies that government policy was the main

mover of the Singapore‟s financial development (S. Tan, 2006; Ariff and Khalid,

2000; Huff, 1994; Lim, 1988; Lee, 1987; Lee, 1983). In a study by Tan (1999), it was

1 Using regression analysis over the period 1978Q1 to 2006Q4, it was found that banking loans growth lagged real GDP growth by 5 quarters with an adjusted R2 of 0.97

70

found that since the late 1960s, financial development in Singapore was a result of

carefully planned strategy at financial restructuring through the offering of special

incentives and deliberate policy measures. A separate study by Peebles and Wilson

(2002) further asserted that the broad combination of legislative, fiscal and

administrative measures (summarized in Appendix 3) has supported Singapore‟s

financial development and helped develop Singapore into an international financial

centre. From the policy perspective, this issue concerning the driving force behind

Singapore‟s financial development (economic growth or financial policy) is critical

for the country‟s policymakers to make informed decisions as the sector develops.

Table 14 also shows that the number of Asian Currency Units (ACUs) more than

doubled between 1978 and 1996. This reflects the rapid development of the Asian

Dollar Market (ADM) over the period. The ADM is an international money and

capital market in foreign currencies. It is the Asian counterpart of the Eurodollar

Market in London. First established in 1968, the ADM was set up as a counterpart to

the euro-currency market in the City of London (Lee, 1983). Financial institutions

which operate in the ADM need MAS approval and are required to set up separate

bookkeeping entities, called the ACUs, for their international currency transactions.

The Bank of America was the first bank allowed to set up dealing operations in

foreign currencies in 1968, which marked the beginning of the development of the

ACUs. To consolidate its first-mover advantage in developing the ACUs as the Asian

centre for foreign currency transactions, the Singapore government offered reduced

profit tax of 10 percent compared to the then 40 percent corporate tax rate, along with

the abolition of withholding tax on interest earned from non-resident deposits (Tan,

2006). Between 1978 and 1996, the assets of ACUs surged substantially from $27

71

billion (in 1978) to $507 billion (in 1996), representing an annual growth rate of 19.3

percent. This significantly outstripped the rate of expansion of bank assets (Chart 11).

Chart 11: Total Assets of Banks and Asian Currency Units (ACUs), 1978-2006

0

100000

200000

300000

400000

500000

600000

700000

800000

19781980

19821984

19861988

19901992

19941996

19982000

20022004

2006

Total ACU

Assets

Total Bank

Assets

Total ACU Assets

and

Total Bank Assets

Asian Financial

Crisis, Jul 07

ACU Assets

growth trend

Banking Assets

growth trend


Increased ACU activities were accompanied by an increase in the number of foreign

banks in Singapore from 68 in 1978 to 128 in 1996 (Table 14). Moreover, the

number of merchant banks and insurance companies also showed increases between

1978 and 1996 (Table 14). Correspondingly, the assets of merchant banks and

insurance companies registered steady increases over the period (Chart 12).

Chart 12: Total Assets of Merchant Banks and Insurance Companies,1978-2006

0.0

20,000.0

40,000.0

60,000.0

80,000.0

100,000.0

120,000.0

19781980

19821984

19861988

19901992

19941996

19982000

20022004

2006

Assets of Merchant Banks

Assets of Insurance

Companies

Merchant Bank Assets

and

Insurance Companies Assets

Asian Financial

Crisis, Jul 07


As banking institutions increased in number, a rising number of stockbroking

companies were also established (Table 14). Stock-market turnover, which is an

indicator of stock-market activities, rose substantially from $17.6 billion in 1978 to

$128 billion in 1996 (Chart 13). The amount of funds raised in the stock-market also

72

increased more than eighteen-fold from $22.7 billion in 1978 to $420 billion in 1996

(Table 19). This contrasted sharply with the small amount of funds raised in the bond

market. The issuance of government bonds or Singapore Government Securities

(SGS), which is the largest segment of Singapore‟s bond market, amounted to a mere

$10.1 billion in 1996 (Table 19).

Chart 13 : Stock-market turnover, 1978-2006

S toc kmarket T urnover

1978-2006

0

50000

100000

150000

200000

250000

300000

350000

1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006

Stockmarket Turnover ($m)

Recession

Asian Financial

Crisis, Jul 07

Recession


S. Tan (2006) suggested that the small SGS market was largely attributable to the

budget surpluses that the Singapore government had been accumulating since the

1980s. These enabled the government to amass substantial reserves thereby negating

the need for bond issuance to raise funds. In addition, many of Singapore‟s large

corporations were also “cash-rich” and had no real need to raise funds through the

issuance of corporate bonds (S. Tan, 2006).

Table 19: Stockmarket capitalization and bond issuance, 1978 and 1996 ($ b)

Stock-market

Capitalization

Issuance of Singapore

Government Securities (SGS)

Issuance of corporate bonds

1978

22.7 1.3 -

1996

420.0 10.1 2.3

Sources: MAS reports, 1978 and 1996

Hence, the development of financial institutions over the period 1978-1996 was

primarily focused on banks, particularly off-shore banking activities, as well as the

stock-market. The less developed bond market in Singapore, particularly in

73

comparison to bond markets in other financial centres around the world, constitutes a

major weakness in Singapore‟s financial sector development (Eichengreen, 2004).

Notwithstanding the purported “under-development” of the bond market, S. Tan

(2006) maintained that Singapore‟s financial development contributed positively to its

economic growth, with the financial sector‟s share of GDP more than doubling from

13.6 percent in 1978 to nearly 30 percent in 1996 (Table 3). Huff (1994, p.345)

further noted that ”by 1990, Singaporeans had benefited considerably city‟s growth as

a financial centre and consequent expansion of employment” as Singapore

successfully diversified away from “dependence on cheap labour and towards higher

value-added, human capital-intensive jobs”.

Nonetheless, as Khalid and Tyabji (2002) aptly noted, Singapore‟s financial

development up to 1996 was characterized by deregulation measures in the 1970s

which focused on interest rate liberalization and the removal of exchange controls.

These measures were followed by “incremental changes” to the regulations in the

1980s and early 1990s to add “breadth” and “depth” to the financial markets such as

introducing cashless payment and reducing transaction costs. Throughout this

period, “the opening up of the banking sector was sidestepped” (Khalid and Tyabji,

2002, p.356). While the substantial growth of the ADM enhanced Singapore‟s status

as an offshore financial hub, Lim (1988) noted that MAS policy was to separate

domestic financial activities from offshore financial activities (ADM) in order to

“insulate” the domestic economy from international financial stability and protect

local banks from “excessive” international competition. This MAS policy appears to

point to a disconcerting incompatibility between the proclaimed government stance

74

towards openness in trade/investments and the simultaneous “protection” of the

domestic banking sector from international competition. More importantly, it raises

the crucial question about whether this “protection” of the domestic banking sector

could have been a major weakness in the Singapore financial sector which adversely

impacted the domestic economy when the Asian financial crisis erupted in 1997.

3.3.2 1997-1998

The Asian financial turmoil, unleashed in July 1997, adversely affected Singapore‟s

short-term financial sector development over 1997-98. Nonetheless, the crisis also

highlighted vital weaknesses in the domestic financial system which were

subsequently addressed through a gamut of government-led changes in financial

policy from 1999. These changes in financial policy, which arguably benefited the

development of Singapore‟s financial sector in the longer term, will be analysed in

Section 3.3.3.

In the immediate period following the onset of the financial crisis in mid-97, short-

term interest spiked, reflecting a liquidity crunch in Singapore‟s financial system

(Chart 6). This prompted the monetary authorities to increase money supply (M2)

sharply by 30 percent to alleviate the tight liquidity environment (Chart 5). The

Singapore dollar weakened against the US dollar, but strengthened substantially

against regional currencies. Over the period 1996-1998, the Singapore dollar

appreciated by 71 percent against the Indonesian rupiah and 26 percent against the

Thai baht (Table 20).

Table 20: Average exchange rates (S$ per foreign currency), 1996 - 1998

Currency 1996 1997 1998

US dollar 1.4101 1.4848 1.6736

Malaysia ringgit 0.5605 0.5353 0.4271

Thai baht 0.0556 0.0488 0.0409

75

100 Indonesian rupiah 0.0606 0.0536 0.0173

Source: MAS reports, 1996, 1997,1998

The financial crisis, which resulted in uncertainties in the region, led to a significant

pullback in ACU lending activities2. The ACUs serve as institutions mobilising

funds from around the world for on-lending to the region.

Chart 14 : Assets of Asian Currency Units (ACUs), 1978-2006 ($m)

AC U As s ets

1978-2006

0

100000

200000

300000

400000

500000

600000

700000

800000

1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006

ACU Assets

Recession

Asian Financial

Crisis, Jul 07

Recession


With the scaling down of turnkey projects in regional economies due to dampened

investor confidence, offshore lending plunged. This is reflected in the decline in

ACU assets (Chart 14). The decline in offshore loans adversely affected the

operations of merchant banks. The total assets of these banks, which amounted to

$66.5 billion in 1997, contracted by 10 percent in 1998. Chart 15 shows that the

growth rates of ACUs tend to move in line with the growth rates of merchant bank

assets. This is unsurprising as ACU assets, comprised largely of inter-bank loans as

well as loans and advances to non-bank customers, accounted for 95 percent of

merchant bank assets. Additionally, the volume of merchant banks‟ underwriting

activities also fell from $2 billion in 1997 to $548 million in 1998.

Chart 15: Growth of Assets of Merchant Banks and ACUs, 1980-2006

2 Asian Currency Unit (ACU) refers to a unit of account for US dollar denominated deposits held in separate accounts in Singapore-based financial institutions.

76

-20.00

-10.00

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

19801982

19841986

19881990

19921994

19961998

20002002

20042006

Growth in ACU Assets

Growth in Merchant Banks'

Assets

Growth in Merchant Bank Assets

and

Growth in ACU Assets

Asian Financial

Crisis, Jul 07


Domestic lending also turned cautious, expanding at a slower rate of 6 percent in

1998 compared to 13 percent in the previous year (Chart 16). The slowdown in

domestic bank loans was led primarily by declines in loans to the manufacturing and

commerce sectors. Locally incorporated banks also reported substantially lower

earnings due to provisions made for their exposure to regional countries (MAS

Annual Report, 1998/99).

Chart 16: Growth in Domestic Bank Loans, 1978-2006

G rowth in Domes tic B ank L oans

1978-2006

-10.0%

-5.0%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006

Growth in Bank Loans

Recessio

n

Asian Financial

Crisis, Jul 07Recession


In line with the general contraction in lending activities, the assets of finance

companies fell in 1998 (Chart 17) as loans and advances plunged 6.3 percent. The

number of finance companies also fell from 23 in 1996 to 19 in 1998 (Table 14).

Chart 17: Assets of Finance Companies, 1978-2006

77

F inanc e C ompanies As s ets

1978-2006

0.0

5,000.0

10,000.0

15,000.0

20,000.0

25,000.0

1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006

Finance companies assets

Recession

Asian Financial

Crisis, Jul 07

Recession


Chart 17 suggests a secular downturn in finance companies assets which began with

the onset of the Asian financial crisis in 1997 and continued beyond the recession in

2002. This, coupled with a similar downtrend in the number of finance companies

(Table 14), seems to reflect a consolidation among finance companies within the

financial sector.

Importantly, the Asian financial turmoil suggested that financial development could

be detrimental, rather than beneficial, to a country‟s economic development. The

crisis spotlighted a number of structural weaknesses in Singapore‟s financial sector

development which adversely affected its economic growth in the short term. In

particular, the excessive “protection” of the domestic banking sector prior to 1997

became a major weakness for the financial sector when the crisis erupted. Foreign

banks were restricted primarily to offshore banking in their operations. The lack of

foreign participation in the domestic banking activities permitted most of the local

banks to be run under tight family control and ownership (e.g. United Overseas Bank,

Overseas Union Bank, Overseas Chinese Banking Corporation). This led to problems

of proper corporate governance and transparency. The disclosure system among

78

domestic banks was weak by international standards, especially with regard to off-

balance sheet items and loan provisions and risk exposures. Ng (1998) argued that

the poor accounting disclosure standard was a key factor underlying the fragility of

the Singapore financial sector which adversely impacted economic growth, causing

real GDP to contract by 0.1 percent in 1998. This appears to cast doubt on the

widely-held thesis that financial development, of itself, is beneficial to economic

growth.

Moreover, without a deposit insurance scheme in the Singapore financial system to

assuage depositors‟ fears of banking collapse, domestic savers tended to “panic” in

attempting to get ahead of the crowd on cash withdrawal at “rumours” of banking

weakness. This, in turn, triggered a widespread panic thus precipitating in the

financial crisis of 1997. Tan (2002, p.8) argued that foreign ownership and control

would have served “as a check on the abuses of the domestic banking system”,

thereby reducing the risk of a banking system crisis. Furthermore, Tan (2002)

maintained that the branches of international banks would have been less vulnerable

to “withdrawal panic” as compared to the local banks.

Additionally, the Asian financial crisis highlights the critical issue of the lack of

development of the domestic bond market which adversely affected Singapore‟s

economic development. As in many Asian countries such as Indonesia and Thailand,

Singapore‟s financial system has relied too heavily on bank financing for investment

funding. Delhaise (1998, p.1) argued that the core reason for the Asian financial

turmoil was that the financial system depended “almost exclusively on commercial

banks” which were “heavily leveraged” and “poorly regulated”. There were no

79

alternative sources of funding because capital markets were “poorly developed”.

Eichengreen (2004) maintained that domestic credit in Singapore had been primarily

raised from the banking sector and the stock-market, with the bond market playing a

substantially smaller role in funding economic activities (Table 18).

Table 18: Total Outstanding External Finance (as percentage of GDP)

Domestic

Credit Provided

by Banking

Sector

Stock market

Capitalization

Outstanding

Domestic Debt

Securities by

Corporate Issuers

Outstanding

Debt Securities

by the Public

Sector

Outstanding

Debt Securities

by Financial

Institutions

Singapore 89.6 166.7 4.7 27.1 15.0

US 161.5 153.5 24.1 82.0 42.4

Europe 123.1 112.6 6.7 48.4 31.3

Source: World Bank and Bank of International Settlements

Arguably, the less developed bond market in Singapore, especially in comparison to

bond markets in the US and Europe, constituted a major weakness in its financial

sector development. Tan (2002, p.35) maintained that “as in the US,

disintermediation of funds from the banking system towards the capital market could

result in a more transparent and efficient financial system”. This is because capital

markets demand “greater transparency” thus making them less susceptible to

information disclosure problems which could spark “withdrawal panic” during a

financial crisis. Thus, it is arguable that Singapore‟s financial development, which

focused primarily on developing the banking sector and stock-market, resulted in

negative consequences for the domestic economy during the 1997 financial crisis.

This, again, appears to undermine the thesis that financial development is, of itself,

beneficial to a country‟s economic development.

3.3.3 1999-2006

The 1997 Asian financial crisis spotlighted several structural weaknesses within the

domestic banking sector which was heavily “protected” from international

80

competition. In 1999, significant banking sector reforms were implemented to

address these structural weaknesses by encouraging more foreign banks to establish

operations in Singapore and making domestic banks compete on par with the

international banks. In this regard, the 40 percent foreign shareholding limit for local

banks, which aimed to guard against foreign control and ownership of the local banks,

was lifted. Additionally, the local banks which had been largely family-owned

enterprises (except for the Development Bank of Singapore) were all required to

divest their non-financial assets to reduce cross-shareholdings across industries. The

separation of financial and non-financial activities of local banks is in line with

international practice to limit the risk of contagion from banking to non-banking

activities when a crisis such as the Asian financial turmoil erupts. By 2006, a deposit

insurance scheme was also established to protect savers against bank runs.

Furthermore, MAS also introduced a series of reform measures to enhance the

“efficiency” and “depth” of capital markets (Khalid and Tyabji, 2002). One of the

measures to improve stock market efficiency was the merger between the domestic

stock exchange (Stock Exchange of Singapore, SES) and the financial futures

exchange (Singapore International Monetary Exchange, SIMEX) in 1999. To

broaden and deepen the domestic bond market, MAS also allowed foreign companies

to issue Singapore-dollar denominated bonds. In 2001, the Singapore government

also issued a 15-year government bond which fostered the development of the

domestic corporate bond sector by providing “a benchmark yield curve to the

Singapore dollar corporate bond market and a much needed boost in its development”

(S. Tan, 2006, p.257). The expansion of domestic corporate bond market, in turn,

helped to meet the longer-term funding needs of private sector businesses. Thus, the

81

issuance of the 15-year government bond provided impetus to the development of the

domestic bond market which was perceived to be “underdeveloped” before the onset

of the Asian financial crisis in 1997 (Eichengreen, 2006).

Notwithstanding these significant policy changes, the assets of ACUs which are a

reflection of off-shore lending activities continued to fall for several years after the

1997 financial crisis before bottoming out in 2002 (Chart 11). This reflected the

massive write-offs in the balance sheets of off-shore banks which resulted from

default of cash-strapped borrowers in the region. The enormous write-offs of bad

debt led to closure of some of the smaller offshore banks, as the risk-reward trade-off

for the region was deemed to be unattractive. The number of off-shore banks

declined from 105 in 1998 to 59 in 2002. By 2006, there were only 43 off-shore

banks with many of them diversifying away from lending or risk-based activities to

fee-based activities such as asset management and private banking. A decade after

the Asian financial crisis, total ACU assets were only 25 percent higher at $698.6

billion in 2006 (Chart 11), representing an annual growth rate of 5.5 percent over the

period 1998-2006. This contrasted sharply with the 19.3 annual growth in ACU

assets in the pre-crisis period between 1978-1996. As the bulk of merchant bank

assets are in the form of ACU assets, the total assets of merchant banks also showed

significantly slower pace of expansion over the same period (Chart 12).

As shown in Chart 18 and Chart 19, by 2006 the ratios of ACU Assets/GDP and

Merchant Bank Assets/GDP had fallen to levels prevailing in the mid-1980s, and well

below their corresponding levels before the 1997 Asian financial crisis. The fall in

the ratio of merchant bank assets to GDP, coupled with the significant decline in the

number of merchant banks from 80 in 1998 to 48 in 2006 (Table 14), suggests a

82

dwindling role of merchant banks in financial intermediation within the Singapore

financial sector in the post-1997 period.

Chart 18 Chart 19 AC U As s ets /G DP

1978-2006

0

1

2

3

4

5

6

7

1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006

ACU Assets/GDP

Recession

Asian Financial

Crisis, Jul 07

Recession

As s ets of Merc hant B ank/G DP

1978-2006

0.0000

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000

0.9000

1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006

Merchant Banks Assets/GDP

Recession

Asian Financial

Crisis, Jul 07

Recession

Sources: MAS/MTI Sources: MAS/MTI

The assets of finance companies, which began falling when the Asian financial

turmoil was unleashed in 1997, continued its decline till 2003 before rebounding

slightly thereafter (Chart 17). This, again, is in stark contrast to the steadily rising

trend in the assets of finance companies before the 1997 crisis. The number of

finance companies also dropped significantly from 19 in 1998 to 3 in 2006 (Table 14).

Taken together, these developments reflected a consolidation among finance

companies in Singapore. More importantly, the developments suggest a diminishing

role for finance companies in the area of financial intermediation amidst the backdrop

of rising economic activities over the period 1999-2006. This is reflected in the

substantial decline in the ratio of finance companies assets to GDP since 1999.

Chart 20: Ratio of Finance Companies Assets to GDP, 1978-2006

As s ets of F inanc e C ompanies /G DP

1978-2006

0.0000

0.0500

0.1000

0.1500

0.2000

0.2500

1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006

Finance Companies Assets/GDP

Recession

Asian Financial

Crisis, Jul 07

Recession


Domestic banking activities became increasingly important in financial intermediation

as off-shore banking and finance companies played considerably smaller roles in this

83

area over the period 1999-2006. This is shown in Chart 21 which indicates that the

ratio of domestic banking assets to GDP, an indication of financial intermediation by

domestic banks, has generally trended upwards since 1997.

Chart 21: Ratio of Domestic Bank Assets to GDP, 1978-2006

Domes tic B anking As s ets /G DP

1978-2006

0

0.5

1

1.5

2

2.5

3

1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006

Domestic Banking Assets/GDP

Recession

Asian Financial

Crisis, Jul 07

Recession

Sources: MAS/MTI

Stock-market activities resumed their uptrend in the post-Asian financial crisis period

from 1999-2006. Chart 22 shows that stock-market capitalization rose from $470.9

billion in 1999 to $589.6 billion in 2006, amounting to a spectacular growth rate of

22.2 per cent per annum. Over the same period, stock-market turnover also rose from

$197 billion in 1999 to $300 billion in 2006 (Chart 13), representing strong annual

growth rate of 40.2 percent.

Chart 22: Stock-market Capitalization, 1978-2006 ($b)

S toc kmarket C apitalization

1978-2006

0

100000

200000

300000

400000

500000

600000

700000

197819801982198419861988199019921994199619982000200220042006

Stockmarket Capitalization ($m)

Recession

Asian Financial

Crisis, Jul 07

Recession


Importantly, the ratios of stock-market capitalization/GDP and stock-market

turnover/GDP are higher over the period 1999-2006 than to the period 1978-1996

84

(Charts 23 and 24). These suggest a higher level of financial intermediation by the

stock-market in the post-Asian financial crisis period.

Chart 23 Chart 24

S toc kmarket C apitalization/G DP

1978-2006

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006

Stockmarket Cap/GDP

Recession

Asian Financial

Crisis, Jul 07

Recession

S toc kmarket T urnov er/G DP

1978-2006

0.000

0.200

0.400

0.600

0.800

1.000

1.200

1.400

1.600

1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006

Stockmarket Turnover/GDP

Recession

Asian Financial

Crisis, Jul 07

Recession

Sources: MAS/MTI Sources: MAS/MTI

The above analyses on various financial institutions and their respective activities

suggest that Singapore‟s financial development was different before and after the

1997 financial turmoil. In particular, off-shore banks, merchant banks and finance

companies were numerically smaller and appear to have significantly retracted their

lending activities after the financial crisis. On the other hand, domestic banks,

insurance companies and the stock-market seem to have taken on substantially larger

roles in financial intermediation over the period 1999-2006 compared to the earlier

period.

Additionally, there are two major developments which tend to support the view that

Singapore‟s financial system has changed following the financial crisis. These

developments are:

(a) emergence and rising importance of new financial industries;

(b) different approach to financial regulation and supervision.

(a) Emerging and rising importance of new financial industries

85

In recent years, Singapore‟s financial sector has diversified from banking and

insurance-related services to fund management and investment advisory services

(Table 13). These industries, which form a large part of the wealth advisory and

treasury services clusters, are considered by MAS to be “sentiment-sensitive”

industries which are strongly tied to conditions in international financial markets.

Importantly, wealth advisory services and treasury services expanded at an annual rate

of 9.5 percent over the period 2001-06, more than double that of the “core clusters”

comprising banking and insurance services (Chart 25)

Chart 25: Growth Rates of Different Financial Clusters, 2001-06 (%)

-30.0

-20.0

-10.0

0.0

10.0

20.0

30.0

40.0

2001 2002 2003 2004 2005 2006

Growth in Core Clusters

Growth in Wealth Advisory

& Treasury Services

Growth in Wealth Advisory &

Treasury Services

v.s.

Growth in Core Clusters

Source: MAS Annual Report 2006/07

The number of investment advisers also jumped from 162 in 1998 to 185 in 2006

(Table 14). In 2006, the increasingly important wealth advisory and treasury services

clusters accounted for nearly half of the expansion of output in the financial services

sector (MAS, 2006/07). .

There are several critical factors underlying the growth of the wealth advisory and

treasury services cluster in recent years. The increasing interests in Asian equities

among global investors; rapidly growing pool of high net-worth individuals in the

region; and increasing sophistication of domestic and regional investors all

contributed to strong growth in the newly emerging wealth advisory and treasury

86

services cluster (MAS, 2006/07). Going forward, the cluster is expected to “underpin

domestic financial services growth in the medium term” (MAS, 2006/07, p.36).

(b) Different approach to financial regulation and supervision

Since the 1990s, global financial deregulation and technological development with the

widespread utilization of the internet caused world financial markets to become more

integrated, thereby intensifying competition and fostering innovation among financial

institutions and systems (Khalid and Tyabji, 2002). Amidst this global development,

there were growing criticisms that the stringent regulatory mechanism adopted by

MAS was complicating business practices and restricting financial innovation

(Eschweiler, 1997). Following the Asian financial crisis in 1997, “MAS embarked on

a fundamental review of its policies in regulating and developing Singapore‟s

financial sector” which were aimed at opening markets to new players and

strengthening existing players “by creating an environment conducive to efficiency

and innovation” (Khalid and Tyabji, 2002, p.356).

S. Tan (2006) asserted that the approach to financial reforms adopted by Singapore

authorities was distinctly different before and after the Asian financial turmoil of

1997. From the 1970s to early 1990s, the approach adopted by these authorities in

attracting international financial institutions and broadening the range of financial

services was “to set strict rules, avoid risky products and put protective barriers

around domestic financial institutions” (S. Tan, 2006, p. 250). However, by the late

1990s, following the 1997 Asian financial crisis, it was found that this approach put

Singapore at a disadvantage vis-à-vis its competitors such as Hong Kong and Sydney

in the innovation of new products and services. Consequently, the Singapore

87

authorities have shifted from a “prescriptive, rule-based” regulatory framework to a

more “flexible, risk-based” style (S. Tan, 2006). Additionally, MAS also revamped

the settlement and payment system in 1998 to enhance the speed and management of

monetary operations. This was aimed at reducing the risk of instability in the

financial system and containing systemic risk arising from bank failures (Khalid and

Tyabji, 2002). Moreover, the new real-time gross settlement system reduces

settlement risk by effecting inter-bank fund settlement on a continual basis,

superseding the previous end-of-day net settlement system (Low, 1998; MAS Annual

Report, 1997/98).

3.4 Conclusion

This chapter reviewed the economic and financial development of Singapore over two

distinct periods: 1978-1996 and 1999-2006. It also assessed developments during the

watershed 1997-1998, which divides the two periods. The critical role of finance in

Singapore‟s economic development was explored in the different phases of the

country‟s financial history. The various types of financial institutions and activities

were also examined in the context of the country‟s financial development.

Importantly, the analyses in this chapter suggest that the economic and financial

development of Singapore were significantly different in the period preceding and the

period following the 1997 Asian financial crisis. Therefore, it seems appropriate to

examine separately the relationship between financial development and economic

growth in the two distinct periods (namely, 1978-1996 and 1999-2006) and assess

how the finance-growth nexus has evolved over the last three decades, particularly

before and after the financial crisis.

88

The next chapter looks at the data relevant for testing this proposed thesis, the

constructs employed, and the methodology adopted for the tests.

90

Chapter 4

METHODOLOGY

4.1 Introduction

Previous chapters provided a review of the literature on the growth-finance nexus.

They also described the financial and economic development of Singapore over the

two major periods 1978-1996 and 1999-2006 as well as the watershed period 1997-

1998. This chapter will summarize the theory underlying the finance-growth

relationship (Section 4.2) and discuss the methods, constructs and indicators used in

past empirical studies (Section 4.3). It will also formulate a bivariate vector auto-

regression (VAR) model for the research and explain the rationale for adopting this

methodology in the study (Section 4.4). Sections 4.5 and 4.6 will examine the

variables employed in the study and outline the sources of data for the variables. The

testing procedures associated with the VAR model will be discussed in Section 4.7.

These tests attempt to address the two research questions concerning (a) the causal

relationship between financial development and economic growth in Singapore over

each of the two periods (1978-1996 and 1999-2006) and (b) changes in the finance-

growth nexus for Singapore over the two development phases. The end date (2006)

was the last year of relevant data at the time of writing. The final section (4.8)

summarizes the foregoing sections.

4.2 Theory underlying the finance-growth relationship

The literature review (Chapter 2) has identified a variety of models to explain the

relationship between financial development and economic growth. These models

suggest different possible causal linkages between the financial sector and the real

economy. Levine (1997, p.691) argued that the finance-growth nexus is best

91

explained by analyzing the function and role played by the financial system as

“financial systems have their biggest growth effects via capital allocation”.

Following Levine‟s (1997) classification, the financial system could be broadly

classified into two main sectors, namely, financial intermediaries and stock markets.

Levine (2005) suggested that financial intermediaries, comprising largely of banks

and other lending institutions such as finance companies, play five important

functions within the financial system. First, the financial intermediaries help to

minimize the costs of acquiring and processing information on the borrowers by

reducing duplication and free-rider problems (see also Diamond, 1984; Boyd and

Prescott, 1986). Financial intermediaries are more cost-efficient than individuals in

gathering, verifying and evaluating information on the borrowers due to economies of

scale. This enhances the efficiency of capital allocation thereby stimulating long-term

economic growth (see also Greenwood and Jovanovic, 1990; King and Levine, 1993).

Second, Levine (2005) argued that financial intermediaries, as creditors to the

borrowing firms, are able to improve corporate governance by closely monitoring the

firms and inducing managers towards maximizing firm value, which small dispersed

shareholders are unable to achieve (see also Berle and Means, 1932; Diamond, 1984).

In helping to tighten corporate governance, productivity is boosted along with

investment and economic growth (see also Bencivenga and Smith, 1993; Sussman,

1993; Harrison, Sussman and Zeira, 1999). Third, financial intermediaries such as

banks help to lower the transactions costs of risky investments by pooling risks

together in a diversified portfolio. Moreover, the financial intermediaries could

facilitate inter-generational risk sharing, thereby reducing risk from the long-term

perspective (see also Allen and Gale, 1997). Additionally, in pooling resources

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among savers with different liquidity preferences, financial intermediaries can reduce

liquidity risks thereby optimizing the allocation of capital and boosting productivity

growth (see also Bencivenga and Smith, 1991). Fourth, financial intermediaries can

effectively mobilize savings from disparate small savers thereby encouraging capital

accumulation which promotes long-term growth (Levine, 2005). Financial

intermediaries are able to pool together the savings of individuals for investment in

large scale projects which would otherwise be constrained to economically inefficient

production (see also Sirri and Tufano, 1995). Bagehot (1873) also argued that

financial intermediaries can enjoy economies of scale in mobilizing resources, thus

resulting in better resource allocation which would promote long-term growth.

Acemoglu and Zilibotti (1997) further showed that by mobilizing savings from

diverse individuals towards investment in a diversified portfolio of risky projects,

investment returns are enhanced with positive impact on economic growth. Finally,

financial intermediaries facilitate the proliferation of financial arrangements which

help to lower transaction costs (Levine,2005). This, in turn, eases exchange (King

and Plosser, 1986; Williamson and Wright, 1994) which leads to productivity gains.

Moreover, the lower costs of transaction also promote specialization and financial

innovation (Greenwood and Smith, 1996) thereby benefiting economic growth in the

longer term.

Levine (2005) further suggested that the stock market plays a critical role in fostering

economic growth by enhancing the efficiency of capital accumulation in a variety of

ways. Firstly, the stock market helps to reduce liquidity risks by allowing small

savers to buy and sell equities quickly and cheaply, while simultaneously allowing

companies to gain access to long-term capital raised through equity issuance (see also

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Levine, 1991; Bencivenga, et al., 1995). This is important because small investors are

generally risk-averse and unwilling to undertake long-term investments which more

profitable but incur larger risks. With a liquid stock market, small savers who acquire

equities can easily sell them to other small savers in the stock market if they wish to

have access to cash. This allows companies which issue the equities (company shares)

to have long-term access to capital which can be channeled to investments with higher

returns. Thus, a liquid stock market improves the allocation of capital within the

economy thereby fostering economic growth in the longer term. Moreover, Levine

(2005) argued that the stock market help to reduce the individuals‟ risks and enhances

the longer-term profitability of corporate investments thereby stimulating savings and

investments which benefit economic growth (see also Demirguc and Levine, 1996).

In addition to the above, Levine (2005) maintained that a rise in the liquidity of the

stock market would tend to motivate investors to undertake more research on the

listed firms (see also Holmstrom and Tirole, 1993; Boot and Thakor, 1997). This

arises because liquid stock markets allow investors who have acquired information

through prior research on the firms to trade and profit from the research before the

information becomes widely available and prices change (see also Kyle, 1984). The

increased research and acquisition of firm-related information in large and liquid

stock markets improve resource allocation, thereby boosting economic growth in the

longer term. Moreover, large and liquid stock markets could help exert corporate

control on firms by facilitating corporate takeovers of companies which are poorly

managed or inefficiently operated (Jensen and Meckling, 1996; Stein 1988). Thus, in

well-developed stock markets, the fear of corporate takeover provides strong

incentives for corporate managers of listed companies to optimize firm efficiency so

94

as to maximize firm value. Furthermore, Levine (2005) suggested that managerial

compensation in well-functioning stock markets could be better linked to corporate

performance as management compensation is tied to stock prices. The alignment of

managerial interests with the firms‟ interests enhances managerial incentives to

achieve greater profitability (see also Diamond and Verrecchia, 1982; Jensen and

Murphy, 1990). This leads to better resource allocation which, in turn, spurs long-

term economic growth.

