corruption and its impact on economic growth: is …

CORRUPTION AND ITS IMPACT ON ECONOMIC GROWTH: IS EAST ASIA SPECIAL?

Nasrul Ali B.Comm (Hons) (Murdoch)

This thesis is presented for the degree of Doctor of Philosophy of the University of Western Australia

UWA Business School 2008

ACKNOWLEDGEMENTS

First and foremost I am indebted to my Creator for granting me the powers of intellect and

rational thought, without which this PhD would never have been accomplished.

I am eternally grateful to my parents, Dr and Mrs Ameer Ali, for their love and support.

I offer my sincerest thanks to A/Prof Yanrui Wu and Dr Abu Siddique for their supervision.

I extend my appreciation to the Commonwealth Government of Australia for granting me

an Australian Postgraduate Award scholarship to support my PhD endeavour.

Many thanks to Dr Robert Rankin, Assistant Governor of the Reserve Bank of Australia.

Dr Rankin encouraged me to do my PhD while I was working for him at the RBA and has

always made time for me out of his busy schedule when I needed advice. I am very

fortunate to have had the support of someone in his position.

A special thanks to Dr Kanishka Jayasuriya who gave up a lot of his time to meet with me

and provide me with access to his resources, and very kindly gave me some much needed

feedback.

I am grateful to the following staff members at Curtin University for giving me ample

lecturing and tutoring opportunities to supplement my income while working on my PhD:

A/Prof Sandra Hopkins; Prof John Evans; A/Prof Lakshman Alles; and Mr Steven Kemp.

Finally, I would like to thank the following people for their assistance and support: Mr and

Mrs Gamini Fernando; Prof Laksiri Jayasuriya; Dr Roy Gilbert; Dr Felix Chan; A/Prof

Meher Manzur; Dr Ruhul Salim; and Ms Ann Tolson and the Western Australian

Department of Industry and Resources.

iii

ABSTRACT

The 1997 Asian Financial Crisis raised serious questions about the nature of East Asia’s

rise to economic prosperity, once labelled as a ‘miracle’ by the World Bank. In particular,

East Asian governments were criticised for allowing rampant corruption to pervade their

economies. At a conceptual level, the overwhelming majority of studies argue that

corruption, defined as the misuse of public office for private gain, has impeded growth.

Empirically, many studies have shown the detrimental impact of corruption on economic

growth but few have analysed the particular effect of corruption on East Asia’s economic

growth in the years leading up to the 1997 Crisis, a period characterised by superior

economic growth rates against the backdrop of corruption. This study seeks to fill that gap.

By virtue of its clandestine nature, any study on corruption is subject to measurement

limitations and this study is no exception. The only available data on corruption are indices

published by a handful of various international organisations. Each of these indices follows

a similar format: they are based partly or wholly on surveys of the corporate sector in each

of the sample countries, the results of which are converted into corruption scores and used

to rank the sample countries. Although there is a general consistency in rankings across the

different indices, the survey questions tend to equate corruption with bribery. In one survey

which questioned respondents about corruption and bribery in separate questions, the

results indicated that the two are not necessarily synonymous at least in the minds of

respondents.

A brief analysis of the nature of corruption within East Asia reveals why the tendency to

equate corruption with bribery can be misleading, and therefore raises doubts about the

credibility of the aforementioned corruption indices. Many countries in East Asia are

shown to harbour a network of patron-client relationships within a centralised framework.

This led to strong rent-seeking behaviour from the corporate sector which became a key

component of the development oriented strategy adopted by the rulers of the day.

Government support for rent-seekers was consistently monitored and subject to

performance requirements. This aligned the interests of the corporate sector with those of

the government and ultimately led to strong economic growth. Countries such as South

Korea, Indonesia and Malaysia are good examples of this whilst a notable exception is the

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Philippines, where the system was heavily decentralised and rulers preferred personal profit

above social development and economic prosperity.

When using the available corruption indices as measures of corruption in a corruption-

growth model that is applied to cross-sectional data covering 141 countries in 1996,

corruption is found to have a significant positive relationship with economic growth for two

of the corruption indices. However, no particular significant relationship is found to exist

for East Asian countries within the sample. The corruption indices are then combined to

produce a single index of corruption which is then used in a corruption-growth model and

applied to panel data covering 33 countries over a twenty year period from 1984 to 2003.

This time the corruption variable is found to have a significant positive relationship with

economic growth for East Asian countries (excluding Singapore) during 1986-1996.

Finally, the concept of rent-seeking is examined as an alternative to the typical principal-

agent model of corruption used in the literature, based on its strong resonance with the

particular nature of corruption in East Asia. A measure of rent-seeking is developed, and

using cross-sectional data for 57 countries in 1996 reveals that rent-seeking has a

significant positive relationship with economic growth.

v

TABLE OF CONTENTS

ACKNOWLEDGEMENTS iii

ABSTRACT iv

TABLE OF CONTENTS vi

LIST OF FIGURES ix

LIST OF TABLES xi

LIST OF ACRONYMS xiii CHAPTER 1

INTRODUCTION 1

1.1 Corruption and Economic Growth 1

1.2 Corruption Behind the East Asian Miracle? 4

1.3 Thesis Outline 7

CHAPTER 2

LITERATURE REVIEW: CONCEPTUAL ISSUES 10

2.1 Introduction 10

2.2 The Concept of Corruption 11

2.3 Economic Models of Corruption 13

2.4 Conclusion 27

CHAPTER 3

LITERATURE REVIEW: EMPIRICAL STUDIES 28

3.1 Introduction 28

3.2 Empirical Studies Analysing Corruption and Growth 28

3.3 Empirical Studies on Rent-Seeking 34

3.4 Conclusion 41

Appendix to Chapter 3 42

CHAPTER 4

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CORRUPTION IN EAST ASIA 67

4.1 Introduction 67

4.2 Cronyism 67

4.3 Mutual Hostage Situations 70

4.4 Centralised vs Decentralised Corruption 72

4.5 Patron-Client Networks 75

4.6 Malaysia’s Rent-Seeking Experience 77

4.7 Singapore’s Secret 84

4.8 The China Syndrome 84

4.9 Conclusion 85

CHAPTER 5

MEASURING CORRUPTION 86

5.1 Introduction 86

5.2 Corruption Indices 86

5.3 Correlation Between Indices 99

5.4 Conclusion 103

CHAPTER 6

CORRUPTION AND GROWTH: A CROSS-COUNTRY STUDY 105

6.1 Introduction 105

6.2 Developing A Corruption-Growth Model 106

6.3 Data Issues 109

6.4 Estimation Results 114

6.5 Endogeneity 120

6.6 Corruption and Growth in East Asia 126

6.7 Comparing Results with Existing Studies 130

6.8 Conclusion 133


CHAPTER 7

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CORRUPTION AND GROWTH II: A PANEL DATA ANALYSIS 156


7.2 Corruption Indicators 156

7.3 A Corruption-Growth Model using Panel Data 168

7.4 Estimation Results and Preliminary Analysis 169

7.5 East Asia: A Corrupt-Growth Club? 178

7.6 Sensitivity Analysis 183

7.7 Comparison with Other Studies 183

7.8 Conclusion 186


CHAPTER 8

RENT-SEEKING AND ECONOMIC GROWTH: EMPIRICAL EVIDENCE 195


8.2 Measurement of Rent-Seeking 196

8.3 Empirical Analysis 202

8.4 Conclusion 216

CHAPTER 9

SUMMARY AND CONCLUDING REMARKS 217


9.2 Summary of Findings 217

9.3 Concluding Remarks 219

REFERENCES 222

viii

LIST OF FIGURES

1.1 Economic Growth (East Asia vs Rest of the World, 1986-1996) 4

1.2 Corruption and Economic Growth in East Asia, 1995 7

2.1 Corruption Without Theft 14

2.2 Corruption With Theft 14

2.3 Andvig’s (1991) Multiple Equilibria Model 16

2.4 Monopoly Welfare Losses 20

2.5 Net Effect of Rent-Seeking with Value-Reducing Rents 21

2.6 Net Effect of Rent-Seeking with Value-Enhancing Rents 23

2.7 Rents Based on Transfers 24

3.1 Rent-Protection 39

4.1 Natural Resource Rents 79

4.2 Market for Loans 83

4.3 Rent Effect of Financial Restraint 83

5.1 CPI vs WDR(q12n) 100

5.2 CPI vs WDR(q14) 101

5.3 WDR(q12n) vs WDR(q14) 102

5.4 CPI vs WCY 102

5.5 KKZ vs WCY 103

6.1 Corruption (KKZ) and Economic Growth, 1996 113

7.1 Average Corruption in East Asia vs Rest of the World (All Indices, 1984-2003) 158

7.2 Average Corruption in East Asia vs Rest of the World (Composite Index, 1984-2003) 160

7.3 Average Corruption (STD1, 1984-2003) 161




7.7 Corruption for Selected Countries (STD4, 1984-2003) 163

7.8 Average Corruption (ICRG Index, 1984-2001) 165

7.9 Average Economic Growth (1984-2003) 166

7.10 Corruption vs Economic Growth (Average, 1986-1996) 167

8.1 Rents based on Value-Reducing Transfers 198

8.2 Rents based on Value-Enhancing Transfers 199

8.3 Rent-Seeking and Economic Growth, 1996 207

8.4 Residual Plot (Model 11, Table 8.6) 215

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9.1 Corruption vs Economic Growth, 2005 220

x

LIST OF TABLES

A3.1 A Summary of Existing Studies on Corruption and Growth 42

A3.2 A Summary of Selected Rent-Seeking Studies 55

5.1 CPI 2006, Selected Countries 91

5.2 Corruption Indices used in Empirical Studies 98

5.3 Summary of Major Corruption Indices 104

6.1 Descriptive Statistics 114

6.2 Correlation Matrix 115

6.3 Results using KKZ Index 116

6.4 Sensitivity Analysis using Alternate Corruption Indices 119

6.5 Sensitivity Analysis using Composite Indices 120

6.6 Controlling for Endogeneity using KKZ Index 123

6.7 Sensitivity Analysis of Endogeneity (Alternate Indices) 125

6.8 Corruption and Economic Growth in East Asia, 1996 126

6.9 Including an East Asian Dummy Variable 128

6.10 Including an East Asian Dummy Variable and Controlling for Endogeneity 129

6.11 Regression Results from Existing Studies 131

A6.1 Results using ICRG Index 134

A6.2 Results using CPI 136

A6.3 Results using WCY Index 138

A6.4 Results using KKZ-ICRG Index 140

A6.5 Results using KKZ-WDR Index 142

A6.6 Results using ICRG-WDR Index 144

A6.7 Controlling for Endogeneity (ICRG Index) 146

A6.8 Controlling for Endogeneity (CPI) 148

A6.9 Controlling for Endogeneity (WCY Index) 149

A6.10 Controlling for Endogeneity (KKZ-ICRG Index) 150

A6.11 Controlling for Endogeneity (KKZ-WDR Index) 152

A6.12 Controlling for Endogeneity (ICRG-WDR Index) 154

7.1 Corruption in East Asia (Composite Index, 1995-2003) 159


7.3 Regression Results using CPI 171

7.4 Regression Results using ICRG Index 173

7.5 Regression Results using WCY Index 174

7.6 Regression Results using Composite Index 176

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7.7 Controlling for Endogeneity (Composite Index) 177

7.8 Using Dummy Variables 179

7.9 Using Dummy Variables and Controlling for Endogeneity 180

7.10 Using Dummy Variables and Controlling for Endogeneity (Excluding

Singapore and 1984/1985) 182

7.11 Sensitivity Analysis 184

7.12 Regression Results from Existing Studies 185

A7.1 Sample of Countries 187

A7.2 Controlling for Endogeneity (CPI) 188

A7.3 Controlling for Endogeneity (ICRG Index) 190

A7.4 Controlling for Endogeneity (WCY Index) 192

A7.5 Controlling for Endogeneity (Composite Index, Excluding Singapore and 1984/1985) 194

8.1 Rent-Seeking as % of Budget, 1996 205

8.2 Average Rent-Seeking as % of Budget, 1970-1985 206

8.3 Descriptive Statistics 208


8.5 Regression Results 211

8.6 Controlling for Endogeneity 213

xii

LIST OF ACRONYMS

ADB Asian Development Bank AFDB African Development Bank BF Bertelsmann Foundation BI Business International CPI Corruption Perceptions Index DUP Directly Unproductive Profit-seeking EIU Economist Intelligence Unit FDI Foreign Direct Investment FH Freedom House GCR Global Competitiveness Report GDP Gross Domestic Product GFS Government Financial Statistics GI Global Insight GNP Gross National Product ICRG International Country Risk Guide IMD International Institute for Management Development IMF International Monetary Fund KKZ Kaufmann, Kraay and Zoido-Lobaton KMT Kuomintang LDP Liberal Democratic Party MIG Merchant International Group NEP New Economic Policy OECD Organisation for Economic Co-operation and Development PERC Political and Economic Risk Consultancy PPP Purchasing Power Parity SAR Special Administrative Region SIMA Statistical Information Management and Analysis TI Transparency International UK United Kingdom UMNO United Malays National Organisation UN United Nations UNECA United Nations Economic Commission for Africa UNESCO United Nations Educational, Scientific and Cultural Organization US United States WCY World Competitiveness Yearbook WDR World Development Report WEF World Economic Forum

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CHAPTER 1

INTRODUCTION

“East Asia has a remarkable record of high and sustained economic

growth. From 1965 to 1990 the twenty-three economies of East Asia

grew faster than all other regions...Most of this achievement is

attributable to seemingly miraculous growth in just eight

economies…Seemingly, the rapidly growing economies in East Asia

used many of the same policy instruments as other developing

economies, but with greater success. Understanding which policies

contributed to their rapid growth, and how is a major question for

research…”

- World Bank (1993, pp.v and 1).

The World Bank’s comments above highlight East Asia’s phenomenal growth rates prior to

1997, despite many institutions having deemed the region to be plagued with corruption.

The fundamental question this dissertation therefore seeks to answer is whether the impact

of corruption on East Asia’s economic growth prior to the 1997 Asian Financial Crisis may

in fact have been positive, thus challenging the general view that corruption impedes

growth. This introductory chapter presents a concise overview of the purpose of this

dissertation. It begins with a discussion about the relationship between corruption and

economic growth and identifies a section of the literature which finds that, contrary to

popular belief, corruption can enhance economic growth. The specific research objectives

of this study are presented in Section 1.2, followed by an outline of the dissertation.

1.1 Corruption and Economic Growth

One of the earliest studies to tackle the issue of corruption and economic growth was Leff

(1964), in which it was argued that corruption could in fact increase efficiency in an

economy. This occurs through the use of ‘speed money’, allowing entrepreneurs to avoid

bureaucratic delay by paying bribes. This theory was later reinforced by Huntington (1968).

Bribes also act as an incentive for bureaucrats to perform efficiently in the absence of

1

satisfactory pay structures (Rahman et al., 2000). Bardhan (1997) argues that if private

firms are bidding in bribes for a government contract then it follows that the highest bidder

must necessarily be the lowest-cost firm. Thus, awarding the contract to the firm offering

the highest bribe should maintain allocative efficiency (see Beck and Maher, 1986; Lien,

1986).

Backman (1996) acknowledges that a “candid appraisal of corruption would show that it is

not always nefarious to growing economies”. He argues that “corrupt practices in the public

decision making of developing economies could spur growth, particularly if they frustrate

inappropriate and anti-development government policies” (p.1). Further, “economies where

corruption is a way of life may arguably be more efficient than slightly corrupt economies

where…there are just a few players, making it less likely that the winner will be the bidder

who most values the tender” (p.1). Backman asserts that smuggling is an example of a

corrupt behaviour that “has almost always had a positive economic effect, as long as the

goods being smuggled are not of an illicit nature” (p.2). Backman explains that smuggling

circumvents trade barriers and “puts producers in direct contact with consumers and avoids

a government attempting to get in the way…[so that] the producers become wealthier, the

consumers happier, and pressure is put on domestic producers to be more competitive”

(p.2). In this way, general economic welfare is enhanced.

The overwhelming majority of the literature, however, argues that corruption slows down

the wheels of commerce and reduces efficiency, by institutionalising itself through its

chronic persistence. This ultimately impedes economic growth because by “forming

perverse patron-client relationships with the bureaucracy and state machinery and by

diverting resources and talent from productive purposes to corrupt practices, corrupt

investors hamper the overall prosperity of the nation by reducing economic growth by

adversely affecting the quality and quantity of investment” (Rahman et al., 2000, p.4).

The theoretical debate over the effect of corruption on an economy has produced many

empirical studies aimed at establishing a link between corruption and economic growth.

Using a growth accounting approach with economic growth regressed against an index of

corruption as the chief explanatory variable, it is generally found that corruption leads to

reduced domestic investment, reduced foreign direct investment and overblown

2

government expenditure. Corruption also distorts the composition of government

expenditure away from essential sectors towards less efficient but more manipulatable

public projects (Wei, 1999).

A seminal study in this field was Mauro (1995) (and his subsequent 1997 study) which

found that corruption leads to reduced economic growth. However, in his 1997 study

Mauro acknowledged that there were difficulties associated with the measurement of

corruption, and argued that the corruption indices that are used (based on surveys of the

corporate sector’s perception of corruption levels) are highly subjective and therefore not

entirely accurate. Perhaps more interestingly, the indices also fail to distinguish between

corruption at an elite level (such as government ministers receiving gifts from the ruling

regime) and corruption at a grassroots level (such as customs officials accepting bribes to

allow passage of illegal goods into the country). The latter is how corruption is typically

viewed in the literature and was conceptualised by Shleifer and Vishny (1993) in their

principal-agent model of corruption, where the government is seen as the principal and its

bureaucrats are agents who sometimes extort bribes at the expense of serving the

government’s interests.1 In fact this view of corruption is rather narrow and fails to capture

the ‘elite’ form of corruption, as Mauro (1997) described it. The result of this modelling

error is a failure to accurately measure the effect of corruption on economic growth in

regions where the so-called ‘elite’ form of corruption thrives, such as in East Asia. The

present study argues that this form of corruption can in fact be measured, and modelled,

when it is viewed as rent-seeking.

Mauro’s findings were supported by Rahman et al. (2000), amongst many others. However,

Li et al. (2000) argued that the effect is not as pronounced as in Mauro (1995). Intriguingly,

they claim that “during 1980-94 Asia did not pay the price paid elsewhere for corruption; in

other words, corruption may indeed have acted as grease money in Asia during this period”

(p.18). Rock and Bonnett (2004) attempted to analyse this inconsistency too. They

investigated the East Asian corruption-growth relationship from a political perspective, and

suggest that corruption impedes growth and investment in most developing countries but 1 Shleifer and Vishny (1993) also consider the issue of centralised corruption, where more than one good is sought after by the public and where more than one bureaucrat can offer those goods. They identify three possible scenarios based upon the behaviour of the different bureaucrats – collusion, independent supply, or competition. Centralisation in reference to East Asian corruption will be explored in Chapter 4.

3

increases growth in some East Asian economies. The East Asian experience is therefore of

considerable interest as explained in the next section.

1.2 Corruption Behind the East Asian Miracle?

Between 1986 and 1996, the average annual economic growth rate in East Asia was nearly

7 per cent, while in the rest of the world it was between 2 and 3 per cent, as shown in

Figure 1.1 (World Bank, 2005).2 Such high growth rates are common amongst many other

countries, but what made East Asia different was that it was able to sustain these rates over

such a long period. Further, income inequality in East Asia had also declined. Achieving

both those feats was unique and laudable (World Bank, 1993).

Figure 1.1: Economic Growth (East Asia v Rest of the World, 1986-1996)

0

1

2

3

4

5

6

7

8

9

10

1986 1988 1990 1992 1994 1996

East Asia

Rest of the World

Source: Compiled using data obtained from the World Bank (2005).

By definition a miracle is beyond explanation, however the World Bank, in its 1993 report,

identified the following factors as being responsible for East Asia’s growth:

• private domestic investment; 2 For the purpose of this study, East Asia refers to Singapore; Malaysia; South Korea; Thailand; Hong Kong SAR; Indonesia; the Philippines; and Taiwan. However, the World Bank’s 1993 report used Japan instead of the Philippines. In the empirical analysis, Taiwan is excluded due to unavailability of data.

4

• high savings rates that funded the investment;

• growth in manufactured exports based on absorption of technology;

• declining population growth rates which led to high consumption per capita;

• high productivity growth;

• increase in labor force skills; and,

• strong government intervention.

Interestingly, the International Monetary Fund (IMF) is of the view that the economic

success of East Asia seemed to confirm the convictions of two opposing schools of

economic thought. On the one hand, neoclassical liberalists called for governments to

ensure property rights, law and order, and adequately provide public goods without high tax

rates and price controls at a microeconomic level, and to ensure stable and low inflation, a

strong financial and banking system, open markets, and stable and realistic exchange rates

without excessive budget deficits. On the other hand, the revisionists believed that market

imperfections were bound to exist in the poorer countries, as production creates

externalities, credit is limited, and firms engage in unfair trade practices, and thus called for

an interventionist government to ensure that the economy developed through the

acquisition of technology and the allocation of funds for productive investment purposes.

The governments in East Asia had met all of these requirements and had done so with great

success (Sarel, 1996).

Many more factors were highlighted by other scholars and institutions, but all their analyses

shared one commonality – they were in praise of the economic success of East Asia. The

world was seeking to draw lessons from the East Asian miracle as it became a blueprint for

economic prosperity.

The World Bank’s 1993 report cited earlier was entitled ‘The East Asian Miracle:

Economic Growth and Public Policy’. Eight years later, the World Bank released another

publication entitled ‘Rethinking the East Asian Miracle’ (Stiglitz and Yusuf, 2001). The

reason for the rethinking was the 1997 Asian Financial Crisis. In July 1997, Thailand

devalued its currency in response to severe speculative attacks on the baht. What followed

over the next 18 months was a series of consecutive devaluations in the region (in response

5

to similar attacks). Even renowned economist Paul Krugman admitted that he was 90

percent wrong about East Asia, his only consolation being that everyone else was 150

percent wrong (Institute for International Monetary Affairs, 1999). The IMF (1998) pointed

to problems in East Asian banking systems, somewhat ironically given that it formerly

praised their governments for promoting the integrity of their banking systems (Sarel,

1996). Stanley Fischer (1998), then Deputy Director of the IMF, blamed crony capitalism

as a major cause of the crisis.

Fischer’s claim that ‘crony capitalism’ contributed to the downfall of East Asia is most

intriguing. For the ten years prior to 1997, East Asia was not only sporting high growth

rates, but high corruption rates as well. Figure 1.2 illustrates this fact. The dashed lines

represent the world average, and the corruption level is measured on a scale of zero to ten,

with ten indicating extreme corruption.3 With the exception of Singapore and Hong Kong,

the chart shows that the East Asian ‘miracle’ economies were all experiencing levels of

economic growth and corruption that were above the world average in 1995. This seems to

contradict the general view in the literature that corruption tends to impede economic

growth.

The particular relationship between corruption and economic growth in East Asia has been

largely overlooked in the literature. The present study investigates this relationship, and

seeks to determine whether corruption may have partly contributed to high growth in East

Asia during the years leading up to 1997, when the region was engulfed by a financial crisis

that led to a sharp decline in growth rates. Specifically, this study will:

• critically analyse the existing measures of corruption;

• develop alternative corruption indices;

• undertake both cross-sectional and panel data analysis;

• analyse the particular nature of corruption in East Asia; and,

• develop a technique for measuring rent-seeking, which will then be used as a proxy

for corruption.

3 Corruption scores were taken from Transparency International’s Corruption Perceptions Index.

6

Figure 1.2: Corruption and Economic Growth in East Asia, 1995

0123456789

10

0 1 2 3 4 5 6 7 8 9 10

Economic Growth (%)

Cor

rupt

ion

Lev

el

Singapore

Malaysia

Thailand

Indonesia

Taiwan

Philippines

Hong Kong SAR

South Korea

Source: Compiled using data obtained from Transparency International (1995) and the World Bank

(2005).

1.3 Thesis Outline

This dissertation begins with an overview of existing literature followed by empirical

analysis. Though the relationship between corruption and growth represents the core theme

of the study, East Asia will feature prominently in the analysis as the region is of particular

interest. The remainder of this section describes the specific outline of the dissertation.

Chapter 2 provides a review of the literature that describes the concept of corruption. The

first half of the chapter focuses on the definition of corruption, and provides examples of

how corruption is typically defined in the literature. Following on from this, the chapter

then explains how corruption is modelled in an economy. The most popular model in this

regard is the principal-agent model of corruption pioneered by Shleifer and Vishny (1993).

This model is simple yet provides a model of corruption that is consistent with the way

corruption is defined in the literature. The second half of the chapter introduces rent-

seeking as an alternative model of corruption, and one that captures a very different

dimension of corruption that is largely overlooked in the literature.

7

Chapter 3 extends the literature review by providing a summary of the empirical studies

that analyse the effect of corruption on economic growth. A breakdown of these studies is

provided, showing each study’s theme, data and results. While there are many studies, this

chapter reveals two significant gaps in the literature. First, many of the studies that employ

regression techniques have not used panel data analysis. Second, hardly any studies analyse

East Asia exclusively. These are the gaps that the present study seeks to fill. A summary of

empirical studies involving rent-seeking is also presented and once more it is revealed that

few studies have analysed East Asia, and most studies suffer from an inability to produce a

reliable measure of rent-seeking. This provides the basis for the analysis in Chapter 8.

Chapter 4 provides a brief review of corruption in East Asia. Drawing on the handful of

studies that have applied the rent-seeking model of corruption to East Asia, the chapter

explains the nuances of corruption in East Asia through the analysis of patron-client

networks between the business sector and the government.

In Chapter 5, the issue of measuring corruption is examined. This issue is integral to any

study on corruption. A survey of corruption indices published by a handful of institutions

such as Transparency International and Political Risk Services Inc. is presented. Each index

is critically analysed, and compared with other indices to test their correlation and

determine whether the indices produce consistent country rankings. One particular index

(based on the 1997 World Development Report) reveals that corruption is not viewed

synonymously with bribe-paying, contrary to what the literature assumes. A selection of

these indices is chosen to represent corruption in the context of a cross-sectional regression

model.

Chapter 6 presents a cross-sectional model to be employed in the analysis of 141 countries

for the year 1996. Having covered the corruption data in the previous chapter, the economic

control variables to be used in the model are listed and explained. Regression results are

shown initially using the Kaufmann, Kraay and Zoido-Lobaton (KKZ) index as the

measure of corruption, followed by results using other indices (and pairs of indices). The

model is then adjusted to control for endogeneity problems. Dummy variables are also

included to test for the particular relationship between corruption and economic growth in

East Asia. Finally, the results are compared with those of similar existing studies.

8

Chapter 7 presents a corruption-growth analysis based on panel data. Some of the indices in

Chapter 5 are combined to form a composite index of corruption, and this is used in a

corruption-growth model covering 33 countries between 1984 and 2003. As per the

previous chapter, endogeneity is controlled for and dummy variables are included to

analyse the East Asian region. A sensitivity analysis is conducted using different forms of

the composite index (i.e., combined in different ways). The results are compared with those

of similar existing studies.

Chapter 8 then provides an analysis of results of the application of a corruption-growth

model using rent-seeking as an alternative measure of corruption. The rent-seeking model

considered is a modified version of that introduced by Katz and Rosenberg (1989). The

model is applied to a sample of 104 countries in 1996. Due to differing data sources the

sample is not the same as those used in earlier chapters. Finally, Chapter 9 provides a

summary of the dissertation’s findings and some concluding remarks.

9

CHAPTER 2

LITERATURE REVIEW: CONCEPTUAL ISSUES

“I cannot help but notice that ethnic Europeans have an infinite

capacity to convince themselves that whatever it is they may be

doing at the moment is right, is just, is proper…We accept

democracy, but not the liberal democracy of the West…Our

governments may not be the cleanest and the most incorruptible in

the world, but we do care for our people and our country enough to

work hard to develop and progress, to industrialise and build

prosperous economies…Remember that the Western ideologues

have been wrong so many times before.”

- Former Malaysian Prime Minister Dr Mahathir Mohamad.4

2.1 Introduction

Typically, corruption is viewed as something harmful. Dr Mahathir’s comments above

warn about the dangers of following ideologies simply because they are practised by the

powerful and the influential. Further, his comments suggest that corruption should not be

the single criterion by which governments should be judged. What matters, in Dr

Mahathir’s view, is the prosperity and progress of its society as it is this responsibility with

which governments are charged. Indeed, if one were to use the economic performance of

East Asia in the late 1980s/early 1990s to judge the governments of the region, one should

be showering them with praise for their miraculous achievement in that period. But what is

interesting is that those very same economies were also plagued with corruption in that

period, at least as far as Transparency International and other institutions were concerned.5

In order to investigate whether countries can achieve strong economic growth while

simultaneously being corrupt, one needs to understand the conceptual issues behind

corruption and its impact on economic growth. This chapter examines the extant literature

analysing corruption and its effects on economic growth. It begins by explaining how 4 This is an excerpt of the Malaysian Prime Minister’s speech at the final banquet of the Southern African International Dialogue at Swakopmund, Namibia on 29 July 1998 (Burling, 1998). 5 Other institutions providing measurements of corruption include the World Bank, the International Institute for Management Development, and Political Risk Services Inc.. For more information, see Chapter 4.

10

corruption is defined in the literature, and outlines the theoretical issues associated with

corruption. The chapter then summarises the empirical literature studying both the direct

link between corruption and growth, and indirect effects via other determinants of growth.

2.2 The Concept of Corruption

Although literature on corruption and its impact on economic growth is quite rich, there is

hardly any agreement amongst the researchers about a definition of corruption (Sandholtz

and Koetzle, 2000; Lui, 1996; Jain, 2001; Bardhan 1997). It varies from study to study due

to the application of various measures to quantify corruption. It also depends on the type of

corruption. What follows is an exhibition of different views about the concept and types of

corruption based on the main studies in this area.

Shleifer and Vishny (1993, p.599) define corruption as the “sale by government officials of

government property for personal gain”. This refers to the collection of bribes in exchange

for providing permits or licences, or prohibiting the entry of competitors into the market.

While Wei and Wu (2001, p.3) accept this definition they prefer to think of corruption a

little more broadly “as a short-hand for ‘poor public governance,’ which can include not

only bureaucratic corruption, but also deviations from rule of law or excessive and arbitrary

government regulations”. Huang and Wei (2003, p.4) define corruption as “the erosion of a

government’s ability to collect revenue through formal tax channels [arising] through the

outright theft by tax officials, through hiding of taxable income by taxpayers, or through

practices whereby tax inspectors collaborate with taxpayers to reduce the latter’s tax

obligation in exchange for a bribe.”

Sandholtz and Koetzle (2000) point out that all definitions of corruption share three

common elements, namely:

• a distinction between the public and private sectors;

• an exchange of either a good or service by a government employee

(bureaucrat) in return for some inducement; and,

• an element of impropriety (as a result of a breach of either existing norms, or

the law).

11

These elements are brought together by Bardhan (1997) who defines corruption as “the use

of public office for private gains, where an official (the agent) entrusted with carrying out a

task by the public (the principal) engages in some sort of malfeasance for private

enrichment which is difficult to monitor for the principal” (p.1321). However, Bardhan

acknowledges that there are other kinds of corruption (e.g., that which involves only the

private sector, like paying a higher price to a scalper for a movie ticket, or using

connections to gain entry into a restricted venue, etc).6

Some studies, like Sandholtz and Koetzle (2000), refrain from defining corruption and

identify corruption by its characteristics. Lui (1996, p.26) assert that corruption is a rent-

seeking activity induced by an absence of a perfectly competitive market; that it is illegal;

and that it involves “some degree of power, which can be interpreted as a form of human

capital acquired through inheritance or investment”.7 Chakrabarti (2001, p.7) employs a

similar strategy, stating that “a corrupt activity must satisfy three criteria – it must have a

positive expected economic value to its perpetrators, it must have some risk of socio-legal

censure associated with it and it must adversely affect the economy”.

Jain (2001) takes a step further and identifies three types of corruption. The first type occurs

at an elite level, where the political rulers exploit their power to formulate policies that

serve their interests instead of the public interest. The second type is bureaucratic

corruption which is akin to Bardhan’s definition of corruption where bureaucrats extort

bribes in exchange for supplying a government good or service. A third type is legislative

corruption where the voting power of legislators can be heavily influenced under the guise

of ‘gift-giving’.

Bhagwati (2000, p.4) also makes a distinction between two types of corruption – rent-

creating and profit-sharing. Under rent-creating corruption cronies are rewarded by the

ruling power with monopolies in production or distributive activity; and under profit-

sharing corruption cronies are rewarded with a share in profit-making enterprises so that

“the cronies have an incentive to make the pie as big as they can”. Bhagwati concedes that

profits “siphoned off by cronies could well cut into tax revenues that build useful

6 See Argandona (2003). 7 The concept of rent-seeking will be explained later in the chapter.

12

infrastructure” and may be “reinvested productively by the cronies whereas tax revenues

may have been wasted”.

In spite of the various types of corruption, the most popular definition of corruption in the

literature is the misuse of public office for private gain. This definition of corruption refers

to the bureaucratic corruption that Bardhan (1997), Jain (2001) and others use in their

study. This will also be the definition used in the present study. However, Tullock (1996) is

of the view that this definition is by no means the most appropriate. He rightly identifies a

glaring problem with the definition of economic corruption as bribe paying: “People say

that corruption is taking action…for the secret reason of private benefit…However, as a

rough rule of thumb, most people who talk about corruption are thinking not about a

congressman who votes for crop restrictions because he thinks that will win elections but

about somebody who actually takes bribes” (p.11).

Tullock’s (1996) point highlights a problem in the corruption literature. As will be seen in

the empirical studies of corruption, a negative relationship between corruption and growth

is commonly found based on the popular definition of corruption as bribe-paying which

ignores the different types of corruption that exist in different countries. This myopic view

of corruption is also present in the theoretical issues surrounding corruption. These will be

discussed in the next section.

2.3 Economic Models of Corruption

Having defined corruption the next step is to introduce some economic models of

corruption. The most popular one in the literature is the principal-agent model of corruption

and this will be analysed first, before an alternative model is proposed.

2.3.1 A Principal-Agent Model of Corruption

At the theoretical level the effect of corruption on an economy is typically viewed from a

‘principal-agent’ perspective. Basically bureaucrats acting as agents for the government –

or the principal – have an opportunity to be corrupt and extract a private gain (bribe) for

themselves. Shleifer and Vishny (1993) developed this basic corruption model. The model

13

assumes that the bureaucrat exclusively possesses a particular government-produced good,

such as an import licence or the right to use a government road, valued at price P1. The

good is assumed to be homogeneous, and a demand curve, D, exists for this good from the

private sector (see Figures 2.1 and 2.2). The bureaucrat has the opportunity to restrict the

quantity of the good (which translates in practice to delaying the issue of an import licence

for example).

Figure 2.1: Corruption without Theft Figure 2.2: Corruption with Theft

P P

Q

P1 + B

MR

Q

P1

MRD D

Q1 Q1

B

P1

Source: Shleifer and Vishny (1993, pp.602-603).

The model also assumes that the government does not monitor the bureaucrat’s actions, so

he can restrict supply without fear of detection from his superiors. Essentially the

bureaucrat is a monopolist and his sole aim is to maximise the bribes he can collect in

supplying the government good. The model also assumes that there is no cost involved for

the bureaucrat in producing the good since the government is paying the cost, though in

reality the bureaucrat may need to exert some personal effort.

To firstly determine the marginal cost to the bureaucrat, two situations need to be

distinguished. In the case without theft, the bureaucrat keeps the bribe that is paid (B) and

hands over the price of the good (P1) to the government. In this case, the marginal cost is

simply the price of the good, P1, and the bribe is the bureaucrat’s profit. The profit-

maximising quantity supplied is Q1, where marginal revenue equals marginal cost (P1).

14

This situation is illustrated in Figure 2.1. It is interesting to note that, in the case without

theft, the bribe resembles a commodity tax – in particular a revenue-maximising

commodity tax when marginal cost is equal to the price of the good, P1. In the situation

with theft the bureaucrat only charges the bribe, which he keeps for himself, and gives

nothing to the government. Thus he simply hides the actual sale from the government. If

the transaction is hidden then the bureaucrat does not incur a cost in providing the good,

therefore his marginal cost is zero. This situation is shown in Figure 2.2. Once again, the

profit-maximising quantity supplied is Q1, where marginal revenue equals marginal cost

(which is zero in this case). The price that the buyer pays equals the bribe to the bureaucrat,

which may in fact be lower than the actual price. In practice this may equate to a customs

officer allowing passage of contraband in exchange for a bribe which is lower than the

official duty (Shleifer and Vishny, 1993).

If the assumption that the bureaucrat’s corrupt practices cannot be detected by the

government is relaxed, the situation remains largely unchanged. The bureaucrat will simply

set the bribe at a level such that the benefits of corruption outweigh the costs of the penalty

imposed if the bureaucrat is caught. If the penalty is proportionate to the size of the bribe,

the bureaucrat may reduce the bribe and increase output. If the penalty is proportionate to

the number of people who pay the bribe (through a higher probability of someone

registering a complaint), the bureaucrat may raise the level of the bribe and restrict supply

(for further discussion, see Becker and Stigler, 1974; Rose-Ackerman, 1975; and Klitgaard,

1988).

Shleifer and Vishny (1993) also explain how corruption can spread quickly due to

competition. If bureaucrats receive jobs from their superiors by paying for them, clearly

those who extort the most bribes will be able to offer the highest bids. Amongst buyers,

competition will also see an increase in corruption. Those who pay bribes will lower their

costs and competition will force other buyers to follow suit or fall out of the market.

However, this will only occur in the case of corruption with theft. In the other case bribes

actually raise the buyers’ costs and there is an incentive for them to expose the corrupt

bureaucrats.

15

Ehrlich and Lui (1999) argue that bureaucratic corruption exists in all societies, during all

stages of economic development and under different political regimes. However, different

countries have different levels of corruption and liberal economists will blame the

regulatory state for its elaborate system of permits and licences. Bardhan (1997) claims that

this is an inadequate explanation, because countries like Mexico are perceived to be more

corrupt than South Korea and Taiwan despite the latter having higher levels of government

intervention. Another explanation is that different countries have different social norms as

shown in Andvig’s (1991) basic model in Figure 2.3.

Figure 2.3: Andvig’s (1991) Multiple Equilibria Model

B M Curve

A

C

O

N Curve

Source: Andvig (1991) cited in Bardhan (1997, p.1331).

On the diagram, the horizontal axis represents the number of corrupt bureaucrats, the end

point of which represents a situation where every bureaucrat is corrupt, and O marks the

origin where there are zero corrupt bureaucrats. The vertical axes measure the payoffs to

the bureaucrats. The M and N curves describe the marginal benefit for a corrupt and an

honest bureaucrat respectively. At the lower end of the horizontal axis, where there are few

corrupt bureaucrats, the benefit for the honest bureaucrat is higher than that of his corrupt

counterpart. However, as the number of corrupt bureaucrats increases, the benefit for the

honest bureaucrat declines (because he is being undercut by corrupt bureaucrats) and

eventually becomes negative when everybody is corrupt. The benefit for the corrupt

bureaucrat however increases, but only temporarily as competition in bribes forces the

16

benefit down. But the payoff for the corrupt bureaucrat is still positive when everybody is

corrupt (Bardhan, 1997).

The three equilibrium states are identified by A, B and C on Figure 2.3. A and C are stable

because either corruption is zero or rampant. At any point in between, the economy will

either move towards A (if there is low corruption) or C (if there is high corruption). Point B

represents an equilibrium where there is no difference between being corrupt or honest, but

it is unstable. This is because it requires only one extra bureaucrat (either honest or corrupt)

to enter the scene and sway the economy either towards A or C (Bardhan, 1997). This

model therefore explains why corruption may persist in some countries while it is never

allowed to develop in others.8

2.3.2 Rent-Seeking: An Alternative Model of Corruption

The term rent-seeking was first coined by Krueger (1974) although the concept was

originally introduced by Tullock (1967). As its name suggests, rent-seeking is defined as

the use of resources and effort in obtaining, maintaining or transferring rents (Khan,

2000a). In Krueger (1974) rent-seeking is shown to be competitive particularly in the case

where rents arise out of quantitative restrictions on trade. An import licence amounts to

such a quantitative restriction similar in effect to a tariff. Like Tullock (1967), Krueger

(1974) suggests that there would be resources invested in competing for that rent (import

licence) and uses the import licence on intermediate goods as an example. Such licences are

often allocated in proportion to the capacity of firms, so naturally the greater the investment

in increasing capacity the greater the expected receipt of import licences. Mitchell (1993)

argues that this will result in higher capital stock for firms, which will thus improve social

welfare.

Where government officials decide on how to allocate licences, firms can devote resources

to influencing the probability or expected size of allocations. According to Sobel and

Garrett (2002) this represents the distinction between direct and indirect rent-seeking.

Direct rent-seeking would involve monetary payments or ‘in-kind’ benefits such as free

8 For further discussion on the mechanisms through which the equilibrium points are reached, see Cadot, 1987; Andvig and Moene, 1990; and Tirole, 1996.

17

meals and gifts that are given directly to a government official. Indirect rent-seeking refers

to advertising campaigns and other lobbying that is not directed at any individual

specifically. In their study, Sobel and Garrett (2002) looked at rent-seeking industries in the

US and found that significantly more enterprises were situated in state capital areas than in

noncapital areas, reflecting a considerable degree of rent-seeking.9

Negative Impact of Rent-seeking

Bhagwati (1982) claims that the rent-seeking behaviour identified by Krueger (1974) and

Tullock (1967) falls under what he terms directly unproductive, profit-seeking activities, or

DUP activities for short. These include any activities that “yield pecuniary returns but do

not produce goods or services that enter a utility function directly or indirectly via increased

production or availability to the economy of goods that enter a utility function” (Bhagwati,

1982, p.989). Three common examples of DUP activities are tariff-seeking lobbying, tariff

evasion, and premium seeking for given import licences. Bhagwati (1982) asserts that these

particular activities do not enhance or contribute to the productivity of an economy as they

arise out of a initially distorted situation and do not alleviate the distortion. Other DUP

activities however can create distortion from an undistorted initial situation, leave the initial

undistorted situation unchanged, and alleviate distortion from a distorted initial situation.

Pecorino (1992) finds that rent-seeking (a DUP activity) can contribute to growth through

acquisition of productive human capital. Murphy et al. (1993) distinguish between private

rent-seeking which deals with contests for existing stocks of wealth, and public rent-

seeking which refers to the government restricting licences and import quotas etc. The latter

is deemed to be more harmful to growth as it hampers innovation which in turn impedes

growth.

