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THE IMPACT OF POLITICO-ECONOMIC INSTITUTIONS

ON ECONOMIC PERFORMANCE: EVIDENCE FROM

EAST ASIA AND LATIN AMERICA, 1990-2009

Pananda Chansukree

A Dissertation Submitted in Partial

Fulfillment of the Requirements for the Degree of

Doctor of Philosophy (Development Administration)

School of Public Administration

National Institute of Development Administration

2012

ABSTRACT

Title of Dissertation The Impact of Politico-Economic Institutions on Economic

Performance: Evidence from East Asia and Latin America,

1990-2009

Author Ms. Pananda Chansukree

Degree Doctor of Philosophy (Development Administration)

Year 2012

How do the political and economic institutions of a country affect its

economic performance? What is the appropriate structure of politico-economic

institutions for economic development? These questions have been of significant

interest among not only economists and political scientists, but also policymakers in

all countries around the world. This is because knowledge of the relationship between

politico-economic institutions and economic performance will enable them to develop

political and economic institutions which are conductive to their countries’ economic

growth and efficiency.

The objective of this study is three-fold: to study the political and economic

institutions in East Asia and Latin America, to examine the relationship between

politico-economic institutions and economic performance in selected East Asian and

Latin American countries over the period of 1990-2009; and to help improve policy

decisions with respect to institutional building and economic efficiency in developing

countries. This study employs a time-series, cross-country analysis. Unlike most

empirical studies on the subject which take only political institutions into

consideration, this study determines the effects of both political and economic

institutions on economic performance. These effects need to be studied together in

order to capture various aspects of institutions. In this study, the comparison of East

Asia and Latin America is driven by the belief that Latin America has much to learn

from East Asian countries’ economic success.

iv

This study adopts both time-series and cross-country approaches rather than

only a cross-country approach, which is more common. Using cross-country data

from 10 East Asian countries and 17 Latin American countries over the period from

1990 to 2009, this study relies on secondary data by employing cross-country

economic and political data from several sources. Descriptive statistics reveal that

East Asia is ahead of Latin America in terms of economic performance gauged by

economic growth, unemployment, poverty, and income inequality. In addition, the

political institutions in East Asia are more effective than those in Latin America,

while the economic institutions in East Asia are less effective than those in Latin

America.

The results from the empirical analysis indicate that in the full sample, the

institutional factor which has a significant impact on annual growth rates of GDP per

capita is rule of law. However, the relationship is in an unexpected way. The

institutional factor which has a significant influence on unemployment rates in East

Asia and Latin America is economic freedom. That is, the more economic freedom,

the lower are the unemployment rates. It was also found that that regulatory quality

has a significant and negative impact on the percentage of the population falling

below the poverty line. In addition, the institutional factor which has a significant

impact on income inequality in East Asia and Latin America is political rights.

However, the relationship is in an unexpected way. That is, the greater the political

rights, the higher is income inequality.

In the case of East Asia, there is no institutional factor that has a significant

impact on annual growth rates of GDP per capita. The institutional factors which have

an impact on unemployment rates in East Asia are political rights, press freedom, rule

of law, and control of corruption. Political rights and rule of law have the expected

impact on unemployment rates, while press freedom and control of corruption have

an unexpected impact. The institutional factor which affects the percentage of the

population falling below the poverty line in East Asia is economic freedom.

Nevertheless, the relationship is in an unexpected way. The finding of this study

indicates that economic freedom does not guarantee poverty reduction. Moreover,

press freedom and economic freedom have the expected impact on income inequality

in East Asia, while control of corruption has an unexpected impact.

v

As for Latin America, the institutional factor that affects annual growth rates

of GDP per capita in an unexpected way is control of corruption. It was found that the

greater the control of corruption, the lower are the annual growth rates of GDP per

capita. The institutional factors which impact unemployment rates in Latin America

include civil liberties, press freedom, regulatory quality, and control of corruption.

Civil liberties have an unexpected impact on unemployment rates, whereas press

freedom, regulatory quality, and control of corruption have the expected impact. With

regard to poverty, the findings of this study suggest that an increase in press freedom

and the rule of law reduces the percentage of the population falling below the poverty

line. Furthermore, the institutional factor which affects income inequality in Latin

America is regulatory quality. The finding indicates that when regulatory quality is

improved, income distribution will be more equal.

The findings and insights in this study will enable developing countries in

East Asia and Latin America to identify areas regarding their politico-economic

institutions that require improvement. In this way, adequate policies aimed at creating

a functional and growth-enhancing institutional framework would be implemented.

The research outcomes may also be beneficial to other developing countries, and

perhaps less-developed countries, which desire to develop the appropriate structure of

political and economic institutions for future development.

ACKNOWLEDGEMENTS

The writing of this dissertation has been one of the most significant academic

challenges I have ever faced. I would like to take this opportunity to acknowledge and

express thanks to several individuals to whom I owe my deepest gratitude. First and

foremost, my utmost gratitude goes to Professor Dr. Ponlapat Buracom for his

devotion and support as my dissertation adviser. I also would like to express my

gratitude to Dr. Amornsak Kitthananan for being my dissertation co-adviser and to

Professor Dr. Anusorn Limmanee for being my committee chairperson. Without their

support, patience and guidance, this dissertation would not have been completed. Last

but not the least, my special appreciation goes to my family for their understanding

and support throughout this incredible challenge.

Pananda Chansukree

September 2012

TABLE OF CONTENTS

Page

ABSTRACT iii

ACKNOWLEDGEMENTS vi

TABLE OF CONTENTS vii

LIST OF TABLES xiii

LIST OF FIGURES xvii

CHAPTER 1 INTRODUCTION 1

1.1 Statement of the Problem 1

1.2 Significance of the Study 3

1.3 Objectives of the Study 4

1.4 Scope of the Study 4

1.5 Limitations of the Study 5

CHAPTER 2 LITERATURE REVIEW AND CONCEPTUAL FRAMEWORK 7

2.1 Theories of Economic Growth 7

2.1.1 Neoclassical Growth Theory 8

2.1.2 Endogenous Growth Theory 9

2.1.3 New Institutional Theory 11

2.1.3.1 Definition of Institutions 13

2.1.3.2 Features of Institutions 13

2.1.3.3 Functions of Institutions 15

2.1.3.4 Measurement of Institutions 16

2.2 Approaches to Development 18

2.2.1 The Institutional Approach 18

2.2.2 The Development Approach 19

2.2.3 Empirical Evidence on the Impact of Institutions 19

on Economic Performance

viii

2.3 Political Institutions 20

2.3.1 Democracy 21

2.3.1.1 Concept of Democracy 21

2.3.1.2 Measures of Democracy 23

2.3.1.3 Theoretical Perspectives on Democracy and 24

Economic Growth

2.3.1.4 Empirical Evidence on the Impact of Democracy 29

on Economic Growth

2.3.2 Governance 31

2.4 Economic Institutions 33

2.5 Economic Performance 34

2.5.1 Economic Growth as an Indicator of Economic Performance 34

2.5.1.1 Economic Growth Criterion of Development 35

2.5.1.2 Measures of Economic Growth 35

2.5.2 Other Indicators of Economic Performance 35

2.6 Conceptual Framework 36

CHAPTER 3 RESEARCH METHODOLOGY 47

3.1 Sample Selection 47

3.2 Data Collection 54

3.3 Data Analysis 59

CHAPTER 4 COMPARING EAST ASIA AND LATIN AMERICA 62

4.1 Differences in Political Institutions 62

4.2 Differences in Economic Institutions 68

4.3 Differences in Fundamental Socio-Economic Factors 71

4.4 Explaining Divergent Economic Performance 78

CHAPTER 5 DESCRIPTIVE STATISTICS AND DATA ANALYSIS: 82

POLITICO-ECONOMIC INSTITUTIONS AND ECONOMIC

PERFORMANCE IN EAST ASIA AND LATIN AMERICA

5.1 Descriptive Statistics 82

5.2 Data Analysis 87

5.2.1 The Impact of Politico-Economic Institutions on Annual 88

Growth Rates of GDP per capita

ix

5.2.2 The Impact of Politico-Economic Institutions on 92

Unemployment Rates

5.2.3 The Impact of Politico-Economic Institutions on the 95

Percentage of the Population Falling below the Poverty Line

5.2.4 The Impact of Politico-Economic Institutions on Income 98

Inequality

CHAPTER 6 DATA ANALYSIS: POLITICO-ECONOMIC INSTITUTIONS 102

AND ECONOMIC PERFORMANCE IN EAST ASIA

6.1 The Impact of Politico-Economic Institutions on Annual 102


6.2 The Impact of Politico-Economic Institutions on 104

Unemployment Rates

6.3 The Impact of Politico-Economic Institutions on the Percentage 108

of the Population Falling below the Poverty Line

6.4 The Impact of Politico-Economic Institutions on Income 112

Inequality

CHAPTER 7 DATA ANALYSIS: POLITICO-ECONOMIC INSTITUTIONS 117

AND ECONOMIC PERFORMANCE IN LATIN AMERICA

7.1 The Impact of Politico-Economic Institutions on Annual 117


7.2 The Impact of Politico-Economic Institutions on 121

Unemployment Rates

7.3 The Impact of Politico-Economic Institutions on the 124


7.4 The Impact of Politico-Economic Institutions on Income 128

Inequality

CHAPTER 8 DISCUSSIONS OF RESULTS 132

8.1 Discussions of the Impact of Politico-Economic Institutions 132

on Economic Performance in East Asia and Latin America


Annual Growth Rates of GDP per capita

x


Unemployment Rates


Percentage of the Population Falling below the

Poverty Line


Income Inequality

8.2 Comparisons of the Impact of Politico-Economic Institutions 135

on Economic Performance in East Asia and That in Latin

America

8.2.1 The Impact of Politico-Economic Institutions on Annual 136



Unemployment Rates




Income Inequality

CHAPTER 9 CONCLUSIONS 146

9.1 Major Findings 146

9.2 Policy Implications 148

9.3 Theoretical Contributions 150

9.4 Suggestions for Further Research 151

BIBLIOGRAPHY 153

APPENDICES 173

Appendix A-1: Annual Growth Rates of GDP per Capita (%), 1990-2009 174

Appendix A-2: Unemployment (% of Total Labor Force), 1990-2009 178

Appendix A-3: Poverty (% of Population Falling below the Poverty Line), 182

1990-2009

Appendix A-4: Income Inequality, 1990-2009 186

Appendix B-1: Investment Rates (% of GDP), 1990-2009 190

xi

Appendix B-2: Gross National Savings (% of GDP), 1990-2009 194

Appendix B-3: Population Growth Rates (%), 1990-2009 198

Appendix C-1: Life Expectancy at Birth (Years), 1990-2009 202

Appendix C-2: Adult Literacy Rates (Total) (% of People Aged 15 and 206

Above), 1990-2009

Appendix C-3: Combined Gross Enrollment (Total) (%), 1990-2009 210

Appendix D-1: Political Rights, 1990-2009 214

Appendix D-2: Civil Liberties, 1990-2009 218

Appendix D-3: Press Freedom, 1990-2009 222

Appendix E-1: Government Effectiveness, 1990-2009 226

Appendix E-2: Regulatory Quality, 1990-2009 230

Appendix E-3: Rule of Law, 1990-2009 234

Appendix E-4: Control of Corruption, 1990-2009 238

Appendix F-1: Protection of Property Rights, 1990-2009 242

Appendix F-2: Economic Freedom, 1990-2009 246

Appendix G: SPSS Output for the Impact of Politico-Economic 250

Institutions on Annual Growth Rates of GDP per Capita in

East Asia and Latin America

Appendix H: SPSS Output for the Impact of Politico-Economic 269

Institutions on Unemployment Rates in East Asia and

Latin America

Appendix I: SPSS Output for the Impact of Politico-Economic 287

Institutions on the Percentage of the Population Falling

Below the Poverty Line in East Asia and Latin America

Appendix J: SPSS Output for the Impact of Politico-Economic 308

Institutions on Income Inequality in East Asia and

Latin America

Appendix K: SPSS Output for the Impact of Politico-Economic 329


East Asia

Appendix L: SPSS Output for the Impact of Politico-Economic 344

Institutions on Unemployment Rates in East Asia

xii

Appendix M: SPSS Output for the Impact of Politico-Economic 359


Below the Poverty Line in East Asia

Appendix N: SPSS Output for the Impact of Politico-Economic 375

Institutions on Income Inequality in East Asia

Appendix O: SPSS Output for the Impact of Politico-Economic 390


Latin America

Appendix P: SPSS Output for the Impact of Politico-Economic 408

Institutions on Unemployment Rates in Latin America

Appendix Q: SPSS Output for the Impact of Politico-Economic 425


Below the Poverty Line in Latin America

Appendix R: SPSS Output for the Impact of Politico-Economic 446

Institutions on Income Inequality in Latin America

BIOGRAPHY 466

LIST OF TABLES

Tables Page

2.1 Subjective Indicators of Democracy 24

2.2 Studies of Democracy, Autocracy, Bureaucracy and Growth 30

2.3 Theoretical Sources of the Variables 37

3.1 GNP per Capita, US Dollars ($) 48

3.2 Unemployment (% of total labor force) 48

3.3 Poverty Headcount Ratio at National Poverty Line (% of population) 49

3.4 Income Inequality (the ratio the income share of the top quintile to 50

that of the bottom quintile)

3.5 Real GDP Growth per Capita, 1990-1999 and 2000-2009 52

3.6 Measurements and Sources of the Variables 57

4.1 Freedom House’s 2010 Indices for Political Rights and Civil Liberties 63

4.2 WGI’s 2009 Indices for Government Effectiveness, Regulatory 66

Quality, Rule of Law, and Control of Corruption

4.3 The Wall Street Journal and the Heritage Foundation’s 2011 Index 68

For Property Rights

4.4 Fraser Institute’s 2009 Index for Economic Freedom 70

4.5 Investment Rates, 1990-1999 and 2000-2009 72

4.6 Gross National Savings, 1990-1999 and 2000-2009 73

4.7 Population Growth Rates, 1990-1999 and 2000-2009 74

4.8 Life Expectancy at Birth, 1990-1999 and 2000-2009 75

4.9 Adult Literacy Rates, 1990-1999 and 2000-2009 76

4.10 Combined Gross Enrollment, 1990-1999 and 2000-2009 77

5.1 Summary Statistics for East Asia and Latin America 82

5.2 Summary Statistics for East Asia 85

5.3 Summary Statistics for Latin America 86

xv

5.4 Correlation Matrix of the Variables Used to Test the Impact of 89

Politico-Economic Institutions on Annual Growth Rates of GDP

per Capita

5.5 Multiple Regression Analysis of the Significant Predictor Variables 91

and Annual Growth Rates of GDP per Capita


Politico-Economic Institutions on Unemployment Rates


and Unemployment Rates


Politico-Economic Institutions on the Percentage of the Population

Falling below the Poverty Line


and the Percentage of the Population Falling below the Poverty Line


Politico-Economic Institutions on Income Inequality


and Income Inequality



per capita


and Annual Growth Rates of GDP per Capita









and the Percentage of Population Falling below the Poverty Line

xvi







per capita


and Annual Growth Rates of GDP per capita









and the Percentage of the Population Falling below the Poverty Line





8.1 Comparisons of East Asia and Latin America 136

LIST OF FIGURES

Figures Page

2.1 Conceptual Framework 46

8.1 The Impact of the Significant Variables on Annual Growth Rates of 133

GDP per Capita in East Asia and Latin America

8.2 The Impact of the Significant Variables on Unemployment Rates in 134


8.3 The Impact of the Significant Variables on the Percentage of the 134

Population Falling below the Poverty Line in East Asia and Latin

America

8.4 The Impact of the Significant Variables on Income Inequality in East 135

Asia and Latin America

8.5 The Impact of the Significant Variables on Annual Growth Rates of 137

GDP per Capita in Latin America


East Asia


Latin America


Population Falling below the Poverty Line in East Asia


Population Falling below the Poverty Line in Latin America

8.10 The Impact of the Significant Variables on Income Inequality in 144

East Asia

8.11 The Impact of the Significant Variables on Income Inequality in 144

Latin America

CHAPTER 1

INTRODUCTION

1.1 Statement of the Problem

How do the political and economic institutions of a country affect its

economic performance? What is the appropriate structure of politico-economic

institutions for economic development? These questions have been of significant

interest among not only economists and political scientists, but also policymakers in

all countries around the world. This is because knowledge of the relationship between

politico-economic institutions and economic performance will enable them to develop

political and economic institutions which are conductive to their countries’ economic

growth and efficiency.

In the past, studies on the factors affecting economic performance tended to

focus on socio-economic factors such as savings, investment, human capital

development, and technological progress. However, such socio-economic factors fail

to capture a number of aspects that explain economic performance. Due to the

limitations of socio-economic factors, an increasing number of studies have tried to

focus on institutional factors, particularly political institutions and economic

institutions. While a large number of studies focus on democracy, which is a major

measure of political institutions, only a small number of studies focus on other

measures of political institutions and on economic institutions.

Moreover, even though there are numerous empirical studies on the effect of

political institutions–normally perceived as democracy–on economic growth (Aghion,

Alesina and Trebbi, 2008; Anyiwe and Oziegbe, 2006; De Hann and Siermann, 1995;

Helliwell, 1992; Kurzman, Werum and Burkhart, 2002; Mahmood, Azid and

Siddiqui, 2010; Narayan and Smyth, 2006; Przeworski and Limongi, 1993; Rodrik,

1997; Sirowy and Inkeles, 1990; Weede, 1983), there is a limited number of studies

2

which determine the effect of both political and economic institutions on economic

performance. As pointed out by Knack and Keefer (1995), due to data limitations,

empirical research into cross-country sources of growth has been restricted to a

narrow examination of the role of institutions. Instead of taking economic institutions

into consideration, researchers have relied upon measures of political institutions.

These sets of variables capture only some aspects of institutions. In fact, economic

institutions, particularly economic freedom and the protection of property rights, are

of primary importance to economic performance because they influence the structure

of economic incentives in society (Acemoglu, Johnson and Robinson, 2004).

An example of studies taking both political and economic institutions into

consideration is Boko’s (2002) research on the impact of institutional factors on

economic growth in African countries. His study, however, employs only economic

freedom to represent economic institutions (Boko, 2002). Another study is by Scully

(1988), who examines the effect of the institutional framework on economic growth

in 115 market economies. In his study, the variable employed to represent economic

institutions is economic liberty measured in two ways, economic systems and

economic freedom (Scully, 1988).