Notwithstanding the potential economic benefits arising from the development of

bank-based and market-based financial systems, it is arguable that financial

development could also adversely impact on economic growth. In the case of banks,

which are essentially issuers of debt (i.e. lenders), there could be an inherent bias

towards “excessive prudence” which hampers business innovation and retards

economic development (Morck and Nakamura, 1999). This is corroborated in the

study by Weinstein and Yafeh (1998) which found that Japanese firms with “close

ties” to banks tended to adopt conservative, slow growth strategies and earned lower

profits compared to firms which are not closely related with the banks. Rajan and

Zingales (2003) argued that banks could weaken the corporate governance of firms as

the bank-based system involves relationship building, thus making it difficult for bank

managers to bankrupt the under-performing firms. Moreover, powerful bankers who

maintain strong relationships with incompetent firm managers could effectively block

outsiders from removing these incapable managers (Black and Moersch, 1998). Thus,

bank-based financial development could undermine corporate governance thus

leading to sub-optimal allocation of resources within the economy (Levine, 2005).

95

In the case of market-based system, the development of the stock market could also

adversely affect economic growth. This is because a well-developed stock market

which efficiently and speedily provides information to investors would tend to

dissuade the investors from allocating resources towards research in identifying

technological innovations which could enhance economic growth (Stiglitz, 1985).

Moreover, Shleifer and Summers (1988) argued that the liquidity of stock markets

could facilitate “harmful” takeovers in a myopic investment environment, thus

adversely affecting resource allocation and hindering economic growth.

4.3 Methods, Constructs & Indicators Used in Past Empirical Studies

There are four main methods undertaken in past studies to investigate the finance-

growth nexus, namely, historical country-case studies, cross-section regression

analysis, panel data studies, and time-series vector autoregression (VAR) modeling.

This section reviews these four methods and examines the variables used in past

research to reflect the two main constructs, namely financial development and

economic growth.

4.3.1 Historical Country-Case Studies

Some of the studies adopt a historical case-study approach to analyze the relationship

between financial and economic development. Using country-case studies, Cameron

(1967) examined the historical relationship between banking development and the

early stages of industrialization for seven countries – England (1750-1844), Scotland

(1750-1845), France (1800-1870), Belgium (1800-1875), Germany (1815-1870),

Russia (1860-1914) and Japan (1868-1914). Without using formal statistical

analyses, Cameron carefully examined the legal, economic and financial linkages

96

between banks and industries in the seven countries during their industrialization

process. The systematic chronological case studies provided detailed and thick

description of the evolution of the financial systems as the economies developed, and

the interactions between financial intermediaries, financial markets and government

policies in each of the countries. Using a similar historical country case-study

approach, Haber (1991) compared industrial and capital market development in

Brazil, Mexico and the United States over the period 1830-1930. The study found

that capital market development influenced industrial output and economic

development in the three countries.

Arguably, historical country-case studies could provide rich information on the

finance-growth nexus. However, as Levine (1997) points out, such studies tend to

hinge crucially on the researcher‟s “subjective evaluations” of financial performance

and often fail to systematically control for other factors which might influence

economic growth.

4.3.2 Cross-section Regression Analysis

Cross-sectional studies, which primarily employ regression analysis, constitute

another important method used to examine the relationship between financial


In a major cross-country empirical study of 80 countries to assess Schumpeter‟s view

that financial development promotes economic growth, King and Levine (1993)

developed four indicators to measure the services provided by financial

intermediaries. These four indicators, which were jointly used to provide a “richer

97

picture” of financial development, included (a) ratio of liquid liabilities to GDP to

measure financial depth; (b) ratio of bank credit to total credit to measure financial

intermediation by banks; (c) ratio of credit issued to non-financial private firms to

total domestic credit; and (d) ratio of credit issued to non-financial private firms to

GDP. The last two indicators were intended to examine where the financial system

distributes assets as a financial system that primarily funds private firms is deemed to

provide more financial services than one that mainly funds state enterprises. On

economic growth, King and Levine (1993) constructed three different measures as

indicators. These economic growth indicators were (a) growth rate of real GDP per

capita; (b) growth rate of capital per capita to measure the rate of physical capital

accumulation; and (c) growth rate of total factor productivity (TFP) to measure

improvements in economic efficiency. The study used cross-country regressions to

assess the strength of the correlations between financial development and economic

growth indicators. After controlling for other factors which might affect economic

growth such as fiscal and monetary policies and exchange rate, the study found that

each of the four financial indicators was statistically significant in accounting for each

of the three growth variables.

In another cross-sectional study on 78 countries over the period 1976-1993, Levine

and Zervos (1998) focused on stock-markets and banks in the financial sector and

assessed their impact on economic growth. The study developed six stock-market

development indicators (i) ratio of value of domestically listed stocks to GDP to

measure the size of stock-market capitalization; (ii) ratio of the value of the trades of

domestic shares to the total value of listed domestic shares; (iii) ratio of the value of

the trades of domestic shares to GDP; (iv) Capital Asset Pricing Model (CAPM)

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integration measure of α; (v) Arbitrage Pricing Theory (APT) integration measure of

α; and (vi) twelve-month rolling standard deviation of market returns as a measure of

stock-market volatility. The indicators for (ii) and (iii) were intended to measure the

liquidity in the stock-market, while indicators for (iv) and (v) were used to measure

the degree of integration with world financial markets to provide evidence that market

integration spurs economic growth. On banking development, the study used only

one indicator – ratio of loans made by commercial banks and other deposit-taking

banks to GDP. Levine and Zervos (1998) argued that this “bank credit” indicator is

superior to traditional financial depth measure of M2/GDP as it isolates credit issued

by banks, as opposed to credit extended by the central bank or other intermediaries.

The study also used four indicators for measuring economic growth. These were (a)

real per capita GDP growth; (b) real per capita physical stock growth; (c) productivity

growth; and (d) savings rate. Using cross-country regressions, the study found that

bank credit was highly correlated with the growth indicators. Moreover, the liquidity

indicators were also positively and significantly correlated with all four growth

indicators at the 5 percent level. These results suggested that financial development,

as reflected in increased stock-market liquidity and banking development, positively

affects economic growth.

Contrary to the finding of Levine and Zervos (1998) that financial development

promotes economic growth, Ram‟s (1999) cross-sectional study of 95 countries found

little support for this Schumpeterian view. The study used financial depth, measured

as the ratio of current-price liquid liabilities to GDP, as an indicator for financial

development. Economic growth was measured by the growth of real GDP per capita.

Using these indicators, Ram (1999) initially examined the covariation between

99

financial development and economic growth in each country over the period 1960-

1989. The mean of the 95 correlation coefficients was – 0.06, suggesting very little

relationship between economic growth and a key proxy for financial development.

Subsequently, using regressions on cross-country data across three sub-groups, Ram

observed a huge parametric heterogeneity and negligible or negative association

between financial development and economic growth.

Many other cross-sectional studies, using various indicators for financial development

and economic growth, have been undertaken. These studies include research by De

Gregario and Guidotti (1995), Berthelemy and Varoudakis (1997) and Rajan and

Zingales (1998). Some of the studies noted that there were problems in the

construction of appropriate indicators to reflect the underlying constructs, namely,

financial development and economic growth. De Gregario and Guidotti (1995), for

example, found that the weak relationship between financial and economic

development in some countries was partly attributable to the indicators used for

financial development which focused on the banking sector, while major financial

development took place outside the banking system in those countries.

While past cross-sectional studies on the finance-growth were useful in casting some

light and highlighting problems associated with the research, the critical issue of

endogeneity (or simultaneity) of the variables has been ignored. This is particularly

important in the study of the finance-growth relationship, as empirical evidence is

utilized to test whether financial development “leads” or “follows” economic growth.

Cross-sectional studies, which utilize contemporaneously dated regressors in

regression analysis, could only show correlations among the empirical constructs used

100

to reflect financial development and economic growth. These studies generally do not

provide any indication on the direction of causation (in the Granger sense) between

financial development and economic growth, which is the crux of the research3. As

Levine (2005, p.897) admitted, the cross-sectional studies by King and Levine (1993)

and Levine and Zervos (1998) “do not settle the issue of causality” as it is possible

that “financial markets develop in anticipation of economic activity”4 so that finance

becomes a “leading indicator rather than a fundamental cause” of economic growth.

4.3.3 Panel data studies

Panel data studies attempt to overcome the simultaneity problem by including a time

dimension in the cross-sectional research sample. The additional time dimension in

the panel or longitudinal data set provides a means for testing Granger causality

between the two key constructs in the finance-growth nexus, namely, financial


Levine, Laoyza and Beck (2000) employed the panel data approach to examine the

relationship between financial intermediary development and economic growth. The

study covered 77 countries over the period 1960-1995. Financial intermediary

development was measured by private credit growth while economic growth was

measured by real per capita GDP growth. In averaging the data over seven non-

overlapping five-year periods, the study found that financial intermediary

development had a positive impact on economic growth, after adjusting for

simultaneity bias in the data.

3 Although Levine (1998, 1999) has implemented the instrument variables approach in some cross-sectional studies to overcome the simultaneity problem. 4 Arguably, this specific criticism by Levine (2005) regarding cross-sectional studies applies equally to results derived from the standard Granger-causality tests.

101

In a recent panel data study by Tang (2006), three aspects of financial development

were examined, namely, stock-market, banking sector and capital flows. This

longitudinal study of Asia-Pacific Economic Cooperation (APEC) countries over the

period 1981-2000 implemented pooled ordinary least squares (OLS) estimation

technique to assess the finance-growth relationship. In using the basic specifications

of the growth model developed by Levine, Laoyza and Beck (2000) and Edison et al.

(2002), the study developed a modified growth model expressed as follows:

Log (Growth) = Bo log (LiqLiab) + B1 log (CommBank) + B2 log (BankCred)

+ B3 log (MktCap) + B4 log (ValTrade) + B5 log (Turnover)

+ B6 log (CapFlow) + B7 log (Capinfl) + B8 log (Company)

The constructs and indicators used in the study are shown in the following template.

Construct Indicator

Economic

Growth

Growth = growth rate of per capita gross domestic product (GDP)

Stockmarket

development

MktCap = ratio of total value of stocks listed on the domestic stock market to GDP

ValTrade = ratio of total value of stocks being traded on the domestic stock market divided by

GDP

Turnover = ratio of total value of stocks being traded divided by total value of stocks listed on

the domestic stock market

Company = number of domestic companies listed on the domestic stock market

Banking sector

development

CommBank = ratio of commercial bank assets divided by the total of commercial banks and

central bank‟s assets

BankCred = ratio of total private sector loans made by commercial banks and other deposit-

taking banks to the GDP

Capital flow

CapFlow = ratio of foreign direct direct investment and portfolio inflows and outflows divided

by GDP

Capinfl = ratio of foreign direct direct investment and portfolio inflows divided by GDP

Using the above indicators, Tang (2006) found that among the three financial sectors,

only the stock-market was significant in promoting growth, especially among

developed APEC countries. The positive relationship between the stock-market and

economic growth remained robust even after controlling for simultaneity bias.

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Like cross-sectional studies, many panel data studies have been undertaken in recent

years (Benhabib and Spiegel, 2000; Rousseau and Wachtel, 2000; Loayza and

Ranciere, 2002; Beck and Levine, 2004; Rioja and Valev, 2004). Arguably, the panel

data approach possesses several major advantages over the cross-sectional approach.

With panel data, the regression equations are able to better exploit the combination of

time series and cross-sectional variation in the observations. Moreover, by examining

the time series of cross section observations, panel data affords a study of the

dynamics of change (Baltagi, 1995). More importantly, panel data allows for the

expanded use of instrumental variables for all regressors including lagged variables,

thus providing a means for controlling the endogeneity problem among variables in

the finance-growth relationship.

4.3.4 Time-series analysis

Another type of studies on the finance-economic nexus pertains to the use of time-

series econometric techniques including vector autoregression (VAR) analysis and

related Granger (1969) causality test.

In a seminal study by Jung (1986) on 56 countries using annual data for different

periods from the 1950s to the 1980s, the Granger causality test was applied to analyze

the causal relationship between financial development and economic growth. In the

study, economic growth was measured by the rate of change of per capita GDP (or

GNP) while financial development was proxied by two different indicators. The two

financial development indicators were (a) currency ratio which is the ratio of currency

to M1 (b) monetization variable which is the ratio of M2 to GDP (or GNP). Jung

(1986) argued that in the early stages of economic development, the currency ratio

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should fall with real growth as a result of asset diversification and an increase in more

“non-currency” transactions. Moreover, Jung also maintained that financial assets

tend to accumulate as the economy grows. The monetization variable, which reflects

the size of the financial sector vis-à-vis the real economy, should thus increase over

time if the financial sector develops faster than the real sector and vice-versa. The

study concluded that there was evidence to indicate that less developed countries are

characterized by the causal direction running from financial to economic

development, while developed countries tended to show the reverse causal direction.

In another major study by Demetriades and Luintel (1996) on India, vector-

autoregression (VAR) techniques were employed to examine the effects of banking

sector controls on the process of financial deepening. Banking sector controls were

measured directly from information concerning various types of interest rate

restrictions, reserve and liquidity requirements, and directed credit programmes. The

six types of interest rate controls, as well as directed credit programmes, were

measured by dummies, while data on minimum reserve and liquidity requirements

was collected as an indicator of financial repression in line with the McKinnon-Shaw

view. Using the principal components method, indices were subsequently constructed

to summarize the different types of banking sector policies. Demetriades and Luintel

(1996) argued that this innovation enabled the study to quantify the effects of

“repressionist policies” independently of interest rate effects. Moreover, following

King and Levine (1993), the study used the ratio of bank deposit liabilities to nominal

GDP as a proxy for financial depth along with real GDP per capita to measure real

income level. All the variables, except interest rates and dummy variables, were

subsequently transformed into natural logarithms, so that their first differences

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represent logarithmic growth rates. The Unrestricted Error Correction Method of

Estimation (UECM) was used following unit root tests of the variables which

indicated that their first differences were stationary. The empirical results suggested

that banking sector controls had overwhelmingly negative effects on India‟s financial

development. Moreover, the study also found that financial policies affected

economic development through their effects on financial deepening.

Using the multi-variate VAR methodology, Xu (2000) investigated the effects of

financial development on domestic investment and output in 41 countries between

1960-1993. Three variables were used in the research, namely, real GDP, real

investment and an index of financial development. Real GDP and real investment

were obtained from dividing their respective nominal values by the appropriate

deflators. Xu argued that as the “common practice” is the belief that the provision of

financial services is positively related to the financial intermediary sector, it would be

appropriate to use the level of monetization as a “pertinent proxy” for measuring the

level of financial development. The index of financial development was thus

constructed from the ratio of liquid liabilities in the formal financial intermediary

sector to GDP, whereby the liquid liabilities themselves were calculated from the sum

of money and quasi money (M2) less currency. With a multi-variate VAR approach,

Xu (2000, p.343) maintained that the methodology is able to accommodate “different

economic and institutional arrangement in each country”, thus avoiding the

assumption of homogeneity of economic structures in cross-sectional studies.

Importantly, the time series coupled with VAR provide a useful way to deal with the

simultaneity problem among financial development, domestic investment and output.

Additionally, using impulse-response analysis which takes account of the dynamic

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feedback among financial development, domestic investment and GDP, Xu argued

that the impulse response functions allow for the identification of the long-term

cumulative effects of financial development on investment and growth. The study

concluded that domestic investment is an important channel through which financial

development positively influences economic growth.

In a recent study by Shan (2005), quarterly time-series data over the period 1985-1998

from ten OECD countries and China were used to estimate the vector autoregression

(VAR) model for testing Patrick‟s (1966) hypothesis that financial development

“leads” economic growth. In departing from the Granger causality approach

(Granger, 1969), impulse response function and variance decomposition (Enders,

2004) were applied in the study to examine the dynamic relationships between

variables in the VAR system. The VAR model was derived from growth and finance

models, which provided the main variables for the study of the growth-finance nexus.

Shan (2005) maintained that growth theory suggests that economic growth, defined as

the rate of change of real GDP, is determined by a number of factors. The growth-

inducing factors include (a) investment rate measured by the rate of change of total

capital expenditure; (b) productivity growth measured by the rate of change of a

weighted average of labour and capital productivity; (c) trade openness measured by

the ratio of the sum of exports and imports to GDP; and (d) labour force growth

measured by the rate of change of the labour force. Based on financial theory, Shan

(2005) used total credit as an indicator for financial development, arguing that the

ratios of M2/GDP and M3/GDP used in past studies (Sims, 1972; King and Levine,

1993; Cole, Scot and Wellons, 1995) were inappropriate as they were indicators of

“financial depth” rather than financial development per se. Moreover, as total credit

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in the economy also depends partly on government policy (Juttner, 1994), Shan

included the official interest rate, measured by the overnight cash rate, in the VAR

model as an indicator of government stance on monetary policy. Additionally, the

rate of change of the main stock-market index was used as an indicator to measure the

development of stock-market in the financial sector which is not captured by total

credit. Further, the rate of change of consumer price index was also used to account

for the effects of inflation on the financial aggregates. The study concluded that

“little evidence was found from the variance decomposition analysis that financial

development „leads‟ economic growth in the eleven countries in the sample” (Shan,

2005, p1366).

Many other time-series studies involving the Granger causality tests and VAR

techniques have been undertaken. Empirical studies which applied the Granger

causality tests include research by Fritz (1984), Spears (1991), Murinde and Eng

(1994) and Demetriades and Hussein (1996). In more recent studies, the vector auto-

regression (VAR) technique seems to be more widely used. The testing results from

time-series studies were generally mixed and the causality patterns appeared to vary

across different countries. Research by Luintel and Khan (1999) and Shan and

Morris (2002) using the VAR method suggested bi-directional causality between

financial development and economic growth. On the other hand, a study by Al-

Tamimi, Al-Awad and Charif (2001) which utilized the Granger causality test and

impulse response function (IRF) analysis suggested no clear relationship in the

finance-growth nexus.

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Taken together, it is notable that most of the past research on the finance-growth

relationship employed single-equation approach using cross-section or panel data.

These studies, which generally utilized average observations of cross-country data

over long periods, face a number of difficulties in the following areas:

(a) The studies generally assume that all countries in the study have a stable growth

path which is unlikely to occur (Quah, 1993).

(b) The studies attach the same weighting to large and small economies, as all

countries are assumed to be homogenous (Ram, 1999; Maddala and Wu, 2000).

(c) The studies often make the strong assumption that all countries within the sample

have common economic structures and technologies (Arestis and Demetriades,

1997; Neusser and Kugler, 1998; Sinha and Marci, 1999; Ram, 1999, Xu, 2000).

Arguably, as different economies are at different stages of economic development,

their economic structures are likely to differ.

(d) In aggregating across countries to obtain average observations for cross sectional

studies, important country-specific developments and policies which influence

economic development are ignored (Evans, 1995).

(e) Even if a significant causal relationship is found in a large sample of countries for

cross sectional studies, this conclusion only represents an average relationship

which cannot be generalized and applied to individual countries within the sample

(Demetriades and Hussien, 1996).

(f) As correlation does not imply causation, there are problems associated with

statistical inferences from cross-country regressions (Lervine and Zervos, 1998).

Hence, Arestis and Demetriades (1997) asserted that a time-series approach, rather

than a cross-sectional approach, would be more appropriate for assessing the

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relationship between financial development and economic growth. Ram (1999)

further suggested that future research on this relationship should focus more on

individual-country studies rather than cross-country or panel data analysis.

4.4 Vector auto-regression (VAR) Model

Past studies suggest that the vector auto-regression (VAR) model5 has been used to

investigate the relationship between financial development and economic growth in

the study. In the proposed study, a bivariate autoregressive model will be employed

where the two constructs are financial development (xt) and economic growth (yt).

The basic time- series equations for the VAR model can be written as:

yt = β10 + γ11 y t -1 + γ12 x t -1 + uyt …….. equation (i)

xt = β20 + γ21 y t -1 + γ22 x t -1 + uxt …….. equation (ii)

In equations (i) and (ii), β10, β12, β20, β21, γ11, γ12, γ21, and γ22 are coefficients to be

estimated in the model. Each equation contains an error term (uyt or uzt) that could be

contemporaneously correlated with each other but is uncorrelated with its own lagged

values. Moreover, more lags in the endogenous variables Y and X can be added to

the above equations. Importantly, as causality tests are sensitive to the lag length in

the VAR model, it is critical to determine the optimal number of lags in the model.

The appropriate lag length for each of the two endogenous variables (Y and X) can be

estimated using the Aikaike Information Criterion (AIC) or Schwarz Bayes Criterion

(SBC).

5 The VAR is used in the broad sense to include VAR in levels, VAR in first differences as well as the Vector Error Correction Model (VECM). This will be further elucidated in section 4.7.4.

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The VAR model is preferred to the cross-sectional and panel data methods of analyses

because of two important reasons. First, the VAR model, which consists of a system

of inter-related lagged (autoregressive) time-series, supplies a useful tool to capture

all the interactions and feedback among the variables in the time-series model. This is

undertaken using the impulse response function, which provides an important tool for

assessing the dynamic impact of random disturbances in one variable on other

variables in the system. Second, unlike structural (or simultaneous) equation models

which often require pre-determined assumptions concerning the underlying

relationship among the model variables, the VAR model is non-structural. This

implies that the VAR model does not require any “incredible restrictions” (Sims, 1972)

to identify the model and treats all variables within the model as endogenous. The

VAR model is therefore an “atheoretical empirical model …that can be used as a

framework for formal examination of inter-relationships within a given data set

without the need to specify a theoretical framework a priori (Groenewold, 2003,

p.458)”. This critical attribute in the VAR model is particularly relevant to the study,

as the literature review (Chapter 2) suggests that the theory underlying the finance-

growth nexus might not be rich enough to allow for tight specifications of the

relationships among the variables. In this case, it would be advantageous to employ

the VAR model which does not assume a priori relationships among the variables.

Importantly, in utilizing the VAR model in the study, it does not imply that the

theoretical models and implications outlined in the earlier chapters are invalid. On the

contrary, by not presupposing a given relationship between financial development and

economic growth, the VAR model allows for the data to “speak for themselves” in

capturing the dynamic relationships among the variables.

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A separate VAR model will be constructed for the whole period as well as for each of

the two sub-periods, namely 1978-1996 and 1999-2006. The rationale for choosing

the different sub-periods for each VAR model follows the analysis undertaken in

Chapter 3. Notably, the 1997 Asian financial crisis created a watershed in the

economic and financial development of Singapore. Therefore, it seems appropriate to

separately establish a VAR model before and after the 1997 financial crisis to

facilitate subsequent comparison and analyses (which will be undertaken in the next

chapter).

4.5 Variables

The literature review has identified a number of indicators employed in past studies to

analyse the relationship between financial development and economic growth. In this

study two main indicators, namely real per capita GDP and financial loans over

nominal GDP, are selected to represent the two key constructs, namely economic

growth and financial development respectively. The rationale underlying the choice

of the indicators will be amplified in the subsequent section. Moreover, other

indicators which were employed in past empirical studies to reflect the two constructs

(economic growth and financial development) will also be explored in order to check

for the robustness of the results. This will be further discussed in section 4.7.7.

4.5.1 Real per capita GDP

Past studies have employed various indicators to measure economic development.

These indicators include the industrial production index, real GDP and real GDP per

capita. Some studies have utilized the industrial production index as a proxy for

economic development (Gupta, 1984; Deidda and Fattouh, 2002). Admittedly, the

industrial production index might not be a good indicator for economic development

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as industrial output constitutes a small proportion of total economic activities. Other

studies have chosen real GDP as an indicator for economic development (Murinde

and Eng, 1994; Chang, 2002; Bhattacharya and Sivasubramaniam, 2003). However,

Sen (1988) has argued that the economic development of a country encompasses more

than its economic growth and capital accumulation. To capture the broader definition

of economic development, which includes the quality of life and standard of living of

the country‟s population, the real per capita GDP is commonly used (G. Tan, 1999).

A large number of cross-sectional and time-series studies have employed real per

capita GDP as an indicator for measuring economic development (Jung, 1986; King

and Levine, 1993, Demetriades and Luintel, 1996; Levine and Zervos, 1998, Ram,

1999; Khalid and Tyabji, 2002, Thangavelu and Ang, 2004, Chang and Caudill, 2005).

The real GDP refers to the total real (constant dollar) value of goods and services

produced in the domestic economy. The real per capita GDP which will be employed

in this time-series study is computed as the ratio of the real GDP of the country to its

total domestic population. The real GDP data are obtained from the Economic

Survey of Singapore which is published by the Ministry of Trade and Industry (MTI)

on a quarterly basis. However, as population statistics are only published on an

annual basis by MTI in the Yearbook of Statistics, the population growth rate is

assumed to be constant over the four quarters of the year in the computation of real

per capita GDP for each quarter.

Though real per capita GDP is primarily selected as an indicator for economic growth,

the robustness of the results will be explored by using real GDP as an alternative

indicator. This will be discussed later in section 4.7.7.

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4.5.2 Financial loans over nominal GDP

The construction of an appropriate indicator for financial development is complicated

by the wide diversity of financial services and large array of institutions associated

with various functions of financial intermediation (Thangavelu and Ang, 2004).

Three indicators have largely been employed in past studies to reflect the impact of

financial intermediation on economic development. A commonly used indicator to

measure banking sector development is the financial loans ratio, which is measured as

the ratio of financial loans made by commercial banks and other deposit-taking banks

to nominal GDP (Levine and Zervos, 1998; Levine, Laoyza and Beck, 2000; Edison

et al., 2002, Tang, 2006). A second indicator is the ratio of M2 to nominal GDP (Jung,

1986; Murinde and Eng,1994). A third and less commonly used indicator is the

financial assets ratio, which is measured as the ratio of total financial assets to

nominal GDP (Shaw, 1973).

Levine and Zervos (1998) argued that the financial loans ratio (financial loans/GDP)

is superior to the traditional measure of M2/GDP as it isolates credit issued by banks,

as opposed to credit extended by the central bank. Moreover, the financial assets ratio

(financial assets/GDP) is arguably a measure of financial depth rather than financial

development per se (Shan, 2005). On the other hand, the financial loans ratio

(financial loans/GDP) attempts to capture the supply of credit and loanable funds to

the private sector, which ultimately influence the “quality” and “quantity” of

investments that impact on long term economic development (Khalid and Tyabji,

2002). Thus, this study employs the ratio of financial loans to nominal GDP as an

indicator for financial development as it more accurately reflects the function of

financial intermediaries in channeling funds to the private sector for investments and

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economic development. In the study, the aggregated loans of commercial banks,

offshore banks, merchant banks and finance companies will be compiled from the

Monthly Statistical Bulletin published by the Monetary Authority of Singapore. The

nominal GDP will be obtained from the quarterly Economic Survey of Singapore

published by the Ministry of Trade and Industry.

4.5.3 Stock-market turnover over nominal GDP

From the perspective of stock-market development, the ratio of stock-market turnover

to nominal GDP is commonly used as an indicator for stock market activities within

the financial system (Levine and Zervos, 1998, Thangevelu and Ang, 2004; Tang,

2006). This indicator reflects the level of liquidity in the stock market, which in turn,

influences the efficient functioning of the stock market in terms of acquisition of

information, savings mobilization, corporate control and risk diversification among

firms. A well-functioning stock market which enhances the quality of these related

services would benefit economic growth (Levine and Zervos, 1998). Thus, for the

purposes of this study, the ratio of stock-market turnover to nominal GDP is

employed as a measure of stock-market development. The data on stock-market

turnover is obtained from the Monthly Statistical Bulletin published by the Monetary

Authority of Singapore.

4.6 Data Sources, Study Period and Statistical Tools

In the study, quarterly time-series data from 1978 to 2006 are used to analyze the

relationship between financial and economic development. As explained in Chapter

3, the year 1978 is chosen as it marked the start of the “complete liberalization of the

foreign exchange market in Singapore” (Murinde and Eng, 1994, p.396) and the

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commencement of Singapore as an international financial centre. Furthermore, the

data are divided into two distinct periods: 1978-1996 and 1998-2006. The first data

period 1978-1996 is chosen as it corresponds to Singapore‟s rapid stage of economic

and financial development and its growing emergence as an Asian NIE. As explained

in Chapter 3 the Asian financial crisis, which erupted in July 1997, represented a

watershed in Singapore‟s economic and financial development. The second data

period 1998-2006 is chosen to correspond to another phase of financial deregulation

and liberalization, as 1998 marked the beginning of a wide range of reforms and

restructuring measures in the Singaporean financial sector (S. Tan, 2006).

Economic data on nominal GDP and real GDP are obtained from the Economic

Survey of Singapore which is published quarterly by the Ministry of Trade and

Industry (Singapore), MTI. Population statistics are obtained from the Yearbook of

Statistics, which is also published by MTI. Financial data on stock market turnover

and loans of financial intermediaries are obtained from the Monthly Statistical

Bulletin published by the Monetary Authority of Singapore, MAS. The nomenclature

for the various data series in the publications are detailed below.

Data Data description in publication Name of publication Source of

publication

Nominal GDP

GDP at current market prices Economic Survey of

Singapore (Quarterly)

MTI

Real GDP

GDP at 2000 market prices Economic Survey of

Singapore (Quarterly)

MTI

Population

Population at mid-year Yearbook of Statistics

(Annual)

MTI

Stock market

turnover

Turnover value (Singapore Exchange

Securities Trading Ltd – SGX-ST.)

Monthly Statistical Bulletin MAS

Loans of

financial

intermediaries

Bank‟s loans and advances including

bill financing

Monthly Statistical Bulletin MAS

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The E-views statistical software package is employed for various statistical tests

including determining the optimal lag length, test for cointegration, developing the

vector error correction model (VECM), and test for causality in the VECM

framework.

4.7 Testing Procedures

The testing procedure involves several steps which are outlined below. Importantly,

the testing procedure hinges crucially on the stationarity of the data, which has

implications on the causality tests to be undertaken.

A time series Xt is said to be stationary if its expected value and population variance

are independent of time and if the population covariance between its value at time t

and time (t + s) only depends on s and not on t. Thus, the following constitutes a

stationary time series:

Xt

= β Xt-1 + εt …… equation (iii)

where – 1 < β < 1 and εt is considered to be white noise with a mean of 0 and a

constant variance with no autocorrelation. In this case, it can be shown that the

expected value of Xt is 0 and hence independent of time t. Moreover, it can also be

shown that the population variance is also independent of time t and the population

covariance between its value at time t and time (t + s) only depends on s and not on t.

Consequently, the time series in equation (iii) is considered to be stationary.

However, for the time series given in equation (iii), the data is said to be non-

stationary if β = 1 as it becomes a random walk series which is expressed as follows:

Xt

= Xt-1 + εt …… equation (iv)

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Consequently, if the time series in equation (iv) starts from X0 at time 0, its value at

time t will be given by:

Xt

= X0 + ε1 + ε2 + …. + εt ….. equation (v)

Unlike the stationary time series in equation (iii), the non-stationary time series in

equation (v) contains an innovation or shock (ε) which is permanently built into the

series. Consequently, the non-stationary time series incorporates the sum of the

shocks in each period (ε1 + ε2 + …. + εt ) which is thus said to be integrated. In

contrast, the stationary time series in equation (iii) with β < 1 implies that each shock

is exponentially attenuated and tends towards 0 when t becomes large. In this case,

the expected value of Xt in equation (iv) is only independent of t for a fixed X0.

Importantly, it can be shown that the population variance is directly proportional to

time t. Moreover, it can also be shown that the population covariance between its

value at time t and time (t + s) depends not only on s but also on t. Thus, the time

series in equation (iv) is considered to be non-stationary because both the population

variance and covariance are not independent of time.

Taken together, equations (iii) and (iv) form the basis for the most common test for

stationarity of data series. Thus, in practice, the test for data stationarity becomes a

test for β = 1 or unit root. When unit root is present (i.e. β = 1), the data is considered

to be non-stationary. This will be further explained in subsequent section (4.7.1).