According to Baumol (1990) such harmful activities have existed in many areas of society

as far back as the 12th century where the legal system was used for rent-seeking purposes.

In one case, operators of two dams sued each other repeatedly for over a century until one

eventually ran the other out of business due to an inability to pay court fees. However in

some cases the rent-seeking ended up being productive where entrepreneurs competed for

grants of land and patents of monopoly from monarchs, albeit by chance since there was no

9 Rent-seeking can also occur within firms, signalling internal governance problems and firm inefficiency. See Vining (2003) for more details on this phenomenon.

18

tendency for the monarchs to use efficiency as a criterion for issuing such grants/patents.

Ultimately though, Baumol (1990) argues that the contribution to production depends on

whether resources are devoted to innovation or to rent-seeking and this in turn depends on

the relative payoffs of each alternative. Murphy et al. (1991) build on this point stating that

the allocation of talent towards rent-seeking can be damaging to an economy in the long

run. They find that countries with a higher proportion of engineering colleges grow faster

than those with a higher proportion of law concentrators, and lawyers represent an

allocation of talent towards rent-seeking.

Tullock (1993) takes Bhagwati’s argument one step further and highlights that rent-seeking

behaviour must not only be unproductive, but it must result in a loss to society as a whole

in order for it to be classified as rent-seeking. For example, a person who finds a cure for

cancer and then patents his finding in order to maximise his wealth is not an example of

rent-seeking, because the society still benefits from the cure. However, if the person then

obtained a law that prevented the import of a new and more effective cure of cancer

because he wanted to make profits from his own, older and less effective cure, then that

would be classified as rent-seeking since the public is prevented from receiving the benefit

of the newer cure. This illustrates that although rent-seeking behaviour may appear to result

in a complete welfare loss, it may in fact produce a positive outcome for society.

One of the most popular types of rents are monopoly rents. These exist when a single firm

has a monopoly over the production of a particular good, and is able to collect a rent equal

to the monopolist’s profit. Tullock (1993) uses the situation of a monopoly to illustrate

rent-seeking and demonstrate its social costs.10 In the case of a monopoly, rent-seeking

results in a welfare loss (Tullock, 1993). In Figure 2.4, Point C represents the cost of the

good, and P1 represents the price charged by the monopolist. The shaded area represents the

welfare loss to society when the monopolist extracts a rent because there is less quantity

supplied. However this is not deemed to be the only loss. Tullock (1993) argues that there

are further losses. Tullock claims that costs will actually increase when there is a shift from

competition to a monopoly. This is because the monopolist is no longer as willing to invest

the effort or resources needed to find the least costly way to produce the good in question,

10 See Posner (1975) for a detailed discussion of the social costs of a monopoly.

19

because the monopolist knows he can charge a higher price anyway. Palda (2000) expands

on this issue of high-cost producing firms. In the simple example of two firms competing

for a broadcast licence, if the firm with the higher transmission cost wins the rent then more

of society’s resources will be devoted to broadcasting.

Figure 2.4: Monopoly Welfare Losses P

Q

P1

MR D

A

B

Rent

C

Source: Tullock (1993, p.10).

Another loss lies in the monopolist’s efforts to maintain his monopoly. The monopolist

would have to invest resources in order to prevent other competitors from entering the

market. This investment in defence of monopoly powers can be classified as rent-seeking

behaviour.11 So Tullock (1993) argues that at least part, if not all, of the rectangle P1ABC

should also represent a welfare loss as it incorporates the resources invested in defending

the monopolist’s powers. The problem lies in identifying this loss in reality. As Sobel and

Garrett (2002) point out, the indirect expenditure is a major component of the overall rent-

seeking expenditure but by its very nature it is difficult to identify in practice – a problem

commonly referred to as the Tullock paradox (Tullock, 1998). This leads to the finding in

the literature that the observed social costs of rent-seeking will be much lower than what is

predicted in theory.

The net effect of the rent-seeking behaviour by a monopolist is the sum of the lost inputs

that are invested in rent-seeking and the social benefit or loss resulting from the actual rent 11 For an interesting comparison between rent-seeking models and conflict models, see Neary (1997).

20

itself. Khan (2000b) illustrates this net effect using Figure 2.5. The diagram assumes that a

particular economy can either produce cars or grain with its resources, and shows the

production possibility frontier PP for all possible combinations. The world price lines are

represented by the downward sloping straight lines. The efficient position for the economy

is the point A, where the world price is tangential to PP. Suppose that an import restriction

is imposed on cars (not shown on the diagram), raising the domestic price of cars. This will

result in a shift in domestic production from A to B. More cars will be produced locally at

the expense of grain, and society becomes worse off. As inputs are held constant, the value

of the lost output at world prices is the social loss. To compare the values at B and A, the

world price line is drawn through point B. Thus XY becomes the deadweight social loss,

which is the same loss identified by Tullock (1993) as the shaded area in Figure 2.4.

Figure 2.5: Net Effect of Rent-Seeking with Value-Reducing Rents

Cars

B A

B’

P’ P

P’

PP is the production possibility frontier A is the efficient output mix B is the output mix after an import restriction on cars P’P’ is the frontier after rent-seeking activities XY is the (negative) social value of the restriction ZY is the additional rent-seeking cost

P

Z Y X Grain Source: Khan (2000b, p.81).

However, domestic car producers can begin lobbying politicians to maintain the import

restrictions, or impose further ones, and grain producers can be expected to lobby for

similar restrictions that would provide them with rents. This rent-seeking behaviour uses up

resources that could otherwise have been used to produce cars and grain. Therefore the loss

of inputs from production towards rent-seeking results in a shrinkage of the production

possibility frontier from PP to P’P’. Consequently the economy shifts from B to B’. To

measure the cost of this rent-seeking, a world price line is drawn through B’ and the

21

resultant difference between B and B’ is YZ. Therefore the net effect on society becomes

the sum of XY (deadweight social loss) and YZ (rent-seeking cost), yielding XZ (Khan,

2000b). One strand of the literature has considered the impact of deregulation of

monopolies in order to recover the deadweight social losses. However Poitras and Sutter

(1997) contend that the monopolist will invest resources to defend its position, while

reformers will expend effort in advocating deregulation, so that the result may see the gains

from deregulation being outweighed by its costs.12

What is the total value of the social cost? That depends on the measure of rent-seeking. It is

difficult to find an appropriate measure of rent-seeking. Cole and Chawdhry (2002) use the

number of registered interest groups in the US as a measure of rent-seeking. Del Rosal and

Fonseca (2001) analyse the Spanish coal mining industry and measure rent-seeking by the

magnitude of labour unrest. In other words, when workers go on strike, the costs to the firm

and the economy are a proxy for the total social cost. There are other ways of measuring

rent-seeking, and these will be covered in Chapter 8.

Positive Impact of Rent-seeking

Figure 2.5 illustrated the effect of a ‘value-reducing’ rent, i.e. a rent that has a negative

effect on society. But recall that there can also be rents that are beneficial to society. Khan

(2000b) uses Figure 2.6 to show the effect of a ‘value-enhancing’ rent. In Figure 2.6 the

import restrictions still result in increased domestic car production, from A to B, but B now

lies on a different production possibility frontier, P”P”. This can happen if the restriction

induces ‘learning’, i.e. the restriction is only for a limited time and subject to domestic car

producers learning to use technology to produce better cars.13 However, the cost of the

rent-seeking behaviour will see this new frontier fall short of P”P”, and end up as P’P’. In

spite of this, the net effect is XY less YZ, which gives XZ – a net social benefit. Bhagwati

and Srinivasan (1980) similarly show how rent-seeking can be welfare improving.

It is worth noting that although the government is in charge of imposing the import

restriction, it may only be doing this in response to lobbying from the private sector. This is

12 See Dewey (2000) for an in-depth discussion of rent-seeking and deregulation, and Drook-Gal, Epstein and Nitzan (2004) for an analysis of the mechanics of privatisation in the context of rent-seeking. 13 It is assumed that both grain and car producers will eventually benefit from learning.

22

a possible explanation as to why monopolies do not necessarily arise out of economies of

scale, but rather as a result of firms succeeding in finding preferential treatment from the

government which can use its powers to impede competition (Lambsdorff, 2002).

Figure 2.6: Net Effect of Rent-Seeking with Value-Enhancing Rents

Cars

B

A

B’

P”

P

PP is the initial production possibility frontier A is the efficient output mix P”P” is the frontier after import restrictions induce learning B is the output mix after the import restriction on cars P’P’ is the frontier after rent-seeking B’ is the output mix after rent-seeking XY is the potential social value of the restriction ZY is the rent-seeking cost

P’

Source: Khan (2000a, p.82).

P P’ P” X Z Y Grain

As Figure 2.6 shows, rent-seeking can be value enhancing when the rents are injected back

into the system productively and not simply wasted. This is known as the transfer of rents.

Rents based on transfers can include not only taxes and subsidies, but also transfers which

convert public property into private property. They are classified as rents because the

income is greater than any alternative of the recipients. However not all transfers are rents.

Pension payments and unemployment benefits are associated with a previous saving or

contribution similar to an insurance premium and are not classified as rents (Khan, 2000a).

Tullock (1993) argues that the most desirable outcome of rent-seeking is where the costs

are zero and the result is a transfer of wealth rather than its dissipation.

The effect of the transfer consists of two dimensions. On one side, the valuation of the

transfer by the ‘loser’ and the ‘gainer’ determines the welfare effect on society. Since a

poor person is likely to value a dollar greater than a rich person, then a transfer from the

23

poor to the rich would result in lower social welfare. The other dimension relates to the

effect on the incentives of the ones being taxed, which is usually negative.

Figure 2.7: Rents Based on Transfers

D

E

C

B A

MC MC + tax

Transferred to other sectors

Demand

Price in taxed sector

Q2 Q1 Output of taxed sector Source: Khan (2000a, p.37).

Figure 2.7 shows that when a tax is imposed upon the ‘losers’, the marginal cost of

production is increased. This results in a lower quantity produced. The rectangle ABCD

represents the tax, or rent, which is then transferred to another sector. The triangle BCE is

the deadweight loss that represents the social cost of the fall in output. This situation is very

similar to the monopoly rent described earlier where rents were transferred from consumers

to firms, with the only difference being that there the transfer was organised through the

price mechanism, whereas here it is through the political mechanism (Khan, 2000a). Katz

and Rosenberg (2000) show that corporate taxes tend to favour rent-seeking by established

firms and discriminate against new and zero profit firms.

Rent-seeking and Corruption

Some types of rent-seeking behaviour can be viewed as a form of corruption. Indeed rent-

seeking incorporates a wide spectrum of activities, many of which are morally and legally

acceptable in some societies. The issue however is whether these practices would otherwise

be frowned upon outside these societies, and if so, whether they are (or ought to be)

captured in global corruption rankings. Later in Chapter 4, the nature of corruption in East

24

Asia will be analysed and it will be shown that the corrupt practices often occur under the

guise of rent-seeking behaviour. To explore the connection between the two phenomena,

consider Shleifer and Vishny’s (1993) model of corruption, which can be viewed from the

perspective of a private corporation (assume a monopolist) seeking a particular unique

licence. The corporation may engage in rent-seeking behaviour in order to ensure that it

receives the licence, and that the licence is not given to another corporation which may

threaten the original firm’s monopoly status. Figure 2.1 now becomes a representation of

the private firm’s situation, instead of the bureaucrat’s (in Shleifer and Vishny’s original

model). Once again, welfare losses arise. The first is due to the restricted quantity supplied

as a result of charging a high price (which is the monopolist’s rent). The second is due to

the higher input costs incurred because, as Tullock (1993) argued, the monopolist sacrifices

the resources required to keep input costs at a minimum in exchange for reaping higher

revenue (although this was irrelevant in the case of the corrupt bureaucrat). And the third

loss is due to rent-seeking behaviour, in trying to secure the licence from the bureaucrat.

This behaviour must be recognised as a welfare loss, because the monopolist is investing

resources purely and simply to extract a rent, and this investment does not yield any benefit

for society as a whole. Lambsdorff (2002) is of the view that the situation is no different to

a beggar who mutilates himself to exact more sympathy from passers-by and thus more

charity.

In fact, Lambsdorff (2002) argues that the rent-seeking model is a better way of analysing

corruption than the principal-agent approach. The principal-agent model assumes that the

principal (government) is not corrupt, rather it is the agent (bureaucrat) who bends or

breaks the rules devised by the principal to serve his own interests. Further, the model

assumes that the government also has complete control over the legal framework, and

consequent rewards and penalties. Lambsdorff feels that these assumptions are unrealistic,

especially in societies where the public sector is a corrupt entity. It is too simplistic to

assume that the government must be immune to corruption because it is in control of

legislation. Lambsdorff cites the case of Thailand, where people are prohibited from taking

(stealing) leaves or pebbles from certain areas. However, the Forestry Department was not

motivated by environmental protection incentives, rather it officially converted the areas in

question into tourist attractions or destroyed them for gas pipelines, purely for the benefit of

25

the department. To fully capture the effect of corruption and the welfare loss on society,

Lambsdorff (2002) believes the rent-seeking model is more appropriate.14

The nature of rent-seeking behaviour can determine whether it is perceived to be an act of

corruption. If the rent-seeking firm were to pay money to a politician, this would be

deemed as corruption. But if the same firm were to undertake extensive advertising, starting

political campaigns and engaging public relation agencies, this would not be seen as corrupt

behaviour (Lambsdorff, 2002). This illustrates the difference between direct and indirect

rent-seeking, as highlighted by Sobel and Garrett (2002). The government’s response to the

rent-seeking behaviour can also be perceived as being corrupt. In fact, this has become the

backbone behind the argument in favour of favouritism or ‘nepotism’ in government.

Pedersen (1997) shows how the government’s attempt to serve its own interests in the

context of rent-seeking has led to uneven distribution of benefits within developing

countries. Colombatto (2003) also provides an interesting characterisation of corruption

based on rent-seeking in different institutional contexts, i.e. developed countries,

totalitarian countries and transitional countries. Konstantin (2003) illustrates how unequal

countries like Russia are plagued by rent-seeking, as a result of government inefficiency.

Where corruption is a selling cost for the private supplier (rent-seeking), Fedeli and Forte

(2003) show that a centralised regime causes higher corruption. Mohtadi and Roe (2003)

finds that young and mature democracies achieve faster economic growth than countries in

mid stages of democratisation, producing a U effect, as a result of rent-seeking. Emerson

(2002) argues that in less developed countries, the high degree of corruption contributes to

industrial dualism – a large number of small, traditional firms and a handful of large,

modern firms. More corrupt countries tend to have fewer and larger modern firms, and

lower social welfare (and thus greater deadweight loss). Government rents are also higher.

Emerson (2002) finds a positive relationship between the degree of corruption in a country

and its percentage of large firms.

14 For an extensive discussion on similar bureaucratic corruption in China, see Lu (1999) and Sharpe (2001).

26

2.4 Conclusion

The most popular definition of corruption in the literature is the ‘misuse of public power to

provide private gain’. It is this definition which provides the basis for the principal-agent

model of corruption developed by Shleifer and Vishny (1993), which is how corruption is

typically modelled in studies examining its impact on an economy. However, corruption

can also occur in the form of rent-seeking. This process of private corporations establishing

patron-client networks with governments in order to secure rents is an alternative method of

modelling the effect of corruption on an economy. This study uses both frameworks in the

analysis of how corruption impacts upon growth, particularly in East Asia. The question

now is what have empirical investigations of this effect yielded? The next chapter seeks to

provide an answer to this question.

27

CHAPTER 3

LITERATURE REVIEW: EMPIRICAL STUDIES

Each country in Asia will chart its own way forward. Every country

wants to be developed and wealthy. They will adopt and adapt those

features or attributes of successful countries which they think will help

them succeed. If these features work and improve their rate of

progress, they will be permanently incorporated. If they do not work

or cause difficulties, they will be abandoned.

- Former Singaporean Prime Minister Lee Kuan Yew.15

3.1 Introduction

The comments of the former Singaporean Prime Minister echo the sentiments of Dr

Mahathir’s comments in the previous chapter. The architect of Singapore’s rise to

economic prosperity, Lee, makes it abundantly clear that Asian countries do not necessarily

need to follow the policies of other successful nations. Singapore clearly stands out

amongst its East Asian counterparts. Consistently appearing in the top ten in Transparency

International’s Corruption Perceptions Index, Singapore provides a perfect example of what

heights a country can reach in the absence of a bribe-seeking bureaucracy. But if other East

Asian countries can produce economic growth similar to that of Singapore’s in the presence

of corruption, then what conclusion does one reach? Is Singapore an anomaly? What is the

true impact of corruption on economic growth? This chapter provides a concise summary

of empirical studies concerning corruption and economic growth and presents a framework

for this study’s own analysis in later chapters.

3.2 Empirical Studies Analysing Corruption and Growth

The theoretical debate over the effect of corruption on investment, economic growth,

political stability and economic integration has led many scholars to conduct empirical

15 National University of Singapore, 2006.

28

research with the aim of establishing a link between corruption and these economic

variables. These authors mainly employed the growth accounting approach to examine the

impact of corruption on economic growth. An index of corruption (measured in various

ways as will be seen in Chapter 5) is used as the main explanatory variable. Literature in

this area is too vast and Table 3A.1 in the appendix to this chapter provides a summary of

the major studies in this area since 1995.

Wei (1999) provides a concise review of this material, which generally finds that corruption

leads to reduced domestic investment, reduced foreign direct investment, overblown

government expenditure, and distorted composition of government expenditure away from

essential sectors towards less efficient but more manipulatable public projects. Particularly

in the case of Asia, Wei concludes that there is no evidence to suggest that corruption in

that part of the world is anything but a hindrance to economic development. It is worth

noting that Wei begins the review with an anecdote about a Chinese restaurateur who

received a National Outstanding Private-Sector Worker Award and whose restaurant

consequently achieved such a high reputation that it was frequently patronised by

bureaucrats who never paid their bills, thus leading to the eventual closure of the business.

This is a somewhat surprising opening by Wei, having earlier dismissed anecdotal evidence

(that may suggest corruption is beneficial to growth) because “there is a limit to what

anecdotes can tell us” (p.3).

Wei’s anecdote is meant to demonstrate how corruption can kill a small business, but it

actually does a better job at explaining the difference between corruption in Asia, and

corruption in the rest of the world. One might argue that the bureaucrats who never paid

their bills were actually being repaid by the restaurateur for the award that was conferred

upon him, in recognition for the success of his business. The restaurant may truly have been

successful, and in any other culture the bureaucrats would probably have given the owner

an award anyway, but in Asia this is seen as a favour, and the restaurateur had to repay it.

Regardless, if the business was so successful on its own merits, one would expect that the

ongoing business from the restaurant’s other clientele would surely have compensated for

the lost revenue from the bureaucrats’ unpaid bills.

29

Most of the literature does indeed support Wei’s assertion that the link to economic growth

is deemed to be significant and negative, consistent with the ‘efficiency reducing’ view

discussed in Chapter 1 (Mauro, 1995 and 1997; Rahman et al., 2000). Poirson (1998)

argues that a reduction in corruption levels will only translate to an improvement in growth

in the long-term. However, Vehovar and Jager (2003) show that an improvement in growth

will not necessarily result in improvements in governance, particularly in the case of

Slovenia. Kaufmann (2003, p.1) argues that there has been “scant progress worldwide in

recent times in improving rule of law and governance, in controlling corruption, and in

improving institutional quality -- although there is clearly variance across countries”. Why

is it that countries do not strive to improve their institutions and root out corruption, and

remain caught in a circle of corruption and low economic growth? Mauro (2002) believes

one explanation lies in the absence of incentives for individuals to fight corruption when it

has become widespread. In fact, Chakrabarti (2001) claims that social corruption is derived

from individual corruption levels optimally chosen by agents with varying risk aversion

and human capital. Chakrabarti uses a multi-generational economy with heterogeneous

agents to show that there are locally stable equilibrium corruption levels with certain

socioeconomic determinants. In the worst case, corruption can become so rampant that it

stifles all economic activity.

Other studies have found corruption to have a positive impact on growth. For example,

Mendez and Sepulveda (2001) demonstrate that corruption enhances growth at low levels

of incidence, but reduces it at high levels. This implies the existence of some level of

corruption that maximises long-term growth.

Some studies have explored the effects of corruption on other variables that ultimately will

impact on economic growth. The literature in this area finds that corruption leads to

reduced investment. Expanding on his 1995 study, Mauro (1997) shows that a decline in a

country’s perceived level of corruption leads to an increase in its investment rate. Rahman

et al. (2000) also build on Mauro’s (1995) model and agree that corruption is significantly

and negatively associated with gross domestic investment, and can also drive away foreign

investment. In Africa, Gyimah-Brempong (2002) reveals that corruption leads to decreased

income per capita, indirectly caused by decreased investment.

30

According to Wei (1997), a rise in either the tax rate on multinational firms or the

corruption level in a host country reduces inward FDI. In particular, Wei concludes that

there is no support for the hypothesis that corruption has a smaller effect on FDI into East

Asian host countries. Wei (1997a) further states that the less predictable the level of

corruption, the greater its impact on foreign direct investment. In other words, corruption

resembles an unpredictable, random tax that adds to risk and uncertainty. An increase in

corruption is therefore no different to an increase in corporate tax. Interestingly,

Smarzynska and Wei (2000) suggest that not only does host country corruption reduce

inward FDI, but it also results in a shift towards joint ventures as opposed to wholly-owned

firms.

Vinod (2003) takes the analysis one step further, arguing that not only is corruption

negatively associated with investment, but that corrupt countries will place capital controls

to prevent capital flight to less corrupt countries, and uncovers a significant correlation

between corrupt countries and capital controls. On the issue of capital restrictions, Dreher

and Siemers (2004) assert that higher corruption induces stricter restrictions and vice versa.

Further, capital account restrictions induce higher corruption in the short-term but reduce

corruption in the long-term. Wei and Wu (2001) examine a possible linkage between the

composition of capital inflows and corruption, building on Wei (2000). The results suggest

that poor public governance is associated with a higher loan-to-FDI ratio (as well as a

country’s inability to borrow internationally in its own currency). This is said to be

associated with a higher incidence of a currency crisis, thereby suggesting that corruption

may increase the probability of a currency crisis.

The link between corruption and the composition of government expenditure is also worth

exploring because “most economists think that the level and type of spending undertaken

by governments do matter for economic performance” (Mauro, 1997, p.10). Mauro claims

that government spending on education as a ratio to GDP is negatively and significantly

correlated with corruption. Other components of government expenditure also exhibit a

similar connection with corruption, but only education remains significant when the level

of per capita income is used as an additional explanatory variable. As Mauro explains, this

control accommodates the relationship known as Wagner’s law – that government

expenditure as a percentage of GDP tends to rise as a country becomes richer. Mauro also

31

finds that government spending on defence or transportation displays no significant

relationship with corruption. There is also no evidence that corruption leads to excessive

expenditure on ‘white elephant’ projects. Fisman and Gatti (2002) produce evidence of a

strong negative relationship between fiscal decentralisation in government expenditure and

corruption. Bohn (2003) argues that political instability may lead to underinvestment in

infrastructure or anti-corruption measures. The main finding is that a political instability

threshold exists, below which a government will refuse to invest. Pellegrini and Gerlagh

(2004) reach a similar conclusion, asserting that corruption indirectly impedes growth

through political instability.

Tanzi and Davoodi (1997) challenge the notion that high levels of public expenditure

contribute to growth. They argue that corruption is likely to increase the number of projects

undertaken in a country, and enlarge their size and complexity. This results in an increase

in the share of public investment in GDP; a fall in the average productivity of that

investment; and a possible reduction in other essential areas of spending such as education

and health. Controlling for GDP per capita, Tanzi and Davoodi observe that high levels of

corruption are indeed associated with higher public investment, lower government

revenues, lower spending in essential areas, and lower quality of public infrastructure.

Generally, corruption encourages public investment while reducing its productivity. In a

similar vein, Del Monte and Papagni (2001) show that the efficiency of public expenditure

is lower in regions where corruption is higher, and that corruption has a negative effect on

economic growth of Italian regions. Ellis and Fender investigate the tension between the

need for the government to rectify a market failure and supply a public good, and the

opportunity that this provides for corruption. They claim that when potential governments

compete, “instead of driving corrupt payments to zero the competition leads to the

maximisation of the present discounted value of private sector utility subject to a constraint

imposed by the level of irreducable (sic) corruption” (Ellis and Fender, 2003, p.3).

On government valuation, Depken and LaFountain (2004) explore the effects of corruption

on state bond ratings in the US and find that more corrupt states have lower bond ratings,

implying that taxpayers in those states must pay a premium for debt. In East Asia, Fons

(1999) identifies a correlation between credit risk and corruption. As for the private sector,

Lee and Ng (2002) investigate the relation between corruption and international corporate

32

valuation. Their results show that firms from more (less) corrupt countries trade at

significantly lower (higher) market multiples, implying that corruption has significant

economic consequences for shareholder value. Interestingly, Fisman and Svensson (2000)

reveal that growth in the private sector is also impeded by corruption – specifically, bribery

curbs growth more than taxation in Uganda.

Corruption is also connected to competition. Emerson (2006) investigates the interaction

between corrupt government officials and industrial firms, and the results indicate that

corruption is antithetical to competition, and also that higher education and more civil

liberties (a proxy for democracy) have a mitigating effect on corruption. Compte et al.

(2005) observe that corruption can affect competition in government procurement auctions.

If the agent administering the market accepts bribes, this can result in high public spending

and inefficient allocation of resources.

On the relationship between government revenue and corruption, Hwang (2002) finds

corruption as being negatively and significantly related to domestic tax revenue as well as

total amount of government revenue over GDP. Also, Hwang states that international trade-

tax revenue as a proportion of total government revenue is negatively and significantly

related to corruption. Fjeldstad and Tungodden (2003) show that an increase in corruption

may raise revenues in the short run, but in general the opposite will be the case in the long

run. Johnson et al. (1999) present a similar argument in a slightly different manner. First,

they find that the share of the unofficial economy in GDP is higher in the presence of

tighter regulation, as this provides more opportunity for officials to employ corrupt

practices. A higher share of the unofficial economy is then correlated with lower tax

revenue as a percent of GDP, thus an increased level of corruption will lead to lower tax

revenue.

Huang and Wei (2003) analyse the implications of corruption for central banking.

According to their study, the optimal inflation targeting for a high-corruption country is

generally different from that for a low-corruption country, and that fixed exchange rates are

more difficult to sustain for high-corruption countries as the inflation rate (in the anchor

country) may be too low from the viewpoint of the countries that adopt the exchange rate

arrangements. Related to the issue of inflation, Braun and Di Tella (2004) argue that agents

33

can inflate prices for goods needed to start an investment project. High and variable

inflation is assumed to increase the cost of monitoring the agent, and this is found to result

in higher corruption and lower investment.

Alesina and Weder (2002) study the connection between corruption and foreign aid. They

could not produce any evidence that bilateral or multilateral aid goes disproportionally to

less corrupt countries. Interestingly though, their study reveals that while Australia and

Scandinavian countries give more foreign aid to less corrupt countries, the US does the

inverse. Generally, an increase in aid is found to increase corruption. Ali and Isse (2003)

concur with this relationship. However, Tavares (2003) uses geographical and cultural

distance to the donor countries as instrumental variables to assess causality between foreign

aid and corruption, and finds the inverse to be true, i.e. that foreign aid decreases

corruption.

3.3 Empirical Studies on Rent-Seeking

In Chapter 2, rent-seeking was introduced as an alternative dimension of corruption. There

is a considerable amount of literature analysing the impact of rent-seeking on an economy,

and what follows is a survey of those studies, which are also concisely summarised in Table

3A.2 in the appendix to this chapter.

3.3.1 Rent-Seeking Expenditure

Many studies have analysed the expenditure on rent-seeking, as this is deemed to be a

significant determinant of the social cost of rent-seeking (Amegashie, 1999; Baye et al.,

1999; Dixit, 1987). One might assume that the greater a player spends on rent-seeking, the

greater the player’s chances of winning the rent. However there are other factors that

influence rent-seeking expenditure. For instance, Amegashie (1999) considers a rent-

seeking scenario where part of the rent is fixed and the remainder is dependent on the

extent of lobbying. This is likened to applications for a position that is subject to a

minimum salary which may be increased depending on the quality of the successful

applicant. It is also similar to institutions that compete for a research grant which has a

minimum value for the successful institution, but the amount may be raised depending on

34

the attractiveness of the research proposal. Amegashie (1999) finds that in such cases, an

increase in the number of rent-seekers may lead to a fall in the aggregate rent-seeking

expenditure. In a later study, Amegashie (2002) reveals that the magnitude of rent-seeking

expenditure can also depend on whether the rent is being awarded by a single administrator

or a committee. Baye et al. (1999) show that, in some cases, rent-seekers may end up

spending more than the rent itself is worth.

Dixit (1987) considers a situation where individuals expend effort to increase their

probability of securing a particular rent, such as when firms compete in investment to yield

a profitable innovation, or when bribes are paid to government officials to secure a

particular licence or contract. In the case of the latter, if two contenders for the rent are not

equally efficient then Dixit (1987) argues that the more efficient user, if given first access

to the government official, would offer a higher bribe than in the case of simultaneous

access. However Glazer and Hassin (2000) find that in sequential contests, earlier movers

do not make more profits than later movers, and profits are lower than in simultaneous

contests.16

Haan and Schoonbeek (2003) contrast rent-seeking with auctions. In an auction, all players

exert effort by submitting a bid and the highest bid wins the auction. Under rent-seeking,

the outcome is stochastic. The competitors will still exert effort by lobbying or bribing but

this only serves to increase their probability of winning the rent, and decrease the

probability of their rivals winning it. In practice, Haan and Schoonbeek argue that hybrid

forms of rent-seeking contests and regular auctions exist where a bid is offered by each

competitor but other factors are also taken into account, thus allowing room for the

contestants to increase their probability of winning. They also find that there is

underdissipation of rent in these circumstances. Lockard (2003) also examines the

randomisation in the rent-seeking game and provides a detailed analysis of how this can

impact on the rent-seeking expenditures. Davis and Reilly (1998) claim that more rents are

dissipated in perfectly discriminating auctions where the highest bidder wins, than in

lotteries where relative bids determine the chance of winning.

16 See Morgan (2003) for a theoretical comparison between sequential and simultaneous contests.

35

Valuation of the rent-seeking competitors also plays a role in determining rent-seeking

behaviour. The findings of Epstein and Nitzan (2002) demonstrate that the aggregate

expected payoffs of the players are positively related to their valuation. Similarly, Baumol

(1990) argues that it is these payoffs that determine whether the players will engage in rent-

seeking behaviour. Nti (1999) shows that the probability of winning is tied to the valuations

of the contestants. The contestant with the higher valuation will exert more effort than the

lower valuation contestant but both will allocate the same fraction of their valuations to

rent-seeking. This implies that the higher valuation player becomes the favourite to win the

rent. If the favourite’s valuation increases, then he or she will exert more effort but the

other contestant will actually reduce their effort. On the other hand, if the other contestant’s

valuation is increased, then both players will increase their efforts. Expected profits

therefore increase with a player’s valuation but decrease with the valuation of the

competitor. In a similar vein, Stein (2002) shows that a player’s expenditure will depend on

the expenditure of competitors.

Vogt, Weimann and Yang (2002) argue that rent-seeking will necessarily result in an

efficient outcome. In their study, it was found that in an open-ended game players have no

control over their competitor’s response once they make a bid, but the threat of escalation

was enough to encourage the players to settle for an efficient outcome. Sometimes

competition between rent-seekers can lead to dissipation of the rent, in which case Baik and

Lee (2003) show that it is beneficial for players to combine and form a strategic group,

which will lead to lower rent dissipation than if players compete. In the context of trade

policy, Aidt (1997) finds that if players’ bargaining power is balanced, then cooperation

can lead to free trade. However if the power is unbalanced then cooperation can lead to

increased trade protection. Cooperation between consumers and workers has also been

shown to reduce social losses (Rama, 1997).

3.3.2 Rent-Seeking and Political Involvement

An important element of the rent-seeking process is the role played by the government.

Earlier, the rent-seeking process was found to result in a social loss as shown in the typical

case of a monopoly. But this loss is dependent on the size of the rent, which is ultimately

determined by the government. The firm can engage in rent-seeking behaviour not only to

36

win this rent, but also to maximise it by pressuring the government to raise the size of the

rent. However, Epstein and Nitzan (2003a) contend that the public will also exert their own

pressure on the government to keep the price (and thus the size of the rent) as low as

possible (rent-avoidance) in order to protect their surplus. In a later study, Epstein and

Nitzan (2003b) show that this consumer opposition will actually influence the monopolist’s

decision on what price to set, which will end up not being the profit-maximising price.

Faith (2002) holds a pessimistic view about rent-seeking and political involvement. If firms

seek to secure an import licence for example, competition will eventually drive these rents

down to zero, which tends to happen where rent-seeking is not actively encouraged by the

political sector (Che and Gale, 1997). Once that happens, Faith (2002) argues that rent-

seeking will jump to a higher level, where firms will try to gain control over the authority

that issues the licence. Again, if this is circumvented by legislation that may perhaps direct

all licence revenues to the treasury, firms may further engage in rent-seeking behaviour to

ensure that this revenue is spent on projects that are beneficial to the firms. Faith (2002)

suggests a way around this, by offering pools of funds to firms that are fixed, such that the

degree of rent-seeking behaviour has no impact on how much funds are available to a

particular firm. But this may not necessarily be in the government’s best interests, as it may

wish to encourage rent-seeking for efficiency reasons.17 For example, the government may

prefer a monopoly over perfect competition because there would be less rent-seeking

behaviour and therefore less wastage (Lambsdorff, 2002). However, as Lambsdorff argues,

if the size of the rent is dependent on the rent-seeking activity, then a monopoly would

result in more wastage than a competitive market. Further, in a competitive market there are

unequal degrees of rent-seeking behaviour and no guarantee that the rents will accrue to the

firm that invests the most resources in rent-seeking. And competing firms introduce an

element of product quality through availability of choice, which is non-existent in a

monopoly market. The monopolist is also not required to reveal any information about its

production efficiency, while this information would be provided by competing firms.

On the other hand, Sutter (1999) finds that if a government allows secure rights to rents,

firms may end up making political investments to create new additional rights. Related to

17 Boyce (1998) provides an in-depth analysis of the manner in which governments can allocate rents, and the impact this has on rent-seeking.

37

this point is what Tullock (1993) refers to as the ‘transitional gains trap’. This theory

suggests that if the government were to subsidise a particular industry, it would only result

in transitional gains. The entry of new firms into the market (attracted by the subsidy)

would lower whatever superior profits were being made as a result of the subsidy. The

economy will become smaller and less efficient. The problem now is that the government

would not be able to withdraw the subsidy as it would induce more rent-seeking behaviour

in order to retain the rent.

Having already invested resources into seeking a rent, firms will then engage in rent

protection to avoid the transitional gains trap. Tullock (1993) cites the Rowley and Tollison

(1986) model of rent-seeking and rent-protection, as shown in Figure 7.1 where firms

engage in rent-seeking behaviour to secure a full monopoly right, worth price A and

quantity Qm. With perfect competition, the price could be driven down to E and quantity

produced would expand to Qc. This is the outcome for which consumers would lobby the

government. Equilibrium price is therefore situated at F, which is somewhere between A

and E. The government dictates this price, as a result of lobbying from both firms and

consumers. This political price F is deliberately positioned closer to A than E, because the

lobbying power of firms is assumed to be greater than that of consumers. The cost to the

firms is represented by the rectangle FCIE, which is assumed to exceed the size of the

consumers’ cost represented by the trapezoid ABCF (all expenditures represent social

waste since society receives no benefit, only the rent-seeking firm does). The social cost of

the resulting partial monopoly is thus equal to the social cost of a full monopoly (assuming

no rent avoidance by consumers) described earlier in the discussion. Firms will continue to

lobby the government to protect their rents, and thus the government will be reluctant to

withdraw any rents.

38

Figure 3.1: Rent-Protection

Source: Tullock (1993, p.71).

E

Qm Qr Qc Q

P

Demand

LRMC I D

C

H G

B

F

A

The nature of the government has also been analysed in the literature.18 For example,

Mudambi et al. (2002) find that plurality electoral systems are more resistant to the political

demands of rent-seeking than proportional systems. They also find that having fewer

election districts reduces rent-seeking opportunities. Similarly, Spindler and de Vanssay

(2003) reveal that constitutional design has an impact on rent-seeking, and vice versa. Sato

(2003) explores the welfare impact of fiscal decentralisation in the presence of rent-

seeking, and reveals mixed results. Competition leads to lower capital tax revenue under a

decentralised government, which leads to a reduced source of available rents, but reduced

tax revenue may also stimulate further political activities. Thus the impact of

decentralisation on rent-seeking behaviour is ambiguous. Aidt (2003) finds that in the

absence of political competition, rent-seeking expenditure becomes self-limiting. An

analysis of the trade-off between private and public rent-seeking is provided in Boldrin and

Levine (2004).

3.3.3 Rent-Seeking in Specific Industries

Many studies have analysed rent-seeking with reference to particular industries. Abbot and

Brady (1999) studied the nature of rent-seeking within the US telecommunications

industry, and their findings suggest that government regulation has retarded competition

and innovation in that industry through the actions of rent-seeking agents. Law (2003) 18 McNutt (1997) uses political tenure to derive a measure of rent-seeking.

39

focuses on the oligopolistic US food industry and observes that regulation of that industry

did not arise as a result of rent-seeking, contrary to public thinking. Based on a study of the

same industry, Bhuyan (2000) finds evidence that corporate political activities (or rent-

seeking behaviour) are higher in industries that are highly concentrated, large in employee

size and sales and deeper in debt. Goel (2003) analyses rent-seeking in research markets

and finds that greater rent-seeking by a rival lowers an academic’s own profit-maximising

research and rent-seeking activity, and that greater research spending by a rival has the

same effect especially when the probability of the academic’s own innovation is low.

McMillan (2004) explores the effect of rent-seeking on public school productivity. Gramm

(2003) considers how rent-seeking affects bank mergers and acquisitions, and finds that

protests by interest groups against these impose significant time costs on merger and

acquisition applications.

Migue and Marceau (1993) study rent-seeking in the context of environmental pollution,

and consequent tax/subsidy policies. They find that pollution taxes and subsidies give rise

to rent-seeking and dissipation of rents, which adversely affects the success of anti-

pollution programs. In a similar vein, Damania (1999) finds that prevalence of pollution

emission standards is a direct result of rent-seeking behaviour, and they are more likely to

be proposed by political parties which represent environment interest groups. Torvik (2002)

studies natural resource rents, and argues that a greater amount of natural resources

increases rent-seeking and thus reduces the number of entrepreneurs running productive

firms. The consequent drop in income exceeds the increase in income from the natural

resource, thus more natural resources lead to lower welfare. In a rather obscure area of the

literature, Scully (1997) analyses rent-seeking in the context of democide and genocide, and

finds that such rent-seeking results in a 20% loss in wealth.

Banerjee (2001) provides an interesting analysis of rent-seeking in the sugarcane industry

in the Maharashtra province of India. Sugarcane farmers hold membership with a

cooperative, which agrees to buy sugar from the farmers at a uniform price before

converting the sugarcane into sugar and selling it on the market at a higher price. The board

of the cooperative consists of mainly large sugarcane farmers. The law in India prevents the

cooperative from providing its members with lump-sum transfers (resulting from profits),

so the most logical way of rewarding farmers for their efforts is to raise the price paid for

40

sugarcane. Since this is a uniform price, all farmers would benefit from this upward

adjustment in price. However, the board of the cooperative engage in what is known as

dharmodaya, where the profits earned by the cooperative are siphoned off and used for

investments (such as temples, schools, etc) which end up benefiting the board members

disproportionately. Thus, the board engages in rent-seeking behaviour by lowering the price

paid for sugarcane to maximise the available rents, and then investing the rents into projects

which ultimately benefit the board members.

Khwaja and Mian (2005) find evidence of corruption in Pakistan’s banking sector. They

have evidence that politically connected firms receive preferential treatment from

government banks. The extent of rent-seeking by these firms increases with the strength of

the politician, and falls with the degree of electoral participation in his constituency. Nearly

2% of GDP is lost to this manner of rent-seeking every year.

3.4 Conclusion

There are a multitude of studies analysing corruption’s impact on growth and, as this

chapter has shown, the overwhelming majority of them have concluded that the impact is

detrimental. However, the relationship between corruption and growth has always been

examined from a very broad perspective, both in terms of the time period in focus, and the

number of countries. The same is true for studies analysing the relationship between rent-

seeking and economic growth. Most notably, few studies have attempted to analyse the

East Asian region and, in particular, the period before the 1997 Asian Financial Crisis when

those countries were achieving high rates of economic growth in spite of similar degrees of

corruption. Within this context, even fewer studies have attempted to use rent-seeking as a

proxy for corruption. The present study will seek to fill in these gaps. However, the nature

of corruption in East Asia is an issue that is worthy of independent analysis. This will be

the subject of the next chapter.

41

42

Appendix to Chapter 3

Table A3.1: A Summary of Existing Studies on Corruption and Growth

Author (year) Subject Data Methodology Results Alesina and Weder (2002)

Corruption, foreign aid

ICRG index 1982-95, WDR index 1997, BI index 1980-83, WCY index 1996, CPI 1997; political rights index 1974-89 from Gastil (1990); average debt relief per capita 1989-97 from Easterly (1999); investment as a percentage of GDP, private capital flows 1975-1995 from World Bank; voting at UN, years as a colony from Alesina and Dollar (2000); real GDP per capita from Heston et al. (various years); openness from Sachs and Warner (1995)

Regression Increased aid leads to increased corruption

Ali and Isse (2003)

Corruption, economic growth

ICRG index 1982-90, CPI 1995-99; economic freedom from Gwartney and Lawson (1997); ethnicity from Mauro (1997) and Easterly and Levine (1997); political freedom from Freedom House; rule of law from Political Risk Services; secondary school enrolment 1975; govt spending/GDP, real GDP per capita from World Bank

Regression Higher corruption causes lower economic growth both now and in the future, no effect of corruption on investment/GDP

Table A3.1: A Summary of Existing Studies on Corruption and Growth (cont’d) Author (year) Subject Data Methodology Results Bohn (2003) Corruption,

political instability, public investment

Parsimonious model framework

Political instability may lead to underinvestment in infrastructure or anti-corruption measures; a threshold exists, below which a govt will refuse to invest.

Braun and Di Tella (2004)

Corruption, investment, inflation

ICRG index 1980-94; inflation from IMF; imports to GDP ratio from Heston et al. (various years); political rights index from Gastil (various years); central bank independence index from Cukierman et al. (1992)

Regression High inflation increases costs of monitoring agents, resulting in higher corruption and lower investment.