Actually, economic institutions need to include not only economic freedom,

but also protection of property rights. As pointed out by Norton (2003), countries

with high economic growth tend to be equipped with good economic institutions –

well specified property rights and economic freedom. According to Na-Chiengmai

(2001), property rights are an important factor in determining economic activity. In a

market economy, the ability to accumulate and have legal protection for private

property is a significant motivating force. Without adequate protection of property

rights, individuals and firms will not have the incentive to invest in physical or human

capital or adopt more efficient technologies (Acemoglu et al., 2004).

Another research gap is that most empirical studies on the effect of institutions

on economic performance are based on developed countries (Kaldaru and Parts, 2008;

Seputiene, 2009). While institutions in developed nations tend to be stable and

relatively uniform, institutions in developing nations tend to be in a flux and vary

considerably across time and space. Thus, the experience of developing countries

with a range of institutions provides a rich laboratory for learning about the effect of

3

institutional arrangements (Lin and Nugent, 1995; Rodrik and Rosenzweig, 2010;

Shirley, 2008). Therefore, this study intends to do some initial work to bridge these

research gaps.

1.2 Significance of the Study

Due to the inconclusive evidence on the relationship between democracy and

economic growth, this study aims to re-examine the empirical relationship between

these two variables using data from selected developing countries. However, this

study goes further than previous studies by incorporating the effects of both political

and economic institutions on economic performance. The basic premise of this study

is that institutions, defined as the implicit and explicit rules by which the members of

a society interact, shape the economic behavior of agents and help explain the

economic performance of countries.

This study contributes to the existing knowledge of the relationship between

democracy and economic growth, as well as the new institutional economics, in four

ways. First, it estimates the long run effects of democracy on economic performance

within the institutional framework, which also incorporates governance and economic

institutions. This study, therefore, enhances knowledge and understanding of how the

institutional framework impacts economic performance. Second, this study offers

empirical results that are based on developing countries which are a great laboratory

due to their diverse institutions. Thus, this study complements previous research on

the relationship between democracy and economic growth and on the new

institutional economics in developing countries. The third contribution is that this

study is one of few which investigate the impact of institutional factors on various

measures of economic performance. Most previous studies on this issue only used

either the level of output (GDP, GDP per capita, GNP per capita, and GDP per

worker) or the growth of output (GDP growth, GDP per capita growth, and GDP per

worker growth) as the proxy of economic performance (Efendic, Pugh and Adnett,

2011). By employing many measures, the findings of this study better reflect how

institutions affect various aspects of economic performance. The last contribution of

this study is the adoption of both time-series and cross-country approaches rather than

4

only a cross-country approach, which is more common. By employing the time-series

approach, the researcher is able to investigate how changes in one variable over time

affect another variable, and thus address the issue of long-run causality between

variables. By adopting the cross-country approach, the researcher can produce

findings which are complimentary with case studies in advancing our understanding

of the growth process. This is because although case studies can generate novel

hypotheses, claims based on case studies that are not supported by cross-country

regressions require close scrutiny (Rodrik, 2002).

The findings and insights in this study will enable developing countries to

identify areas regarding their politico-economic institutions that require improvement.

In this way, adequate policies aimed at creating a functional and growth-enhancing

institutional framework would be implemented. The research outcomes may also be

beneficial to other developing countries, and perhaps less-developed countries, which

desire to develop the appropriate structure of political and economic institutions for

future development.

1.3 Objectives of the Study

The objectives of this study are as follows:

1. To study political and economic institutions in East Asia and Latin

America;

2. To examine the relationship between politico-economic institutions

and economic performance in selected East Asian and Latin American countries over

the period of 1990-2009;

3. To help improve policy decisions with respect to institutional

building and economic efficiency in developing countries.

1.4 Scope of the Study

The focus of this study is on the impact of politico-economic institutions on

economic performance in selected developing countries in East Asia and Latin

America. This study employs a time-series, cross-country analysis. According to

5

Gwartney, Holcombe and Lawson (2006), it is necessary to consider a lengthy time

period when analyzing the impact of institutional factors on economic performance.

This is for two reasons, which are that over a long time period, short-term effects

such as business cycles will be minimized, and that changes in institutional quality

tend to have effects on economic outcomes only with lags.

1.5 Limitations of the Study

This study has a number of limitations which should be addressed in further

research. First, a much larger sample should be required for greater precision. This

study does not cover developed countries because the study’s focus is on the

developing world, which has been largely overlooked by researchers in the field of

institutional economics. However, future research might draw from a greater number

of countries, including both developed and developing countries, to ensure that the

research results can be applied to countries at different levels of development.

Second, this study focuses on only political and economic institutions. In fact, there

are other institutions that might affect economic performance which are not included

in this study, such as legal institutions and social institutions. Therefore, other types

of institutions should be examined in future studies. Third, since this study and most

existing studies on the relationship between democracy and economic growth rely on

surveys conducted by Freedom House, future research should utilize other democracy

indexes such as the Economist Intelligence Unit’s Democracy Index to see whether

the results are different from those found in previous studies.

Fourth, further research should overcome this study’s limitations in terms of

statistical analysis. The authors of future studies should assume that the relationships

among economic variables are better characterized by a nonlinear specification. As

suggested by Lee et al. (2004), in some cases, ‘nonlinear models can provide better

economic insights’ (p.2). Moreover, further research should extend this study by

using a time-lagged regression analysis. Research on the impact of politico-economic

institutions on economic performance which takes the issue of time lags into

consideration would produce more accurate research results. Finally, better indicators

of institutions, better instruments, and different techniques are necessary in order to

6

confirm the robustness of previous findings as well as this study’s findings. The hope

is that further research will not only create a better understanding of the relationship

between existing institutions and economic performance, but will also help to design

new institutions conducive to economic growth.

CHAPTER 2

LITERATURE REVIEW AND CONCEPTUAL FRAMEWORK

Literature that is appropriate for the context of this study is literature on the

theories of economic growth, the broad approaches to development, political institutions,

economic institutions, and economic performance.

2.1 Theories of Economic Growth

The concept of economic development and its factors has changed over time.

Traditionally, economic development has been seen as determined by physical and

natural capital, technology, and also human capital. Nevertheless, differences in the

speed of economic development among countries with similar factor endowments and

production technologies have called for the introduction of new factors of economic

development in the last decade of the 20th century. Among alternative explanations,

economists have recently focused on the contribution of formal institutions and social

capital to economic growth and development (Grabowski, Self and Shields, 2007;

Kaldaru and Parts, 2008; Rodrik, Subramanian and Trebbi, 2002).

The history of economic growth theories can be divided into a number of

major trends, starting with the emergence of the discipline in the 1950s and 1960s.

This initial phase was characterized by a structuralist approach which stressed the role

of government in economic development. The failure of government triggered a split

into three schools of thought: the neoclassical approach, the reformist approach, and

the dependency approach. The 1980s were the heyday of the neoclassical approach,

which emphasizes the market mechanism (Goto, 1997).

In the 1990s, however, there were changes that signaled a second paradigm

shift for economic growth theories. Even though this shift did not negate the

prevailing trend toward the neoclassical theory, it is significant in the sense that it has

8

led to the emergence of neo-institutional approaches, the new growth model

approach, and the capability approach, in addition to the political economic approach

to development (Goto, 1997). Nevertheless, the theories of economic growth that are

the focus of this study include neoclassical growth theory, endogenous growth theory,

and new institutional theory.

2.1.1 Neoclassical Growth Theory

The neoclassical growth theory represents the seminal contribution to the

classical theory of growth. Important contributions to the neoclassical growth theory

came from the work done by Robert Solow, who developed a simple growth model

and won the Nobel Prize in economics in 1987 for his work. Solow’s (1956) growth

model expands on the Harrod-Domar formulation by adding a second factor, labor,

and introducing a third independent variable, technology, to the growth equation. His

model shows that ‘if there were no technological progress, then the effects of

diminishing returns would eventually cause economic growth to cease’ (Aghion and

Howitt, 1997: 11). According to neoclassical growth theory, output growth results

from one or more of three factors: increases in labor quantity and quality (through

population growth and education), increases in capital (through saving and

investment), and improvements in technology (Todaro and Smith, 2003).

Since the neoclassical growth theory assumes that the rate of technological

progress is determined by a scientific process that is separate from, and independent

of, economic forces, it implies that economists can take the long-run growth rate as

given exogenously from outside the economic system. In other words, the long-run

growth rate is determined outside of the model. A common prediction of neoclassical

growth models is that an economy will always converge towards a steady state rate of

growth, which depends only on the rate of technological progress and the rate of labor

force growth (Howitt, 2008).

Empirical evidence offers mixed support for the neoclassical growth model.

Limitations of the model include its failure to take into consideration entrepreneurship

(which may be a catalyst behind economic growth) and strength of institutions (which

facilitates economic growth). In addition, it does not explain how or why technological

progress occurs (Todaro and Smith, 2003). Most importantly, neoclassical theory

9

does not adequately meet the needs of less-developed economies. It seems that

neoclassical economists are unable to provide a convincing explanation of the

development process and its dynamic aspects (Goto, 1997). Moreover, its analytical

approach is too mechanistic and deterministic to be capable of capturing the essential

elements of the interactions among actors and thus misses important strategic issues

in the course of economic development (Yanagihara, 1997). These failings have led

to the development of endogenous growth theory, which endogenizes technological

progress and/or knowledge accumulation as well as better explains the development

process (Todaro and Smith, 2003).

2.1.2 Endogenous Growth Theory

The poor performance of neoclassical theories in illuminating the sources of

long-term economic growth has led to a widespread dissatisfaction with traditional

growth theory. The endogenous growth theory provides a theoretical framework for

analyzing long-run economic growth that is determined by forces that are internal to

the economic system rather than by forces outside that system (Todaro and Smith,

2003). According to Romer (1994 quoted in Turnovsky, 2001: 1), the endogenous

growth theory has been motivated by several issues, including the following:

(i) an attempt to explain aspects of the data not addressed by the

neoclassical model; (ii) a more satisfactory explanation of international

differences in economic growth rates; (iii) a more central role for the

accumulation of knowledge; and (iv) a larger role for the instruments

of macroeconomic policy in explaining the long-run growth process

Likewise, Todaro and Smith (2003) point out that the principal motivations of the

endogenous growth theory are to explain both growth rate differentials across

countries and a greater proportion of the growth observed. More succinctly,

endogenous growth theorists seek to explain the factors that determine the rate of

growth of the GDP that is left unexplained and exogenously determined in the

neoclassical growth model. They believe that long-term economic growth is

10

dependent not only on saving of and investment in physical capital, but also on other

factors especially human capital development.

The basis of human capital lies in the theories of Theodore Schultz, an

economist at the University of Chicago who was awarded the Nobel Prize in

economic sciences in 1979. In the early 1960s, Schultz produced his ideas of human

capital as a way of explaining the advantages of investment in education to improve

agricultural outputs. The logical next step was to expand this linkage between better

education and improved productivity as a benefit for the economy as a whole. Schultz

demonstrated that the yield on human capital in the US economy was larger than that

based on physical capital, such as new plants and machinery.

One of the very early proponents of human capital theory is Gary Becker, the

1992 Nobel Prize winner in economics. Becker (1975) advocates that human capital

is considerably important for the process of economic development. Apart from

proposing different kinds of human capital creation, he also places an emphasis on the

significance of investment in human capital as a key for sustainable economic

development or even an engine of economic development itself. The investment in

human capital can be undertaken by various ways, such as formal education and on-

the-job training. Another prominent scholar that advocates the theory of human

capital is Jacob Mincer. In his seminal work, Mincer (1974) puts forward the

important role that education plays in promoting human capital, which in turn leads to

economic development or at least sustains the level of economic development.

Although the endogenous growth theory overcomes some shortcomings of the

neoclassical growth theory, it has faced some theoretical criticisms. One of the main

weaknesses of endogenous growth theory is the collective failure to explain the

conditional convergence reported in the empirical literature. Another frequent

criticism is related to the cornerstone assumption of diminishing returns to capital.

Some contend that endogenous growth theory has proven no more successful than

neoclassical growth theory in explaining the income divergence between the

developing and developed worlds (Parente, 2001).

11

2.1.3 New Institutional Theory

The new institutional theory revived interest in growth theory and modified

the way in which most economists study the determinants of economic growth. It is

an attempt to incorporate a theory of institutions into economics. However, contrary

to many earlier attempts to overturn or replace neoclassical theory, ‘the new

institutional economics builds on, modifies, and extends neoclassical theory to permit

it to come to grips and deal with an entire range of issues heretofore beyond its ken’

(North, 1993: 1).

The goal of the new institutional theory is to overcome the important

limitations of mainstream neoclassical economics (Nabli and Nugent, 1989). In

mainstream neoclassical economics, considerable attention has been paid to four main

types of constraints: individual preferences, technological opportunities, physical and

human capital endowments, and market opportunities. In such analyses, the

institutional framework has almost invariably been taken for granted, and in many

cases has even been altogether omitted. This leaves the analysis of institutional

constraints to non-economists. While the analyses of non-economists are rich in

descriptive details and contain numerous useful insights, ‘they tend to be relatively

light in their ability to provide either reliable generalizations or a sound logical basis

for policy choices’ (Nabli and Nugent, 1989: 1334).

The new institutional economics departs from the neoclassical theory in that it

abandons instrumental rationality–the assumption of neoclassical theory that has

made it an institution-free theory (North, 1993). According to North (1993: 1), in a

world of instrumental rationality, ‘institutions are unnecessary; ideas and ideologies

do not matter; and efficient markets–both economic and political–characterize

economies’. In addition, ‘it recognizes high incidence of market imperfections in the

economy, especially in early stages of development’ (Yanagihara 1997: 7). This

recognition leads to its adoption of market-enhancing government policy, aimed at

facilitating the private sector’s capacity to overcome coordination problems and other

market imperfections (Yanagihara, 1997). Another important break with the neoclassical

school is that the new institutional economics explicitly treats the firm as an

organization with its internal coordination mechanisms. To this extent, the new

12

institutional theory enables one to relate the ingredients of each economic agent to its

behavior (Yanagihara, 1997).

This theory focuses attention on the institutions that shape the incentive

structure, which may either propel or impede productive activity within society (Ali

and Crain, 2002). According to North (1990), ‘a great deal of economic performance,

across both space and time, can be explained by variations in institutions’ (quoted in

Davis, 2009: 1). In the new institutional theory, special emphasis has been placed on

political institutions. As pointed out by North (1993: 2), this approach models

political institutions ‘as a critical factor in the performance of economies’ and ‘as the

source of the diverse performance of economies’. The influence of the new

institutional theory pioneered by North has been profound. It has not only attracted

the attention of a large number of social scientists, but also influenced the amount of

attention devoted to questions of institutional design (Davis, 2009).

A common theme in the new institutional theory is that societies that have

adopted infrastructures that favor production over diversion have typically done so

through effective government (e.g., a strong judiciary and policies that secure

property rights) (Ali and Crain, 2002). As a result of numerous studies carried out in

the field of the New Institutional Economics, institutions are widely considered by

scholars as a key factor in explaining differences in economic performance across

diverse economies.

Over the past decade, a lot of emphasis has been given to the creation and

development of good institutions as a necessary condition for economic growth

(Gagliardi, 2008; Grabowski et al., 2007; Presbitero, 2006; Seputiene, 2009). It has

become clear that property rights, appropriate regulatory structures, the quality and

independence of the judiciary, and bureaucratic capacity can no longer be taken for

granted in many settings and that they are of utmost importance to initiating and

sustaining economic growth. An implicit assumption that these institutions would

arise endogenously and effortlessly as a by-product of economic growth has been

substituted by the view that they are essential pre-conditions and determinants of

growth (Rodrik, 2002). Due to the importance of institutions, the meaning, features,

functions, and measurement of institutions need to be clarified.

13

2.1.3.1 Definition of Institutions

There is no consensus concerning the appropriate definition of

institutions (Davis, 2009; Nabli and Nugent, 1989; Rhodes, Binder and Rockman,

2006). According to North (1981), institutions are ‘a set of rules, compliance

procedures, and moral and ethical behavioral norms designed to constrain the

behavior of individuals in the interests of maximizing the wealth or utility of

principles’ (quoted in Glaeser, La Porta, Lopez-de-Silanes and Shleifer, 2004: 275).

They are made up of formal constraints (rules, laws, constitutions), informal

constraints (norms of behaviors, conventions, and self imposed codes of conduct),

and their enforcement characteristics (North, 1993; 1994). It is the admixture of rules,

norms, and enforcement characteristics that determines economic performance

(North, 1993).

Institutions are defined by Greif (2006) as ‘a system of social factors

that conjointly generate a regularity of behavior’ (cited in Davis, 2009: 3). Both North

and Greif emphasize the social factors which influence behavior, as opposed to the

features of the natural environment or factors purely internal to individuals (Davis,

2009). According to Nabli and Nugent (1989: 1335), an institution is ‘a set of

constraints which governs the behavioral relations among individuals or groups’.

Rodrik (2000: 2) defines institutions as ‘a set of humanly devised behavioral rules

that govern and shape the interactions of human actions, in part by helping them to

form expectations of what other people will do’. It is obvious that a key word that

these and other definitions share is “constraints,” which need to be reasonably

permanent or durable (Glaeser et al., 2004).

2.1.3.2 Features of Institutions

In order to understand the linkages between institutions and economic

performance, some features of institutions must be understood. The first key feature

of institutions is the nature of rules and constraints. The nature of the rules and

constraints of institutions is a feature which is explicitly stated in most definitions.

These rules and constraints are defined by Ostrom (1986 quoted in Nabi and Nugent,

1989: 1335) as ‘prescriptions commonly known and used by a set of participants to

order repetitive, interdependent relationship’. According to Nabli and Nugent (1989),

it is important in terms of institutional analysis to consider sets of rules rather than

14

single rules separately. Only rules as sets or configurations are considered as basic

features of institutions.

The second feature is institutions as subjective constructs. North (1993)

places a strong emphasis on the “mental construct” or the “subjective model” of

individuals as a major factor affecting institutional change. Institutions are not

considered objective phenomena, but subjective mental constructs or “artifacts” that

think and act through the medium of human beings. However, the subjective nature of

institutions does not preclude their objective manifestations (e.g., constitution or

traffic signal), their susceptibility to objective influence (e.g., economic crisis and

war), or the objective nature of their ultimate impact. Institutions are, therefore,

subjective in terms of their origins and operations, but objective in terms of their

manifestations and impacts (Saleth and Dinar, 2004).