Stationarity is critical in the VAR model because OLS estimates of the coefficients

are unbiased and consistent only when the time series are stationary. When the data

are non-stationary, the least squares estimators will be biased and inconsistent,

thereby invalidating the associated hypothesis tests (such as the t-test and F-test) in

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relation to the relevant coefficients. Consequently, the regression coefficient of a

purported explanatory variable may appear to be significantly different from zero

when that variable is actually not a determinant of the dependent variable. As Toda

and Phillips (1993) put it, non-stationary time series exhibit non-standard distributions

which result in “nuisance parameters” and inconsistent diagnostic statistics.

From the perspective of the VAR model, data stationarity implies that the mean,

variance and auto-covariance at various lags for each time series within the model are

constant over time. This allows for the adjustment process to be modeled using an

array of equations with fixed coefficients which can be estimated from past data. On

the other hand, if the time series data are non-stationary, it implies that the

relationship between two or more variables in the model could arbitrarily change over

time (except in the special case of cointegration), thus leading to problems in

estimating their inter-relationships and the construction of the VAR model.

Moreover, using Monte Carlo experiments, Granger and Newbold (1974)

demonstrated that “spurious” regression results can arise from using non-stationary

time series.

4.7.1 Unit root test for data non-stationarity

The formal test for data non-stationarity can be undertaken by testing for the existence

of unit root for each time series using the Augmented Dickey-Fuller (ADF) test with

the optimal number of lags pre-selected using the AIC or SBC (as discussed in section

4.4). The ADF test follows the pioneering works of Dickey and Fuller (1979) which

re-expresses equation (iii) in terms of first difference:

ΔXt

= (β – 1) Xt-1 + εt ……… equation (vi)

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The parameter of interest in equation (vi) is the coefficient of Xt-1 . If (β – 1) = 0 [i.e.

β =1 ], then the time series is said to contain a unit root, which implies that the data is

non-stationary. This is because when β =1 (i.e. unit root exists), equation (vi)

becomes the direct result of the random walk series ( i.e. Xt = Xt-1 + εt in equation

(iv)) which is non-stationary. On the other hand, when │β│ < 1, then equation (vi)

becomes the direct consequence of the stationary time series expressed in equation (iii)

(i.e. Xt = β Xt-1 + εt with │β│ < 1 since it is inadmissible that │β│ > 1 because the

time series would become explosive).

Additionally, if there are more lagged differences in Xt such as the following

sequence: Xt

= β1 Xt-1 + β2 Xt-2 + εt …… equation (vii)

then the first difference could be written as:

ΔXt

= (β1 + β2 – 1) Xt-1 – β2 ΔXt-1 + εt ……… equation (viii)

In this case, the ADF test is to assess the null hypothesis that the coefficient of Xt-1 is

equal to 0 (i.e. β1 + β2 – 1 = 0). If the null hypothesis (of the presence of unit root)

cannot be rejected, then the time series is said to be non-stationary.

In undertaking the ADF test, it is critical to note that the test tends to have a low

power. Thus, a failure to reject the null hypothesis (regarding the presence of a unit

root) does not always mean that the time series is non-stationary. Nonetheless, if the

null hypothesis concerning the presence of a unit root is rejected, then the time series

is considered to be stationary. Moreover, the ADF test has non-standard distribution

which requires the use of simulated critical values from the original paper by Dickey

and Fuller (1979) in order to analyse the test results.

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Another important weakness of the ADF unit root test is that it fails to take account of

a structural break in the data series. Perron (1989) has shown that a stationary series

with a break in level and/or trend could lead to erroneous conclusions of non-

stationarity. To address this problem, the Zivots and Andrews (1992) procedure,

which allows the break date to be data-determined, will be employed to test for unit

root with an unknown break date.

4.7.2 Determine the order of integration of time series

In cases where the ADF test indicates that the time series is non-stationary (i.e. cannot

reject the null hypothesis regarding the presence of a unit root), differencing can help

to transform the non-stationary process into a stationary one. Thus, for the non-

stationary random walk series in equation (iv), taking the first difference (with β = 1)

yields the following result:

ΔXt

= Xt – Xt-1 = εt ……… equation (ix)

The sequence in equation (ix) reflects a stationary process with a constant population

mean and variance which are both independent of time. As the non-stationary time

series is transformed into a stationary process by differencing once, it is said to be

integrated of order 1 and denoted as I(1). If a series is made stationary by

differencing twice, then it is considered to be integrated of order 2 or I(2). Thus, by

definition, a time series which is stationary in terms of its levels (i.e. needs no

differencing) is described as I(0).

For non-stationary time series [i.e. not I(0)], the ADF test can be performed on the

first or second differences to determine whether the series is I(1) or I(2). Box,

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Jenkins and Reinsel (1994) suggest that most time series are either I(0) or I(1) and

occasionally I(2).

4.7.3 Cointegration test

Engle and Granger (1987) pointed out that even if two time series are non-stationary

(i.e. unit root exists in the time series data), a linear combination of the time series can

still be stationary. When this occurs, the non-stationary time series are said to be

cointegrated. Thus, if Xt and Yt are two non-stationary time series such that:

Yt = β1 + β2 Xt + ut ……….. equation (x)

then Xt and Yt are said to be cointegrated if the disturbance term (ut), which reflects

the error in the linear combination of Y and X, is a stationary time series.

Cointegration enables two non-stationary variables to be combined in a stable long-

run relationship between the variables. Importantly, for the variables to be

cointegrated, they must be integrated of the same order. When the variables in a

model are cointegrated, any short-run divergence away from equilibrium will be

moderated by long-run forces. This can be demonstrated by examining the

disturbance term, ut, which can considered as a measure of the deviation between the

components of the model:

ut = Yt – β1 – β2Xt ……….. equation (xi)

Consequently, if the there is a stable long-run relationship between Xt and Yt, there

will be a limit to the divergence between the two variables. Hence, even if the two

time series (Xt and Yt) are non-stationary, the disturbance term (or error term) ut will

be stationary. In this study, cointegration testing aims to examine whether there is a

stable long-run relationship between financial development and economic growth.

121

Cointegration can be tested using the Engle-Granger methodology which involves

several steps. First, pretest the two key variables of financial development (Xt) and

economic growth (Yt) to determine that they are integrated of the same order which

cointegration necessitates. This could be undertaken using the ADF test to infer the

number of roots in each variable. If the two variables (financial development and

economic growth) are integrated of different order, then they cannot be cointegrated.

Second, if the variables are integrated of the same order, use Ordinary Least Squares

(OLS) regression to estimate the long-run equilibrium relationship between the

variables. The long run equilibrium relationship between financial development (Xt)

and economic growth (Yt) can take the following form:

Yt

= β1 + β2Xt + ut …… equation (xii)

Stock (1987) suggested that if the variables are cointegrated, the OLS regression

yields “super-consistent” estimator of β1 and β2, which implies that β1 and β2 converge

faster than in OLS models using stationary variables. Third, determine the residual

sequence from the OLS regression on equation (xii), which could be denoted as u t.

Finally, using the regression residuals, the ADF test can be performed to determine

whether the variables (Xt and Yt) are cointegrated. Thus, consider the following

autoregression of the residuals:

Δ u

t.

= a1 u

t-1 + εt …… equation (xiii)

If the null hypothesis that a1 = 0 cannot be rejected, then the conclusion is that the

residual series contains a unit root and hence the variables Xt (financial development)

and Yt (economic growth) are not cointegrated. Conversely, rejection of the null

hypothesis (H0 : a1 = 0) means that the residual sequence is stationary, thereby

implying that Xt (financial development) and Yt (economic growth) are cointegrated.

122

Intuitively, the Engle-Granger test for cointegration examines the stationarity of the

(saved) regression residuals ( u t.) because when the residuals (or error terms) are

stationary, it implies that the error terms (residuals) have a constant mean and hence

will not get too large. Consequently, using equation (xi), it follows that Xt (financial

development) and Yt (economic growth) will not diverge from each other indefinitely.

Thus, the underlying intuition is that if the regression residuals ( u t.) are stationary,

then Xt (financial development) and Yt (economic growth) will tend to trend together,

thereby implicitly pointing to a long run relationship between the two variables which

cointegration implies.

Enders (2004) suggested that while the Engle-Granger (1987) test for cointegration

can be “easily implemented”, it has important defects. Enders (2004) noted that the

results of the cointegration test might be different if the variables in equation (xii) are

reversed. Another problem is that Engle-Granger (1987) cointegration test employs a

two-step estimator, whereby residuals are initially generated from estimating the long

run relationship between the variables and these generated residuals are subsequently

tested for stationarity to determine whether the variables are cointegrated. Enders

(2004) argued that this two-step procedure is problematic as any error in the first step

will be “carried over” into the second step.

Given the problems associated with the Engle-Granger (1987) procedure, the test for

cointegration in the study follows the multivariate cointegrating technique of

Johansen (1988; 1992) and Johansen and Juselius (1990) as preferred in the literature

on econometrics. Moreover, using the Monte Carlo procedure, Gonzalo (1994)

found that Johansen‟s (1988) cointegrating technique outperforms four other

123

cointegration methods in the estimation and testing of cointegrated. relationships.

Johansen (1988) and Johansen and Juselius (1990) proposed two test statistics for the

presence of cointegration: the trace (λtrace) and the maximum eigenvalue (λmax)

statistics. They also provided the critical values of λtrace and λmax which were obtained

from simulation studies. These critical values could be used to determine whether the

variables are cointegrated. If the variables are cointegrated, it implies that there is a

stable long-run relationship between the variables.

Importantly, as with the case of testing for unit roots with structural breaks, there is

also a need to test for cointegration with structural breaks. This is because a test for

cointegration which fails to take account of a break in the long-run relationship will

have a low power (Harris and Sollis, 2003). To address this problem, the Gregory and

Hansen (1996) test for cointegration with structural breaks will be employed. The

Gregory and Hansen (1996) test allows for a break in mean and/or trend at a pre-

determined date while simultaneously maximizing the chances of finding two

variables to be cointegrated (Groenewold, 2003).

Nonetheless, even if the variables are tested to be cointegrated (with ot without

structural breaks), it merely indicates that there is an underlying long-run relationship

between the variables. The presence of cointegration between two variables does not

provide any indication of the direction of causality between the variables. Causality

tests will be undertaken to examine the direction of causation between the variables.

124

4.7.4 Model for causality test

In the study, the model used as a framework for tests of causality between financial

development and economic growth hinges crucially on the stationarity/non-

stationarity and cointegration/non-cointegration of the data:

(i) If all the time series in the model are I(0) (i.e. unit root is not found to be present

in the levels of the time series), the causality test will employ the straightforward

F-test on the levels of the variables in the VAR model.

(ii) If all the time series are found to be I(1) but not cointegrated, then the F-test can

be applied on the first difference in the VAR model variables to test for causality.

(iii)If the data are found to contain a mixture of I(0) and I(1) series with at least two

I(1) and all the I(1) variables are not cointegrated, then the F-test will be applied

to the VAR model with each variable appropriately differenced to achieve

stationarity.

(iv) If all the time series are I(1) (i.e. all the time series are found to be stationary after

taking the first differences for each series) and cointegrated, then the vector-error

correction model (VECM) can be used to test for causality in the variables.

Granger (1988) suggested that when two I(1) variables are cointegrated, it implies that

causality exists in at least one direction. The Error Correction Model (ECM) was first

introduced by Sargan (1964) in the econometric literature and developed further by

Davidson, Henry, Srba and Yeo (1978). Granger (1988) and Miller and Russek (1990)

suggested that the standard Granger causality test might incorrectly find no causal

relationship between two non-stationary variables which are cointegrated, thereby

rendering the standard Ganger (1969) causality test invalid. The VECM is therefore a

restricted VAR with cointegration restrictions to test for causality between two non-

stationary I(1) series which are cointegrated. Granger (1988) suggested that when

125

two variables are cointegrated, the time series can be formulated in error correction

form where the changes in the dependent variables are modeled against the lagged

changes in the dependent variables and the error correction term. Thus, the bivariate

VECM can be formulated as follows:

∆Yt = α1 +

1

1

n

i

ai ∆Yt-i +

1

1

m

i

γi ∆Xt-i – π1 u t-1 + εyt …… equation (xiv)

∆Xt = α2 +

1

1

n

i

bi ∆Xt-i +

1

1

m

i

λi ∆Yt-i – π2 u t-1 + εxt …… equation (xv)

where u t-1 (error correction term) = Yt-1 – β1 – β2Xt-1

The VECM incorporates two sources of causation: error correction term ( u t-1) and

lagged difference terms. The error correction term ( u t-1), which is obtained from the

saved residuals in estimating the long-run relationship between the variables Xt

(financial development) and Yt (economic growth), measures the long-run causal

relationship between the variables. Since the two variables (Xt and Yt) are

cointegrated, it follows that u t-1 will also be stationary. This, coupled with the I(1)

characteristics of the two variables, imply that all the variables in the VECM are

stationary thereby enabling OLS estimation on equations (xiv) and (xv) to yield

unbiased and consistent estimates of the coefficients. The OLS estimated coefficients

on the lagged difference terms (γi and λi) provide a means for assessing the short-run

causal relationship between Xt and Yt. From equation (xiv), we can say that ∆X

(changes in financial development) causes ∆Y (changes in economic growth) in the

Granger sense if all the γi are significantly different from zero. Likewise, from

equation (xv), we can say that ∆Y (changes in economic growth) causes ∆X (changes

in financial development) in the Granger sense if all the λi are significantly different

from zero.

126

4.7.5 Impulse response function

Following the estimation of an appropriate model, the estimated model will be

employed to compute the impulse response function (IRF). Runkle (1987) suggests

that the impulse response function (IRF) constitutes the centrepiece of VAR analysis.

The IRF traces out the dynamic response of a dependent variable in the VAR system

to shocks in the error terms. Thus, having established the model underpinning the

relationship between two variables Xt (financial development) and Yt (economic

growth), we could assess the dynamic adjustment process of a (one standard deviation)

stochastic shock (or innovation) in the error term ut of one of the variables on all the

variables in the system. The IRF traces out the impact of such shocks for a few

periods into the future.

4.7.6 Break point analysis

Having tested for causality using the appropriate VAR model and computed the

impulse response functions for the VAR model in each of the two different periods

(namely 1978-1996 and 1998-2006), break point analysis will be undertaken. The

Chow (1960) test, which involves the standard F-statistic, will be employed to analyse

the structural stability of the VAR coefficients in the two sample periods (1978-1996

and 1998-2006). Different break points around the break date (1996-1998) could

also be experimented to check on the robustness of the findings (Groenewold, 2003).

4.7.7 Checking result robustness

As discussed earlier, the robustness of the results will be further explored and/or

validated by experimenting with the use of other proxy variables as alternative

indicators in the model.

127

While a large majority of the studies employed real per capita GDP as a measure for

economic development, a small number of studies utilized real GDP as an indicator

for economic growth. These included studies by Murinde and Eng (1994), Xu (2000)

and Bhattarcharya and Sivasubramanian (2003). Following the latter studies, real

GDP will be used as an indicator for economic development in the various tests to

check for robustness of the results.

Moreover, in measuring financial development, a commonly used indicator to reflect

the impact of stock market development on economic growth is the turnover ratio

(Arestis and Demetriades, 1997). The turnover ratio is computed as the ratio of stock

market turnover to nominal GDP Levine and Zervos (1998) argued that the turnover

ratio provides a measure of the level of dynamism of the stock market as it captures

the level of transactional activities in the stock market which impact on economic

growth. Arguably, the turnover ratio thus reflects the level of liquidity in the stock

market, which in turn, influences the efficient functioning of the stock market in terms

of acquisition of information, savings mobilization, corporate control and risk

diversification among firms (Thangevelu and Ang, 2004; Tang, 2006). A well-

functioning stock market which enhances the quality of these related services would

benefit economic growth (Levine and Zervos, 1998). Thus, to test for the robustness

of the results, the study will employ the turnover ratio as alternative indicator for

financial development to encapsulate the impact of the stock market on economic

growth.

128

4.8 Conclusion

This chapter reviewed the theoretical framework underlying the finance-growth

relationship and the methodologies employed in past studies. It established a

bivariate VAR model for the study and identified the key variables, sources of data

and statistical tools to be employed for testing the causal relationship between

financial development and economic growth. The testing procedures for causality

tests within the VAR model were also described along with impulse response function

analysis, break point analysis as well as checks on result robustness. The main

findings of the various tests will be discussed in the next chapter.

130

Chapter 5

DATA ANALYSIS I

5.1 Introduction

In the preceding chapter, the methods used in past empirical studies to examine the

finance-growth nexus, the proposed statistical methodology for the current research,

the data collection and testing procedures employed in the studies were described in

detail. The results of the statistical investigation will be presented over three separate

chapters - Chapters 5, 6 and 7. Chapter 5 provides the results on the battery of tests

aimed at determining the stationarity, order of integration and cointegration of the

variables employed. Chapter 6 serves to provide the findings for the relationship

between financial development and economic growth in Singapore using the Granger

causality test and impulse response analyses. Impulse response analyses assess the

finance-growth nexus by examining the dynamics of the responses of financial

development on economic growth and vice-versa. Chapter 7 reports on the robustness

tests which are further undertaken to assess the causality results obtained in the

preceding chapter.

In this chapter, the main variables in the vector auto-regression (VAR) model

employed in the study and the framework of analysis will be outlined in Section 5.2.

In Section 5.3, the stationarity of the time series associated with each key variable is

examined using the standard unit root test, with the order of integration of the time

series determined when data stationarity is achieved. The long-run relationship

between the variables, which is analysed using the cointegration test, will be

examined in Section 5.4. Section 5.5 summarizes and concludes on the results of the

various preliminary tests performed on the time series data.

131

5.2 Main Variables and Framework of Analysis

5.2.1 Main variables

The study employs a total of four different variables to examine the relationship

between financial development and economic growth in Singapore over the period 1st

quarter 1978 to 4th

quarter 2006. The definition of each of the four variables is given

in Table 5.1 below:

Table 5.1: Definition of variables Variable Definition

Y Real per capita GDP

L Ratio of banking loans to nominal GDP

G Real GDP

T Ratio of stock-market turnover to nominal GDP

The real per capita GDP (Y) and real GDP (G) are expressed in logarithm form (ln) so

that first differences can be interpreted as “continuously-compounded rates of

change” (Groenewold, 2003, p.460).

The trends in real per capita GDP (Y), ratio of banking loans to nominal GDP (L),

real GDP (G) and the ratio of stock-market turnover to nominal GDP (T) are

illustrated below.

Real Per Capita GDP (1978Q1-2006Q4)

0

2000

4000

6000

8000

10000

12000

14000

1978

Q1

1979

Q2

1980

Q3

1981

Q4

1983

Q1

1984

Q2

1985

Q3

1986

Q4

1988

Q1

1989

Q2

1990

Q3

1991

Q4

1993

Q1

1994

Q2

1995

Q3

1996

Q4

1998

Q1

1999

Q2

2000

Q3

2001

Q4

2003

Q1

2004

Q2

2005

Q3

2006

Q4

Asian Financial

Crisis, Jul 07

132

Banking Loans Over Nominal GDP (1978Q1-2006Q4)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

1978

Q1

1979

Q2

1980

Q3

1981

Q4

1983

Q1

1984

Q2

1985

Q3

1986

Q4

1988

Q1

1989

Q2

1990

Q3

1991

Q4

1993

Q1

1994

Q2

1995

Q3

1996

Q4

1998

Q1

1999

Q2

2000

Q3

2001

Q4

2003

Q1

2004

Q2

2005

Q3

2006

Q4

Asian Financial

Crisis, Jul 07

Real GDP (1978Q1-2006Q4)

0

10000

20000

30000

40000

50000

60000

1978

Q1

1979

Q2

1980

Q3

1981

Q4

1983

Q1

1984

Q2

1985

Q3

1986

Q4

1988

Q1

1989

Q2

1990

Q3

1991

Q4

1993

Q1

1994

Q2

1995

Q3

1996

Q4

1998

Q1

1999

Q2

2000

Q3

2001

Q4

2003

Q1

2004

Q2

2005

Q3

2006

Q4

Asian Financial

Crisis, Jul 07

Stockmarket Turnover Over Nominal GDP (1978Q1-2006Q4)

0

0.5

1

1.5

2

2.5

3

1978

Q1

1979

Q2

1980

Q3

1981

Q4

1983

Q1

1984

Q2

1985

Q3

1986

Q4

1988

Q1

1989

Q2

1990

Q3

1991

Q4

1993

Q1

1994

Q2

1995

Q3

1996

Q4

1998

Q1

1999

Q2

2000

Q3

2001

Q4

2003

Q1

2004

Q2

2005

Q3

2006

Q4

Asian Financial

Crisis, Jul 07

Over the period 1978-2006, the time series for real per capita GDP (Y) and real GDP

(G) tended to exhibit long-term positive trends. Importantly, both time series appear

to be more volatile in the post-1997 Asian financial crisis period compared to that in

the pre-crisis period. The share of banking loans to nominal GDP (L) rose steadily

133

from 1978-1985 with the emergence of Singapore as a regional offshore financial

centre (as explained in Chapter 3). While the recessions in 1985 and 2001 tended to

cause the share of banking loans to nominal GDP to fall, this ratio remained relatively

higher over the period 1999-2006 compared to the period 1978-1996. While the time

trend of the ratio of stockmarket turnover to nominal GDP (T) is less pronounced than

the other variables, a regression of T against a time trend suggests that the coefficient

is significant. Thus, the ratio of stockmarket turnover to nominal GDP (T) also

broadly follows a time trend, with spikes in stockmarket activities corresponding to

the bull phases such as those in 1993, 1999 and 2004.

5.2.2 The Framework of Analysis

As a bivariate vector autoregression (VAR) model is employed for the study (see

Chapter 4), the framework of analysis involves investigating the characteristics of the

two variables within the model and the relationships (if any) between the variables.

The two main variables are Y (real per capita GDP) and L (ratio of banking loans to

nominal GDP), which are initially used in the bivariate VAR model as proxies for

economic growth and financial development respectively. Moreover, as discussed in

Chapter 2, the literature suggests a difference between stock market and bank

financing in terms of their impact and linkages with economic growth. Thus, while

the variable L is intended to capture banking sector development, another variable T

(ratio of stock market turnover to nominal GDP) is separately employed to capture

stock market activities in the financial sector. Additionally, to test for the robustness

of results, the variable G (real GDP) is used as an alternative indicator for economic

growth. The underlying rationale for choosing each of the proxy variables has been

explained in Chapter 4 (Methodology). Thus, the results of data analysis are

134

thoroughly examined by investigating the four possible combinations of the economic

activity/financial development variables in the bivariate VAR model, namely (i) Y

and L (ii) G and T (iii) Y and T and (iv) G and L.

For the purposes of estimation, a different VAR model is constructed for each of the

two periods, i.e. a VAR model is constructed for the first period (1st quarter 1978 to

4th

quarter 1996) and another VAR model established for the second period (1st

quarter 1998 to 4th

quarter 2006). The reason for separating the two periods is the

watershed 1997 Asian financial crisis which is explained in Chapter 3. Comparative

analysis of the VAR models in the two periods (namely 1978Q1-1996Q4 and

1998Q1-2006Q4) encapsulates the crux of the research focus, which is to examine the

causal links between financial development and economic growth before and after the

1997 Asian financial crisis.

5.3 Unit Root Test and Order of Integration

5.3.1 Unit root test

As explained in Chapter 4 (section 4.7.1), the unit root test is to determine whether the

time series associated with each of the four variables (namely Y, L, G and T) is

stationary. The null hypothesis in the unit root test for each time series is that unit

root exists, which implies that the time series is non-stationary.

The detailed results of unit root testing using the Augmented Dickey Fuller (ADF)

test for the four variables are given in Appendices 4A to 4C. For each variable, the

unit root test is undertaken with and without trend and with the lag length ranging

from 1 to 8. Additionally, the test is performed on each time series for the full sample

period 1978Q1-2006Q4, as well as for the two sub-sample periods of 1978Q1-

135

1996Q4 and 1998Q1-2006Q4. Table 5.2 shows the unit root testing results for the

four variables (in levels) with/without trend and a lag length of one.

Table 5.2: Unit Root Testing for Variables in Levels

1978Q1-2006Q4 1978Q1-1996Q4 1998Q1-2006Q4

ADF Test Statistic ADF Test Statistic ADF Test Statistic

Variable

(Level)

Without

Trend

With

Trend

Variable

(Level)

Without

Trend

With

Trend

Variable

(Level)

Without

Trend

With

Trend

Y -1.397 -3.245 Y 0.826 -3.291 Y -0.415 -2.164

L -2.420 -1.894 L -2.108 -1.890 L -1.434 -2.342

G -2.255 -2.405 G 0.292 -2.509 G 0.220 -1.604

T -4.319 * -5.584 * T -3.285 * -3.897 * T -3.167 * -4.040 *

The critical 5 per cent value for ADF test (no trend) = – 2.889 and ADF test (with trend) = – 3.452

* represents significance at the 5 per cent level

Table 5.2 suggests that, except for the variable T, the respective Augmented Dickey

Fuller (ADF) test statistic for the time series of each variable (Y, L and G) in the three

periods (1978Q1-2006Q4, 1978Q1-1996Q4, 1998Q1-2006Q4) is larger than its

corresponding critical value at the 5 percent level of significance. Hence, for the

variables Y, L and G, the results indicate that we cannot reject the null hypothesis that

unit root exists in the time series, that is we cannot reject that the time series for Y, L

and G are non-stationary. Appendices 4A to 4C suggest that except for a few lag

periods (lag 2, 5, 6 and 7) for the variable Y in the sub-period 1978Q1-1996Q4, the

ADF results obtained regarding the non-stationarity of the variables Y, L and G are

robust with respect to the lag length ranging from one to eight and the existence/non-

existence of a trend in the testing equation for all three periods (1978Q1-2006Q4,

1978Q1-1996Q4, 1998Q1-2006Q4). Thus, we conclude that the time series for

variables Y, L and G are non-stationary in their levels. Importantly, as the time series

for the variables Y, L and G are not stationary, it follows that Ordinary Least Squares

(OLS) estimation cannot be performed using these variables as the least squares

estimators will be biased and inconsistent.

136

In the case of the variable T, the ADF test is also robust with respect to the

existence/non-existence of a trend in the testing equation but only with a small lag

length of one and two. It is therefore critical to test for the optimal lag length of the

variable T. Using three different criteria for determining the optimal lag length,

namely, the Akaike Information Criterion (AIC), the Schwarz Bayes Criterion (SBC)

and the Hannan-Quinn Criterion (HQC), the results indicate that the optimal lag

length of T (for the three periods, 1978-2006, 1978-1996 and 1998-2006) with or

without a trend is one. However, over the period 1978-2006, the Akaike Information

Criterion (AIC) suggests that the optimal lag length for variable T (no trend) is two.

Nonetheless, the ADF test shows that T with no trend and a lag length of two is also

stationary in its levels (see Appendix 4A). Thus, with or without a trend and with an

optimal lag length of one or two, the ADF statistic suggests that we reject the null

hypothesis of non-stationarity of T which implies that we conclude the variable T is

stationary (in levels).

5.3.2 Determining the order of integration of the variables

For the variables Y, L and G, further investigation in unit root testing is undertaken on

the first difference of each time series to determine whether stationarity can be

achieved by differencing. This procedure for determining the order of integration is

explained in the previous chapter (section 4.7.2). The detailed results of unit root

testing on the first difference in the time series of each variable (Y, L, and G) with

and without trend are given in Appendices 5A to 5C. Table 5.3 shows the unit root

testing results for Y, L and G (in first differences) with/without trend and a lag length

of one:

137

Table 5.3: Unit Root Testing for Variables in First Differences

1978Q1-2006Q4 1978Q1-1996Q4 1998Q1-2006Q4

ADF Test Statistic ADF Test Statistic ADF Test Statistic

Variable

(1st Diff)

Without

Trend

With

Trend

Variable

(1st Diff)

Without

Trend

With

Trend

Variable

(1st Diff)

Without

Trend

With

Trend

Y -10.938* -10.961* Y -9.769* -9.711* Y -4.895* -4.822*

L -8.384* -8.497* L -6.105* -6.126* L -4.828* -4.820*

G -10.609* -10.633* G -9.507* -9.441* G -4.726* -4.695*

The critical 5 per cent value for ADF test (no trend) = – 2.887 and ADF test (with trend) = – 3.450


Table 5.3 indicates that for the variables Y, L and G, the respective Augmented

Dickey Fuller (ADF) test statistic for the first difference in each time series (with and

without trend) in the three periods (1978Q1-2006Q4, 1978Q1-1996Q4, 1998Q1-

2006Q4) is significant at the 5 percent level. These results are largely robust for

different lag lengths ranging from one to eight. Importantly, the results are robust for

various optimal lag lengths selected using the Akaike Information Criterion (AIC), the

Schwarz Bayes Criterion (SBC) and the Hannan-Quinn Criterion (HQC) for each

variable in first difference (for the three periods, 1978-2006, 1978-1996 and 1998-

2006) with or without a trend (see Appendices 5A to 5C). Consequently, the results

imply that the null hypothesis of non-stationarity of the time series (in first differences

of each of the variable Y, L and G) is rejected. Hence, the time series for the

variables Y, L and G are stationary in their first differences and can thus be

considered to be integrated of order 1 or I(1). As the time series for variable T is

found to be stationary in its level, it is said to be integrated of order 0 or I(0).

5.4 Cointegration Test

Having tested that the time series for variables Y, L and G are non-stationary in their

levels but stationary in their first differences (i.e. integrated of order 1), the

138

cointegration test is applied to assess whether a linear combination of Y and L or a

linear combination of G and L can be stationary. As the time series for G is I(1)

while the time series for T is I(0), it follows that the variables G and T cannot be

cointegrated. Similarly, since the time series for Y is found to be I(1) and the time

series for T is found to be I(0), which imply that Y and T are not integrated of the

same order, it follows that Y and T cannot be cointegrated.

As explained in Chapter 4 (section 4.7.3), two different cointegration tests, namely the

Engle-Granger test and the Johansen test, are separately undertaken to determine

whether the variables Y and L (in a bivariate VAR model) can be cointegrated. These

two cointegration tests are also separately applied on the variables G and L (in a

bivariate VAR model) to assess whether the two variables can be cointegrated.

5.4.1 Engle-Granger Test for Cointegration The Engle-Granger test for cointegration involves testing for the stationarity of the

saved residuals ( u t) from the OLS regression of variable Y (representing real per

capita GDP) on the variable L (representing the ratio of banking loans to nominal

GDP). The saved residuals ( u t) are tested for stationarity using the Augmented

Dickey Fuller (ADF) test. The detailed results of the ADF test on the saved residuals

( u t) in the three different periods (1978Q1-2006Q4, 1978Q1-1996Q4, 1998Q1-2006Q4)

with/without trend and different lag lengths are shown in Appendices 6A to 6C.

Table 5.4 shows the unit root testing results for u t with/without intercept and a lag

length of one:

139

Table 5.4: Engle-Granger Test - Unit root testing of the saved residuals ( u t)

Variables

ADF Test Statistic of u t

1978Q1-2006Q4 1978Q1-1996Q4 1998Q1-2006Q4

Without

Intercept

With

Intercept

Without

Intercept

With

Intercept

Without

Intercept

With

Intercept

Y and L -0.819 -0.802 -0.481 -0.459 -2.584* -2.556

G and L -0.676 -0.659 -0.185 -0.154 -2.411* -2.377

The critical 5 per cent value for ADF test (without intercept) = – 1.945 and for ADF test (with

intercept) = – 3.398


For variables Y and L, the ADF test statistic of the saved residual ( u t) in the full

sample period 1978Q1-2006Q4 and the sub-sample period 1978Q1-1996Q4 (with or

without intercept) is larger than the critical value at the 5% level, thus implying that

we cannot reject the null hypothesis that unit root exists in the residual series ( u t) in

both these periods. Similarly, for variables G and L, the ADF test statistic of the

saved residual ( u t) in both these periods, namely 1978Q1-2006Q4 and 1978Q1-

1996Q4 (with or without intercept) is also larger than the critical value at the 5% level,

thus implying the non-stationarity of the residual series ( u t). Moreover, as shown in

Appendices 3A and 3B, the ADF results which imply the non-stationarity of the

residual series ( u t) for the Y-L variables G-L variables in the two periods are not

sensitive to the lag length. However, for the sub-sample period 1998Q1-2006Q4, the

ADF statistic seems to suggest that the residual series ( u t) without intercept for the

two pairs of variables Y-L and G-L are stationary, though they remain non-stationary

when an intercept is added in testing the residual series. Nonetheless, close scrutiny

of the detailed results from the residual series for the Y-L and G-L variables at

different lags and with/without intercept (as shown in Appendix 6C) indicates that the

ADF statistic is mostly larger than the critical value at the 5% level, thus providing

support for the broad conclusion that the residual series ( u t) of the Y-L and G-L

variables are non-stationary. Hence, on the whole, we could conclude that the

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residual series ( u t) of the two pairs of variables Y and L as well as G and L are non-

stationary for all three periods, namely 1978Q1-2006Q4, 1978Q1-1996Q4 and

1998Q1-2006Q4. Following the methodology outlined in Chapter 4, this implies that

the variables Y and L as well as the variables G and L are not cointegrated in each of

the three periods (1978Q1-2006Q4, 1978Q1-1996Q4 and 1998Q1-2006Q4). We

therefore conclude that the Engle-Granger test suggests that the variables Y and L as

well as the variables G and L are not cointegrated.