Chakrabarti (2001)

Link between actions of individuals and national corruption levels

General equilibrium model

Stable equilibrium levels of corruption depend primarily on degree of risk aversion of individuals, proportion of national income spent on anti-corruption vigilance and level of human capital in society.

Compte et al. (2005)

Corruption, competition

Auction model Corruption can affect competition in government procurement auctions, bribe-seeking agents results in high public spending and inefficient resource allocation.

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Table A3.1: A Summary of Existing Studies on Corruption and Growth (cont’d) Author (year) Subject Data Methodology Results

Del Monte and Papagni (2001)

Corruption, investment, economic growth

Number of crimes against the public administration per million employees, share of real private investment on real GDP, ratio of high school enrolment on the labour force, and shares of govt consumption in GDP are all taken from time series (1963-91)

Regression based on single equation ADL model

Negative effect of corruption on growth; significant negative relationship between corruption and private investment, and efficiency of public investment.

Depken and LaFountain (2004)

Corruption, state bond ratings

Number of federal public corruption convictions per 100,000 residents in state from US Department of Justice; bond ratings from Moody’s, Standard and Poor’s, Fitch; tax burden per capita from US Tax Foundation; total state debt to government revenue ratio, real per capita state debt load and population from Statistical Abstract of the US; real per capita income from US Bureau of Economic Analysis; state unemployment rate from US Bureau of Labor Statistics

Regression Higher corruption associated with lower bond ratings, as taxpayers pay premium for debt.

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Table A3.1: A Summary of Existing Studies on Corruption and Growth (cont’d) Author (year) Subject Data Methodology Results Dreher and Siemers (2004)

Corruption, capital account restrictions

ICRG index 1987-99; capital account restrictions data from Grilli and Milesi-Ferreti (1995); govt and opposition fractionalisation, socialist system of govt from Beck et al. (2001); competitive nomination index from Banks (2002); free press index from Freedom House; fixed exchange rate IMF; share of protestants from Treisman (2000) and CIA; openness, govt revenue, ln(GDP per capita), illiteracy rate, monetary growth, gross domestic savings, ln(population), GDP growth from World Bank; banking and currency crises from Capiro and Klingenbiel (2003)

Regression, summary statistics

Capital restrictions induce higher corruption in earlier years, but reduce corruption in later years.

Ellis and Fender (2003)

Opportunities for corruption when govt is required to rectify a market failure and supply a public good

Ramsey model of economic growth

When potential govts compete, instead of driving corrupt payments to zero the competition leads to the maximisation of the present discounted value of private sector utility subject to a constraint imposed by the level of irreducible corruption.

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Table A3.1: A Summary of Existing Studies on Corruption and Growth (cont’d) Author (year) Subject Data Methodology Results Emerson (2006) Corruption,

competition, democracy

Equilibrium model

Competition and corruption are negatively related, higher education and more civil liberties (proxy for democracy) have a mitigating effect on corruption in a country.

Fisman and Gatti (2002)

Corruption, fiscal decentralisation in govt expenditure

ICRG index 1982-90; decentralisation measured by expenditure of state/local govt as a proportion of total govt expenditure 1980-95 from IMF; ln(per capita GDP) 1960-90 from Heston et al. (various years); civil liberties 1972-95 from Banks; schooling 1960-90 from Barro-Lee (1993); population, imports/GDP from World Bank; expenditure/GDP 1980-85 from Barro (1991); legal system origin from La Porta et al (1998); indicators of colonial affiliation from CIA

Regression Significant negative relationship between corruption and fiscal decentralisation.

Fisman and Svensson (2000)

Corruption, taxation, firm growth

Reported bribes in Uganda Shillings, sales growth 1995-1997, reported tax payment gross sales in Uganda Shillings 1995, foreign ownership percentage, age of firm from Ugandan Industrial Enterprise Survey

Regression Taxation and bribery negatively correlated with growth, negative impact of bribery is greater.

Fjeldstad and Tungodden (2003)

Corruption, tax revenue

Theoretical Corruption increases tax revenue in the short run, but reduces it in the long run.

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Table A3.1: A Summary of Existing Studies on Corruption and Growth (cont’d) Author (year) Subject Data Methodology Results Gyimah-Brempong (2002)

Corruption, economic growth, income equality

CPI 1993-99 for African countries; real GDP growth, real GNP growth, Gini coefficient, income per capita, gross investment/GDP, gross national savings/GDP, imports/GDP, education, ethno-linguistic fractionalisation for African countries from World Bank

Regression Corruption decreases the growth rate of income per capita directly by reducing the productivity of existing resources, and indirectly through reduced investment.

Huang and Wei (2003)

Corruption, central banking

Equilibrium model

Optimal inflation-targeting level for a highly corrupt country is different to that of a less corrupt country.

Hwang (2002) Corruption, government revenue

BI index 1980-83, Levine-Loayza-Beck (1995) dataset 1982-1995, CPI 1996-98; ratio of government revenue to GDP in 1980, 1985, 1990, 1995 from World Bank

Regression Corruption positively and significantly associated with taxes on international trade over current govt revenue, negatively and significantly related to domestic tax revenue and total government revenue over GDP.

Johnson et al. (1999)

Corruption, tax revenue

Equilibrium model

Higher corruption leads to lower tax revenue.

Kaufmann (2003)

Corruption, governance, institutional quality

Aggregate governance indicators; Executive Opinion Survey by WEF

Descriptive statistics, time series

Little improvement in governance over the years.

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Table A3.1: A Summary of Existing Studies on Corruption and Growth (cont’d) Author (year) Subject Data Methodology Results Kaufmann and Wei (1999)

Corruption, regulatory burden and delay, cost of capital

GCR index 1996-97, WDR index 1997 Regression Positive correlation between corruption and management time wasted with bureaucracy, regulatory burden, and cost of capital.

Li et al. (2000) Corruption, income distribution, Gini differentials, economic growth

ICRG index 1982-94; real GDP per capita 1980-92 from Heston et al. (various years); black market premium from Barro and Lee (1994); schooling data from Nehru et al (1995); M2/GDP, imports/GDP, export/import price-change differential, govt spending/GDP, average arable land, urbanisation ratio, population growth rate, initial land Gini coefficient from World Bank and Heston et al. (various years); Gini coefficients from Deininger and Squire (1996)

Regression Inequality is low when corruption is high or low, but high when corruption is medium; corruption explains Gini differential between industrial and developing countries; significant negative relationship between corruption and growth but not as strong as in Mauro (1995).

Mauro (1995) Corruption, economic growth

BI index 1980-1983; GDP per capita at PPP 1980, average investment 1980-85, growth in GDP per capita 1960-85 from Heston et al. (1988); ethnolinguistic fractionalisation index 1960 from Taylor and Hudson (1972)

Regression Significant negative relationship between corruption and investment, and corruption and growth.

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Table A3.1: A Summary of Existing Studies on Corruption and Growth (cont’d) Author (year) Subject Data Methodology Results Mauro (1997) Corruption,

economic growth, investment, expenditure

ICRG index 1982-95 and BI index 1980-83; 1970-85 averages of govt spending on defence, education, social security, public investment, and total govt expenditure from Barro (1991); 1985 data on expenditure on education and health from Devarajan et al. (1993); composition of public investment by sector from Easterly and Rebelo (1993)

Regression, correlation

Significant negative relationship between corruption and economic growth and investment, and corruption and spending on education and transfer payments; causality indeterminable.

Mauro (2002) Corruption, economic growth, persistent relationship

Strategic complementarities and multiple equilibria models

Countries appear to be stuck in a cycle of corruption and low economic growth due to lack of incentives to fight corruption.

Mendez and Sepulveda (2001)


ICRG index 1982-92 as proxy for 1960-92; cross-sectional sample with averages for population growth, income per capita, GDP growth, investment/GDP, expenditure/GDP 1960-92 from Heston et al. (various years); schooling from Barro and Lee (2000), economic freedom and political instability index from Fraser Institute

Regression, t-tests Corruption is growth enhancing at low levels of incidence and growth reducing at high levels of incidence, implying the existence of a positive level of corruption that maximises long-run growth.

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Table A3.1: A Summary of Existing Studies on Corruption and Growth (cont’d) Author (year) Subject Data Methodology Results Pellegrini and Gerlagh (2004)


CPI 1980-85; gross investment/GDP 1975-96, income and growth from Heston et al. (various years); average years of schooling in 1975 for people over 25, political instability 1970-85 from Barro and Lee; trade openness 1965-90 from Sachs and Warner (1995); legal origins from World Bank

Regression Corruption reduces growth through its impact on investment, schooling, trade openness and political instability.

Poirson (1998) Economic security, investment, economic growth

Ten ICRG ratings to measure institutional quality; political rights and civil liberties ratings from Freedom House; economic data 1980-95 from IMF; schooling data from World Bank; all data from 1984-95

Regression Reduction in corruption will only improve growth in the long-run.

Rahman et al. (2000)

Corruption, economic growth, investment (domestic, foreign)

ICRG index 1991-97; schooling/area/ distance 1985 from Barro and Lee (1994); Inv/GDP 1990-97, Consumption/GDP 1990-97, GNP per capita 1985 from World Bank

Regression Significant negative relationship between corruption and growth, domestic investment, FDI.

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Table A3.1: A Summary of Existing Studies on Corruption and Growth (cont’d) Author (year) Subject Data Methodology Results Rock and Bonnett (2004)

Corruption, economic growth, investment

BI index 1980-83, CPI 1988-92, World Bank corruption index 1997-98, ICRG 1984-96; real GDP per capita in PPP$ 1996, gross domestic investment/GDP, population growth rate, deviation of PPP value of the investment deflator from sample mean in 1960 from Heston et al. (various years); secondary enrolment ratio 1960, trade/GDP, govt consumption expenditures/GDP from World Bank; ethnolinguistic fractionalisation index from La Porta et al. (1999)

Regression Corruption impedes growth and investment in most developing countries, but increases growth in East Asia.

Smarzynska and Wei (2000)

Corruption, foreign investor’s preference for a joint venture versus a wholly owned subsidiary

WDR index; CPI 1999; EBRD Survey regarding FDI in Eastern Europe and former USSR 1989-95; R&D, advertising, firm-size, diversification data 1993 from Worldscope database; GDP 1993 from EBRD; distance from Rudloff (1981); unit labour costs from Havlik (1996); corporate tax rate from PWPaineWebber

Regression Corruption reduces inward FDI and shifts the ownership structure towards joint ventures; technologically more advanced firms are found to be less likely to engage in joint ventures; US firms are found to be more averse to joint ventures in corrupt countries than investors of other nationalities (possibly due to the U S Foreign Corrupt Practices Act).

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Table A3.1: A Summary of Existing Studies on Corruption and Growth (cont’d) Author (year) Subject Data Methodology Results Svendsen (2003) Corruption, social

capital, economic growth

CPI 2000; World Values Survey measuring trust and density of civic participation from Inglehart et al. (1998); GDP per capita 1975-2000 in current dollars and adjusted for PPP differences from World Bank

Descriptive statistics

High levels of corruption = low levels of trust = low levels of economic growth.

Tanzi and Davoodi (1997)

Corruption, public investment, expenditure

Combined index 1980-95 based on BI index 1980-83 and ICRG index 1982-95; investment and expenditure data from IMF; GDP and real per capita GDP from World Bank

Regression High levels of corruption associated with higher public investment, lower govt revenues, lower spending in essential areas, and lower quality of public infrastructure; corruption increases public investment while reducing productivity.

Tavares (2003) Corruption, foreign aid

ICRG index; GDP per capita, International aid/GDP from World Bank; ethnolinguistic fractionalisation, political rights, religion, origin of legal system from LaPorta et al. (1999); population, OECD membership, public expenditure/GDP from Barro and Lee (1994)

Regression Increased aid leads to decreased corruption.

Vehovar and Jager (2003)

Corruption, governance, economic growth

Aggregate governance indicators from World Bank

Cluster analysis Economic growth does not automatically translate to an improvement in governance.

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Table A3.1: A Summary of Existing Studies on Corruption and Growth (cont’d) Author (year) Subject Data Methodology Results Vinod (2003) Corruption,

economic growth, cost of capital, FDI

CPI 2001; PricewaterhouseCoopers Survey 2001 on cost of capital penalty due to corruption; FDI, Trade, GDP for 2001 from IMF/World Bank; exchange controls data from IMF

Correlation coefficients, t-statistics

Negative correlation between corruption and cost of capital, corruption and capital controls; significant positive relationship between corruption and FDI/GDP, and corruption and Trade/GDP.

Wei (1997) Corruption, FDI BI index 1980-83 and CPI; 2-year bilateral FDI flows 1990-91 from OECD; 1989 host countries’ tax rates from Hines and Desai (1996) and Pricewaterhouse Coopers; GDP and population from IMF; wage and labour compensation from International Labor Org; barriers to investment from 1996 World Competitiveness Report; literacy and schooling from World Bank (1995)

Regression An increase in the tax rate on MNCs or corruption level in host govts leads to reduced FDI; no evidence that investors are less sensitive to corruption in East Asia; American investors less averse to host country corruption in spite of Foreign Corrupt Practices Act.

Wei (1997a) Corruption, FDI GCR index 1997, BI index 1980-83, CPI; 1991 FDI from OECD; tax rates 1989 from Price Waterhouse; GDP from IMF; distance and language from Frankel et al. (1995)

Significant, negative relationship between corruption-induced uncertainty and FDI.

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Table A3.1: A Summary of Existing Studies on Corruption and Growth (cont’d) Author (year) Subject Data Methodology Results Wei (2000) Corruption, capital

inflows GCR index, WDR index, CPI 1998, Neumann’s (1994) German exporters’ index; bilateral bank lending from BIS; bilateral FDI flows from OECD; distance from Rudloff (1981); language from CIA; GDP from World Bank; exchange rate volatility from IMF; legal system origin and accounting standards from La Porta et al. (1999)

Regression Corruption in capital importing countries affects volume and composition of inflows – distortion away from FDI and towards foreign bank loans; significant negative relationship between corruption and inward FDI.

Wei and Wu (2001)

Corruption, composition of capital inflows

Combined GCR and WDR indices 1997; bilateral FDI data 1994-96 from OECD; bilateral bank lending data and term structure of bank lending from Consolidated International Claims of BIS Reporting Banks on Individual Countries; FDI restrictions and incentives data taken from PricewaterhouseCoopers

Regression, summary statistics, correlation coefficients

Corruption is associated with a higher loan-to-FDI ratio, and an inability to borrow internationally in its own currency.

Zhang (2000) Corruption, volatility in the business cycle

Cross-sectional and panel data Regression Negative relationship between corruption and average long-term rate of economic growth, and between corruption and business cycle volatility.

Author (year) Subject Result (Variables) Aidt (1997) Cooperative rent-seeking and trade

policy If competition is balanced, then cooperative lobbying leads to free trade. If it is unbalanced, cooperation may lead to increased protection.

Aidt (2003) Political competition and distributive programs with different deadweight costs

Rent-seeking expenditures tend to be self-limiting in the absence of competition.

Amegashie (1999) Rent-seeking – aggregate expenditure and number of rent-seekers

Aggregate rent-seeking expenditure is inversely related to number of rent-seekers.

Amegashie (2002) Rent-seeking expenditure, committees/single administrators and probabilistic voting

Assuming probabilistic voting, relative magnitudes of rent-seeking expenditures depend on the relative weighted sensitivities to rent-seeking efforts of the committee and the single administrator. Distribution of voting powers of committee members affects rent-seeking efforts.

Baik and Lee (2001) Rent dissipation When one strategic group is formed, both members and non-members benefit, and rent dissipation is smaller than under individual rent-seeking. When two or more groups are formed, nobody benefits and dissipation is greater.

Banerjee (2001) Rent-seeking in India Rent-seeking is prevalent in the Maharashtra sugar industry, and the resulting inefficiency is heavily influenced by asset inequality.

Baye et al. (1999) Rent-seeking and overdissipation Total rent-seeking expenditure can exceed the value of the rent.

Table A3.2: A Summary of Selected Rent-Seeking Studies

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Table A3.2: A Summary of Selected Rent-Seeking Studies (cont’d)

Author (year) Subject Result (Variables) Bhagwati and Srinivasan (1980) Revenue-seeking Revenue-seeking and rent-seeking may be welfare improving.

Bhuyan (2000) Rent-seeking and welfare loss in US

food manufacturing industries Rent-seeking is imperfect and corporate political activities are higher in industries that are highly concentrated, large in employee size and sales and deeper in debt. (Average sales per firm in industry; industry concentration ratio; total number of employees (production and non-production); number of product lines in industry; ratio of total liabilities to total assets; ratio of net income before income tax and net sales)

Boldrin and Levine (2004) Rent-seeking and innovation Contrary to popular opinion, public rent-seeking does not always result in less private rent-seeking or welfare improvement.

Boyce (1998) Rent-seeking in natural resource quota allocations – role of the political body

Allocatively efficient default policy does not minimise social costs, and maximises rent-seeking. Competitive post-allocation market reduces rent-seeking, but does not minimise social cost even with an efficient default policy. But forcing winners in political redistributions to compensate losers achieves efficient allocation with zero rent-seeking.

Che and Gale (1997) Rent dissipation and budget constraints

Higher levels of rent-seeking expenditure result in lower rent dissipation.

Chin and Chou (2004) Rent-seeking and economic growth Magnitude of effects of the government encouraging production over diversion on output per worker depends on the economic agents’ propensity for rent-seeking.

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Subject Result (Variables)

Author (year) Cole and Chawdhry (2002) Rent-seeking and growth Rent-seeking has a negative impact on economic growth.

(Economic growth, investment rate, educational attainment, taxes and public services, production costs, industry mix, number and size of interest groups)

Corchon (2000) Allocative effects of rent-seeking In the case of two agents who equally value an object, rent-seeking has no effect on resource allocation. If there are two agents with different valuations of the object or more than two agents, rent-seeking affects resource allocation.

Damania (1999) Political lobbying and choice of environmental instruments

Prevalence of pollution emission standards is a result of rent-seeking behaviour, and they are more likely to be proposed by political parties which represent environment interest groups.

Davis and Reilly (1998) Rent-seeking and the impact of institutional arrangements and rent-defending activity

More rents are dissipated in discriminating auctions where the highest bidder wins, and the introduction of a rent-defending buyer reduces social costs.

Del Rosal and Fonseca (2000) Measurement of social costs of rent-seeking via labour unrest in Spanish mining industry

Provides a measurement of rent-seeking via labour unrest. (Lost days due to strikes, opening/closure of mining firms, number of pits, employment in mining industry)

Dewey (2000) Rent-seeking and regulation When two parties compete to influence regulatory decisions, gross surpluses may be detrimental to both. A simultaneous increase in both parties’ marginal lobbying costs benefits the low-cost group and harms the high-cost group.

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Author (year) Dixit (1987) Rent-seeking and effect of

precommitment With two asymmetric players, the favourite will commit effort at a higher level than that in a Nash equilibrium, and the underdog at a lower level. This applies in rent-seeking.

Drook-Gal et al. (2004) Rent-seeking and privatisation Selfishness of the government will reduce the sale price of the company and increase probability of privatisation, and a budgetary constraint on the rent-seeking of government employees has the same effect.

Emerson (2002) Rent-seeking in less developed countries

The high degree of corruption in developing country governments contributes to the dual nature of their industrial structure. The higher the degree of corruption, the fewer (and larger) are the formal firms, the lower is social welfare and the greater is dead-weight loss, and the higher are government rents. An examination of the size distribution of 16 countries and their degree of corruption shows that the degree of corruption is a good predictor of the percentage of large firms in an economy. (Number of firms that employ 50 or more employees, Corruption Perceptions index, GNP per capita)

Epstein and Nitzan (2002) Rent-seeking and asymmetric valuations

Aggregate expected payoff of the players is positively related to their valuation.

Epstein and Nitzan (2003a) Endogenous determination of monopoly price

Political culture of the government significantly influences the price set by a monopoly.

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Author (year) Epstein and Nitzan (2003b) Social cost of rent-seeking as a result

of consumer opposition Consumer opposition to monopoly rents affects the choice of price which thus determines the social cost. So the monopolist does not simply set the profit maximising price.

Faith (2002) Rent-seeking and fixed-share pools in government

A fixed-share pool is a given amount of resources to be divided among a given number of claimants. Each share is fixed so there is no rent-seeking. However, if claimants have sharing rights to more than one pool, rent-seeking in the form of transferring resources from a pool where one has a small share to a pool where one has a large share will generally occur.

Fedeli and Forte (2003) Rent-seeking with public co-financing of private projects and centralisation of government

Where corruption is a selling cost for the private supplier (rent-seeking), a centralised regime causes higher corruption because of the higher number of private suppliers of competing projects.

Glazer and Hassin (2000) Rent-seeking expenditure and sequential contests

In sequential contests, earlier movers do not make more profits than later movers; profits lower than in simultaneous contests.

Goel (2003) Rent-seeking in research markets Greater rent-seeking by a rival lowers own profit-maximising research and rent-seeking activity. Greater research spending by a rival also has the same effect, especially when the probability of own innovation is low.

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Author (year) Gramm (2003) Regulatory rent-seeking based on

protests against bank mergers and acquisitions

Protests impose significant time costs on merger and acquisition applications submitted by banks, so rent-seeking community groups may extract payments to forestall a protest. Protesters are not benevolent (using protests as a means to enhance social welfare). (Waiting time in days; dummy variable for protested application; asset size of the lead bank in a merger or acquisition; asset size of the target bank; dummy variable for a lead bank with a CRA rating of Outstanding; variables concerning the region of the country where the lead bank is located and whether the application represents an intra- or inter-state acquisition; year in which application was submitted)

Haan and Schoonbeek (2003) Rent-seeking with efforts and bids Assuming that on top of rent-seeking players also submit bids which are payable if they win, a Nash equilibrium eventuates where each player submits the same bid, which is equal to the sum of total efforts. This results in underdissipation of rent.

Itaya and Sano (2003) Rent-seeking with negative expected payoffs

In a multi-period setting, players will not only determine their expenditures, but will also choose a mixed strategy on whether to stay in or exit from rent-seeking competition in each period.

Katz and Rosenberg (1989) Measurement of rent-seeking Provides a methodology for measuring the waste associated with rent-seeking. (Government spending)

Katz and Rosenberg (2000) Rent-seeking and corporate taxation Corporate tax reduces rent-seeking, and favours rent-seeking by established firms and discriminates against zero-profit and new firms.

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Author (year) Khwaja and Mian (2005) Rent-seeking in Pakistan Political corruption exists in the banking sector in Pakistan,

politically connected firms receive preferential treatment from banks. (Bank loans to corporations, election information)

Konstantin (2003) Rent-seeking and property rights In unequal societies, the ability to maintain private protection of property rights can lead to low economic growth, high inequality of income and widespread rent-seeking.

Lambsdorff (2002) Rent-seeking and corruption As a model of corruption, the rent-seeking concept fails to recognise the impact of corruption on the size of the rent; the possibility of a corrupt government; and the narrower range of interests sought by corruption compared to ordinary lobbying. When these are taken into account, rent-seeking is less harmful than corruption.

Krueger (1974) The concept of rent-seeking Theoretical paper – no empirical analysis.

Law (2003) Rent-seeking in the food industry Pure food regulation helped solve an asymmetric information problem in the market, and was not the result of rent-seeking by traditional food producers. (Food prices)

Lockard (2003) Rent-seeking and randomised decision mechanism (sortition)

Randomisation of collective decision making procedures attenuates rent-seeking expenditures.

McMillan (2004) Rent-seeking and public education In the presence of competition, rent-seeking public schools find it optimal to reduce productivity.

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Author (year) McNutt (1997) Rent-seeking and political tenure Measurement of rent-seeking based on costs of tenure.

(Percentage of incumbent politicians in the lower house returned at a general election; voter turnout)

Migue and Marceau (1993) Rent-seeking and pollution taxes/subsidies

Pollution taxes and subsidies give rise to rent-seeking and dissipation of rents, which adversely affects the success of anti-pollution programs.

Mohtadi and Roe (2003) Rent-seeking, democracy and growth Young and mature democracies grow faster than countries in mid stages of democratisation, producing a U effect, as a result of rent-seeking.

Morgan (2003) Rent-seeking expenditure and simultaneous vs sequential contests

When two risk neutral ex ante identical agents compete, sequential contests are ex ante Pareto superior to simultaneous contests.

Mudambi et al. (2002) Rent-seeking and electoral institutional structure

Electoral institution structure has a significant impact on rent-seeking opportunities. (Level of economic reform; change in economic reform; dummy variable for presidential or parliamentary system; electoral districts per million registered voters; literacy rate; regional dummy for Eastern Europe and Latin America; military spending as a percentage of GDP; years of independence)

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Author (year) Murphy et al. (1991) Impact of talent on growth via rent-

seeking Countries with higher proportion of engineering colleges grow faster, and those with a higher proportion of law concentrators grow slower, since lawyers act as a catalyst for lobbying or rent-seeking behaviour. (Investment; primary school enrolment; government consumption; incidence of revolutions and coups; initial GDP; engineering and law enrolments)

Murphy et al. (1993) Rent-seeking and growth Rent-seeking can lead to multiple equilibria. Public rent-seeking can hurt innovation, which can hamper growth.

Neary (1997) Rent-seeking and conflict Conflict models involve greater relative expenditure on wealth-redistribution activities than do rent-seeking models.

Nti (1999) Rent-seeking and asymmetric valuations

Increasing the underdog’s valuation induces both players to increase their efforts. Increasing the favourite’s valuation increases his effort but decreases the effort of the underdog. Expected profits increase with a player’s valuation but decreases with the valuation of the competitor.

Palda (2000) Rent-seeking and high-cost producers If the high-cost producers win the rent, the social cost will be higher due to the higher cost. The loss becomes worse if the rent-seeking expenditure is also higher than the more efficient rival.

Pecorino (1992) Effect of rent-seeking on growth via capital accumulation

When agents allocate time as an effort towards obtaining an import licence, the effect on growth depends on whether the agent specialises in rent-seeking or not.

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Author (year) Pedersen (1997) Rent-seeking in developing countries Social welfare disparity in developing countries is a result of

uneven distribution of political influence, which is explained by the rent-seeking model.

Poitras and Sutter (1997) Rent-seeking and deregulation Establishing regulation dissipates monopoly profits, so the gain from deregulation is limited to the cost of the monopoly. Potential welfare gains exceed the cost of deregulation.

Posner (1975) Rent-seeking and social costs Assuming that competition to obtain a monopoly transforms monopoly profits into social costs, public regulation is a larger source of social costs than a private monopoly.

Rama (1997) Rent-seeking and labour unions When incorporating rent-seeking expenditure by labour unions, participation of workers and consumers reduces the social cost of restrictive regulation.

Sato (2003) Rent-seeking, tax competition and fiscal decentralisation

Rent-seeking accounts for political distortions which may be mitigated in the process of fiscal decentralisation, while tax competition results in economic distortions associated with decentralisation. Welfare evaluation should be based on the balance of the political gain and the economic cost.

Scully (1997) Rent-seeking and democide/genocide Rent-seeking associated with democide results in a loss of 20% of wealth.


Author (year) Subject Result (Variables) Sobel and Garrett (2002) Rent-seeking and social costs Rent-seeking can be indirect (lobbying efforts, advertising

campaigns etc) or in-kind (gifts etc). Rent-seeking can be measured. Presence of a political institution in a city reduces the level of traditional economic activity (manufacturing etc) thus representing a social opportunity cost. (Size of rent-seeking industries in capital cities compared with noncapital cities, and tested for statistical differences)

Sobel and Garrett (2002) Rent-seeking and social costs Rent-seeking can be indirect (lobbying efforts, advertising campaigns etc) or in-kind (gifts etc). Rent-seeking can be measured. Presence of a political institution in a city reduces the level of traditional economic activity (manufacturing etc) thus representing a social opportunity cost. (Size of rent-seeking industries in capital counties compared with noncapital counties, and tested for statistical differences)

Spindler and de Vanssay (2003) Rent-seeking and constitutional design Constitutional design affects post-constitution rent-seeking.

Stein (2002) Asymmetric rent-seeking Not all players will make positive rent-seeking expenditures. If a player increases expenditure, the strongest player will follow suit.

Sutter (1999) Stable rights to rents and efficiency Securing rights to receive transfers increases rent-seekers’ incentives to make political investments creating new transfers.

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Author (year) Subject Result (Variables) Torvik (2002) Natural resource rent-seeking, and

income/welfare A greater amount of natural resources increases rent-seeking and reduces the number of entrepreneurs running productive firms. The consequent drop in income exceeds the increase in income from the natural resource. More natural resources thus lead to lower welfare.

Tullock (1998) Rent-seeking and subsidies Expands the rent-seeking model to incorporate government subsidies.

Vining (2003) Firm inefficiency Rent-seeking is an example of internal governance failure.

Vogt, Weimann and Yang (2002)

Rent-seeking and efficiency Real-world rent-seeking expenditures are lower than what standard rent-seeking models predict.

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CHAPTER 4

CORRUPTION IN EAST ASIA

You cannot expect your civil servants to be totally clean when you

underpay them, underpay your civil servants in relation to what the

market is paying the rest of the community.

- Chief Executive of Hong Kong SAR Donald Tsang.19

4.1 Introduction

The focus of this study is on corruption in East Asia before the 1997 Asian Financial Crisis.

Only a handful of studies have attempted to show that corruption in East Asia resonates

with the concept of rent-seeking, and presents an argument for using rent-seeking as an

alternative to the Shleifer and Vishny (1993) principal-agent model in the analysis of

corruption. At a broader level, there is also an argument for revising the perception that

corruption is synonymous only with bribe paying. What follows is a concise review of the

few studies in this strand of the literature, and their application of the rent-seeking model to

explain East Asian corruption is quite telling. It is shown that the corruption within East

Asia is of a complex and diverse nature.

4.2 Cronyism

To begin with, one needs to remember that Asian business and politics was plagued by

cronyism and nepotism, and as Kang (2003) explains, this is an example of how rent-

seeking behaviour can enhance efficiency in an economy. The rent-seeking model

discussed in Chapter 2 incorporated an element of political involvement. In other words the

government is no longer free of any responsibility as in the Shleifer and Vishny (1993)

model, rather it has a direct role to play in determining the rents available and how they are

transferred. The situation can also be reversed – the government can extract rents from the

private sector and transfer them back into the economy.

19 Keatley (2006, p.6).

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Kang (2003) links cronyism with rent-seeking particularly in Asia. Cronyism refers to

family and personal relations, bribery and corruption, patron-client relations and collusion.

It occurs when preference is given to friends or colleagues, resulting in a potential conflict

of interest (Gyimah-Boadi, 2000). According to Khatri et al. (2006), cronyism requires that

one party offers something of value to another, implicitly requiring a reciprocal exchange,

where both parties are connected in some form of social network (e.g. friendship, kinship,

etc). What makes this a cost to society is when this exchange is at a cost to a third party,

who holds a superior claim to the valued resource. When cronyism is mixed with

government and politics, it is seen as an impediment to economic growth because it implies

that decisions are based upon non-market principles, and involves rent-seeking. Kang

(2003) primarily argues that cronyism can reduce transaction costs, particularly in

developing countries where such costs to doing business are prohibitively high. Most

developing countries have relatively weaker legal, political, and economic institutions than

developed countries, so information about market conditions and potential is scarce and

difficult to obtain, thus making investments and property rights insecure. Further, long-term

commitments become less appealing because of the risk of political and economic

conditions and actors changing quickly. Capital markets also do not function efficiently in

developing countries, which introduces an element of uncertainty into political or economic

decision-making. Lloyd-Ellis and Marceau (2003) show that in the early stages of

economic development, these credit market imperfections will actually induce rent-seeking.

Once the economy develops, productive activities become more secure and profitable and

rent-seeking is abandoned.

In the case of Asia, cronyism offers a way to circumvent all of these problems. Cronyism is

synonymous with guanxi, a Chinese word literally meaning ‘connection’ or ‘relationship’

(Khatri et al., 2006). These close personal relationships offer a level of trust that is lacking

elsewhere, which leads to better information and ability to enter into long-term contracts.

Further, cronyism can actually reduce transaction costs. Kang (2003) asserts that the parties

involved in cronyism are mutual hostages of each other – in other words, each party

(political and business elite) has the ability to harm the other but is deterred by the damage

that can be inflicted by the other. Rent-seeking costs can be lower if the business elite

operates in the form of a cartel, since the consequent collusion reduces the extent of

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competition for rents and thus reduces the investment of resources towards rent-seeking

(Baik and Lee, 2001).

Within East Asia two polar examples of cronyism can be identified. Where there are an

excessively large number of parties, the result is chaotic. Economic policies will fluctuate

wildly as players try to side with the ruling party, and the investment environment in

general will be unpredictable so transaction costs will be high. This was largely the

situation in the Philippines. At the other extreme, there is only one centralised player with

whom all other players try to side, an example of which is Indonesia. On the surface,

economic decisions can be made quickly and efficiently, and implementation is just as easy

because players only need to deal with one party. However, this stability is offset by the

risk of less durability through a crisis or a transition period. Such periods can threaten

stability of the ruling party, which then raises transaction costs and renders property rights

meaningless. Much of the cronyism in East Asia involved intermarriage among elites. Once

the ruling political party had established itself, the business sector was allowed to influence

policy decisions, so access to the state was the key to economic success (Kang, 2003).

Moran (1998) makes an interesting observation about the study of corruption and rent-

seeking with respect to Asia. The extant literature according to Moran (1998) tends to

restrict corruption to the political elite, which took kickbacks by virtue of their position and

power but were in no way connected to the bureaucracy. In fact, the bureaucracy was

deeply intertwined with the political elite and was itself corrupt. Kong (2004) expands on

this point, drawing a distinction between the neoclassical approach to corruption and the

political power approach. The former is more widely acknowledged in the literature, and

regards corruption as a result of excessive government intervention that results in market

distortions. To rid countries of corruption, neoclassicists argue that these countries should

expand the scope of the market and model their political and economic institutions on those

of Western market democracies. But Kong (2004) argues that this erroneously views

corruption as a homogenous phenomenon, and does not provide an answer to the question

of why some countries are developmental orientated in spite of high levels of corruption?

Under the political power approach, Kong (2004) suggests that corruption should be

viewed from both a political and an economic perspective, and understood within the

context of historical conditions and political power contestation and distribution. This

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would be more accurate in the case of East Asia. In fact, it highlights the difference

between neoliberalism, where a free market democracy is the means and end of

development, and developmentalism, where a market democracy is achieved but only after

some illiberal means are employed along the way.

Using the political power approach, Kong (2004) shows that in East Asia there was an

asymmetrical distribution of political power in favour of the executive. In South Asia

however, this distribution of power was not only between the government and business

sector but also between the government and the middle class. This yielded a secondary set

of patron-client networks in addition to the state-business relationship. But in the case of

East Asia, the government could make the most of its relationship with the business sector,

maximising rents by encouraging growth (ensuring that firms performed well), without

worrying about having to make payments to a third party (e.g. the middle class in the case

of South Asia). The point to be made here is that as long as corruption or rent-seeking is

centralised, it is not a hindrance to economic development. This point will be examined in

a later section.

4.3 Mutual Hostage Situations

In the case of South Korea, a mutual hostage situation existed between the business sector

and the state which led to enhanced growth, but not before the corrupt rule of Syngman

Rhee hampered growth in the 1950s. Rhee was receiving substantial foreign aid from the

US in return for being an anti-communist ally, and also milked the private sector in

exchange for providing rents in the form of licences to import scarce goods, control of

Japanese firms at undervalued prices, etc. All the proceeds went straight to Rhee, his

political party and the police bureaucracy (Moran, 1998).

A mutual hostage situation between the state and the private sector began to develop when

Park Chung Hee came to power via a coup in 1961. Park implemented a Japanese style of

corporatism in South Korea where national development was intertwined with social

cohesion (Kang, 2003). Unlike the British, the Japanese did not rule by having local

supporters and administrators. Instead, they used Japanese colonial administrators who

transferred over a quarter of South Korea’s arable land to Japanese entrepreneurs and

71

corporations. The aim was to convert South Korea into a productive base for Japan, instead

of maintaining it as a captive market at a low cost like the British did (Khan, 2000b). All

this meant that sectional interests were to be marginalised, and both the labour force and the

private sector needed to accept the state authority in favour of national development. Those

businesses that followed government guidelines and shifted to export-led growth received

benefits from the government. Some businesses received assistance by virtue of their

regional origins or personal relationships with the government elite, but this assistance was

quickly reduced or withdrawn upon signs of weak performance. Against this backdrop,

civil society groups were also weak and marginalised. This autonomy from civil society

groups allowed South Korea to nationalise corruption (Moran, 1998).

Initially, businesses were keen to stay on the right side of the government, and thus patron-

client networks were established. Under Korea’s first five-year economic development plan

in 1962, the government encouraged business activities amongst entrepreneurs, providing

public support and subsidise to those who made investments in preferred industries. Added

to this was an environment of rising foreign investment as a result of US pressure on South

Korea to shift to export-oriented industries in order to lower US foreign aid. As private

finance flowed into South Korea, rent-seeking behaviour from foreign corporations saw

over US$8 million of funds being diverted to the ruling party during the 1971 elections, in

attempts to secure participation in the profitable economic conditions (Lee and McNulty,

2003; Moran, 1998). Thus Korean businesses (or chaebol) were highly leveraged and relied

upon the government for finance. This reflects the centralisation of corruption in South

Korea as outlined in the Shleifer and Vishny (1993) model. According to the model, if

bureaucrats work together or collude in the offering of bribes, it is possible for the

aggregate level of bribes to be maximised, instead of maximising individual bribes (which

would ultimately reduce total output). At the same time, the government wanted to

encourage the formation of large conglomerate firms which were accounting for large

percentages of the South Korean economy. The chaebols needed the government for

finance, but the government could never threaten to cut off credit because of the effect it

would have on the economy. Further, politicians relied upon the chaebols for financing

their campaigns (Kang, 2003). Thus the balance had started to tip in the chaebols’ favour,

and they began to possess and exercise more power, giving them advantages over smaller

firms in almost all aspects of business activities and jeopardising the interests of suppliers,

72

competitors, and customers. In 1996, the 30 largest chaebols accounted for 40% of

shipments, 18.1% of employment, and 38.2% of value added in the mining and

manufacturing sector (Shin, 2002). The corruption that existed in South Korea was

motivated by political survival and individual greed, and resulted in economic development

because of the mutual hostage situation between the government and the chaebols. Once

democratisation began to take hold, the business sector began to exert more control

resulting in a situation of state capture.

4.4 Centralised vs Decentralised Corruption

In the Philippines however, in the early period of democracy, there was no mutual hostage

situation. The business sector would establish close ties with the ruling party (which

happened to be just as weak as the opposition), and once the rulers failed to deliver, the

support would wane and shift toward the opposition. Thus political power consistently

swung between both sides, with the business sector milking each for its own benefit.

Essentially, the politicians were buying votes from the business sector. This is reflected by

excessive budget deficits in election years (Kang, 2003).

Between 1971 and 1997, the Philippine economy grew at an average of 3.8% per year

(World Bank, 2005). This is significantly lower than the average growth of its Asian

neighbours, and it is largely due to the decentralised corruption that prevailed. Shleifer and

Vishny (1993) asserted that when complementary goods (say, fire and water and police

department licences sought after by a builder) are being offered by a single bureaucrat, the

bribe charged for the first good will not be set too high as it may reduce demand for the

second good. In other words, centralisation forces the corrupt official to consider aggregate

rent collection instead of individual bribes. But if the complementary goods were being

sold by two different competing bureaucrats, each would seek to maximise his own bribes

and this could result in lower aggregate rent collection. Thus, decentralised corruption

results in greater inefficiencies. In the Philippines in 1968, it was found that an entrepreneur

would need to go through 54 procedural steps in order to release an importation, of which

42 involved the payment of a bribe. The bureaucracy in the Philippines was not subject to

centralisation as far as corruption was concerned, largely because the elite had a strong

economic base outside of the state, unlike in Indonesia and Thailand for example

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(Hutchcroft, 2000). Without centralisation, an element of unpredictability was introduced in

dealings with the bribery within the bureaucracy and therefore transaction costs for

businesses were increased along with inefficiency.

Like in South Korea, the Philippine business sector was driven by family based

conglomerates, but the difference was that there was more than one family. This raised

transaction costs and lowered economic efficiency for three reasons. First, rent-seeking

costs were much higher because of the increased competition. Second, the increased

competition also meant that property rights were unstable. And third, demands on the state

were increased due to the different competing families and the impediments to collusion

(geographical amongst others) (Kang, 2003). There was also weak monitoring of rents, so

firms were not given an incentive to invest the rents productively. Once Marcos came to

power in the Philippines, the cronyism became less decentralised as he capitalised upon a

disorganised business sector and created oligarchs that depended upon him for survival. But

Marcos never instituted restrictions on his power or those of his cronies, so the rent-seeking

behaviour became unproductive and highly inefficient (Hutchcroft, 2000). Once again there

was no mutual hostage situation, so the Marcos government simply preyed on the economy

and sought to maximise its own rents without any regard for development.

While Marcos failed, Suharto succeeded. Indonesia’s case is a perfect example of

centralised corruption. Under the rule of President Suharto from 1966-1998, the Indonesian

economy was quite literally transformed from rags to riches. Unlike in the Philippines, the

ruling regime had incentives to prevent unrestrained plundering and inefficiency once it had

established itself as an authoritarian power. The Suharto government most notably

implemented a policy of balanced budgeting, promising not to spend more than what was

collected from tax revenue and foreign aid. The Suharto era was punctuated with fiscal

discipline, and even the government spending itself was equitable and positive in terms of

distribution of wealth. However, beyond the apparent discipline lay a substantial degree of

off-budget fiscal activity. A major example of this was Suharto’s control over Pertamina,

the state oil company. Its profits were siphoned off by Suharto and used to finance

industrial development plans that were largely unsuccessful until the central bank was

eventually called in to bail out the firm and pay off the significant debt that was owing to

international creditors (MacIntyre, 2000).

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The use of state enterprises to finance government projects was one form of off-budget

fiscal activity, that also had inflationary effects given that the central bank was one of the

state organisations involved. In addition to this, private firms were also encouraged

(unofficially) to contribute to similar government projects. In some cases, wealthy

businessmen contributed funds to assist the government by undertaking financially

pessimistic projects, purely and simply to maintain patron-client relations with the Suharto

government and indeed Suharto himself (MacIntyre, 2000). Even members of the political

elite competed amongst themselves to partake in Suharto’s spoils. The business sector in

Indonesia was never allowed to organise and develop a strong base, instead it had to rely

wholly on the ruling Suharto family for survival. This was partly due to the ethnic

breakdown of the business sector. The ethnic Chinese minority in Indonesia was less than

4% of the population yet held the majority of the private sector. Due to their minority

status, the Chinese capitalists were politically vulnerable and had no choice but to look to

Suharto for protection. This is in stark contrast to the South Korean chaebols, which never

suffered any social resistance and did not need the government’s protection (Kang, 2003).