Path dependence is the third feature of institutions. The evolution of

institutions and their performance implications is strongly influenced by their path-

dependent nature. Path dependency means that history does matter; that is, the

direction and scope of institutional change cannot be separate from its early course or

past history (Saleth and Dinar, 2004). North (1993) points out that the network

externalities, economies of scope, and complementarities that exist with a given

institutional matrix make institutional change overwhelmingly incremental and path

dependent. Since informal institutions play an important role in the incremental way

in which institutions evolve, they remain a major source of path dependence. Informal

institutions change more slowly than formal institutions. Thus, there is always tension

between altered formal rules and persisting informal rules (Gagliardi, 2008; Saleth

and Dinar, 2004).

Institutions are also characterized by stability and durability. According

to Saleth and Dinar (2004), the features of institutions that are important from the

standpoint of institutional change are their relative durability, self-reinforcing nature,

and persistence. Nabli and Nugent (1989) also point out that institutions should have

some degree of stability; otherwise, they would not have an institutional character.

The relative durability aspects of institutions make institutional change gradual and

incremental in nature (North, 1993). Even when conquest or revolution suddenly

changes formal institutions, the informal rules derived from the formal rules continue

15

to have their hold, providing institutional continuity and stability (Saleth and Dinar,

2004).

The fifth feature of institutions is their hierarchic nature and nestedness.

Institutions are not a single entity but comprise a number of fundamentally linked and

carefully structured components. Since these components assume the form of either a

single rule or a subset of sequentially nested rules, institutions can be viewed as a

constellation of hierarchically nested rules. Institutions, whether as part of the

institutional environment or institutional arrangements, are mutually nested and

structurally embedded within each other (Saleth and Dinar, 2004).

Finally, institutions are characterized by embeddedness and complementarity.

Although the factors governing institutions can range from pure market selection or

transaction cost criteria to cultural, social, and political requirements, institutions

themselves are embedded with and complementary to each other. Therefore, formal

institutions are embedded within informal institutions and the former cannot be

effective without the latter. For instance, market institutions are embedded within

social and political institutions at both national and regional levels. This embedded

character ensures the prerequisites for the operation of market institutions.

Institutional embeddedness also has contextual and spatial dimensions. In view of

these dimensions, markets and their institutional substitutes, such as hierarchies,

networks, and alliances, are constantly influenced by socioeconomic transformation,

technical change, and the changing status of regions and nation-states (Saleth and

Dinar, 2004).

2.1.3.3 Functions of Institutions

The most basic function of institutions is ‘to economize, i.e. to allow

one or more of the agents to improve their welfare without making others worse off,

or to allow them to attain a higher level of their objectives within their constraints’

(Lin and Nugent, 1995: 2307). There may be several important and quite distinct

means of achieving this basic economizing function of institutions. One of these is by

taking advantage of potential economies of scale, specialization, and/or external

economies. Numerous institutions, both market institutions and non-market institutions,

can perform this function. This indicates the potential for competition among

alternative institutional arrangements (Lin and Nugent, 1995).

16

Another means of improving welfare is to prevent individuals and

groups from making mistakes. An important institutional mechanism for avoiding

mistakes is collecting more and better information and making that information

available to decision makers. Information is relevant not only for present decisions

but, due to the effects of the evaluations of present decisions on future decisions, also

for future decisions. In other words, one of the institutional mechanisms for avoiding

future mistakes may be a better information system that allows mistakes to be

discovered quickly and that alerts decision-makers not to repeat their mistakes (Saleth

and Dinar, 2004).

Prominent among economizing institutions are property-right institutions

which internalize externalities. Property rights are the formal and informal rules that

delimit an individual’s or group’s rights over the assets that they possess. While

property rights may be a crucial ingredient in the economizing function of

institutional change, they also illustrate the importance of the other basic function of

an institution; namely, that of redistribution. In any case, improving one’s own

position at the expense of others, i.e. the redistributive function, may be a primary

function or motive for many institutional arrangements (Saleth and Dinar, 2004).

Other functions of institutions include contributing to solving problems

in the coordination of agents’ plans, helping to promote cooperative behavior and

overcome opportunism, making agents internalize externalities, and reducing

uncertainty. Institutions support the formation of social capital and of a historical

experience of collective action which, in turn, positively affect the likelihood to

credibly commit to cooperative strategies (Gagliardi, 2008).

2.1.3.4 Measurement of Institutions

Even though there is a strong theoretical case supporting the importance

of institutions in the organization of economic activity, the corresponding empirical

case has been hampered by the lack of information on countries’ institutional quality

and also by problems related to its measurement (Gagliardi, 2008). A lot of

institutions and research centers provide measures of institutional quality; however, it

is very difficult to disentangle which are the best (Presbitero, 2006).

To measure institutions, the literature has focused on several datasets.

The first dataset, used initially by Knack and Keefer (1995) and Hall and Jones

17

(1999), and more recently by Acemoglu et al. (2001), is survey indicators of

institutional quality from the International Country Risk Guide (ICRG) produced by

Political Risk Services (Glaeser et al., 2004). On a monthly basis since 1980, the

ICRG has produced political, economic, and financial risk ratings for countries

important to international business. It rates 130 countries according to 22 components

grouped into three major categories of risk: political, financial, and economic.

The second set of data, used most recently by Rodrik et al. (2002), is an

aggregated index of mostly survey assessments of government effectiveness collected

by Kaufmann et al. (2002) (Glaeser et al., 2004). The Government Effectiveness

Index is a measure of "the quality of public service provision, the quality of the

bureaucracy, the competence of public servants, and the independence of the civil

service from political pressures." This index describes the ability of governments to

effectively deliver public services and to make policy.

The third set, which comes from the Polity IV dataset collected by

political scientists, aims directly at measuring the limits of executive power (Glaeser

et al., 2004). Polity IV contains coded annual information on regime authority

characteristics and transitions for all independent states in the global state system and

covers the years 1800-2009. It consists of six component measures that record key

qualities of executive recruitment, constraints on executive authority, and political

competition. It also records changes in the institutionalized qualities of governing

authority.

The fourth widely-used dataset is the IRIS institution quality indicators.

The IRIS dataset was originally constructed in 1993 by Steve Knack and Philip

Keefer for the IRIS Center at the University of Maryland, based on data obtained

from the International Country Risk Guide. The dataset includes computed scores for

six variables: corruption in government, rule of law, bureaucratic quality, ethnic

tensions, repudiation of contracts by government, and risk of expropriation. Knack

produced subsequent issues of the data for an ongoing series of working papers from

the IRIS Center. In its current form, IRIS-3 contains data for the period 1982-1997.

The last dataset, which is based on the World Bank’s long-standing

research program, is the Worldwide Governance Indicators (WGI). The WGI offers a

useful snapshot of some perceptions of a country’s quality of governance. They have

18

captured six key dimensions of governance, Voice and Accountability, Political

Stability and Lack of Violence, Government Effectiveness, Regulatory Quality, Rule

of Law, and Control of Corruption from 1996 to the present. The WGI measure the

quality of governance in over 200 countries, based on close to 40 data sources

produced by over 30 different organizations worldwide and have been updated on an

annual basis since 2002.

2.2 Approaches to Development

At present, the countries around the world are facing two major development

challenges: how to ignite growth and how to establish democracy. Economic research

has identified two broad approaches to confronting these challenges. The first

approach emphasizes the need to start with democracy and other checks on

government in order to achieve economic growth, whereas the second approach

emphasizes the need for human and physical capital accumulation to start the process

(Glaeser et al., 2004).

According to Glaeser et al. (2004), these two approaches share some important

similarities. Both of them emphasize the need for secure property rights to support

investment in human and physical capital, and they both see such security as a public

policy choice. Nevertheless, the institutional approach sees the pro-investment

policies as a consequence of political constraints on government, while the development

approach sees these policies in poor countries largely as choices of their typically

unconstrained leaders.

2.2.1 The Institutional Approach

This approach emphasizes the need to start with democracy and other checks

on government as the mechanisms for securing property rights. With such political

institutions in place, investment in human and physical capital, and therefore

economic growth, is expected to follow. This approach was stressed by Montesquieu

(1748) and Smith (1776), as well as by the new institutional economics literature.

More recently, the literature on economic growth, beginning with the early contributions

of Knack and Keefer (1995) and Mauro (1995), has turned to the effects of good

19

institutions on economic growth. It is fair to say that recent studies, including those of

Hall and Jones (1999), Acemoglu et al. (2001; 2002), Easterly and Levine (2003),

Dollar and Kraay (2003), and Rodrik et al. (2004), have reached close to an

intellectual consensus—that the political institutions of limited government cause

economic growth (Glaeser et al., 2004).

2.2.2 The Development Approach

The reverse idea, namely that growth in income and human capital causes

institutional improvement, is most closely associated with the work of Lipset (1960).

He believed that educated people are more likely to resolve their differences through

negotiation and voting than through violent disputes. Education is necessary for

courts to operate and to empower citizens to engage with government institutions.

Literacy encourages the spread of knowledge about the government’s malfeasance.

According to this approach, countries differ in their stocks of human and social

capital, and institutional outcomes depend to a large extent on these endowments.

Empirically, Lipset’s hypothesis–that growth leads to better political institutions–has

received considerable support in the work of Przeworski and his associates (2000)

and Barro (1999) (Glaeser et al., 2004).

2.2.3 Empirical Evidence on the Impact of Institutions on Economic

Performance

The impact of institutions on economic performance is indirect because

institutions do not produce goods or services. According to the institutional approach,

both the amount and productivity of resources depend on the institutional

environment. Well-defined institutions reduce uncertainty, decrease macroeconomic

volatility, stimulate specialization, lower transaction costs, and thus foster investments

and innovation (Seputiene, 2009).

Although there are a large number of empirical studies on the impact of

institutions on economic performance, the results have been inconclusive. A leading

example of empirical studies supporting the proposition that institutions cause growth

is a study conducted by Knack and Keefer (1995). It aims to quantify the relationship

between institutions, investment, and growth by using alternative indicators. The

research findings strongly indicate that institutions that protect property rights are

20

crucial to economic growth and to investment. The effect of institutions on growth

persists even after controlling for investment. This suggests that the security of

property rights affects not only the magnitude of investment, but also the efficiency

with which inputs are allocated. Similarly, de Long and Shleifer (1993) found that

good institutions in the form of predictable and stable rules of law, efficiency

bureaucracy, and property rights security are linked with economic performance.

Another leading example is research by Acemoglu, Johnson, and Robinson

(2001). They treat European colonialism as a natural experiment and hypothesize that

European colonizers imposed different types of institutions on their former colonies

depending on whether those colonies were suitable for European settlement. The

research result is that institutions have a large effect on economic performance.

Kaufmann, Kraay and Zoido-Lobaton (1999; 2002) also provide convincing

evidence that institutions matter for development in terms of per capita incomes,

infant mortality, and adult literacy. In addition, a study carried out by Seputiene

(2009) aims to explore and quantify the relationship of the countries’ income level

with institutional environment, geography, and openness to trade across the European

Union countries. It was found that a strong and positive link between various

measures of institutions and economic development was established, and primacy of

institutions over openness to trade and geography was supported.

Meanwhile, some researchers have found that institutions have no impact on

economic growth. An interesting example is a study conducted by Glaeser et al.

(2004) which reveals that the evidence that institutions cause economic growth, as

opposed to growth improving institutions, is non-existent. In this study, the OLS

cross-country evidence for 1960-2000 provides no support for the claim that

“institutions cause growth.”

2.3 Political Institutions

The study of political institutions is one of the founding pillars of political

science (Rhodes et al., 2006). According to March and Olsen (2006: 7), ‘political

institutions define basic rights and duties, shape and regulate how advantages,

burdens, and life-chances are allocated in society, and create authority to settle issues

21

and resolves conflicts’. In other words, political institutions determine both the

constraints and incentives faced by key players in a given society (Pereira and Teles

2011). As pointed out by Moe (2005), political institutions create incentives that

influence the strategic choices made by political actors. They are in some sense the

rules of the game in political life, and are themselves created to solve political

problems. Due to the importance of political institutions, this study investigates the

impact of political institutions–measured chiefly by democracy and governance–on

economic performance.

2.3.1 Democracy

According to the institutional approach explained above, democracy–a

measure of political institutions–is extremely essential for economic growth. Therefore,

the concept of democracy and its measures need to be elaborated.

During the 20th century, ‘democracy broke out across the world like a measles

epidemic’ (Polidano, 2002: 260). In other words, there was a significant expansion in

the number of sovereign states and the number of democratic governments (Brochado

and Martins, 2005). From the 1970s to the early 1990s, authoritarian regimes

collapsed one after the other (Polidano, 2002). According to Potter (1997), in the mid-

1970s about 68 percent of all the countries in the world were governed by

authoritarian regimes; by the mid-1990s this figure had dropped to 26 percent (cited

in Polidano, 2002). Thus, the 20th century has been considered by Freedom House as

the “democratic century” (Brochado and Martins, 1995).

2.3.1.1 Concept of Democracy

The word “democracy,” from the Greek, means ‘rule by the people’

(UNDP, 2002: 54). The term “democracy” can be defined in several ways. One may

stress political democracy, economic democracy or social democracy (Ersson and

Lane, 1996). In this research, the focus is on ‘what is most commonly labeled as

‘political democracy’, which implies the existence of extensive political rights and

civil liberties, in addition to contestation between parties’ (Ersson and Lane, 1996: 50).

In the political aspect, democracy is defined as ‘a system whereby the

whole of society can participate, at every level, in the decision-making process and

keep control of it’ (Boutros-Ghali, 2002: 9). Another definition is provided by Rivera-

22

Batiz and Rivera-Batiz (2002: 135), who define political democracy as ‘a political

regime where the population of a country chooses its government leaders and is thus

to influence public policy without undue restrictions or limitations’. The two major

components of political democracy are political rights and civil liberties (Ersson and

Lane, 1996). Freedom House (2000 quoted in UNDP, 2002: 36) defines political

rights as ‘the freedoms that enable people to participate freely in the political

process’, and civil liberties as ‘the freedom to develop views, institutions and

personal autonomy apart from the state’.

According to De Hann and Siermann (1995: 182), a country is considered as

a democracy if there is a regime in which

(i) meaningful and extensive competition exists among individuals

and organized groups for all elective positions of government power,

at regular intervals and excluding the use of force; (ii) no major

(adult) social group is excluded from this competition and (iii) a

sufficient level of civil and political liberties exists to ensure the

integrity of political competition and participation.

Democracy is based on two fundamental principles, which are

participation and accountability (UNDP, 2002). Participation is a concept which

includes ‘the involvement of people not only in choosing political representatives but

also in being included and empowered in the process through which decisions are

reached in the various layers of society’ (Rivera-Batiz and Rivera-Batiz, 2002).

Hence, in democratic countries, people have the right to participate in the

management of public affairs (UNDP, 2002). As for accountability, it enables people

to have the right to access information on government activities, to petition the

government, and to seek redress through impartial administrative and judicial

mechanisms (UNDP, 2002).

The foundation of political democracy is the full observance of human

rights. The promotion of those rights as well as the respect of differences and of

freedom of speech and thought are indispensable preconditions for democracy. The

holding of free, fair, and regular elections based on universal suffrage is another

23

necessary precondition for the existence of a democratic regime (Boutros-Ghali,

2002; UNDP, 2002). Justice is also a precondition of democracy. According to

Boutros-Ghali (2002: 11), ‘justice guarantees the exercise of democracy as it serves

to enforce the principle of equality before the law, the right of all individuals to

express their opinion within the society to which they belong, and the right to be

heard and to put their case’. Therefore, democracy is viable only if there is a reliable

and independent judicial system.

Sen (2001) points out that there are three different ways in which

democracy enriches the lives of the citizens. First, democracy provides more political

and civil freedoms, which are crucial for the good living of individuals as social

beings. Political and social participation has intrinsic value for human life and well-

being. Second, democracy plays an instrumental role in enhancing the hearing that

people get in expressing and supporting their claims to political attention, including

the claims of economic needs. It provides political incentives for responsive and good

governance. Third, democracy has constructive importance in providing the citizens

with an opportunity for the debate and discussion which helps formulate a system of

values and priorities.

2.3.1.2 Measures of Democracy

Many authors claim that ‘it is meaningful to distinguish between states

that are more or less democratic, meaning thus that democracy is a measurable

property’ (Ersson and Lane, 1996: 50). Based on this idea, a series of indicators or

measures of democracy have been constructed. According to the UNDP (2002),

democracy measures aim to show the extent of political rights and civil liberties

available to the citizens of given polities. Although there are a lot of measures

available for democracy, there is no unambiguous, uncontroversial measure. There

are two types of measures, both with drawbacks. The first type is objective measures,

such as date of the most recent election and voter turnout or the existence of

competitive elections. Objective measures, however, may not reflect all aspects of

democracy. Another type is subjective measures which are ‘based on expert opinions

about a country’s degree of democracy’ (UNDP, 2002: 36). Because of being

subjective, they are open to disagreement and perception biases.

24

Nevertheless, since ‘a truly democratic government requests the

citizens’ widespread and substantive participation and the responsibility of the people

that have the power’, the use of subjective measures ‘constitutes the most appropriate

approach for the reception of this qualitative concept’ (Brochado and Martins, 1995).

Table 2.1 provides some of the subjective indicators that intend to capture the

extension of democracy (UNDP, 2002). These indices rely on three sources: the

Polity IV Dataset, Freedom House Indices, and the World Bank Governance

Indicators Dataset.

Table 2.1 Subjective Indicators of Democracy

Indicator Source Range

Polity score Polity IV dataset -10 (less democratic)

University of Maryland to 10 (most democratic)

Civil liberties Freedom House 1.0-2.5 free

3.0-5.0 partly free

6.0-7.0 not free

Political rights Freedom House 1.0-2.5 free

3.0-5.0 partly free

6.0-7.0 not free

Press freedom Freedom House 0-3 free

31-60 partly free

61-100 not free

Voice and accountability World Bank Governance -2.5 to 2.5; higher is better

Indicators Dataset

2.3.1.3 Theoretical Perspectives on Democracy and Economic Growth

In the social science debate about political democracy and economic

growth, there are three theoretical perspectives describing their relationship. Those

perspectives are referred to as the “conflict,” “compatibility,” and “skeptical”

perspectives, respectively (Sirowy and Inkeles, 1990).