5.4.2 Johansen Test for Cointegration

Following the discussion in section 4.7.2 (Chapter 4), the Johansen test for

cointegration involves assessing two test statistics as proposed by Johansen (1988)

and Johansen and Juselius (1990): (a) trace (λtrace) and (b) maximum eigenvalue (λmax).

These two test statistics are compared with the critical values of λtrace and λmax to

determine whether the variables are cointegrated. In computing the two test statistics,

it is important to assess whether an intercept and/or trend should enter into the short-

run model (VAR model) or the long-run model (co-integrating equation, CE), or both

models in the bivariate system. In general, there are five distinct models that can be

considered:

(i) Model 1: No intercept or trend in the CE or VAR. This means that there are

no deterministic components in the data or in the cointegrating relations. This

model is unlikely to occur in practice as the intercept is generally needed to

account for adjustments in the units of measurements of the variables.

(ii) Model 2: Intercept (no trend) in the CE and no intercept or trend in the VAR.

This means that there are no linear trends in the data and the intercept is

restricted to the long-run model (i.e. the cointegrating equation).

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(iii) Model 3: Intercept in CE and VAR, but no trends in CE and VAR. In this

case, there are no linear trends in the levels of the data but both the short-run

(VAR) and long-run (CE) models could fluctuate around the intercept.

(iv) Model 4: Intercept in CE and VAR, linear trend in CE and no trend in VAR.

In this case, the trend is included in the CE to take account of exogenous

growth in the long-run relationship.

(v) Model 5: Intercept and quadratic trend in the CE with an intercept and linear

trend in the VAR. This model, however, is difficult to interpret from the

economics viewpoint as it tends to suggest a continuously increasing or

decreasing rate of change which seems implausible.

Thus, though there are five different models in testing for cointegration, Model 1 and

Model 5 are ruled out as they are unlikely to happen (Asteriou and Hall, 2007).

Consequently, the Johnasen cointegration test is undertaken for Models 2, 3 and 4.

The detailed results of the cointegration test on Models 2, 3 and 4 using the Johansen

method for the variables Y and L and the variables G and L for different lag lengths

are shown in Appendices 7A to 7C. Table 5.5 and Table 5.6 show the Johansen trace

statistic and the Maximum Eigenvalue for the respective combinations of the two

variables (Y and L as well as G and L) for Models 2, 3 and 4 with a lag length of one.

Table 5.5: Johansen Trace Test

Johansen Trace Statistic

Variables

1978Q1-2006Q4 1978Q1-1996Q4 1998Q1-2006Q4

Model

2

Model

3

Model

4

Model

2

Model

3

Model

4

Model

2

Model

3

Model

4

Y and L 38.10* 13.66 24.02 30.32* 9.74 32.92* 19.71 13.36 31.50*

G and L 53.04* 17.04 25.60 40.47* 8.748 20.94 21.94* 11.80 26.49*

For Model 2: Critical 5% value (trace test) = 20.262; Critical 5% value (Maximum Eigenvalue) = 15.892

For Model 3: Critical 5% value (trace test) = 15.495; Critical 5% value (Maximum Eigenvalue)= 14.265



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Table 5.6: Johansen Maximum Eigenvalue Test

Johnsen Maximum Eigenvalue Value

Variables

1978Q1-2006Q4 1978Q1-1996Q4 1998Q1-2006Q4

Model

2

Model

3

Model

4

Model

2

Model

3

Model

4

Model

2

Model

3

Model

4

Y and L 32.07* 12.92 12.91 26.86* 9.58 23.35* 11.98 11.97 23.21*

G and L 46.92* 16.59 17.40 36.66* 8.73 14.25 12.43 11.72 21.38*





The null hypothesis in the Johansen trace and eigenvalue tests is that there is no

cointegration between the variables. For the variables Y and L over the period

1978Q1-2006Q4, the results of the Johansen cointegration test indicate that the trace

statistic and the maximum eigenvalue are both smaller than their corresponding

critical values in Models 3 and 4. This implies that the null hypothesis that there is no

cointegration between Y and L cannot be rejected at the 5 percent level of

significance. We therefore conclude that the variables Y and L are not cointegrated in

Models 3 and 4. This is consistent with the earlier findings of the Engle-Granger

cointegration test that these two variables (Y and L) are not cointegrated over this

period (1978Q1-2006Q4). Additionally, over the period 1998Q1-2006Q4, the

results of the Johansen cointegration test between Y and L also indicate that the trace

statistic and the maximum eigenvalue are both smaller than their corresponding

critical values in Models 2 and 3, implying no cointegration between the two

variables.

For the variables G and L over the period 1978Q1-1996Q4, the results of the

Johansen cointegration test (using the trace statistic and the maximum eigenvalue)

also indicate that there is no cointegration between G and L in Models 3 and 4. This

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is also consistent with the earlier findings of the Engle-Granger cointegration test that

G and L are not cointegrated over the period 1978Q1-2006Q4.

However, the results of the Johansen cointegration test (as shown in Tables 5.5 and

5.6) for the variables Y and L and the variables G and L are sensitive to the

underlying model in the testing equation. Moreover, the results of the cointegration

test are also sensitive to the lag length (see Appendices 7A to 7C). Thus, on the

whole, the findings of the Johansen cointegration test are somewhat ambiguous as the

trace test and the maximum eigenvalue test provide inconclusive results.

Nonetheless, taking into account the earlier results of the Engle-Granger cointegration

test, which suggest that the variables Y and L and the variables G and L are not

cointegrated over each of the three periods (1978Q1-2006Q4, 1978Q1-1996Q4 and

1998Q1-2006Q4), the balance of evidence seems to point to no cointegrating

relationship between the variables Y and L and the variables G and L over each of the

three periods.

We can, therefore, summarize that the variables Y (real per capita GDP) and L (ratio

of banking loans to nominal GDP) and the variables G (real GDP growth) and L (ratio

of banking loans to nominal GDP) are not cointegrated over each of the three periods,

1978Q1-2006Q4, 1978Q1-1996Q4 and 1998Q1-2006Q4. Similarly, the variables G

(real GDP growth) and T (ratio of stock-market turnover to nominal GDP) and the

variables Y (real per capita GDP) and T (ratio of stock-market turnover to nominal

GDP) are also not cointegrated over all the three periods. Nonetheless, in view of the

ambiguous results in the Johansen tests with regard to the existence of cointegration

between the variables (as explained above), Chapter 7 will also explore the

implications in the results if the variables were assumed to be cointegrated.

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Importantly, following the methodology laid out in Chapter 4, with the variables Y, L,

G tested to be I(1) while variable T is I(0) along with the finding that the four pairs of

variables (Y-L, G-T, Y-T and G-T) are not cointegrated, we can work with a

straightforward VAR model in the first differences of Y, L and G and in the levels of

T. The results of the causality test employing the VAR model will be presented in the

next chapter.

5.5 Conclusion

In this chapter we performed a battery of tests on the data underlying the variables

employed in the study to determine the stationarity and cointegration of the variables.

The data testing was performed over the three periods: 1978(1)-2006(4), 1978(1)-

1996(4) and 1998(1)-2006(4). Four proxy variables were chosen for the analysis –

real per capita GDP (Y), real GDP (G), share of bank loans to nominal GDP (L) and

share of turnover to GDP (T). For each variable, the Augmented Dickey Fuller

(ADF) test was employed to test for the stationarity and order of integration of the

time series. The ADF test indicated that Y, G and L were integrated of order one (i.e.

I(1)) while T was stationary in its levels (i.e. I(0)). Using the four variables, four

bivariate VAR models were estimated to test for the finance-growth relationship: (i) Y

and L (ii) G and T (iii) Y and T (iv) G and L. As the variables G and T and variables

Y and T could not be cointegrated (since Y and G were I(1) while T was I(0)),

cointegration tests were applied on the variables Y and L and the variables G and L.

The results of the cointegration tests suggested that the latter two pairs of variables (Y

and L as well as G and L) were also not cointegrated. These results determine the

model of causality test which constitutes the next stage of analysis in the study (in

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Chapter 6). Nonetheless, in Chapter 7, robustness tests will be undertaken to explore

the use of a Vector Error Correction Model (VECM) based on the assumption of

cointergation in the variables Y and L and the variables G and L.

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Chapter 6

DATA ANALYSIS II

6.1 Introduction

In Chapter 5 a battery of tests was performed on the time series data to determine the

stationarity, order of integration and cointegration/non-cointegration of the variables

employed in the study. Following the data testing in the previous chapter, this chapter

will present the results of the causality tests undertaken on the model variables in

relation to the finance-growth nexus. In Section 6.2, Granger causality test is applied

to the model variables to assess the causal relationship between financial development

and economic growth. Section 6.3 analyses the generalized impulse response

functions to assess the dynamics of the interaction between the financial sector and

the real economy. To further examine the dynamics of the finance-growth

relationship, cumulative impulse response functions will be generated and presented

in Section 6.4. Section 6.5 summarizes and concludes the results of the causality

tests in incorporating the outcomes of the Granger test results and impulse response

analyses.

6.2 Granger Causality Test

As discussed in Chapter 4, the bivariate VAR model will be employed to examine the

relationship between financial development and economic growth. The Granger

causality test can be performed to test for the causality between pairs of variables in

the VAR model. In the VAR model involving Y (real per capita GDP) and L (ratio of

banking loans to nominal GDP), the variable Y is said to Granger cause the variable L

when, ceteris paribus, L can be predicted with greater accuracy by past values of Y

rather than not using past values. Similarly, in the VAR model involving G (real

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GDP) and T (ratio of stock market to nominal GDP), the variable G is said to Granger

cause the variable T when (ceteris paribus) T can be more accurately predicted by

using past values of Y than by not using past values of Y. Using the same principle,

Granger causality test can also be applied to the variables Y and T as well as the

variables G and L to determine the causality between the variables in the separate

VAR models.

6.2.1 Lag length selection for Granger causality test in VAR model

In performing the Granger causality test, it is necessary to determine the appropriate

lag length for the vector autoregression (VAR) model within which the test is to be

conducted. The Granger causality test, which is undertaken under a VAR framework,

is sensitive to the lag length because the VAR model postulates each variable to be a

function of its own lagged values and the lagged values of other variables in the

endogenous model.

There are various measures to determine the optimal lag length in the VAR model.

These measures, commonly referred to as information criteria, include the Akaike

information criterion (AIC), the Schwarz (or Bayesian) information criterion (SBC)

and the Hannan-Quinn (HQ) criterion. Information criteria measures attempt to

provide the trade-off between model fit (of the VAR) and parsimony of the lagged

endogenous variables in the VAR. They are computed based on the likelihood for a

model, penalized by the number of parameters. For two models that share the same

likelihood value (which implies that both models fit the data equally well), the model

with less lagged endogenous variables (i.e. more parsimonious model) will incur a

smaller penalty and is thus superior based on the information criterion. The AIC,

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SBC and HQ measures differ in the penalty that is applied to the additional

parameters as reflected in the computations below:

AIC = T log│Ω│ + 2 (m2p + m)

SBC = T log│Ω│ + log (T) (m2p + m)

HQ = T log│Ω│ + 2 (log (T)) (m2p + m)

where T is the sample size in the model, log│Ω│ is the log determinant of the error

covariance for the model, m is the number of endogenous variables and p is the

number of lags (for the variables). The best model is one with p yielding the smallest

value in the relevant information criterion.

Using E-views, the three different information criteria provide the optimal lag length

for the VAR model shown in Appendix 8. However, different criteria for deciding lag

length such as the Akaike, Schwarz and Hannan-Quinn information criteria yield

inconsistent results with regard to the optimal lag length. The optimal lag length

ranges between one and seven depending on the criteria used. Following the lead of

Groenewold (2003), the optimal lag length is selected after further inspection of the

autocorrelations of the residuals to ensure the absence of first to fourth order

autocorrelation at the 5 percent level for all equations. Using this procedure, the

optimal lag length for each VAR model in different periods can be shown as follows:

Table 6.1: Optimal lag length selected for the VAR model in different periods

Model Variables (in VAR) 1978(1) – 2006(4) 1978(1) – 1996(4) 1998(1) – 2006(4)

Y and L 5 7 7

G and T 5 4 1

Y and T 5 7 1

G and L 5 4 7

6.2.2 Results of the Granger causality test

As the variables Y, L, G are all found to be I(1) while the variable T is I(0) and the

four pairs of variables (Y-L, G-T, Y-T, and G-L) are not cointegrated (following the

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data testing in the preceding chapter), the Granger causality test is performed on the

first differences of Y, L, G and in levels of T. The results of the Granger causality

test, obtained from E-views, are presented in Table 6.2 (below). The respective VAR

models in each period (1978(1) – 2006(4), 1978(1) – 1996(4) and 1998(1) – 2006(4))

employ the appropriate lag length as reflected in Table 6.1. The tabulated F statistics

and p values in Table 6.2 enable us to make the inference to either accept or reject the

null hypothesis that one variable does not Granger cause the other variable in the

model.

Table 6.2: Results of Granger causality test

Model

Period

Lag

Null hypothesis (H0)

F statistic

p

value

Causal

Inference

Y & L

1978(1) – 2006(4)

5

dL does not Granger cause dY 3.200 0.010 Reject H0

dY does not Granger cause dL 2.727 0.024 Reject H0

1978(1) – 1996(4)

7

dL does not Granger cause dY 1.264 0.286 Accept H0

dY does not Granger cause dL 1.858 0.095 Accept H0

1998(1) – 2006(4)

7

dL does not Granger cause dY 1.936 0.144 Accept H0

dY does not Granger cause dL 3.323 0.030 Reject H0

G & T

1978(1) – 2006(4)

5

T does not Granger cause dG 2.054 0.078 Accept H0

dG does not Granger cause T 2.253 0.055 Accept H0

1978(1) – 1996(4)

4

T does not Granger cause dG 1.993 0.107 Accept H0


1998(1) – 2006(4)

1

T does not Granger cause dG 4.465 0.043 Reject H0


Y & T

1978(1) – 2006(4)

5

T does not Granger cause dY 1.276 0.281 Accept H0

dY does not Granger cause T 0.598 0.701 Accept H0

1978(1) – 1996(4)

7



1998(1) – 2006(4)

1



G & L

1978(1) – 2006(4)

5

dL does not Granger cause dG 3.127 0.012 Reject H0

dG does not Granger cause dL 3.220 0.010 Reject H0

1978(1) – 1996(4)

4

dL does not Granger cause dG 1.720 0.155 Accept H0

dG does not Granger cause dL 0.450 0.772 Accept H0

1998(1) – 2006(4)

7

dL does not Granger cause dG 1.700 0.195 Accept H0

dG does not Granger cause dL 3.601 0.022 Reject H0

The results of the causal relationship between financial development and economic

growth, as indicated by the Granger causality test, are summarized in Table 6.3:

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Table 6.3: Summary of the causal relationship between financial and economic development

Model

Period

Causal Relationship

Y & L

1978(1) – 2006(4) Bilateral causal relationship between dL and dY

1978(1) – 1996(4) No causal relationship between dL and dY

1998(1) – 2006(4) Unidirectional causality from dY to dL

G & T

1978(1) – 2006(4) No causal relationship between dG and T


1998(1) – 2006(4) Unidirectional causality from T to dG

Y & T

1978(1) – 2006(4) No causal relationship between dY and T

1978(1) – 1996(4) No causal relationship between dY and T


G & L

1978(1) – 2006(4) Bilateral causal relationship between dL and dG

1978(1) – 1996(4) No causal relationship between dL and dY

1998(1) – 2006(4) Unidirectional causality from dG to dL

For the stock market, the Granger causality results suggest no causal relationship

between stock market activities and economic growth over the two periods: 1978(1)-

2006(4) and 1978(1)-1996(4). However, over the period 1998(1)-2006(4), there is

evidence that stock market activities Granger cause economic development. For the

banking sector, the Granger causality results suggest a bilateral causal relationship

between bank sector development and economic growth over the period 1978(1)-

2006(4) and unidirectional causality from economic growth to banking development

over the period 1998(1)-2006(4). However, over the period 1978(1)-1996(4), the

Granger causality results suggest no causal relationship between banking and

economic development. The Granger causality test results, therefore, are not clear-cut

but depend on the variables used and sub-period over which they are estimated.

Moreover, while the Granger causality tests provide some evidence on the

relationship between financial development and economic growth (particularly with

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respect to banks), they do not show the relative magnitude of the impact of one sector

on the other nor the dynamics of the causal relationship between the financial sector

and the real economy over time. Arguably, these two weaknesses of the Granger

causality results can be addressed using impulse response analyses which will be

covered in subsequent sections of this chapter. Thus, we move on to estimate the

VAR model which will be employed to generate the impulse response functions for

subsequent analyses.

6.3 Model Specification and Estimation of Results

6.3.1 Model specification and estimation of the full-sample, 1978(1)-2006(4)

Like the Granger causality tests, the optimal lag length is critical for estimating the

VAR model. This is because the variables in the VAR are endogenous, which means

that each variable is predicted using its own lagged values and lagged values of other

variables. The VAR models are estimated using the optimal lag lengths determined

on the basis of the various criteria as reported in Table 6.1. For the full-sample period

1978(1) – 2006(4), an optimal lag length of five is employed for estimating each of

the four VAR models: (i) Y and L (ii) G and T (iii) Y and T and (iv) G and L. These

four VAR models are reported in Tables 6.4A, 6.4B, 6.4C and 6.4D.

Table 6.4A: Estimated VAR model with variables Y and L, full sample, 1978(1)-2006(4) Regressor/Test d ln (Y) equation d L equation

d ln (Y)-1 0.1006 (0.95) -1.8788 (-2.06) *

d ln (Y)-2 -0.1863 (-2.14) * -0.1890 (-0.39)

d ln (Y)-3 -0.2246 (-2.58) * 0.5640 (1.16)

d ln (Y)-4 0.5160 (6.11) * 0.4001 (0.09)

d ln (Y)-5 -0.3105 (-3.06) * 1.9961 (3.52) *

d (L)-1 -0.2860 (-1.51) 0.1861 (1.75)

d (L)-2 0.0025 (0.13) -0.1744 (-1.64)

d (L)-3 -0.0228 (-1.19) 0.1734 (1.62)

d (L)-4 0.0658 (3.52) * -0.1092 (-1.05)

d (L)-5 -0.0328 (-1.68) 0.2453 (2.25) *

Constant 0.0123 (3.46) * -0.0097 (-0.49)

Adjusted R2 0.4264 0.1057

Note: The variables are: d L = first difference of the ratio of banking loans to nominal GDP, d ln(Y) =

first difference of the log of real per capita GDP. Numbers in parentheses beside estimated coefficients are absolute values of the t-ratio.


152

Table 6.4B: Estimated VAR model with variables G and T, full sample, 1978(1)-2006(4) Regressor/Test d ln (G) equation T equation

d ln (G)-1 0.1886 (1.93) -0.4740 (-0.33)

d ln (G)-2 -0.1786 (-2.24) * -0.7143 (-0.61)

d ln (G)-3 -0.1450 (-1.82) 0.3693 (0.32)

d ln (G)-4 0.5005 (6.14) * -2.0601 (-1.80)

d ln (G)-5 -0.2733 (-2-.88) * 0.2768 (0.20)

T-1 0.0056 (0.79) 0.6093 (5.90) *

T-2 -0.0012 (-0.16) 0.1885 (1.61)

T-3 0.0108 (1.33) -0.0010 (-0.01)

T-4 -0.0187 (-2.30) * -0.1112 (-0.93)

T-5 0.0054 (0.75) 0.1149 (1.09)

Constant 0.0140 (2.91) * 0.1764 (2.51) *

Adjusted R2 0.3681 0.4853

Note: The variables are: d ln(G) = first difference of log of real GDP, T = ratio of stock-market turnover to nominal GDP. Numbers in parentheses beside estimated coefficients are absolute values of the t-ratio.


Table 6.4C: Estimated VAR model with variables Y and T, full sample, 1978(1)-2006(4) Regressor/Test d ln (Y) equation T equation

d ln (Y)-1 0.1425 (1.48) -0.5167 (-0.35)

d ln (Y)-2 -0.2245 (-2.77) * -0.6055 (-0.49)

d ln (Y)-3 -0.1886 (-2.33) * 0.4593 (0.38)

d ln (Y)-4 0.4527 (5.72) * -1.9145 (-1.60)

d ln (Y)-5 -0.2882 (-3.10) * 0.3085 (0.22)

T-1 0.0048 (0.70) 0.6101 (5.94) *

T-2 -0.0016 (-0.21) 0.1883 (1.60)

T-3 0.0108 (1.37) -0.0043 (-0.04)

T-4 -0.0176 (-2.22) * -0.1149 (-0.96)

T-5 0.0039 (0.55) 0.1133 (1.07)

Constant 0.1186 (2.74) * 0.1624 (2.49) *

Adjusted R2 0.3740 0.4828

Note: The variables are: d ln(Y) = first difference of the log of real per capita GDP, T = ratio of stock-market turnover to nominal GDP. Numbers in parentheses beside estimated coefficients are absolute values of the t-ratio.


Table 6.4D: Estimated VAR model with variables G and L, full sample, 1978(1)-2006(4) Regressor/Test d ln (G) equation d L equation

d ln (G)-1 0.1808 (1.71) -1.1892 (-2.11) *

d ln (G)-2 -0.1205 (-1.47) -0.0805 (-0.18)

d ln (G)-3 -0.1693 (-2.07) * 0.6861 (1.56)

d ln (G)-4 0.5743 (7.15) * 0.1457 (0.34)

d ln (G)-5 -0.3180 (-3.13) * 2.0883 (3.83) *

d (L)-1 -0.0230 (-1.18) 0.1708 (1.64)

d (L)-2 0.0085 (0.44) -0.1773 (-1.71)

d (L)-3 -0.0193 (-1.00) 0.1742 (1.68)

d (L)-4 0.0681 (3.56) * -0.1035 (-1.01)

d (L)-5 -0.0407 (-2.05) * 0.2461 (2.32) *

Constant 0.0144 (3.39) * -0.0234 (-1.03)

Adjusted R2 0.4168 0.1248

Note: The variables are: d ln(G) = first difference of log of real GDP, d L = first difference of the ratio of

banking loans to nominal GDP. Numbers in parentheses beside estimated coefficients are absolute values of the t-ratio.


The equations in the four models have varying explanatory power, with adjusted R2

ranging from 12.5 percent to 48.5 percent. The coefficients of the equations show

153

mixed results in terms of their statistical significance, with a small number of

coefficients which are significant at the five percent level while a large number of

coefficients are not statistically significant.

The VAR models in Table 6.4A and Table 6.4D seem to provide some evidence for a

bilateral relationship between banking sector development and economic growth. In

the VAR model for variables Y and L (Table 6.4A), the dln(Y) equation indicates that

the lagged share of banking loans (d(L)-4 ) is significant. Moreover, in the dL

equation, the lagged real per capita GDP growth (d ln(Y)-1 and d ln(Y)-5 ) is also

significant in determining changes in the share of banking loans to GDP. This

suggests a bilateral positive relationship between banking sector development

(proxied by dL) and economic growth (proxied by dlnY). The robustness of this

result is affirmed in the subsequent VAR model involving variables G and L (Table

6.4D). Table 6.4D shows that in the VAR model on variables G and L, the lagged

share of banking loans (d(L)-4 and d(L)-5 ) is significant in determining economic

growth (proxied by dlnG). Furthermore, lagged real GDP growth (d ln(G)-1 and d

ln(G)-5 ) is also significant in determining changes in the share of banking loans to

GDP (proxied by dL) in the model. These preliminary findings support the view that

there is a positive causal relationship between banking sector development and

economic growth in Singapore. These findings also seem to be consistent with the

Granger causality results (in section 6.2.2) which suggest a bilateral causal

relationship between bank financing and economic activities.

While there appears to be a mutual causal relationship between the banking sector and

the real economy (as reflected in Tables 6.4A and 6.4D), the relationship between the

stock market and the economy seems to be somewhat different. In the VAR model on

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G and T (Table 6.4B) and the VAR model on Y and T (Table 6.4C), the lagged share

of turnover to GDP (T-4) has a small negative impact on economic development

(proxied by dlnG and dlnY respectively) which is significant (at the 5 percent level).

On the other hand, economic development does not seem to have any significant

impact on stock market activities, as none of the economic growth coefficients in the

dT equations are significant (see Tables 6.4B and 6.4C). These results tend to

suggest that stock market development in Singapore has a small negative impact on

economic growth, while economic development does not impact on stock market

activities at all. Thus, from the perspective of the finance-growth nexus in Singapore,

Tables 6.4B and 6.4C suggest a unidirectional relationship going from the stock

market to the real economy (with stock market activities having a negative impact on

economic development) while economic growth does not affect stock market

development. These results, however, do not seem to be in line with the findings

from the Granger causality tests (in section 6.2.2) which suggest that there is no

causal relationship between stock market activities and the real economy. More

importantly, we should also be cautious in interpreting the coefficients of the lagged

variables in each VAR model as it is possible that two coefficients which are

individually insignificant might be jointly significant.

6.3.2 Test for structural break

As discussed in Chapter 3, the financial and economic development of Singapore has

gone through significant structural changes after the 1997 Asian financial crisis. As

any structural break would tend to cause the estimated model to be unstable, it is

appropriate to test for structural break in the model at the end of 1996. The Chow test

of structural stability which is conducted on each of the four models produced the

following results as shown in Table 6.5:

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Table 6.5: Chow test for structural stability

Model Variables Test statistic Probability value

Y and L (First VAR model) 8.9215 ** 0.0003

G and T (Second VAR model) 87.6247 ** 0.0001

Y and T (Third VAR model) 74.1665 ** 0.0001

G and L (Fourth VAR model) 39.3800 ** 0.0001

The null hypothesis is that there is no structural break at 1997Q2

** represents significance at the 1 per cent level

The null hypothesis for the Chow test is that there is no structural break for the

selected break-point at the second quarter of 1997. In each model, the Chow test of

structural stability produces a test statistic which is significant at the one per cent level.

The results of the Chow test clearly indicate a structural shift in all the four models at

the second quarter of 1997.

To test for the robustness of the results, different break-points were experimented for

the watershed period of 1997-98, which corresponds to the onset of the Asian

financial crisis and the subsequent economic adjustment process in Singapore (as

explained in Chapter 3). The Chow test results were robust in indicating a significant

break over the period 1997(1) and 1997(4) for all the four models. These results are

consistent with the study by Tilak and Choy (2007) which found that there was a

structural shift in the major “macroeconomic series” such as real GDP and

manufacturing value-added during the Asian financial crisis in 1997. We therefore

proceed to estimate separate models for the two sub-periods: 1978(1) – 1996(4) and

1998(1) – 2006(4). The comparative analyses of the VAR model in the two different

sub-sample periods will be discussed in the next section.

6.3.3 Sub-sample analysis

As autcorrelation problem arises when the lag length of five (which was employed for

the full sample period 1978(1)-2006(4)) is applied to the four VAR models over the

two different sub-sample periods (i.e. 1978(1)-1996(4) and 1998(1)-2006(4)), the

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optimal lag length is re-selected for the VAR models in the sub-samples. However,

like the full sample estimates (explained in section 6.2.1), the Akaike, Schwarz and

Hannan-Quinn information criteria to determine the optimal lag length again yield

inconsistent results with the optimal lag ranging between one and seven. Table 6.6

(summarized from the detailed results in Appendix 5) shows the lag length selected

for each VAR model in the sub-sample after ensuring the absence of autocorrelation

in the equations. In the sub-sample for G and L over the period 1978(1) – 1996(4),

the lag length of 4 (selected by Schwarz information criterion) and the lag length of 7

(selected by the Akaike information criterion) both indicated no autocorrelation in the

residuals. Nonetheless, following Stock and Watson (2003), we choose the model

with the lowest number of lags as the preferred model (i.e. 4-lag model is selected).

Table 6.6: Selected lag length of VAR for sub-sample periods 1978(1) – 1996(4) and 1978(1) – 1996(4)

Variables in

VAR model

Selected lag length for VAR model

sub-sample period 1978(1) – 1996(4)


sub-sample period 1998(1) – 2006(4)

Y and L 7 7

G and T 4 1

Y and T 7 1

G and L 4 7

For the two different sub-sample periods, the estimated VAR models using the

appropriate lag lengths (as indicated in Table 6.6) are shown in Tables 6.7, 6.8, 6.9,

and 6.10. While the resultant coefficients associated with the lag variables in each of

the VAR model provide some clue on the impact of lagged changes in one variable on

the other variable, caution should be taken against making too much of the individual

coefficient significance as it is possible that two coefficients that are individually

insignificant could be jointly significant.

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Table 6.7: Estimated VAR model with variables Y and L over 1978(1)-1996(4) and 1998(1)-2006(4)

Sub-sample, 1978(1)-1996(4) Sub-sample, 1998(1)-2006(4) Regressor d ln (Y) equation d L equation Regressor d ln (Y) equation d L equation

d ln (Y)-1 -0.1274 ( -1.02) -0.9112 (-1.33) d ln (Y)-1 0.0324 (0.09) -3.3960 (-1.94)

d ln (Y)-2 -0.0725 (-0.55) -0.3578 (-0.50) d ln (Y)-2 -0.2162 (-0.58) -3.0664 (-1.66)

d ln (Y)-3 0.1387 (1.06) 0.3814 (0.54) d ln (Y)-3 -0.6000 (-1.44) 1.5376 (0.74)

d ln (Y)-4 0.4676 (4.94) * 1.0548 (2.03) * d ln (Y)-4 0.7256 (1.69) -4.5434 (-2.12) *

d ln (Y)-5 -0.2495 (-2.06) 2.1498 (3.25) * d ln (Y)-5 0.0311 (0.09) -0.8775 (-0.51)

d ln (Y)-6 -0.2979 (-2.22) 1.2007 (1.64) d ln (Y)-6 0.1565 (0.50) -2.5364 (-1.64)

d ln (Y)-7 -0.4235 (-3.15) * 0.7591 (1.03) d ln (Y)-7 0.5979 (1.88) -5.1535 (-3.25) *

d (L)-1 0.0002 (0.01) 0.1295 (0.89) d (L)-1 -0.0447 (-0.76) -0.4042 (-1.37)

d (L)-2 0.0038 (0.14) -0.1226 (-0.85) d (L)-2 0.0070 (0.11) -0.5891 (-1.86)

d (L)-3 -0.0097 (-0.38) 0.0626 (0.45) d (L)-3 -0.0388 (-0.57) 0.0019 (0.01)

d (L)-4 0.0370 (1.46) 0.0334 (0.24) d (L)-4 0.1170 (1.82) -0.6793 (-2.11) *

d (L)-5 -0.0447 (-1.72) 0.1227 (0.87) d (L)-5 0.0412 (0.83) -0.4590 (-1.85)

d (L)-6 -0.0366 (-1.39) 0.2120 (1.47) d (L)-6 0.0235 (0.57) -0.2995 (-1.45)

d (L)-7 -0.0120 (-0.46) 0.0819 (0.57) d (L)-7 0.0798 (1.85) -0.7065 (-3.28) *

Constant 0.0207 (4.16) * -0.0516 (-1.90) Constant 0.0049 (0.43) 0.0937 (1.65)

Adjusted R2 0.6541 0.1162 R2 0.4896 0.3809 Note: The variables are: d ln(Y) = first difference of the log of real per capita GDP, d L = first difference of the ratio of banking loans to nominal GDP. Numbers in parentheses beside estimated coefficients are absolute values of the t-ratio and * represents significance at the 5 per cent level

Table 6.8: Estimated VAR model with variables G and T over 1978(1)-1996(4) and 1998(1)-2006(4)

Sub-sample, 1978(1)-1996(4) Sub-sample, 1998(1)-2006(4) Regressor d ln (G) equation T equation Regressor d ln (G) equation T equation

d ln (G)-1 -0.0624 (-0.69) 1.0246 (0.72) d ln (G)-1 0.0329 (0.18) -1.3579 (-0.49)

d ln (G)-2 -0.0800 (-0.89) 0.6150 (0.43) T-1 0.0235 (2.11) * 0.5272 (3.04) *

d ln (G)-3 -0.0400 (-0.46) 1.3746 (0.99) Constant -0.0050 (-0.55) 0.4275 (2.99) *

d ln (G)-4 0.6790 (7.74) * -1.1591 (-0.84) Adjusted R2 0.0966 0.1894

T-1 -0.0009 (-0.10) 0.6594 (5.15) *

T-2 0.0003 (0.04) 0.1381 (0.93)

T-3 0.0182 (1.94) 0.0247 (0.17)

T-4 -0.0219 (-2.78) * -0.1025 (-0.82)

Constant 0.0113 (2.26) * 0.1008 (1.28)

Adjusted R2 0.5639 0.5425 Note: The variables are: d ln(G) = first difference of log of real GDP, T = ratio of stock-market turnover to nominal GDP. Numbers in parentheses beside estimated coefficients are absolute values of the t-ratio and * represents

significance at the 5 per cent level

Table 6.9: Estimated VAR model with variables Y and T over 1978(1)-1996(4) and 1998(1)-2006(4)

Sub-sample, 1978(1)-1996(4) Sub-sample, 1998(1)-2006(4) Regressor d ln (Y) equation T equation Regressor d ln (Y) equation T equation

d ln (Y)-1 -0.0513 (-0.38) -2.1354 (-0.86) d ln (Y)-1 0.0423 (0.24) -1.7126 (-0.62)

d ln (Y)-2 -0.0526 (-0.39) 4.2110 (1.71) T-1 0.0194 (1.78) 0.5287 (3.12) *

d ln (Y)-3 0.0953 (0.71) 2.5380 (1.03) Constant -0.0060 (-0.65) 0.4243 (2.97) *

d ln (Y)-4 0.5272 (5.59)* -1.2752 (-0.74) Adjusted R2 0.0539 0.1931

d ln (Y)-5 -0.2213 (-1.75) 3.0006 (1.30)

d ln (Y)-6 -0.2517 (-1.96) * -4.1195 (-1.75)

d ln (Y)-7 -0.3109 (-2.31) -1.2188 (-0.49)

T-1 -0.0046 (-0.58) 0.7365 (5.06)*

T-2 0.0037 (0.37) -0.0147 (-0.08)

T-3 0.0145 (1.50) 0.0945 (0.53)

T-4 -0.0110 (-1.17) -0.0364 (-0.21)

T-5 -0.0106 (-1.17) -0.2021 (-1.21)

T-6 0.0103 (1.12) 0.0639 (0.38)

T-7 0.0006 (0.08) 0.1412 (1.02)

Constant 0.0146 (3.15)* 0.1005 (1.18)

Adjusted R2 0.6369 0.5229 Note: The variables are: d ln(Y) = first difference of the log of real per capita GDP, T = ratio of stock-market turnover to nominal GDP. Numbers in parentheses beside estimated coefficients are absolute values of the t-ratio and * represents significance at the 5 per cent level.