From 1966 to 1997, before the advent of the Asian financial crisis, Indonesia averaged

growth of over 7 per cent per year. In the five years preceding Suharto’s takeover, growth

only averaged 2 per cent per year (World Bank, 2005). Despite the excessive rent-seeking,

the economy was still able to achieve high growth rates. The key to this lies in the

aforementioned incentives in restraining plundering and excessive inefficiency. Once again,

using the Shleifer and Vishny (1993) model, when complementary goods are being

provided by the same bureaucrat the level of the bribe extorted will be kept down in order

to maximise overall rent collection. Suharto was effectively a monopolist in this context,

and was careful not to allow excessive and unrestricted corruption which would have had a

detrimental effect on aggregate rent collection. In one case, a member of the political elite

wanted to fund a jet aircraft project and sought assistance from Suharto. Despite allowing

the project to go ahead, Suharto imposed two restrictions. While it would have been easy to

pull funds from the budget or from the government’s hidden reserves (unofficial bank

accounts), Suharto ensured that the funds came from off-budget sources. This restricted the

size of the funds. Secondly, a respected and independent member of the cabinet (not a

crony of the project’s founder) was placed in charge of managing the funds, so as to ensure

arm’s length accounting (MacIntyre, 2000).

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In another case, Suharto’s grandson Ari Sigit used his corporation as a middleman to help

provide shoes for Indonesian schoolchildren. In an agreement reached with the Education

Department, schoolchildren were obligated to purchase shoes from Ari Sigit’s company at

an above-market price which would have yielded profits of around US$80 million a year.

The public were furious that the president’s grandson was ripping off poor Indonesian

families, and the public outcry eventually saw Suharto disapprove the plan and force the

Education Department to abandon it (Backman, 1999). These cases reflect Suharto’s

awareness of his centralised position and the need to ensure that no single sector enriched

itself at the expense of the entire pyramid, atop which Suharto sat (MacIntyre, 2000).

4.5 Patron-Client Networks

Connections have always been important in Asia. According to Luo (2002) illicit business-

government links are important in the analysis of corruption in Asia. But these connections

do not necessarily operate in the same way. For example, where the government of South

Korea had to deal with rival political elites, consequent political contestation, and a

distinction between state and business, the Taiwanese government did not have to contend

with the same factors. Taiwanese political leaders allowed relatively little direct private-

sector input in policy-making decisions, and instead chose to establish political ties with

small and medium sized enterprises and farmers (Byun, 2001). The ruling Chinese

Nationalist Party or kuomintang (KMT) was never impeded the way governments were in

South Asia (as discussed above) because the local elite was kept divided and was never

allowed to collude. The competition amongst the local elite for government ‘side payments’

limited their influence over rent collection from the KMT. While in South Korea, the

business sector gained significant bargaining power after democratisation, in Taiwan the

situation was somewhat more chaotic. Since the KMT controlled a significant proportion of

the business sector, it maintained a position as a powerful business force. But the sheer

quantity of assets that were available to be appropriated by the private sector introduced

several other players into the game, including underground criminal organisations. This

gave rise to a web of connections between the different players (Kong, 2004).

Patron-client networks were established all over East Asia in many different ways. In

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Thailand, the ruling elite had strong roots within the bureaucracy unlike in the Philippines.

However, there was still excessive corruption within the bureaucracy due to separate

factions within the elite. The Thai Chinese played a role similar to the Malaysian Chinese,

but the difference was that they intermarried with the locals and were linguistically

integrated. There was effectively a three-way power tussle between the military-

bureaucratic group, the Bangkok-based Thai Chinese capitalists, and provincial capitalists

(Khan, 2000b). This meant that the bureaucracy, although centralised, was weak,

fragmented and not oriented towards development unlike in South Korea. The military-

bureaucratic group had held power in the 1950s, but as the regime shifted towards

democracy in recent years the power of provincial constituencies (providing 80% of seats

in the House of Representatives) increased and provided opportunities for patron-client

networks. Although competition for power amongst the elite was a recipe for disaster in the

case of the Philippines, in Thailand the difference was that the bureaucracy was the source

of power and prestige, whereas the Philippine elite already had an economic base outside

the state. Fortunately, the macroeconomic management of the Thai economy was entrusted

to a few agencies that were insulated from bureaucratic politics, and thus was enable to

enforce fiscal discipline (Rock, 2000).

In the midst of this power tussle, the Thai Chinese entrepreneurs (who were excluded from

the public and political arenas prior to the 1970s) began to capitalise upon patron-client

relations with the military and sought protection from harassment by bribe-seeking

bureaucrats in return for offering financial support. Rent-seeking became highly

competitive and there was consistent entry into rent-providing industrial sectors by

excluded capitalists (both ethnic Thai and Thai Chinese), seeking to establish links with

members of the ethnic Thai elite (Doner and Ramsay, 2000). These patron-client networks

ultimately contributed to economic growth, which averaged over 7% per year between

1971 and 1990, and then nearly 9% in the first half of the 1990s (World Bank, 2005). The

contribution is no more evident than in Thailand’s massive textile industry. Several textile

firms were established in the 1950s by commercial bankers, who had links to the ruling

military through their banks. By 1960, there were 20 such firms founded by 16 groups of

investors. This was followed by the government passing legislation to protect property

rights in the pursuance of an import-substitution policy. This allowed the textile firms to

form alliances with foreign (mainly Japanese) firms and further enhance their property

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rights. With the factional rivalry within the elite, new capitalists entered the scene and

increased competition for rents. While larger established firms used their political

connections to seek restrictions on textile capacity to protect their interest, newer firms used

their own connections to circumvent these restrictions. Competition eventually led to an

oversupply in the domestic market. Fortunately, cooperation between the private sector as a

whole and the government eventually led to exporting in the 1970s (Doner and Ramsay,

2000).

The entrance into exporting led to the problem of allocation of export quotas to competing

firms. The government ended up allocating quotas on the basis of past exports – those who

exported more were given greater quotas. Consequently, larger established firms with

strong political connections received the most quotas. However, they also tended to be

more inefficient than smaller firms (Doner and Ramsay, 2000). One reason for this is that

the technology needed to enhance productivity was so basic that the rents did not require

learning and monitoring for the adoption of the technology and efficient operation (Khan,

2000b). As the business sector strengthened itself, and as Thailand shifted towards a

democracy, the business sector (dominated by large family-based composites) began to use

electoral politics to further its own interests. Government control of trade associations

declined and their representatives made their way into the public sector (Rock, 2000). The

situation is now similar to South Korea.

4.6 Malaysia’s Rent-Seeking Experience

The creation and distribution of rents in Malaysia stem largely from the implementation of

the New Economic Policy (NEP) by the government in 1971. This policy was aimed at

removing inter-ethnic economic disparities between the majority Malays and the minority

Chinese which led to riots in May 1969 (Abu Bakar and Hasan, 2003). These disparities

stretch as far back as Malaysia’s colonial days. The Malays had always been marginalised

from the capitalist sector, and were largely involved with the rural sector except for the elite

Malays who helped form the state administration. The Chinese were, and still are, more

involved with the private sector and took advantage of business opportunities. During

colonial times, land and other natural resources were controlled by the British and were

allocated to mainly European (instead of Chinese) interests. The Malays were appeased

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(though some might argue bribed) by pensions, positions in the state administration and

even privileged access to those same resources. Naturally, larger concessions of better land

went into British hands, and thus received better infrastructure development. Malaysia

inherited this property rights regime from its colonisers. In summary, the Malays welcomed

the government’s NEP while the Chinese remained sceptical as the government

intervention seemed to come at their expense (Jomo and Gomez, 2000).

State intervention was a necessary component of the NEP which sought to achieve inter-

ethnic parity in occupations and corporate wealth ownership. The government intervened

primarily in fiscal resource allocation, public sector ownership and control of business

enterprises. A large number of public enterprises were established in all economic sectors,

either as statutory bodies established by legislation or as private corporations (Rasiah and

Shari, 2001). Many had subsidiaries and joint ventures. The number of such entities grew

from 109 in 1970 to 1,014 in 1985, and were often charged with increasing public debt and

being inefficient. At the same time, the public sector share of GNP had increased from 29%

in 1970 to about 58% in 1981, but later fell to 25% in 1993. The contribution of state-

owned enterprises to economic growth also declined, and its capital productivity has

steadily decreased. These entities were also weak in transparency and accountability. Out of

900 such enterprises identified in 1984, annual returns were only available for 269, which

themselves reported an annual loss of RM137.3 million. This represents inefficient state

intervention that was geared towards (necessarily, as some might argue) favouring the

politically dominant but economically weak Malay community (Jomo and Gomez, 2000).

Following the implementation of the NEP, economic growth for Malaysia surged to record

levels. Between 1961 (four years after independence) and 1970 Malaysia was averaging

growth of 6.5% per year. In the next four years under the NEP, average annual growth

jumped to 8.8% (World Bank, 2005). By this stage, oil prices were beginning to rise rapidly

and the government began to tap into significant natural resource rents, although the oil

shock in 1975 saw growth plummet to 0.8% in that year for Malaysia (World Bank, 2005).

Maximisation of these rents was not only efficient but socially beneficial. To explain this,

Khan (2000a) considers the case of a lake that produces fish as an example. If one assumes

the rate of renewal of fish in the lake is fixed, then increasing the rate at which the fish are

extracted raises the marginal cost. To ensure efficient allocation of resources, the amount of

If the lake did not belong to the fishery, then anyone would be free to extract fish from the

lake. Whatever rents would accrue to the fishery would then be dissipated as a result of

overfishing. As the quantity of fish extracted rises beyond Q1, the marginal cost exceeds the

price so the total rent is reduced depending on the extent of overfishing. Each fisherman

will keep fishing as long as the price of selling the last fish covers the cost of catching it. If

there are a large number of fishermen and fish, then the cost of the last fish will appear to

be the average cost. No fisherman would be concerned with the marginal cost, and the

quantity extracted would amount to Q2, where price equals average cost. Now the total rent

accrued by the fishery is the triangle ABC minus the negative rent as a result of

competition, represented by the triangle CDE. The total rent could be zero or quite possibly

negative. This demonstrates the need for maintaining scarcity rents through the creation of

property rights (Khan, 2000a).

Source: Khan (2000a, p.34).

Q1 Q2 Output (fish)

fish extracted should be such that the marginal cost equals the marginal benefit, as

illustrated in Figure 4.1. The demand curve is assumed to be flat, because the particular

lake in question represents a small component of the total demand for fish. For efficient

allocation of resources, Q1 worth of fish should be extracted, i.e. where marginal cost

equals price. The fishery therefore earns a rent represented by the triangle ABC, similar to a

producer’s surplus.

D

A

F

B E C

Demand

MC

AC

Price

Figure 4.1: Natural Resource Rents

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80

According to Jomo and Gomez (2000), two natural resource industries stand out in the case

of Malaysia; petroleum and logging. Malaysia became a net oil exporter since the mid-

1970s. In 1974 against the backdrop of rising oil prices, legislation was passed to give the

federal government jurisdiction over petroleum resources. These resources were hitherto in

the hands of state governments, along with other natural resources such as land, water,

forests and minerals, under the post-colonial federal constitution. Although the government

distributed petroleum royalties among the states, the national petroleum corporation

Petronas was in the hands of the federal government along with petroleum revenues.

Petronas is a corporation with a strong international credit rating, ranking 28th in the 2004

Fortune Global 500 (Petronas, 2004a) and raking in nearly US$10 billion in before-tax

profits in 2004 (Petronas, 2004). However, Petronas was used by the government to finance

commercially unviable projects such as the Dayabumi project in the mid-1980s and the

Kuala Lumpur City Centre (the ‘Twin Towers’) which was the world’s tallest building in

the mid-1990s. Petronas was also involved in the bailing out of Bank Bumiputra Malaysia

Berhad on two separate occasions. This has not significantly affected Petronas’ standing in

the business community largely because of its success in capturing and retaining petroleum

rents (Jomo and Gomez, 2000).

The story is not quite the same for the logging industry. Logging companies are not taxed

greatly, thus making it difficult to accommodate the real costs of reforestation and the

enforcement of logging-related regulations. Timber companies do not pay much income

tax. Jomo and Gomez (2000) assert that their financial statements reveal losses or modest

profits. State governments receive a royalty on logs extracted of some 1% of the timber

price. Loggers therefore minimise their tax liabilities and maximise retained earnings by

undervaluing the type, nature, quality, and volume/quantity of timber extracted. Extraction

and exportation of wood is often accompanied with some bribery of officials to reduce tax

liabilities. In response to this, the government raised taxes, but this only increased tax

evasion (Jomo and Gomez, 2000).

Along with increased state intervention, measures were being taken to increase Malay

participation in commerce as part of the NEP. In 1975, legislation was passed to give the

government greater control over manufacturing enterprises. Manufacturing licences could

be revoked if Malays were not being employed in sufficient numbers, or being given

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sufficient control of corporations (Rasiah and Shari, 2001). The latter was achieved by

inducing corporations to sell discounted shares to Malays. Similar legislation applied to

foreign-owned corporations. Protests by the Chinese led the government to eventually

provide some concessions, without altering the fundamental purpose of the legislation.

Despite this, many Chinese elected to conceal the extent of their investments by setting up

complicated cross-holding networks, while others diversified their operations overseas, and

some pooled their resources together. It became necessary for both Chinese and foreign

corporations to forge close business ties with politically influential Malays (Jomo and

Gomez, 2000). It should however be noted that although the government attempted to

increase the Malay share in commerce from 2% to 30% by 1990, the target for the Chinese

share was set at 40% – which was not only above that of the Malays, but was also above the

level enjoyed by the Chinese at the time of the implementation of the NEP. The remaining

30% was the target for the foreign share, which was at 65% when the NEP was

implemented (Snodgrass, 1995). Between 1976 and 1984 growth averaged nearly 8% per

year, before a recession struck in 1985 and 1986. After the recession, growth surged once

again averaging nearly 9% until the impact of the Asian financial crisis in 1998 (World

Bank, 2005).

As part of its redistributive policies, patron-client networks were set up by the ruling United

Malays National Organization (UMNO), established after the 1969 riots. UMNO became

the dominant Malay party in the ruling coalition (Barisan Nasional). The objective of the

patron-client networks was to transfer rents from the Chinese capitalists to the political

leadership of UMNO, via legal taxes and illegal extractions. These were then used to

provide jobs for Malays in state owned enterprises and subsidised loans through the

banking system. Given the natural dominance held by the Chinese in the private sector, the

rents collected through legal taxes were already large enough and the need for illegal

extractions was less. The political Malay elite who were the recipients of these rents, later

engaged in their own rent-seeking behaviour to maintain their political support amongst the

wider Malay community (Khan, 2000b). The effect of the rent transfer from the Chinese to

the Malays occurred in a similar fashion to what was illustrated in Figure 2.7 in Chapter 2.

Fortunately for Malaysia, the implementation of the NEP did not dampen the confidence of

the Chinese capitalists, and they realised that they had no choice but to adapt to the

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changing economic climate (Snodgrass, 1995). The Chinese continued to drive the

economy, and at the same time, the rents that were being transferred to the Malays resulted

in learning despite less monitoring by the government than was in the case of South Korea

(Khan, 2000b). The privatisation of the public monopolies created a “new rentier elite”

(Jomo and Gomez, 2000, p.295) in the corporate sector, not just politically influential and

economically powerful, but also forced to be more competent in order to maximise rents.

The massive North-South Highway project was completed well ahead of time by the

company (inexperienced in road construction) that secured the tender, in order to collect

tolls sooner. Thus rent-seeking and re-allocation of rents was efficiency enhancing for

Malaysia, and helped the economy to grow. However, since the rents were being

transferred to an intermediate, politically powerful Malay class, this restricted access to

resources by Chinese capitalists that would have otherwise induced learning on their part.

This explains why technological progress in Malaysia depended heavily on foreign direct

investment (Khan, 2000b).

The rents that were transferred from the Chinese to the Malays were also in the form of

subsidised loans from banks. Financial sector rents in Malaysia need to be understood in

the context of financial restraint, rather than financial repression. The latter involves the

government extracting rents from the financial sector. The former involves the government

creating rent opportunities for the financial sector. It is the more efficient of the two, and is

what prevails in Malaysia. The government created rents, and allowed the private sector to

compete in securing those rents. Thus the efficiency of resource allocation is left to the

private sector agents (Fay and Jomo, 2000).

Fay and Jomo (2000) illustrate how interest rate controls can lead to the creation of

financial sector rents in Figure 4.2. The equilibrium interest rate is r0, where the household

funds’ supply curve intersects the corporate funds’ demand curve. If the government was to

intervene and regulate deposit rates, rents can be captured by financial intermediaries,

defined as the difference between the lending rate, rL, and the deposit rate, rd. The lending

rate is higher than what it would be in the absence of government intervention, allowing

banks to capture rents from both the private sector (rL – r0) and households (r0 – rd).

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Figure 4.2: Market for Loans

r

Source: Fay and Jomo (2000, p.306).

The rent effect of financial restraint is quite large. As banks recognise the rents that are

available, they will seek to capture these rents by inducing greater savings by households

by providing greater security for deposits. This is based on the notion that deposit security

dominates interest rates as an incentive for households to save. Fay and Jomo (2000)

illustrate the result of this effect in Figure 4.3.

Figure 4.3: Rent Effect of Financial Restraint

r

Source: Fay and Jomo (2000, p.307). The increased savings is represented by a shift in the supply curve from S to S1 which leads

to a new equilibrium rate of rL1. Assuming the deposit rate is still controlled by the

government, banks are able to capture rents equal to rL1 – rd. Under this scenario, firms are

actually better off because they are receiving a greater quantity of loans (Qd > Q0) at a

much lower interest rate (rL1 < r0). Through government regulation, competition which

S1

D

S

r0

rL1

rd

Q0 Qd Q

Qd Q0 Q

S

D

rL

r0

rd

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would have eliminated these rents was restricted. Banks enjoyed an interest margin

(between lending and deposit rates) of between 4 and 5 per cent between 1984 and 1995. In

accordance with the government’s policy along inter-ethnic lines, priority lending

guidelines were also imposed on banks. Certain sectors of the economy were given access

to funds at reasonable costs, such as the Malay business community, small businesses,

housing etc. After deregulation, although the number of priority sectors declined the Malay

business community’s priority status was not affected, and was instead subject to a target of

20 per cent of outstanding loans (Fay and Jomo, 2000).

4.7 Singapore’s Secret

One country that is noticeably missing from the preceding discussion of East Asian

corruption is Singapore. As will be seen in Chapters 5 and 6, Singapore is a striking outlier

in the corruption data for East Asia. For all intents and purposes, Singapore appears to be

the cleanest Asian country and consistently boasts corruption rankings more akin to those

of the Nordic countries rather than its neighbouring counterparts. Few studies have

analysed the Singaporean experience, but Sam (2005) points to a number of features that

underlie Singapore’s secret to combating graft. Firstly, Singapore pays competitive salaries

to its public sector officials, reducing the distinction prevalent in many other countries

between the public and private sectors. The Singaporean bureaucracy is also extremely

efficient, providing simple easy-to-follow procedures that reduce the opportunity for firms

to bribe civil servants. Finally, strong political will and tough anti-corruption laws have

enabled the tiny city-state to remain relatively free of the corruption that has plagued its

neighbours.

4.8 The China Syndrome

The preceding sections have attempted to show how corruption in East Asia is more

synonymous with rent-seeking than it is with ordinary bribe paying. One exception to this

however is the case of China. Corruption through rent-seeking prevails in China, where

public agents seek profits generated by their monopolies over certain resources or power

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for their own gains, however Chinese bureaucrats allegedly extort bribes as well.20 Lu

(1999) points to three common practices by state agencies: illicitly levied fines; imposed

fees; and apportionments. These agencies perform services for a fee when in fact they

should be provided free of charge. Most of these charges are also imposed without

accompanying legislation. These so-called ‘extrabudgetary funds’ were estimated at 38.43

billion yuan in 1995, and 42 billion yuan in 1997. What makes these even more striking is

that although the private sector pays such huge amounts, the revenues of the state treasury

are in fact decreasing, losing approximately 100 billion yuan in tax revenue each year due

to tax evasion and under-reporting of profits. This appropriation of rents also takes place in

rural China, accounting for an average of 2.5 percent of peasants’ annual income in 1991.

In Shanxi province in 1992, some villages were paying 13.98 percent of their income to

various agencies, while in Henan province in 1991 it was over 15%. The government

agencies ranged from finance, land management, and town planning to water conservation,

public health, law enforcement and postal services (Lu, 1999). China is a very special case,

and warrants a separate independent analysis due to its unique characteristics. Such an

analysis is beyond the scope of this study.

4.9 Conclusion

This chapter has reviewed the peculiar nature of corruption in East Asia. Corruption has

manifested itself in different ways across the region, however the preceding discussion has

shown that the rent-seeking model is rather successful in explaining the corruption in East

Asia. This provides the basis for using rent-seeking as a proxy for corruption in a

corruption-growth model. Before any empirical analysis can be embarked upon, a

measurement of corruption must be developed. This is the subject of the next chapter.

20 See Su and Littlefield (2001) for an analysis of rent-seeking in China and how it is reconciled with the concept of guanxi, or the interpersonal relationships or connections that are often used to seek ‘favours’.

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CHAPTER 5

MEASURING CORRUPTION

“Corruption is not a natural disaster: it is the cold, calculated theft of

opportunity from the men, women and children around the world who are

least able to protect themselves. It must be taken seriously.”

- Transparency International’s Chief Executive David Nussbaum (2005).

5.1 Introduction

By virtue of its clandestine nature, corruption is not easily measurable. In fact, this has been

the greatest limitation to empirical research into this phenomenon. How does one measure

the extent of bribery in a country? Or the extent of patron-client networks that exist

between the government and the private sector? Such data is not easily available. One

reason has already been mentioned, that corruption is clandestine in nature and not visible.

Another reason is that governments of corruption nations would not be the first to publish

statistics revealing their corrupt practices to the rest of the world. The onus naturally lies

therefore on independent organisations to make their own assessments. There exist only a

handful of such sources that have attempted to provide reliable measures of corruption, and

these take the form of indices which rank countries on the basis of their corruption ‘scores’.

These indices will later be used in revised form in the empirical component of this study.

The purpose of this chapter is to critically analyse these indices and identify the best

possible index or combination of indices.

5.2 Corruption Indices

In the empirical studies involving corruption, the overwhelming majority has relied upon

one of the following six indices published by various international organisations as a

measure of corruption. Each of these indices is discussed in this section:

• the Corruption Perceptions Index (CPI) published by Transparency International

(TI);

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• the International Country Risk Guide (ICRG) published by Political Risk Services

Inc.;

• the Business International index (BI - now incorporated into the Economist

Intelligence Unit’s publications);

• the 1997 World Development Report (WDR) published by the World Bank;

• the Global Competitiveness Report (GCR) published annually by the World

Economic Forum (WEF); and

• the World Competitiveness Yearbook (WCY) published annually by the

International Institute for Management Development (IMD).

Corruption Perceptions Index (CPI)

Transparency International (TI) has published the CPI annually since 1995.21 As its name

suggests, the index provides data on perceptions of corruption in countries. It is a

composite index based on surveys of the corporate sectors in various countries. The most

recent edition, the 2007 CPI, was based on 14 surveys sourced from 12 independent

institutions, covering 180 countries. The institutions were:

• The Asian Development Bank (ADB);

• The African Development Bank (AFDB);

• The Bertelsmann Foundation (BF);

• Freedom House (FH);

• The Economist Intelligence Unit (EIU);

• Global Insight (GI);

• The Institute for Management Development, Lausanne (IMD);

• The Political and Economic Risk Consultancy, Hong Kong (PERC);

• The World Economic Forum (WEF);

• The World Bank;

• Merchant International Group (MIG); and

21 TI also published a Bribe Payers Index in 1999 and 2002, but this index is rarely used in the literature. It is based upon surveys conducted in only 15 countries which measure respondents’ perceptions about the propensity for foreign companies to pay bribes in those countries. These perceptions are then used as a measure of corruption in the host countries of the foreign companies.

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• United Nations Economic Commission for Africa (UNECA).

According to TI, the sources provided by the institutions listed above were selected after

meeting certain criteria. Primarily, the sources needed to provide a ranking of nations

which is only possible if the same methodology is used in each country surveyed. Further,

the sources needed to measure corruption on its own, without being interlaced with other

related issues such as political instability or nationalism (TI, 2007). For this reason, the

‘Corruption in Government’ index from the International Country Risk Guide (ICRG) was

not included in the CPI. In its 2003 edition of the CPI, TI argued that the ICRG index does

not measure corruption, but rather the political risk involved in corruption. Interestingly, TI

claimed that this can be misleading since corruption only leads to political instability if it is

not tolerated (TI, 2003).

In the 2007 edition of the CPI, data was extracted from the sources for the two years

preceding 2007 when available. These were averaged to “reduce abrupt variations in

scoring that might arise due to random effects” (TI, 2007, p.3). As this is done for each

annual edition, year-to-year comparisons are still possible though TI points out that

methodologies may change each year, which could affect a country’s score (2007).

Consistent with the literature (Bardhan, 1997), all 12 sources defined corruption as the

misuse of public power for private benefit. Some sources distributed surveys to the

corporate sectors in the countries, while others relied upon their own panels of experts to

make assessments about corruption levels. Specifically:

• the ADB, AFDB and World Bank issued surveys asking about the prevalence of

ineffective audits, conflicts of interest, policies being biased towards narrow

interests, policies distorted by corruption, and public resources diverted to private

gain;

• the BF asked its local correspondents to prepare country reports and quantitatively

assess the enforcement of corruption and the effectiveness of anti-corruption

measures;

• the IMD issued a survey asking elite businessmen whether bribing and corruption

prevail or do not prevail in the economy;

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• the PERC asked expatriate businessmen how bad they considered corruption to be

in the country they work as well as in their home country;

• the EIU defines corruption as the misuse of public office for personal (or party

political) financial gain and asks its panel of experts to assess the incidence of

corruption;

• MIG asked its correspondents to assess the level of corruption in the form of

bribery;

• FH’s panel of experts assessed factors including the implementation of

anticorruption initiatives, public perceptions of corruption, laws on financial

disclosure and conflict of interest, protections for whistleblowers, and the media’s

coverage of corruption;

• GI assessed the likelihood of encountering corrupt officials and other such groups;

• UNECA asked its panel of experts to assess the level of corruption in areas such as

the legislature, judiciary, the executive level, and tax collection in each country; and

• the WEF, in its Global Competitiveness Report, asked respondents to what extent

their firms made undocumented extra payments or bribes connected with exports

and imports; public utilities; annual tax payments; public contracts; loan

applications; influencing laws and policies to favour selected business interests; and

getting favourable judicial decisions (TI, 2007)

A key point that TI emphasises is that the questions listed above measure only the degree of

corruption, in line with the aims of the CPI. TI acknowledges that this ignores the important

distinction between nepotism and corruption in the form of monetary transfers, but argues

that both are merely forms of the same ‘corruption’ phenomenon (TI, 2007).

The CPI is created using the ranks of countries according to each source. This data is then

standardised using matching percentiles. Common sub-samples from a new source and the

previous year’s CPI, and the largest value in the CPI become the standardised value for the

country ranked best by the new source. For example, assume a source ranks only five

countries in the order UK (4.2), Singapore (3.9), China (2.8), Malaysia (2.7) and India

(2.4). In the 2004 CPI these countries obtained the scores 8.6, 9.4, 3.2, 5.1 and 2.9,

respectively. Matching percentiles would now assign UK the best score of 9.4, Singapore

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second best with 8.6, and so on. This method is preferred because all standardised scores

are within a range of 0-10, which is consistent with the range for each source (TI, 2007).

The standard deviation of the index at this point will be much smaller than that of previous

CPIs, and this trend will continue unless a further adjustment is made. A beta-

transformation is made to preserve the standard deviation of the previous year’s CPI while

still keeping the scores between 0 and 10. Each score, X, is transformed according to the

following function:

1

10 × ∫ (X/10)α-1 (1-X/10)β-1 dX (5.1) 0

The parameters α and β must be found such that the resultant mean and standard deviation

is as desired. Once this is achieved, the CPI is complete. To check for validity, correlations

are generated between each of the sources used in the CPI. In 2003, the average Pearson

correlation was 0.87 and the corresponding Kendall’s rank correlation was 0.72 (TI, 2003).

Table 5.1 shows a selection of the 2007 CPI rankings.

The CPI’s biggest selling point is that it is an index of indices. The fact that the CPI is

based on several sources helps to give credibility to its results. For example, if country X

received a high corruption rating from only one source, and a clean rating from the

remaining nine sources, it would not be featured in the high end of the CPI rankings. But if

that particular source was used independently, one might be tempted to believe that country

X was indeed corrupt. These sorts of biases disappear, the more sources are used to ensure

consistency in the ratings.

It is this feature that makes the CPI one of the most popular measures of corruption in the

literature (see Table 5.2). However, the trouble with the CPI is that its definition of

corruption is limited to the payment of bribes to bureaucrats. Although this is the way

corruption is defined in the literature, it fails to pick up the alternative dimension of

corruption highlighted by this study, namely the rent-seeking that arises out of patron-client

networks that exist between the government and the private sector in many countries.

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Table 5.1: CPI 2007, Selected Countries

Country Rank Score

New Zealand 1 9.4

Denmark 1 9.4

Finland 1 9.4

Singapore 4 9.3

Sweden 4 9.3

Iceland 6 9.2

Netherlands 7 9.0

Switzerland 7 9.0

Norway 9 8.7

Canada 9 8.6

Hong Kong SAR 14 8.3

Malaysia 43 5.1

South Korea 43 5.1

Thailand 84 3.3

Philippines 131 2.5

Indonesia 143 2.3

Source: Transparency International (2007).

International Country Risk Guide (ICRG)

The ICRG was first created in 1980 by the editors of International Reports, a weekly

newsletter on international finance and economics. In 1992, ICRG moved to the Political

Risk Services Group, a ratings firm based in the US (Political Risk Services Inc., 2004).

The ICRG model develops composite risk ratings based on risk factors and their bearing on

business or investments. The model is based on a set of 22 components grouped into three

major categories of risk: political, financial, and economic. For each country, the model

provides a rating for each component in the form of a score, similar to the process used by

TI.

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The ‘political risk’ category contains a component labelled ‘corruption’. The ICRG (2004,

p.5) states that “the most common form of corruption met directly by business is financial

corruption in the form of demands for special payments and bribes connected with import

and export licenses, exchange controls, tax assessments, police protection, or loans”. The

measure of ‘corruption’ is said to be “more concerned with actual or potential corruption in

the form of excessive patronage, nepotism, job reservations, ‘favor-for-favors’, secret party

funding, and suspiciously close ties between politics and business” though no information

is given on exactly how this is measured, other than a statement that results are collected

through the completion of surveys (p.5).

However, according to Howell (2001), the corruption ratings by the ICRG are strongly

linked to government stability and accountability. Howell (2001, p.26) states that the

“highest risk ratings tend to signify an accountable democracy whose government has been

in office for less than five years” and the lowest ratings are “usually given to one-party

states and autarchies (sic)”. This is consistent with TI’s claim that the ICRG ratings are

political in nature. Another problem with the ICRG is its scale. Until 2000, the scores were

all integers between 0 and 6 (6 indicating the absence of corruption). Such a narrow range

meant that a lot of countries remained at the same level, even though the corruption level

may have changed. In other words, it would have to take a significant change in corruption

to make a country move by one point on the scale, since there were only seven points in

total. In 2001 the points were doubled when half-points (0.5) were introduced. However,

this did little to alleviate the problem.

Another problem is the rankings under the ICRG. For example, in 1996, the US, the UK,

France and Singapore were all at the same level as Nicaragua, Namibia, Libya,

Mozambique and Guatemala, and were deemed more corrupt than Costa Rica, Cyprus and

South Africa. This makes the ICRG less marketable as an accurate measure of corruption,

however it is still as popular as the CPI in the literature only because it provides data over a

much longer timeframe (at least fifteen years), while the CPI was only introduced in 1995.

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Business International (BI)

The BI index is no longer in existence and has been incorporated into the Economist

Intelligence Unit’s publications on risk. Mauro (1995) provides an insight into this index,

which covered 56 country risk factors for 68 countries during 1980-1983. In particular,

Mauro (1995, p.684) focused on nine indicators, namely:

• Political change (institutional) – possibility that the institutional framework will be

changed within the forecast period by elections or other means;

• Political stability (social) – conduct of political activity, both organised and

individual, and the degree to which the orderly political process tends to disintegrate

or become violent;

• Probability of opposition group takeover – likelihood that the opposition will come

to power during the forecast period;

• Stability of labour – degree to which labour represents possible disruption for

manufacturing and other business activity;

• Relationship with neighbouring countries – this includes political, economic and

commercial relations with neighbours that may affect companies doing business in

the country;

• Terrorism – the degree to which individuals and businesses are subject to acts of

terrorism;

• Legal system and judiciary – efficiency and integrity of the legal environment as it

affects business, particularly foreign firms;

• Bureaucracy and red tape – the regulatory environment foreign firms must face

when seeking approvals and permits and the degree to which it represents an

obstacle to business;

• Corruption – the degree to which business transactions involve corruption or

questionable payments.

Mauro (1995, p.684) selects these indicators because they are assessed independently of

macroeconomic variables, and also because “they refer to the interests of any firm

operating in the country in question, rather than specifically to foreign-owned multinational

companies”. Mauro then takes the simple average of the scores for each indicator for the

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period 1980-83. A high score corresponds to a better institutional quality. In his 1997 paper

Mauro used the same BI index but also included the ICRG index, averaged for the period

1982-95.

1997 World Development Report (WDR)

The 1997 World Development Report was published by the World Bank and included an

Executive Survey that sought to capture private sector perceptions of institutional

uncertainty. The main part of the survey consisted of five sections, as follows:

• the predictability of laws and policies – uncertainties created by the legislative

process;

• political instability and security of property – uncertainty arising from transfer of

government power;

• government-business interface – government obstacles to doing business;

• law enforcement and bureaucratic red tape – degree of corruption and whether it is

a predictable transaction cost or an uncertainty; and,

• government efficiency in providing services – degree of efficiency in the

government provision of health care, utilities, roads, etc (World Bank, 1997).

Countries were then ranked based on their responses to the survey questions. Five of the

eight questions that explored corruption under the ‘law enforcement and bureaucratic red

tape’ section were in the form of statements, and respondents were meant to indicate the

degree to which the statement was true by using a scale of 1 to 6, with 1 indicating the

statement was always true, while a 6 meant that the statement was never true. The questions

were as follows:

Question 14: It is common for firms in my line of business to have to pay some

irregular “additional payments” to get things done.

Question 15: Firms in my line of business usually know in advance about how

much this ”additional payment” is.

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Question 16: Even if a firm has to make an “additional payment” it always has to

fear that it will be asked for more, e.g. by another official.

Question 17: If a firm pays the required ”additional payment” the service is

usually also delivered as agreed.

Question 18: If a government agent acts against the rules I can usually go to

another official or to his superior and get the correct treatment.

In addition to the survey, a credibility index was also designed to measure the reliability of

the survey responses. The index was the simple mean of the average answers to five

‘subindicators’ which was then normalised such that the index for high-income OECD

countries was set to one. The first subindicator measured the predictability of rulemaking,

or the extent to which entrepreneurs have to cope with unexpected changes in rules of

policies. The second assessed the subjective perception of political instability, or whether

government changes were signs of dramatic policy ‘surprises’. A third subindicator dealt

with security of persons and property, to see whether entrepreneurs had confidence in the

security prowess of the authorities. Predictability of judicial enforcement was captured by

the fourth subindicator, measuring the degree of uncertainty arising out of arbitrary

enforcement of rules by the judiciary. And the final subindicator measured corruption,

defined as the practice of entrepreneurs having to make irregular additional payments to get

things done (World Bank, 1997).

Ahmad (2003) relied upon the WDR for three indices measuring the perception of

corruption. Two of the indices were based on the responses to questions 14 and 16 above.

However, Ahmad (2003, p.10) noted that these results may be “misleading as these two

indices reflect the perception of the same group of respondents”. Therefore, Ahmad also

used the CPI. However, Ahmad also used the responses to Question 12n, which fell under

the section on ‘government-business interface’. The question asked respondents to judge

how problematic corruption was for doing business, again using a scale of 1 to 6. The

WDR index was only published once (i.e., for one year) and therefore is severely limited in

its scope.

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Global Competitiveness Report (GCR)

The Global Competitiveness Report is produced jointly by the World Economic Forum and

the Harvard Institute for International Development on an annual basis. The Report is also

based on surveys. In the 2003-04 edition, respondents were asked the following questions

on corruption:

Question 7.01 How commonly are bribes paid in connection with import and export

permits?

Question 7.02 How commonly are bribes paid when getting connected with public

utilities?

Question 7.03 How commonly are bribes paid in connection with annual tax

payments?

Respondents were required to provide a score on a scale of one to seven. These scores were

then combined to produce the corruption index (WEF, 2004). Wei and Wu (2001)

combined both the WDR and the GCR for their measure of corruption. Johnson et al.

(1998) also used the GCR, but used data from eight different questions covering tax burden

as reported by the firm; regulatory burden; government intervention in the enterprise

sector; regulatory discretion and enforcement; extent of bribery payments; police

effectiveness; and labour regulations. Unfortunately, editions of the GCR prior to 2000 are

no longer in print and therefore this source is unable to be used in this study. Fortunately

though, the GCR data is subsumed in the CPI scores.

World Competitiveness Yearbook (WCY)

The WCY is published on an annual basis by the International Institute for Management

Development (IMD), providing a measure of the competitiveness of nations. Some 320

competitiveness criteria were used in the 2003 edition to rank 59 countries and regional

economies (Rosselet-McCauley, 2003). In addition to compiling statistics, an executive

survey is also conducted every year to measure perceptions of competitiveness. According

to IMD, the survey “is an in-depth 116-point questionnaire sent to executives in top- and

middle management in all of the economies” ranked in the Yearbook (Rosselet-McCauley,

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2003, p.3). The aim of the survey is to quantify issues that cannot be easily measured, one

of which is ‘corruption’. Corruption falls under the criteria of “Institutional Framework”,

which in turn is part of the “Government Efficiency” competitiveness factor.22 IMD states

that the surveys are sent to “executives who represent a cross-section of the business

community in each country or region” and are asked to “evaluate the present and expected

competitiveness conditions of the economy in which they work and have resided during the

past year, drawing from the wealth of their international experience, and thereby ensuring

that the evaluations portray an in-depth knowledge of their particular environment”

(Rosselet-McCauley, 2003, p.5). In 2003, 4,256 responses were received from the 59

economies surveyed (Rosselet-McCauley, 2003).

Alesina and Weder (1999, p.1128) refrained from using a single index or creating one,

preferring instead to use all available cross-country measures of corruption (including the

WCY), arguing that “[w]hile we would not trust 100 percent any specific index, we feel

more confident if a certain pattern of results is consistent for every measure of corruption”.

Alesina and Weder used seven indicators of corruption from six different sources, the first

of which is the ICRG. The second source is a survey conducted for the 1997 WDR, from

which two indicators are extracted measuring frequency of bribe-paying and the importance

of different obstacles to business. A third source of corruption data is from Standard and

Poor’s, and a fourth is the BI index. The last two sources are the WCY, and the CPI. Table

5.2 shows the extent to which each of the indices mentioned above is used in the literature.

Other Measures of Corruption

In some cases, corruption is measured without the use of the major indices. For instance,

Del Monte and Papagni (1997) measure corruption as the number of crimes committed

against the public administration per million employees. This data was sourced from the

national statistical bureau in Italy. Similarly, Depken and LaFountain (2004) measure

corruption as the number of federal public corruption convictions per 100,000 residents in a

particular state, sourced from the Public Integrity section of the US Department of Justice.

22 There are four competitiveness factors that are used to assess countries, namely economic performance; government efficiency; business efficiency; and infrastructure. Some 243 criteria fall under these factors, of which 116 are assessed through the Executive Survey.

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Table 5.2: Corruption Indices used in Empirical Studies

Authors CPI ICRG BI WDR GCR WCY Ahmad (2003) Alesina and Weder (2002) Ali and Isse (2003) Braun and Tella (2001) Dreher and Siemers (2004) Emerson (2006) Fisman and Gatti (2002) Hwang (2002) Johnson et al. (1998) Kimbro (2002) Lee and Ng (2002) Leite and Weidemann (1999) Li et al. (2000) Mauro (1995) Mauro (1997) Mendez and Sepulveda (2001) Poirson (1998) Rahman et al. (2000) Smarzynska and Wei (2000) Svendsen (2003) Tanzi and Davoodi (1997) Tavares (2003) Treisman (2000) Vinod (2003) Wei (1997) Wei and Wu (2001)

Some studies have used other obscure measures of corruption in addition to the five major

indices. For example, Alesina and Weder (2002) also used data from Standard and Poor’s.

Emerson (2006) used data from the World Audit Organization’s World Democracy Audit

publication. Hwang (2002) drew on the Levine-Loayza-Beck (1999) dataset. Johnson et al.

(1999) also used the Impulse Exporter Bribery Index constructed from a survey of German

business people conducted in 1992-94 by Peter Neumann at Impulse (a German business

publication).

The World Bank (2004) released an index of Governance Indicators which measure six

dimensions of governance covering 199 countries and territories in 1996, 1998, 2000 and

2002. The data is extracted from 25 sources constructed by 18 different organisations. In

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this sense only, the index is similar to the CPI in that it is a composite index. One of the

dimensions of governance is labelled as “Control of Corruption”, drawing on the individual

measures of corruption in the 25 data sources. But this corruption index (hitherto referred to

as the KKZ index) is in fact markedly different from the CPI, and in fact was intended to

supersede the CPI.23 Whereas the CPI sometimes used two and three years of data from the

same source and treated them as separate sources, the KKZ index only used data from one

year at a time. There are also significant differences in the way the countries’ scores are

generated (Kaufmann et al. 2003).