The conflict perspective claims that ‘economic growth is hindered by

the democratic organization of the polity’ (De Hann and Siermann, 1995: 177). In

25

other words, democracy and economic development are viewed as competing

concerns (Sirowy and Inkeles, 1990). According to this perspective, to achieve

successful and rapid economic growth, an authoritarian regime that suppresses or

delays the extension of basic civil and political rights and the development of

democratic procedures and institutions is required (De Hann and Siermann, 1995;

Sirowy and Inkeles, 1990: 129). Sirowy and Inkeles (1990) point out that there are

three major reasons for supporting such a claim:

1) dysfunctional consequences of “premature” democracy act, in

turn, to slow growth, 2) democratic regimes are largely unable to

implement effectively the kinds of policies considered necessary to

facilitate rapid growth, 3) the uniqueness of the present world-

historical context requires pervasive state involvement in the

development process, which is in turned unduly fettered by political

democracy.

In this view, an authoritarian regime has the superior ability to generate

economic growth indirectly since it fosters social and political stability, allows

insulation from outside influence, and is able to muster single-minded strength.

Moreover, authoritarian regimes can facilitate rapid economic growth directly

through a number of mechanisms. Some of these mechanisms are ‘their ability to

exert firmer control over labor and labor markets, their greater efficiency in the

allocation of resources, their ability to use coercion to break traditional patterns, and

their capacity to collectively organize and direct economic policies’ (Sirowy and

Inkeles, 1990: 130). Perhaps the most frequently-noted mechanism, however, is its

effect on consumption and saving. According to Sirowy and Inkeles (1990), since

democratic governments are preoccupied with issues of redistribution rather than

accumulation, the allocation of national income is likely to be biased toward

consumption and away from saving. An increased demand for immediate

consumption and the lack of capital reduce investment and retard economic growth

(De Hann and Siermann, 1995). Democracy is thus inimical to economic development.

More authoritarian regimes, in contrast, can tolerate the degree of restraint in

26

consumption necessary for maximizing the rate of growth as well as pursue policies

benefiting a minority at the expense of the majority, and thereby foster the

accumulation of needed capital (Sirowy and Inkeles, 1990).

Therefore, advocates of the conflict perspective argue that developing

countries with strong central planning under an authoritarian form of government will

experience more rapid economic growth than will those with democratic regimes

(Dick, 2001; Sirowy and Inkeles, 1990). The strong advocates of this perspective are

Hoover (1957) and Huntington (1968). Hoover (1957) asserts that authoritarian states

are inherently better at achieving economic growth than democratic states (cited in

Ryan, 2000). His viewpoint is supported by Huntington (1968), whose influence has

caused the conflict model to acquire widespread acceptance (Przeworski and Limongi,

1993). He also believes that democracy is inimical to economic development (cited in

De Haan and Siermann, 1995). Although this perspective was more popular in the

past, it still has its proponents. For instance, the former leader of Singapore, Lee Kuan

Yew, believes that what a country needs to develop is discipline more than

democracy because the exuberance of democracy leads to indiscipline and disorderly

conduct, which are inimical to development (De Hann and Siermann, 1995).

Proponents of the compatibility perspective sharply object to the charges

levied by proponents of the conflict perspective. According to Sirowy and Inkeles

(1990: 132), ‘although the compatibility model concedes that economic development

requires an authority to enforce contracts, ensure law and order, and so on, they

strongly disagree with the assumption that development needs to be commanded in all

respects by a central authority’. Fundamental to this perspective is the claim that the

political institutions essential for economic development tend to exist and function

effectively under democratic rule. These institutions include:

the rule of law, which protects property rights; individual liberties

that foster creativity and entrepreneurship; freedom of expression,

which ensures the production and unimpeded flow of information;

and institutional checks and balances that prevent the massive theft

of public wealth often observed in autocracies (Pei, 2001: 29).

27

Advocates of the compatibility perspective view democracy and

economic development as very much compatible, actually working to support one

another (Sirowy and Inkeles, 1990). They believe that a democratic regime is most

suitable for fostering sustained and equitable economic development. Among the

proponents of the compatibility perspective, ‘there is the sentiment that although

authoritarian rule may, on some occasions, generate a more rapid rate economic

development in the short run, democratic rule is more conductive to a sustained,

sectorially balanced, and equitable growth in the long run’ (Sirowy and Inkeles, 1990:

134). From their viewpoint, democratic processes and the existence of civil liberties

and political rights generate the conditions which are most conductive to economic

development (Ersson and Lane, 1996; De Hann and Siermann, 1995). This is because

a market economy, which involves special social institutions, is likely to go hand in

hand with institutions that protect civil and political rights; that is, with political

democracy. If a market economy is expected to operate well, the kind of social

institutions that are available only in democracies is required. Hence, democracy

indirectly promotes sustained and equitable economic development by strengthening

the market economy, which tends to outperform other economic systems on

development criteria including growth, quality of life or level of human development

(Ersson and Lane, 1996).

In addition, those that support this perspective believe that democracy

tends to rely on the cooperation of the people. In order to achieve such broadly based

cooperation among citizens, it is necessary to develop social conditions that meet the

needs of the population. This implies that social inequalities tend to be less

pronounced in democratic countries. In the long run, therefore, democracies enhance

economic growth as well as promote social equality because they foster a community

of well-educated people, who in turn produce highly-qualified industrial outputs,

further strengthening both cooperation and growth (Ersson and Lane, 1996).

According to De Hann and Siermann (1995), various arguments have

been put forward to support the compatibility perspective. First, democratic regimes

may be more effective in reforming the economy than authoritarian ones. Second,

democratic government may perform better with respect to the security of property,

which is the foundation for material progress. The basic argument for this point of

view is that authoritarian leaders cannot promise credibly that current policies

28

securing property rights will last. Finally, some authors have found a positive

relationship between economic freedom and rates of growth. If this association is

robust and if economic freedom and political freedom are positively correlated,

democracy will also be positively correlated with economic growth. This viewpoint

has been put forward in an article in The Economist (1994 quoted in De Hann and

Siermann, 1995) which states that democracy entrenches economic freedom, and in

doing so underpins economic growth. Another influential study that supports this

view is by Bhalla (1997). He thinks that ‘free markets and a free society are the

important ingredients to rapid economic development’ (Bhalla, 1997: 228).

Theorists that subscribe to the skeptical perspective doubt whether any

systematic relationship exists between democracy and economic development (De

Hann and Siermann, 1995; Sirowy and Inkeles, 1990). According to Ersson and Lane

(1996: 49), the skeptical perspective admits that ‘it may well be the case that

democracy and development go together in the long run, but it emphasizes that

‘democracy in itself has little direct impact upon development’. In other words,

having a democratic government alone matters very little for economic growth

(Sirowy and Inkeles, 1990). Instead, the emphasis should be placed on the intervening

factors that may have an impact on the interaction between them (Ersson and Lane,

1996). Such intervening factors include, for example, the kind of policies pursued, the

nature of the political party system (two-party versus multi-party), the level and form

of state intervention into the economy, the pattern of industrialization pursued (labor-

intensive versus capital-intensive), and the cultural environment (De Hann and

Siermann, 1995; Sirowy and Inkeles, 1990). Therefore, the skepticism in this perspective

derives from the contention that additional factors operate to intervene in the direct

link between democracy and economic development. This skepticism embedded in

this perspective also includes ‘the idea that different political systems are capable of

adopting the same economic policy, suggesting that the effects of political systems on

growth are negligible’ (Feng, 2003: 320).

According to Przeworski (1992), the skeptical perspective has been

strengthened by several empirical studies on the relationships between democracy and

development which are inconclusive: some studies support the conflict perspective,

whereas others support the compatibility perspective. This skeptical standpoint can be

29

found in many studies. For example, Barsh (1992 quoted in Ersson and Lane, 1996: 49)

concludes in an article that:

Democracy does not appear to “cause” growth, but economic

differentiation certainly creates demands for democratic participation,

which governments must then meet the interests of further economic

growth and political stability. Democracy is neither a “quick fix” for

development problems, nor a substitute for resources. Over the long

run, however, democracy and development can become reinforcing.

Przeworski and Limongi (1993) also point out in their article that we simply do not

know ‘whether democracy fosters or hinders economic growth’ (p.64). The same

conclusion is offered by Weede (1983). 2.3.1.4 Empirical Evidence on the Impact of Democracy on Economic

Growth

One of the themes in the study of social science which has received

considerable attention in recent years is the relationship between democracy and

economic growth (Baum and Lake, 2003; Feng, 2003). There are numerous thinkers

and scholars that have written on this relationship (Pei 2001). All of them agree that

there is a close relationship between democracy and economic growth (Boutros-

Ghali, 2002). However, while it has been accepted that economic growth makes most

countries become increasingly democratic, there has been no consensus on the impact

of democracy on economic growth (De Hann and Siermann, 1995; Iqbal and You,

2001). The impact of democracy on economic development has been the subject of

scholarly research for centuries (Pei, 2001; Rivera-Batiz, 2002; Stiglitz, 2001).

Nevertheless, the existing statistical evidence on the relationship between democracy

and economic growth has been inconclusive (Przeworski and Limongi, 1993; Rivera-

Batiz and Rivera-Batiz, 2002).

Table 2.2 provides a list of 18 studies of democracy, autocracy,

bureaucracy, and growth, summarized by Przeworski and Limongi (1993). Among

these studies which generated 21 findings, eight were in favor of democracy, eight

were in favor of authoritarianism, and five discovered no difference. Therefore,

‘overall, these studies present a very mixed and confusing picture with regard to the

30

effect of democracy on economic growth’ (Sirowy and Inkeles, 1990: 137). What is

puzzling is that ‘among the 11 results published before 1988, eight found that

authoritarian regimes grew faster, while none of the nine results published after 1987

supported this finding’ (Przeworski and Limongi, 1993: 60). Nevertheless, the most

widely accepted of the current studies is another study of Barro which is not reported

in Table 2.2. In this study, he reports a curvilinear effect of democracy on economic

growth (Baum and Lake, 2003). According to Barro (1997), at low levels of

democracy, more democracy is better for growth; at high levels, however, more

democracy is inimical to growth.

Table 2.2 Studies of Democracy, Autocracy, Bureaucracy, and Growth

Author Sample Time Frame Finding

Przeworski (1966) 57 countries 1949-1963 dictatorships at medium

development level grew faster

Adelman and

Morris (1967)

74 underdeveloped

countries (including

communist bloc)

1950-1964 authoritarianism helped less

and medium developed

countries

Dick (1974) 59 underdeveloped

countries

1959-1968 democracies developed slightly

faster

Huntington and

Dominguez (1975)

35 poor nations the 1950s authoritarian grew faster

Marsh (1979) 98 countries 1955-1970 authoritarian grew faster

Kormendi and

Meguire (1985)

47 countries 1950-1977 democracies grew faster

Kohli (1986) 10 underdeveloped

countries

1960-1982 no difference in 1960s;

authoritarian slightly better in

1970s

Landau (1986) 65 countries 1960-1980 authoritarian grew faster

Sloan and Tedin

(1987)

20 Latin American

countries

1960-1979 bureaucratic-authoritarian

regimes do better than

democratic ones; traditional

dictatorships do worse

31

Table 2.2 (Continued)

Author Sample Time Frame Finding

Marsh (1988) 47 countries 1965-1984 no difference between regimes

Pourgerami (1988) 92 countries 1965-1984 democracies grew faster

Scully (1988, 1992) 115 countries 1960-1980 democracies grew faster

Barro (1989) 72 countries 1960-1985 democracies grew faster

Grier and Tullock

(1989)

59 countries 1961-1980 democracy better in Africa and

Latin America; no regime

difference in Asia

Remmer (1990) 11 Latin American

countries

1982-1988 democracy faster, but result

statistically insignificant

Pourgerami (1991) 106 less developed

countries

1986 democracies grew faster

Helliwell (1992) 90 countries 1960-1985 democracy has a negative, but

statistically insignificant, effect

on growth

Source: Przeworski and Limongi (1993)

2.3.2 Governance

Although democracy is the most important and most widely used measure of

political institutions, democratization alone cannot enhance economic performance.

High economic performance cannot be achieved unless developing countries meet the

basic requirements regarding political institutions, such as rule of law, efficient

bureaucracy, corruption-free government, and political constraints on executives

(Bloch and Tang, 2004). According to the Overseas Development Institute (2006: 1),

‘Government effectiveness, an efficient bureaucracy and rule of law are associated

with better economic performance’. Therefore, apart from democracy, this study also

includes government effectiveness, regulatory quality, rule of law, and control of

corruption to represent political institutions. These can be referred to as

“governance.”

32

Governance is a broad and ample concept. According to the World Bank

(2006: 2), governance refers to ‘the traditions and institutions by which authority in a

country is exercised’. This considers the process by which governments are selected,

monitored and replaced, the capacity of the government to effectively formulate and

implement sound policies, and the respect of citizens and the state of the institutions

that govern the economic and social interactions among them. According to the

UNDP, governance is defined as ‘the exercise of economic, political and administrative

authority to manage a country’s affairs at all levels’ (UNDP, 1997: 2). It consists of

the mechanisms, processes, and institutions through which citizens and groups

articulate their interests, exercise their legal rights, meet their obligations, and

mediate their interests (UNDP, 1997). There has been growing recognition of the link

between good governance and successful economic development (The World Bank,

2006). The indicators of governance included in this study are government

effectiveness, regulatory quality, rule of law, and control of corruption. This is

because empirical analysis shows that government effectiveness, regulatory quality,

and rule of law have a positive influence on economic development, particularly

economic growth (Zhuang, de Dios and Lagman-Martin, 2010). This study also

examines the impact of corruption control on economic performance since previous

empirical studies revealed that corruption is statistically associated with economic

growth and income inequality (Zhuang et al., 2010).

A leading example is a study conducted by Campos and Nugent (1999) which

investigates the impact of governance on development performance in East Asia and

Latin America. In this study, the indicators of governance are quality of bureaucracy,

transparent policy-making, accountable executive, strong civil society, and rule of

law. Development performance was gauged by per capita income, infant mortality,

and illiteracy. It was found that in the full sample, all governance characteristics had

expected effects on development performance, and that the relative importance of

governance characteristics varied by region. That is, the institution that plays an

important role in improving development performance is the quality of bureaucracy,

while in the case of Latin America the prominent role appears to be played by the

effectiveness of rule of law. Another example is research conducted by Shafique and

Haq (2006) which examines the effects of governance on economic growth and

33

income inequality. Governance was measured by political stability, government

effectiveness, regulatory quality, rule of law, and control of corruption. The findings

revealed that all governance indicators had the expected impact on economic growth

and income inequality.

2.4 Economic Institutions

Many distinguished scholars such as John Locke, Adam Smith, and Douglass

North have emphasized the importance of economic institutions. In other words, they

believe that economic institutions matter for economic growth. According to

Acemoglu et al. (2004), the institutions which are of primary importance to economic

performance are the economic institutions in society such as the structure of property

rights and the degree of economic freedom. Therefore, this study uses economic

freedom and protection of property rights as the indicators of economic institutions.

Economic institutions are important because they influence the structure of economic

incentives in society, help allocate resources to their most efficient uses, and

determine who gets profits, revenues, and residual rights of control. Although trade

openness and geography may also affect economic performance, differences in

economic institutions are the fundamental cause of cross-country differences in

economic development (Acemoglu et al., 2004; Bloch and Tang, 2004; Rodrik et al.,

2002). The main reason is that economic institutions determine not only the aggregate

economic growth potential of the society, but also a range of economic outcomes,

including the distribution of resources in the future (Acemoglu et al., 2004). As

pointed out by Pei (1999), the most effective means of achieving high economic

performance is to develop institutions governing economic activities and increasing

economic freedom. Despite their significant importance, economic institutions are

rarely glimpsed either in the development literature or in empirical studies based on

new institutional economics.

Recently, there has been a growing number of empirical studies on the impact

of economic institutions on economic performance. An example is research

conducted by Hasan, Quibria, and Kim (2003) which explored the relationship

between economic freedom and poverty in over 40 developing countries. The major

34

empirical result that emerged from their research is that indicators of economic

freedom such as trade openness and size of the government are robustly associated

with poverty reduction. In other words, economic freedom plays an important role in

reducing poverty. Another example is a study conducted by Prasad (2003) which

investigates the impact of property rights on overall economic performance in Fiji.

The findings of this study indicate that well-defined property rights are vital for

transitional economies that are undergoing major structural changes.

2.5 Economic Performance

What do we mean by economic performance? Many studies on the relationship

between institutions and economic performance equate economic performance with

growth in per capita income. This reflects a very limited definition of economic

performance. For both ethical and ethical reasons, policymakers should care about

other aspects of a society’s economic performance, including unemployment,

poverty, and inequality. This in turn implies that scholars ought to go beyond

analyzing the relationship between institutions and aggregate levels of economic

output, and also consider how institutions affect the distribution of resources (Davis,

2009).

2.5.1 Economic Growth as an Indicator of Economic Performance

According to Cypher and Dietz (2004), economists typically measure the level

of development of a nation using two broad methodologies. The first methodology is

by using the income per person or economic growth criterion which suggests that

‘income levels are reasonably good approximate measures for comparing the level of

development of nations and that income per person can serve as a logical surrogate

for gauging overall social progress’ (Cypher and Dietz, 2004: 28). Another methodology

is based on the argument that ‘development is such a complex, multi-faceted notion

that it should be conceived from the outset as considerably broader than income and

hence can only be measured by entirely different standards’ (Cypher and Dietz, 2004: 29).

Nevertheless, Cypher and Dietz (2004) suggest that it is simpler and more convenient

to use a measure of income per capita as a substitute gauge for the broader goals of

35

development characterizing the ultimate objectives of nations. Furthermore, there is

empirical evidence to support the claim that per capita income is highly correlated

with key measures that attempt to capture the broader goals of economic, social, and

political development.

2.5.1.1 Economic Growth Criterion of Development

Within mainstream economics, ‘development stands for economic growth,

that is growth in output as indicated by standard measures like GNP/GDP, often

expressed on a per capita basis’ (Ersson and Lane, 1996: 55). Economic growth is

also defined by Thomas (2000: 31) as ‘a continued increase in the size of an economy

(its GDP), i.e. a sustained increase in output over a period’. Economists often use a

nation’s per capita income as a measure for evaluating the overall level of national

development and welfare, and then the rate of growth of income per capita can be

used to determine the progress a nation makes over time (Cypher and Dietz, 2004).