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Table 6.10: Estimated VAR model with variables G and L over 1978(1)-1996(4) and 1998(1)-2006(4)

Sub-sample, 1978(1)-1996(4) Sub-sample, 1998(1)-2006(4) Regressor d ln (G)

equation

d L equation Regressor d ln (G) equation d L equation

d ln (G)-1 -0.1022 (-1.14) -0.4650 (-1.06) d ln (G)-1 0.1807 (0.50) -3.9402 (-2.39) *

d ln (G)-2 -0.1028 (-1.14) 0.1440 (0.33) d ln (G)-2 -0.1287 (-0.34) -3.6029 (-2.06) *

d ln (G)-3 -0.0761 (-0.86) -0.2768 (-0.65) d ln (G)-3 -0.5292 (-1.24) 1.1762 (0.60)

d ln (G)-4 0.6469 (7.49) 0.0864 (0.21) d ln (G)-4 0.8955 (2.03) * -4.9331 (-2.43) *

d (L)-1 -0.0469 (-1.76) 0.3481 (2.70) * d ln (G)-5 0.0093 (0.03) -0.6037 (-0.37)

d (L)-2 -0.0051 (-0.17) -0.0500 (-0.36) d ln (G)-6 0.2299 (0.68) -2.4438 (-1.58)

d (L)-3 0.0374 (1.28) 0.0779 (0.55) d ln (G)-7 0.6104 (1.81) -4.9253 (-3.17) *

d (L)-4 -0.0437 (-1.60) 0.0114 (0.09) d (L)-1 -0.0449 (-0.72) -0.4628 (-1.61)

Constant 0.0127 (2.32)* 0.0202 (0.76) d (L)-2 0.0114 (0.17) -0.6884 (-2.20) *

Adjusted R2 0.5548 0.1037 d (L)-3 -0.0408 (-0.56) -0.0829 (-0.25)

d (L)-4 0.1291 (1.84) -0.7563 (-2.34) *

d (L)-5 0.0239 (0.44) -0.4631 (-1.87)

d (L)-6 0.0229 (0.50) -0.3638 (-1.72)

d (L)-7 0.0724 (1.51) -0.7717 (-3.49) *

Constant 0.0004 (0.02) 0.1644 (2.15) *

Adjusted R2 0.4360 0.4124 Note: The variables are: d ln(G) = first difference of log of real GDP, d L = first difference of the ratio of banking loans to nominal GDP. Numbers in parentheses beside estimated coefficients are absolute values of the t-ratio and * represents significance at the 5 per cent level.

A comparison between the full sample period 1978(1) – 2006(4) and the sub-sample

period 1978(1) – 1996(4) show substantial rise in adjusted R2 values for all the

equations in the four VAR models except the d L equation in the VAR model

involving G and L (in Table 6.10). For the sub-sample period 1998(1) – 2006(4), the

d ln (Y) and d L equations (in VAR model for Y and L) and the d ln (G) and d L

equations (in VAR model for G and L) yield higher adjusted R2 values compared to

corresponding equations in the full-sample period (compare adjusted R2 in Tables 6.7

and 6.10 with adjusted R2 in Tables 6.4A and 6.4D). Taken together, the explanatory

power of the equations in the two sub-periods (namely 1978(1) – 1996(4) and 1998(1)

– 2006(4)) generally tend to be higher than those in the full sample period 1978(1)-

2006(4). This provides further evidence of the structural break in 1997 associated

with the Asian financial crisis, and the usefulness of estimating separate VAR models

in the two sub-periods in analyzing the results.

For the VAR model involving Y and L (Table 6.7), the d L equation suggests that

lagged changes in ln (Y) (i.e. d ln (Y)-4 and d ln (Y)-5 ) have significant positive

159

impact on the share of banking loans in GDP in the pre-Asian financial crisis period

from 1978(1)-1996(4). However, in the post-crisis period from 1998(1)-2006(4), the

d L equation suggests that lagged changes in ln (Y) (i.e. d ln (Y)-4 ) has a significant

negative impact on the share of banking loans in GDP. Moreover, in both periods, all

the coefficients in the d ln (Y) equations are not significant suggesting that economic

growth has no impact on banking sector development.

In the VAR model involving G and T (Table 6.8), the d ln (G) equation shows that

stock market activities (proxied by T) has a small negative impact on economic

growth (proxied by d ln (G)) in the pre-crisis period 1978(1)-1996(4). However, in

the post-crisis period 1998(1)-2006(4), the d ln (G) equation suggests that stock-

market activities has a small positive impact on economic development. Additionally,

in both periods, the T equations indicate that economic growth has no impact on

stock-market development.

For the VAR model involving Y and T (Table 6.9), all the coefficients in the d ln (Y)

and T equations are not significant. This seems to indicate that that there is no

relationship between economic development (proxied by d ln (Y)) and stock-market

activities (proxied by T) in the two sub-sample periods.

In the VAR model for G and L (Table 6.10), the d ln (G) and d L equations suggest

that there is no relationship between economic development ((proxied by d ln (G))

and financial development (proxied by d L) over the period 1978(1)-1996(4).

However, in the post-Asian financial crisis period 1998(1)-2006(4), the d L equation

indicates that lagged changes in economic activities (d ln(G)-1, d ln(G)-2 and d ln(G)-4)

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have significantly negative effects on financial development. This corroborates the

earlier finding in the VAR model on Y and L (Table 5.10), which suggests that

economic growth has an adverse impact on financial development in the post-Asian

financial crisis period. Additionally, all the coefficients in the d ln (G) equations are

not significant in both periods, which further support the finding for the VAR model

on Y and L (Table 5.10) that economic growth has no impact on banking sector

development.

In summary, the above analysis employing various VAR models provides evidence

that the finance-growth nexus has changed in the post-Asian financial crisis period

from 1998(1)-2006(4) as compared to that in the pre-crisis period from 1978(1) –

1996(4). In the VAR model involving Y and L (Table 6.7), the relationship between

banking sector development and economic growth changes from a positive

relationship in the pre-crisis period to a negative relationship in the post-crisis period.

In the VAR model for G and T (Table 6.8), the relationship between stock market

development and economic growth changes from a negative relationship in the pre-

crisis period to a positive relationship in the post-crisis period. In the VAR model

involving G and L (Table 6.9), the finance-growth relationship appears to be non-

existent in the pre-crisis period (1978(1)-1996(4) but exists in the post-crisis period

with economic growth adversely affecting financial development over the period

1998(1)-2006(4). Nonetheless, the VAR model on Y and T (Table 6.10) suggests no

relationship between stock market activities and economic development in both

periods.

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Notwithstanding these findings, as Groenewold (2003) suggests, these conclusions are

based on the significance of individual coefficients which could be misleading

because of multi-colinearity problems. Moreover, the coefficients are unable to

reveal the “dynamic interrelationships” among the endogenous variables in the model,

which is critical in VAR analysis. These dynamic effects can be captured by

simulating the model using the impulse response function, which will be discussed in

the following section.

6.4 Generalised Impusle Response Analysis

The impulse response function (IRF) is the main tool for analyzing the dynamic

properties of the VAR model. The IRF involves providing a one-time shock to a

particular variable6 in the VAR model and observing its effects transmitted through

the dynamic lag structure to all the endogenous variables in the model (including the

shocked variable itself). Thus, the IRF traces the dynamic effects of a once-off shock

on the current and future values of all the endogenous variables in the VAR model.

In this section, the analysis of the IRFs will be undertaken over the three periods,

namely over the full sample period from 1978(1)-2006(4) and the two sub-sample

periods, 1978(1) – 1996(4) and 1998(1) – 2006(4), with the focus on the finance-

growth relationship. Thus, the focus of the analysis is whether there has been a

change in the relationship between financial development and economic growth over

time. To facilitate comparison of the IRFs, the figures for the full sample and the

different sub-samples will be grouped together.

6 Strictly speaking, we do not shock the variable as all the variables in the VAR are endogenous. Instead, we shock a particular error or group of errors in the equation which contains the variable.

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The Paseran and Shin (1998) procedure, which employs generalized impulses, is used

to generate the IRFs as shown in Figures 6.1 to 6.4 (below). This approach has an

advantage over the Choleski approach, which is sensitive to the variable ordering in

the VAR. However, the innovation shocks in the Pesaran and Shin (1998) method are

not orthogonal and hence “cannot simply be added up as they can in the Choleski

approach“ (Groenewold, 2003, p.466). In this chapter, generalized impulse response

functions will be estimated to assess the finance-growth nexus. In the next chapter

(Chapter 7), the Choleski approach to impulse response analysis will be undertaken to

test for the robustness of the results. Importantly, the IRFs for each sample period are

reported with two-standard error bounds to provide a rough guide to the sampling

error associated with the computations. As the confidence bounds are relatively broad,

some caution has to be exercised in interpreting the conclusions.

6.4.1 GIRF analysis for VAR model involving Y and L

Figure 6.1 shows the the generalized IRFs of innovation shocks to dL and dln(Y) in

the VAR model involving Y (real per capita GDP) and L (share of banking loans to

GDP) which is estimated over the three periods (1978(1)–2006(4), 1978(1)–1996(4)

and 1998(1)–2006(4)). A ten-period horizon is employed so that the dynamics of the

adjustment process resulting from the innovation shock is allowed to work through

the VAR system.

Importantly, following the Granger causality tests undertaken in section 6.2, impulse

response analyses serve to throw further light on the issue of causality between

financial development and economic growth. With a ten-period horizon employed in

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the analyses, the impulse response functions enable the dynamic causality between

financial and economic development to be traced out over time.

Figure 6.1 - Generalized IRFs of shocks to dL and dln in the three periods:

1978(1)-2006(4), 1978(1)-1996(4) and 1998(1)-2006(4)

(a) Full sample period (5-lag VAR): 1978(1) – 2006(4)

-.02

-.01

.00

.01

.02

.03

1 2 3 4 5 6 7 8 9 10

Response of dln(Y) to dL

-.10

-.05

.00

.05

.10

.15

1 2 3 4 5 6 7 8 9 10

Response of dL to dLn(Y)

Response to Generalized One S.D. Innovations ± 2 S.E.

(b) Sub-sample (7-lag VAR) for pre-Asian financial crisis period: 1978(1) – 1996(4)

-.010

-.005

.000

.005

.010

.015

.020

1 2 3 4 5 6 7 8 9 10


-.04

.00

.04

.08

.12

1 2 3 4 5 6 7 8 9 10

Response of dL to dln(Y)


(c) Sub-sample (7-lag VAR) for post-Asian financial crisis period: 1998(1) – 2006(4)

-.03

-.02

-.01

.00

.01

.02

.03

1 2 3 4 5 6 7 8 9 10


-.12

-.08

-.04

.00

.04

.08

.12

1 2 3 4 5 6 7 8 9 10



(d) Comparing GIRF responses in the two sub-sample periods


-0.06

-0.04

-0.02

0

0.02

0.04

0.06

1 2 3 4 5 6 7 8 9 10

1978-1996 1998-2006

` ``


-0.012

-0.01

-0.008

-0.006

-0.004

-0.002

0

0.002

0.004

0.006

1 2 3 4 5 6 7 8 9 10

1978-1996 1998-2006

164

For the full sample period 1978(1) - 2006(4) (in Figure 6.1(a)), changes in the real per

capita GDP growth (dln(Y)) have an adverse impact on the share of banking loans to

GDP (dL) in the short term (1 to 2 quarters), with the effects gradually becoming

positive in the medium term (3 to 6 quarters) but fading out in the longer term (7 to 10

quarters). Similarly, over the same full sample period, changes in the share of

banking loans to GDP (dL) have a negative impact on real per capita GDP growth

(dln(Y)) in the short term (1 to 2 quarters). Nonetheless, these negative effects (of

banking development on economic growth) taper off rapidly in the medium and

longer term. Thus, Figure 6.1(a) suggests that there is negative bi-directional causal

relationship between banking and economic development in the short term (1 to 2

quarters), but uni-directional causality from economic growth to banking development

in the medium term (3 to 6 quarters) with the effects fading off in the longer term (7

to 10 quarters). The net long-run effects of the finance-growth relationship between

the banking sector and the real economy will be discussed in the context of

cumulative impulse response analyses in section 6.5.

As the VAR model is a non-structural model, it does not allow for definitive

economic interpretation of the results. Nonetheless, it is interesting to speculate on

some possible economic mechanisms underlying the results. A case in point is that

the initial negative effects of economic growth on banking sector development and

vice-versa appear to be counter-intuitive. A possible reason for the initial negative

impact of economic growth on banking development is that rapid economic growth

raises the profits of firms thereby increasing the firms‟ cash flows and their

availability of internal funds, which in turn, reduce the need for corporate borrowing

and bank loans in the short term (Dornbusch, Fischer and Startz, 2001). On the other

165

hand, the negative impact of banking sector development on economic growth could

be accounted for by banks‟ inherent bias towards financial prudence which tends to

impede corporate innovation and growth (Levine, 2002). This problem could be

particularly acute among countries with weak financial institutions, especially in

economies where the government plays a dominant role in financial intermediation.

In a recent study by Nili and Rastad (2007) it was found that the weakness of financial

institutions in oil exporting countries, which resulted from a proliferation of

government-owned banks, leads to financial development having a dampening effect

on the quality of investment and growth in these economies. Similarly, in a separate

study by Zhang (2003) it was found that there was a significant negative relationship

between banking sector development and economic growth in eight Asian economies

over the period 1960-1999 due largely to inefficient loan distribution by financial

intermediaries in those economies. Another possible explanation for the negative

impact of financial development on economic growth could be that an expansion in

banking loans increases the money supply thereby generating inflationary pressure

which, in turn, causes the real interest rates to fall. The fall in real interest rates tend

to induce depositors to withdraw their funds from the banks thus tightening liquidity

in the financial system and stifling investments and GDP growth (Rosseau and

Wachtel, 2000). Indeed, in a recent study by Hung (2003) using endogenous growth

model, it was found that financial development tends to raise inflation and reduce

economic growth.

For the sub-sample period 1978(1) - 1996(4) (in Figure 6.1(b)), the patterns of the IRF

responses for shocks in dL on dln(Y) appear to be largely similar to those observed

for the full sample period. Notably, in the short term (1 to 2 quarters), there is a

166

negative bi-directional causal relationship between banking sector development and

economic growth. Nonetheless, Figure 6.1(b) indicates positive uni-directional

causality from economic growth to banking development in the medium term (3 to 6

quarters) with the positive effects fading out in the longer term (7 to 10 quarters).

In the sub-sample period from 1998(1) - 2006(4), Figure 6.1(c) largely points to the

same conclusion as that in Figure 6.1(b). Nonetheless, in the medium term (3 to 6

quarters), the positive effects of economic growth (dln(Y)) on banking development

(dL) appear to have become more volatile over the period 1998(1) - 2006(4)

compared to that in the earlier period from 1978(1) – 1996(4). Thus, while changes

in the real per capita GDP growth (dln(Y)) still have a negative impact on dL (share

of banking loans to GDP in the short term and a positive impact (on dL) in the

medium term, the effects tend to fluctuate more widely between positive and negative

territory. Similarly, the response of dln(Y) to a shock in dL seems to fluctuate around

a larger band compared to that in the earlier period from 1978(1) – 1996(4).

Figure 6.1(d) shows a comparison of the generalized impulse response functions

(GIRFs) without the two-standard error bounds over the two sub-sample periods:

1978(1) – 1996(4) and 1998(1) – 2006(4). To ensure that the shock sizes are the

same, re-scaling of the shock sizes is undertaken by multiplying the GIRFs in the

second sub-period (1998(1) – 2006(4)) by the ratio of the residual standard deviation

in the first sub-period (1978(1) – 1996(4)) to that in the second sub-period (1998(1) –

2006(4)) (Groenewold, 2003). Arguably, this procedure is legitimate as the IRFs are

linear in the shock. From the perspective of banking sector development, Figure

6.1(d) seems to suggest that the finance-growth relationship has become more

167

variable during post-crisis period compared to that in the pre-crisis period. This result

is consistent with the findings in Chapter 3 which suggests that the Singapore

economy has become more volatile since the 1997 Asian financial crisis, thus causing

the finance-growth nexus to become more erratic.

6.4.2 GIRF analysis for VAR model involving G and T

Figure 6.2 shows the generalized IRFs corresponding to the innovation shocks to T

(ratio of stockmarket turnover to GDP) and dln(G) (GDP growth) in the respective

VAR models estimated for the three periods (1978(1) – 2006(4), 1978(1)-1996(4) and

1998(1)-2006(4)). The IRFs in Figure 6.2 attempt to capture the dynamic effects of

the inter-relationships between stockmarket activities (T) and economic growth

(dln(G)) in the short term (1 to 2 quarters), medium term (3 to 6 quarters) and the

longer term (7 to 10 quarters).

Figure 6.2 – Generalized IRFs of shocks to T and dln(G) in the three periods:

1978(1)-2006(4),1978(1)-1996(4) and 1998(1)-2006(4)


-.01

.00

.01

.02

.03

1 2 3 4 5 6 7 8 9 10

Response of dlnG to T

-.2

-.1

.0

.1

.2

.3

.4

1 2 3 4 5 6 7 8 9 10

Response of T to dlnG



-.010

-.005

.000

.005

.010

.015

.020

1 2 3 4 5 6 7 8 9 10

Response of dlnG to T

-.1

.0

.1

.2

.3

.4

1 2 3 4 5 6 7 8 9 10

Response of T to dln(G)


168


-.01

.00

.01

.02

.03

1 2 3 4 5 6 7 8 9 10

Response of dln(G) to T

-.2

-.1

.0

.1

.2

.3

.4

.5

1 2 3 4 5 6 7 8 9 10





0

0.02

0.04

0.06

0.08

0.1

0.12

1 2 3 4 5 6 7 8 9 10

1978-1996 1998-2006

` ``


-0.004

-0.002

0

0.002

0.004

0.006

0.008

1 2 3 4 5 6 7 8 9 10

1978-1996 1998-2006

For the full sample period (in Figure 6.2(a)), the impact of an innovation shock in T

on dln(G) is positive in the short term (1 to 2 quarters), but the positive effects are

rapidly dissipated in the medium to longer term. Similarly, dln(G) has a significant

positive impact on T initially, but the effects quickly wear beyond 1 to 2 quarters.

This contrasts with the findings in Table 6.4B, which indicates that the effect of

dln(G) on T is insignificant, thus illustrating the importance of assessing the full

dynamics of the inter-relationships among variables in the model. Importantly, the

IRF analysis for the full sample period of the VAR model involving G and T suggests

a positive bi-directional causal relationship between stock-market development and

real GDP growth in the short term (1 to 2 quarters) but the linkages between the stock

market and real economy tend to fade out in the medium to longer term (3 to 10

quarters).

169

For the sub-sample period 1978(1) – 1996(4) (as shown in Figure 6.2(b)), an

innovation shock to dln(G) appears to have a significant positive effect on T in the

first 4 quarters, suggesting that Singapore‟s real GDP growth has a positive impact on

its stock-market development in the short to medium term. Over the same sample

period, an innovation shock in T also has a positive impact on dln(G), though the

positive effect similarly dies out after around 4 quarters. This suggests that stock-

market development in the Singapore financial sector has a positive impact on

domestic growth in the short to medium term. Thus, in the near to medium term,

there appears to be a mutually reinforcing causal relationship between stock market

development and economic growth in Singapore over the period 1978(1) – 1996(4),

with stock-market activities stimulating economic growth while stronger economic

growth, in turn, feeds back to sustain stock-market development.

For the sub-sample period 1998(1) – 2006(4) (in Figure 5.2(c)), an innovation shock

in T does not seem to have a significant impact on dln(G) as the impulse responses are

not significant under a two-standard error criterion. Similarly, an innovation shock in

dln(G) also does not have a significant effect on T under a two-standard error

criterion. Thus, using a two-standard error bound, there is 95 percent probability that

the impulse responses are insignificant in both directions. These results tend to

suggest no causal relationship between stock-market development and economic

growth over the period 1998(1) – 2006(4).

Figure 6.2(d) attempts to compare the GIRFs in assessing the dynamic relationship

between the stock market and the real economy before and after the 1997 Asian

financial crisis. As explained earlier for Figure 6.1(d), the innovation shocks of the

170

impulse responses for Figure 6.2(d) are re-scaled to ensure comparability of the

GIRFs. Importantly, compared to the pre-Asian financial period from 1978(1)-

1996(4), the mutually reinforcing effects of stock-market activities and GDP growth

seems to be less persistent in the post-crisis period (1998(1) – 2006(4)). This could be

due to the financial deregulation in Singapore in the post-1997 period (explained in

Chapter 3) which opened up of the domestic stockmarket to international capital

flows, thereby weakening the relationship between stockmarket activities and

economic growth. These results are in line with the findings by Groenewold (2003)

which found that financial deregulation in Australia at end-1983 tended to weaken the

relationship between the share market and the rest of the economy in the post-

regulation period.

6.4.3 GIRF analysis for VAR model involving Y and T

Figure 6.3 shows the generalized IRFs (GIRFs) corresponding to the innovation shocks

to T and dln(Y) in the respective VAR models estimated for the three periods

(1978(1) – 2006(4), 1978(1)-1996(4) and 1998(1)-2006(4)). The GIRFs in Figure 6.3

are intended to check for the robustness of the results obtained in Figure 6.2 by

substituting real per capita GDP growth (dln(Y)) for real GDP growth (dln(G)) in the

VAR model. The analysis therefore involves a comparison of the GIRFs in Figure 6.3

with corresponding GIRFs in Figure 6.2 to examine whether the relationship between

the stockmarket and the real economy has changed after the 1997 Asian financial

crisis.

171

Figure 6.3- Generalized IRFs of shocks to T and dln(Y) in the three periods:

1978(1)-2006(4), 1978(1)-1996(4) and 1998(1)-2006(4)


-.01

.00

.01

.02

.03

1 2 3 4 5 6 7 8 9 10

Response of dln(Y) to T

-.2

-.1

.0

.1

.2

.3

.4

1 2 3 4 5 6 7 8 9 10

Response of T to dln(Y)



-.010

-.005

.000

.005

.010

.015

.020

1 2 3 4 5 6 7 8 9 10


-.2

-.1

.0

.1

.2

.3

.4

1 2 3 4 5 6 7 8 9 10




-.01

.00

.01

.02

.03

1 2 3 4 5 6 7 8 9 10


-.2

-.1

.0

.1

.2

.3

.4

.5

1 2 3 4 5 6 7 8 9 10





-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

1 2 3 4 5 6 7 8 9 10

1978-1996 1998-2006

` ``


-0.006

-0.004

-0.002

0

0.002

0.004

0.006

0.008

1 2 3 4 5 6 7 8 9 10

1978-1996 1998-2006

172

Notably, the shapes of the IRFs in all three periods in Figure 6.3 (for VAR involving

G and T) are roughly the same as the IRFs in Figure 6.2 (for VAR involving variables

Y and T) over the corresponding periods. Importantly, as shown in Figures 6.3(d), the

mutually beneficial effects of stock-market activities and economic development

seem to die out more quickly after 1 to 2 quarters in the post-crisis period compared to

around 4 quarters in the pre-crisis period. This lends support to the conclusion that

the positive bi-directional relationship between the stock market and the real economy

seems to have weakened in the post-1997 period.

6.4.4 GIRF analysis for VAR model involving G and L

Figure 6.4 shows the generalized IRFs corresponding to the innovation shocks to

dln(G) and dL in the VAR models to explore the dynamic interaction between

financial development and economic growth over the three periods (1978(1) –

2006(4), 1978(1)-1996(4) and 1998(1)-2006(4)). The IRFs in Figure 6.4 are aimed at

checking the robustness of the results in Figure 6.1 by substituting real GDP growth

(dln(G)) for real per capita GDP growth (dln(Y)) in the VAR model. The analysis

thus involves comparing the IRFs in Figure 6.4 with corresponding IRFs in Figure 6.1

to assess the finance-growth nexus in the banking sector over the three periods.

Figure 6.4 – Generalized IRFs of shocks to dL and dln(G) in the three periods:

1978(1)-2006(4), 1978(1)-1996(4) and 1998(1)-2006(4)


-.02

-.01

.00

.01

.02

.03

1 2 3 4 5 6 7 8 9 10

Response of dln(G) to dL

-.08

-.04

.00

.04

.08

.12

1 2 3 4 5 6 7 8 9 10

Response of dL to dln(G)


173


-.01

.00

.01

.02

1 2 3 4 5 6 7 8 9 10


-.04

.00

.04

.08

.12

1 2 3 4 5 6 7 8 9 10




-.03

-.02

-.01

.00

.01

.02

.03

1 2 3 4 5 6 7 8 9 10


-.12

-.08

-.04

.00

.04

.08

.12

1 2 3 4 5 6 7 8 9 10





-0.06

-0.04

-0.02

0

0.02

0.04

0.06

1 2 3 4 5 6 7 8 9 10

1978-1996 1998-2006

` ``


-0.014

-0.012

-0.01

-0.008

-0.006

-0.004

-0.002

0

0.002

0.004

0.006

1 2 3 4 5 6 7 8 9 10

1978-1996 1998-2006

Visual inspection of the shapes of the generalized impulse response functions (GIRFs)

in all three periods in Figure 6.4 (for VAR involving G and L) suggest that they are

broadly similar to those in the GIRFs for Figure 6.1 (for VAR involving variables Y

and L). The GIRF results in Figure 6.4 thus corroborate the results discussed in the

earlier section 6.4.1 (pertaining to GIRFs in Figure 6.1). Importantly, comparing

Figure 6.4 (b) and Figure 6.4(c), it also appears that there is greater volatility in the

inter-relationship between financial development and economic growth in the post-

174

crisis period compared to that in the pre-crisis period. This result is further

corroborated in Figure 6.4(d) which compares the GIRFs in the pre-crisis and post-

crisis periods from the perspective of finance-growth linkages.

6.5 Cumulative Impusle Response Analysis

To further examine the results of the impulse response analyses, cumulative impulse

response functions are generated to examine the impact of the innovation shocks as

accumulated responses rather than period-by-period responses. Cumulative IRFs

serve to provide an additional perspective to the simulations obtained from the

original IRFs. The cumulative IRFs represent the effects of the original shock (to the

error in the first-difference form of the model) on the level of the variable. Moreover,

the cumulative IRFs allow for an overall assessment of the net result of the positive

and negative effects in the simulated IRFs. Cumulative IRFs can be generated using

generalized shocks or Choleski shocks to the endogenous variables in the VAR

models. Nonetheless, it was found that the cumulative response functions for

generalized and Choleski approaches were qualitatively similar, thus only the

cumulative generalized impulse response functions (cumulative GIRFs) are presented

in this section.

6.5.1 VAR model on Y and L

Figure 6.5 produces the cumulative generalized impulse responses of innovation

shocks to dL and dln(Y) in the VAR models over the three periods: 1978(1) – 2006(4),

1978(1) – 1996(4) and 1998(1) – 2006(4). The cumulative GIRFs in Figure 6.5 differ

from the original GIRFs shown in Figure 6.1 as the latter show the generalized

impulse response of dln(Y) to dL (and vice versa) whereas the cumulative GIRFs

175

display the effect on ln(Y) of the same shock to dL (and vice-versa). Thus, the

cumulative GIRFs show the cumulative positive and negative effects of the

innovation shocks on the levels of the variable in the long-run.

Figure 6.5 – Cumulative GIRFs of shocks to dL and dln(Y) in the three periods:

1978(1)-2006(4), 1978(1)-1996(4) and 1998(1)-2006(4)


-.02

-.01

.00

.01

.02

.03

.04

1 2 3 4 5 6 7 8 9 10

Accumulated Response of dln(Y) to dL

-.2

-.1

.0

.1

.2

.3

1 2 3 4 5 6 7 8 9 10

Accumulated Response of dL to dln(Y)

Accumulated Response to Generalized One S.D. Innovations ± 2 S.E.


-.02

-.01

.00

.01

.02

.03

1 2 3 4 5 6 7 8 9 10


-.08

-.04

.00

.04

.08

.12

.16

.20

1 2 3 4 5 6 7 8 9 10




-.04

-.02

.00

.02

.04

.06

1 2 3 4 5 6 7 8 9 10


-.3

-.2

-.1

.0

.1

.2

1 2 3 4 5 6 7 8 9 10



(d) Comparison of Cumulative GIRFs over 2 periods: 1978-1996 and 1998-2006

Accumulated Response

of dL to dln(Y)

-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

1 2 3 4 5 6 7 8 9 10

1978-1996 1998-2006


of dLn(Y) to dL

-0.016

-0.014

-0.012

-0.01

-0.008

-0.006

-0.004

-0.002

0

1 2 3 4 5 6 7 8 9 10

1978-1996 1998-2006

176

The cumulative GIRFs in Figures 6.5 (a), (b) and (c) tend to reinforce the earlier

findings in section 6.4.1 that there is a negative bilateral causality between banking

sector development and economic growth in the short-term (1 to 2 quarters). In

comparing the cumulative GIRFs over the two sub-sample periods (Figure 6.5(d)), it

appears that economic growth tends to have a positive long-term impact on banking

sector development in the pre-crisis period 1978(1) – 1996(4) but a negative long-

term impact on banks in the post-crisis period 1998(1) – 2006(4). On the other hand,

bank intermediation tends to have a negative effect on economic growth in both the

pre-crisis and post-crisis periods (as shown in Figure 6.5(d)).