5.3 Correlation Between Indices

To determine whether there is any consistency in the approaches adopted in each index, the

correlation coefficients between selected pairs of indices were obtained. As a starting point,

a typical year before the 1997 financial crisis (namely, 1996) will be used for analysis as

that year is also the focus of empirical exercises in Chapter 8. Of the six major indices, only

the CPI, WDR, and WCY have data for 1996.24 The KKZ index was also available, but this

will be discussed later. A total of 22 countries appeared in all three indices. All scores were

converted to a common scale of 0 – 10, with 0 indicating extreme bribery or corruption, and

10 indicating zero bribery or corruption. The WDR scores were separated according to the

specific question that was asked. Only two questions were considered: Question 12n, which

asked respondents to rate the level of corruption; and Question 14, which asked respondents

to rate the level of bribery. When plotting these results against those of the CPI the results

are intriguing as shown in Figures 5.1 and 5.2. The correlation coefficient in both Figures is

0.87. However by looking at the graphs, one can see the striking difference between the

ratings under each of the WDR questions. None of the scores for question 14 are less than

4, so the data appears slightly skewed (Figure 5.2).

23 KKZ is an acronym for the surnames of the World Bank staff who first constructed this index: D. Kaufmann, A. Kraay and P. Zoido-Lobaton. 24 Although the WDR was published in 1997, the survey results were obtained in late 1996. The CPI and WCY survey results are always obtained in the same year of publication. To ensure consistency, the 1996 CPI and WCY data are used in conjunction with the WDR data.

100

Figure 5.1: CPI v WDR(q12n)

0

1

2

3

4

5

6

7

8

9

10

4 5 6 7 8 9 10

CPI-96

WD

R-9

7(q1

2n)

0 1 2 3

Note: WDR(q12n) refers to the scores based on responses to Question 12n from the WDR survey. Source: Compiled using data obtained from Transparency International (1996) and the World

Bank (1997).

In Figure 5.3, the two WDR indices are plotted against each other. The correlation

coefficient between the two WDR indices was 0.85. Interestingly, most of the countries

appear above the dashed line in the graph, which represents a low score for Question 12n,

and a high score for Question 14.25 This translates to a high level of corruption, but a low

level of bribery. This indicates that, at least in the minds of the respondents, there is a

substantial difference between bribery and corruption. The finding is also at odds with the

general definition of corruption in the literature, which, as shown in Chapter 2, implicitly

includes bribery. This inconsistency renders the WDR somewhat less appealing as an

accurate measure of corruption. However, it provides support for exploring alternative

dimensions of corruption, such as rent-seeking, in Chapter 8.

25 The line of best fit for Figure 4.3 has an intercept of 4.67, which is an indication of the skewness of the data.

Figure 5.2: CPI v WDR(q14)

0

1

2

3

4

5

6

7

8

9

10

0 1 2 3 4 5 6 7 8 9 10

CPI-96

WD

R-9

7(q1

4)

Note: WDR(q14) refers to the scores based on responses to Question 14 from the WDR survey. Source: Compiled using data obtained from Transparency International (1996) and the World Bank

(1997).

The sample size between the CPI and the WCY only is 44 countries compared to the 28

between the CPI and WDR (with some overlapping countries). The latter is much smaller

because the WDR covers a very sparse range of countries – the only two Asian countries in

its sample are India and Malaysia. Figure 5.4 plots the CPI against the WCY for 1996. This

time the correlation coefficient is 0.97, implying a stronger relationship.

Figure 5.5 plots the relationship between the WCY and KKZ indices in 1996. The

correlation coefficient is 0.98, based on a sample of 46 countries, indicating an extremely

high level of consistency in their assessment of corruption levels. Given this strong

relationship, and the consistency in the scale, the two indices were averaged to provide a

single index for use in a cross-sectional model analysing the effect of corruption on growth.

Based on the average of the two indices, most of the East Asian members in the sample

have scores lower than the mean of 5.53. In fact, the average for East Asia is 4.42. When

Singapore is excluded (as an outlier with an average score of 9.14), the average falls to 3.89

indicating a high level of corruption.

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Figure 5.3: WDR(q12n) v WDR(q14)

0

1

2

3

4

5

6

7

8

9

10

0 1 2 3 4 5 6 7 8 9 10

WDR-97(q12n)

WD

R-9

7(q1

4)

Note: WDR(q12n) and WDR(q14) refer to the scores based on responses to Question 12n and

Question 14 from the WDR survey respectively. Source: Compiled using data obtained from the World Bank (1997).

Figure 5.4: CPI v WCY

0

1

2

3

4

5

6

7

8

9

10

0 1 2 3 4 5 6 7 8 9 10

CPI-96

WC

Y-9

6

Source: Compiled using data obtained from Transparency International (1996) and the

International Institute for Management Development (1996).

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Figure 5.5: KKZ v WCY

0

1

2

3

4

5

6

7

8

9

10

0 1 2 3 4 5 6 7 8 9 10

KKZ-96

WCY

-96

Source: Compiled using data obtained from the World Bank (2004) and the International Institute

for Management Development (1996).

5.4 Conclusion

This chapter has identified the major sources of corruption data used in the extant literature.

It has been established that the CPI and the ICRG index have been the most popular ones

(Table 5.2). Table 5.3 summarises the major indices.

Although the CPI is clearly superior to the ICRG index, the latter is popular because it was

introduced as early as 1980 while the CPI was only introduced in 1995. The BI index and

the WDR index all attempted to provide similar measures of corruption, however they were

only published occasionally and are now no longer in existence. The WDR index also

harboured an apparent inconsistency in its measurement of corruption. The GCR continues

to be published, however gaining access to issues prior to 2000 has not been successful as

they are no longer in print. The timeframe of this study’s empirical investigation for both

cross-sectional (1996) and panel data analysis (1984-2003) renders the GCR less worthy of

inclusion, however it should be noted that the GCR is one of the sources in the CPI so it

will still make a contribution. The next chapter will provide a cross-sectional analysis of the

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effect of corruption on economic growth based on the relevant data for the year 1996 (the

year immediately preceding the Asian Financial Crisis).

Table 5.3: Summary of Major Corruption Indices

Index Features Corruption Perceptions Index

- 1995 – present - composite index - based on surveys of corporate sector

International Country Risk Guide

- 1984 – present - based on surveys - strongly linked to government stability and

accountability

Business International

- 1980 – 1983 - no longer in publication - based on nine indicators, one of which relates to

corruption and bribery

World Development Report

- 1997 only - based on surveys, one section of which related to

level of law enforcement and bureaucratic red tape

- includes credibility index to measure reliability of survey responses

Global Competitiveness Report

- 1979 – present - based on surveys with specific questions relating

to extent of bribery - editions prior to 2000 no longer in print - subsumed into the CPI

World Competitiveness Yearbook - 1989 – present - based on surveys with sections relating to

government efficiency and insitutional framework

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CHAPTER 6

CORRUPTION AND GROWTH: A CROSS-COUNTRY STUDY

“Anti-corruption and building a clean government are an important

strategic mission…We cannot slack off for one moment…If a ruling party

cannot maintain flesh-and-blood ties with the mass people, if it loses the

people’s support, it will lose its vitality.”

- Chinese President Hu Jintao26.

6.1 Introduction

As explained in Chapter 3, there exists a substantial amount of literature that seeks to

investigate the connection between corruption and economic growth. The greatest

limitation to these studies is the way in which they measured corruption. In Chapter 5 the

most popular measures were reviewed and some alternative methods were proposed. The

purpose of this chapter is to explore the effect of corruption on economic growth in East

Asia before the 1997 Asian Financial Crisis. By the definition of corruption established in

Chapter 2, how corrupt are the East Asian countries? How do they measure up against the

rest of the world? Did corruption contribute to the spectacular economic growth that was

achieved by those countries prior to 1997? This chapter seeks to shed some light on these

questions.

The chapter begins by presenting the methodology for a cross-sectional model to analyse

the effect of corruption on growth. The derivation of this model will be based on previous

studies of corruption and growth. This will be followed by a summary of the data required

and its means of collection, including the corruption variable. Finally, the results of the

application of the model to the data will be presented and discussed accordingly.

26 This is an excerpt from a speech by the Chinese President to the Chinese Communist Party on its 85th anniversary (Cody, 2006, p.A14).

105

6.2 Developing A Corruption-Growth Model

After settling upon a means of measuring corruption, the corruption variable together with a

number of control variables was included in a model with the rate of economic growth as

the dependent variable. This section describes examples of the control variables used in the

extant literature. The examples are pertinent as they form the framework upon which the

model in the present study was constructed.

Mauro (1997) performed a regression between the 1960-85 average rate of annual growth

in GDP per capita and the corruption level in over 100 countries. Population growth, the

level of secondary education and an initial level of GDP were used as control variables and

drawn from the Barro (1991) dataset. This data was sourced from the IMF’s Government

Finance Statistics (GFS) and the United Nations Educational Scientific and Cultural

Organization (UNESCO). The data for industrial countries was added to the Devarajan,

Swaroop and Zou (1993) dataset of developing countries to increase the sample to 95

countries sourced from the GFS in 1985. Mauro also employed three instrumental variables

in his study to address potential endogeneity bias.27

Ali and Isse (2003) employed a regression model similar to Mauro (1997). They also

included a number of other variables in their study. An economic freedom index was taken

from Gwartney and Lawson (1997) for more than 100 countries, measuring “the extent to

which economic agents are free to use the market mechanism for the allocation of resources

and the extent to which property rights are protected” (Ali and Isse, 2003, p.462). Ethnicity

was used because “the domination of one ethnic group in the political arena of an economy

creates differential access to power” (p.463). Corruption was thus used to level the political

and economic playing field. Further, Ali and Isse argued that corrupt bureaucrats would

favour their family first, followed by their ethnic group, and finally their country. This

suggested that ethnically fragmented societies were more likely to be corrupt. Mauro

(1997) incorporated a similar variable in his study, so it was no surprise that Ali and Isse

took the index from an earlier study by Mauro in 1995, as well as from Easterly and Levine

27 These variables were related to ethnolinguistic fractionalisation, which measures the probability that two randomly selected persons from a given country will not belong to the same ethnolinguistic group (Ali and Isse, 2003).

106

(1997). A political freedom index taken from Freedom House was also used because Ali

and Isse suggest that the more free a country’s media is, the more transparent its

government will be forced to become. A rule of law index (from Political Risk Services) for

the 1982-1990 period was incorporated into the study, measuring soundness of political

institutions, strength of judicial systems, and orderly successions of power. Strong rankings

on these criteria are deemed by Ali and Isse (2003) to make a country less susceptible to

corruption. The secondary school enrolment rate in 1975 was also used, though Ali and

Isse’s contention here is that a higher level of education will make public servants more

aware of what behaviour is deemed to be corrupt, and will discourage them from engaging

in this behaviour out of loyalty to the nation (stemming from increased national pride). In

other words, secondary education is seen as an explanator of corruption rather than

economic growth. The centralisation of a country’s government power was also used, but

as a binary variable instead of an index, because centralisation can lead to higher corruption

through increased collusion amongst bureaucrats, but so too can decentralisation through

increased opportunities provided at each level of government. Finally, government

expenditure as a share of GDP was included on the basis that “the larger the size and scope

of the public sector, the greater the likelihood of corrupt behaviour” (p.463). The present

study differs from Ali and Isse (2003) in several ways. The corruption measure employed

in the present study is wider as it is based on five corruption indices, compared to two

indices used in Ali and Isse (2003). The time period analysed in the present study is also

longer than that of Ali and Isse (2003), and whilst the latter found corruption to be

negatively associated with economic growth, the present study reveals some evidence to the

contrary as will be seen later.

Li et al. (2000) used a slightly different model, focusing on growth in different time

periods: 1980-1984; 1985-1989; and 1990-1992. Lagged values for schooling and financial

development were used to account for the endogeneity problem. They used five-year

averages for all the variables in the model. The control variables used were initial level of

GDP, primary years of schooling, financial development (money supply M2 over GDP),

openness (imports over GDP), terms-of-trade shocks (difference of changes in export price

and changes in import price), black market premium, government spending (as a fraction of

GDP), average arable land, the urbanisation ratio, the population growth rate and the initial

distribution of asset as measured by the initial land Gini coefficient. Most of the data was

107

taken from World Bank national accounts and from Summers and Heston (1994). The black

market premium data was taken from Barro and Lee (1994). Primary years of schooling

data was extracted from Nehru et al. (1995).

A slightly more complex model was employed by Rahman et al. (2000) to investigate the

impact of corruption on economic growth. Specifically, the authors augmented Mauro’s

model by including statistically significant regional dummies, using the ICRG corruption

index for 1991-1997 as it is more recent than the BI index, and condensed the length of the

sample period to 1990-1997 instead of Mauro’s 26 year period from 1960-1985. Further,

the authors “demonstrate the sensitivity of the effect of corruption in the presence of

various other policy, geographic and demographic variables that have been widely used in

empirical growth literature” (p.7). The corruption data was provided on a monthly basis for

130 countries, and Rahman et al. (2000) averaged this to obtain annual values for the 1991-

1997 period. Data for schooling, area and distance to the capitals of the world’s 20 major

exporters (weighted by values of bilateral imports) was taken from Barro and Lee (1994),

while the remaining data was extracted from the World Bank’s SIMA database. Control

variables included initial quantity of human capital which was proxied by the gross

secondary school enrolment rate in 1985. Initial quality of human capital was also used, and

measured by the pupil/teacher ratio in secondary school in 1980. Initial GDP was taken as

the log of GDP per capita in 1985.

An interesting variation of the standard corruption-growth model appears in Mendez and

Sepulveda (2001). The conditioning set of variables include measures of initial human

capital and GDP, investment and government shares of GDP, population growth rate,

political instability and regional dummies. However Mendez and Sepulveda included a

quadratic corruption term in the equation, allowing the model to capture the growth

maximising level of corruption which is ignored in a linear model.

Given the above sample of regression models, the framework for the model to be used in

the present study can now be established. As shown above, the empirical literature

investigating the link between corruption and economic growth relies upon regression

analysis, and this study will follow suit. Typically, regression involves economic growth

(measured by annual percentage change in real GDP) as the dependent variable. Corruption

108

is introduced as an independent variable, along with other control variables. The regression

model for this chapter, based on cross-sectional data in 1996, is therefore as follows:

k

уi = α + βCi + ∑ λjZij + μi (6.1)

j=1

where у = rate of economic growth

C = corruption index

Z = set of control variables

μ = error term

α, β and λ are unknown parameters to be estimated

6.3 Data Issues

Economic Growth Indicator

The measure of growth in all studies is based upon GDP, typically expressed as an annual

percentage change. GDP growth is used in Mendez and Sepulveda (2001), Li et al. (2000),

Ali and Isse (2003), Svendsen (2003), and Mauro (1995), to name a few. Rahman et al.

(2000) used GNP instead of GDP because the focus was on the influence of foreign direct

investment on growth, so the growth variable needed to account for foreign production. In

most cases, real GDP per capita is used to factor in the growth in population (Mauro, 1995;

Ali and Isse, 2003; Svendsen, 2003; Tanzi and Davoodi, 1997), however this study will

simply include population growth as an independent variable as it allows the study to

identify and isolate the individual effect of population growth on economic growth. GDP

data in the literature was sourced mostly from the IMF, World Bank, or Heston et al.

(various years). Economic growth in this study will be represented by the variable

‘GROWTH’, and the relevant data was extracted from the World Bank’s World

Development Indicators (2005) database.

Corruption Variable

The corruption variable differs somewhat considerably between studies. Mauro’s (1997)

corruption index was a simple average of the ICRG and BI corruption indices when both

were available, and the ICRG alone for all other times. The ICRG data was the average

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from 1982-1995 for 100 countries, while the BI data consisted of the 1980-1983 average

for 67 countries.

Ali and Isse (2003) analysed corruption in two different time periods: the 1980s and the

1990s. Corruption in the 1980s was based on information collected from Political Risk

Services, from 1982-1990. Li et al. (2000) used the same source in their study, but over the

time period 1982-1994. Corruption in the 1990s was based on Transparency International’s

CPI from 1995-1999. Ali and Isse reversed the rankings of both indices (such that a lower

value means lower corruption) and then compressed the scale to a range of 0 to 1.

Mendez and Sepulveda (2001) used the ICRG corruption index from Political Risk

Services. Since this index is not available before 1982, the authors used the average of the

index for 1982-1992 as an approximation for the corruption level present during the entire

period. Further, they assumed that the same levels of corruption held for 1960-1984. This

was based on a 0.86 correlation coefficient for the Transparency International corruption

index from 1980-1996.

In the current study, the World Bank’s KKZ index will initially be used as the measure of

corruption in the cross-sectional regression model. Alternate indices will then be used as

part of a sensitivity analysis. In all regression results however, the corruption variable will

be represented as ‘CORRUPT’.

Control Variables

One of the most popular control variables used as a determinant of economic growth is

education, and this is typically represented by levels of secondary enrolment (Mauro, 1997;

Ali and Isse, 2003). However, some studies have used primary years of schooling (Li et al.,

2000), and others have used both primary and secondary school enrolment (Mendez and

Sepulveda, 2001). One would argue that education improves the quality of a country’s

human capital, which will then feed into productivity and ultimately yield growth in an

economy. In the following analysis, secondary education (measured as the number of

students enrolled in secondary education as a percent of official population in

corresponding age group) will be represented by the variable ‘SECENROL’. Population

growth is another standard control variable, used by nearly every study either as an

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independent variable or subsumed into a per capita GDP measure of economic growth. As

population increases, one would expect an increase in consumption and the size of the

labour force, both of which would increase demand and output in an economy. Population

growth (measured as the annual percentage change in population) in this study will be

referred to as ‘POPRATE’ in the discussion of results. Gross secondary enrolment rates and

population growth rates were both extracted from the World Bank’s World Development

Indicators database (2005).

Furthermore, the gross fixed capital formation of countries will also be included as another

control variable. According to the World Bank (2005), gross fixed capital formation

consists of outlays on additions to the fixed assets of the economy, which include land

improvements (fences, ditches, drains); plant, machinery, and equipment purchases; and the

construction of roads, railways, schools, offices, hospitals, private residential dwellings,

and commercial and industrial buildings. One would therefore expect the growth rate of

gross fixed capital formation (represented by the variable ‘GFCFRATE’) to be related to

economic growth.

In addition, the following control variables will also be used in the current study:

• LNGDP85 is the natural logarithm of GDP in 1985 used to represent initial income,

which is expected to have a negative relationship with growth as the higher the

initial level of GDP, the less scope there is for ‘catch-up’ or improvement upon this

level.

• EXPRATE represents the annual growth rate of exports of goods and services. This

includes the value of all goods and other market services provided to the world such

as merchandise, freight, insurance, travel, and other nonfactor services. Factor and

property income such as investment income, interest, and labor income, is excluded.

It is expected that if the growth rate in exports rises, this should lead to an increase

in economic growth due to increased production.

• EXPGDP expresses the annual value of exports (as defined above) as a percentage

of GDP. An increase in this variable would also tend to raise economic growth due

to increased production.

111

• MKTCAP represents the total market capitalisation of listed domestic companies as

a proportion of GDP. Market capitalisation is calculated as the share price times the

number of shares outstanding. Listed domestic companies are the number of

domestically incorporated companies listed on the country’s stock exchanges at the

end of the year. One may expect that if firms increase in value and the share price

rises, then the value of their business is rising and this contributes to increased GDP

and thus economic growth.

• REALINT is the real interest rate prevailing in a country, measured as the deposit

interest rate less the rate of inflation measured by the GDP deflator. If interest rates

rise, this would raise the cost of borrowing and would tend to impede production by

firms, so GDP would possibly fall as rates continue to rise.

• GVEXGDP represents annual general government final consumption expenditure as

a proportion of GDP. This includes all current expenditures for purchases of goods

and services by all levels of government, excluding most government enterprises. It

also includes capital expenditure on national defence and security. Increased

government spending is likely to increase GDP through the multiplier effect.

• HITECHEX: expresses the proportion of manufactured exports that are ‘high-

technology exports’. These are deemed to be goods produced by industries (based

on U.S. industries) that rank in the top 10 according to research and development

expenditures. Manufactured exports are the commodities such as chemicals and

related products, basic manufactures, manufactured articles, and machinery and

transport equipment. As exports become more hi-tech, one might expect production

capabilities to expand and grow, and thus the value of GDP will rise to reflect the

greater value of exports28.

In the regression model, the question of endogeneity needs to be addressed. Ali and Isse

(2003) and Mauro (1997) employed instrumental variables to tackle this problem. Li et al.

(2000) used lagged values of certain variables such as the secondary enrolment variable.

This study will follow the Li et al. (2000) procedure of using one-year lags.

28 Some control variables have been omitted from the present study’s analysis due to lack of reliable data.

112

As the focus of this study is the connection between growth and corruption in pre-1997

East Asia, 1996 data was collected and applied to the regression model outlined in the

previous section. To begin with, the World Bank’s KKZ index was used as a measure of

corruption. This covered a total of 151 countries and the corruption scores were

standardised with a mean of zero and a standard deviation of 1. In this study, 5 was added

to each score to raise the mean to 5 whilst retaining the standard deviation. This removed

the issue of negative scores. Figure 6.1 illustrates the general relationship between

corruption and economic growth in 1996. It is clear from Figure 6.1 that Tajikistan and

Suriname are outliers in the sample, so they were excluded from the analysis. Taiwan was

also excluded due to limited data availability. Economic growth was not available for a

further seven countries, so this left a sample of 141 countries.29

Figure 6.1: Corruption (KKZ) and Economic Growth, 1996

-20

-15

-10

-5

0

5

10

15

20

25

3 4 5 6 7 8

Corruption

Eco

nom

ic G

row

th (%

)


Suriname

Tajikistan

29 Refer to Figure 1.2 for an illustration of the particular relationship between corruption and economic growth in East Asia.

113

6.4 Estimation Results

Table 6.1 provides some descriptive statistics for the variables in this analysis (corruption;

gross fixed capital formation growth rate; population growth rate; gross secondary

enrolment rate; exports as a proportion of GDP; exports growth rate; market capitalisation

as a proportion of GDP; real interest rate; government expenditure as a proportion of GDP;

and high-technology exports as a proportion of manufactured exports). Table 6.2 reveals

the correlation matrix for these variables. There appear to be no instances of

multicollinearity as no correlations are 0.7 or greater. Regression results are outlined in

Table 6.330.

Table 6.1: Descriptive Statistics

Variable Mean Std. Dev. Min Max No. of Obs

CORRUPT 4.13 3.63 -10.14 12.22 141

GFCFRATE 21.28 6.44 7.13 42.50 134

POPRATE 1.58 1.29 -2.39 6.83 148

SECENROL 65.34 34.17 5.25 148.25 146

EXPGDP 37.15 24.88 0.97 170.84 136

EXPRATE 8.32 12.50 -28.16 86.68 126

MKTCAP 39.44 55.22 0.07 304.58 92

REALINT 8.12 16.41 -94.28 71.17 111

GVEXGDP 15.12 6.05 4.52 33.01 134

HITECHEX 9.64 12.51 0.00 58.89 101

LNGDP85 23.27 2.81 17.15 30.63 169

Source: Author’s own calculation based on data obtained from the World Bank (2005).

30 It is conventional practice not to report p-values of F statistics. Instead, standard errors of coefficients are reported.

114

Variable 1 2 3 4 5 6 7 8 9 10 11 1. GROWTH 1.00 -0.09 0.17 0.13 0.13 0.20 -0.05 -0.29 -0.16 0.14 -0.17

2. EXPGDP 1.00 0.40 -0.07 0.52 0.03 -0.20 0.24 0.19 0.45 -0.14

3. GFCFRATE 1.00 -0.19 0.32 -0.10 0.01 0.19 0.07 0.38 0.08

4. EXPRATE 1.00 -0.01 0.12 -0.12 -0.16 -0.06 0.04 -0.16

5. MKTCAP 1.00 0.20 -0.22 0.22 -0.02 0.47 0.28

6. POPRATE 1.00 -0.18 -0.60 -0.31 -0.05 -0.37

7. REALINT 1.00 0.10 -0.05 -0.14 -0.11

8. SECENROL 1.00 0.53 0.37 0.64

9. GVEXGDP 1.00 0.13 0.20

10. HITECHEX 1.00 0.27

11. LNGDP85 1.00

115

Table 6.2: Correlation Matrix

Source: Author’s own calculation based on data obtained from the World Bank (2005).

116

Table 6.3: Results using KKZ Index

Variable 1 2 3 4 5

CONSTANT -4.765 (10.19) -5.223 (3.42) -5.723 (3.09) -3.451 (2.45) -1.447 (2.24)

CORRUPT 0.656 (0.77) 0.502 (0.71) 0.755 (0.66) 0.503 (0.52) 1.077 (0.50)*

GFCFRATE 0.244 (0.07)** 0.262 (0.07)** 0.263 (0.07)** 0.239 (0.06)** 0.163 (0.05)**

POPRATE 1.258 (0.62)* 0.846 (0.51) 0.818 (0.50) 0.514 (0.40) 0.156 (0.40)

SECENROL -0.011 (0.03) -0.032 (0.03) -0.044 (0.02) -0.037 (0.02) -0.045 (0.02)*

EXPGDP -0.021 (0.03) -0.022 (0.02) -0.020 (0.02) -0.023 (0.02) -0.032 (0.02)*

EXPRATE 0.046 (0.06) 0.067 (0.05) 0.061 (0.05) 0.073 (0.04)* 0.033 (0.03)

MKTCAP -0.005 (0.01) -0.004 (0.01) -0.004 (0.01)

REALINT 0.034 (0.04) -0.011 (0.03)

GVEXGDP 0.044 (0.10) 0.157 (0.09) 0.171 (0.08)* 0.128 (0.07) 0.020 (0.07)

HITECHEX 0.045 (0.04) 0.037 (0.04) 0.030 (0.04) 0.020 (0.03)

LNGDP85 -0.077 (0.37)

Adjusted R2 0.24 0.21 0.23 0.23 0.14

F-value 2.55 2.68 3.38 4.31 3.81

Sample size 56 64 72 91 122

Notes: Coefficients are presented with standard errors in parentheses. *Significant at 5% level. **Significant at 1% level.

Variable 6 7 8 9

CONSTANT -1.104 (2.15) -0.973 (2.11) 0.011 (1.95) 0.541 (1.93)

CORRUPT 1.080 (0.49)* 1.146 (0.45)* 0.961 (0.44)* 0.819 (0.43)

GFCFRATE 0.159 (0.05)** 0.161 (0.05)** 0.154 (0.05)** 0.126 (0.05)**

POPRATE 0.138 (0.40)

SECENROL -0.044 (0.02)* -0.049 (0.01)** -0.049 (0.01)** -0.049 (0.01)**

EXPGDP -0.032 (0.02)* -0.032 (0.02)* -0.021 (0.01)

EXPRATE 0.033 (0.02) 0.034 (0.02)

MKTCAP

REALINT

GVEXGDP

HITECHEX

LNGDP85

Adjusted R2 0.15 0.15 0.14 0.14

F-value 4.49 5.41 6.44 7.92

Sample size 123 123 131 133

Notes: Coefficients are presented with standard errors in parentheses.

Table 6.3: Results using KKZ Index (cont’d)

117

*Significant at 5% level. **Significant at 1% level.

In Table 6.3, all variables were included in Model 1 and the least significant control

variable (excluding corruption) was removed in a stepwise technique until only significant

variables remained. This occurred in Model 9, which identified four control variables to be

significant in the presence of corruption. The corruption variable itself was not significant

at the 5% level. Its coefficient was positive, and given that a low value in the KKZ index

reflects high corruption, this indicated a negative (though statistically insignificant)

relationship with economic growth. Note that the sample size increases each time a variable

is omitted. This is due to the gaps that exist in the data. Based on the preceding analysis, it

appears that when using the KKZ index as a measure of corruption there appears to be no

significant relationship between corruption and economic growth. But could this be due to

the KKZ index itself? In other words, would other corruption indices have yielded different

results? Table 6.4 below shows the results of the corruption-growth model based on

alternate measures of corruption, i.e. the ICRG, CPI and WCY indices. Only the final

models are reported after following the stepwise elimination technique. For the complete

tables, refer to the appendix to this chapter.

Table 6.4 shows that when using the WCY index and the CPI, there does not appear to be a

significant relationship between corruption and economic growth. However, when using the

ICRG index the corruption coefficient is negative and significant at the 5% level. The

reverse scale of the ICRG index implies that corruption has a positive effect on economic

growth. Specifically, for every unit decrease in the ICRG index (reflecting increased

corruption) economic growth rises by 0.565 percentage points. However the R2 is

extremely low at 3%. Having used single indices, the next step was to combine pairs of

indices, building on the analysis in Section 5.4 of Chapter 5. In creating the new composite

indices, each index was standardised and 5 was added to each score. This yielded indices

with a standard deviation of 1 and a mean of 5. The simple average of each pair of indices

then constituted a new composite index. For some of these pairings, there were not many

common countries so only the largest samples were used. Table 6.5 shows the results using

each of three composite indices. Once again, only results from the final models are reported

and full tables are in the appendix to this chapter.

118

Table 6.4: Sensitivity Analysis using Alternate Corruption Indices

Variable ICRG CPI WCY

CONSTANT 6.461 (0.96) 0.602 (1.27) -1.852 (1.39)

CORRUPT -0.565 (0.26)* -0.069 (0.12) 0.027 (0.11)

GFCFRATE 0.168 (0.04)** 0.200 (0.05)**

POPRATE 0.967 (0.31)** 0.952 (0.36)*

SECENROL

EXPGDP

EXPRATE

MKTCAP

REALINT

GVEXGDP

HITECHEX

LNGDP85

Adjusted R2 0.03 0.34 0.41

F-value 4.63 9.93 11.18

Sample size 121 53 45


As with Table 6.4, the results in Table 6.5 are mixed. Only the model using the KKZ-ICRG

index reveals that corruption is significant at the 5% level. Consistent with the results in

Table 6.4, the coefficient is negative, implying that an increase in the index value

(reflecting decreased corruption) leads to an increase in economic growth. The R2 is

extremely low at 4%, which is also consistent with Table 6.4. So far there appears to be

some evidence to suggest that corruption is positively related to economic growth. At the

same time however, the explanatory power of the model is too low to lend any credibility to

this evidence.

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Table 6.5: Sensitivity Analysis using Composite Indices

Variable KKZ-ICRG KKZ-WDR ICRG-WDR

CONSTANT 6.165 (2.01) 20.058 (6.61) -2.192 (4.26)

CORRUPT -0.767 (0.36)* 0.783 (0.84) 0.679 (0.71)

GFCFRATE 0.120 (0.07)*

POPRATE 1.722 (0.59)**

SECENROL

EXPGDP -0.078 (0.03)*

EXPRATE

MKTCAP

REALINT

GVEXGDP

HITECHEX

LNGDP85 -0.729 (0.34)*

Adjusted R2 0.04 0.11 0.13

F-value 3.65 2.90 4.52

Sample size 116 48 49


6.5 Endogeneity

To control for endogeneity issues, a series of lagged control variables were introduced into

all of the models above. These are as follows:

• GFCFRATE1 is the one-year lag of the annual rate of growth in gross fixed capital

formation, and is included because economic growth could potentially provide for

an increase in the investment in fixed capital in the following year.

• POPRATE1 represents the one-year lag of the annual growth rate in the population

growth rate. It is sometimes argued that economic growth may lead to an increase in

the population growth rate through the provision of better services (e.g. health,

education etc).

120

• SECENROL1 is the one-year lag of the gross secondary enrolment rate. As

mentioned earlier, the provision of better education services resulting from growth

could potentially lead to an increase in secondary enrolment.

• EXPRATE1 and EXPGDP1 correspond to the one-year lagged growth rate of

exports and the one-year lagged exports as a proportion of GDP respectively. It

could be argued that economic growth could lead to an increased production of

goods for foreign consumption, a capability financed by the increased revenue as a

result of higher economic growth.

• MKTCAP1 represents the one-year lag of total market capitalisation of listed

domestic companies as a proportion of GDP. One may expect that if firms increase

in value and the share price rises, then the value of their business is rising and this

contributes to increased GDP and thus economic growth. However, the economic

growth could then raise business confidence and spur more corporations to invest in

more projects, thus raising the share price and the value of market capitalisation.

• REALINT1 is the one-year lag of the real interest rate prevailing in a country. If

interest rates rise, this would raise the cost of borrowing and would tend to impede

the production by firms, so GDP would possibly fall as rates continue to rise.

However, falling growth rates could in turn lead to a fall in interest rates in an

attempt to lower borrowing costs and stimulate firms to borrow and invest, and

consumers to borrow and spend.

• GVEXGDP1 represents the one-year lag of annual general government final

consumption expenditure as a proportion of GDP. Increased government spending

is likely to increase GDP through the multiplier effect, but the increased GDP may

in turn increase the total revenue for the government and expand its budget,

allowing it to increase its expenditure.

• HITECHEX1 expresses the one year-lag of the proportion of manufactured exports

that are ‘high-technology exports’. As exports become more technologically

advanced, one might expect production capabilities to expand and grow, and thus

the value of GDP will rise to reflect the greater value of exports. As GDP grows,

firms and the government become richer and are able to invest more into research

and development, which could in turn lead to more high-technology exports.

121

A notable omission in the group of lagged variables is initial income (LNGDP85). The

reason for this is that the growth rate in later years cannot have any impact on income (or

anything) in earlier years. Table 6.6 shows the use of lagged variables in the presence of

corruption using the KKZ index as a measure of corruption. In the final model, only two of

the lagged variables are significant, namely the gross fixed capital formation rate and the

secondary enrolment rate. Interestingly, these are the one-year lags of the same two

variables that were significant in the final model of the analysis using the original control

variables (see Table 6.3 above). Their coefficients have the same sign indicating the same

direction of effect, except now the issue of endogeneity has been addressed.31 The R2 is

also higher at 20% compared to 14% in the previous model (though still rather low).

However corruption is still not found to be significant, as was the case in Table 6.3 earlier.

Table 6.7 shows a sensitivity analysis of these results in the presence of the alternate

corruption indices used earlier (both single and composite).

Table 6.7 reveals that the corruption variable is found to be significant in five of the

models, but the results are mixed. When using the ICRG and CPI Indices on their own, the

corruption coefficient is negative which implies a positive effect on economic growth. Both

coefficients are also statistically significant; at the 5% level when using the ICRG index,

and at the 1% level when using the CPI index. This represents a unique finding that

challenges the results found in many of the previous studies, and will be discussed further

in Section 6.6. However, when using the WCY and ICRG-WDR Indices the coefficient is

both positive and significant at the 5% level.

31 Granger causality tests would be required to establish causality, however such tests rely on the availability of longer time series data. As a second best alternative, one year lags are introduced to tackle the issue of endogeneity.

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Variable 1 2 3 4 5

CONSTANT 13.914 (8.48) 12.872 (7.35) 12.089 (6.06) 4.296 (5.64) 4.495 (5.52)

CORRUPT 0.125 (0.74) 0.444 (0.70) 0.427 (0.69) 0.054 (0.63) 0.074 (0.61)

GFCFRATE1 0.055 (0.04) 0.065 (0.03) 0.065 (0.03) 0.089 (0.03)** 0.090 (0.03)**

POPRATE1 -0.389 (0.61) -0.474 (0.58) -0.463 (0.57) -0.276 (0.48) -0.281 (0.47)

SECENROL1 -0.030 (0.03) -0.049 (0.03)* -0.049 (0.02)* -0.043 (0.02) -0.044 (0.02)

EXPGDP1 -0.006 (0.02) -0.004 (0.02)

EXPRATE1 0.071 (0.04) 0.062 (0.04) 0.062 (0.04) 0.008 (0.04)

MKTCAP1 0.003 (0.01) 0.002 (0.01) 0.001 (0.01)

REALINT1 0.009 (0.05)

GVEXGDP1 0.033 (0.09) 0.068 (0.09) 0.068 (0.08) 0.032 (0.08) 0.027 (0.08)

HITECHEX1 0.082 (0.04) 0.068 (0.04) 0.066 (0.04) 0.057 (0.04) 0.063 (0.04)

LNGDP85 -0.408 (0.31) -0.372 (0.28) -0.341 (0.23) 0.060 (0.22) 0.053 (0.22)

Adjusted R2 0.12 0.15 0.17 0.17 0.20

F-value 1.67 2.13 2.41 3.07 3.80

Sample size 55 63 63 80 81

Table 6.6: Controlling for Endogeneity using KKZ Index


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Variable 6 7 8 9

CONSTANT 5.279 (2.16) 5.302 (2.11) 4.786 (1.97) 1.085 (1.88)

CORRUPT 0.206 (0.51) 0.208 (0.50) 0.118 (0.49) 1.219 (0.45)

GFCFRATE1 0.082 (0.02)** 0.082 (0.02)** 0.080 (0.02)** 0.373 (0.01)**

POPRATE1 -0.283 (0.41) -0.283 (0.41)

SECENROL1 -0.041 (0.02)* -0.041 (0.02)* -0.031 (0.01)* -0.052 (0.01)**

EXPGDP1

EXPRATE1

MKTCAP1

REALINT1

GVEXGDP1 0.004 (0.07)

HITECHEX1 0.061 (0.03) 0.060 (0.03) 0.054 (0.03)

LNGDP85

Adjusted R2 0.22 0.23 0.24 0.20

F-value 5.28 6.41 7.94 10.55

Sample size 90 90 90 116

Table 6.6: Controlling for Endogeneity using KKZ Index (cont’d)


124


Variable ICRG CPI WCY KKZ-ICRG KKZ-WDR ICRG-WDR

CONSTANT 6.145 (1.01) 3.489 (0.71) 3.790 (1.28) 7.650 (1.88) 0.974 (3.18) -1.563 (3.89)

CORRUPT -0.575 (0.27)* -0.357 (0.11)** 0.371 (0.17)* -0.692 (0.36) 1.402 (0.75) 1.949 (0.93)*

GFCFRATE1 0.068 (0.03)* 0.100 (0.02)** 0.110 (0.03)** 0.070 (0.03)*

POPRATE1

SECENROL1 -0.047 (0.02)** -0.067 (0.02)** -0.073 (0.02)**

EXPGDP1

EXPRATE1 0.094 (0.03)** 0.166 (0.04)**

MKTCAP1

REALINT1

GVEXGDP1

HITECHEX1 0.067 (0.03)*

LNGDP85

Adjusted R2 0.07 0.51 0.47 0.06 0.15 0.16

F-value 4.94 13.64 10.46 4.44 6.29 5.68

Sample size 106 50 44 106 63 49

Table 6.7: Sensitivity Analysis of Endogeneity (Alternate Indices)


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6.6 Corruption and Growth in East Asia

The preceding section has provided mixed results concerning the relationship between

corruption and economic growth using different measures of corruption. As this study

focuses on East Asian economies, some dummy variables are introduced to control for the

specific corruption-growth relationship in those economies. The rationale for this particular

analysis lies in the peculiar levels of corruption and growth that were seen in East Asia

prior to the 1997 Asian Financial Crisis, relative to the rest of the world. This can be seen in

Table 6.8 which does not include Taiwan due to data unavailability.

Table 6.8: Corruption and Economic Growth in East Asia, 1996

Region Growtha KKZb ICRGb CPIb WCYb

East Asia 6.9 0.48c 3.9 5.0 4.5 Rest of the world 4.3 -0.03 3.3 5.4 5.7 East Asia excl. Singapore 6.8 0.21 3.8 4.3 3.7 Rest of the world incl. Singapore 4.3 -0.01 3.3 5.5 5.8 Sample size for all regions 183 150 128 53 45

Notes: aAverage percentage growth rates. bFigures shown are averages. cUnlike the other indices, the KKZ index is not on a scale of 0 to 10, rather it is measured in standard deviation from the average so that 0.48 indicates the corruption level was 0.48 standard deviations above the mean corruption level.

Source: Compiled using data obtained from the World Bank (2002 and 2005), Political Risk Services Inc (1996), Transparency International (1996) and the International Institute for Management Development (1996).

In terms of economic growth, East Asia was far ahead of the rest of the world in 1996. Its

corresponding corruption level was also higher (given that a lower corruption index score

reflects a higher level of corruption) under the CPI and WCY indices. The distinction is not

as apparent under the KKZ and ICRG indices because these indices comprised much larger

samples of countries, which included many African, East European and South American

countries, all of whom suffer from considerable corruption. Table 6.8 also shows that when

Singapore is excluded from the East Asian sub-group, the difference in economic growth

between East Asia and the rest of the world contracts slightly whilst the corresponding gap

126

between corruption levels increases markedly. Though under the KKZ and ICRG indices

East Asia is still considered to be less corrupt than the rest of the world (due to the larger

sample), the gap narrows when Singapore is left out of East Asia. This reflects the

peculiarity of Singapore as an East Asian country that experienced ‘miraculous’ economic

growth prior to 1997 but was not plagued by corruption. Singapore consistently appears in

the top ten countries of all the corruption indices.

To test for the particular corruption-growth relationship in East Asia, a dummy variable

was introduced into the regression models. The dummy variable, Da, takes on a value of 1

for East Asian countries, and zero for all other countries. This is then multiplied by the

corruption score to investigate the interaction with corruption (Da*C). Both these dummy

variables are included in all the models reported (i.e., for each different measure of

corruption and in the presence of lagged variables) and the results are shown in Tables 6.9

and 6.10. Table 6.9 shows the inclusion of the dummy variable into the models without

controlling for endogeneity, while Table 6.10 reports the results after controlling for

endogeneity. Models involving the WDR index were excluded because the WDR sample

only contained one or two East Asian countries. Singapore was not given a value of 1 under

the dummy despite being an East Asian country, as it consistently appears as an almost

corruption-free country in all the indices (i.e., an outlier). Unfortunately, the dummy

variable and its interaction with corruption were not significant in any of the models. This

could be attributable to the fact that there were only seven East Asian countries in the

sample. This is an extremely small number and may not be statistically sufficient to yield a

significant result. To overcome this problem, in the next chapter panel data will be used.