2.5.1.2 Measures of Economic Growth

The two most common measures used for measuring economic growth

are GNP and GDP. According to Cypher and Dietz (2004: 31), GNP is ‘the total

value of all income (= value of final output) accruing to residents of a country,

regardless of the source of that income, that is, irrespective of whether such income is

derived from sources within or outside the country’, and GDP is ‘the total value (=

value of final output) of all income created within the borders of a country, regardless

of whether the ultimate recipient of that income resides within or outside the country’.

Since the GNP measure indicates the sum total of new final goods and services

available to a country’s residents for their final use, it is a proximate gauge of the

material welfare or well-being of the residents. On the other hand, the GDP measure,

which is more purely an index of the value of all new products and services occurring

within a country’s frontiers, provides information on the pace of total production in a

country.

2.5.2 Other Indicators of Economic Performance

Although economic growth is the most commonly used indicator of economic

performance, it cannot reflect all aspects of economic performance. Therefore, other

36

indicators of economic performance are necessary. The other indicators included in

this study are unemployment, poverty, and income inequality.

According to the International Labor Organization (ILO), unemployment

occurs when people are without jobs and they have actively looked for work in the

past four weeks. The unemployment rate is a measure of the prevalence of

unemployment and it is calculated as a percentage by dividing the number of

unemployed individuals by all individuals currently in the labor force. Poverty is an

economic condition of lacking both money and the basic necessities needed to live

successfully. There are many measurements of poverty; however, the measure used in

this study is the percentage of the population living in poverty. As for income

inequality, it is the unequal distribution of household or individual income across the

various participants in the economy. This study will use an alternative measure of

income inequality, which is the ratio of the income share of the top quintile (20%) to

that of the bottom quintile (20%).

2.6 Conceptual Framework

After examining the broad approaches to development and the theoretical

perspectives describing the relationship between democracy and economic growth

mentioned above, the conceptual framework of this study will be developed from the

institutional approach and the compatibility perspective. In other words, this research

will be based on the assumption that good institutions and democracy generate

economic growth. This is because there has been a strong support for the proposition

that good institutions enhance economic performance. Moreover, it has been widely

accepted that democracy and economic development are complementary, and they

reinforce each other (Boutros-Ghali, 2002). According to the UNDP (2002), there are

good reasons to believe that democracy and economic growth are compatible. ‘With

just two exceptions, all of the world’s richest countries–those with per capita incomes

above $20,000 (in 2000 purchasing power parity)–have the world’s most democratic

regimes’ (UNDP, 2002: 56). Furthermore, the theory that authoritarianism is good for

economic growth is extremely weak (Pei, 2001). Thus, the institutional approach and

the compatibility perspective are of this study’s interest. The theoretical sources of the

dependent and independent variables in this study are shown in Table 2.3.

37

Table 2.3 Theoretical Sources of the Variables

Dependent Variable Theory

Annual growth rates of GDP per capita Economic performance

Unemployment rates Economic performance

Population falling below the poverty line Economic performance

Income inequality Economic performance

Independent Variable Theory

Investment rates Neoclassical growth theory

Gross national savings Neoclassical growth theory

Population growth rates Neoclassical growth theory

Life expectancy at birth Endogenous growth theory

Adult literacy rates Endogenous growth theory

Combined gross enrollment Endogenous growth theory

Political rights New institutional theory

Civil liberties New institutional theory

Press freedom New institutional theory

Government effectiveness New institutional theory

Regulatory quality New institutional theory

Rule of law New institutional theory

Control of corruption New institutional theory

Protection of property rights New institutional theory

Independent Variable Theory

Economic freedom New institutional theory

The dependent variable in this study is economic performance gauged by

several measures, including economic growth, unemployment, poverty, and income

inequality. To measure economic growth, the annual growth rates of GDP per capita

will be employed. Unemployment will be presented as the percentage of total labor

force. As for the measurement of poverty, this study will use the percentage of the

population falling below the poverty line. With regard to income inequality, this

study will use an alternative measure, which is the ratio of the income share of the top

38

quintile (20%) to that of the bottom quintile (20%) due to the lack of comprehensive

Gini data.

Since economic performance is not only affected by institutional factors, it is

necessary to include other factors according to neoclassical growth theory and

endogenous growth theory. Thus, six other independent variables are included in the

model. Theoretically, there are strong reasons to believe that each of these variables

influences economic growth. Empirically, it has been found in most cross-sectional

analyses that these variables correlate with growth.

The inclusion of investment rates, gross national savings, and population

growth rates is suggested by neoclassical growth theory. Since the level of investment

is proportional to the level of the GDP, the investment rates are employed as a proxy

for the level of investment. In many empirical studies (e.g. Sinha, 1999), investment

is found to be positively associated with economic growth. Gross national saving is

the percentage of the GDP that is saved by households across a country. It is the main

source of funds available for domestic investment in new capital goods. Capital

accumulation, in turn, is a key driver of productivity gains and rising living standards

(Mankiw, 2005). It has been found that a higher rate of national savings would lead to

higher economic growth (Agrawal and Sahoo, 2009; Sajid and Sarfraz, 2008). As for

population growth, it may inhibit economic growth. This is because when the rate of

population growth is high, the large number of new workers entering the workforce

serves to reduce total capital per worker. This causes the capital stock per worker to

fall, resulting in lower levels of economic growth (Drury et al., 2006). Growth-related

factors according to neoclassical growth theory contribute to the first three main

hypotheses of this study, together with their sub-hypotheses:

H1: There is a significant relationship between investment rates and economic

performance.

H1-1: Investment rates have a positive effect on annual growth rates of

GDP per capita.

H1-2: Investment rates have a negative effect on unemployment rates.

H1-3: Investment rates have a negative effect on the percentage of the

population falling below the poverty line.

H1-4: Investment rates have a negative effect on income inequality.

39

H2: There is a significant relationship between gross national savings and


H2-1: Gross national savings have a positive effect on annual growth

rates of GDP per capita.

H2-2: Gross national savings have a negative effect on unemployment

rates.

H2-3: Gross national savings have a negative effect on the percentage

of the population falling below the poverty line.

H2-4: Gross national savings have a negative effect on income

inequality.

H3: There is a significant relationship between population growth rates and


H3-1: Population growth rates have a negative effect on annual growth


H3-2: Population growth rates have a positive effect on unemployment

rates.

H3-3: Population growth rates have a positive effect on the percentage


H3-4: Population growth rates have a positive effect on income

inequality.

The independent variables representing endogenous growth theory include life

expectancy at birth, adult literacy rates, and the combined primary, secondary, and

tertiary gross enrollment ratio. Economists point out that ‘the overall health of

workers allows for greater productivity, since workers are more able to work

diligently, for longer hours, and without succumbing to disease or debilitation’ (Drury

et al., 2006: 128). The typical quantitative measure of health is the log of average life

expectancy as used by Barro (1997). In addition, endogenous growth theory argues

that economic growth is generated from within a system as a direct result of internal

processes. More specifically, the theory notes that the enhancement of a nation's

human capital will lead to economic growth. An important dimension of human

capital is education, which is typically gauged by adult literacy rates and gross

40

enrolment ratio. Studies (e.g. Johnston, 2004) consistently find that adults with better

literacy skills are more likely to be employed, and to earn more, than those with

poorer literacy skills. Thus, an increase in adult literacy rates in the long run leads to

higher economic growth rates. As for combined gross enrollment, it is the number of

students enrolled in primary, secondary, and tertiary levels of education, regardless of

age, as a percentage of the population of theoretical school age for the three levels.

Through the massive dropout of a large number of children, the aggregate rate of

human capital accumulation in the national economy declines and hence hampers

economic growth (Seebens and Wobst, 2003). The results of many empirical studies

(e.g. Peaslee, 1967; Sadeghi, 1996; Seebens and Wobst, 2003) show a positive

relationship between school enrollments and economic growth. Human capital factors

in accordance with endogenous growth theory contribute to three other hypotheses,

accompanied by their sub-hypotheses:

H4: There is a significant relationship between life expectancy at birth and


H4-1: Life expectancy at birth has a positive effect on annual growth


H4-2: Life expectancy at birth has a negative effect on unemployment

rates.

H4-3: Life expectancy at birth has a negative effect on the percentage of

the population falling below the poverty line.

H4-4: Life expectancy at birth has a negative effect on income inequality.

H5: There is a significant relationship between adult literacy rates and


H5-1: Adult literacy rates have a positive effect on annual growth rates of

GDP per capita.

H5-2: Adult literacy rates have a negative effect on unemployment rates.

H5-3: Adult literacy rates have a negative effect on the percentage of the


H5-4: Adult literacy rates have a negative effect on income inequality.

41

H6: There is a significant relationship between combined gross enrollment

and economic performance.

H6-1: Combined gross enrollment has a positive effect on annual

growth rates of GDP per capita.

H6-2: Combined gross enrollment has a negative effect on unemployment

rates.

H6-3: Combined gross enrollment has a negative effect on the percentage


H6-4: Combined gross enrollment has a negative effect on income

inequality.

According to new institutional theorists, it is in political institutions that the

ultimate reasons for economic success or failure have to be sought (Galjart, 2006;

Hanson, 2007). Therefore, political institutions are the first set of institutions in this

study. There are two broad measures of political institutions: democracy and

governance. The independent variables representing democracy, which is the most

important measure of political institutions, are political rights, civil liberties, and

press freedom. This is because political rights and civil liberties are the major

components of political democracy, which are equally important for developed and

developing countries (Bahmani-Oskooee and Goswami, 2006; Ersson and Lane,

1996). According to Hanson (2007), political rights and civil liberties serve as

intrinsic makers since state institutions that serve to protect political rights and civil

liberties tend to protect economic rights as well. Indeed, Sala-i-Martin (1997) and

Sturm and De Haan (2005) found that respect for political rights and civil liberties is

robustly related to economic growth. Although freedom of the press is a cherished

right of the people, it is different from other liberties of the people in that it is both

individual and institutional. It applies not just to an individual’s right to publish ideas,

but also to the right of print and broadcast media to express political views and to

cover and publish news. A free press is, therefore, one of the foundations of political

democracy (U.S. Department of State's Bureau of International Information Programs,

1999). This is the reason why press freedom is considered as a separated independent

variable in this research. This contributes to the following three hypotheses, together

with their sub-hypotheses:

42

H7: There is a significant relationship between political rights and economic

performance.

H7-1: Political rights have a positive effect on annual growth rates of

GDP per capita.

H7-2: Political rights have a negative effect on unemployment rates.

H7-3: Political rights have a negative effect on the percentage of the


H7-4: Political rights have a negative effect on income inequality.

H8: There is a significant relationship between civil liberties and economic

performance.

H8-1: Civil liberties have a positive effect on annual growth rates of GDP

per capita.

H8-2: Civil liberties have a negative effect on unemployment rates.

H8-3: Civil liberties have a negative effect on the percentage of the


H8-4: Civil liberties have a negative effect on income inequality.

H9: There is a significant relationship between press freedom and economic

performance.

H9-1: Press freedom has a positive effect on annual growth rates of GDP

per capita.

H9-2: Press freedom has a negative effect on unemployment rates.

H9-3: Press freedom has a negative effect on the percentage of the


H9-4: Press freedom has a negative effect on income inequality.

Apart from democracy, institutions need to be represented by governance,

which is another measure of political institutions so that all aspects of political

institutions are captured. In this study, the independent variables representing

governance include government effectiveness, regulatory quality, rule of law, and

control of corruption. This contributes to four other hypotheses, along with their sub-

hypotheses:

43

H10: There is a significant relationship between government effectiveness


H10-1: Government effectiveness has a positive effect on annual growth


H10-2: Government effectiveness has a negative effect on unemployment

rates.

H10-3: Government effectiveness has a negative effect on the percentage


H10-4: Government effectiveness has a negative effect on income

inequality.

H11: There is a significant relationship between regulatory quality and


H11-1: Regulatory quality has a positive effect on annual growth rates of

GDP per capita.

H11-2: Regulatory quality has a negative effect on unemployment rates.

H11-3: Regulatory quality has a negative effect on the percentage of the


H11-4: Regulatory quality has a negative effect on income inequality.

H12: There is a significant relationship between rule of law and economic

performance.

H12-1: Rule of law has a positive effect on annual growth rates of

GDP per capita.

H12-2: Rule of law has a negative effect on unemployment rates.

H12-3: Rule of law has a negative effect on the percentage of the


H12-4: Rule of law has a negative effect on income inequality.

H13: There is a significant relationship between control of corruption and


H13-1: Control of corruption has a positive effect on annual growth rates

of GDP per capita.

H13-2: Control of corruption has a negative effect on unemployment

rates.

44

H13-3: Control of corruption has a negative effect on the percentage of

the population falling below the poverty line.

H13-4: Control of corruption has a negative effect on income inequality.

According to the new institutional economics, economic performance is

influenced not only by political institutions, but also by economic institutions.

Therefore, this study employs protection of property rights and economic freedom as

the independent variables representing economic institutions. Property rights are

defined as ‘the ability of people to exercise authority over the resources they own’

(Mankiw, 2008: 262). Empirical research (Asoni, 2008; Gould and Gruben, 2001;

Kwan and Lai, 2000; Park and Ginarte, 1997; Norton, 2003) has shown that an

economy-wide respect for property rights is an important prerequisite for economic

growth. Economic freedom refers to ‘the degree to which a market economy is in

place, where the central components are voluntary exchange, free competition, and

protection of persons and property’ (Berggren, 2003: 193). Numerous studies have

shown that greater economic freedom is associated with higher levels of economic

growth and human well-being (Barro, 1996; Faria and Mintesinos, 2009; Farr, Lord

and Wolfenbarger, 1998; Grubel, 1998; Norton, 2003; Wu and Davis, 1999). These

contribute to the two other hypotheses, accompanied by their sub-hypotheses:

H14: There is a significant relationship between protection of property rights


H14-1: Protection of property rights has a positive effect on annual

growth rates of GDP per capita.

H14-2: Protection of property rights has a negative effect on

unemployment rates.

H14-3: Protection of property rights has a negative effect on the

percentage of the population falling below the poverty line.

H14-4: Protection of property rights has a negative effect on income

inequality.

H15: There is a significant relationship between economic freedom and


45

H15-1: Economic freedom has a positive effect on annual growth rates of

GDP per capita.

H15-2: Economic freedom has a negative effect on unemployment rates.

H15-3: Economic freedom has a negative effect on the percentage of the


H15-4: Economic freedom has a negative effect on income inequality.

Apart from institutions, economic performance is also affected by geography

(Bloch and Tang, 2004; Norton, 2003; Rodrik, 2002). As pointed out by Rodrik

(2002: 6), geography influences economic performance because it is ‘an important

determinant of the extent to which a country can become integrated with world

markets, regardless of the country’s own trade policies’. Therefore, a set of

geographical variables need to be included as control variables. These variables are

land area, the population size in millions, distance from the equator, and

landlockness. However, since land area, distance from the equator, and landlockness

are constant over time, only the population size will be employed to represent

geography. Trade openness, which is generally measured as the ratio of exports plus

imports to GDP, is also included as a control variable because there is evidence that

trade openness is associated with economic performance (Baharom, Habibullah and

Royfaizal, 2008; Bloch and Tang, 2004; Harrison, 1996; Kandiero and Chitiga,

2003).

The conceptual framework of this research (Figure 2.1) is shown on the

following page.

46

Figure 2.1 Conceptual Framework

- Political rights - Civil liberties - Press freedom

Democracy

Economic Performance

- Life expectancy at birth - Adult literacy rates - Combined gross enrollment

- Protection of property rights - Economic freedom

Economic Institutions

Growth-Related Factors

Human Capital Factors

- Annual growth rates of GDP per capita - Unemployment rates - Population falling below the poverty line - Income inequality

- Investment rates - Gross national savings - Population growth rates

- Government effectiveness - Regulatory quality - Rule of law - Control of corruption

Governance

CHAPTER 3

RESEARCH METHODOLOGY

Using cross-country data from selected developing countries over the period

from 1990 to 2009, this research evaluated the effect of politico-economic institutions

on economic performance. The countries investigated in this study are selected

developing countries in East Asia and Latin America.

3.1 Sample Selection

In the 1970s, it became clear that some East Asian countries achieved higher

economic growth than almost all other developing countries, particularly Latin American

countries. The difference in economic performance of East Asian and Latin American

economies has spawned academic and political debate. The four East Asian tigers –

Hong Kong, South Korea, Singapore, and Taiwan–grew extremely rapidly at an

average of over 6.0 percent a year in per capita terms between 1960 and 2000. On the

other hand, many countries in Latin America recorded less than 1.0 percent growth

during the same period (De Gregorio and Lee, 2003). The high growth of East Asian

countries, compared to the poor performance of Latin American economies, leads

directly to the question of what the fundamental factors are that explain such

differences, and what should be done to stimulate economic growth. Compared to

East Asia, Latin America’s growth performance was disappointing. The rankings for

GNP per capita for 1995, 1998, 2005, and 2009 for 10 East Asian countries and 17

Latin American countries included in this study reveal that East Asia is ahead of

Latin America (see Table 3.1).

48

Table 3.1 GNP per Capita, US Dollars ($)

East Asia 1995 1998 2005 2009 Latin

America 1995 1998 2005 2009

China 620 750 1736 3692 Argentina 8030 8970 4466 7423 Hong Kong 22990 23670 N/A 30923 Bolivia 800 1000 1009 1700 Indonesia 980 680 1279 2080 Brazil 3640 4570 3455 7949 Malaysia 3890 3600 4963 6732 Chile 4160 4810 5865 8691 Philippines 1050 1050 1304 2004 Colombia 1910 2600 2292 4895 Singapore 26730 30060 27842 37542 Costa Rica 2610 2780 4589 6182 South Korea 9700 7970 10975 17315 Ecuador 1390 1530 2628 4083 Taiwan N/A 10855 16764 N/A El Salvador 1610 1850 2445 3322 Thailand 2740 2200 3065 3719 Guatemala 1340 1640 2403 2611 Vietnam 240 330 623 1032 Honduras 600 730 1192 1870 Mexico 3320 3970 7154 7835 Nicaragua 380 420 906 1085 Panama 2750 3080 4626 6513

Paraguay 1690 1760 1275 2336 Peru 2310 2460 2612 4102 Uruguay 5170 6180 4359 9168 Venezuela 3020 3500 4807 11317 Mean 7660 8117 7617 11671 Mean 2631 3050 3299 5475

Source: World Bank Database

In addition to economic growth, East Asia has performed better than Latin

America in terms of unemployment, poverty, and income inequality. Table 3.2

provides unemployment rates as the percentage of the total labor force of the East

Asian and Latin American countries included in this study in 1995, 1998, 2005, and

2009. It is obvious that on average, East Asia had lower unemployment rates than

Latin America in all the years under consideration.