6.5.2 VAR model on G and T

Figure 6.6 illustrates the cumulative GIRFs for innovation shocks to T and dln(G) in

the VAR model on the levels of G and T for the three periods:1978(1) – 2006(4),

1978(1) – 1996(4) and 1998(1) – 2006(4). Over the period 1978(1) – 1996(4), Figure

6.6(b) suggests that stock-market activities and economic growth have a positive

causal relationship in the short and medium terms (1 to 6 quarters) with the mutually

reinforcing effects fading out in the longer term (7 to 10 quarters) even though the

cumulative effects remain positive. This is consistent with the findings in section

6.4.2. Over the period 1998(1) – 2006(4), Figure 6.6(c) shows that that the

cumulative effects of T on dln(G) as well as the cumulative effects of dln(G) on T are

largely stable beyond the initial one to two quarters. Figure 6.6(d) indicates that the

cumulative positive effects in the finance-growth linkages (for the equities market)

tend to rise steadily in the pre-crisis period (1978(1) – 1996(4)) for up to 6 quarters

several quarters whereas the cumulative positive effects between stock-market

activities and economic growth tend to stabilize beyond 1 to 2 quarters in the post

177

crisis period (1998(1) – 2006(4)). Taken together, these results lend further support

to the view that the finance-growth nexus, from the perspective of stock-market

development, has weakened after the 1997 Asian financial crisis.

Figure 6.6 – Cumulative GIRFs of shocks to T and dln(G) in the three periods:

1978(1)-2006(4), 1978(1)-1996(4) and 1998(1)-2006(4)


-.01

.00

.01

.02

.03

.04

1 2 3 4 5 6 7 8 9 10

Accumulated Response of dln(G) to T

-0.5

0.0

0.5

1.0

1.5

2.0

1 2 3 4 5 6 7 8 9 10

Accumulated Response of T to dln(G)



-.01

.00

.01

.02

.03

.04

.05

1 2 3 4 5 6 7 8 9 10


-0.4

0.0

0.4

0.8

1.2

1.6

2.0

1 2 3 4 5 6 7 8 9 10




-.01

.00

.01

.02

.03

.04

.05

1 2 3 4 5 6 7 8 9 10


-0.4

0.0

0.4

0.8

1.2

1 2 3 4 5 6 7 8 9 10



178



of T to dln(G)

0

0.1

0.2

0.3

0.4

0.5

0.6

1 2 3 4 5 6 7 8 9 10

1978-1996 1998-2006


of dLn(G) to T

0

0.005

0.01

0.015

0.02

1 2 3 4 5 6 7 8 9 10

1978-1996 1998-2006

6.5.3 VAR model on Y and T

Figure 6.7 exhibits the cumulative IRFs for innovation shocks to T and dln(Y) in the

VAR model on the levels of Y and T for the three periods: 1978(1) – 2006(4),

1978(1) – 1996(4) and 1998(1) – 2006(4). Close examination of Figure 6.7 indicates

that the cumulative impulse response functions generated from the VAR model on Y

and T are very similar to those generated from the VAR model on G and T (in section

6.5.2 above).

Figure 6.7 – Cumulative IRFs of shocks to T and dln(Y) in the three periods:

1978(1)-2006(4), 1978(1)-1996(4) and 1998(1)-2006(4)


-.01

.00

.01

.02

.03

.04

1 2 3 4 5 6 7 8 9 10

Accumulated Response of dln(Y) to T

-0.5

0.0

0.5

1.0

1.5

2.0

1 2 3 4 5 6 7 8 9 10

Accumulated Response of T to dln(Y)



-.01

.00

.01

.02

.03

.04

1 2 3 4 5 6 7 8 9 10


-0.4

0.0

0.4

0.8

1.2

1.6

2.0

1 2 3 4 5 6 7 8 9 10



179


-.01

.00

.01

.02

.03

.04

1 2 3 4 5 6 7 8 9 10


-0.4

0.0

0.4

0.8

1.2

1 2 3 4 5 6 7 8 9 10





of T to dln(Y)

0

0.1

0.2

0.3

0.4

0.5

0.6

1 2 3 4 5 6 7 8 9 10

1978-1996 1998-2006


of dLn(Y) to T

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

1 2 3 4 5 6 7 8 9 10

1978-1996 1998-2006

Thus, on the whole, the cumulative GIRFs in Figure 6.7 reinforce the findings in the

preceding section (section 6.5.2) that there is positive bilateral causality between the

stock market and the real economy. However, as indicated earlier, the positive

linkages between stock-market activities and economic growth tend to become

weaker over the period 1998(1) – 2006(4) compared to that in earlier period 1978(1) –

1996(4) - reflecting a weakening of the finance-growth nexus after the 1997 Asian

financial crisis.

6.5.4 VAR model on G and L

Figure 6.8 shows the cumulative generalized impulse responses of innovation shocks

to dL and dln(G) in the VAR models over the three periods: 1978(1) – 2006(4),

1978(1) – 1996(4) and 1998(1) – 2006(4). For the full sample period 1978(1) –

2006(4), visual inspection of Figure 6.8(a) indicates that the cumulative GIRFs

estimated for the VAR model on G and L are largely similar to those estimated in

Figure 6.5(a) for the VAR model on Y and L (in section 6.5.1). However, in the sub-

180

sample periods ((1978(1) – 1996(4) and 1998(1) – 2006(4)), the cumulative GIRFs in

Figures 6.8(b) and 6.8(c) are slightly different from those in Figures 6.5(b) and 6.5(c)

for the VAR model on Y and L. Importantly, Figure 6.8(b) suggests a negative

bilateral causality between banking development and economic growth in the short-

term (1 to 2 quarters) with the cumulative effects remaining negative in both

directions in the longer term (7 to 10 quarters) over the period 1978(1) – 1996(4).

While Figure 6.8(c) similarly indicates negative bilateral causality between bank

intermediation and economic growth in the short-term (1 to 2 quarters) and longer

term (7 to 10 quarters), the finance-growth relationship tends to become more volatile

in the post-crisis period 1998(1) – 2006(4). This corroborates the earlier findings that

there is greater volatility in the (negative) linkages between financial development

and economic growth in the post-crisis period compared to that in the pre-crisis period.

Figure 6.8 – Cumulative IRFs of shocks to dL and dln(G) in the three periods:

1978(1)-2006(4), 1978(1)-1996(4) and 1998(1)-2006(4)


-.03

-.02

-.01

.00

.01

.02

.03

.04

1 2 3 4 5 6 7 8 9 10

Accumulated Response of dln(G) to dL

-.12

-.08

-.04

.00

.04

.08

.12

.16

.20

1 2 3 4 5 6 7 8 9 10

Accumulated Response of dL to dln(G)



-.04

-.02

.00

.02

.04

.06

1 2 3 4 5 6 7 8 9 10


-.2

-.1

.0

.1

.2

.3

1 2 3 4 5 6 7 8 9 10



181


-.08

-.04

.00

.04

.08

.12

1 2 3 4 5 6 7 8 9 10


-.6

-.4

-.2

.0

.2

.4

1 2 3 4 5 6 7 8 9 10





of dL to dln(G)

-0.14

-0.12

-0.1

-0.08

-0.06

-0.04

-0.02

0

1 2 3 4 5 6 7 8 9 10

1978-1996 1998-2006


of dLn(G) to dL

-0.025

-0.02

-0.015

-0.01

-0.005

0

1 2 3 4 5 6 7 8 9 10

1978-1996 1998-2006

Taking into account the above results from cumulative GIRFs and the original GIRFs,

we can summarize the impulse response analyses in the following table:

Table 6.11: Summary of IRF results on causality between financial development and economic growth

Economic Growth (Y)*

Short-term

(1 to 2 quarters)

Medium-term

(3 to 6 quarters)

Cumulative long-

term effects

(7 to 10 quarters)

Banking

sector

development

(L)

Pre-1997

Asian

financial

crisis

Negative bi-

directional causality

between Y and L

Positive causality from Y

to L

No causality from L to Y

Negative

effect of Y on

L and vice

versa

Post-1997

Asian

financial

crisis

Negative bi-


between Y and L

Positive causality from Y

to L with increased

volatility in the finance-

growth nexus

No causality from L to Y

Negative

effect of Y on

L and vice

versa

Stock-

market

development

(T)

Pre-1997

Asian

financial

crisis

Positive bi-


between Y and T

Positive bi-directional

causality between Y and

T

Positive effect

of Y on T and

vice versa

Post-1997

Asian

financial

crisis

No causality

between Y and T

No causality between Y

and T

Positive effect

of Y on T and

vice versa

Note: The IRF results are qualitatively the same when Y (real per capita GDP) is substituted for G (real

GDP growth) in the VAR models

182

Hence, impulse response analyses (using generalized and cumulative IRFs) suggest

different causality patterns between financial development and economic growth in

Singapore (as outlined in Table 6.11) for the stock market and banks. Importantly,

the IRF results suggest negative bi-directional causality between banking

development and economic activities in the short-term. While the VAR model is non-

structural and does not allow for definitive economic interpretation of the results, it is

interesting to speculate on possible economic factors underlying the results.

Arguably, the initial negative impact of economic growth on banking development

could be explained by firms‟ abundant availability of internal funds arising from

higher profits as the economy expands, thus resulting in lower corporate borrowing

and bank loans in the short term (Dornbusch, Fischer and Startz, 2001). On the other

hand, the initial negative impact of banking sector development on economic growth

could be accounted for by the weakness of the banking system (Nili and Rastad, 2007;

Zhang, 2003). As explained in Chapter 3, the weakness of the Singapore financial

system could be attributable to government‟s over-protection of domestic banks by

separating domestic financial activities from offshore financial activities so as to

“insulate” the domestic economy from the vagaries of financial turbulence and shelter

the local banks from “excessive” international competition (Lim, 1988; Khalid and

Tyabji, 2002). Additionally, the lack of transparency among the largely family-

owned banks such as the United Overseas Bank (UOB), Overseas Union Bank (OUB)

and the Overseas Chinese Banking Corporation (OCBC) could also have contributed

to the weakness in the Singapore banking system. With a weak banking system,

financial intermediation results in inefficient distribution of loans thus adversely

affecting the quality of investment and growth in the domestic economy (Nili and

Rastad, 2007; Zhang, 2003).

183

Moreover, IRF analyses on the linkages between banks and the real economy also

indicate that there is a change in the finance-growth relationship after the 1997 Asian

financial crisis. From the perspective of banking sector development, the finance-

growth nexus appears to be more volatile after the 1997 financial crisis. This could be

explained from the earlier analyses of economic and financial development in

Singapore (Chapter 3) which indicate that the financial development of Singapore

could have entered into more volatile phase in the post-crisis period due to the

emergence of new industries such as wealth advisory and treasury services, which are

sensitive to volatile market sentiments. Additionally, as S. Tan (2006) pointed out, the

de-regulation of banks associated with a policy shift away from a “prescriptive, rule-

based” regulatory framework in the pre-crisis period to a more “flexible, risk-based”

style in the post-crisis period could have provided more “nimbleness” for banks to

innovate financial products which are riskier and tied to volatile market development.

Additionally, from the perspective of stock-market development, the mutually

reinforcing bilateral relationship between stock-market activities and economic

development (in the short to medium term) before the financial crisis erupted was

found to be less persistent in the post-crisis period. Arguably, this could be

attributable to the financial deregulation measures implemented in the post-1997

period (explained in Chapter 3) which were aimed at enhancing the “efficiency” and

“depth” of capital markets in Singapore (Khalid and Tyabji, 2002). The deregulatory

measures served to open up the domestic stock market to international capital flows,

thus weakening the relationship between stock-market activities and economic

growth. These results are consistent with the findings by Groenewold (2003) which

found that financial deregulation in Australia in 1983 tended to weaken the

184

relationship between the stock market and the real economy in the post-regulation

period.

6.6 Conclusion

In this chapter, we tested for the causal relationship between financial development

and economic growth over the period 1978(1) – 2006(4) and assessed the way in

which the finance-growth nexus (if any) could have changed before and after the 1997

Asian financial crisis. The Granger causality test was conducted to examine the

causality between the four pairs of variables – Y-L, G-T, Y-T and G-L. The

appropriate lag length in the Granger causality test was selected using the standard

Akaike, Schwarz-Bayes and Hannan-Quinn criteria with the additional requirement to

ensure the absence of autocorrelation in the optimal lag choice. Following evidence

of causality among some pairs of variables (such as Y-L, G-T, and G-L), the VAR

model was estimated for all the four pairs of variables (Y-L, G-T, Y-T and G-L) to

further investigate the causality among the variables as a means to understanding the

relationship between financial development (as proxied by L and G) and economic

growth (as proxied by Y and G).

The four estimated VAR models (Y-L, G-T, Y-T and G-L) in the full sample period

(1978(1) – 2006(4)) performed poorly with relatively low adjusted R2 as all the

models failed the standard test for structural stability at the break point of 1997(2)

chosen to coincide with the onset of the Asian financial crisis. We therefore

estimated and simulated the four VAR models over two sub-sample periods:

1978(1) – 1996(4) and 1998(1) – 2006(4). On the whole, the four models performed

185

substantially better in separate sub-samples, lending evidence of a structural break in

1997 when the Asian financial crisis erupted.

The estimated equations in the VAR models (Y-L, G-T, Y-T and G-L) for the sub-

samples provided some evidence regarding the change in the finance-growth

relationship in the pre- and post-crisis periods. In the VAR model involving Y and L,

the finance-growth nexus seemed to have changed from a positive unidirectional

relationship to a negative unidirectional relationship going from banking sector

development to economic growth. In contrast, the VAR model for G and T suggests a

negative unidirectional relationship from stock-market development to economic

growth in the pre-crisis period, but this unidirectional relationship (from the stock

market to the real economy) became positive in the post-crisis period. In the VAR

model involving G and L, there seemed to be no relationship between the financial

sector and the real economy in the pre-crisis period but economic activities were

found to have an adverse impact on banking sector development in the post-crisis

period. On the other hand, the VAR model for Y and T indicated no relationship

between financial development and economic growth before and after the 1997 Asian

financial crisis. Notwithstanding these preliminary findings, it was argued that multi-

collinearity problems among the coefficients in the estimated equations posed

problems for their interpretation.

To supplement the analysis, generalized impulse response functions and cumulative

impulse response functions were separately generated for the four VAR models (Y-L,

G-T, Y-T and G-L) in order to reveal the dynamic (quarter-by-quarter) inter-

relationships among the endogenous variables in the finance-growth nexus and to

186

investigate whether this relationship has changed after the 1997 Asian financial crisis.

As the VAR model is non-structural, caution will be exercised in interpreting the

economic mechanisms underlying the results. The impulse response analyses for the

VAR model involving Y and L as well as the VAR model involving G and L both

indicated negative bi-directional causality between banking sector development and

economic growth in the short term (1 to 2 quarters), but positive uni-directional


quarters). Importantly, the impulse response functions in both VAR models

(involving Y and L as well as G and L) suggested that the finance-growth relationship

in the banking sector had become more volatile in the post-crisis period. This result

is consistent with the conclusions in Chapter 3 which indicates that both the domestic

financial sector and the Singapore economy as a whole have become more volatile in

the post-1997 period, thus resulting in a finance-growth nexus that is more erratic.

Additionally, the VAR models involving Y and T as well as G and T both suggested a

positive bi-directional causal relationship between stock-market activities and

economic growth in the short to medium term (1 to 6 quarters) in the pre-crisis period,

but no causality between stock-market and economic development in the post-crisis

period. This supports the view that there is a weaker bilateral relationship between

stock market activities and economic growth in the post-1997 period following the

Asian financial crisis. The next chapter will further test for the robustness of these

results from the perspective of the finance-growth nexus in Singapore.

188

Chapter 7

DATA ANALYSIS III

7.1 Introduction

In Chapter 6, the main findings of the causality tests between financial development

and economic growth were presented. In this chapter, a series of robustness tests

employing impulse response analyses will be undertaken to scrutinize the results

obtained in the previous chapter. In Section 7.2, separate impulse response functions

(IRFs) will be computed for different lags in cases where the optimal lag lengths were

found to differ from the selected option. In Section 7.3, the Choleski impulse

response functions will be generated to compare with the generalized IRF results

shown in the preceding chapter. To further compare the IRF results, Section 7.4 will

examine generalized impulse response functions which are generated from VECM

models in cases where the pairs of variables (namely Y-L and G-L) show signs of

cointegration (as explained in Chapter 5). Section 6.6 summarizes and concludes

the results of the robustness tests undertaken in Chapter 7 in relation to the previous

chapter.

7.2 Impulse Response Analyses for Different (VAR) Lags

In section 6.4 of the preceding chapter, the impulse response functions were generated

for the full sample (1978(1) – 2006(4)) and the sub-samples (1978(1) – 1996(4) and

(1998(1) – 2006(4)) for each of the VAR models (involving Y and L, G and T, Y and

T as well as G and L) employing the appropriate lag length which was selected

primarily using the Akaike information criterion (in addition to ensuring the absence

of autocorrelation in the residuals). This previously selected lag length for the

different VAR models (in different periods) as discussed in Chapter 6 is shown in the

following table:

189

Table 7.1: Selected lag length of VAR model in different periods

Variables in

VAR model


1978(1) – 2006(4) 1978(1) – 1996(4) 1998(1) – 2006(4)

Y and L 5 7 7

G and T 5 4 1

Y and T 5 7 1

G and L 5 4 7

To check for the robustness of the results, this section will perform impulse response

analyses on different lag lengths selected for the VAR models in the full sample and

sub-samples using alternate lag selection criteria, such as the more conservative

Schwarz-Bayes criterion (as shown in Appendix 5).

7.2.1 VAR model (with different lag length) on Y and L

Figure 7.1 shows the generalized IRFs of innovation shocks to dL and dln(Y) for the

VAR model on Y (real per capita GDP) and L (share of banking loans to GDP) for the

three periods (1978(1) – 2006(4), 1978(1) – 1996(4) and 1998(1) – 2006(4)).

Comparing Figure 7.1 with Figure 6.1 (in the preceding chapter), it is notable that the

initial negative effects of economic growth on banking development and vice-versa

still persist in the short term (1 to 2 quarters) using lower lags in the VAR (which

were selected using the Schwarz-Bayes criterion). Nonetheless, the negative effects

tend to fade more quickly, with largely zero impulse responses between the variables

Y and L beyond 3 quarters, thus suggesting a neutral relationship between economic

and banking development in the medium term (3 to 6 quarters). This contrasts with

the findings in Figure 6.1 (previous chapter) which suggested positive uni-directional


quarters). Thus, the “dynamic causality” (Lutkepohl, 1991) patterns between banking

and economic development are sensitive to the lag length, with shorter lags resulting

190

in weaker causality patterns in the medium term (3 to 6 quarters) though the causality

patterns in the short term (1 to 2 quarters) remain largely similar.

Figure 7.1 – Generalized IRFs of shocks to dL and dln(Y) in the three periods:

1978(1)-2006(4), 1978(1)-1996(4) and 1998(1)-2006(4)


-.02

-.01

.00

.01

.02

.03

1 2 3 4 5 6 7 8 9 10


-.10

-.05

.00

.05

.10

.15

1 2 3 4 5 6 7 8 9 10




-.010

-.005

.000

.005

.010

.015

.020

1 2 3 4 5 6 7 8 9 10


-.050

-.025

.000

.025

.050

.075

.100

1 2 3 4 5 6 7 8 9 10




-.03

-.02

-.01

.00

.01

.02

.03

1 2 3 4 5 6 7 8 9 10


-.2

-.1

.0

.1

.2

1 2 3 4 5 6 7 8 9 10



191



-0.08

-0.07

-0.06

-0.05

-0.04

-0.03

-0.02

-0.01

0

0.01

1 2 3 4 5 6 7 8 9 10

1978-1996 1998-2006

7


-0.009

-0.008

-0.007

-0.006

-0.005

-0.004

-0.003

-0.002

-0.001

0

0.001

1 2 3 4 5 6 7 8 9 10

1978-1996 1998-2006

7

7.2.2 VAR model (with different lag length) on G and T

Figure 7.2 (a) shows the generalized IRFs corresponding to the innovation shocks to T

(ratio of stock-market turnover to GDP) and dln(G) (real GDP growth) for VAR

model with a lower lag length of one (as selected by the Schwarz Bayes ctiterion) for

the full sample period 1978(1) – 2006(4). Unlike the IRFs generated from the earlier

5-lag VAR model in Figure 6.2(a), the IRFs in for the 1-lag VAR model in Figure

7.2(a) (for the full-sample period 1978(1) – 2006(4)) tend to suggest no relationship

between the stock market and the real economy. For the sub-sample periods

1978(1) – 1996(4) and 1998(1) – 2006(4), the Schwarz Bayes criterion yields the

same optimal lag length as the Akaike information criterion (as reported earlier in

section 6.4.2 of Chapter 6) in the VAR models. As the two lag length criteria produce

the same outcome, longer lag lengths of 7 and 4 are chosen for VAR models in the

sub-sample periods 1978(1) – 1996(4) and 1998(1) – 2006(4) respectively. Using a

lag length of 7 for the VAR model in the sub-sample period 1978(1) – 1996(4), the

GIRFs (in Figure 7.2 (b)) suggest positive bi-directional causality between stock-

market and economic development in the short term (1 to 2 quarters) and the medium

term (3 to 6 quarters). This is similar to the earlier findings (in section 6.4.2 of

Chapter 6) where a lower lag length of 4 was employed in the VAR model. Moreover,

192

using a lag length of 4 for the VAR model in the sub-sample period 1998(1) – 2006(4),

the GIRFs (in Figure 7.2 (c)) suggest no “dynamic causality” between stock-market

activities and economic growth in the short to medium terms. This, again, is

consistent with the earlier results obtained in Figure 6.2(c) (section 6.4.2 in Chapter 6)

where a lag length of 1 was employed in the VAR model. As the focus of the study

is to assess the finance-growth nexus before and after the Asian financial crisis, the

GIRFs in Figure7.2(b) and 7.2(c) continue to indicate a positive bi-directional

causality between stock-market activities and economic growth in the short to

medium terms (1 to 6 quarters) in the pre-crisis period (1978(1) – 1996(4)), with the

finance-growth linkages becoming weaker in the post-crisis period (1998(1)–2006(4)).

Figure 7.2 – Generalized IRFs of shocks to T and dln(G) in the three periods:

1978(1)-2006(4), 1978(1)-1996(4) and 1998(1)-2006(4)


-.01

.00

.01

.02

.03

.04

1 2 3 4 5 6 7 8 9 10


-.1

.0

.1

.2

.3

.4

1 2 3 4 5 6 7 8 9 10




-.010

-.005

.000

.005

.010

.015

.020

1 2 3 4 5 6 7 8 9 10


-.2

-.1

.0

.1

.2

.3

.4

1 2 3 4 5 6 7 8 9 10



193


-.02

-.01

.00

.01

.02

.03

1 2 3 4 5 6 7 8 9 10


-.4

-.2

.0

.2

.4

.6

1 2 3 4 5 6 7 8 9 10





-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

1 2 3 4 5 6 7 8 9 10

1978-1996 1998-2006

7


-0.006

-0.004

-0.002

0

0.002

0.004

0.006

0.008

1 2 3 4 5 6 7 8 9 10

1978-1996 1998-2006

7

7.2.3 VAR model (with different lag length) on Y and T

Figure 7.3 shows the IRFs associated with the innovation shocks to T and dln(Y) in

the VAR models for the three periods: 1978(1) – 2006(4), 1978(1) – 1996(4) and

1998(1) – 2006(4). For Figures 7.3(a) and 7.3(b), corresponding to the sample

periods 1978(1) – 2006(4) and 1978(1) – 1996(4) respectively, the lag lengths in the

VAR models are selected from the Schwarz Bayes criterion - which differ from those

in the VAR models presented in Figure 6.3 (in the preceding chapter) where the lag

lengths were selected using the Akaike information criterion. For the sub-sample

period 1998(1) – 2006(4) in Figure 7.3(c), both lag-length criteria yield the same

outcome hence a higher lag length of 4 is chosen to throw more light on the impulse

response analysis.

194

Importantly, visual inspection of the IRFs in Figure 7.3 suggests that they are

qualitatively similar to those in Figure 6.3. Nonetheless, the impulse response effects

are uniformly insignificant in Figure 7.3(a) with a lower lag-length of 1 whereas they

are significant in the short term (1 to 2 quarters) in Figure 6.3(a) with a higher lag

length of 5, indicating that lag length does affect the outcome. Thus, to supplement

the analysis, we experimented by gradually increasing the lag length (numerically

from 2 to 5) in the VAR model and found that insignificance sets in with a lag length

of 4.

With regard to the finance-growth nexus, the IRFs in Figure 7.3 serve to reinforce the

findings in the preceding section (section 7.2.2) that there is a positive bi-directional

causal relationship between the stock market and the real economy in the short term

(1 to 2 quarters), but the finance-growth linkages seemed to have weakened in the

medium to longer term (3 to 10 quarters) in the post-crisis period (compared to that in

the pre-crisis period).

Figure 7.3 – Generalized IRFs of shocks to T and dln(Y) in the three periods:

1978(1)-2006(4), 1978(1)-1996(4) and 1998(1)-2006(4)


-.01

.00

.01

.02

.03

1 2 3 4 5 6 7 8 9 10


-.1

.0

.1

.2

.3

.4

1 2 3 4 5 6 7 8 9 10



195


-.010

-.005

.000

.005

.010

.015

.020

1 2 3 4 5 6 7 8 9 10


-.1

.0

.1

.2

.3

.4

1 2 3 4 5 6 7 8 9 10




-.02

-.01

.00

.01

.02

.03

.04

1 2 3 4 5 6 7 8 9 10


-.4

-.2

.0

.2

.4

.6

1 2 3 4 5 6 7 8 9 10





-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

1 2 3 4 5 6 7 8 9 10

1978-1996 1998-2006

7


-0.004

-0.002

0

0.002

0.004

0.006

0.008

1 2 3 4 5 6 7 8 9 10

1978-1996 1998-2006

7

7.2.4 VAR model (with different lag length) on G and L

Figures 7.4 (a) and Figure 7.4(c) show the GIRFs generated from innovation shocks

to dln(G) and dL in the VAR models employing a shorter lag length (one) using the

Schwarz-Bayes criterion for the periods 1978(1) – 2006(4) and 1998(1) – 2006(4).

These differ from the GIRFs generated from the VAR models with longer lag

selection as shown in Figure 6.4(a) and Figure 6.4(c) of Chapter 6. For Figure 6.4(b),

the VAR model over the period 1978(1) – 1996(4) employed a lag length of seven

196

using the Akaike information criterion. For the sub-sample period 1978(1) – 1996(4),

Figure 7.4 (b) suggests a significant negative impact of dln(G) on dL in third quarter,

with the effects reversing to become significantly positive in the seventh quarter.

Over the same period, Figure 7.4(b) also suggests no causality from dL to dln(G). For

the sub-sample period 1998(1) – 2006(4) in Figure 7.4(c), the IRF results corroborate

the earlier findings in section 6.4.4 (Chapter 6) that economic growth and banking

development tend to exhibit a negative bi-directional causality in the short term (1 to

2 quarters), but no casual relationship in the medium to longer term (3 to 10 quarters).

Nonetheless, with a shorter lag length of one, the impulse responses in Figure 7.4 (c)

tend to converge more quickly than those in Figure 6.4(c) which employed a longer

lag length of seven.

Figure 6.4 – Generalized IRFs of shocks to dL and dln(G) in the three periods:

1978(1)-2006(4), 1978(1)-1996(4) and 1998(1)-2006(4)


-.02

-.01

.00

.01

.02

.03

.04

1 2 3 4 5 6 7 8 9 10


-.08

-.04

.00

.04

.08

.12

.16

1 2 3 4 5 6 7 8 9 10



(b) Sub-sample (7-Lag VAR) for pre-Asian financial crisis period: 1978(1) – 1996(4)

-.010

-.005

.000

.005

.010

.015

.020

1 2 3 4 5 6 7 8 9 10


-.04

.00

.04

.08

1 2 3 4 5 6 7 8 9 10



197


-.03

-.02

-.01

.00

.01

.02

.03

1 2 3 4 5 6 7 8 9 10


-.2

-.1

.0

.1

.2

1 2 3 4 5 6 7 8 9 10





-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

1 2 3 4 5 6 7 8 9 10

1978-1996 1998-2006

7


-0.008

-0.006

-0.004

-0.002

0

0.002

0.004

1 2 3 4 5 6 7 8 9 10

1978-1996 1998-2006

7

Thus, the above analyses involving the selection of alternate lag lengths in the VAR

models for the full sample (1978(1) – 2006(4)) and sub-samples (1978(1) – 1996(4)

and 1998(1) – 2006(4)) suggest that the IRF results are robust with respect to different

lag lengths. Hence, we could conclude that the causality patterns between financial

development and economic growth (as discussed in the previous chapter) are largely

insensitive to the lag length selected in the VAR models.

7.3 Choleski Impulse Response Functions

To further test for the robustness of the results which employed generalized IRF in the

preceding chapter, the Choleski approach to IRF analysis is undertaken in this section.

As it is well known that the Choleski approach is sensitive to the variable ordering in

the innovations, different variable ordering was experimented with in this exercise.

Nonetheless, the outcome of varying the order of the variables does not seem to result

198

in qualitatively different results for the IRFs. This is because the correlation of the

equation errors for each VAR model is generally found to be low (below 0.15). The

Choleki IRFs are presented in this section to facilitate a direct comparison with the

generalized IRFs presented in the section 6.4 (Chapter 6) in testing for the robustness

of the results obtained earlier.

In generating the Choleski IRF for each model, it is notable that the cross-effect on

the second listed variable is the same as the generalized IRF which is discussed in

section 6.4 (Chapter 6). Consequently, the Choleski IRF associated with cross-effect

on the second listed variable (which is similar to the generalized IRF) will not be

reported. Hence, in the VAR model on Y and L, the Choleki IRF starting from zero

which corresponds to a particular variable ordering (dL, dln(Y)) will be combined

with the Choleki IRF starting from zero which corresponds to the alternate variable

ordering (dln(Y), dL). Similarly, in the other VAR models (involving G and T, Y

and T as well as G and L), the Choleski IRFs starting from zero which are generated

from two different variable orderings will be combined. To ensure clarity in the

presentation, the specific variable ordering of each Choleki IRF is annotated

accordingly as shown in Figures 7.5, 7.6, 7.7 and 7.8 in the subsequent sections.

7.3.1 VAR model on Y and L

Figure 7.5 shows the Choleski IRFs generated from innovation shocks to dL and

dln(Y) in the VAR model involving Y (real per capita GDP) and L (share of banking

loans to GDP) for the three periods: 1978(1) – 2006(4), 1978(1) – 1996(4) and

1998(1) – 2006(4). To facilitate the comparison between the Choleski IRFs and the

generalized IRFs, a ten-period horizon is similarly employed to allow for the

dynamics of the adjustment process to work through the system. Figure 7.5 shows

199

that the negative bilateral causality between banking development (dL) and economic

growth (dln(Y)), which was earlier found to exist using generalized IRF analysis in

chapter 6 (section 6.4.1), seems to be mitigated in the Choleski approach.

Nonetheless, this is simply an artifact of the Choleski diagonalisation whereby the

generated IRF always starts from zero, thus resulting in a smaller short-term effect.

Taking out the short-term effects, the Choleski IRFs in Figure 7.5 are qualitatively

similar to those in Figure 6.1 (Chapter 6), thus corroborating the findings on the

finance-growth linkages for the banking sector in chapter 6 (section 6.4.1).

Importantly, Figure 7.5(d), which compares the finance-growth relationship before

and after the 1997 crisis, underlines the increased volatility of the finance-growth

nexus in the post-crisis period.

Figure 7.5 – Choleski IRFs of shocks to dL and dln(Y) in the three periods:

1978(1)-2006(4), 1978(1)-1996(4) and 1998(1)-2006(4)


-.04

.00

.04

.08

.12

1 2 3 4 5 6 7 8 9 10


Variable ordering: dL, dln(Y),

Response to Cholesky One S.D. Innovations ± 2 S.E.