127

Variable KKZ ICRG CPI WCY KKZ-ICRG

CONSTANT 0.685 (2.00) 6.473 (0.97) -0.654 (1.50) -2.180 (1.55) 1.891 (2.20)

Da 7.896 (11.05) 1.380 (6.45) 7.350 (7.37) 2.194 (2.20) 4.626 (10.09)

Da*C -1.234 (2.10) 0.323 (1.64) -1.321 (1.40) -0.591 (0.49) -0.693 (1.91)

CORRUPT 0.869 (0.44)* -0.606 (0.27)* -0.029 (0.13) 0.063 (0.12) 0.396 (0.49)

GFCFRATE 0.105 (0.05)* 0.155 (0.06)* 0.204 (0.06)** 0.134 (0.05)*

POPRATE 1.017 (0.32)** 1.007 (0.37)*

SECENROL -0.049 (0.01)** -0.039 (0.01)**

EXPGDP

EXPRATE

MKTCAP

REALINT

GVEXGDP

HITECHEX

LNGDP85

Adjusted R2 0.13 0.04 0.33 0.40 0.13

F-value 4.97 2.59 6.05 6.91 4.34

Sample size 132 121 53 45 114

Table 6.9: Including an East Asian Dummy Variable


128


Variable KKZ ICRG CPI WCY KKZ-ICRG

CONSTANT 0.771 (1.89) 6.473 (0.97) 2.007 (0.97) 1.743 (0.90) 6.549 (2.09)

Da 9.740 (10.64) 1.380 (6.45) 8.191 (7.03) 3.489 (2.19) 0.842 (11.56)

Da*C -1.365 (2.02) 0.323 (1.64) -1.211 (1.34) -0.548 (0.51) 0.122 (2.18)

CORRUPT 1.268 (0.45)** -0.606 (0.27)* -0.017 (0.13) 0.009 (0.13) -0.758 (0.37)

GFCFRATE1 0.037 (0.01)** 0.106 (0.03)** 0.106 (0.04)** 0.080 (0.06)

POPRATE1 0.909 (0.33)** 1.228 (0.42)**

SECENROL1 -0.053 (0.01)**

EXPGDP1

EXPRATE1

MKTCAP1

REALINT1

GVEXGDP1

HITECHEX1

LNGDP85

Adjusted R2 0.22 0.04 0.39 0.37 0.03

F-value 7.31 2.59 7.75 6.11 1.99

Sample size 116 121 53 45 116

Table 6.10: Including an East Asian Dummy Variable and Controlling for Endogeneity


129


6.7 Comparing Results with Existing Studies

The question now is how these results compare with findings from other empirical studies

analysing the relationship between corruption and economic growth. The studies presented

in Table 6.11 experimented with a variety of regression models, so in order to ensure

accurate comparisons with this study’s results, the statistics quoted are from the smallest

economic growth models (in terms of number of variables involved) that included

corruption, population growth and education as primary independent variables. Mauro

(1997) found a significant positive coefficient (i.e. a negative relationship) for his

corruption variable. Using a 2SLS regression on the same data, Mauro found that the

relationships are the same but the estimated coefficients are in fact larger, when the index

of ethnolinguistic fractionalisation is used as an instrumental variable to address possible

endogeneity bias. Multivariate regressions (both OLS and 2SLS) using other determinants

of economic growth produced similar results, and again the coefficients were larger under

the 2SLS method.

Ali and Isse (2003) found that corruption in the 1980s is strongly correlated with corruption

in the 1990s, which is interpreted as a sign that corruption was persistent over the years,

and the longer it persists the more endemic it becomes. Descriptive statistics for the

independent variables show that the mean and median of corruption in the 1990s were

higher than those for the 1980s. However, Ali and Isse pointed out that this could also be

explained by the fact that the data came from different organisations, and the survey

questions may have been quite different. Turning to the regression model, Ali and Isse

employed a few control variables, namely initial GDP, the population growth rate, and the

secondary school enrolment rate. When corruption in the 1980s is added to this model, the

coefficient is found to be significantly negative. It should be noted though that the R-

squared value for all but one of the regressions is not higher than 54%. Ali and Isse

conceded that corruption could be a function of economic growth, raising an endogeneity

problem. Running the regression using 2SLS with ethnolinguistic fractionalisation used as

an instrumental variable (similar to Mauro (1997)), Ali and Isse found that ethnolinguistic

fractionalisation is not correlated with economic growth, but has a significant negative

relationship with corruption. This suggests that higher corruption causes lower economic

growth.

130

Study Dependent Variable Corruption Population Growth Education R2

Mauro (1997) Growth in GDP per capita 0.004** -0.412* 0.040** 0.31

Mauro (1995) Growth in GDP per capita 0.003* -0.395* 0.031** 0.27

Rahman et al. (2000) Growth in GNP per capita 0.510 -0.310 2.380 0.43 (Adj.)

Li et al. (2000) Growth in real GDP per capita

(PPP adjusted) -0.439 -1.607* -0.361 0.33

Mendez and Sepulveda (2001) Growth in GDP per capita 0.003* -0.664* -0.000* 0.81

Poirson (1998) Growth in GDP per capita 0.828** -1.215** 0.017 0.72 (Adj.)

Ali and Isse (2003) Growth in real GDP per capita -0.500 -0.167 0.548 0.46

Notes: Li et al. (2000) and Ali and Isse (2003) reversed their corruption indices, such that a higher index value reflected a higher level of corruption.

Table 6.11: Regression Results from Existing Studies

131


Li et al. (2000) found that the corruption variable is negative but not significant. However,

when a dummy variable for Asian countries is introduced, Li et al. (2000, p.18) found that

corruption had “a far less deleterious effect on growth in Asia than elsewhere”, though that

effect was still negative. In particular, the authors state that “during 1980-94 Asia did not

pay the price paid elsewhere for corruption; in other words, corruption may indeed have

acted as grease money in Asia during this period”. Adding further control variables to the

model reduces the significance of the corruption coefficient, but maintains its negative

effect. When adding government spending into the model, the variable itself is not

significant, but corruption has a greater effect on growth when government spending is

higher. Also, corruption reduces growth rates in countries where the distribution of land is

more unequal (as measured by the initial land Gini). However, the R-squared values for all

the growth-corruption regressions are never higher than 37%, indicating poor predictive

power.

Given these results, it is clear from Table 6.11 that none of the studies mentioned have

found corruption to have a positive impact on economic growth (statistically significant or

otherwise). As Rahman et al. (2000, p.12) concluded, “the focal variable, corruption, is

both economically and statistically significant”. This makes the findings in this chapter

unique. One reason why the result challenges the existing literature is the measure of

corruption in the present study. None of the studies in Table 6.11 used the World Bank’s

KKZ index.

Population growth was used in all studies, and yielded a consistent result. The coefficient is

negative in all studies, implying that slower population growth will result in higher

economic growth. However, the coefficient for the population growth rate was positive in

all the models in this chapter. This may be connected to the fact that economic growth in

the studies in Table 6.11 is measured on a per capita basis, unlike in the present study.

The results for education are mixed. Mauro (1995) and Mauro (1997) used secondary

enrolment rates in 1960, while Ali and Isse (2003) used the same in 1975. Rahman et al.

(2000) used gross secondary enrolment rate (same as the present study) in 1985, and also

used pupil/teacher ratio in secondary schools in 1985 (which was meant to measure the

quality of human capital, while the former measured the quantity of human capital). Li et

132

al. (2000) used primary years of schooling as a proxy for education. Mendez and Sepulveda

(2001) used total enrolment in both primary and secondary education in 1960 (but only the

secondary education coefficient is quoted in Table 6.11). Poirson (1998) used secondary

enrolment ratio as a proxy for education. In the present study, education was mostly found

to be insignificant, and this is consistent with findings by Poirson (1998), Li et al. (2000),

Rahman et al. (2000) and Ali and Isse (2003).

6.8 Conclusion

This chapter applied a simple regression using cross-sectional data to analyse the impact of

corruption on economic growth with particular focus on East Asia. Using data from 1996, a

typical year prior to the 1997 Asian Financial Crisis, a variety of corruption indices were

used in corruption-growth models. In most models, corruption was not found to be

significant. But in two particular models, one using the Political Risk Services Inc.’s ICRG

index and the other using the International Institute for Management Development’s WCY

index, corruption yielded a significant (5% level) negative coefficient, which implied that

there exists a significant positive relationship between corruption and economic growth. In

comparison with other studies, this represented a unique result as corruption was always

found to be negatively associated with economic growth. The inclusion of a dummy

variable representing East Asian countries did not produce any different results.

One limitation of these results is the measure of corruption. By virtue of its clandestine

nature, any measure of corruption is likely to be criticised for its accuracy, but this is not

something that can be avoided. Another limitation may have been the time period. Indeed,

this was a cross-sectional analysis of 141 countries but only one year was used. There may

have existed some peculiarities in that year that potentially distorted the results. Also, there

were not many East Asian countries in the sample. To address these issues, an analysis of

panel data is necessary. This will be the subject of the next chapter.

133

Variable 1 2 3 4 5 6

CONSTANT -3.261 (10.29) -5.028 (3.06) -2.274 (2.59) -0.106 (2.87) -2.852 (2.67) 3.904 (2.22)

CORRUPT -0.374 (0.46) -0.146 (0.46) -0.222 (0.42) -0.802 (0.41) -0.694 (0.42) -0.805 (0.39)*

GFCFRATE 0.214 (0.07)** 0.246 (0.07)** 0.217 (0.07)** 0.168 (0.08)* 0.138 (0.08) 0.126 (0.07)

POPRATE 1.717 (0.68)* 1.813 (0.64)** 1.323 (0.56)* 1.030 (0.50)* 0.293 (0.41)

SECENROL 0.022 (0.03) 0.163 (0.02) 0.005 (0.02)

EXPGDP -0.024 (0.03) -0.028 (0.02) -0.036 (0.02) -0.043 (0.02) -0.036 (0.02) -0.031 (0.02)

EXPRATE 0.008 (0.07) 0.197 (0.06) 0.628 (0.05) 0.073 (0.06)

MKTCAP -0.001 (0.01) -0.003 (0.01)

REALINT 0.033 (0.05) -0.016 (0.03) -0.019 (0.03) -0.050 (0.04) -0.033 (0.03) -0.037 (0.03)

GVEXGDP 0.040 (0.10) 0.066 (0.10) 0.048 (0.09) 0.210 (0.04)* 0.075 (0.08) 0.069 (0.08)

HITECHEX 0.055 (0.04) 0.038 (0.04) 0.038 (0.04) 0.055 (0.04) 0.072 (0.04) 0.070 (0.04)

LNGDP85 -0.039 (0.37)

Adjusted R2 0.27 0.26 0.21 0.17 0.08 0.09

F-value 2.77 2.96 2.97 2.76 1.93 2.18

Sample size 54 57 66 68 74 74

Table A6.1: Results using ICRG Index


134



Variable 7 8 9 10 11

CONSTANT 4.598 (2.06) 4.382 (1.70) 4.314 (1.71) 4.460 (1.38) 6.461 (0.96)

CORRUPT -0.663 (0.35) -0.655 (0.31)* -0.679 (0.32)* -0.605 (0.27)* -0.565 (0.26)*

GFCFRATE 0.116 (0.07) 0.119 (0.07) 0.091 (0.06) 0.100 (0.05)

POPRATE 0.272 (0.28)

SECENROL

EXPGDP -0.031 (0.02) -0.026 (0.02)

EXPRATE

MKTCAP

REALINT -0.038 (0.03)

GVEXGDP

HITECHEX 0.073 (0.04) 0.058 (0.03) 0.040 (0.03)

LNGDP85

Adjusted R2 0.09 0.07 0.06 0.05 0.03

F-value 2.48 2.78 2.91 3.94 4.63

Sample size 74 91 115 116 121

Table A6.1: Results using ICRG Index (Cont’d)


135


Variable 1 2 3 4 5

CONSTANT 3.101 (8.60) -2.210 (2.58) -1.425 (2.30) -1.322 (1.87) -1.423 (1.59)

CORRUPT -0.026 (0.22) 0.063 (0.25) 0.858 (0.24) 0.019 (0.19) 0.007 (0.15)

GFCFRATE 0.166 (0.06)* 0.223 (0.06)** 0.215 (0.06)** 0.215 (0.06)** 0.216 (0.05)**

POPRATE 1.167 (0.59) 1.304 (0.57)* 1.111 (0.52)* 1.086 (0.41)* 1.092 (0.40)**

SECENROL 0.014 (0.02) 0.003 (0.02) 0.002 (0.02)

EXPGDP -0.008 (0.02) -0.012 (0.02) -0.021 (0.01) -0.021 (0.01) -0.021 (0.01)

EXPRATE -0.027 (0.05) -0.000 (0.05)

MKTCAP -0.005 (0.01) -0.007 (0.01) -0.003 (0.01) -0.003 (0.01) -0.003 (0.01)

REALINT 0.044 (0.04) -0.035 (0.03) -0.040 (0.03) -0.040 (0.03) -0.040 (0.02)

GVEXGDP -0.093 (0.09) -0.024 (0.09) -0.010 (0.08) -0.008 (0.08)

HITECHEX 0.054 (0.03) 0.034 (0.03) 0.030 (0.03) 0.031 (0.03) 0.031 (0.03)

LNGDP85 -0.161 (0.41)

Adjusted R2 0.42 0.36 0.40 0.41 0.43

F-value 3.63 3.44 4.21 4.87 5.71

Sample size 41 44 45 45 45

Table A6.2: Results using CPI


136


Variable 6 7 8 9

CONSTANT -1.075 (1.53) -0.781 (1.51) -1.444 (1.41) 0.602 (1.27)

CORRUPT -0.012 (0.15) -0.005 (0.15) 0.026 (0.14) -0.069 (0.12)

GFCFRATE 0.202 (0.05)** 0.196 (0.05)** 0.200 (0.05)** 0.168 (0.04)**

POPRATE 1.318 (0.36)** 1.149 (0.36)** 1.184 (0.35)** 0.967 (0.31)**

SECENROL

EXPGDP -0.026 (0.01) -0.020 (0.01) -0.018 (0.01)

EXPRATE

MKTCAP

REALINT -0.034 (0.02) -0.032 (0.02)

GVEXGDP

HITECHEX 0.022 (0.03)

LNGDP85

Adjusted R2 0.43 0.35 0.35 0.34

F-value 6.71 6.18 8.03 9.93


Table A6.2: Results using CPI (cont’d)


137


Variable 1 2 3 4 5

CONSTANT 7.490 (8.33) 7.453 (8.15) 6.072 (7.02) 6.011 (6.80) -1.591 (2.09)

CORRUPT 0.118 (0.20) 0.110 (0.16) 0.102 (0.16) -0.005 (0.15) 0.054 (0.17)

GFCFRATE 0.138 (0.07) 0.138 (0.07) 0.136 (0.07) 0.146 (0.06)* 0.206 (0.06)**

POPRATE 1.048 (0.63) 1.076 (0.50)* 1.111 (0.48)* 0.670 (0.43) 0.918 (0.46)

SECENROL -0.002 (0.03)

EXPGDP -0.007 (0.02) -0.007 (0.02)

EXPRATE -0.038 (0.06) -0.039 (0.05) -0.039 (0.05)

MKTCAP -0.007 (0.01) -0.007 (0.01) -0.008 (0.01) -0.006 (0.01) -0.008 (0.01)

REALINT 0.081 (0.05) 0.080 (0.05) 0.087 (0.04) 0.080 (0.04) -0.035 (0.03)

GVEXGDP -0.110 (0.09) -0.113 (0.08) -0.113 (0.08) -0.076 (0.08) -0.007 (0.09)

HITECHEX 0.060 (0.03) 0.060 (0.03) 0.058 (0.03) 0.040 (0.03) 0.024 (0.03)

LNGDP85 -0.269 (0.29) -0.272 (0.28) -0.223 (0.24) -0.217 (0.24)

Adjusted R2 0.47 0.49 0.51 0.49 0.39

F-value 3.94 4.50 5.15 5.46 4.68

Sample size 37 37 37 38 41

Table A6.3: Results using WCY Index


138


Variable 6 7 8 9

CONSTANT -1.707 (1.58) -1.963 (1.52) -1.511 (1.47) -1.852 (1.39)

CORRUPT 0.045 (0.14) 0.077 (0.13) 0.020 (0.12) 0.027 (0.11)

GFCFRATE 0.207 (0.06)** 0.222 (0.05)** 0.206 (0.05)** 0.200 (0.05)**

POPRATE 0.927 (0.44)* 1.006 (0.42)* 0.790 (0.39)* 0.952 (0.36)*

SECENROL

EXPGDP

EXPRATE

MKTCAP -0.008 (0.01) -0.007 (0.01)

REALINT -0.036 (0.03) -0.035 (0.03) -0.036 (0.03)

GVEXGDP

HITECHEX 0.024 (0.03)

LNGDP85

Adjusted R2 0.41 0.42 0.41 0.41

F-value 5.62 6.93 8.22 11.18



Table A6.3: Results using WCY Index (cont’d)

139


Variable 1 2 3 4 5

CONSTANT -5.348 (10.71) -1.260 (9.08) -1.274 (8.34) -3.337 (3.57) 4.474 (3.58)

CORRUPT 0.003 (0.76) 0.196 (0.74) 0.198 (0.64) 0.516 (0.64) -0.855 (0.60)

GFCFRATE 0.221 (0.07)** 0.202 (0.07)** 0.202 (0.07)** 0.234 (0.07)** 0.142 (0.08)

POPRATE 1.785 (0.68)* 0.510 (0.46) 0.509 (0.40) 0.617 (0.38) 0.339 (0.41)

SECENROL 0.016 (0.03) 0.000 (0.03)

EXPGDP -0.023 (0.03) -0.036 (0.02) -0.036 (0.02) -0.038 (0.02)* -0.036 (0.02)

EXPRATE -0.001 (0.07)

MKTCAP -0.003 (0.01) 0.004 (0.01) 0.004 (0.01) 0.001 (0.01)

REALINT 0.038 (0.05) 0.046 (0.04) 0.046 (0.04) -0.005 (0.03) -0.031 (0.04)

GVEXGDP 0.019 (0.10) -0.079 (0.10) -0.079 (0.08) -0.083 (0.08) 0.082 (0.09)

HITECHEX 0.053 (0.04) 0.067 (0.03) 0.067 (0.03)* 0.054 (0.03) 0.074 (0.04)

LNGDP85 0.014 (0.37) -0.015 (0.34) -0.014 (0.31)

Adjusted R2 0.26 0.23 0.24 0.22 0.07

F-value 2.67 2.69 3.05 3.11 1.82

Sample size 54 59 59 62 74

Table A6.4: Results using KKZ-ICRG Index


140


Variable 6 7 8 9

CONSTANT 6.071 (3.01) 6.102 (3.01) 6.157 (2.54) 6.165 (2.01)

CORRUPT -1.028 (0.56) -0.772 (0.48) -0.809 (0.43) -0.767 (0.36)*

GFCFRATE 0.129 (0.07) 0.118 (0.07) 0.120 (0.07) 0.120 (0.07)*

POPRATE

SECENROL

EXPGDP -0.029 (0.02) -0.030 (0.02) -0.025 (0.02)

EXPRATE

MKTCAP

REALINT -0.036 (0.03) -0.036 (0.03)

GVEXGDP 0.077 (0.09)

HITECHEX 0.072 (0.04) 0.074 (0.04) 0.059 (0.03)

LNGDP85

Adjusted R2 0.08 0.08 0.06 0.04

F-value 2.02 2.26 2.55 3.65


Table A6.4: Results using KKZ-ICRG Index (Cont’d)


141


Variable 1 2 3 4 5

CONSTANT 14.070 (24.96) 13.883 (24.20) 24.152 (13.41) 7.255 (10.46) 11.884 (5.61)

CORRUPT 0.817 (1.19) 0.986 (1.04) 0.396 (0.84) -0.086 (0.82) 1.013 (0.88)

GFCFRATE 0.431 (0.16)* 0.427 (0.15)* 0.342 (0.11)** 0.228 (0.10)* 0.120 (0.11)

POPRATE 2.588 (1.35) 2.299 (0.97)* 1.549 (0.71)* 1.875 (0.66)** 0.768 (0.65)

SECENROL 0.022 (0.07)

EXPGDP -0.131 (0.09) -0.133 (0.09) -0.136 (0.05)* -0.051 (0.04) -0.084 (0.04)*

EXPRATE 0.329 (0.18) 0.331 (0.17) 0.195 (0.10) 0.150 (0.08) 0.057 (0.07)

MKTCAP -0.012 (0.01) -0.011 (0.01)

REALINT -0.107 (0.10) -0.107 (0.09) -0.117 (0.07)

GVEXGDP 0.303 (0.23) 0.336 (0.20) 0.309 (0.16) 0.344 (0.15)* -0.021 (0.15)

HITECHEX 0.135 (0.12) 0.143 (0.12) 0.170 (0.09) 0.093 (0.08)

LNGDP85 -1.254 (0.91) -1.219 (0.87) -1.370 (0.48)** -0.626 (0.36) -0.582 (0.36)

Adjusted R2 0.31 0.35 0.45 0.37 0.11

F-value 2.02 2.36 3.75 3.82 1.85

Sample size 26 26 31 39 48

Table A6.5: Results using KKZ-WDR Index


142


Variable 6 7 8 9

CONSTANT 11.709 (7.78) 12.839 (7.64) 13.962 (7.58) 20.058 (6.61)

CORRUPT 0.997 (0.86) 0.905 (0.85) 1.013 (0.84) 0.783 (0.84)

GFCFRATE 0.118 (0.11) 0.117 (0.11)

POPRATE 0.811 (0.56) 0.833 (0.55) 0.870 (0.55)

SECENROL

EXPGDP -0.085 (0.04)* -0.066 (0.03) -0.078 (0.03)*

EXPRATE 0.058 (0.07) -0.084 (0.06)

MKTCAP

REALINT

GVEXGDP

HITECHEX

LNGDP85 -0.585 (0.35) -0.590 (0.35) -0.591 (0.35) -0.729 (0.34)*

Adjusted R2 0.13 0.14 0.14 0.11

F-value 2.21 2.53 2.86 2.90


Table A6.5: Results using KKZ-WDR Index (cont’d)


143


Variable 1 2 3 4 5

CONSTANT 13.738 (27.85) 13.454 (26.89) 13.864 (26.40) 37.002 (15.92) 28.261 (15.66)

CORRUPT 0.481 (1.68) 0.298 (1.30) 0.211 (1.27) -0.634 (1.01) 0.106 (0.95)

GFCFRATE 0.354 (0.15)* 0.347 (0.14)* 0.323 (0.13)* 0.307 (0.11)* 0.313 (0.12)*

POPRATE 2.029 (1.49) 2.208 (1.08) 2.248 (1.06) 1.353 (0.79) 1.176 (0.81)

SECENROL -0.011 (0.06)

EXPGDP -0.119 (0.10) -0.115 (0.09) -0.112 (0.09) -0.183 (0.06)** -0.160 (0.06)*

EXPRATE 0.159 (0.26) 0.132 (0.20)

MKTCAP -0.010 (0.02) -0.011 (0.01) -0.011 (0.01)

REALINT -0.100 (0.11) -0.096 (0.10) -0.092 (0.10) -0.147 (0.08) -0.077 (0.07)

GVEXGDP 0.304 (0.28) 0.280 (0.24) 0.237 (0.23) 0.318 (0.19)

HITECHEX 0.192 (0.14) 0.191 (0.13) 0.208 (0.13) 0.284 (0.10)** 0.235 (0.10)*

LNGDP85 -0.982 (0.98) -0.955 (0.93) -0.883 (0.91) -1.553 (0.56)* -1.201 (0.54)*

Adjusted R2 0.28 0.32 0.35 0.44 0.39

F-value 1.87 2.19 2.48 3.84 3.68

Sample size 26 26 26 30 30

Table A6.6: Results using ICRG-WDR Index


144


Variable 6 7 8 9 10

CONSTANT 7.074 (11.62) -7.121 (5.32) -3.725 (4.46) -1.076 (4.30) -2.192 (4.26)

CORRUPT 0.564 (0.94) 0.633 (0.82) 0.691 (0.69) 0.744 (0.71) 0.679 (0.71)

GFCFRATE 0.247 (0.11)* 0.273 (0.11)* 0.179 (0.10)

POPRATE 1.924 (0.75)* 2.356 (0.57)** 1.825 (0.57)** 1.692 (0.58) 1.722 (0.59)**

SECENROL

EXPGDP -0.074 (0.04) -0.043 (0.04) -0.071 (0.03)* -0.043 (0.03)

EXPRATE

MKTCAP

REALINT

GVEXGDP

HITECHEX 0.104 (0.08) 0.030 (0.07)

LNGDP85 -0.506 (0.41)

Adjusted R2 0.31 0.30 0.18 0.14 0.13

F-value 3.74 4.47 3.70 3.70 4.52

Sample size 37 41 49 49 49

Table A6.6: Results using ICRG-WDR Index (cont’d)


145


Table A6.7: Controlling for Endogeneity (ICRG Index)

Variable 1 2 3 4 5

CONSTANT 13.750 (8.52) 14.121 (7.50) 13.474 (6.10) 8.654 (5.58) 6.838 (1.69)

CORRUPT

GFCFRATE1 0.074 (0.04) 0.087 (0.04)* 0.087 (0.04)* 0.085 (0.02)** 0.084 (0.02)**

POPRATE1 -0.244 (0.63) -0.349 (0.59) -0.339 (0.58) -0.573 (0.51) -0.578 (0.46)

SECENROL1 -0.014 (0.02) -0.024 (0.02) -0.024 (0.02) -0.040 (0.02)* -0.043 (0.02)*

EXPGDP1 -0.007 (0.02) -0.003 (0.02)

EXPRATE1 0.066 (0.04) 0.057 (0.04) 0.057 (0.04) 0.043 (0.04) 0.022 (0.03)

MKTCAP1 0.004 (0.01) 0.004 (0.01) 0.004 (0.01)

REALINT1 0.004 (0.05)

GVEXGDP1 0.073 (0.09) 0.123 (0.08) 0.123 (0.08) 0.161 (0.08)* 0.142 (0.07)

HITECHEX1 0.091 (0.04)* 0.080 (0.04) 0.079 (0.04)* 0.069 (0.04) 0.070 (0.03)*

LNGDP85 -0.371 (0.32) -0.351 (0.29) -0.327 (0.24) -0.094 (0.22)

Adjusted R2 0.17 0.20 0.21 0.23 0.24

F-value 1.96 2.50 2.83 3.91 4.54

Sample size 54 62 62 77 81

-0.566 (0.34) -0.620 (0.37) -0.715 (0.41)


146

-0.717 (0.41) -0.592 (0.41)


Variable 6 7 8 9 10

CONSTANT 7.122 (1.63) 5.600 (1.01) 6.318 (0.92) 7.039 (1.15) 6.145 (1.01)

CORRUPT -0.603 (0.33) -0.626 (0.33) -0.490 (0.33) -0.905 (0.32)** -0.575 (0.27)*

GFCFRATE1 0.088 (0.02)** 0.087 (0.02)** 0.083 (0.02)** 0.076 (0.03)* 0.068 (0.03)*

POPRATE1 -0.544 (0.46)

SECENROL1 -0.042 (0.02)* -0.027 (0.01)* -0.018 (0.01)

EXPGDP1

EXPRATE1

MKTCAP1

REALINT1

GVEXGDP1 0.131 (0.07) 0.119 (0.07)

HITECHEX1 0.081 (0.03)** 0.069 (0.03)* 0.059 (0.03)* 0.040 (0.03)

LNGDP85

Adjusted R2 0.25 0.25 0.23 0.13 0.07

F-value 5.51 6.29 7.02 4.94 4.94

Sample size 82 82 82 83 106

Table A6.7: Controlling for Endogeneity (ICRG Index, cont’d)


147


Variable 1 2 3 4 5 6 7 8

CONSTANT 4.866 (8.28) 3.279 (7.17) 2.445 (1.51) 2.423 (1.48) 2.643 (1.21) 3.192 (1.14) 2.393 (0.96) 3.489 (0.71)

CORRUPT -0.137 (0.22) -0.124 (0.21) -0.118 (0.20) -0.122 (0.20) -0.096 (0.16) -0.148 (0.15) -0.257 (0.12)* -0.357 (0.11)**

GFCFRATE1 0.106 (0.04)** 0.110 (0.03)** 0.114 (0.03)** 0.113 (0.03)** 0.112 (0.03)** 0.093 (0.02)** 0.094 (0.02)** 0.100 (0.02)**

POPRATE1 0.601 (0.56) 0.639 (0.54) 0.644 (0.45) 0.635 (0.45) 0.563 (0.34) 0.456 (0.30) 0.490 (0.30)

SECENROL1 0.007 (0.02) 0.005 (0.02) 0.005 (0.02) 0.005 (0.02)

EXPGDP1 -0.006 (0.02) -0.004 (0.02) -0.003 (0.01)

EXPRATE1 0.102 (0.04)* 0.095 (0.04)* 0.099 (0.03)** 0.099 (0.03)** 0.100 (0.03)** 0.089 (0.03)** 0.098 (0.03)** 0.094 (0.03)**

MKTCAP1 -0.002 (0.08) -0.003 (0.01) -0.004 (0.01) -0.005 (0.01) -0.004 (0.01)

REALINT1 -0.012 (0.05)

GVEXGDP1 -0.068 (0.08) -0.083 (0.07) -0.082 (0.07) -0.081 (0.07) -0.075 (0.06) -0.076 (0.06)

HITECHEX1 0.074 (0.03)* 0.066 (0.03)* 0.063 (0.03)* 0.062 (0.03)* 0.063 (0.03)* 0.058 (0.02)* 0.061 (0.02)* 0.067 (0.03)*

LNGDP85 -0.108 (0.31) -0.031 (0.27)

Adjusted R2 0.44 0.45 0.48 0.49 0.50 0.53 0.53 0.51

F-value 3.92 4.69 5.75 6.62 7.73 10.33 11.87 13.64

Sample size 42 46 48 48 48 50 50 50

Table A6.8: Controlling for Endogeneity (CPI)

148


Variable 1 2 3 4 5 6 7 8

CONSTANT 13.380 (7.80) 14.894 (7.15) 15.935 (6.74) 17.815 (6.22) 14.362 (5.76) 14.354 (5.71) 4.103 (1.20) 3.790 (1.28)

CORRUPT 0.146 (0.20) 0.163 (0.20) 0.148 (0.19) 0.167 (0.19) 0.116 (0.18) 0.821 (0.18) 0.140 (0.17) 0.371 (0.17)*

GFCFRATE1 0.102 (0.04)* 0.101 (0.04)* 0.095 (0.04)* 0.087 (0.03)* 0.084 (0.03)* 0.084 (0.03)* 0.093 (0.03)** 0.110 (0.03)**

POPRATE1 0.325 (0.61)

SECENROL1 -0.015 (0.02) -0.022 (0.02) -0.020 (0.02) -0.026 (0.02) -0.027 (0.02) -0.031 (0.02) -0.038 (0.02)* -0.047 (0.02)**

EXPGDP1 -0.013 (0.02) -0.013 (0.02) -0.017 (0.01) -0.019 (0.01)

EXPRATE1 0.122 (0.05)* 0.128 (0.05)* 0.130 (0.05)* 0.130 (0.04)** 0.126 (0.04)** 0.131 (0.04)** 0.131 (0.04)** 0.166 (0.04)**

MKTCAP1 -0.005 (0.01) -0.004 (0.01)

REALINT1 0.067 (0.08) 0.069 (0.08) 0.059 (0.08)

GVEXGDP1 -0.093 (0.09) -0.085 (0.09) -0.078 (0.08) -0.073 (0.08) -0.052 (0.08)

HITECHEX1 0.080 (0.03)* 0.081 (0.03)* 0.078 (0.03)* 0.069 (0.03)* 0.053 (0.03) 0.056 (0.03) 0.052 (0.03)

LNGDP85 -0.405 (0.29) -0.441 (0.28) -0.485 (0.26) -0.520 (0.24)* -0.399 (0.23) -0.412 (0.22)

Adjusted R2 0.48 0.50 0.51 0.52 0.51 0.51 0.49 0.47

F-value 4.07 4.58 5.21 6.27 6.72 7.88 8.81 10.46

Sample size 37 37 37 40 40 40 42 44

Table A6.9: Controlling for Endogeneity (WCY Index)

149


Variable 1 2 3 4 5

CONSTANT 14.041 (8.97) 13.724 (7.89) 13.451 (6.60) 9.938 (6.02) 8.567 (2.47)

CORRUPT -0.657 (0.71) -0.609 (0.70) -0.611 (0.70) -0.823 (0.60) -0.761 (0.55)

GFCFRATE1 0.073 (0.04) 0.085 (0.04)* 0.085 (0.04)* 0.088 (0.03)** 0.085 (0.02)**

POPRATE1 -0.225 (0.64) -0.317 (0.60) -0.313 (0.59) -0.574 (0.51) -0.580 (0.47)

SECENROL1 -0.013 (0.02) -0.027 (0.02) -0.027 (0.02) -0.039 (0.02) -0.041 (0.02)*

EXPGDP1 -0.006 (0.02) -0.001 (0.02)

EXPRATE1 0.068 (0.04) 0.060 (0.04) 0.060 (0.04) 0.047 (0.04) 0.024 (0.03)

MKTCAP1 0.004 (0.01) 0.003 (0.01) 0.003 (0.01)

REALINT1 0.007 (0.05)

GVEXGDP1 0.063 (0.09) 0.106 (0.09) 0.106 (0.09) 0.163 (0.08)* 0.142 (0.07)

HITECHEX1 0.087 (0.04)* 0.073 (0.04) 0.072 (0.04) 0.068 (0.04) 0.071 (0.03)*

LNGDP85 -0.331 (0.32) -0.291 (0.29) -0.280 (0.24) -0.070 (0.22)

Adjusted R2 0.14 0.16 0.18 0.22 0.23

F-value 1.80 2.18 2.47 3.74 4.37

Sample size 54 62 62 77 81

Table A6.10: Controlling for Endogeneity (KKZ-ICRG Index)


150


Variable 6 7 8 9 10

CONSTANT 8.850 (2.39) 7.498 (2.09) 7.592 (2.11) 10.091 (2.17) 7.650 (1.88)

CORRUPT -0.777 (0.54) -0.829 (0.54) -0.600 (0.52) -1.239 (0.44)** -0.692 (0.36)

GFCFRATE1 0.090 (0.02)** 0.089 (0.02)** 0.085 (0.02)** 0.079 (0.03)** 0.070 (0.03)*

POPRATE1 -0.536 (0.46)

SECENROL1 -0.040 (0.02)* -0.025 (0.01) -0.018 (0.01)

EXPGDP1

EXPRATE1

MKTCAP1

REALINT1

GVEXGDP1 0.128 (0.07) 0.116 (0.07)

HITECHEX1 0.084 (0.03)** 0.073 (0.03)* 0.061 (0.03)* 0.051 (0.04)

LNGDP85

Adjusted R2 0.24 0.24 0.22 0.13 0.06

F-value 5.23 5.98 6.70 4.94 4.44

Sample size 82 82 82 83 106

Table A6.10: Controlling for Endogeneity (KKZ-ICRG Index, cont’d)


151


Variable 1 2 3 4 5

CONSTANT 43.937 (28.50) 48.353 (23.77) 26.682 (18.03) 20.200 (9.79) 1.872 (9.61)

CORRUPT -0.827 (1.09) -0.897 (1.03) -0.070 (1.07) -0.153 (1.03) 0.828 (1.05)

GFCFRATE1 0.021 (0.07)

POPRATE1 -2.247 (1.92) -2.447 (1.75) -2.039 (1.58) -1.773 (1.43) -0.537 (0.95)

SECENROL1 -0.087 (0.07) -0.092 (0.06) -0.127 (0.06)* -0.121 (0.06)* -0.098 (0.05)*

EXPGDP1 -0.066 (0.09) -0.079 (0.07) -0.027 (0.06)

EXPRATE1 0.149 (0.07) 0.149 (0.07) 0.109 (0.08) 0.104 (0.08) 0.044 (0.07)

MKTCAP1 0.015 (0.02) 0.017 (0.01) 0.013 (0.01) 0.010 (0.01)

REALINT1 -0.085 (0.11) -0.099 (0.10)

GVEXGDP1 0.384 (0.25) 0.409 (0.22) 0.356 (0.20) 0.342 (0.19) 0.079 (0.16)

HITECHEX1 0.200 (0.13) 0.222 (0.10) 0.138 (0.09) 0.114 (0.07) 0.087 (0.08)

LNGDP85 -1.345 (0.94) -1.482 (0.80) -0.703 (0.67) -0.469 (0.38) 0.109 (0.41)

Adjusted R2 0.21 0.26 0.18 0.21 0.07

F-value 1.57 1.84 1.69 1.96 1.43

Sample size 25 25 29 29 39

Table A6.11: Controlling for Endogeneity (KKZ-WDR Index)


152


Variable 6 7 8 9 10

CONSTANT 4.679 (4.35) 3.466 (3.73) 4.575 (3.37) 4.887 (3.25) 0.974 (3.18)

CORRUPT 0.500 (0.85) 0.432 (0.83) 0.385 (0.83) 0.359 (0.80) 1.402 (0.75)

GFCFRATE1

POPRATE1 -0.436 (0.79)

SECENROL1 -0.078 (0.04)* -0.063 (0.03)* -0.052 (0.02)* -0.051 (0.02)* -0.067 (0.02)**

EXPGDP1

EXPRATE1 0.039 (0.05) 0.041 (0.05) 0.036 (0.05)

MKTCAP1

REALINT1

GVEXGDP1 0.091 (0.14) 0.098 (0.14)

HITECHEX1 0.089 (0.07) 0.075 (0.06) 0.069 (0.06) 0.075 (0.06)

LNGDP85

Adjusted R2 0.07 0.09 0.10 0.11 0.15

F-value 1.57 1.86 2.22 2.88 6.29

Sample size 47 47 47 48 63

Table A6.11: Controlling for Endogeneity (KKZ-WDR Index, cont’d)


153


Variable 1 2 3 4 5

CONSTANT 21.362 (29.84) 20.039 (18.31) 13.358 (11.78) 10.160 (8.19) 5.014 (7.71)

CORRUPT 0.191 (1.69) 0.213 (1.60) -0.600 (1.11) -0.553 (1.08) 0.045 (0.99)

GFCFRATE1 0.068 (0.08) 0.070 (0.07) 0.084 (0.04) 0.085 (0.04)* 0.093 (0.03)*

POPRATE1 -0.093 (1.62)

SECENROL1 -0.032 (0.05) -0.030 (0.04) -0.031 (0.03) -0.035 (0.03) -0.058 (0.03)

EXPGDP1 -0.022 (0.09) -0.019 (0.06) -0.017 (0.05)

EXPRATE1 0.147 (0.07) 0.146 (0.07) 0.123 (0.06)* 0.124 (0.06)* 0.109 (0.06)

MKTCAP1 0.002 (0.02) 0.001 (0.01)

REALINT1 -0.027 (0.11) -0.024 (0.09) -0.042 (0.06) -0.038 (0.06)

GVEXGDP1 0.191 (0.22) 0.184 (0.18) 0.321 (0.15)* 0.318 (0.15)* 0.329 (0.14)*

HITECHEX1 0.095 (0.12) 0.091 (0.09) 0.099 (0.07) 0.080 (0.05) 0.054 (0.05)

LNGDP85 -0.841 (1.01) -0.802 (0.72) -0.437 (0.47) -0.321 (0.36) -0.166 (0.35)

Adjusted R2 0.14 0.20 0.27 0.29 0.36

F-value 1.37 1.62 2.25 2.61 3.93

Sample size 26 26 32 32 37

Table A6.12: Controlling for Endogeneity (ICRG-WDR Index)


154


Variable 6 7 8 9 10

CONSTANT 1.864 (3.70) -2.254 (4.14) -2.053 (4.03) -0.211 (3.83) -1.563 (3.89)

CORRUPT -0.036 (0.83) 1.477 (0.94) 1.457 (0.92) 1.512 (0.93) 1.949 (0.93)*

GFCFRATE1 0.092 (0.03)** 0.077 (0.04) 0.078 (0.04)* 0.077 (0.04)

POPRATE1

SECENROL1 -0.064 (0.02)** -0.082 (0.03)** -0.080 (0.02)** -0.064 (0.02)** -0.073 (0.02)**

EXPGDP1

EXPRATE1 0.104 (0.05)* 0.013 (0.05)

MKTCAP1

REALINT1

GVEXGDP1 0.327 (0.13)* 0.223 (0.16) 0.219 (0.16)

HITECHEX1 0.054 (0.05)

LNGDP85

Adjusted R2 0.39 0.21 0.23 0.21 0.16

F-value 5.08 3.58 4.55 5.35 5.68

Sample size 40 49 49 49 49

Table A6.12: Controlling for Endogeneity (ICRG-WDR Index, cont’d)


155


CHAPTER 7

CORRUPTION AND GROWTH II: A PANEL DATA ANALYSIS

“Wait and see how I do. If I am really bad, you can blame me.

Are all rich men bad?”

– Former Thai Prime Minister Thaksin Shinawatra.32

7.1 Introduction

In Chapter 6 the connection between corruption and economic growth was examined using

data of 1996, a typical year before the 1997 Asian Financial Crisis. The empirical analysis

presents evidence of a potentially positive relationship between the two variables

suggesting that high levels of corruption can indeed be synonymous with similar levels of

growth. This was true for all countries in the sample, not just East Asia. But as the analysis

was based on cross-sectional data, it begs the question of whether the results were only

particular to that 1996 sample. In this chapter, panel data will be used to investigate the

relationship between corruption and growth with special focus on East Asia, for 33

countries over the period 1984-2003. The chapter will begin with an analysis of corruption

data to be used in the model. This will be followed by an outline of the modified

corruption-growth model to allow for the use of panel data, and will go on to provide a

summary of the extra data required and its means of collection. The results will then be

presented and discussed accordingly.

7.2 Corruption Indicators

For the panel data model, corruption data was taken from the following three sources:

• the ICRG index for 1984-2001;

• the WCY index for 1991-2003; and

• the CPI for 1995-2003.

32 This was the Thai Prime Minister’s response to an accusation that he used his wealth to buy power, soon after an election victory (Anonymous, 1999, p.1).

156

Taiwan is excluded from the analysis due to lack of data leaving 33 countries in the

sample.33 Figure 7.1 illustrates the average corruption levels for East Asia and the rest of

the world using each of the three aforementioned indices, and it is clear that East Asia

consistently exhibits higher average corruption levels than other countries in the sample.

To estimate the corruption-growth model, the different indices need to be combined in

some manner to construct a single index of corruption. The most obvious technique would

be to take a simple average of the CPI, the WCY index and the ICRG index for the entire

sample period 1984-2003. However, due to the availability of each index in differing time

periods, not all years were an average of all three indices. For some years, like 1984, only

one index (ICRG) was available. The WCY and CPI values were on identical scales of 0 to

10, where 0 reflected extreme corruption, however the ICRG scale was 0 to 6 so its values

needed to be converted to fit the scale of the WCY and CPI data. This was achieved by

dividing each ICRG score by 6 and then multiplying it by 10. A sample of the corruption

data for East Asia using the resulting composite index is shown in Table 7.1. The data

reveals that overall the corruption level was rather high in the region, though Singapore and

Hong Kong seem to perform relatively well with the former recording extremely low levels

of corruption across all indices. Japan is close behind in the third place, followed by

Malaysia and the remaining countries. Indonesia almost always features last. The strong

performance of Singapore will prove to be significant in the empirical analysis later in the

chapter.

33 See Table A7.1 in the appendix to this chapter for the complete list of countries. Excluding Taiwan, only 33 countries appear in all three of the nominated corruption indices.