Table 3.2 Unemployment (% of total labor force)

East Asia 1995 1998 2005 2009 Latin America

1995 1998 2005 2009

China 2.9 3.1 4.2 4.3 Argentina 18.8 12.8 10.6 8.6 Hong Kong 3.2 4.6 5.6 5.2 Bolivia 3.6 N/A 5.4 N/A Indonesia N/A 5.5 11.2 7.9 Brazil 6.0 8.9 9.3 8.3 Malaysia 3.1 3.2 3.5 3.7 Chile 4.7 6.3 8.0 9.7 Philippines 8.4 9.4 7.7 7.5 Colombia 8.7 15.0 11.3 12.0 Singapore 2.7 2.7 5.6 5.9 Costa Rica 5.2 5.6 6.6 7.8 South Korea 2.1 7.0 3.7 3.6 Ecuador 6.9 N/A 7.7 6.5

49


East Asia 1995 1998 2005 2009

Latin America

1995 1998 2005 2009

Taiwan N/A N/A N/A N/A El Salvador 7.6 7.3 7.2 7.3 Thailand N/A 3.4 1.3 1.2 Guatemala N/A N/A N/A N/AVietnam N/A 2.3 N/A N/A Honduras 3.2 4.0 4.2 N/A Mexico 6.9 3.6 3.5 5.2 Nicaragua 16.9 13.2 5.6 N/A Panama 14.0 14.0 9.8 6.6

Paraguay 3.4 5.3 5.8 N/A Peru 7.1 7.8 11.4 6.3 Uruguay 10.2 10.0 12.2 7.3 Venezuela 10.2 11.0 11.4 7.6 Mean 3.7 4.6 5.4 4.9 Mean 8.3 8.9 8.1 7.8


According to Table 3.3, the percentage of thepopulation falling below the

national poverty line in East Asia was much lower than that in Latin America. On

average, less than one-fifth of the total population in East Asia lived below the

poverty line. Moreover, the percentage of population living in poverty had gradually

declined. On the contrary, nearly half of the Latin American population lived below

the poverty line and the percentage of people living in poverty had increased during

1995-2005.

Table 3.3 Poverty Headcount Ratio at National Poverty Line (% of population)


1995 1998 2005 2009

China N/A 4.6 N/A N/A Argentina N/A N/A N/A N/AHong Kong N/A N/A N/A N/A Bolivia N/A N/A 59.6 N/AIndonesia N/A N/A 16.0 14.2 Brazil 35.1 34.0 30.8 21.4 Malaysia N/A N/A N/A 3.8 Chile N/A 21.6 N/A 15.1 Philippines N/A N/A N/A 26.5 Colombia N/A N/A 45.0 40.2 Singapore N/A N/A N/A N/A Costa Rica 23.5 22.1 23.8 21.7 South Korea N/A N/A N/A N/A Ecuador 39.3 44.7 N/A 36.0 Taiwan N/A N/A N/A N/A El Salvador 47.5 44.6 35.1 37.8 Thailand N/A N/A N/A 8.1 Guatemala N/A N/A N/A N/AVietnam N/A 37.4 N/A N/A Honduras 67.8 63.3 65.8 58.8 Mexico N/A 63.7 47.0 N/A Nicaragua N/A 47.9 46.2 N/A Panama N/A N/A N/A N/A

50


East Asia 1995 1998 2005 2009

Latin America

1995 1998 2005 2009

Paraguay N/A 36.1 38.6 35.1 Peru N/A 42.4 48.7 34.8 Uruguay N/A N/A N/A 20.9 Venezuela N/A 50.4 43.7 28.5 Mean N/A 19.8 16.0 13.2 Mean 42.6 42.8 44.0 31.9


The contrast in economic performance between the two regions is even more

apparent when considering income distribution. Whereas most East Asian countries

have achieved substantial redistribution in the context of a successful shared-growth

strategy, Latin American countries are still at the top of world rankings of inequality

in income distribution (Dellepiane-Avellaneda, 2010). As shown in Table 3.4, East

Asia has less income inequality than Latin America in all the years under study.

Table 3.4 Income Inequality (the ratio of the income share of the top quintile to that

of the bottom quintile)


1995 1998 2005 2009

China N/A N/A 9.6 N/A Argentina 13.8 15.7 15.7 12.9 Hong Kong N/A N/A N/A N/A Bolivia N/A N/A 30.6 N/A Indonesia N/A N/A 5.2 N/A Brazil 29.2 29.2 21.9 20.2 Malaysia 12.0 N/A N/A 11.4 Chile N/A 17.3 N/A 13.4 Philippines N/A N/A N/A 8.3 Colombia N/A N/A 21.5 22.5 Singapore N/A 9.8 N/A N/A Costa Rica 12.6 11.8 12.9 14.3 South Korea N/A 4.7 N/A N/A Ecuador 15.6 18.5 18.8 13.9 Taiwan N/A N/A N/A N/A El Salvador 14.7 28.8 18.1 14.4 Thailand N/A 7.6 N/A 7.0 Guatemala N/A 19.3 N/A N/A Vietnam N/A 5.5 N/A N/A Honduras 19.3 28.9 33.2 30.0 Mexico N/A 13.1 N/A N/A Nicaragua N/A 45.8 7.6 N/A Panama 43.2 37.9 21.4 15.8

Paraguay 25.7 28.5 16.7 16.7 Peru N/A 21.5 14.3 14.1 Uruguay 9.4 10.4 11.3 11.0 Venezuela 12.7 14.2 18.9 N/A Mean 12.0 6.9 7.4 8.9 Mean 19.6 22.7 18.8 16.6

Source: Author’s calculations based on World Bank Database

51

In the 1980s and 1990s, a large number of social scientists, particularly

economists, tried to discover the causes behind East Asia’s economic success. Some

of them focused on a single country, while some compared two or more countries

within the region. Only at the end of the 1980s did scholars begin to compare the

development experience of some East Asian countries with that of one or more

countries in another region, most often Latin America (Galjart, 2006). Since the early

1990s, an increasing number of comparative studies of the East Asian and the Latin

American developmental experiences have been carried out. Most of these studies

emphasize the enormous differences existing between both regions in historical,

institutional, economic, social, political, and cultural factors (Silva, 2006). It has been

found by many researchers, such as De Gregorio and Lee (2003) and Evans (1987),

that the difference in economic performance between these two regions can largely be

explained by differences in fundamental growth factors, such as investment rate,

human resources, fertility, institutional quality, macroeconomic stability, and the

degree of trade openness.

It has become widely accepted among development economists that East

Asian countries’ and Latin American countries’ differences in policies and

institutions have led to the success of the former and the failure of the latter (Boyd,

2006). While seven of the eleven countries labeled by Knack (2003) as “catch-up

countries” from 1960 to 1998 are from East Asia (Singapore, Hong Kong, Japan,

Taiwan, South Korea, Malaysia, and Thailand), not even one is from Latin America

(Dellepiane-Avellaneda, 2010). Not surprisingly, many Latin American countries,

particularly Argentina, Uruguay, Venezuela, Nicaragua, and Peru, have behaved as

‘fall-back countries’. Table 3.5 shows the averages of real GDP growth per capita of

selected East Asian and Latin American countries. It is obvious that there has been

growth divergence between the two regions.

52

Table 3.5 Real GDP Growth per Capita, 1990-1999 and 2000-2009

East Asia 1990-1999 2000-2009 Latin America 1990-1999 2000-2009

China 8.75 9.61 Argentina 3.16 2.57 Hong Kong 2.04 3.55 Bolivia 1.70 1.74 Indonesia 3.28 3.77 Brazil 0.13 2.05 Malaysia 4.53 2.84 Chile 4.67 2.58 Philippines 0.52 2.60 Colombia 0.99 2.37 Singapore 4.37 3.20 Costa Rica 2.91 2.20 South Korea 5.24 3.91 Ecuador -0.08 3.35 Taiwan NA 3.67 El Salvador 3.66 1.69 Thailand 4.21 3.11 Guatemala 1.69 0.92 Vietnam 5.51 6.00 Honduras 0.25 2.32 Mexico 1.66 0.83 Nicaragua 0.81 1.51 Panama 3.51 3.97

Paraguay 0.07 0.32 Peru 1.37 3.77 Uruguay 3.04 2.12 Venezuela 0.30 2.08 Mean 4.27 4.23 Mean 1.75 2.14


According to Fukuyama and Marwah (2000), one of the major factors underlying

economic growth is institutional effectiveness. It is now widely recognized within the

development community that apart from having the correct economic policies, a

country must also have competent institutions to administer them. It may even be the

case that in certain circumstances, even a wrong-headed policy administered by a

strong institution will lead to better results than a good policy administered by a weak

institution.

There is no question that there has historically been a huge gap between East

Asia and Latin America in terms of institutional effectiveness, despite the fact that

there is as yet no firm consensus on exactly what constitutes institutional effectiveness or

how to measure it. An institution can be said to be effective if it is able to set clear-cut

goals for itself and achieve them. In economic policymaking, it is absolutely critical

that the government agency in charge focuses on long-term economic growth and be

protected from pressures to divert resources towards the many rent-seeking claims

that exist in the larger society. Needless to say, the bureaucrats administering such a

53

policy need to have a high degree of professional competence, and to be free of any

personal corruption (De Gregorio and Lee, 2003; Fukuyama and Marwah, 2000).

Many countries in East Asia, such as Hong Kong, Singapore, and South

Korea, have succeeded in creating effective institutions. On the other hand, most of

the countries in Latin America have had strong states and weak institutions. Having a

relatively low degree of institutional effectiveness is not necessarily an insurmountable

obstacle to development if countries are able to match their governmental ambitions

to their real capabilities. The focus on provision of public goods, such as education,

infrastructure, and rule of law, should be complementary with long-term economic

planning, rather than resulting in politicized bureaucracies concerned with their own

prosperity. East Asia differs from Latin America insofar as this matching of

institutions to capabilities has been carried out more effectively across the region.

However, not every Asian country has been able to create effective institutions. Some

countries such as Indonesia, the Philippines, and China, have been plagued by high

levels of official corruption (De Gregorio and Lee, 2003; Fukuyama and Marwah,

2000).

The comparison of East Asia and Latin America is often driven by the belief that

Latin America has much to learn from East Asian countries’ economic performance

(Boyd, 2006). The spectacular economic success achieved by East Asian countries,

especially the newly industrializing countries (NICs), ‘has led scholars and policymakers

to look more closely at this development experience to discover if any useful lessons

could be learned by other developing countries, and Latin American in particular’ (Kay,

2006: 21). Therefore, the comparison of these two regions is extremely valuable.

At the level of theory, the comparison of East Asian and Latin American

economic performance is significant because many theories of economic and political

development, most notably dependency theory and bureaucratic authoritarianism theory,

were originally based on Latin American cases. East Asia provides an excellent

opportunity to test these theories (Fukuyama and Marwah, 2000).

In order to make meaningful comparisons, certain limitations must be

imposed on the regions in question. This study will follow the study conducted by

Fukuyama and Marwah (2000) by concentrating on the larger and more successful

societies, and making some, perhaps, arbitrary exclusions. In the case of Latin

54

America, communist Cuba and the small states of the Caribbean will be excluded,

while the relatively poor countries of Central America will be included. In the case

of East Asia, communist North Korea and authoritarian Burma will be excluded,

whereas communist China and Vietnam will be included because they have opened

their economies to market forces in recent years. The countries included in this study

are China, Hong Kong, Indonesia, Malaysia, the Philippines, Singapore, South Korea,

Taiwan, Thailand, and Vietnam from East Asia as well as Argentina, Bolivia, Brazil,

Chile, Columbia, Costa Rica, Ecuador, El Salvador, Guatemala, Honduras, Mexico,

Nicaragua, Panama, Paraguay, Peru, Uruguay, and Venezuela from Latin America.

3.2 Data Collection

This research relied on secondary data or existing statistics by employing the

cross-country economic and political data from several sources. To measure

economic growth, the annual growth rates of GDP per capita during the period of

1990 to 2009 reported by the World Bank were used (Appendix A-1). The annual

data published by the World Bank in its World Development Report are a source

which provides a consistent and reliable series of data available to researchers around

the world (Cypher and Dietz, 2004). Data regarding unemployment (Appendix A-2),

poverty (Appendix A-3), and income inequality (Appendix A-4) were obtained from

the World Bank’s most popular dataset–World Development Indicators (WDI). The

WDI is the primary World Bank collection of development indicators, compiled from

officially-recognized international sources. It presents the most current and accurate

global development data available, and includes national, regional and global

estimates.

Investment rates (Appendix B-1) and gross national savings (Appendix B-2)

were collected from surveys conducted by the International Monetary Fund (IMF),

called the World Economic Outlook (WEO). As for population growth rates

(Appendix B-3) and life expectancy at birth (Appendix C-1), data will also be

obtained from the World Development Indicators (WDI). Data regarding adult

literacy rates (Appendix C-2) and combined gross enrollment (Appendix C-3) were

collected from the Human Development Index (HDI), which has been used since

1990 by the United Nations Development Programme.

55

To measure the level of political democracy, the Freedom House indices of

political rights (Appendix D-1), civil liberties (Appendix D-2), and press freedom

(Appendix D-3) were utilized. Since Freedom House’s surveys rely on a wide range

of sources and cover long time series, they have been widely used by researchers

(UNDP, 2002). Freedom House has annually surveyed political rights and civil

liberties around the world from 1973 to the present. Political rights ratings are based

on an evaluation of three subcategories: electoral process, political pluralism, and

participation. With respect to civil liberties ratings, they are based on an evaluation of

four subcategories: freedom of expression and belief, associational and organizational

rights, rule of law, and personal autonomy and individual rights. The indices of

political rights and civil liberties range from 1 to 7, with a value of 1 representing the

strongest level of democracy and 7 the weakest. In 1997, Freedom House published

an assessment of freedom of the press with data from 1980 to the present (UNDP,

2002). Its examination of the level of press freedom is based on three broad

categories: the legal environment, the political environment, and the economic

environment. The Press Freedom Index ranges from 0 to 100. A value of 0 represents

the highest level of press freedom and 100 represents the lowest level.

To measure government effectiveness (Appendix E-1), regulatory quality

(Appendix E-2), rule of law (Appendix E-3), and control of corruption (Appendix E-

4), this study relied on the Worldwide Governance Indicators (WGI). Government

effectiveness captures the perceptions of the quality of public services, the quality of

the civil service and the degree of its independence from political pressures, the

quality of policy formulation and implementation, and the credibility of the

government’s commitment to such policies. Regulatory quality captures the perceptions of

the ability of the government to formulate and implement sound policies and

regulations that permit and promote private sector development. As for rule of law, it

captures the perceptions of the extent to which agents have confidence in and abide

by the rules of society, and in particular the quality of contract enforcement, property

rights, the police, and the courts, as well as the likelihood of crime and violence.

Control of corruption captures the perceptions of the extent to which public power is

exercised for private gain. The reason why the other two dimensions of the WGI,

which are voice and accountability as well as political stability and absence of

56

violence, are excluded from this study is that they overlap with the indicators of

democracy mentioned above.

To measure the protection of property rights (Appendix F-1), data were

obtained from the Index of Economic Freedom conducted by the Wall Street Journal

and the Heritage Foundation. The Index of Economic Freedom measures ten

components of economic freedom, assigning a grade to each using a scale from 0 to

100, where 100 represents the maximum freedom. However, this study focused on

only one component–Property Rights. Since the Index relies on many sources of

information on property rights, data were reliable. The property rights component is

an assessment of the ability of individuals to accumulate private property, secured by

clear laws that are fully enforced by the state. It measures the degree to which a

country’s laws protect private property rights and the degree to which its government

enforces those laws. It also assesses the likelihood that private property will be

expropriated and analyzes the independence of the judiciary, the existence of

corruption within the judiciary, and the ability of individuals and businesses to

enforce contracts.

Regarding the degree of economic freedom (Appendix F-2), the annual survey

Economic Freedom of the World (EFW) was employed. The EFW is an indicator of

economic freedom produced by James Gwartney and Robert Lawson, and is

published by the Canadian Fraser Institute. Since it uses a definition of economic

freedom similar to laissez-faire capitalism, it has been more widely used than any

measure of economic freedom. Its use stems in part from the longer time period

covered (data exist from 1980 to present), and the fact that this index is constructed

from third party information. The EFW index measures the degree of economic

freedom in five major areas: (1) size of government; (2) legal structure and security of

property rights; (3) access to sound money; (4) freedom to trade internationally; and

(5) regulation of credit, labor, and business. The scores range from 0 to 10, with 10

representing the highest possible strength of economic freedom.

Details regarding the measurement and the source of each variable included in

this study are shown in Table 3.6.

57

Table 3.6 Measurements and Sources of the Variables

Variable Measurement Source

1. Annual growth rates of GDP per capita

This variable is based on constant local currency. GDP per capita is gross domestic product divided by midyear population. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources.

The World Bank

2. Unemployment rates

Unemployment refers to the share of the labor force that is without work but available for and seeking employment.

The World Bank

3. Population falling below the poverty line

This variable is measured as the percentage of the population living below the national poverty line. National estimates are based on population-weighted subgroup estimates from household surveys.

The World Bank

4. Income inequality In this study, income inequality is measured as the ratio of the income share of the top quintile (20%) to that of the bottom quintile (20%).

The World Bank

5. Investment rates Investment rates refer to the share of total GDP that is devoted to investment fixed assets.

The International Monetary Fund

6. Gross national savings

Gross national savings are the sum of private and public savings. They are

The International Monetary Fund

calculated as GDP minus final consumption expenditure (total consumption).

7. Population growth rates

Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage.

The World Bank

8. Life expectancy at birth

This variable indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.

The World Bank

9. Adult literacy rates This variable refers to the percentage of the population aged 15 and older who can, with understanding, both read and write a short simple statement on their everyday life.