-.008

-.004

.000

.004

.008

.012

.016

.020

.024

1 2 3 4 5 6 7 8 9 10


Variable ordering: dln(Y), dL



-.04

-.02

.00

.02

.04

.06

.08

.10

.12

1 2 3 4 5 6 7 8 9 10


Variable ordering: dL, dln(Y)


-.010

-.005

.000

.005

.010

.015

.020

1 2 3 4 5 6 7 8 9 10


Variable ordering: dln(Y), dL


200


-.12

-.08

-.04

.00

.04

.08

.12

1 2 3 4 5 6 7 8 9 10


Variable ordering: dL, dln(Y)


-.03

-.02

-.01

.00

.01

.02

.03

1 2 3 4 5 6 7 8 9 10


Varible ordering: dln(Y), dL


(d) Comparing Choleski impulse responses in the two sub-sample periods

Variable ordering: dL, dln(Y) Variable ordering: dln(Y), dL


-0.06

-0.04

-0.02

0

0.02

0.04

0.06

1 2 3 4 5 6 7 8 9 10

1978-1996 1998-2006

Response of dn(Y) to dL

-0.006

-0.004

-0.002

0

0.002

0.004

0.006

0.008

0.01

1 2 3 4 5 6 7 8 9 10

1978-1996 1998-2006

7.3.2 VAR model on G and T

Figure 7.6 shows the Choleski impulse responses of shocks to T and dln(G) in the

VAR model involving G (real GDP) and T (share of turnover to GDP) over the three

periods: 1978(1) – 2006(4), 1978(1) – 1996(4) and 1998(1) – 2006(4). Taking into

account the smaller short-term effects due to the Choleski diagonalisation, the

dynamic impulse responses in the medium to long term (3 to 10 quarters) are

qualitatively similar for the Choleski and generalized IRFs. Thus, the IRF results on

G and T using the Choleski approach tend to support the findings in the earlier

chapter (which employed the generalized IRF approach) that there is bilateral

causality between stock-market development and economic growth in the medium

term (3 to 6 quarters) before the 1997 Asian financial crisis, but the causal

201

relationship between the stock market and the real economy weakened considerably

after the crisis. This is particularly evident in Figure 7.6(d), which shows that the

Choleski-based impulse responses die out more rapidly in the post-crisis period as

compared to those in the pre-crisis period.

Figure 7.6 – Choleski IRFs of shocks to T and dln(G) in the three periods:

1978(1)-2006(4), 1978(1)-1996(4) and 1998(1)-2006(4)


-.2

-.1

.0

.1

.2

.3

.4

1 2 3 4 5 6 7 8 9 10


Variable ordering: T, dln(G)


-.010

-.005

.000

.005

.010

.015

.020

.025

.030

1 2 3 4 5 6 7 8 9 10


Variable ordering: dln(G), T



-.1

.0

.1

.2

.3

.4

1 2 3 4 5 6 7 8 9 10




-.010

-.005

.000

.005

.010

.015

.020

1 2 3 4 5 6 7 8 9 10





-.2

-.1

.0

.1

.2

.3

.4

.5

1 2 3 4 5 6 7 8 9 10




-.010

-.005

.000

.005

.010

.015

.020

.025

.030

1 2 3 4 5 6 7 8 9 10




202


Variable ordering: T, dln(G) Variable ordering: dln(G), T


-0.03

-0.02

-0.01

0

0.01

0.02

0.03

0.04

1 2 3 4 5 6 7 8 9 10

1978-1996 1998-2006

Response o f dln(G) to T

-0.004

-0.002

0

0.002

0.004

0.006

0.008

1 2 3 4 5 6 7 8 9 10

1978-1996 1998-2006

7.3.3 VAR model on Y and T

Figure 7.7 shows the Choleski-based IRFs corresponding to innovation shocks in T

and dln(Y) in the VAR models for the three periods. Visual inspection of Choleski

IRFs in Figure 7.7 again suggests a close similarity with Figure 6.3 (in Chapter 6).

Thus, the Choleski IRF analysis on the VAR model involving Y and T lends further

credence to the conclusions on the dynamic causality between stock-market

development and economic growth. Importantly, Figure 7.7 supports the view that

the positive bilateral effects between the stock market and the real economy tend to

become weaker in the post-1997 Asian financial crisis period. This is consistent with

the earlier findings in section 6.4.3 (Chapter 6) which indicates a weaker relationship

between the stock market and the real economy after the 1997 Asian financial crisis.

Figure 7.7 – Choleski IRFs of shocks to T and dln(Y) in the three periods:

1978(1)-2006(4), 1978(1)-1996(4) and 1998(1)-2006(4)


-.2

-.1

.0

.1

.2

.3

.4

1 2 3 4 5 6 7 8 9 10


Variable ordering: T, dln(Y)


-.010

-.005

.000

.005

.010

.015

.020

.025

.030

1 2 3 4 5 6 7 8 9 10


Variable ordering: dln(Y), T


203


-.2

-.1

.0

.1

.2

.3

.4

1 2 3 4 5 6 7 8 9 10




-.010

-.005

.000

.005

.010

.015

.020

1 2 3 4 5 6 7 8 9 10





-.2

-.1

.0

.1

.2

.3

.4

.5

1 2 3 4 5 6 7 8 9 10




-.010

-.005

.000

.005

.010

.015

.020

.025

.030

1 2 3 4 5 6 7 8 9 10





Variable ordering: T, dln(Y) Variable ordering: dln(Y), T


-0.04

-0.02

0

0.02

0.04

0.06

0.08

1 2 3 4 5 6 7 8 9 10

1978-1996 1998-2006


-0.004

-0.002

0

0.002

0.004

0.006

1 2 3 4 5 6 7 8 9 10

1978-1996 1998-2006

7.3.4 VAR model on G and L

Figure 7.8 exhibits the Choleski IRFs of innovation shocks in dln(G) and dL for the

VAR model involving G (real GDP) and L (share of banking loans to GDP) in the

three periods. As explained earlier in section 7.3.1, the adverse short-term (1 to 2

quarters) effect of banking development (proxied by dL) on economic growth

(proxied by dln(G)) and vice versa appear more subdued in the Choleski approach due

204

to the diagonalisation process which generates the impulse response functions from

the starting point of zero. Taking out this short-term effect, the longer term (3 to 10

quarters) shapes of the Choleski-based IRFs tend to mirror the shapes of the

generalized IRFs in Figure 6.4 (Chapter 4). As indicated in Figure 7.8(d), the causal

relationship between the banking sector and the rest of the economy appears to have

become more volatile in the post-Asian financial crisis period (1998(1) – 2006(4)) as

compared to that in the pre-crisis period (1978(1) – 1996(4)). This lends further

support to the view that the finance-growth nexus, from the perspective of banking

sector development, has become more erratic after the 1997 Asian financial crisis.

Figure 7.8 – Choleski IRFs of shocks to dL and dln(G) in the three periods:

1978(1)-2006(4), 1978(1)-1996(4) and 1998(1)-2006(4)


-.08

-.04

.00

.04

.08

.12

1 2 3 4 5 6 7 8 9 10


Variable ordering: dL, dln(G)


-.008

-.004

.000

.004

.008

.012

.016

.020

.024

1 2 3 4 5 6 7 8 9 10


Variable ordering: dln(G), dL



-.04

-.02

.00

.02

.04

.06

.08

.10

1 2 3 4 5 6 7 8 9 10




-.010

-.005

.000

.005

.010

.015

.020

.025

1 2 3 4 5 6 7 8 9 10




205


-.12

-.08

-.04

.00

.04

.08

.12

1 2 3 4 5 6 7 8 9 10




-.03

-.02

-.01

.00

.01

.02

.03

1 2 3 4 5 6 7 8 9 10





Variable ordering: dL, dln(G) Variable ordering: dln(G), dL


-0.06

-0.04

-0.02

0

0.02

0.04

0.06

1 2 3 4 5 6 7 8 9 10

1978-1996 1998-2006

Response of dn(G) to dL

-0.006

-0.004

-0.002

0

0.002

0.004

0.006

0.008

0.01

1 2 3 4 5 6 7 8 9 10

1978-1996 1998-2006

7.4 GIRFs Generated from Cointegrated Systems (VECM)

To further test for the robustness of the results, impulse response functions will be

generated using the Vector Error Correction Model (VECM) in cases where

cointegration tests (in chapter 5) suggest the possibility of cointegration in the VAR

models. Given the findings in section 5.2.2 (Chapter 5) that the variables Y, L and G

are I(1) while the variable T is I(0), it implies that the pairs of variables Y-T and G-T

cannot be cointegrated as cointegration between variables can only occur when they

are integrated of the same order (explained in chapter 4). Thus, it is only meaningful

to estimate VECMs for the pairs of variables Y-L and G-L in instances where

cointegration tests (in chapter 5) pointed to the prospect of cointegration of the

variables.

206

Using the detailed results of the cointegration tests (as shown in Appendices 4A, 4B

and 4C), the following lags are selected for the different models of VECMs involving

Y-L and G-L in the three different periods (1978(1)-2006(4), 1978(1)-1996(4) and

1998(1)-2006(4)):

Table 7.2: Selection of lag length for VECMs for Y-L and G-L in the three different periods

Selected lag length for VECM in Y and L Selected lag length for VECM in G and L

1978-2006 1978-1996 1998-2006 1978-2006 1978-1996 1998-2006

Model 2 6 8 6 6 8 2

Model 3 5 3 4 6 3 8

Model 4 4 5 1 6 5 5

Model 2: VECM with intercept (no trend) in the CE and no intercept or trend in the VAR Model 3: VECM with intercept in CE and VAR, but no trends in CE and VAR Model 4: VECM with intercept in CE and VAR, linear trend in CE and no trend in VAR

For each model in the VECM, the appropriate lag length is chosen for cases where

both the trace test and the maximum eigenvalue test jointly indicate the presence of

cointegration. Impulse response functions were subsequently generated for each

VECM in the respective model using the selected lag length as indicated in Table 7.2.

7.4.1 GIRFs from VECM involving Y and L

Figure 7.9 shows the generalized impulse response functions (GIRFs) generated from

the VECM involving Y and L over the three periods: 1978(1)-2006(4), 1978(1)-

1996(4) and 1998(1)-2006(4). As a large number of the GIRFs generated from the

VECMs were found to generally converge after about 30 periods (rather than after 10

periods), a 30-period horizon is employed in the analyses. However, there were still

some GIRFs which had not converged and did not appear to be converging even after

30 periods. These non-convergent GIRFs, which were particularly distinct for shocks

from L to ln(Y), provide support for using the original formulation dL and dln(Y).

207

Notably, the GIRFs in Figure 7.9 appear to be broadly similar in shapes to the

cumulative GIRFs generated for the VAR model for Y and L (as shown in section

6.5.1 in Chapter 6). Moreover, regardless of the model and lag length of the VECM,

the generated impulse response functions from the VECMs suggest initial (for t=1 and

t=2) adverse effects of banking activities (L) on economic growth (ln(Y)) and vice-

versa during the pre- and post-1997 Asian financial crisis periods. This finding,

which indicates negative bilateral causality between banking sector development and

economic growth in the short term (1 to 2 quarters) before and after the 1997 financial

crisis, is consistent with earlier findings on the finance-growth nexus as explained in

the preceding sections. Additionally, the range of fluctuation of the impulse response

functions appear to be larger in the post-crisis period (1998(1)-2006(4)) compared to

that in the pre-crisis period (1978(1)-1996(4)), suggesting more variability in the

finance-growth nexus after the 1997 crisis. Finally, the GIRFs generated from the

VECMs (Models 2 and 3) in Figure 7.9 (c) suggest that banking development has a

positive long-term effect on economic activities in the post-crisis period.

Figure 7.9 – VECM-generated GIRFs of shocks to L and ln(Y) in the three periods:

1978(1)-2006(4), 1978(1)-1996(4) and 1998(1)-2006(4)

(a) Full sample period: 1978(1) – 2006(4) Model 2: VECM (6 Lag) with intercept (no trend) in the CE and no intercept or trend in the VAR

-.06

-.04

-.02

.00

.02

.04

5 10 15 20 25 30

Response of Ln(Y) to L

-.10

-.05

.00

.05

.10

.15

5 10 15 20 25 30

Response of L to Ln(Y)

Response to Generalized One S.D. Innovations

208

Model 3: VECM (5 lag) with intercept in CE and VAR, but no trends in CE and VAR

-.02

-.01

.00

.01

.02

.03

5 10 15 20 25 30


-.10

-.05

.00

.05

.10

.15

5 10 15 20 25 30



Model 4: VECM (4 lag) with intercept in CE and VAR, linear trend in CE and no trend in VAR

-.02

-.01

.00

.01

.02

.03

5 10 15 20 25 30


-.08

-.04

.00

.04

.08

.12

5 10 15 20 25 30



(b) Sub-sample for pre-Asian financial crisis period: 1978(1) – 1996(4) Model 2: VECM (8 Lag) with intercept (no trend) in the CE and no intercept or trend in the VAR

-.04

-.03

-.02

-.01

.00

.01

.02

5 10 15 20 25 30


-.04

.00

.04

.08

.12

5 10 15 20 25 30




-.02

-.01

.00

.01

.02

5 10 15 20 25 30


-.050

-.025

.000

.025

.050

.075

.100

5 10 15 20 25 30



209


-.02

-.01

.00

.01

.02

.03

5 10 15 20 25 30


-.08

-.04

.00

.04

.08

.12

5 10 15 20 25 30



(c) Sub-sample for post-Asian financial crisis period: 1998(1) – 2006(4) Model 2: VECM (6 Lag) with intercept (no trend) in the CE and no intercept or trend in the VAR

-.04

-.02

.00

.02

.04

.06

5 10 15 20 25 30


-.12

-.08

-.04

.00

.04

.08

.12

.16

5 10 15 20 25 30




-.02

-.01

.00

.01

.02

.03

5 10 15 20 25 30


-.12

-.08

-.04

.00

.04

.08

.12

5 10 15 20 25 30




-.03

-.02

-.01

.00

.01

.02

.03

5 10 15 20 25 30


-.2

-.1

.0

.1

.2

5 10 15 20 25 30



210

7.4.2 GIRFs from VECM involving G and L

Figure 7.10 exhibits the generalized impulse response functions (GIRFs) generated

from the VECM involving G and L over the three periods: 1978(1)-2006(4), 1978(1)-

1996(4) and 1998(1)-2006(4). Like the GIRFs obtained from the VECM involving Y

and L (in the preceding section), a substantial number of GIRFs generated from the

VECM involving G and L were found to converge after 30 periods. Consequently, a

30-period time horizon was chosen for the impulse response analyses. Nonetheless

there were some GIRFs, particularly those generated from shocks of L to ln(G), which

did not appear to be converging even after 30 periods. These non-convergent GIRFs

provide further reason for using the original formulation of dL and dln(G).

Visual inspection of the GIRFs in Figure 7.10 suggests that they are roughly similar in

shapes to the cumulative GIRFs generated for the VAR model for G and L (as shown

in section 6.5.4 in chapter 6). Moreover, Figures 7.10(b) and 7.10(c) suggest that

banking activities (proxied by L) and economic growth (proxied by ln(G)) have

mutually adverse effects on each other in short term (1 to 2 quarters) during the pre-

and post-crisis periods. This provides further support for the view that there is

negative bilateral causality between banking and economic development in the short

term (1 to 2 quarters) before and after the 1997 Asian financial crisis. Furthermore,

the GIRFs generated from the VECM (Model 3) in Figure 7.10 (c) provide some

evidence that banking activities have positive long-term effects on economic growth

in the post-crisis period.

211

Figure 7.10 – VECM-generated GIRFs of shocks to L and ln(G) in the three periods:

1978(1)-2006(4), 1978(1)-1996(4) and 1998(1)-2006(4)

(a) Full sample period: 1978(1) – 2006(4) Model 2: VECM (6 Lag) with intercept (no trend) in the CE and no intercept or trend in the VAR

-.08

-.04

.00

.04

.08

5 10 15 20 25 30

Response of Ln(G) to L

-.10

-.05

.00

.05

.10

.15

5 10 15 20 25 30

Response of L to Ln(G)



-.03

-.02

-.01

.00

.01

.02

.03

5 10 15 20 25 30


-.10

-.05

.00

.05

.10

.15

5 10 15 20 25 30




-.03

-.02

-.01

.00

.01

.02

.03

5 10 15 20 25 30


-.10

-.05

.00

.05

.10

.15

5 10 15 20 25 30



(b) Sub-sample for pre-Asian financial crisis period: 1978(1) – 1996(4) Model 2: VECM (8 Lag) with intercept (no trend) in the CE and no intercept or trend in the VAR

-.08

-.06

-.04

-.02

.00

.02

.04

5 10 15 20 25 30


-.10

-.05

.00

.05

.10

.15

.20

5 10 15 20 25 30



212


-.03

-.02

-.01

.00

.01

.02

.03

5 10 15 20 25 30


-.08

-.04

.00

.04

.08

.12

5 10 15 20 25 30




.00

.01

.02

.03

5 10 15 20 25 30


-.04

.00

.04

.08

.12

.16

5 10 15 20 25 30



(c) Sub-sample for post-Asian financial crisis period: 1998(1) – 2006(4) Model 2: VECM (2 Lag) with intercept (no trend) in the CE and no intercept or trend in the VAR

-.08

-.04

.00

.04

.08

5 10 15 20 25 30


-.2

-.1

.0

.1

.2

5 10 15 20 25 30




-.02

-.01

.00

.01

.02

.03

5 10 15 20 25 30


-.15

-.10

-.05

.00

.05

.10

5 10 15 20 25 30



213


-.03

-.02

-.01

.00

.01

.02

.03

.04

5 10 15 20 25 30


-.2

-.1

.0

.1

5 10 15 20 25 30



7.5 Summary of Robustness Test Results

Following the battery of robustness tests undertaken in sections 7.2, 7.3 and 7.4 we

can broadly summarize the outcome in Table 7.3 (below).

Table 7.3: Summary of robustness test results on the finance-growth nexus

Economic Growth (Y)*

Short-term

(1 to 2 quarters)

Medium-term

(3 to 6 quarters)

Cumulative long-

term effects

(7 to 10 quarters)

Banking

sector

development

(L)

Pre-1997

Asian

financial

crisis

Negative bi-


between Y and L

OR

Negative causality

from Y to L

Positive causality

from Y to L

OR

No causality

between Y and L

Negative effects

of Y on L and

vice versa

Post-1997

Asian

financial

crisis

Negative bi-


between Y and L

OR

Negative causality

from Y to L

Positive causality

from Y to L with

increased volatility

in the finance-

growth nexus

OR

No causality

between Y and L

Negative effects

of Y on L and

vice versa

OR

Positive effect

of L on Y

Stockmarket

development

(T)

Pre-1997

Asian

financial

crisis

Positive bi-


between Y and T

OR

Positive causality

from Y to T

Positive bi-

directional

causality between

Y and T

Positive effect

of Y and T and

vice versa

Post-1997

Asian

financial

crisis

No causality

between Y and T

No causality

between Y and T

Positive effect

of Y and T and

vice versa

Note: The IRF results are qualitatively the same when Y (real per capita GDP) is substituted for G (real

GDP growth) in the VAR models

214

The results largely confirm the findings in Chapter 6 regarding the different causality

patterns between financial development and economic growth in Singapore in the

short and medium terms as well as the cumulative long-term effects of each sector on

the other. Though the robustness tests provide a few more possibilities in the finance-

growth relationship in the short and medium terms, they largely corroborate the key

findings in the previous chapter. Importantly, like the results obtained in the Chapter

6, the robustness test results in this chapter continue to point to a change in the

finance-growth relationship after the 1997 Asian financial crisis. The finance-growth

relationship was found to be more volatile in the banking sector after 1997 following

the Asian financial crisis. The positive bilateral causality between stockmarket

activities and economic development in the short to medium term (1 to 6 quarters)

during the pre-crisis period was also found to less persistent in the post-crisis period.

The robustness tests therefore largely confirm the findings in Chapter 6 that structural

changes and financial de-regulation in the post-1997 period have led to a weakening

of the finance-growth linkages in the Singapore economy.

7.6 Conclusion

In this chapter we have undertaken a series of robustness tests using impulse response

analyses to assess the validity of the results obtained in the previous chapter. In

employing different lag lengths, adopting the Choleski approach and generating

generalized impulse responses from VECMs in cases where the variables could be

cointegrated, the impulse response analyses suggest that the earlier results (obtained

in Chapter 6) regarding the dynamic causality between financial development and

economic growth are largely robust in the short and medium terms. For the banking

sector, there is negative bilateral causality between financial and economic

215

development in the short term but positive causality from economic growth to

financial development in the medium term. The finance-growth linkages are also

found to be more volatile in the post-1997 Asian financial crisis period compared to

that in the pre-crisis period. With regard to the stock-market, it was found that there

is positive bi-directional causality between GDP growth and stock-market activities in

the short and medium terms before the onset of the 1997 Asian financial crisis.

However, there is no significant evidence of causal linkages between the stock market

and the real economy in the post-crisis period. This could be attributable to financial

deregulation in the post-crisis period, which resulted in a weakening of the finance-

growth nexus over the period. The policy implications and inferences of these results

will be discussed in the concluding chapter that follows.

216

Chapter 8

SUMMARY AND CONCLUSION

8.1 Introduction

In the preceding chapter the robustness of our earlier conclusions regarding the

direction of causality between economic growth and financial development was

extensively tested. The results of this robustness-testing were also reported together

with the underlying reasons for the direction of causality between the financial sector

and the real economy were also explained. This final chapter, which draws the study

to a conclusion, consists of six substantive sections. Section 8.2 provides a summary

of the research while Section 8.3 summarizes the key findings in the study. Section

8.4 highlights the main implications of the causality results obtained from the study.

Section 8.5 suggests some limitations of the study and Section 8.6 indicates the areas

of further research. Section 8.7 concludes the study.

8.2 Summary of Research

The literature suggests a wide range of economic models for analyzing the finance-

growth nexus. These economic models include the McKinnon-Shaw model, neo-

Structuralist model and endogenous growth models. They provide varying reasons

for different causality patterns between financial development and economic growth.

Nevertheless, economic theory does not provide a clear guide on the direction of

causation between the financial sector and the real economy.

The early empirical literature, in particular, did not address causality specifically but

assumed it to be from financial development to growth. Where the empirical

literature does address the causality question, the evidence is very much mixed and

217

can be broadly classified under five major categories: (a) uni-directional causality

from financial development to economic growth (b) uni-directional causality from

economic growth to financial development (c) bi-directional causality between

financial development and economic growth (d) no relationship between financial

development and economic growth (e) negative effects of financial development on

economic growth.

Most of the empirical literature is based on multi-country, cross-section or panel data

sets. Single-country time-series studies, which are relatively fewer in number, also

have their value, particularly in the analysis of the causality patterns between financial

and economic development. Time-series approach is more appropriate than a cross-

sectional approach for assessing the relationship between financial development and

economic growth as different countries are at different stages of economic

development. There are only three single-country time-series studies which

specifically focused on the relationship between financial development and economic

growth in Singapore (Murinde and Eng, 1994; Ariff and Khalid, 2000; Khalid and

Tyabji, 2002). None of these studies has employed the long time frame found in this

study, or has analyzed the finance-growth nexus at different stages of Singapore‟s

economic development in assessing whether the finance-growth relationship could

change over time.

In this study, the vector auto-regression (VAR) model was employed to investigate

the relationship between financial development and economic growth. The VAR

model was employed as it is appropriate for analyzing a system of interrelated time

series and assessing the dynamic impact of a change in one variable on all

218

endogenous variables of the model. The VAR model is non-structural in the sense

that it does not require any “incredible restrictions” (Sims, 1972) to identify the model

and treats all variables within the model as endogenous. Hence, the VAR model is

essentially an “atheoretical empirical model …that can be used as a framework for

formal examination of inter-relationships within a given data set without the need to

specify a theoretical framework a priori ”(Groenewold, 2003, p.458).

There were two different variables used to represent economic growth, namely real

GDP and real GDP per capita. With regard to indicators for financial development,

the variable financial loans over nominal GDP was used to represent banking sector

development while the variable stock-market turnover over nominal GDP was used to

represent stock-market activities. The study utilized quarterly time-series data from

1978 to 2006. Four different bivariate VAR models were constructed to investigate

the finance-growth relationship:

(i) VAR model on real GDP per capita (Y) and financial loans over nominal GDP (L)

(ii) VAR model on real GDP (G) and financial loans over nominal GDP (L)

(iii)VAR model on real GDP (G) and stock-market turnover over nominal GDP (T)

(iv) VAR model on real GDP per capita (Y) and stock-market turnover over nominal

GDP (T)

For the purposes of estimation, a different VAR model is constructed for (a) the

whole sample period 1978-2006 and (b) for each of the two different sub-periods

1978-1996 and 1998-2006. A battery of tests was undertaken on the data underlying

the variables used in the study to determine the stationarity and cointegration of the

variables. The Augmented Dickey Fuller (ADF) test indicated that the variables Y, G

and L were integrated of order 1 (i.e. I(1)) while the variable T was stationary in its

219

levels (i.e. I(0)). The ADF test results implied that the variables G and T and the

variables Y and T could not be cointegrated (as cointegration can only occur if the

variables were integrated of the same order). The results of cointegration tests further

suggested that the other two pairs of variables, namely Y and L as well as G and L,

were also not cointegrated.

The Granger causality test was conducted to examine the causality between the four

pairs of variables – Y-L, G-T, Y-T and G-L. Following evidence of causality among

some pairs of variables (such as Y-L, G-T and G-L), the VAR model was estimated

for all the four pairs of variables to further investigate the causality among the

variables as a means to understanding the relationship between financial development

(as proxied by L and T) and economic growth (as proxied by Y and G). The

estimated equations in the four bivariate VAR models (Y-L, G-T, Y-T and G-L)

provided some evidence regarding the change in finance-growth relationship in the

pre- and post-crisis periods. Nonetheless, it was argued that multi-collinearity among

the variables tended to pose problems for the “accuracy” with which the coefficients

could be estimated thus resulting in problems for the interpretation of the coefficients

in the estimated equations. Consequently, further analysis was undertaken employing

impulse response functions which were generated for the four bivariate VAR models

(Y-L, G-T, Y-T and G-L) in order to reveal the dynamic (quarter-by-quarter) inter-

relationships among the endogenous variables in the finance-growth nexus.

The impulse response function (IRF) serves as a critical tool for analyzing the

dynamic relationships between the variables. The IRF traces the dynamic effects of a

once-off shock in the error of the equation which contains the variable on the current

220

and future values of all endogenous variables in the VAR model. The results of the

impulse response analyses in relation to the four VAR models are summarized in the

subsequent section. Additionally, the results of robustness tests employing impulse

response analyses in the four VAR models are also posted and analyzed in the

following section.

8.3 Summary of Findings

In testing for the causal relationship between financial development and economic

growth over the period 1978(1) – 2006(4), the Granger causality test was conducted to

examine the causality between the four pairs of variables – Y-L, G-T, Y-T and G-L.

The appropriate lag length in the Granger causality test was selected using the

standard Akaike, Schwarz-Bayes and Hannan-Quinn criteria with the additional

requirement to ensure the absence of autocorrelation in the optimal lag choice.

Following evidence of causality among some pairs of variables (such as Y-L, G-T,

and G-L), the VAR model was estimated for all the four pairs of variables (Y-L, G-T,

Y-T and G-L) to further investigate the causality among the variables as a means to

understanding the relationship between financial development (as proxied by L and

G) and economic growth (as proxied by Y and G).

The four estimated VAR models (Y-L, G-T, Y-T and G-L) in the full sample period

(1978(1) – 2006(4)) performed poorly with relatively low adjusted R2 as all the

models failed the standard test for structural stability at the break point of 1997(2)

chosen to coincide with the onset of the Asian financial crisis. The four VAR models

were therefore estimated and simulated over two sub-sample periods: 1978(1) –

1996(4) and 1998(1) – 2006(4). On the whole, the four models performed

221

substantially better in separate sub-samples, lending evidence of a structural break in

1997 when the Asian financial crisis erupted.

The Granger causality results suggest no causal relationship between stock market

activities and economic growth over the two periods: 1978(1)-2006(4) and 1978(1)-

1996(4). However, over the period 1998(1)-2006(4), there is evidence that stock

market activities Granger cause economic development. For the banking sector, the

Granger causality results suggest a bilateral causal relationship between bank sector

development and economic growth over the period 1978(1)-2006(4) and

unidirectional causality from economic growth to banking development over the

period 1998(1)-2006(4). However, over the period 1978(1)-1996(4), the Granger

causality results suggest no causal relationship between banking and economic

development. Taken together, the estimated equations in the VAR models (Y-L, G-T,

Y-T and G-L) for the whole sample period (1978-2006) and the sub-sample periods

(1978-1996 and 1998-2006) provide some evidence on the causal linkages between

financial development and economic growth over the period 1978-2006 as well as a

change in the finance-growth relationship in the pre- and post-crisis periods.

Nevertheless, while the Granger causality tests indicate a causal relationship between

financial development and economic growth (particularly with respect to banks), they

do not show the relative magnitude of the impact of one sector on the other nor the

dynamics of the causal relationship between the financial sector and the real economy

over time. Thus, impulse response functions were generated to examine the dynamic

inter-relationships between the financial sector and the real economy.

222

The Paseran and Shin (1998) procedure, which utilizes generalized impulses, was

employed to generate the period-by-period IRFs for each of the four bivariate VAR

models (Y-L, G-T, Y-T and G-L). To further examine the results of the impulse

response analyses, cumulative impulse response functions were generated to assess

the impact of the innovation shocks as accumulated responses rather than period-by-

period responses.

On the basis of impulse response analyses, it was found that there were different

causality patterns between financial development and economic growth in Singapore

for the stock market and banks. The IRFs suggested negative bi-directional causality

between banking development and economic activities in the short-term (1 to 2

quarters). While the VAR model is non-structural (i.e. it does not allow for definitive

economic interpretation of the results), it is interesting to consider the possible

economic mechanisms underlying the results. The initial negative impact of

economic growth on banking development could be explained by the higher profits of

firms resulting from economic growth which enable firms to have more access to

internal funds and hence less need for corporate borrowing and bank loans in the short

term (Dornbusch, Fischer and Startz, 2001). Conversely, the initial negative impact

of banking sector development on economic growth could be explained in terms of

the weakness of the banking system (Nili and Rastad, 2007; Zhang, 2003).

The weakness of the Singaporean financial system could be due to the government‟s

excessive protection of domestic banks by segregating between domestic financial

activities and offshore financial activities in order to shelter the domestic economy

from unforeseen financial turmoil in international markets and shield local banks from

223

competition by overseas banks (Lim, 1988; Khalid and Tyabji, 2002). The lack of

transparency among the largely family-owned local banks added to the weakness in

the Singapore banking system. As a result of this weakness, financial intermediation

leads to an inefficient distribution of loans thereby undermining the quality of

investment and growth in the economy (Nili and Rastad, 2007; Zhang, 2003).

Furthermore, from the perspective of banking sector development, the IRFs also

suggested that there was a change in the finance-growth relationship after the 1997

Asian financial crisis. The linkages between banks and the real economy appeared to

be more volatile in the aftermath of 1997 financial crisis. This could be because the

financial development of Singapore might have entered into a more volatile phase of

evolution in the post-1997 period due to the growth of new financial sectors such as

wealth advisory and treasury services, which are vulnerable to fluctuations in market

sentiments. Another reason for the more variable finance-growth nexus in relation to

banking sector development could be the de-regulation of banks in 1998 which

involved a shift in government policy away from a “prescriptive, rule-based”

regulatory framework in the pre-crisis period to a more “flexible, risk-based” style in

the post-crisis period which allowed banks to innovate financial products that are

riskier and tied to volatile market development (S. Tan, 2006).

From the perspective of stock-market development, the IRFs also found that there was

bilateral causality between stock-market activities and the real economy (in the short

to medium term) over the period 1978-1996. Thus, prior to the onset of the 1997

Asian financial crisis, the study indicated that there was a mutually causal relationship

between stock-market development and economic growth in Singapore with stock-

224

market activities stimulating economic growth while stronger economic growth, in

turn, fed back to sustain stock-market development. However, over the period 1998-

2006, the study suggested that there was no causal relationship between stock-market

development and economic growth. Hence, the mutually beneficial linkages between

stock-market activities and economic development (in the short to medium term) prior

to the onset of the 1997 Asian financial crisis seemed to be less persistent in the post-

crisis period. This could be due to de-regulatory measures implemented for capital

markets in Singapore during the post-1997 period which opened up the domestic

stock market to international capital flows thereby weakening the relationship

between stock-market activities and economic growth (Khalid and Tyabji, 2002).

These results are in line with the research findings by Groenewold (2003) which

suggested that the 1983 financial deregulation in Australia tended to weaken the

relationship between stock market development and economic growth in the post-

regulation period.

The study also undertook a series of robustness-testing which employed impulse

response analyses. Separate impulse response functions (IRFs) were generated for

different lags in cases where the optimal lag lengths suggested different results.

Moreover, the Choleski impulse response functions were also generated to compare

with the results obtained from generalized IRF analysis. Additionally, generalized

impulse response functions were generated from Vector Error Correction Models

(VECMs) for cases where the pairs of variables (namely Y-L and G-L) showed signs

of cointegration. Importantly, the robustness tests were undertaken for

methodological completeness and thoroughness to test, not only the specification of

the model, but also the conclusions of the study.

225

The robustness tests which employed impulse response analyses using different lag

lengths, adopting the Choleski approach and generating generalized impulse response

functions from Vector Error Correction Models (VECMs) in cases where the

variables could be cointegrated, tended to lend support to the conclusions of the study.