157

0

1

2

3

4

5

6

7

8

9

10

1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

CPI

/WC

Y I

ndex

0

1

2

3

4

5

6

ICR

G Index

Source: Author’s own calculation based on data obtained from Transparency International (various years), the Institute for Management

Development (various years), and Political Risk Services Inc. (various years) .

Figure 7.1: Average Corruption in East Asia vs Rest of the World (All Indices, 1984-2003)

East Asia - CPI

Rest of the sample - CPI

158

Rest of the sample - WCY

East Asia - ICRG

East Asia - WCY

Rest of the sample - ICRG

Table 7.1: Corruption in East Asia (Composite Index, 1995-2003)

1995 1996 1997 1998 1999 2000 2001 2002 2003 Mean

Hong Kong 7.1 7.0 7.3 7.8 7.7 7.7 7.9 8.2 8.0 7.6

Indonesia 1.9 2.7 2.7 2.0 1.7 1.7 1.9 1.9 1.9 2.0

Japan 6.7 7.1 6.6 5.8 6.0 6.4 7.1 7.1 7.0 6.6

Malaysia 5.3 5.3 5.0 5.3 5.1 4.8 5.0 4.9 5.2 5.1

Philippines 2.8 2.7 3.1 3.3 3.6 2.8 2.9 2.6 2.5 2.9

Singapore 9.3 8.8 8.7 9.1 9.1 9.1 9.2 9.3 9.4 9.1

South Korea 4.3 5.0 4.3 4.2 3.8 4.0 4.2 4.5 4.3 4.3

Thailand 2.8 3.3 3.1 3.0 3.2 3.2 3.2 3.2 3.3 3.1

Mean 5.0 5.2 5.1 5.1 5.0 5.0 5.2 5.2 5.2

Note: Scores are on a scale of 0 (highly corrupt) to 10 (not corrupt). Source: Author’s own calculation based on data obtained from Transparency International (various years),

the Institute for Management Development (various years), and Political Risk Services Inc. (various years).

A graphical illustration of the corruption trends in East Asia and the rest of the sample

using the composite index for the entire period of 1984-2003 is shown in Figure 7.2, and

reveals that the average corruption score for the eight East Asian nations is well below that

of the remaining 25 countries, reflecting a higher level of corruption. However, the merging

of the three indices by taking a simple average may not necessarily be the best approach. A

potential problem evident in Figure 7.1 is that the three indices appear to show different

levels of corruption, on average, over the sample period. The ICRG index has an average

corruption level that appears higher than that of the CPI, which is in turn higher than that of

the WCY. This may explain the downward trend in the composite index in Figure 7.2.

To address this issue the corruption data for each index was standardised. The mean and

standard error required for the standardisation process were calculated using five different

techniques, namely,

• STD1 – calculating a mean for each year, then calculating the mean of those means

and the corresponding standard error;

• STD2 – calculating a mean for each country, then calculating the mean of those

means and the corresponding standard error,

• STD3 – calculating a mean and standard error for all corruption observations;

159

• STD4 – calculating a mean for each year and the corresponding standard error

(then standardising each country’s corruption observation by the mean and standard

error for that year only); and,

• STD5 – calculating a mean for each country and the corresponding standard error

(then standardising each country’s corruption observation by that country’s mean

and standard error).34

Figure 7.2: Average Corruption in East Asia v s Rest of the World

(Composite Index, 1984-2003)

0

1

2

3

4

5

6

7

8

9

10

1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

Rest of the sample

East Asia

Source: Author’s own calculation based on data obtained from Transparency International (various years), the Institute for Management Development (various years), and Political Risk Services Inc. (various years).

This procedure was repeated for each separate index so that there were a total of five

different datasets of corruption, with each dataset comprising the three standardised indices

(ICRG, WCY and CPI). Figures 7.3-7.7 plot the standardised corruption data.35 For STD4,

the average corruption for each year will necessarily be zero so charts for selected countries

are shown instead.

34 For all techniques, the ICRG scores were left in their original scale of 0-6. 35 Figures 7.3-7.7 are all products of the author’s own calculation based on data obtained from Transparency International (various years), the Institute for Management Development (various years), and Political Risk Services Inc. (various years).

160

Figure 7.3: Average Corruption (STD1, 1984-2003)

-3.00

-2.50

-2.00

-1.50

-1.00

-0.50

0.00

0.50

1.00

1.50

1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

WCY

ICRG

CPI


-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

ICRG

CPI

WCY

161


-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

ICRG WCY

CPI


-2.00

-1.50

-1.00

-0.50

0.00

0.50

1.00

1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

WCY

ICRG

CPI

162

Figure 7.7: Corruption for Selected Countries (STD4, 1984-2003)

Australia

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

WCY

CPI

ICRG

Brazil

-1.80

-1.60

-1.40

-1.20

-1.00

-0.80

-0.60

-0.40

-0.20

0.00

1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

ICRG

CPI

WCY

163

Figure 7.7: Corruption for Selected Countries (STD4, 1984-2003 – cont’d)

Malaysia

-1.20

-1.00

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

United States

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

WCYCPI

WCY

CPI

ICRG

ICRG

Figures 7.3-7.6 reveal similar trends for all corruption indices regardless of which

standardisation technique is used, though the same is not easily discernable in Figure 7.7.

However, in Figures 7.3-7.6, the ICRG measure appears to provide a consistent pattern of

164

above average corruption scores (reflecting low corruption) prior to 1992/93, followed by a

significant deterioration in those scores (reflecting increasing levels of corruption). This

pattern appears to be independent of the standardisation technique employed, and indicates

that the ICRG index is suggesting that the countries in the present sample have on average

become increasingly corrupt in the last ten years of the sample period. Figure 7.8 plots the

average raw ICRG data for all countries over time and confirms this pattern. The diagram

shows that the average corruption rating for countries in the present sample was hovering

between 4.4 and 5.0 from 1984 to 1992, before plummeting to 3.6 in 2001. There is no

available information to suggest that the ICRG has deliberately assessed global corruption

levels to be rising in the last ten years of the sample period, so it is quite likely that this is

simply a pattern unique to the present sample.

Figure 7.8: Average Corruption (ICRG Index, 1984-2001)

3.0

3.2

3.4

3.6

3.8

4.0

4.2

4.4

4.6

4.8

5.0

1984 1986 1988 1990 1992 1994 1996 1998 2000

Source: Compiled using data obtained from Political Risk Services Inc. (various years).

Having created five standardised datasets of corruption (STD1 – STD5), a simple average

of the three indices was taken to provide a single composite index of corruption for each of

the five datasets. These five composite indices will later be used to test for sensitivity of

results based upon the original composite index, which was a simple average of the raw

165

corruption scores, in order to determine whether the simple average technique was reliable

and accurate.

Having already highlighted a significant difference in corruption levels between East Asia

and the rest of the countries in the sample it is worth considering whether a similar

relationship exists for economic growth. A comparison of economic growth between the

two groups, East Asia and the rest of the sample, is shown in Figure 7.9. The chart reveals

that between 1986 and 1996, East Asian economic growth was far superior to that of the

rest of the sample. The spike in 1997 is clearly attributable to the effects of the 1997 Asian

Financial Crisis. Of some interest is the smaller spike evident in 1985. According to the

World Bank’s 1986 World Development Report, the Asian region was suffering from a

substantial fall in exports due to the preceding slowdown in the US economy, and also from

a drop in commodity prices. This is confirmed in Daquila (2005), who also argues that

higher interest rates (stemming from higher rates in the US) reduced consumption and

investment in Southeast Asian countries which led to a fall in output. Daquila (2005) cites

Booth (1992) who finds that the trough of the slowdown of the Asian economies was in

Figure 7.9: Average Economic Growth (1984-2003)

-8

-6

-4

-2

0

2

4

6

8

10

1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

(%)

Rest of the sample

East Asia


166

1985, with recovery commencing in 1986. Focusing purely on the period between 1986 and

1996 makes the difference in growth rates between East Asia and the rest of the sample far

more apparent.36

The preceding discussion suggests it would be worthwhile to investigate the correlation

between corruption and economic growth for East Asia during the period 1986-1996.

Figure 7.10 plots corruption (using the composite index based on a simple average) against

economic growth for the East Asian countries in that period. The black dots in the chart

represent the East Asian nations while the smaller grey dots represent the rest of the

sample. The dashed lines indicate the averages for the entire sample. Most of the East

Asian countries are in the northwest quadrant of the chart because of their higher than

average corruption levels and growth rates. This provides the basis for an analysis of the

particular relationship between corruption and economic growth in East Asia.

Figure 7.10: Corruption vs Economic Growth (Average, 1986-1996)

-2-10123456789

10

0 1 2 3 4 5 6 7 8 9 10

Corruption

Eco

nom

ic G

row

th (%

)

ThailandSingapore

Malaysia

South KoreaIndonesia

Hong Kong

PhilippinesJapan

Source: Compiled using data obtained from Transparency International (various years), the Institute for Management Development (various years), Political Risk Services Inc. (various years) and the World Bank (2005).

36 See Figure 1.1 in Chapter 1.

167

7.3 A Corruption-Growth Model using Panel Data

The same corruption-growth model as described in Chapter 6 will be employed in this

chapter and applied to panel data. The model will take the following form: k

уit = α + βCit + ∑ λjZijt + μit (7.1)

j=1


C = corruption index


μ = error term


The panel dataset employed here covers a time period spanning 20 years from 1984 to 2003

for 33 countries. As in Chapter 6, one-year lags of the control variables will be used to

control for endogeneity. All the economic data for the empirical modelling are drawn from

the World Bank’s World Development Indicators database (2005). The dependent variable

is economic growth represented by the variable GROWTH. The same control variables

used in the previous chapter will be employed in the current model with two additions as

follows:

• CURACC: rate of growth in current account balance (annual percentage change).

The current account balance is the sum of net exports of goods, services, net

income, and net current transfers.

• EDUCGDP: public expenditure on education as a percentage of GDP. This includes

expenditure on education by local, regional and national governments.

Table 7.2 reveals the correlation coefficients between each variable as part of the test for

multicollinearity during the year 1995 (using the ICRG index as the corruption variable).

GVEXGDP appears to be strongly correlated with SECENROL and EDUCGDP across all

samples. For this reason, GVEXGDP will be excluded from the regression model. There do

not appear to be any other signs of multicollinearity.

168

7.4 Estimation Results and Preliminary Analysis

Regression results are outlined in Tables 7.3-7.5. GROWTH was used as the dependent

variable and initially regressed against all the independent variables with the results being

presented in column 1 in each table. The least significant variable was then removed and

the regression was performed once again. This process was repeated, with each set of

regression results reported in a separate column, until only CORRUPT and significant

independent variables (at least at the 5% level) remained. For all regressions, the Hausman

test was used to determine whether a Fixed Effects or Random Effects model was

appropriate. The Hausman test statistic is provided for each regression together with its

corresponding p-value. A p-value less than 0.05 (5% level significance) indicates that the

Fixed Effects model is preferred.

169


Variable 1 2 3 4 5 6 7 8 9 10 11 12 1. CORRUPT 1 0.09 -0.05 -0.36 0.10 -0.36 -0.20 0.69 0.23 0.54 0.08 0.64

2. EXPGDP 1 0.23 0.14 0.68 0.52 -0.24 -0.10 0.42 -0.28 0.60 -0.21

3. GFCFRATE 1 -0.05 0.31 0.27 -0.48 -0.16 -0.07 -0.06 0.23 -0.16

4. EXPRATE 1 0.10 0.38 -0.03 -0.34 -0.23 -0.44 0.31 -0.31

5. MKTCAP 1 0.57 -0.27 -0.17 0.05 -0.32 0.65 -0.14

6. POPRATE 1 0.04 -0.61 -0.02 -0.61 0.44 -0.48

7. REALINT 1 -0.16 -0.27 0.01 -0.43 -0.06

8. SECENROL 1 0.29 0.74 -0.05 0.57

9. CURACC 1 0.17 0.24 0.10

10. GVEXGDP 1 -0.22 0.74

11. HITECHEX 1 -0.06

12. EDUCGDP 1

Source: Author’s own calculation based on data obtained from Transparency International (various years), the Institute for Management

Development (various years), Political Risk Services Inc. (various years) and the World Bank (2005).

170

Table 7.3: Regression Results using CPI

Variable 1 2 3 4 5

CORRUPT -0.339 (0.31) -0.247 (0.30) -0.222 (0.29) -0.207 (0.29) -0.184 (0.29)

GFCFRATE 0.231 (0.02)** 0.241 (0.02)** 0.244 (0.02)** 0.245 (0.17)** 0.252 (0.02)**

POPRATE 0.381 (0.55) 0.396 (0.53) 0.494 (0.50)

SECENROL -0.048 (0.03) -0.044 (0.02)* -0.046 (0.02)* -0.048 (0.02)* -0.045 (0.02)*

EXPGDP -0.018 (0.04) -0.031 (0.04) -0.039 (0.03) -0.032 (0.03)

EXPRATE 0.032 (0.02) 0.031 (0.02) 0.031 (0.02) 0.029 (0.02) 0.023 (0.02)

MKTCAP 0.015 (0.00)** 0.014 (0.00)** 0.015 (0.00)** 0.014 (0.00)** 0.014 (0.00)**

REALINT 0.011 (0.04)

CURACC -0.039 (0.05) -0.026 (0.05)

HITECHEX 0.044 (0.04) 0.055 (0.04) 0.055 (0.04) 0.052 (0.04) 0.023 (0.03)

EDUCGDP -0.339 (0.32) -0.441 (0.29) -0.426 (0.29) -0.483 (0.28) -0.530 (0.28)

Hausman statistic

(p-value)

31.61

(0.00)

32.41

(0.00)

34.19

(0.00)

28.71

(0.00)

25.71

(0.00)

d.f. 11 10 9 8 7

No. of obs 142 152 152 152 152

Adjusted R2 0.80 0.81 0.81 0.81 0.81

Notes: Coefficients are presented with standard errors in parentheses. Individual fixed effects are not reported. *Significant at 5% level. **Significant at 1% level.

171

Table 7.3: Regression Results using CPI (cont’d)

Variable 6 7 8 9

CORRUPT -0.168 (0.29) -0.218 (0.29) -0.087 (0.12) -0.401 (0.42)

GFCFRATE 0.252 (0.02)** 0.260 (0.014)** 0.294 (0.01)** 0.012 (0.00)**

POPRATE

SECENROL -0.041 (0.02) -0.034 (0.02)

EXPGDP

EXPRATE 0.023 (0.02)

MKTCAP 0.015 (0.00)** 0.015 (0.00)** 0.009 (0.00)** 0.021 (0.01)**

REALINT

CURACC

HITECHEX

EDUCGDP -0.487 (0.27) -0.535 (0.27)* -0.256 (0.17)

Hausman statistic

(p-value)

19.94

(0.00)

12.29

(0.03)

7.69

(0.10)

9.42

(0.02)

d.f. 6 5 4 3

No. of obs 152 153 162 287

Adjusted R2 0.81 0.81 0.81 0.81


.

172

Variable 1 2 3 4 5

CORRUPT 0.200 (0.15) 0.198 (0.15) 0.054 (0.14) 0.094 (0.15) 0.138 (0.14)

GFCFRATE 0.224 (0.01)** 0.224 (0.01)** 0.239 (0.01)** 0.239 (0.01)** 0.264 (0.01)**

POPRATE 0.103 (0.37)

SECENROL -0.007 (0.01) -0.008 (0.01) -0.010 (0.01) -0.008 (0.01)

EXPGDP 0.035 (0.02) 0.035 (0.02) 0.024 (0.02) 0.052 (0.02)** 0.033 (0.02)*

EXPRATE 0.066 (0.01)** 0.066 (0.01)** 0.057 (0.01)** 0.067 (0.02)** 0.067 (0.01)**

MKTCAP 0.010 (0.00)** 0.010 (0.00)** 0.008 (0.00)** 0.008 (0.02)** 0.006 (0.00)**

REALINT 0.014 (0.03) 0.013 (0.03)

CURACC -0.099 (0.03)** -0.102 (0.03)** -0.086 (0.03)** -0.115 (0.03)** -0.065 (0.03)*

HITECHEX 0.016 (0.02) 0.016 (0.02) 0.021 (0.02)

EDUCGDP -0.362 (0.18)* -0.366 (0.18)* -0.520 (0.16)** -0.555 (0.17)** -0.481 (0.15)**

Hausman statistic

(p-value)

30.73

(0.00)

29.14

(0.00)

29.07

(0.00)

31.60

(0.00)

27.82

(0.00)

d.f. 11 10 9 8 7

No. of obs 259 259 282 286 349

Adjusted R2 0.84 0.84 0.83 0.83 0.81

Table 7.4: Regression Results using ICRG Index


173

Individual fixed effects are not reported. *Significant at 5% level. **Significant at 1% level.


CORRUPT -0.007 (0.12) -0.001 (0.11) -0.082 (0.11) -0.115 (0.11) -0.188 (0.12) -0.226 (0.12)

GFCFRATE 0.234 (0.01)** 0.233 (0.01)** 0.247 (0.01)** 0.246 (0.01)** 0.274 (0.01)** 0.274 (0.01)**

POPRATE 0.159 (0.40)

SECENROL -0.012 (0.01) -0.012 (0.01) -0.012 (0.01) -0.010 (0.01)

EXPGDP 0.034 (0.02) 0.036 (0.02) 0.031 (0.02) 0.035 (0.02)* 0.017 (0.02)

EXPRATE 0.060 (0.02)** 0.060 (0.02)** 0.053 (0.02)** 0.066 (0.02)** 0.052 (0.02)** 0.056 (0.02)**

MKTCAP 0.010 (0.00)** 0.010 (0.00)** 0.009 (0.00)** 0.008 (0.00)** 0.007 (0.00)** 0.007 (0.00)**

REALINT 0.018 (0.03) 0.017 (0.03)

CURACC -0.076 (0.04)* -0.080 (0.03)* -0.075 (0.03)* -0.109 (0.03)** -0.078 (0.03)* -0.061 (0.03)*

HITECHEX -0.027 (0.03) -0.028 (0.03) -0.024 (0.03)

EDUCGDP -0.516 (0.19)** -0.523 (0.19)** -0.584 (0.17)** -0.674 (0.18)** -0.668 (0.18)** -0.662 (0.03)*

Hausman statistic

(p-value)

32.46

(0.00)

30.95

(0.00)

31.57

(0.00)

27.14

(0.00)

24.99

(0.00)

26.70

(0.00)

d.f. 11 10 9 8 7 6

No. of obs 235 235 255 257 266 266

Adjusted R2 0.84 0.84 0.84 0.84 0.82 0.83

Table 7.5: Regression Results using WCY Index


174


In all three tables, it is evident that the corruption variable is not a significant explanator of

economic growth. However, although the coefficient for corruption was negative for the

CPI and WCY samples, it was positive for the ICRG sample. The significance of the other

control variables varied across all samples, but the coefficient for the growth rate of gross

fixed capital formation (GFCFRATE) was consistently positive and significant at the 1%

level. The next step is to use the composite index, achieved by taking a simple average of

the three corruption indices, to represent corruption, and re-run the regressions. The results

are presented in Table 7.6.

According to Model 7 in Table 7.6, corruption is still not significant but has a negative

coefficient (reflecting a positive relationship with economic growth). The number of

observations is now 349 as a result of using all three corruption indices, which is the

highest number in all of the regressions performed to this point. The estimated coefficients

of the gross fixed capital formation growth rate, the growth rate in exports and the market

capitalisation of listed companies as a proportion of GDP are all positive explanators of

economic growth with estimated coefficients being significant at the 1% level. Government

expenditure on education as a proportion of GDP appears to have a negative relationship

with economic growth which is also significant at the 1% level.

To take potential endogeneity into consideration, one-year lags of the control variables

were included in the model in place of the original control variables. Table 7.7 shows the

results based on the composite corruption index.37 According to the results, the lagged

gross fixed capital formation growth rate, the lagged exports growth rate, the lagged market

capitalisation value of listed companies as a proportion of GDP, and the lagged current

account balance as a proportion of GDP are all found to be significant and positively

influencing economic growth. Interestingly though, the estimated coefficient for the

corruption variable was also positive and significant at the 1% level. This suggests that low

levels of corruption led to higher levels of economic growth.

37 Results based on the individual corruption indices are provided in the appendix to this chapter. Corruption is found to be significant at the 1% level in the WCY sample, with a positive coefficient reflecting a negative relationship with growth.

175

Variable 1 2 3 4 5 6 7

CORRUPT -0.023 (0.13) -0.024 (0.13) -0.144 (0.12) -0.147 (0.12) 0.245 (0.10) -0.042 (0.10) -0.023 (0.10)

GFCFRATE 0.223 (0.01)** 0.223 (0.01)** 0.240 (0.01)** 0.239 (0.01)** 0.263 (0.01)** 0.259 (0.01)** 0.264 (0.01)**

POPRATE 0.068 (0.37)

SECENROL -0.007 (0.01) -0.007 (0.01) -0.010 (0.01) -0.008 (0.01)

EXPGDP 0.026 (0.02) 0.026 (0.02) 0.018 (0.02) 0.040 (0.02)* 0.029 (0.02)

EXPRATE 0.068 (0.01)** 0.068 (0.01)** 0.058 (0.01)** 0.069 (0.01)** 0.069 (0.01)** 0.075 (0.01)** 0.075 (0.01)**

MKTCAP 0.009 (0.00)** 0.009 (0.00)** 0.008 (0.00)** 0.008 (0.00)** 0.006 (0.00)** 0.007 (0.00)** 0.007 (0.00)**

REALINT 0.014 (0.03) 0.013 (0.03)

CURACC -0.101 (0.03)** -0.103 (0.03)** -0.084 (0.03)** -0.113 (0.03)** -0.065 (0.03)* -0.042 (0.03)

HITECHEX 0.016 (0.03) 0.016 (0.03) 0.013 (0.03)

EDUCGDP -0.393 (0.18)* -0.395 (0.18) -0.558 (0.17)** -0.604 (0.17)** -0.477 (0.15)* -0.466 (0.15)** -0.472 (0.15)**

Hausman statistic

(p-value)

29.60

(0.00)

28.42

(0.00)

30.62

(0.00)

30.77

(0.00)

27.31

(0.00)

25.27

(0.00)

22.71

(0.00)

d.f. 11 10 9 8 7 6 5

No. of obs 259 259 282 286 349 349 349

Adjusted R2 0.84 0.84 0.84 0.83 0.81 0.81 0.81

Table 7.6: Regression Results using Composite Index


176


Variable 1 2 3 4 5 6 7

CORRUPT 0.211 (0.21) 0.338 (0.21) 0.405 (0.18)* 0.368 (0.60) 0.491 (0.15)** 0.445 (0.15)** 0.459 (0.15)**

GFCFRATE1 0.041 (0.02)* 0.066 (0.02)** 0.081 (0.02)** 0.057 (0.02)** 0.084 (0.02)** 0.065 (0.02)** 0.065 (0.02)**

POPRATE1 0.103 (0.54) 0.377 (0.46) 0.489 (0.44) 0.796 (0.36)* 0.440 (0.43) 0.402 (0.43)

SECENROL1 0.011 (0.01) 0.002 (0.01)

EXPGDP1 -0.006 (0.03) 0.065 (0.03) -0.013 (0.03)

EXPRATE1 0.045 (0.03)* 0.031 (0.02) 0.033 (0.02) 0.019 (0.02) 0.047 (0.02)* 0.041 (0.02)* 0.040 (0.02)*

MKTCAP1 0.011 (0.00)** 0.014 (0.00)** 0.018 (0.00)** 0.008 (0.00) 0.013 (0.00)** 0.014 (0.00)** 0.014 (0.00)**

REALINT1 -0.057 (0.04) -0.066 (0.04) -0.023 (0.04) -0.014 (0.04) -0.005 (0.04)

CURACC1 0.030 (0.05) 0.109 (0.05)* 0.208 (0.05)** 0.343 (0.05)** 0.152 (0.04)** 0.157 (0.04)** 0.150 (0.04)**

HITECHEX1 -0.034 (0.04) -0.073 (0.04) -0.082 (0.03)* 0.012 (0.03)

EDUCGDP1 0.005 (0.26)

Hausman statistic

(p-value)

40.20

(0.00)

44.47

(0.00)

53.80

(0.00)

46.77

(0.00)

41.33

(0.00)

41.69

(0.00)

44.98

(0.00)

d.f. 11 10 9 8 7 6 5

No. of obs 259 326 411 411 420 468 468

Adjusted R2 0.54 0.43 0.37 0.59 0.35 0.28 0.28

Table 7.7: Controlling for Endogeneity (Composite Index)

177


7.5 East Asia: A Corrupt-Growth Club?

Since the focus of this study is on the relationship between corruption and growth before

the 1997 Asian Financial Crisis, the dummy variable Db is included. It takes a value of 1 for

those countries that were affected by the Asian Crisis (Hong Kong, Indonesia, Japan,

Malaysia, the Philippines, Singapore, South Korea and Thailand) and 0 for all other

countries. A second dummy variable, Dt, takes on a value of 1 for years up to and including

1997, and 0 for all other years. The purpose of these dummies is to isolate only

observations for the East Asian countries prior to the Crisis, to see if there was a particular

relationship between corruption and economic growth for this sub-sample. The two dummy

variables and their cross-products with the corruption variable are added to Model 7 in

Table 7.6 and the estimated results are shown in Table 7.8. As can be seen in Model 1, both

Dt and its interaction with CORRUPT is not significant. Db is not significant either but its

interaction with corruption is significant. The anomalous standard error of Db is enough to

warrant its removal from the model, along with its interaction with corruption despite its

significance. This produced Model 3. However, neither corruption nor its interaction with

the dummy variable is significant. Table 7.9 repeats the analysis after taking account of

endogeneity, i.e., using Model 7 from Table 7.7.

178

Table 7.8: Using Dummy Variables

Variable 1 2 3

Db 7.885 (.…..)a 7.029 (…...)a

Db*C 0.705 (0.33)* 0.673 (0.32)*

Dt -0.282 (0.73)

Dt*C 0.044 (0.10)

Db*Dt 3.345 (1.30)* 3.098 (1.09)** 1.894 (0.93)*

Db*Dt*C -0.691 (0.27)** -0.652 (0.25)** -0.262 (0.17)

CORRUPT -0.122 (0.15) -0.870 (0.13) 0.020 (0.12)

GFCFRATE 0.258 (0.01)** 0.258 (0.01)** 0.257 (0.01)**

EXPRATE 0.075 (0.01)** 0.076 (0.01)** 0.077 (0.01)**

MKTCAP 0.006 (0.00)* 0.006 (0.00)** 0.007 (0.00)**

EDUCGDP -0.469 (0.16)** -0.455 (0.16)** -0.422 (0.16)**

Hausman statistic

(p-value)

30.86

(0.00)

30.72

(0.00)

25.86

(0.00)

d.f. 11 9 7

No. of obs 349 349 349

Adjusted R2 0.81 0.81 0.81

Notes: Coefficients are presented with standard errors in parentheses. Individual fixed effects are not reported. aStandard error is too large and not reported. *Significant at 5% level. **Significant at 1% level.

179

Table 7.9: Using Dummy Variables and Controlling for Endogeneity

Variable 1 2 3 4 5

Db 0.474 (…..)a

Db*C 0.450 (0.41)

Dt -1.003 (0.92) -0.919 (0.91)

Dt*C 0.173 (0.13) 0.150 (0.12)

Db*Dt 10.499 (1.78)** 9.927 (1.70)** 9.085 (1.40)** 8.790 (1.37)** 8.640 (1.37)**

Db*Dt*C -1.190 (0.32)** -0.998 (0.26)** -0.890 (0.24)** -0.650 (0.22)** -0.617 (0.22)**

CORRUPT 0.050 (0.21) 0.167 (0.18) 0.244 (0.15) 0.135 (0.15) 0.123 (0.15)

GFCFRATE1 0.047 (0.02)** 0.044 (0.02)** 0.043 (0.02)** -0.005 (0.00)

EXPRATE1 0.017 (0.02) 0.019 (0.02) 0.020 (0.02)

MKTCAP1 0.014 (0.00)** 0.015 (0.00)** 0.013 (0.00)** 0.015 (0.00)** 0.015 (0.00)**

CURACC1 0.269 (0.04)** 0.258 (0.04)** 0.248 (0.04)** 0.201 (0.04)** 0.204 (0.04)**

Hausman statistic

(p-value)

45.58

(0.00)

48.60

(0.00)

47.25

(0.00)

27.93

(0.00)

20.20

(0.00)

d.f. 11 9 7 6 5

No. of obs 468 468 468 483 483

Adjusted R2 0.38 0.38 0.38 0.39 0.38


180

181

As shown in Model 5, the corruption variable is statistically insignificant, but its interaction

with the dummy variable is highly significant (at the 1% level). This coefficient, when

added to the corruption coefficient, gives the corruption coefficient for the portion of

observations represented by the dummy Db*Dt, i.e., East Asian countries before 1997. That

is, 0.123 – 0.617 = -0.494, which suggests that for East Asian countries before 1997 the

corruption coefficient is negative and significant, indicating that high corruption led to high

economic growth. However, this analysis can potentially be improved in two ways which

were highlighted previously in the chapter. One relates to the path of economic growth in

the sample period. It was shown earlier that there was a large spike in the growth rates of

East Asia prior to 1986, which may be interfering with the relationship between corruption

and economic growth. Another source of interference is the peculiar nature of Singapore as

an East Asian country that consistently performs very well in the corruption indices,

appearing in the top ten in the company of European and North American nations as the

least corrupt countries. To control for this, observations from 1984 and 1985 were removed

from the sample, and Singapore was also excluded. The analysis in Table 7.7 was repeated

(as Model 7 from that table cannot be used for the new sample) and the results of the final

model after following a stepwise elimination technique are shown as Model 1 in Table

7.1038.

Model 2 in Table 7.10 reports the results after the dummy variables were added to Model 1

along with their interactions with corruption, and Model 5 shows the results after

insignificant variables were eliminated in a stepwise manner. The corruption coefficient is

not significant but remains positive. Given the scale of the corruption indices, this reflects a

negative association with economic growth. More importantly is the coefficient for

Db*Dt*C, which is significant at the 1% level and negative. The resulting corruption

coefficient for East Asian countries before 1997 is therefore 0.227 – 0.881 = -0.654, which

suggests that for East Asian countries before 1997, high corruption led to high economic

growth.

38 See Table A7.4 in the appendix to this chapter for the complete results.

Table 7.10: Using Dummy Variables and Controlling for Endogeneity (Excluding Singapore and 1984/1985)

Variable 1 2 3 4 5

Db 1.968 (…..)a

Db*C 0.633 (0.44)

Dt -0.758 (0.92) -0.694 (0.92)

Dt*C 0.150 (0.13) 0.122 (0.13)

Db*Dt 11.386 (2.03)** 10.653 (1.96)** 9.979 (1.75)** 9.126 (1.40)**

Db*Dt*C -2.330 (0.35)** -1.074 (0.31)** -0.982 (0.29)** -0.881 (0.24)**

CORRUPT 0.293 (0.17) 0.057 (0.21) 0.197 (0.19) 0.264 (0.17) 0.227 (0.15)

GFCFRATE1 0.069 (0.02)** 0.049 (0.02)** 0.046 (0.01)** 0.045 (0.01)** 0.047 (0.01)**

MKTCAP1 0.019 (0.00)** 0.014 (0.00)** 0.014 (0.00)** 0.013 (0.00)** 0.013 (0.00)**

CURACC1 0.210 (0.04)** 0.278 (0.04)** 0.269 (0.04)** 0.262 (0.04)** 0.250 (0.04)**

HITECHEX1 -0.112 (0.03)** 0.011 (0.03) 0.001 (0.03) -0.001 (0.03)

Hausman statistic

(p-value)

56.52

(0.00)

42.17

(0.00)

45.80

(0.00)

42.99

(0.00)

42.99

(0.00)

d.f. 5 11 9 7 7

No. of obs 454 454 454 454 468

Adjusted R2 0.31 0.39 0.39 0.39 0.38


182

7.6 Sensitivity Analysis

To check the reliability and accuracy of the simple-average technique in combining the

three different corruption indices into one composite index, a sensitivity analysis is

performed using Model 5 in Table 7.10. The results are reported in Table 7.11. Model 1 is

the same as Model 5 in Table 7.10. The next five models represent the five different

standardised versions of the composite index as discussed earlier in the chapter. The

estimated corruption coefficient is positive across all models except the one measured by

STD1, but is not significant in any of the models. This is consistent with Model 1. The

binary dummy Db*Dt is positive and significant at the 1% level across all models, and the

size of the coefficient is also similar across all models though half the size of the

corresponding coefficient in Model 1. As for the interaction of the dummy with corruption,

the estimated coefficient is negative across all models and of similar size. In two of the

models (representing STD2 and STD3) Db*Dt*C is also significant at the 5% level. The

resulting corruption coefficient estimate for East Asia prior to 1997 is 0.563 – 1.207 = -

0.644 for STD2 and 0.567 – 1.580 = -1.013 for STD3, both implying a positive relationship

with economic growth consistent with the results in Model 1.

7.7 Comparison with Other Studies

The results from similar studies analysing the effect of corruption on economic growth

using panel data are presented in Table 7.12. The first study, by Li et al. (2000), revealed

that the corruption coefficient is negative, but since their corruption index was reversed this

reflected a positive relationship between corruption and growth. All studies therefore

revealed a similar relationship. Del Monte and Papagni (2001) focused on different regions

within Italy, so the regression model only applied to one country. However the authors

opted for a different corruption measure to the indices used by other studies, namely the

number of crimes committed against the public administration per employee. Gyimah-

Brempong (2003) analysed different countries in Africa so his model examined corruption

as it applies only in Africa. The present study covers a far more broader sample. As for the

specific relationship between corruption and growth in East Asia, only Li et al. (2000)

attempted to explore this area using panel data. Their result shows a significant, negative

coefficient for corruption (and thus reflecting a positive relationship) though less so than

183


Db*Dt 9.126 (1.40)** 4.164 (0.64)** 3.568 (0.65)** 3.456 (0.66)** 3.775 (0.67)** 4.415 (0.57)**

Db*Dt*C -0.881 (0.24)** -0.107 (0.06) -1.207 (0.52)* -1.580 (0.65)* -0.986 (0.58) -0.053 (0.60)

CORRUPT 0.227 (0.15) -0.023 (0.02) 0.563 (0.39) 0.567 (0.45) 0.121 (0.49) 0.317 (0.18)

GFCFRATE1 0.047 (0.01)** 0.053 (0.01)** 0.048 (0.01)** 0.049 (0.01)** 0.049 (0.01)** 0.044 (0.02)**

MKTCAP1 0.013 (0.00)** 0.014 (0.00)** 0.014 (0.00)** 0.014 (0.00)** 0.013 (0.00)** 0.018 (0.00)**

CURACC1 0.250 (0.04)** 0.240 (0.04)** 0.237 (0.04)** 0.243 (0.04)** 0.236 (0.04)** 0.232 (0.04)**

Hausman statistic

(p-value)

42.99

(0.00)

38.70

(0.00)

41.57

(0.00)

40.82

(0.00)

38.91

(0.00)

44.87

(0.00)

d.f. 6 6 6 6 6 6

No. of obs 468 468 468 468 468 468

Adjusted R2 0.38 0.37 0.37 0.37 0.37 0.37

Table 7.11: Sensitivity Analysis


184


Study Dependent Variable Corruption Population

Growth

Education R2 Period No. of Obs.

Li et al. (2000) Growth in real GDP per capita

(PPP adjusted) -0.439 -1.607* -0.361 0.33 1982-1994 87

Gyimah-Brempong

(2003) Growth in GDP per capita 0.243** - 0.284 - 1993-1999 125

Del Monte and

Papagni (2001) Growth in GDP per worker -0.146** - 0.002** 0.56 1984-1999 540

This Study Growth in GDP -0.654 - - 0.38 1984-2003 468

Notes: Li et al. (2000) reversed their corruption indices, such that a higher index value reflected a higher level of corruption. Del Monte and Papagni (2001) use “crimes against the public administration per employee” as a measure of corruption, so a higher value translates to a higher level of corruption.

Table 7.12: Regression Results from Existing Studies

185


186

the corresponding coefficient without including an Asian dummy. Their explanation was

that “during 1980-94 Asia did not pay the price paid elsewhere for corruption; in other

words, corruption may indeed have acted as grease money in Asia during this period”

(p.18).

7.8 Conclusion

This chapter has extended Chapter 6 to provide an insight into the relationship between

corruption and economic growth in East Asia over time. Examination of the path that

corruption and growth had taken over the period 1984-2003 revealed a distinct period

between 1986 and 1996 where the East Asian economies were performing far better than

their global counterparts in terms of growth, but far worse in terms of their perceived level

of corruption. Undertaking analysis of panel data revealed that although corruption had a

negative effect on economic growth globally during the twenty years prior to 2003, it was

positively impacting growth in East Asia during the ten years preceding the 1997 Asian

Financial Crisis. This lends considerable weight to the proposition purported by this study

that corruption played a substantial role in the rise of the East Asian ‘miracle’ economies

during that period. The next chapter takes this analysis one step further by analysing

corruption in a very different and distinct dimension in which it occurs in East Asia, namely

in the context of rent-seeking.

187


Table A7.1: Sample of Countries

Australia Malaysia

Austria Mexico

Belgium Netherlands

Brazil New Zealand

Canada Norway

Denmark Philippines

Finland Portugal

France Singapore

Germany South Korea

Greece Spain

Hong Kong SAR Sweden

Hungary Switzerland

India Thailand

Indonesia Turkey

Ireland United Kingdom

Italy United States

Japan

Variable 1 2 3 4 5

CORRUPT -0.473 (0.45) 0.275 (0.37) 0.275 (0.37) 0.278 (0.37) 0.299 (0.37)

GFCFRATE1 0.026 (0.03) -0.057 (0.02)* -0.057 (0.02)* -0.057 (0.02)* -0.050 (0.02)*

POPRATE1 -0.290 (0.86) 0.024 (0.74)

SECENROL1 -0.050 (0.02)* -0.043 (0.03) -0.043 (0.03) -0.044 (0.02) -0.042 (0.02)

EXPGDP1 0.068 (0.03)** -0.035 (0.04) -0.035 (0.04) -0.036 (0.04)

EXPRATE1 -0.024 (0.02) 0.043 (0.03) 0.043 (0.03) 0.043 (0.03) 0.039 (0.03)

MKTCAP1 0.006 (0.00) 0.007 (0.00) 0.007 (0.00) 0.007 (0.00) 0.007 (0.00)

REALINT1 -0.004 (0.05)

CURACC1 -0.046 (0.07) -0.006 (0.06) -0.006 (0.06)

HITECHEX1 -0.072 (0.05) -0.067 (0.06) -0.067 (0.06) -0.068 (0.06) -0.105 (0.04)**

EDUCGDP1 -0.374 (0.33) -0.456 (0.33) -0.458 (0.33) -0.456 (0.32) -0.447 (0.32)

Hausman statistic

(p-value)

45.66

(0.00)

37.64

(0.00)

35.40

(0.00)

36.70

(0.00)

34.74

(0.00)

d.f. 11 10 9 8 7

No. of obs 167 179 179 179 179

Adjusted R2 0.63 0.42 0.42 0.43 0.43

Table A7.2: Controlling for Endogeneity (CPI)


188


Variable 6 7 8 9

CORRUPT 0.519 (0.41) 0.489 (0.40) 0.015 (0.40) -0.387 (0.42)

GFCFRATE1 -0.128 (0.02)

POPRATE1

SECENROL1 -0.024 (0.02) -0.233 (0.02) -0.022 (0.02)

EXPGDP1

EXPRATE1 0.012 (0.03) 0.011 (0.02)

MKTCAP1 0.010 (0.00)* 0.010 (0.00)* 0.011 (0.00)* 0.022 (0.01)**

REA LINT1

CURACC1

HITECHEX1 -0.092 (0.04)* -0.092 (0.04)* -0.096 (0.04)* -0.100 (0.04)*

EDUCGDP1

Hausman statistic

(p-value)

29.50

(0.00)

17.46

(0.00)

15.25

(0.00)

19.02

(0.00)

d.f. 6 5 4 3

No. of obs 241 241 245 295

Adjusted R2 0.25 0.25 0.29 0.18

Table A7.2: Controlling for Endogeneity (CPI, cont’d)

Notes: Coefficients are presented with standard errors in parentheses. Individual fixed effects are not reported.