The United Nations Development Programme

58



10. Combined gross enrollment

This variable is the number of students enrolled in primary, secondary, and tertiary levels of education, regardless of age, as a percentage of the population of theoretical school age for tthree levels.

The United Nations Development Programme

11. Political rights Political rights refer to the degree of freedom in the electoral process, political pluralism and participation, and functioning of government. Each country is assigned a numerical rating from 1 to 7, with 1 representing the most free and 7 the least free.

Freedom House

12. Civil liberties Civil liberties measure freedom of expression and belief, associational and organizational rights, rule of law, and personal autonomy and individual rights. Each country is assigned a numerical rating from 1 to 7, with 1 representing the most free and 7 the least free.

Freedom House

13. Press freedom Press freedom refers to the degree to which each country permits the free flow of news and information. Countries are given a total score from 0 (best) to 100 (worst).

Freedom House

14. Government effectiveness

This variable captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies. It is measured in units ranging from -2.5 to 2.5, with higher values corresponding to better governance outcomes.

Kaufmann, Kraay, and Mastruzzi

15. Regulatory quality This variable captures perceptions of the ability of the government to formulate and implement sound policiesand regulations that permit and promoteprivate sector development. It is measured in units ranging from -2.5 to


59



2.5, with higher values corresponding to better governance outcomes.

16. Rule of law This variable captures perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence. It is measured in units ranging from -2.5 to 2.5, with higher values corresponding to better governance outcomes.


17. Control of corruption

This variable captures perceptions of the extent to which public power is exercised for private gain. It is measured in units ranging from -2.5 to 2.5, with higher values corresponding to better governance outcomes.


18. Protection of property rights

This variable measures the degree to which a country’s laws protect private property rights and the degree to which igovernment enforces those laws. The score for each country is a number between 0 and 100, with 100 representing the ideal.

The Wall Street Journal and the Heritage Foundation

19. Economic freedom

This variable measures the degree to which the policies and institutions of countries are supportive of economic freedom. The scores range from 0 to 10, with 10 representing the highest possible strength of economic freedom.

Gwartney, Lawson, and Hall

3.3 Data Analysis

This research is the cross-country and time-series analysis of the impact of

politico-economic institutions on economic performance. In this study, the researcher

first analyzed the overall data on both country groups. Then the data on East Asian

countries and Latin American countries were analyzed separately. The results of both

country groups’ equations were compared to find out how their quantitative effects

60

were different. In other words, separating the data into two models – one that includes

only East Asian countries and the other that contains Latin American countries –

provided a more intuitive means to view the differential effects politico-economic

institutions have on East Asian and Latin American countries.

The steps of the data analysis were as follows. First, a general overview was

given by reporting the mean scores and standard deviation of the indicators of

economic performance and the measures of growth-related factors, human capital

factors, democracy, governance, and economic institutions. Second, the bivariate

correlation coefficients between economic performance and each set of variables

were presented. The Pearson’s r value of all independent variables was reported.

Finally, several multivariate regression models were tested, in which the effects on

economic performance were controlled for contextual factors.

The impact of institutional factors on economic performance was estimated by

cross-country regression analysis. The independent variables for the analysis were

selected from the measures presented in the conceptual framework. The relationships

between economic performance and each set of variables were evaluated by

Pearson’s correlation coefficients.

The impact of growth-related factors, human capital factors, democracy,

governance, and economic institutions on economic performance was estimated by

the following equation:

Yi = a + b1GRi + b2HCi + b3DEi + b4GOi + b5EIi

where Yi is economic performance in country i, GRi, HCi, DEi, GOi, and EIi are

growth-related factors, human capital factors, democracy, governance, and economic

institutions respectively. This study was interested in the size, sign, and significance

of the five coefficients b1, b2, b3, b4, and b5. Standardized measures of GRi, HCi, DEi,

GOi, and EIi were used in core regressions so that the estimated coefficients could be

directly compared.

Standard multiple regression analysis was performed with computer program

SPSS. In addition to showing the predictive value of the overall model, standard

multiple regression indicated how well each independent variable predicted the

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dependent variable, controlling for each of the other independent variables. The

nature of the relationship between the dependent variable and each independent

variable was examined by scatter plot and R-square value. Analysis of variance

(ANOVA) was used to test how well the model fits the data.

CHAPTER 4

COMPARING EAST ASIA AND LATIN AMERICA

Before presenting the empirical results and discussions, it is worthwhile to

make meaningful comparisons between East Asia and Latin America in qualitative

aspects. East Asia and Latin America provide interesting cases with which to

compare their economic and political development and their outcomes in terms of

economic performance. This is because even though both regions adopted similar

postwar protective inward-oriented development strategies and have gradually moved

toward democracy, their economic performance has diverged substantially in

subsequent years.

Although it is extremely difficult to compare two regions which are large,

varied, and complex, it is possible to make broad comparisons between their patterns

of economic and political development. Broadly speaking, East Asia has attained

higher and more sustained rates of economic growth throughout the postwar period,

whereas Latin America has been more democratic. During the 1990s, however, these

general differences narrowed, when East Asia became more democratic while

stumbling economically compared to Latin America (Fukuyama and Marwah, 2000).

4.1 Differences in Political Institutions

Political institutions define the structure of the state as well as the political

process. Therefore, they shape the creation and enforcement of economic institutions,

especially economic policy and its administrative implementation. They also

influence the behavior of politicians, political parties, voters and interest groups, and

thus define how institutions are created, altered, and enforced (Borner, Bodmer and

Kobler, 2004).

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In this study, the two broad measures of political institutions are democracy

and governance. According to Freedom House’s latest indices for political rights and

civil liberties (2010), Latin America does better than East Asia. Table 4.1 shows the

ratings on political rights and civil liberties as well as the democratic status of the

sample countries in this study. Among the 17 sample countries in Latin America, 9

countries are considered as “Free” and 8 countries are classified as “Partly Free.”

Meanwhile, among the 10 sample countries in East Asia, only 3 countries are

considered as “Free,” 5 countries are categorized as “Partly Free,” and 2 countries are

classified as “Not Free.” Latin America also has lower mean scores than East Asia in

both categories, indicating that Latin America is more democratic than East Asia.

Table 4.1 Freedom House’s 2010 Indices for Political Rights and Civil Liberties

East Asia PR CL Status Latin America PR CL Status

China 7 6 NF Argentina 2 2 F Hong Kong 5 2 PF Bolivia 3 3 PF Indonesia 2 3 F Brazil 2 2 F Malaysia 4 4 PF Chile 1 1 F Philippines 4 3 PF Colombia 3 4 PF Singapore 5 4 PF Costa Rica 1 1 F South Korea 1 2 F Ecuador 3 3 PF Taiwan 1 2 F El Salvador 2 3 F Thailand 5 4 PF Guatemala 4 4 PF Vietnam 7 5 NF Honduras 4 4 PF Mexico 2 3 F Nicaragua 4 4 PF Panama 1 2 F

Paraguay 3 3 PF Peru 2 3 F Uruguay 1 1 F Venezuela 5 4 PF Mean 4.1 3.5 Mean 2.5 2.8

The main reason why Latin America is more democratic than East Asia is that

democracy came to Latin America much earlier than to East Asia (Fukuyama and

Marwah, 2000). According to Fukuyama and Marwah (2000), at the beginning of the

postwar period, only Japan and the Philippines could be classified as democracies.

However, while Japanese democracy was relatively stable, the Philippines endured

various periods of martial law and dictatorship. In some countries such as Thailand

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and Vietnam, although democratic elections were held, democracy was interrupted by

military coups or communist takeover. In East Asia, the next important democratizations

did not occur until the 1980s.

On the other hand, Latin American countries have had democracy for much

longer. Many countries such as Costa Rica, Colombia, and Venezuela were

established as democracies after gaining independence in the nineteenth century.

Nevertheless, democracy in Latin America was relatively fragile. During the 1960s,

many democratic countries in Latin America were overthrown in military coups and

replaced by bureaucratic-authoritarian regimes (Fukuyama and Marwah, 2000). In the

1980s, the most important change in Latin America’s political sphere was the

transition to democracy after many years of dictatorships in numerous countries

(Morales, 1996).

In addition, there are other possible explanations of why there has been more

democracy in Latin America than in East Asia. Fukuyama and Marwah (2000)

provide four explanations: 1) foreign threats and domestic instability; 2) level of

development; 3) external demonstration effects and influence; and 4) political culture.

With respect to the first explanation, East Asia has been more subject to

external enemies and internal subversion than Latin America. The governments of

South Korea, Taiwan, and Vietnam were formed at mid-century in the crucible of

civil war, and all of them encountered enemies dedicated to overthrowing them.

These external threats had driven many East Asian countries into authoritarianism

due to national-security concerns, so the advent of democracy in East Asia had been

delayed. In contrast, Latin America’s violent nineteenth century was followed by a

relatively peaceful twentieth century. Almost all of the instability experienced by

Latin American countries in this century has been domestic; they have been prone to

high levels of political violence and crime (Fukuyama and Marwah, 2000).

As for the level of economic development, the reason for the relative lack of

democracy in East Asia is that the region as a whole was much poorer than Latin

America at the end of the Second World War. However, at the end of the century,

when many East Asian countries had been ahead of Latin American countries

economically, there were more democracies among the region’s wealthier countries.

One of the reasons why economic development leads to democracy is that it promotes

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the emergence of a broad middle class. The emphasis placed on education by many

East Asian countries such as South Korea and Taiwan has had an impact on creating a

broader middle class, including new elites that are more meritocratic (Fukuyama and

Marwah, 2000).

External demonstration effects and influence mean that countries are

encouraged to adopt democracy by the example of other democracies around the

world. This was obviously a major factor promoting democracy in both East Asia and

Latin America, since the United States exerted substantial influence over its allies’

political development. After dependence in the nineteenth century, most Latin

American countries became democracies partly because of American influence.

Moreover, many of them deliberately based their constitutions on the U.S.

presidential system. The same is true in East Asia. It is not coincident that the

region’s most prominent democracies such as Taiwan and South Korea have had

close relationships with the United States (Fukuyama and Marwah, 2000).

The final explanation is political culture, which satisfactorily explains why

Latin America has been more democratic than East Asia at a relatively low level of

economic development. The simplest explanation is that Latin America has always

perceived itself to be part of Western Christianity, which is closely related to modern

democracy. By contrast, political culture in East Asia is based on the single most

important unifying cultural system called Confucianism, which mandated a high

degree of hierarchy and authority. There is a close association between Confucianism

and authoritarianism (Fukuyama and Marwah, 2000).

Another aspect of political institutions where East Asia and Latin America

differ is concerned with governance, which is broadly defined as ‘the traditions and

institutions that determine how authority is exercised in a country’ (Avellaneda, 2006,

p.2). Economists agree that governance is one of the critical factors which explain the

divergence in economic performance across different countries (Shafique and Haq,

2006). According to the Worldwide Governance Indicators’ latest indices for

government effectiveness, regulatory quality, rule of law, and control of corruption,

regional averages from the year 2009 (Table 4.2) show that East Asia does better than

Latin America in all categories.

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Table 4.2 WGI’s 2009 Indices for Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption

East Asia GE RQ RL CC Latin America GE RQ RL CC China 0.116 -0.2 -0.35 -0.53 Argentina -0.42 -0.9 -0.66 -0.49 Hong Kong 1.757 1.83 1.491 1.845 Bolivia -0.72 -0.98 -1.22 -0.71 Indonesia -0.21 -0.28 -0.56 -0.71 Brazil 0.076 0.177 -0.18 -0.07 Malaysia 0.989 0.335 0.547 0.021 Chile 1.209 1.502 1.251 1.371 Philippines -0.14 0.016 -0.53 -0.71 Colombia 0.041 0.24 -0.44 -0.29 Singapore 2.194 1.835 1.611 2.261 Costa Rica 0.431 0.533 0.563 0.697 South Korea 1.112 0.849 0.999 0.522 Ecuador -0.84 -1.36 -1.28 -0.92 Taiwan 1.061 1.142 0.927 0.573 El Salvador -0.04 0.383 -0.78 -0.17 Thailand 0.152 0.367 -0.13 -0.23 Guatemala -0.69 -0.07 -1.12 -0.6 Vietnam -0.26 -0.56 -0.43 -0.52 Honduras -0.71 -0.24 -0.87 -0.89 Mexico 0.168 0.348 -0.57 -0.27 Nicaragua -1.04 -0.39 -0.83 -0.76 Panama 0.246 0.443 -0.09 -0.26

Paraguay -0.93 -0.41 -0.98 -0.88 Peru -0.36 0.41 -0.66 -0.36 Uruguay 0.688 0.372 0.723 1.22 Venezuela -0.95 -1.69 -1.59 -1.2 Mean 0.68 0.53 0.36 0.25 Mean -0.23 -0.1 -0.51 -0.27

66

67

According to Fukuyama and Marwah (2000), there has been a huge gap

between these two regions in terms of institutional effectiveness, which refers to how

well a political system delivers government services. While many countries in East

Asia have been successful in creating effective institutions, most Latin American

countries have had strong states and weak institutions. Comeau (2003) elaborates this

point by explaining that in most East Asian countries, effective policies have been

carried out by strong states that have established effective institutions and have

strived to adapt in a timely manner to changing needs in the global scene while

achieving remarkable economic growth. The best example for illustrating this point is

Singapore. As shown in Table 4.2, Singapore has high scores on all four aspects of

governance. According to Menon (2007), even though Singapore inherited the same

British model of governance as other Commonwealth states, its governing system has

been well-known for efficiency and competence, particularly in terms of its role in

generating an economic miracle. The governance system of Singapore has been

consistently rated as one of the most politically transparent and least corrupt government,

not only in East Asia, but also in the world. Singapore’s efficient governance is one

of the key factors that have led to its economic success.

On the other hand, Latin America has been characterized by weak or unstable

institutions which have led to the region’s persistent incapacity to implement

appropriate growth strategies (Comeau, 2003). The fact that Latin America has had

weak institutions is particularly true in terms of governance. As pointed out by

Dellepiane-Avellaneda (2009), the low quality of governance in Latin America has

underscored the lack of economic performance in this region. This point can be

illustrated by the case of Bolivia, one of the poorest and most unstable countries in

Latin America. According to Table 8, Bolivia is among the countries which perform

poorly in all aspects of governance. It is clear that misgovernance has been the source

of this country’s poor economic performance (Avellaneda, 2006). Dellepiane-

Avellaneda (2009) also indicates that poor governance, low rates of economic growth,

and high income inequality are inextricably linked in the Latin American region.

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4.2 Differences in Economic Institutions

According to Borner et al. (2004), efficient economic institutions are necessary

for the efficient allocation of scarce resources, particularly in view of inter-temporal

decisions (such as investment and saving). Nevertheless, such institutions are made,

not given, and do not emerge spontaneously from rational self-interest. Economic

institutions of different varieties and degrees of efficiency are shaped by the quality

and form of political institutions.

Apart from political differences, East Asia and Latin America differ with

respect to their economic institutions. In this study, the measures of economic

institutions are the protection of property rights and economic freedom. According to

the Wall Street Journal and the Heritage Foundation’s latest index for property rights,

the regional averages from the year 2011 (Table 4.3) show that East Asian countries

do better than Latin American countries. The countries which retain the highest rating

for property rights are Hong Kong and Singapore. This is not unexpected because

these two countries are widely known for their high-quality legal frameworks, which

provide effective protection of property rights. Secure property rights are the keys of

their economic success. However, there are significant differences across East Asian

countries in terms of property rights. As illustrated in Table 4.3, many East Asian

countries, such as China and Vietnam, are characterized by low level of property

rights protection and weak enforcement.

Table 4.3 The Wall Street Journal and the Heritage Foundation’s 2011 Index for

Property Rights

East Asia Property Rights Latin America Property Rights

China 20 Argentina 20 Hong Kong 90 Bolivia 10 Indonesia 30 Brazil 50 Malaysia 50 Chile 85 Philippines 30 Colombia 50 Singapore 90 Costa Rica 55 South Korea 70 Ecuador 20 Taiwan 70 El Salvador 40

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East Asia Property Rights Latin America Property Rights

Thailand 45 Guatemala 35 Vietnam 15 Honduras 30 Mexico 50 Nicaragua 20 Panama 40

Paraguay 30 Peru 40 Uruguay 70 Venezuela 5 Mean 51 Mean 38.2

According to Akerman, Larsson and Naghavi (2011), the reason why Latin

America has been characterized by weak property rights is that many Latin American

countries chose to open up to trade at an early stage of development. The intuition is

as follows. Since the Latin American landed elite of the 20th century owned abundant

factors, they favored an open economy. In an open economy, prices are determined in

the world market. This triggers a domestic battle for labor. Under free trade, land

owners will find labor more expensive when property rights are enforced because this

will enable capitalists to pay higher wages. This disincentive leads to weak protection

of property rights in an open economy governed by a landed autocrat. According to

Table 4.3, Bolivia and Venezuela are the countries performing the worst in terms of

property rights in Latin America. Since Bolivia has weak institutions, enforcement of

contracts and property rights is uncertain. In Venezuela, contracts and property rights

are also not well respected, and the threat of government expropriation remains high.

The country with the highest rating for property rights in Latin America is Chile,

which has a strong property-rights regime and effective enforcement of property

rights. This is the exceptional case because almost all Latin American countries are

characterized by poor protection of property rights.

In addition, East Asia and Latin America are relatively different with respect

to the degree of economic freedom. According to the latest Economic Freedom of the

World Index, regional averages from the year 2009 (Table 4.4) reveal that there is

more economic freedom in East Asia than in Latin America. However, the difference

is not substantial.

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Table 4.4 Fraser Institute’s 2009 Index for Economic Freedom

East Asia Economic Freedom Latin America Economic Freedom China 6.43 Argentina 5.90 Hong Kong 9.01 Bolivia 6.27 Indonesia 6.50 Brazil 6.19 Malaysia 6.68 Chile 7.77 Philippines 6.46 Colombia 6.21 Singapore 8.68 Costa Rica 7.17 South Korea 7.32 Ecuador 6.04 Taiwan 7.37 El Salvador 7.15 Thailand 6.87 Guatemala 7.07 Vietnam 6.48 Honduras 7.06 Mexico 6.74 Nicaragua 6.82 Panama 7.41 Paraguay 6.57 Peru 7.31 Uruguay 6.90 Venezuela 4.28 Mean 7.18 Mean 6.64

Given Latin America’s history of economic nationalism, the reason why the

numbers are so close is the impact of liberalizing economic reform over the past

decade. According to Fukuyama and Marwah (2000), during the late 1980s and early

1990s, many Latin American countries sought macroeconomic stabilization by

cutting their public sectors, reducing levels of protection and subsidy, privatizing

loss-making public enterprises, and weakening the power of public sector unions.