Taken together, the robustness tests largely confirmed the findings with regard to

changes in the dynamic causality between financial development and economic

growth in Singapore during the pre-1997 and post-1997 Asian financial crisis periods.

The robustness tests corroborated the finding that the finance-growth relationship

tended to become more volatile in the banking sector in the post-crisis period (1998-

2006) as compared to that in the pre-crisis period (1978-1996). Moreover, the

robustness tests also suggested that the positive bilateral causality between stock-

market activities and economic development in the short to medium term (1 to 6

quarters) was weaker in the post-crisis period (1998-2006) as compared to that in the

pre-crisis period (1978-1996). Thus, the robustness tests tended to support the

conclusion that structural changes and financial de-regulation in the post-1997 Asian

financial crisis period could have led to a weakening of the finance-growth nexus in

Singapore.

In summary, the study suggested negative bi-directional causality between banking

development and economic growth in Singapore. From the perspective of banking

sector development, the finance-growth nexus did not remain constant over time and

tended to be more volatile after being subjected to major shocks such as the 1997

Asian financial crisis. Additionally, the study also indicated positive bi-directional

causality between stock-market activities and economic growth in Singapore. From

the perspective of stock-market development, the finance-growth nexus also did not

226

stay constant over time. The mutually beneficial linkages between stock-market

activities and economic growth seemed to be less persistent after being subjected to

major shocks like the 1997 Asian financial crisis.

8.4 Limitations of the Study

There are several limitations in this study. These limitations are commonly associated

with past time-series research on the finance-growth nexus. Nonetheless, the

limitations do not undermine the robustness of the results or the significance of the

findings in the study.

a) The financial industry includes other sectors such as insurance and bond

markets. Additionally, the study highlighted that fee-based activities such as

treasury activities and wealth advisory services are becoming increasingly

important in recent years. The linkages between these sectors within the

financial industry and the rest of the economy have been excluded in the

study.

b) This research is a country-specific study on the Singaporean economy.

Consequently, the relationship between financial development and economic

growth is assessed against the backdrop of socio-economic and political

circumstances which are unique to Singapore. Given the different economic

and political conditions prevailing in other countries, the results on the

finance-growth nexus obtained from this study might not be easily generalized

to other economies.

c) In this study, the two indicators employed for measuring economic

development are real GDP and real GDP per capita. Arguably, both these

227

indicators are not perfect measures of the well-being of a country which

economic development would imply. For example, the extent of

environmental pollution, which undermines the quality of life, is not captured

in the two indicators. Moreover, the statistics (on real GDP and real GDP per

capita) merely reflect population averages which overlook the extent of

unequal distribution of income among the populace.

8.5 Areas of Further Research

The study suggests several areas where further research could be undertaken. These

include:

a) The study adopts a bivariate VAR system involving two key variables, namely

a proxy variable for economic growth (real GDP or real GDP per capita) and

another proxy variable for financial development (bank loans over nominal

GDP or stock-market turnover over nominal GDP). To enrich the analysis, it

might be useful to incorporate additional variables in the VAR system. For

example, Shan (2005) argued that the degree of trade openness as measured by

the ratio of the sum of exports and imports to GDP is a major determinant of

economic growth. This seems reasonable for a small open economy like

Singapore where external trade is more than three times its GDP.

b) Singapore‟s position as an international financial centre is well-supported by

its policy of allowing international capital to flow freely into and out of the

domestic economy without any capital restrictions. Capital inflows provide a

critical source of funds for companies which are listed in the domestic stock

exchange, thus influencing stock-market activities. In this regard, it might be

228

useful to assess the impact of volatile capital flows on stock-market activities

from the perspective of the finance-growth nexus in Singapore. However, the

challenge lies in the span of data availability on capital flows which might be

too short for a meaningful assessment of the finance-growth relationship in the

pre- and post-1997 Asian financial crisis periods.

c) In this study, we have only examined the stock market and banking sectors

within the financial industry to assess their linkages with economic growth.

This is because these two sectors are the two most important and dominant

sectors of the financial market in Singapore. Nonetheless, the financial

industry includes other sectors such as the insurance and derivative markets.

Moreover, the study also suggests that there are rapidly emerging financial

sectors which are becoming increasingly important such as fund management

and wealth advisory services. Thus, to add to our understanding of the

finance-growth nexus in Singapore, it would thus be useful to study the

relationship between a wider range of financial sectors and the economy.

8.6 Conclusion

This research, which explores the relationship between financial development and

economic growth in Singapore, has produced important results. Using a robust

statistical methodology, the study found negative bi-directional causality between

banking development and economic growth in Singapore. From the perspective of

banking sector development, the finance-growth nexus did not remain constant over

time and appeared to be more volatile after the 1997 Asian financial crisis. Moreover,

the study also suggested positive bi-directional causality between stock-market

activities and economic growth in Singapore. From the perspective of stock-market

229

development, the finance-growth nexus also did not stay constant over time, with the

positive bilateral linkages between stock-market activities and economic growth

becoming less persistent after the 1997 Asian financial crisis. While the VAR model

is non-structural and does not allow for definitive economic interpretation of the

results, the study considered some possible economic mechanisms underlying the

results. Though there are arguably several limitations in the study, the robustness of

the results and the significance of the findings remain valid. The study provides a

platform for further research which would enhance our understanding of the finance-

growth nexus in Singapore.

230

Appendix 1

Activities of Key Financial Institutions Operating in Singapore

Type of banking Institution

Permitted activities

Full banks

They may provide the full range of banking transactions such as deposit taking, cheque services, and lending to residents and non-residents. Foreign full banks with QFB privileges may operate a total of 15 locations for sub-branches and/or ATMs. There are currently 29 full banks in Singapore. Five of these are locally-incorporated entities under the three local banking groups (DBS, UOB, OCBC), while the remaining 24 are branches of foreign-incorporated banks. Six of these 24 foreign bank branches have been awarded QFB privileges.

Wholesale banks (known as restricted banks prior to 2001)

They may engage in the same banking activities as full banks. However, they may not accept fixed deposit accounts in Singapore dollars of less than S$250,000 per deposit from non-bank customers; they may not operate savings accounts denominated in Singapore dollars or foreign currency except with the prior approval of the MAS. There are 35 offshore banks in Singapore, all of which are branches of foreign banks.

Offshore banks They may not accept Singapore dollar deposits from residents but banks are allowed to transact freely with other financial institutions. They may accept fixed deposits of S$250,000 or more from non-residents, and extend Singapore dollar loans to residents but not exceeding S$500 million at any one time. There are no restrictions on offshore banks' foreign currency business. They are allowed to engage in Singapore

dollar swaps in respect of proceeds arising from the issue of Singapore dollar bonds managed or arranged by them. Offshore banks may operate only from one office. There are 47 offshore banks in Singapore, all of which are branches of foreign banks.

Merchant banks They may engage in corporate finance, underwriting of share and banks bond issues, mergers and acquisitions, portfolio investment management, management consultancies and other fee-based activities. Merchant banks cannot take deposits or borrow directly from the public, but may do so through banks, finance companies, shareholders and companies controlled by shareholders. As offshore banks, merchant banks can operate only from one office. There are 49 merchant banks in Singapore.

Finance companies

They may accept fixed-term as well as savings deposits, but not demand deposits. They can issue negotiable certificates of deposits (CDs) and grant consumer finance not more than S$5,000. Finance companies with capital funds of at least S$100 million may deal in foreign currency or gold, subject to approval from the MAS. Foreign ownership restrictions in finance companies were lifted in 2002. MAS approval is required when an investor acquires stakes of 5 percent and 20 percent in a finance company. There are three finance companies in Singapore.

Insurance companies

The direct insurance market is predominantly foreign-owned. Since 2000, foreign direct insurers are allowed access into the domestic market with no limit on the number of new entrants. For re-insurers and captive insurance, MAS licensing requirements apply. In the reinsurance market, there are 47 re-insurers with many of them engaging in regional and domestic reinsurance activities. In the life insurance market, there are 14 life insurance providers, with the top two life insurance companies controlling the bulk of total premium income. The general non-life insurance market, which operates primarily through commission-based agencies, focuses on fire and motor vehicle insurance.

Source: S. Tan (2006)

231

Appendix 2

Foreign Full Banks in Singapore: Listed by Levels of Activity

Country Granted

QFB Operates an ACU

SGS market primary dealer

SGS Market secondary dealer

ABN Amro Bank Netherlands Yes Yes Yes No

Citibank United States Yes Yes Yes No HSBC Bank Hong Kong Yes Yes Yes No

Standard Chartered Bank United Kingdom

Yes Yes Yes No Malayan Banking Malaysia Yes Yes No Yes BNP Paribas France Yes Yes No No American Express Bank U.S. No Yes No No Bank of America United States No Yes Yes No Bank of China China No Yes No Yes

Bank of Tokyo-Mitsubishi Japan No Yes No Yes

Calyon Bank France No Yes No Yes JP Morgan Chase United States No Yes No Yes RHB Bank Malaysia No Yes No Yes Bangkok Bank Thailand No Yes No No Bank of East Asia Hong Kong No Yes No No Bank of India India No Yes No No

Bank Negara Indonesia Indonesia No Yes No No HL Bank Malaysia No Yes No No Sumitomo Mitsui Bank Japan No Yes No No

Indian Bank India No No No No Indian Overseas Bank India No No No No Southern Bank Malaysia No No No No

UCO Bank India No No No No

Notes: QFB: Qualifying full bank licence; ACU: Asian Currency Unit; SGS: Singapore Government Securities Source: S. Tan (2006)

232

Appendix 3 Major Financial Sector Policies and Developments in Singapore

Year

Policies and Events

1967 The Board of Commissioner of Currency, Singapore (BCCS) was set up to implement the currency board

system.

1968 The Development Bank of Singapore (DBS) was established to provide financial services to support

industrialisation and general economic development.

The Asian Dollar Market (ADM) was established.

Withholding tax was abolished for foreign depositors.

1971 The Monetary Authority of Singapore (MAS) was established. Restricted licence banks were issued to

concentrate on international business and limited to receive only large time deposits from residents.

1973 The Singapore dollar was floated on a managed basis. Singapore adopted an exchange rate policy based on a

basket of currencies of the main trading partners.

Most foreign exchange controls were gradually dismantled.

The Stock Exchange of Singapore (SES) was set up.

1973 Offshore banking was established to stimulate the expansion of the ADM.

1978 All exchange control regulations were removed. Singapore residents and corporations were free to move funds

and import capital to repatriate profits without restrictions.

1982 Convertibility of domestic currency and notes into gold and 11 foreign currencies on demand was abolished.

1983 MAS Notice 621, which codified the policy of discouraging internationalisation of the Singapore dollar, was

issued.

1987 The Singapore government securities (SGS) market was launched.

The Gold Exchange of Singapore (GES) was restructured and renamed as Singapore International Monetary

Exchange (SIMEX)-the first financial futures and options market in Asia.

1987 Tax incentives to encourage trading of international securities in Singapore were introduced

1988 SESDAQ was established for a second board listing of small companies which could not meet the very strict

regulations of the main board of SES.

1992 MAS Notice 621 was amended to allow the extension of the Singapore dollar credit facilities of any amount to

non-residents, where the Singapore dollar funds were used for activities tied to economic activities in

Singapore

1995 The Central Provident Fund (CPF) was liberalized; members were permitted to place their savings in unit

trusts.

The Government of Singapore Investment Corporation (GIC) decided to allot some S$35 billion funds to

private fund managers, with the condition that they are managed from offices in Singapore.

1996 Incentives were offered to attract major fund management companies to locate in Singapore to manage CPF

and government funds of up to S$1 billion.

233

1998 The start of MAS efforts to liberalise its non-internationalisation policy: Notice 621 was replaced with Notice

757, where the MAS allowed for limited relaxation of policy of non- internationalisation of the Singapore

dollar. Borrowers of the Singapore dollar loans and the Singapore dollar bonds can

now swap their proceeds into foreign currency.

Incentives to foreign banks to build up Singapore debt market though the Approved Bond Intermediary (ABI)

Scheme were introduced.

Foreign companies were allowed to list their shares in Singapore dollar on the local stock market and issue

Singapore dollar bonds as part of the measures to relax non-internationalisation policy.

Keppel Bank merged with Tat Lee Bank, Singapore's first bank merger in 20 years.

1998 DBS acquired Post Office Savings Bank (POSB) for S$1.6 billion, which consolidated DBS's position as the

largest bank in Southeast Asia.

1999 SES and SIMEX were demutualised and merged to develop a single integrated exchange, the Singapore

Exchange (SGX).

Deputy Prime Minister Lee Hsien Loong was appointed Chairman of MAS-this marked the start of many new

reforms and restructuring measures in the banking sector.

MAS announced the first phase of its financial sector liberalisation plan; with the aim to strengthen local banks

through competition and enhance Singapore's position as an

international financial centre.

MAS permitted four Qualifying Full Banks (QFBs) to establish up to 10 locations each, to relocate their

existing branches and to share ATMs among themselves.

Singapore dollar derivative interest rate products were permitted to be traded freely as part of the measures to

relax non-internationalisation policy.

2001 Banks can freely transact Singapore dollar currency options among financial institutions based in Singapore as

part of the measures to relax non-internationalisation policy.

A 15-year government bond was first issued to extend the bond yield so as to grow the SGS and SDCB

markets.

MAS announced the second phase of its financial sector liberalisation plan.

MAS announced that it would award 20 wholesale banking licences over two years.

MAS raised the number of QFB licences by two more, and increased the limit of 10 locations to 15 location

branches.

United Overseas Bank (UOB) acquired Overseas Union Bank (OUB).

OCBC Bank acquired Keppel-Tat Lee Bank.

2001 MAS introduced the ruling for banks to divest non-financial activities by 2004, so as to minimize contagion

risk and conflict of interests.

2002

MAS merged with BCCS and took over the function of currency issuance; MAS became a full-fledged central

bank.

MAS further liberalised its non-internationalisation of Singapore dollar policy. Only two rulings hold:

(i) Non-resident entities are required to swap Singapore dollar loan proceeds into foreign currency when

proceeds are used offshore; and

(ii) Financial institutions are not allowed to extend Singapore dollar credit facilities exceeding S$5 million to

non-resident financial entities if they are believed to be used for speculation against the Singapore dollar

exchange rate.

2003

The grace period for Singapore banks to divest their non-financial businesses was extended by the two years,

up to July 2006, in view of difficult market and economic conditions.

234

2003 MAS fulfilled its promise and awarded eight wholesale bank (WB) licences. All offshore banks will be

upgraded progressively to WB status over time.

2004 A risk-based capital framework for the insurance industry was introduced.

The six QFBs can establish up to 25 service locations from the existing 15, where the 25 locations can be either brick-and-mortar branches of off-site ATM locations. QFBs can share ATMs among themselves.

QFBs can negotiate with local banks on a commercial basis to let their credit card holders obtain cash advances through the local banks' ATM networks.

2005 To promote wealth management, tax exemption for specified income of qualifying foreign charitable purpose trust. This is to promote wealth management. To promote Islamic banking, double stamp duties on Islamic real estate financing transactions were removed

2006 MAS announced the establishment of the Singapore Deposit Insurance Corporation to administer the deposit insurance scheme and manage the deposit insurance fund.

Prime Minster and Minister of Finance, Lee Hsien Loong, announced tax measures in budget speech to build up the depth and breadth of capital markets and further promote risk management and treasury activities in Singapore.

Singapore Exchange (SGX) offered over-the-counter clearing service for oil derivatives and dry bulk forward freight agreements.

Sources: S. Tan (2006); MAS Annual Reports for Various Years

235

Appendix 4A

UNIT ROOT TESTING

Period : 1978Q1-2006Q4 (Full sample)

Variables : Y - Real per capita GDP (log)

L - Share of bank loans to nominal GDP

G - Real GDP (log)

T - Share of stockmarket turnover to nominal GDP

ADF test statistic with no trend

Lag Y(Level ) L(Level ) G(Level ) T(Level )

1 -1.397 -2.42 -2.255 -4.139(S)(H) *

2 -1.27 -2.602 (A)(S)(H) -1.133 -3.139 (A)

3 -1.263 -2.456 -1.153 -2.892

4 -1.342 -2.508 -1.208 -2.946

5 -1.129 -2.296 -0.987 -2.507

6 -1.125 (S)(H) -2.525 -1.001 (A)(S)(H) -2.196

7 -1.377 -2.359 -1.234 -1.993

8 -1.043 (A) -2.224 -0.876 -1.832

ADF test statistic with trend


1 -3.245 -1.894 -2.405 -5.584 (A)(S)(H) *

2 -3.114 -2.418 (A)(S)(H) -2.34 -4.46 *

3 -2.115 -2.173 -1.596 -4.397 *

4 -1.625 -2.36 -1.275 -4.588 *

5 -2.886 -2.058 -2.572 -4.054 *

6 -1.1990 (S)(H) -2.339 -1.761 (A)(S)(H) -3.715 *

7 -3.452 -2.225 -1.632 -3.49 *

8 1.590 (A) -2.094 -1.635 -3.294

A is the optimal lag length set by AIC criterion

S is the optimal lag length set by Schwartz criterion

H is the optimal lag length set by Hanman-Quinn criterion

The critical 5 per cent value for ADF test (no trend) = -2.889

The critical 5 per cent value for ADF test (with trend) = -3.452

* represents significance at the 5 percent level

236

Appendix 4B

UNIT ROOT TESTING

Period : 1978Q1-1996Q4 (Pre-Asian Financial Crisis)

Variables : Y - Real per capita GDP (log) (in level)

L - Share of bank loans to nominal GDP (in level)

G - Real GDP (log) (in level)

T - Share of stockmarket turnover to nominal GDP (in level)



1 -0.826 -2.108 -0.292 -3.285(A)(S)(H) *

2 -0.671 -2.433 (A)(S)(H) -0.129 -2.738

3 -0.335 -2.304 -0.338 -2.590

4 -0.077 -2.386 -0.709 -2.782

5 -0.43 -2.185 -0.151 -2.706

6 -0.127 -2.636 0.168 -2.317

7 -0.308 -2.612 -0.003 -2.093

8 -0.079(A)(S)(H) -2.405 0.444(A)(S)(H) -2.049



1 -3.291 -1.89 -2.509 -3.897(A)(S)(H) *

2 -3.661 * -2.310 (A)(S)(H) -2.229 -3.315

3 -2.312 -2.203 -1.374 -3.364

4 -1.529 -2.321 -0.921 -3.642 *

5 -3.911 (S)(H) * -2.177 -3.078(S)(H) -3.651 *

6 -3.706 * -2.578 -2.724 -3.153

7 -3.479 * -2.642 -2.366 -2.884

8 -2.162 (A) -2.456 -1.452 (A) -2.922







237

Appendix 4C

UNIT ROOT TESTING

Period : 1998Q1-2006Q4 (Post-Asian Financial Crisis)

Variables : Y - Real per capita GDP (log) (in level)

L - Share of bank loans to nominal GDP (in level)

G - Real GDP (log) (in level)

T - Share of stockmarket turnover to nominal GDP (in level)



1 -0.415 (A)(S)(H) -1.434 (A)(S)(H) 0.220 (A)(S)(H) -3.167 (A)(S)(H) *

2 -0.622 -1.88 -0.051 -2.385

3 -0.446 -1.496 0.148 -2.262

4 -0.361 -1.961 0.233 -2.306

5 -0.562 -1.383 -0.048 -1.394

6 -0.037 -0.891 0.487 -0.831

7 0.237 -0.609 0.714 -0.769

8 0.126 -0.363 0.584 -0.302



1 -2.164 (S) -2.342 (A)(S)(H) -1.604 (S)(H) -4.040 (A)(S)(H) *

2 -2.749(A)(H) -2.987 -2.140 (A) -3.094

3 -2.206 -2.691 -1.592 -2.954

4 -1.907 -2.638 -1.301 -3.212

5 -2.23 -1.881 -1.691 -2.099

6 -1.815 -1.648 -1.271 -3.568 *

7 -1.45 -1.459 -1.016 -2.477

8 -1.903 -1.112 -1.489 -1.847







238

Appendix 5A

DETERMINING THE ORDER OF INTERGRATION


Variables : Y - Real per capita GDP (log) (in first difference)

L - Share of bank loans to nominal GDP (in first difference)

G - Real GDP (log) (in first difference)

T - Share of stockmarket turnover to nominal GDP (in first difference)


Lag Y (1st diff ) L (1

st diff ) G (1

st diff )

1 -10.938 * -8.384 (A)(S)(H) * -10.609 *

2 -10.748 * -7.180 * -10.120 *

3 -9.355 * -5.505 * -8.473 *

4 -3.90 * -5.335 * -3.603 *

5 -5.198 (S)(H) * -4.397 * -4.651 (S)(H) *

6 -5.289 * -4.093 * -4.610 *

7 -4.798 * -3.931 * -3.994 *

8 -3.504 (A) * -3.691 * -2.882 (A)


Lag Y (1st diff ) L (1

st diff ) G (1

st diff )

1 -10.961 * -8.497 (A)(S)(H) * -10.633 *

2 -10.785 * -7.317 * -10.155 *

3 -9.428 * -5.644 * -8.534 *

4 -4.049 * -5.480 * -3.664 *

5 -5.265 (S)(H) * -4.584 * -4.709 (S)(H) *

6 -5.425 * -4.259 * -4.733 *

7 -4.851 * -4.085 * -4.024 *

8 -3.530 (A) * -3.864 * -2.891 (A) *







239

Appendix 5B



Variables : Y - Real per capita GDP (log)


G - Real GDP (log)



Lag Y(1st diff ) L(1

st diff ) G(1st diff )

1 -9.769 * -6.105 (A)(S)(H) * -9.507 *

2 -9.887 * -4.944 * -9.190 *

3 -9.317 * -3.871 * -7.994 *

4 -2.615 * -3.407 * -2.315

5 -2.880 * -2.941 * -2.577

6 -3.035 * -2.383 -2.864

7 -4.595 (A)(S)(H) * -2.378 -4.095 (A)(S)(H) *

8 -3.763 * -2.575 -3.108 *


Lag Y(1st diff ) L(1st diff ) G(1st diff )

1 -9.711 * -6.126 (A)(S)(H) * -9.441 *

2 -9.804 * -4.963 * -9.141 *

3 -9.234 * -3.882 * -8.004 *

4 -2.595 -3.371 -2.302

5 -2.861 -2.955 -2.597

6 -3.006 -2.319 -2.844

7 -4.566 (A)(S)(H) * -2.26 -4.115(A)(S)(H) *

8 -3.749 * -2.535 -3.139







240

Appendix 5C



Variables : Y - Real per capita GDP (log) (in first difference)

L - Share of bank loans to nominal GDP (in first difference)

G - Real GDP (log) (in first difference)




1 -4.895 (A)(S)(H) * -4.828 (A)(S)(H) * -4.726 (A)(S)(H) *

2 -4.662 * -4.331 * -4.545 *

3 -4.092 * -3.785 * -3.955 *

4 -3.013 -4.340 * -2.814

5 -2.924 -3.156 * -2.66

6 -2.946 -2.816 -2.501

7 -1.914 -2.763 -1.523

8 -1.841 -2.427 -1.443



1 -4.822 (S)(H) * -4.820 (A)(S)(H) * -1.695 (S)(H)

2 -4.587 (A) * -4.351 * -4.522(A) *

3 -4.021 * -3.634 * -3.942 *

4 -2.951 -4.201 * -2.788

5 -2.889 -3.147 -2.719

6 -2.938 -2.867 -2.612

7 -1.926 -2.819 -1.647

8 -1.920 -2.581 -1.654







241

Appendix 6A

ENGLE-GRANGER COINTEGRATION TEST


Variables: Y - Real per capita GDP (log)


G - Real GDP (log)

ADF test of Ût (Engle-Granger Test )

Variables Y & L Variables G & L

Lag No Intercept With Intercept No Intercept With Intercept

1 -0.819 -0.802 -0.676 -0.659

2 -1.252 (A)(S)(H) -1.229 (A)(S)(H) -1.155(A)(S)(H) -1.132 (A)(S)(H)

3 -1.065 -1.036 -0.979 -0.948

4 -1.110 -1.073 -1.061 -1.021

5 -1.149 -1.095 -0.978 -0.920

6 -0.979 -0.924 -0.926 -0.867

7 -0.944 -0.932 -0.950 -0.879

8 -0.909 -0.838 -0.864 -0.785

Critical 5 percent value for ADF(no intercept) = -1.944

Critical 5 percent value for ADF(with intercept) = -3.398

A is optimal lag length set by AIC criterion

S is optimal lag length set by Schwartz criterion

H is optimal lag length set by Hannan-Quinn criterion


242

Appendix 6B





G - Real GDP (log)




1 -0.481 (S) -0.459 (S)(H) -0.185 -0.154

2 -0.524 (H) -0.46 -0.465 (S)(H) -0.420 (S)(H)

3 -0.425 -0.388 -0.354 -0.262

4 -0.397 -0.336 -0.725 -0.612

5 -1.130 (A) -1.060 -1.208 (A) -1.118

6 -0.651 -0.554 (A) -0.853 -0.724 (A)

7 -0.776 -0.665 -0.800 -0.630

8 -0.624 -0.488 -1.077 -0.885







243

Appendix 6C





G - Real GDP (log)




1 -2.584* (A)(S)(H) -2.556 (A)(S)(H) -2.411* (A)(S)(H) -2.377 (A)(S)(H)

2 -2.844* -2.857 -2.703* -2.699

3 -2.699* -2.790 -2.518* -2.592

4 -1.303 -1.38 -1.266 -1.321

5 -0.810 -0.903 -0.735 -0.813

6 -0.813 -0.921 -0.832 -0.923

7 -0.788 -0.970 -0.847 -1.004

8 -0.815 -1.131 -0.766 -1.041







244

Appendix 7A

JOHANSEN COINTEGRATION TEST




G - Real GDP (log)

Johansen Cointegration test for Y and L

Model 2 Model 3

Model 4

Lag Trace Test

Maximum

Eigenvalue Trace Test

Maximum

Eigenvalue

Trace Test

Maximum

Eigenvalue

1 38.104* 32.067* 13.655 12.915 24.023 12.909

2 62.711* 57.171* 19.945* 18.873 26.607* 19.355

3 82.511* 75.293* 32.282* 31.186* 37.819* 32.329*

4 27.743* 23.715* 17.813* 14.429 30.642* 20.849*

5 (S) 29.663* 23.407* 16.300* 15.571* 22.545 15.956

6 (A) (H) 30.531* 24.268* 16.644* 15.544* 22.608 15.544

7 28.083* 22.313* 15.490 14.824* 21.640 15.779

8 17.064 11.250 9.254 8.621 15.345 9.045

Johansen Cointegration test for G and L

Model 2 Model 3

Model 4

Lag Trace Test

Maximum


Maximum

Eigenvalue

Trace Test

Maximum

Eigenvalue

1 53.037* 46.923* 17.038 16.590 25.596 17.400

2 78.383* 72.857* 27.500* 27.252* 37.065* 31.081*

3 88.576* 82.432* 42.956* 42.700* 54.539* 49.382*

4 29.217* 25.541* 21.189* 21.034* 40.384* 29.520*

5 32.422* 25.783* 20.307* 20.104* 29.795* 22.846*

6 (A)(S)(H) 34.221* 27.366* 21.293* 20.905* 29.367* 22.302*

7 28.314* 22.388* 18.229* 18.050* 29.128* 21.884*

8 19.043 12.719 11.974 11.801 21.715 12.829

Model 2: Intercept (no trend) in the CE and no intercept or trend in the VAR

For model 2: Critical 5% value (trace test ) = 20.262; Critical 5% value (Maximum Eigenvalue) = 15.892

Model 3: Intercept in CE and VAR, but no trends in CE and VAR

For model 3: Critical 5% value (trace test) = 15.495; Critical 5% value (Maximum Eigenvalue)= 14.265

Model 4: Intercept in CE and VAR, linear trend in CE and no trend in VAR

For model 4: Critical 5% value (trace test )= 25.872; Critical 5% value (Maximum Eigenvalue) = 19.387

(A) (S) (H) represent optimal lag selected by Aikake, Schwartz and Hannan-Quinn criteria respectively


245

Appendix 7B





G - Real GDP (log)


Model 2 Model 3

Model 4

Lag Trace Test

Maximum


Maximum

Eigenvalue

Trace Test

Maximum

Eigenvalue

1 30.318* 26.862* 9.739 9.577 32.921* 23.348*

2 51.551* 47.718* 14.350 14.285* 31.517* 20.932*

3 74.901* 70.450* 28.523* 28.507* 49.916* 36.105*

4 16.764 30.193 10.304 10.113 46.493* 38.379*

5 (S) (H) 16.499 10.561 7.235 7.160 31.522* 25.086*

6 19.558 13.384 8.381 8.058 32.763* 24.417*

7 30.790* 25.126* 10.797 10.519 29.601* 20.503*

8 (A) 24.565* 19.008* 9.224 9.163 31.983* 23.983*


Model 2 Model 3

Model 4

Lag Trace Test

Maximum


Maximum

Eigenvalue

Trace Test

Maximum

Eigenvalue

1 40.473* 36.663* 8.748 8.725 20.935 14.249

2 53.452* 50.146* 12.773 12.694 23.282 14.635

3 68.860* 65.504* 30.420* 30.414* 41.688* 32.168*

4 20.219 16.190* 13.104 13.067 37.156* 31.167*

5 (S) 21.013* 16.729* 13.604 13.542 34.661* 25.895*

6 18.908 12.900 9.040 9.033 20.464 12.877

7 32.368* 26.507* 15.079 14.885* 22.263 15.057

8 (A) (H) 25.064* 19.128* 13.287 13.219 23.171 16.582









246

Appendix 7C





G - Real GDP (log)


Model 2 Model 3

Model 4

Lag Trace Test

Maximum


Maximum

Eigenvalue

Trace Test

Maximum

Eigenvalue

1 (S) 19.706 11.978 13.358 11.966 31.498* 23.214*

2 24.396* 14.840 12.411 10.710 30.585* 24.710*

3 21.680* 15.497 10.125 9.897 20.728 14.671

4 (A) (H) 12.580 8.465 4.616 4.528 16.300 11.933

5 16.893 13.463 6.936 6.776 23.373 16.938

6 25.306* 25.231* 12.639 12.594 24.784 13.794

7 24.503* 15.750 13.184 8.947 21.386 13.527

8 19.780 15.973* 12.652 11.459 35.567* 25.504*


Model 2 Model 3

Model 4

Lag Trace Test

Maximum


Maximum

Eigenvalue

Trace Test

Maximum

Eigenvalue

1 21.936* 12.431 11.798 11.717 26.486* 21.379*

2 (S) 29.644* 21.294* 12.277 12.271 29.186* 26.360*

3 24.481* 15.260 10.001 9.661 21.095 19.437*

4 (H) 18.100 13.910 9.989 8.447 21.643 18.243

5 20.274* 14.902 10.542 7.375 26.260* 23.084*

6 23.282* 17.616* 12.403 7.051 31.344* 24.327*

7 30.792* 22.361* 16.551* 12.723 25.360 17.901

8 (A) 27.220* 21.179* 17.526* 15.573* 30.632* 15.654









247

Appendix 8

SELECTION OF OPTIMAL LAG LENGTH OF VAR MODEL

Model

Variables

Period

Akaike Information

Criterion (AIC)

Schwarz

Bayes Criterion (SBC)

Hannan-Quinn

Criterion (HQ)

Y and L

1978(1) – 2006(4)

5*

(No autocorrelation)

4

(3rd

& 4th

order

autocorrelation)

5

(3rd

and 4th

order

autocorrelation)

1978(1) – 1996(4)

7*


4

(1st order

autocorrelation)

5

(2nd

order

autocorrelation)

1998(1) – 2006(4)

7*


1

(2nd

and 4th order

autocorrelation)

1

(2nd

and 4th order

autocorrelation)

G and T

1978(1) – 2006(4)

5*


1

(1st & 4

th order

autocorrelation)

5


1978(1) – 1996(4)

4*


4


4


1998(1) – 2006(4)

1*


1


1


Y and T

1978(1) – 2006(4)

5*


1

(2nd

and 4th order

autocorrelation)

5


1978(1) – 1996(4)

7*


4

(1st order

autocorrelation)

4

(1st order

autocorrelation)

1998(1) – 2006(4)

1*


1


1


G and L

1978(1) – 2006(4)

5*


1

(1st order

autocorrelation)

5


1978(1) – 1996(4)

7

(No Autocorrelation)

4*


4


1998(1) – 2006(4)

7*


1

(1st order

autocorrelation)

1

(1st order

autocorrelation)

* represents the optimal lag length selected for the VAR model

248

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