189


Table A7.3: Controlling for Endogeneity (ICRG Index)

Variable 1 2 3 4 5

CORRUPT 0.278 (0.50) 0.228 (0.23) 0.228 (0.23) 0.228 (0.23) 0.398 (0.24)

GFCFRATE1 0.065 (0.02)** 0.058 (0.02)** 0.058 (0.02)** 0.057 (0.02)** 0.073 (0.02)**

POPRATE1 -0.803 (0.66) 0.015 (0.63)

SECENROL1 -0.011 (0.01) 0.013 (0.01) 0.013 (0.01) 0.013 (0.01) 0.008 (0.01)

EXPGDP1 0.093 (0.04)* 0.053 (0.03) 0.053 (0.03) 0.054 (0.03) 0.085 (0.03)**

EXPRATE1 -0.024 (0.02) 0.033 (0.03) 0.033 (0.03) 0.033 (0.03) 0.023 (0.03)

MKTCAP1 0.002 (0.00)

REALINT1 -0.099 (0.04)* -0.104 (0.05)* -0.104 (0.05)* -0.104 (0.05)* -0.119 (0.05)*

CURACC1 0.126 (0.07) 0.005 (0.06) 0.004 (0.05)

HITECHEX1 -0.078 (0.04) -0.049 (0.04) -0.049 (0.04) -0.049 (0.04) -0.083 (0.04)*

EDUCGDP1 0.519 (0.31) 0.053 (0.29) 0.054 (0.29) 0.056 (0.29)

Hausman statistic

(p-value)

40.86

(0.00)

19.79

(0.00)

29.09

(0.00)

29.05

(0.00)

31.23

(0.00)

d.f. 11 10 9 8 7

No. of obs 239 244 244 244 302

Adjusted R2 0.72 0.50 0.50 0.51 0.38


190

Table A7.3: Controlling for Endogeneity (ICRG Index, cont’d)

Variable 6 7 8 9

CORRUPT 0.467 (0.24)* 0.379 (0.24) 0.299 (0.25) 0.307 (0.25)

GFCFRATE1 0.086 (0.02)** 0.072 (0.02)** -0.005 (0.00)

POPRATE1

SECENROL1

EXPGDP1 0.086 (0.03)** 0.089 (0.03)** 0.065 (0.03)* 0.070 (0.03)**

EXPRATE1 0.028 (0.03) 0.019 (0.03)

MKTCAP1

REALINT1 -0.041 (0.04)

CURACC1

HITECHEX1 -0.092 (0.04)* -0.102 (0.04)* -0.094 (0.04)** -0.100 (0.04)**

EDUCGDP1

Hausman statistic

(p-value)

30.12

(0.00)

31.67

(0.00)

23.23

(0.00)

23.27

(0.00)

d.f. 6 5 4 3

No. of obs 364 400 412 412

Adjusted R2 0.28 0.22 0.20 0.20


191

Variable 1 2 3 4 5

CORRUPT 0.417 (0.17)* 0.168 (0.26) 0.380 (0.17)* 0.365 (0.17)* 0.440 (0.17)**

GFCFRATE1 0.029 (0.02) 0.059 (0.02)** 0.043 (0.02)* 0.044 (0.02)** 0.043 (0.01)**

POPRATE1 -0.068 (0.54)

SECENROL1 0.012 (0.01) -0.001 (0.01) 0.013 (0.01) 0.013 (0.01) 0.003 (0.01)

EXPGDP1 0.010 (0.03) 0.023 (0.03) 0.011 (0.03)

EXPRATE1 0.050 (0.02)* 0.001 (0.02)

MKTCAP1 0.011 (0.00)** 0.003 (0.00) 0.012 (0.00)** 0.012 (0.00)** 0.016 (0.00)**

REALINT1 -0.052 (0.04) -0.082 (0.04) -0.042 (0.04) -0.044 (0.04) -0.075 (0.04)

CURACC1 0.024 (0.05) 0.112 (0.06) 0.028 (0.05) 0.035 (0.04) 0.092 (0.04)*

HITECHEX1 -0.078 (0.04) -0.580 (0.04) -0.079 (0.04)** -0.070 (0.03)* -0.098 (0.03)**

EDUCGDP1 -0.089 (0.26) 0.185 (0.27) -0.161 (0.26) -0.163 (0.26)

Hausman statistic

(p-value)

48.97

(0.00)

35.85

(0.00)

44.92

(0.00)

44.36

(0.00)

46.61

(0.00)

d.f. 11 10 9 8 7

No. of obs 257 257 263 263 332

Adjusted R2 0.56 0.69 0.58 0.58 0.47

Table A7.4: Controlling for Endogeneity (WCY Index)


192


Variable 6 7 8 9 10

CORRUPT 0.382 (0.30) 0.773 (0.19)** 0.721 (0.18)** 0.703 (0.18)** 0.711 (0.18)**

GFCFRATE1 0.019 (0.02) -0.005 (0.00) -0.005 (0.00) -0.005 (0.00)

POPRATE1

SECENROL1

EXPGDP1

EXPRATE1

MKTCAP1 0.011 (0.00)* 0.019 (0.00)** 0.018 (0.00)** 0.018 (0.00)** 0.019 (0.00)**

REALINT1 0.027 (0.04) -0.000 (0.04)

CURACC1 0.293 (0.06)** 0.029 (0.04) 0.057 (0.04)**

HITECHEX1 0.015 (0.04)

EDUCGDP1

Hausman statistic

(p-value)

27.57

(0.00)

32.38

(0.00)

31.87

(0.00)

29.84

(0.00)

22.55

(0.00)

d.f. 6 5 4 4 4

No. of obs 377 381 419 420 420

Adjusted R2 0.57 0.29 0.26 0.26 0.26

Table A7.4: Controlling for Endogeneity (WCY Index, cont’d)


193


Variable 1 2 3 4 5 6 7

CORRUPT 0.211 (0.21) 0.338 (0.21) 0.405 (0.18) 0.415 (0.17)* 0.302 (0.17) 0.311 (0.17) 0.293 (0.17)

GFCFRATE1 0.041 (0.02)* 0.066 (0.02)** 0.081 (0.02)** 0.082 (0.02)** 0.062 (0.02)** 0.063 (0.02)** 0.069 (0.02)**

POPRATE1 0.103 (0.54) 0.377 (0.46) 0.489 (0.44) 0.482 (0.44) 0.397 (0.44)

SECENROL1 0.011 (0.01) 0.002 (0.01)

EXPGDP1 -0.006 (0.03) 0.007 (0.03) -0.013 (0.03)

EXPRATE1 0.045 (0.02)* 0.031 (0.02) 0.033 (0.02) 0.031 (0.02) 0.031 (0.02) 0.030 (0.02)

MKTCAP1 0.011 (0.00)** 0.014 (0.00) 0.018 (0.00)** 0.018 (0.00)** 0.019 (0.00)** 0.018 (0.00)** 0.019 (0.00)**

REALINT1 -0.057 (0.04) -0.066 (0.04) -0.023 (0.04) -0.020 (0.04)

CURACC1 0.030 (0.05) 0.109 (0.05)* 0.208 (0.05)** 0.200 (0.04)** 0.213 (0.04)** 0.208 (0.04)** 0.210 (0.04)**

HITECHEX1 -0.034 (0.04) -0.073 (0.04) -0.082 (0.03)* -0.090 (0.03)** -0.107 (0.03)** -0.110 (0.03)** -0.112 (0.03)**

EDUCGDP1 0.005 (0.26)

Hausman statistic

(p-value)

40.20

(0.00)

44.47

(0.00)

53.80

(0.00)

54.12

(0.00)

54.88

(0.00)

58.57

(0.00)

56.52

(0.00)

d.f. 11 10 9 8 7 6 5

No. of obs 259 326 411 411 454 454 454

Adjusted R2 0.54 0.43 0.37 0.37 0.31 0.31 0.31

Table A7.5: Controlling for Endogeneity (Composite Index, Excluding Singapore and 1984/1985)

194


195

CHAPTER 8

RENT-SEEKING AND ECONOMIC GROWTH:

EMPIRICAL EVIDENCE

“The chaebols are already aware that they will have to change

their practices to keep pace with international standards. Past

governments received donations from them and returned favors.

Our government doesn't owe anything to the chaebols…There no

longer exist any strong ties between the government and the

chaebols; we are now free to follow legally-framed practices. You

can imagine the change.”

– Former South Korean President Kim Dae Jung.39

8.1 Introduction

The chaebols in South Korea have a long history of being connected with the government.

But South Korea was certainly not alone in this regard. Many East Asian countries were

riddled with patron-client networks that gave rise to rent-seeking behaviour.40 This

phenomenon was reviewed in Chapters 2, 3 and 4 as a dimension of corruption, at least as

far as East Asia is concerned. Firms establish connections with the ruling regimes in their

countries and use these connections to secure rents. This is not the same as bribe-paying.

Whereas the Shleifer and Vishny (1993) model identified corruption as the paying of bribes

to bureaucrats in exchange for resources, the rent-seeking model posits that firms receive

the resources by virtue of their relationship with the government, or specifically members

of the ruling elite. Such a model may have theoretical appeal, but does empirical evidence

support it? First of all, how can rent-seeking in East Asian countries be quantified? And

what effect does rent-seeking have on economic growth? Unsurprisingly, there are few

studies that have sought to answer these questions. A significant obstacle to such attempts

is the lack of a reliable measure of rent-seeking. Like corruption, its very nature precludes

its accurate measurement. So the first task of this chapter is to develop a measure of rent-

39 In response to being asked whether he would take harsher action against the chaebols, based on the accusation that they relied heavily on government favours (Krisher, 1998, p.3). 40 See Chapter 2 for a definition of rent-seeking behaviour.

196

seeking. It then replaces the corruption variable in the corruption-growth model developed

in Chapter 6, and the model is then applied in a similar fashion. The results are analysed

accordingly.

8.2 Measurement of Rent-Seeking

One of the limitations of analysing rent-seeking empirically is the absence of data on rent-

seeking. As with corruption, the nature of rent-seeking precludes any accurate measurement

of its extent. Some studies have sought to overcome this problem by introducing a proxy

for rent-seeking. The following section summarises these approaches.

8.2.1 Using Government Transfers as a Proxy

The proxy of government transfers has been used by Naka (2002) and Katz and Rosenberg

(1989). They draw on Tullock’s (1967) original contention that government transfers can

lead to rent-seeking behaviour, and this is reinforced by Khan (2000a). Khan argued that in

Malaysia, patron-client networks were set up by the ruling United Malays National

Organization (UMNO), established after the 1969 riots. UMNO became the dominant

Malay party in the ruling coalition (Barisan Nasional). The objective of the patron-client

networks was to transfer rents from the Chinese capitalists to the political leadership of

UMNO, via legal taxes and illegal extractions.41 These were then used to provide jobs for

Malays in state owned enterprises and subsidised loans through the banking system. Given

the natural dominance held by the Chinese in the private sector, the rents collected through

legal taxes were already large enough and the need for illegal extractions was less. The

political Malay elite who were the recipients of these rents, later engaged in their own rent-

seeking behaviour to maintain their political support amongst the wider Malay community

(Khan, 2000a).

Katz and Rosenberg (1989) analysed this transfer from the government’s point of view,

rather than society. If the government receives taxes from one sector, this is an injection

into its budget which can then be spent elsewhere. Thus Katz and Rosenberg argue that the

41 See Chapter 2.

197

resulting changes in the government’s spending pattern at the margin are a reflection of

rent-seeking behaviour. Figure 8.1 shows how Khan’s (2000a) theory of rent-seeking

through transfers can be viewed from the government’s perspective, in accordance with

Katz and Rosenberg’s (1989) theory.

In Figure 8.1 the original budget constraint faced by the government is BC. As a result of

rent-seeking, the government decides to tax one sector, and shift those funds directly into

other sectors. As a result, the economy moves from point A to point B, representing the

change in composition of the government’s spending. However, as Katz and Rosenberg

(1989) argue, this does not factor in the social cost that arises from rent-seeking. Although

there is now a bigger slice of the pie for non-taxed sectors, the total value of the pie has

diminished by the amount of the rent-seeking. So the budget constraint line actually shifts

downward by the value of the rent-seeking behaviour (which tends towards the size of the

tax but obviously can never exceed it, for that would render the whole rent-seeking process

futile) to BC1 and the economy operates at point C.

Katz and Rosenberg (1989) made an important assumption in their theory. They assumed

that the government transfers occur in response to rent-seeking behaviour, and not out of

altruism toward the public. This challenges the notion of a benevolent government which

was once a standard assumption, but has since been challenged (Lambsdorff, 2002; Ellis

and Fender, 2003). This also adds weight to the neoclassical argument that government

intervention should be kept to a minimum (Kong, 2004). However, Kong (2004) argued

that this does not explain why East Asian countries in particular were able to engage in

these transfers via rent-seeking yet still achieved successful economic development. The

key to this lies in the manner in which the rents are employed by the ‘winners’. Recall

Khan’s (2000a) argument that rents can in fact be value enhancing, if they induce learning

on the part of the recipients. Indeed this was certainly the case in South Korea, where

chaebols were only provided with rents as long as they could continue to perform and

maintain productivity (Kang, 2003). Incorporating this possibility into the analysis yields

Figure 8.2, which is a modification of Figure 8.1 to show a value enhancing transfer.

b)

Q2 Q1 Output of taxed sector

a)

A B

D

E

C

B A

MC MC + tax



Demand

BC

BC1

C

Y1Y0

Y2

X1 X0 Income in taxed sector

Income in all other sectors

(b) Adapted from Katz and Rosenberg (1989, p.136).

Source: (a) Khan (2000a, p.37).

Figure 8.1: Rents based on Value-Reducing Transfers

198

b)

Q2 Q1 Output of taxed sector

a)

A B

C

BC1

BC2

BC

Y2 Y1

Y0

Income in all other sectors

D

E

C

B A

MC MC + tax


Demand


X1 X0 Income in taxed sector

(b) Adapted from Katz and Rosenberg (1989, p.136).

Source: (a) Khan (2000a, p.37).

Figure 8.2: Rents based on Value-Enhancing Transfers

199

200

In Figure 8.2, the economy once again moves along the initial budget constraint BC from

point A to point B, but as a result of the rent recipients investing their rents productively,

the economy is able to achieve growth despite the initial waste of resources. So the budget

constraint initially shifts down from BC to BC1, but the value-enhancing rents push the

economy towards point C, on a new budget constraint BC2 which is higher than the original

(BC).

To test this empirically, one would have to regress growth against rent-seeking behaviour to

determine whether this was the case at least in East Asia. The trick here is to determine a

measure of rent-seeking. Lack of data precludes the use of government transfers, so Katz

and Rosenberg use changes in government expenditure within individual sectors as a proxy

for transfers.42 This assumes that changes in government spending have nothing to do with

government policy and are purely and wholly a result of rent-seeking, and Katz and

Rosenberg acknowledge that this is a limitation in their approach. This technique however

is best suited to the present study as data on government spending is readily available.

8.2.2 Other Measures

There are also a number of other methods employed in order to measure rent-seeking.

Studies such as Bhuyan (2000) benefit from availability of data on precise elements of rent-

seeking. Bhuyan analyses the US food industry in particular and measures rent-seeking by

adding together the salaries, wages and fringe benefits of employees in Political Action

Committees and lobbying organisations; political contributions to federal congressional

candidates; amount spent on legal services; and the amount spent on office space rental by

lobbying organisations. Cole and Chawdhry (2002) measure rent-seeking with several

variables. One variable represents the size of the US state government bureaucracy, which

is defined as the proportion of government employment in a state’s wholesale retail and

trade sector. Another variable reflects the number of interest organisations registered to

lobby in the state’s legislature, and the number of dollars behind each organisation

(measured as the real gross state product divided by the number of organisations)

constitutes a third variable.

42 Naka (2002) was fortunate enough to gain access to data on government transfers in Japan.

201

The availability of data is crucial to the success of any measurement technique. Sometimes

this problem can be circumvented if the analysis focuses on a very specific country, sector

or dimension of rent-seeking. Naka (2002) examines rent-seeking in Japan as a cause for

the slowdown in that country’s economic growth during the 1990s. Of the 47 Japanese

prefectures, those that were strongholds of the ruling LDP party were consistently over-

represented in Parliament as a result of electoral designations. They consequently benefited

disproportionately from shares of budgetary transfers from the government – and this is

what Naka primarily uses as a measure of rent-seeking. Naka (2002) also found that

distribution of public fixed investment expenditure (another measure) across prefectures

was not strictly based on population because some prefectures received greater shares of

investments than others. Specifically, those prefectures in question were mainly small rural

prefectures and are represented by the ruling LDP which has a strong rural base. Of the top

ten recipients of investment expenditure per capita in 1990, only one, Tokyo, was a large

metropolitan prefecture, the rest being small rural ones. Khwaja and Mian (2005) consider

rent-seeking in Pakistan which takes place through loans from government banks given to

‘political’ corporations, i.e., corporations whose director has participated in an election. The

amount of social waste associated with this rent-seeking is measured as a function of the

default rate on the corresponding loans.

One proxy for rent-seeking is the size of the rent and the associated deadweight loss in

Tullock’s (1967) original model of rent-seeking which can be calculated algebraically. For

example, Del Rosal and Fonseca (2000) tackle the issue of labour unrest in the pursuit of

higher wages, and argue that this also is a form of rent-seeking. This unrest is measured as

a function of the prevailing wage rate, the number of lost days of work due to the strikes,

and the price elasticity of labour demand. In a similar manner, Posner (1975) investigates

the social costs associated with the creation of a monopoly, and calculates the rent-seeking

as a function of the resulting fall in output and rise in price, as well as the elasticity of

demand.

Sobel and Garrett (2002) collect data on US industries and divide their sample into

industries based in US capital cities and those based in other cities, to measure the extent of

rent-seeking in capital cities. This is based on the theory that rent-seeking will take place in

capital cities as it is easier to gain direct access to policymakers. For example, Sobel and

202

Garrett measure the number of firms per capita in the legal services sector in US capital

cities, and find that there are nearly one and a half times as many compared to non-capital

cities, which indicates the presence of rent-seeking in capital cities. Murphy et al. (1991)

believe that rent-seeking is a result of misallocated ‘talent’ in an economy. When allocated

towards pure entrepreneurship, talent can lead to efficiency, and engineers are an example

of this. The authors contend that lawyers represent the opposite – their talent is directed

towards rent-seeking which ultimately impedes growth. College enrolments in engineering

are used to measure efficient allocation of talent, while enrolments in law are used as a

proxy for rent-seeking. Mudambi et al. (2002) use an index of economic freedom from

Gwartney, Lawson and Block (1995) as a proxy for rent-seeking, and simply invert the

index values such that a high level of freedom equates to a low level of rent-seeking. The

current study will develop a new measure of rent-seeking based on the Katz and Rosenberg

(1989) approach outlined in Section 8.2.1. This will be explained in the next section on data

issues.

8.3 Empirical Analysis

8.3.1 Data Issues

For the present study, data on government expenditure was collected from the IMF’s

Government Financial Statistics database. To ensure consistency, data entitled “Budgetary

Central Government” was used. This provided amounts of government spending classified

according to the following sectors: general public services; defence; public order and

safety; economic affairs; housing and community amenities; health; recreation, culture and

religion; education; and social protection. A total of 113 countries comprised the initial

sample, but this was narrowed down to 104 after omitting countries for which data was not

available for all sectors.

To explain the Katz and Rosenberg (1989) method, consider the following example of an

economy with $100 budget, and two sectors, A and B. Sector A is allocated $30 (or 30% of

the budget), and Sector B has the remaining $70 (70%). Assume that in the next period, the

government increases its budget by $50 to $150. Out of this increase of $50, $20 goes to A

and $30 goes to B. A now has $50 out of the $150 (or 33%) and B has the remaining $100

203

(67%). Katz and Rosenberg (1989) firstly took the absolute value of the change in

proportions. This gives 3% for A and 3% for B. These values are added together, giving

6%. To avoid double counting, the 6% is divided by 2, giving 3%. Katz and Rosenberg

(1989) argue that this 3% of the budget has been lost to rent-seeking.

However this is a misleading estimation for two reasons. Firstly, the change in A's

proportion, from 30% to 33% is not an increase of 3%, but rather an increase of 3

percentage points. So to classify the 3 as a percentage is erroneous. Also, the dollar value of

the rent-seeking works out to be (20+30)/2 = 25, which is neither 3% of the original budget

of $100, nor of the new budget of $150.

Secondly, the Katz and Rosenberg (1989) method only looks at proportions. So in that

same example, if A had only got $15 out of the extra $50 in the second period, then A's

total would be $45 out of the $150, which is 30%. This represents no change in proportion,

but A has received $15 more funding which in all fairness should be treated as a result of

rent-seeking.

To overcome these two flaws, the present study modified the Katz and Rosenberg (1989)

approach. Instead of looking at proportions, this study analysed the difference between

spending in each sector over consecutive years as a whole number. The absolute value of

these differences in a given year were added up across sectors, and divided by two to avoid

double counting. This gives a number (not a proportion) expressed in the country's local

currency, which represents the value of rent-seeking behaviour. Dividing this number by

the new budget expresses the rent-seeking loss as a proportion of government spending.

This set of values now represents an alternative measure of corruption.43

8.3.2 Preliminary Analysis

Table 8.1 provides the total value of rent-seeking as a proportion of the government’s

budget using the measurement outlined in the previous section. It is not possible to directly

compare the rent-seeking values in Table 8.1 with the data in Chapter 6 because the data on

43 This study acknolwedges that the Katz and Rosenberg (1989) methodology still has some remaining limitations.

204

government budgetary expenditure on which the rent-seeking values are based does not

cover all the countries in the Chapter 6 dataset. However, a quick glance at Table 8.1

reveals that, with the exception of Canada, the more developed countries are concentrated

towards the bottom of the table, indicating a low level of rent-seeking behaviour. This is

consistent with the findings of Katz and Rosenberg (1989) as shown in Table 8.2, which

reveals their index of rent-seeking based on the average rent-seeking between 1970 and

1985 for 20 countries. Due to the data restrictions, only two East Asian countries feature in

the present study, namely, Indonesia and South Korea. This renders any attempt to

investigate the particular effect of rent-seeking on economic growth in East Asia futile.

205

Table 8.1: Rent-Seeking as % of Budget, 1996

Country Rent-Seeking as

% of Budget

Congo, Dem. Rep. of 43.65

Bulgaria 27.21

Vanuatu 26.07

Yemen, Republic of 20.91

Albania 17.79

Burundi 15.77

Romania 15.63

Belarus 15.44

Papua New Guinea 13.70

Mexico 13.57

Poland 12.29

Uruguay 11.47

Mongolia 11.34

Croatia 10.84

Panama 10.73

Myanmar 10.07

Zambia 10.02

El Salvador 9.86

Botswana 9.79

Canada 9.49

Seychelles 9.46

Bolivia 9.12

Indonesia 9.09

Kyrgyz Republic 8.42

Dominican Republic 8.17

Israel 7.84

Algeria 7.71

Belize 7.49

South Korea 7.34


% of Budget

Hungary 6.89

Kenya 6.69

Kuwait 6.67

Vietnam 6.60

India 6.22

Fiji 6.04

Argentina 5.69

Malta 5.50

Ethiopia 5.30

Sri Lanka 5.19

Oman 4.96

Bahamas, The 4.71

Germany 4.47

St. Vincent & The

Grenadines 4.43

Syrian Arab Republic 4.38

Cyprus 4.24

Netherlands 4.18

Greece 4.14

Iceland 3.84

Sweden 3.50

Switzerland 3.50

Spain 3.21

Ireland 2.91

Finland 2.55

United States 2.43

New Zealand 2.33

Norway 2.09

Denmark 1.54

Source: Author’s own calculation based on data obtained from the International Monetary Fund (2005).

206

Table 8.2: Average Rent-Seeking as % of Budget, 1970-1985


% of Budget

Egypt 10.19

Mexico 10.16

Indonesia 7.85

Turkey 7.70

Israel 7.58

Italy 7.31

South Korea 6.08

Chile 5.32

Greece 5.28

Kenya 3.97

Spain 2.92

Australia 2.87

United States 2.80

Canada 2.61

Sweden 2.59

United Kingdom 2.55

Belgium 2.13

Switzerland 2.10

West Germany 1.38

France 1.28

Source: Compiled using data obtained from Katz and Rosenberg (1989).

Source: Compiled using data obtained from the World Bank (2005)

Figure 8.3 illustrates the relationship between rent-seeking (a proxy for corruption) and

economic growth across the 57 countries in Table 8.1 using data for 1996, and indicates a

positive relationship when ignoring the obvious outliers. Descriptive statistics for the

variables in this analysis are shown in Table 8.3. Table 8.4 reveals the correlation matrix

for the chosen variables. As is the case in earlier chapters, there appear to be no signs of

multicollinearity using 0.7 as a benchmark.

-15

-10

-5

0

5

10

15

0 0 40 50

Rent-seekin

Eco

nom

ic G

row

th (%

)

10 2

Figure 8.3: Rent-Seeking and Economic Growth, 1996

Outliers

0 3

g

207

208

Table 8.3: Descriptive Statistics

Variable Mean Std. Dev. Min Max No. of Obs

RENTSKG 9.03 7.20 1.54 43.65 57

GROWTH 3.77 4.12 -16.70 12.41 103

GFCFRATE 6.81 13.86 -34.71 67.66 92

POPRATE 1.28 1.37 -3.93 4.98 104

SECENROL 70.25 30.43 6.64 140.39 100

EXPGDP 39.41 25.07 0.97 170.84 102

EXPRATE 7.89 11.06 -36.10 55.65 97

MKTCAP 32.19 36.90 0.07 180.15 70

REALINT 7.90 12.94 -52.99 63.68 89

CURACC -2.83 7.45 -34.69 22.57 99

GVEXGDP 15.97 6.28 5.01 29.73 103

HITECHEX 10.85 15.10 0.00 78.28 82

EDUCGDP 4.70 1.78 1.34 10.20 73

LNGDP85 23.50 2.18 18.94 29.42 90

Source: Author’s own calculation based on data obtained from the International Monetary Fund

(2005) and the World Bank (2005).

8.3.3 Empirical Model and Results

Having established a measure of rent-seeking, the following model is estimated to

empirically measure the relationship between economic growth and rent-seeking: k

уi = α + βRi + ∑ λjZij + μi (8.1)

j=1


R = rent-seeking value


μ = error term



Variable 1 2 3 4 5 6 7 8 9 10 11 12 13 1. RENTSKG 1.00 -0.24 0.06 -0.51 -0.02 -0.07 -0.47 -0.36 -0.17 -0.20 -0.23 -0.20 -0.31

2. GFCFRATE 1.00 0.00 0.01 0.10 0.18 0.04 0.12 0.02 0.25 0.26 0.24 -0.13

3. POPRATE 1.00 -0.51 0.10 0.00 0.15 -0.04 0.20 -0.25 0.04 -0.29 -0.21

4. SECENROL 1.00 0.24 0.01 0.35 -0.01 0.10 0.47 0.36 0.35 0.54

5. EXPGDP 1.00 -0.02 0.44 -0.09 0.28 0.28 0.38 0.21 -0.30

6. EXPRATE 1.00 -0.10 0.07 -0.04 -0.14 0.07 0.04 0.03

7. MKTCAP 1.00 -0.24 0.49 0.07 0.55 0.09 0.35

8. REALINT 1.00 -0.21 0.04 0.01 -0.09 -0.20

9. CURACC 1.00 0.04 0.03 0.15 0.34

10.GVEXGDP 1.00 0.21 0.67 -0.12

11. HITECHEX 1.00 0.17 0.13

12. EDUCGDP 1.00 -0.08

13. LNGDP85 1.00

Source: Author’s own calculation based on data obtained from the International Monetary Fund (2005) and the World Bank (2005).

209

210

The same set of control variables used in Chapter 6 will be used in this chapter. The only

difference in the model is the corruption variable, which will be represented in this chapter

by the variable RENTSKG, as rent-seeking will be used as a proxy for corruption. As in the

previous two chapters, the issue of endogeneity will also be addressed. The next section

provides the results of the regression analysis.

Preliminary regression results are outlined in Table 8.5. Model 10 shows that while

GFCFRATE, SECENROL and EXPRATE are all statistically significant, the corruption

variable RENTSKG is not. However, the sign of the coefficient is positive, indicating that

higher levels of corruption (in the form of rent-seeking) are associated with slower

economic growth. One issue that may be confounding the results is the existence of

endogeneity. To control for this, one-year lagged values are used to replace all the control

variables excluding LNGDP85. The results are shown in Table 8.6.

Model 11 in Table 8.6 reveals the final model after following a stepwise elimination

technique. The one-year lags of MKTCAP and GVEDGDP are the only significant control

variables, and the RENTSKG coefficient once again is not significant but this time it is a

positive coefficient, indicating that an increase in rent-seeking would lead to an increase in

economic growth. At this stage, an examination of the standardised residual plot shown in

Figure 8.4 revealed an outlier (i.e., outside 2 standard deviations of the mean). When this

outlier was removed, MKTCAP1 was no longer significant and was removed from the

model. Once again, three outliers were identified in the new residual plot, and these were

also removed. The regression was rerun, and the results are shown as Model 12 in Table

8.6. Interestingly, the rent-seeking coefficient is now positive and statistically significant at

the 5% level, indicating that an increase in rent-seeking leads to an increase in economic

growth after controlling for the problem of endogeneity. This result is consistent with the

findings in previous chapters, and shows that rent-seeking does indeed represent a

particular dimension of corruption.

Table 8.5: Regression Results

Variable 1 2 3 4 5

CONSTANT -8.095 (22.08) -0.446 (13.49) -0.472 (13.01) 2.203 (9.81) 3.109 (2.52)

RENTSKG -0.145 (0.63) -0.208 (0.23) -0.208 (0.23) -0.180 (0.18) -0.120 (0.14)

GFCFRATE 0.153 (0.09) 0.109 (0.07) 0.110 (0.07) 0.104 (0.06) 0.091 (0.04)*

POPRATE 0.274 (1.31) 0.264 (0.98) 0.254 (0.83) 0.205 (0.72) 0.413 (0.34)

SECENROL -0.028 (0.06) -0.035 (0.04) -0.035 (0.03) -0.034 (0.03) -0.021 (0.02)

EXPGDP 0.041 (0.09) 0.045 (0.06) 0.045 (0.05) 0.042 (0.04) 0.034 (0.03)

EXPRATE 0.110 (0.22) 0.162 (0.14) 0.162 (0.13) 0.189 (0.11) 0.204 (0.08)*

MKTCAP -0.004 (0.03) -0.006 (0.02) -0.006 (0.02) -0.006 (0.02) -0.005 (0.01)

REALINT 0.027 (0.11) 0.029 (0.07) 0.028 (0.07) 0.181 (0.05) 0.010 (0.04)

CURACC -0.126 (0.22) -0.158 (0.13) -0.159 (0.12) -0.132 (0.09) -0.164 (0.07)*

GVEXGDP -0.073 (0.26) -0.002 (0.11)

HITECHEX 0.017 (0.09) 0.004 (0.06) 0.004 (0.06)

EDUCGDP -0.054 (0.77)

LNGDP85 0.519 (0.71) 0.199 (0.50) 0.201 (0.48) 0.089 (0.37)

Adjusted R2 -0.14 0.12 0.17 0.23 0.33

F-value 0.80 1.29 1.51 1.86 2.79

Sample size 22 28 28 30 33


211

Table 8.5: Regression Results (cont’d)

Variable 6 7 8 9 10

CONSTANT 3.197 (2.21) 5.991 (2.11) 6.036 (2.07) 6.596 (1.53) 5.779 (1.47)

RENTSKG -0.079 (0.12) -0.002 (0.10) 0.002 (0.09) -0.008 (0.09) 0.042 (0.09)

GFCFRATE 0.089 (0.04)* 0.097 (0.04)* 0.096 (0.04)* 0.090 (0.04)* 0.070 (0.03)*

POPRATE 0.376 (0.32) 0.125 (0.33) 0.133 (0.33)

SECENROL -0.019 (0.02) -0.037 (0.02)* -0.036 (0.01)* -0.039 (0.01)** -0.034 (0.01)**

EXPGDP 0.029 (0.02) 0.004 (0.02)

EXPRATE 0.184 (0.08)* 0.070 (0.04) 0.070 (0.04) 0.069 (0.04) 0.090 (0.03)*

MKTCAP -0.006 (0.01)

REALINT

CURACC -0.164 (0.06)* -0.107 (0.06) -0.104 (0.06) -0.095 (0.05)

GVEXGDP

HITECHEX

EDUCGDP

LNGDP85

Adjusted R2 0.36 0.39 0.40 0.42 0.39

F-value 3.31 4.81 5.76 7.04 7.97

Sample size 34 43 43 43 45


212

Table 8.6: Controlling for Endogeneity


CONSTANT 13.617 (13.31) 13.659 (12.31) 12.868 (11.76) 9.889 (10.73) 6.843 (3.78) 6.598 (2.68)

RENTSKG -0.204 (0.31) -0.200 (0.23) -0.216 (0.21) -0.182 (0.17) 0.102 (0.16) 0.102 (0.15)

GFCFRATE1 0.033 (0.06) 0.032 (0.06)

POPRATE1 1.616 (1.64) 1.633 (1.34) 1.867 (1.23) 1.345 (1.12) -0.105 (1.11)

SECENROL1 0.023 (0.03) 0.023 (0.03) 0.023 (0.03) 0.018 (0.02) 0.016 (0.03) 0.017 (0.02)

EXPGDP1 0.062 (0.10) 0.060 (0.06) 0.060 (0.06) 0.044 (0.04) 0.069 (0.04) 0.067 (0.03)*

EXPRATE1 0.073 (0.06) 0.072 (0.05) 0.076 (0.05) 0.058 (0.04) 0.028 (0.04) 0.028 (0.04)

MKTCAP1 -0.045 (0.02) -0.045 (0.02)* -0.044 (0.02)* -0.038 (0.01)* -0.040 (0.02)* -0.040 (0.01)*

REALINT1 0.002 (0.09)

CURACC1 0.325 (0.20) 0.327 (0.18) 0.315 (0.17) 0.215 (0.11) 0.130 (0.13) 0.131 (0.12)

GVEXGDP1 -0.089 (0.12) -0.088 (0.09) -0.081 (0.09) -0.098 (0.07) -0.189 (0.07)* -0.185 (0.06)**

HITECHEX1 -0.053 (0.11) -0.051 (0.09) -0.044 (0.09)

EDUCGDP1 -0.547 (0.40) -0.548 (0.37) -0.556 (0.36) -0.442 (0.03) -0.067 (0.31) 0.051 (0.25)

LNGDP85 -0.282 (0.48) -0.285 (0.44) -0.264 (0.42) -0.123 (0.38)

Adjusted R2 0.48 0.55 0.58 0.58 0.41 0.45

F-value 2.43 3.01 3.52 4.09 2.87 3.44

Sample size 21 21 21 23 25 25


213

Table 8.6: Controlling for Endogeneity (cont’d)

Variable 7 8 9 10 11 12

CONSTANT 7.453 (2.17) 7.121 (1.84) 6.490 (1.67) 6.951 (1.64) 6.967 (1.51) 5.045 (1.02)

RENTSKG -0.038 (0.12) -0.024 (0.11) -0.036 (0.11) -0.037 (0.11) 0.004 (0.10) 0.148 (0.06)*

GFCFRATE1

POPRATE1

SECENROL1 -0.006 (0.02)

EXPGDP1 0.026 (0.03) 0.026 (0.03) 0.033 (0.03)

EXPRATE1 0.043 (0.04) 0.041 (0.03) 0.048 (0.03) 0.047 (0.03)

MKTCAP1 -0.031 (0.01)* -0.032 (0.01)* -0.028 (0.01)* -0.027 (0.01)* -0.025 (0.01)*

REALINT1

CURACC1 0.098 (0.11) 0.092 (0.11)

GVEXGDP1 -0.082 (0.05) -0.091 (0.04)* -0.086 (0.04)* -0.066 (0.03) -0.070 (0.03)* -0.065 (0.02)*

HITECHEX1

EDUCGDP1

LNGDP85

Adjusted R2 0.21 0.24 0.25 0.23 0.20 0.23

F-value 2.14 2.57 2.99 3.28 3.98 7.90

Sample size 31 31 31 31 37 46


214

How do these findings compare with those of previous studies? Only two studies have

performed similar regression analyses in order to determine the impact of rent-seeking on

economic growth. Murphy et al. (1991) did not even attempt to measure rent-seeking,

choosing instead to use the number of law institutions as an independent variable in a

regression against economic growth for 91 observations over the period 1970-1985. The

coefficient was found to be negative but not significant, and the authors argued that the

negative sign implied that rent-seeking was negatively associated with growth based on the

notion that an increase in the number of lawyers would tend to increase the extent of

lobbying. The other study, by Cole and Chawdhry (2002) analysed 344 observations

between 1980 and 1990 and used the number of interest organisations registered to lobby in

a state’s legislature as a proxy for rent-seeking. Their resulting coefficient was also

negative, but significant at the 1% level. Clearly, the results from this study are unique and

far more rigorous given that a reasonable attempt was made to measure rent-seeking and

incorporate it directly into the regression model, rather than simply relying on a proxy.

-2.50 -1.50 -0.50 0.50

Standardised Residual

1.50 2.50

Obs

erva

tion

Figure 8.4: Residual Plot (Model 11, Table 8.6)

215

Outlier

216

8.4 Conclusion

This chapter proposed rent-seeking as an alternative model of corruption. This provides an

alternative to the Shleifer and Vishny (1993) principal-agent model that is popular in the

literature. The purpose of this chapter was to test whether the use of rent-seeking

empirically generated results which are consistent with the use of corruption indexes, and

could therefore be used as a proxy for corruption. In Chapter 6, a cross-sectional analysis

was performed on corruption and growth and it was found that corruption had a positive

impact on economic growth. In this chapter, corruption was measured as rent-seeking

through the introduction of a new rent-seeking index based on the modification of earlier

theories, and the index is comparable to existing corruption indices. The estimated

coefficient was found to be positive and statistically significant at the 5% level suggesting

that rent-seeking is positively related to economic growth.

CHAPTER 9

SUMMARY AND CONCLUDING REMARKS

9.1 Introduction

This study investigated the relationship between corruption and economic growth. In

particular, the study focused on the peculiar relationship between high corruption and high

economic growth in East Asia prior to the 1997 Financial Crisis. The literature to date has

been vast in the field of corruption and growth, yet few studies have attempted to analyse

East Asia exclusively. Even fewer have used panel data analysis and rent-seeking as a

proxy for corruption. This chapter presents a summary of this study’s findings and

identifies ways in which this study’s analysis can be improved. This provides the basis for

further research. The chapter closes with some concluding remarks about corruption and

economic growth in East Asia.

9.2 Summary of Findings

The literature tends to have a myopic view of corruption, viewing it synonymously with

bribe-paying and defining it as the misuse of public office for private gain. Based on this

definition, corruption tends to be modelled under the Shleifer and Vishny (1993) principal-

agent framework. This study argued that such an analysis was too narrow, and failed to

capture the very different ways in which corruption manifests itself. This is no more

apparent than in the case of East Asia. This study has shown that rent-seeking can also be

viewed as a form of corruption in reference to East Asia, conceptually at least. A review of

the empirical literature examining corruption and growth revealed that few studies had

analysed East Asia independently.

To examine the relationship between corruption and economic growth, this study needed

firstly to determine an appropriate measurement of corruption. The only available measures

of corruption are indices published by a variety of international institutions. These indices

were analysed and found to be largely consistent in their ranking of countries based on their

perceived levels of corruption, however it was found that the error of interpreting

217

corruption as mere bribe-paying was highlighted in the World Bank’s World Development

Report (1997). Based on surveys which asked two separate questions about corruption and

bribery, the Report found that while people in a given country deemed it to be highly

corrupt, they did not simultaneously agree that there was a high degree of bribe-paying.

Thus, in the survey respondents’ minds at least, there appeared to be a difference between

corruption and bribery. This lent support to this study’s assertion that corruption should be

viewed a little more broadly.

Moving on to the regression model, a cross-sectional analysis of 141 countries considering

data for 1996 was conducted in Chapter 6 based on the World Bank’s KKZ index. The

results indicated that there was some evidence that corruption was positively related to

economic growth, but the evidence was rather weak. After controlling for possible

endogeneity problems and running a series of sensitivity analyses with different indices in

place of the KKZ, corruption was found to have a significant positive impact on growth in

the presence of two different indices. However, when East Asia is considered separately

through the use of dummy variables, no statistically significant relationship was found.

A panel data analysis was then undertaken on a sample of 33 countries over 20 years (1984-

2003). The corruption indices were averaged to form a composite index, but the model did

not yield any noteworthy results even after using alternative indices in place of the

composite index and after controlling for endogeneity. However, after excluding Singapore

as an outlier in East Asia (due to consistently exhibiting low levels of corruption over time)

and after excluding the years 1985 and 1986 (which saw extraordinarily low growth rates

for East Asia), the results were striking. The resulting corruption coefficient for East Asian

countries before 1997 implied that high corruption led to high economic growth. The

coefficient was significant at the 1% level. A similar result was found when sensitivity

analyses were conducted based on different forms of the composite corruption index (i.e.,

combined using different standardisation techniques).

A concise literature review of empirical studies involving rent-seeking revealed that very

few studies undertook regression analysis using rent-seeking due to measurement

difficulties. This study proposed a measure of rent-seeking by extending the work by Katz

and Rosenberg (1989). Rent-seeking was then substituted for corruption in a cross-sectional

218

model (not enough data was available to warrant panel data analysis) and applied to a

sample of 104 countries in 1996. After controlling for endogeneity the corruption

coefficient was found to be positive and statistically significant at the 5% level. This held

for all countries in the sample which included some East Asian countries, however there

were not enough countries to generate meaningful results for the East Asian region.

9.3 Concluding Remarks

Corruption and Growth

The empirical analysis in this study revealed that a positive relationship existed between

corruption and economic growth in East Asia prior to the 1997 financial crisis. However,

this study is not implying that countries should be corrupt in order to achieve growth.

Rather, this study shows that if a country is already corrupt, the corruption may not

necessarily impede that country’s economic growth. The existing literature overwhelmingly

argues that corruption is an impediment to growth, but this study has identified a particular

region where corruption was synonymous with high levels of economic growth. Figure 9.1

below plots the economic growth of countries who appeared in the 2005 edition of

Transparency International’s Corruption Perceptions Index (CPI) against their corruption

scores. The dashed lines represent the average values for the sample, and the large black

dots mark the seven East Asian countries which have been the focus of this study. Unlike in

the period 1986-1996, the East Asian countries do not congregate in the northwestern

quadrant of the chart, which equates high levels of economic growth with high levels of

corruption (given that a low CPI score reflects a high degree of perceived corruption). This

does not contradict the findings of this study, which do not suggest that corruption is

always synonymous with high economic growth. Rather, it refutes the inverse of that theory

– that low corruption is necessary for high economic growth. In the case of East Asia,

between 1986 and 1996, that was certainly not the case, and provides the basis for further

research into the nature of corruption and its effects on an economy.

219

Part of the problem with existing studies on corruption lies in their measurement of

corruption. Indeed, its very nature precludes its accurate measurement. However, there are

alternative ways in which corruption can be viewed. Bribery is one, and rent-seeking

another, as this study has shown. But there remain many more. For example, if a country is

deemed to be corrupt, one would expect the profits from such corrupt activities to be stored

in bank accounts somewhere. One could investigate the link between a country’s banking

Further Research

Furthermore, this study also reveals how corruption is not necessarily the same as bribe

paying. In Chapter 5, it was shown that one particular corruption index yielded results

which implied that corruption and bribe paying were separate. The analysis in this study

confirmed that rent-seeking is one particular dimension of corruption and that it is more

applicable to East Asia than ordinary bribery, which is how the literature tends to view

corruption.

Source: Compiled using data obtained from the World Bank (2005) and Transparency International (2005)

-3

-1

1

0 1 2 3 4 5 6 7

Corruption

E

3

5

7

9

11

13

15

8 9 10

cono

mic

Gro

wth

(%)

Figure 9.1: Corruption vs Economic Growth, 2005

220

system and the taxation authority. Is there full disclosure from the banks to tax authority?

Do banks disclose amount of funds held and the amount of returns from bank investments?

This data could then be compared with CPI data or other corruption index scores.

One could also look to the opportunities for corruption, such as countries which receive

large amounts of foreign aid. This could be measured against the distribution of wealth

across a country. An investigation into whether politicians’ personal wealth is correlated

with their connection to aid programmes could potentially yield interesting results.

Especially where aid projects are aimed at improving productivity, one could explore

whether the project was successful or not, and in the cases where they were unsuccessful,

whether this tended to occur in countries deemed more corrupt according to the CPI and

other indices. At a broader political level, one could even examine the relationship between

donor countries and the recipient developing countries’ voting behaviour at the United

Nations, i.e., if they receive a large donation from a developed country, do they then vote

for their proposals at the UN?

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