Even though the success of these neoliberal reforms varied widely across the region,

the change that occurred between the 1980s and the 1990s throughout the region was

remarkable and has made Latin America nearly as economically free as East Asia.

According to Table 4.4, Argentina and Venezuela are among the Latin

American countries which perform the worst in terms of economic freedom. The

main reason why these two countries have performed poorly with respect to economic

freedom is disillusionment with the free market or the widespread perception that the

free market has failed (Vasquez, 2004). However, Chile has outperformed all of its

neighbor countries. It holds leadership in Latin America with regard to economic

freedom. Chile's healthy economic freedom comes partly from its openness to global

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trade and investment. Additionally, Chile benefits from a transparent and stable

financial and legal sector, where there is little tolerance for corruption. This

environment has attracted foreign investment, leading to steady economic growth in

various sectors (Ortega, 2010).

In East Asia, ‘people are enjoying free private markets where individuals

making deals on their own behalf or as agents for identifiable individuals to pursue

their own ends for their economic objectives rather than as agent of government’

(Mahmood, Azid, Chaudhry, and Faridi, 2010: 8). It is not surprising that Hong Kong

retains the highest rating for economic freedom in East Asia, followed by Singapore

(see Table 4.4). The foundations of economic freedom in Hong Kong and Singapore

are strong protection of property rights and strong support for the rule of law. In

addition, regulatory efficiency and openness to global trade and investment strongly

support entrepreneurial dynamism in both countries. The East Asian country with the

lowest rating for economic freedom is China (see Table 4.4). Economic freedom in

China rests on fragile foundations. For instance, the judicial system is vulnerable to

political influence and Communist Party directives, and corruption is widespread.

The party’s small leadership group holds ultimate authority, and direct control is

exercised over many aspects of economic activity.

4.3 Differences in Fundamental Socio-Economic Factors

In explaining why East Asia and Latin America have had different economic

performance during the past few decades, it is essential to consider the fundamental

socio-economic factors which also play a very important role in the economic

performance of the two regions. The socio-economic factors determined in this study

include investment rates, gross national savings, population growth rates, life

expectancy at birth, adult literacy rates, and combined gross enrollment.

Table 4.5 shows the averages of the investment rates of selected East Asian

and Latin American countries during the periods of 1990-1999 and 2000-2009. It is

apparent that the investment rates of Latin American countries were lower than those

of the fast growing East Asian countries. According to Agosin (1995: 1)

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the causes for the poor performance of private investment in Latin

America relative to Asia are: considerably slower economic growth;

more stringent domestic credit constraints; the adverse impact of the

debt crisis on Latin American investment, a factor which was absent

in the Asian countries; an important fall in complementary public

investment in Latin America, which did not take place in Asia; and a

greater degree of macroeconomic and relative price instability.

Table 4.5 Investment Rates, 1990-1999 and 2000-2009


China 39.04 41.28 Argentina 18.29 19.10 Hong Kong 29.36 22.44 Bolivia 16.92 15.08 Indonesia 36.00 25.01 Brazil 16.83 17.39 Malaysia 36.35 21.76 Chile 25.38 22.26 Philippines 22.37 16.59 Colombia 21.53 19.69 Singapore 33.79 24.02 Costa Rica 19.62 22.25 South Korea 35.22 29.46 Ecuador 20.68 24.01 Taiwan 25.64 21.61 El Salvador 17.04 16.00 Thailand 36.27 25.90 Guatemala 17.73 19.19 Vietnam 21.18 35.83 Honduras 29.92 27.86 Mexico 26.10 24.78 Nicaragua 22.25 29.58 Panama 22.53 20.79


Source: Author’s calculations based on World Economic Outlook Database, IMF

The differences between East Asia and Latin America with respect to gross

national savings are shown in Table 4.6. It is noticeable that the average of the gross

national savings in East Asia was higher than that in Latin America during both

periods. There are two alternative perspectives on the relationship between national

savings and economic growth. Proponents of the first perspective contend that higher

saving rates lead to higher income growth, resulting in a rise in economic growth

(Loayza, Schmidt-Hebbel, and Serven, 2000). On the other hand, those supporting the

73

second perspective argue that higher economic growth precedes higher national

savings, rather than the reverse. As pointed out by Gavin, Hausmann, and Talvi

(1997: 2), ‘it is only after a sustained period of high growth that saving rates increase’.

According to this view, Latin American countries’ low rates of national savings have

been primarily the consequence of the region’s history of low and volatile economic

growth, whereas the high rates of national savings in East Asian economies has been

due to their high and less volatile rate of economic growth.

Table 4.6 Gross National Savings, 1990-1999 and 2000-2009


China 40.68 46.73 Argentina 15.26 21.38 Hong Kong 32.59 31.96 Bolivia 10.68 18.92 Indonesia 31.18 27.74 Brazil 16.22 16.66 Malaysia 34.34 34.82 Chile 22.58 23.06 Philippines 18.83 18.18 Colombia 16.96 18.31 Singapore 47.53 42.34 Costa Rica 15.17 17.29 South Korea 35.88 31.73 Ecuador 17.66 24.41 Taiwan 29.13 29.00 El Salvador 14.84 12.09 Thailand 33.65 29.20 Guatemala 12.74 14.44 Vietnam 20.41 32.41 Honduras 19.03 21.21 Mexico 22.90 23.36 Nicaragua 5.20 10.71 Panama 18.80 16.04


Source: Author’s calculations based on World Economic Outlook Database, IMF

In terms of population growth rates, which are another growth-related factor,

the average population growth rates in Latin America were higher than those in East

Asia as shown in Table 4.7. In the neoclassical growth model developed by Solow,

higher population growth has a negative impact on economic growth during the transition

to a steady state. This model is well supported by the Harrod- Domar model which

stipulates that ‘population growth forces economies to use their scarce savings to

undertake capital widening rather than capital deepening’ (Klasen and Lawson, 2007:

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4). In addition, ‘rapid population growth forces scarce capital to be spent on nonproductive

segments of the population (e.g., children) and encourages undercapitalization of the

economy, underemployment, low wages, and anemic market demand’ (Crenshaw,

Ameen, and Christenson, 1997). Therefore, Latin America’s low economic growth and

high unemployment compared to East Asia may be partially due to its high population

growth rates. However, it is noteworthy that there has been a decline in the population

growth rates in both regions mainly due to lower fertility rates.

Table 4.7 Population Growth Rates, 1990-1999 and 2000-2009


China 1.13 0.61 Argentina 1.32 0.98 Hong Kong 1.50 0.58 Bolivia 2.23 1.91 Indonesia 1.50 1.27 Brazil 1.56 1.21 Malaysia 2.57 1.88 Chile 1.61 1.08 Philippines 2.23 1.89 Colombia 1.83 1.55 Singapore 3.01 2.31 Costa Rica 2.47 1.75 South Korea 0.95 0.45 Ecuador 1.90 1.15 Taiwan N/A 0.41 El Salvador 1.18 0.41 Thailand 1.01 0.92 Guatemala 2.31 2.46 Vietnam 1.80 1.19 Honduras 2.47 2.02 Mexico 1.68 1.06 Nicaragua 2.16 1.34 Panama 2.03 1.77



Table 4.8 illustrates life expectancy at birth, which indicates the number of

years a newborn infant would live if prevailing patterns of mortality at the time of its

birth were to stay the same throughout its life, of selected East Asian and Latin

American countries. Although the life expectancy at birth in East Asia has been

slightly higher than that in Latin America in both periods, the difference is not

substantial. What is noteworthy is the fact that on average, life expectancy has

increased in both regions. According to many cross-country regression studies (for

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example, Azomahou, Boucekkkine, and Diene, 2008; Bloom, Canning, and Sevilla,

2004; Finlay 2007), life expectancy has a positive and statistically significant effect

on economic growth. A reasonable explanation for this relationship is that a higher

life expectancy tends to lengthen the schooling time, thus generating a better

education and better conditions for economic growth (Azomahou et al., 2008).

Table 4.8 Life Expectancy at Birth, 1990-1999 and 2000-2009


China 69.5 72.4 Argentina 72.5 74.7 Hong Kong 78.8 81.8 Bolivia 60.8 64.5 Indonesia 64.2 69.4 Brazil 68.1 71.5 Malaysia 71.3 73.6 Chile 74.9 80.0 Philippines 67.3 70.9 Colombia 69.4 72.1 Singapore 76.3 79.6 Costa Rica 76.7 78.5 South Korea 73.1 78.1 Ecuador 71.0 74.5 Taiwan N/A 77.2 El Salvador 68.3 70.6 Thailand 68.6 68.5 Guatemala 64.7 69.4 Vietnam 68.9 73.4 Honduras 68.3 71.4 Mexico 72.4 74.6 Nicaragua 66.9 71.7 Panama 73.2 75.1



The quality of human capital–as measured by adult literacy rates and

combined gross enrollment–is also one of the fundamental socio-economic factors

which influence economic performance. According to Table 4.9, East Asia has had

higher adult literacy rates than Latin America, particularly during the period of 1990-

1999. The high level of adult literacy in East Asia reflects the overall high level of

participation in education compared to Latin America. It is also noteworthy that on

average, adult literacy rates have increased in both regions and many countries such

as China, Guatemala, Honduras, and Nicaragua have made considerable progress.

According to Gujjar (2007), literacy is the foundation of all skills and a pre-requisite

76

for economic development. The positive relationship between literacy levels and

economic development is well established.

Table 4.9 Adult Literacy Rates, 1990-1999 and 2000-2009


China 77.8 92.9 Argentina 96.1 97.6 Hong Kong N/A N/A Bolivia 80.0 90.0 Indonesia 81.5 90.3 Brazil 74.6 89.3 Malaysia 82.9 91.4 Chile 94.3 96.4 Philippines 93.6 93.3 Colombia 80.8 92.4 Singapore 89.1 94.1 Costa Rica 92.6 95.8 South Korea 87.6 87.6 Ecuador 88.3 91.0 Taiwan N/A N/A El Salvador 74.1 81.2 Thailand 88.0 93.9 Guatemala 46.0 72.5 Vietnam 87.6 90.3 Honduras 56.9 83.0 Mexico 87.6 92.0 Nicaragua 57.5 77.8 Panama 88.8 93.2


Source: Author’s calculations based on Human Development Index, UNDP

Table 4.10 shows East Asia’s and Latin America’s combined gross enrollment

ratio, which incorporates all three levels of education–primary, secondary, and

tertiary levels. Interestingly, combined gross enrollment was slightly higher in Latin

America than in East Asia. This corresponds to the findings of a study on Latin

American and East Asian secondary education conducted by Gropello (2006). In his

study, it was found that secondary gross enrollment was significantly higher in Latin

America than it was in East Asia. The main reason for the significant increase in

secondary school enrolment rates in Latin America is that during the 1990s,

significant reforms were implemented by many Latin American countries to improve

the coverage, equity, and quality of their secondary education systems. This may be

the major reason why Latin America has higher rates of combined gross enrollment

than East Asia. Another noteworthy fact is that on average, the combined gross

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enrollment ratio in both regions has significantly increased. Moreover, all countries

included in this study have made substantial progress in terms of combined gross

enrollment. One of the main reasons for the considerable increase in the combined

gross enrollment ratio is that ‘as countries become wealthier, enrollment rates

increase’ (Gropello 2006: 27).

Table 4.10 Combined Gross Enrollment, 1990-1999 and 2000-2009


China 53.6 68.4 Argentina 79.5 88.6 Hong Kong 69.8 74.4 Bolivia 63.1 85.6 Indonesia 61.4 67.0 Brazil 66.8 87.7 Malaysia 59.1 70.9 Chile 71.3 81.8 Philippines 75.0 79.6 Colombia 57.3 76.9 Singapore 61.8 85.0 Costa Rica 64.7 71.9 South Korea 78.9 97.1 Ecuador 70.2 N/A Taiwan N/A N/A El Salvador 55.5 72.0 Thailand 49.7 76.9 Guatemala 43.0 67.4 Vietnam 48.6 62.5 Honduras 59.3 72.9 Mexico 65.3 78.6 Nicaragua 55.1 71.3 Panama 66.6 79.1


Source: Author’s calculations based on Human Development Index, UNDP

It is widely accepted that education plays a vital role in improving the quality

of human capital, particularly in developing countries. The improved quality of

human capital enhances overall economic performance. According to previous

research (for example, Hanushek, Woessmann, Jamison, and Jamison 2008; Stevens

and Weale 2003), there is a strong and positive relationship between education and

economic performance. According to Ozturk (2001), education promotes economic

growth by raising people’s productivity and creativity and by promoting entrepreneurship

and technological advances. In addition, it can greatly reduce unemployment rates

since it creates a skilled and educated workforce. Education also plays a very crucial

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role in raising the poor from poverty by increasing the value and efficiency of their

labor, thus leading to a reduction in income inequality.

4.4 Explaining Divergent Economic Performance

The differences in political institutions, economic institutions, and fundamental

socio-economic factors explained above account for the divergent economic

performance of East Asia and Latin America. Most of the growth divergence between

East Asia and Latin America occurred in the last quarter of the twentieth century. The

record of economic growth of the Latin American region as a whole was particularly

weak during the 1980s and 1990s, which was the period when the region was

implementing widespread economic reforms. By comparison, real GDP per capita

and other indicators of economic performance were significantly higher for East Asia

than for Latin America during these same time periods (Elson 2006). According to

Fukuyama and Marwah (2000), East Asia’s and Latin America’s divergent economic

performance can be explained by three major factors: 1) starting endowments of

capital, labor, and natural resources; 2) quality of economic policies; and 3) quality of

institutions.

With regard to starting endowments, East Asia on the whole has no particular

advantages over Latin America. For instance, Elson (2006) points out that natural

endowments are not a significant differentiating factor between East Asia and Latin

America. Both regions contain countries that are resource-rich and resource-poor.

Therefore, it is very difficult to see any clear correlations between starting endowments

and long-term economic performance in either region. In East Asia, Korea, Taiwan,

Hong Kong, and Singapore have achieved very high rates of growth without natural

resources or large populations, in contrast to resource-rich countries such as Malaysia

and Indonesia or populous ones such as China. Likewise, Venezuela’s and Mexico’s

oil resources have not led to high sustained rates of growth (Fukuyama and Marwah,

2000).

On the contrary, over much of the past four decades, the quality of economic

policies has been higher in East Asia than in Latin America. The two regions have

had very clear differences in economic policies. While high-growth, high-income

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countries in East Asia maintained stable macroeconomic fundamentals, Latin

America emphasized economic nationalism over sustainable policies for much of the

period up to the 1980s (Fukuyama and Marwah, 2000). According to Comeau (2003: 487),

East Asia did better than Latin America at maintaining a more stable macroeconomic

environment by ‘consistently keeping inflation and debt in check, stimulating private

initiatives through enhanced economic freedom, and fostering productivity through

greater human capital accumulation’. This caused standards of living and distributive

equity to improve steadily. Productivity in manufacturing also increased at a fast and

sustained pace. Moreover, since price and wage inflation remained under control,

most East Asian economies achieved a highly-competitive factor cost position. On the

other hand, inflation in Latin America was often rampant and hindered business

confidence. Due to the higher risk of holding domestic financial assets in an

environment where prices were disruptive, financial intermediation of savings and

investment was discouraged.

Another major difference in economic policies between the two regions is the

impact of state intervention. East Asian countries hardly pursued orthodox liberal

strategies of economic development. Japan, Korea, and Taiwan all put in place

comprehensive industrial policies, where the government used its control over credit

allocation and licensing to guide broad sectoral transitions. Furthermore, many Asian

countries employed capital controls of various sorts, including sharp limits on foreign

direct investment (FDI), and discouraged their consumers from buying imported

goods. In spite of government intervention, East Asia achieved unprecedented rates of

economic growth. On the other hand, there is no Latin American country that pursued

a highly-interventionist industrial policy and actually succeeded in achieving

sustained economic growth. Despite achieving some success with industrial policy in

the 1960s, Brazil quickly ran into trouble financing oil deficits during the 1970s and

ended up in a severe debt crisis. The reason underlying the differences in the impact

of state intervention between the two regions is that apparently similar interventionist

policies were carried out in more effective ways in East Asia than in Latin America.

For instance, although East Asian governments protected infant industries, they

forced them to be export-oriented by not subsidizing them when they were able to

compete in international markets (Fukuyama and Marwah, 2000).

80

Perhaps the most important reason for the differences in long-term economic

performance has been the superior quality of institutions in East Asia (Fukuyama and

Marwah, 2000). As pointed out by Elson (2006: 110), a number of surveys which

measure the effectiveness of institutions ‘show clear weaknesses for Latin America in

comparison with East Asia across virtually all dimensions and consistently over time’.

Moreover, a study conducted by Comeau (2003) reveals that the institutional

environments of the two regions is an important factor contributing to their divergent

economic performance–East Asia’s relative success and Latin America’s poor


Fukuyama and Marwah (2000) point out that historically, there has been a

huge gap between the two regions in terms of institutional effectiveness. An

institution can be considered effective if it is able to set clear-cut goals for itself and

to achieve them. In economic policy-making, the government agency in charge needs

to focus on long-term economic growth and be protected from pressures to divert

resources toward rent-seeking claims. In addition, the bureaucrats administering such

a policy need to have high professional competence and refrain from any personal

corruption. Many East Asian countries have succeeded in creating effective

institutions. Public institutions in Japan, Korea, and Taiwan have been administered

by highly-trained professional bureaucrats that are protected from the personal and

political temptations that plague government bureaucracies in Latin America. Hong

Kong and Singapore also have governments with a high degree of institutional

effectiveness, stemming mainly from British administration.

By contrast, most countries in Latin America have had weak institutions. That

is, they inherited a tradition of strong bureaucratic centralization and economic

dirigisme from Spain and Portugal, but did not inherit or establish institutions that

promoted effective administration. In the 19th century, most Latin American

governments had state bureaucracies that were completely politicized or else under

the personal patronage of political leaders. Civil service reform was implemented

much later than in Europe and in some cases not at all (Fukuyama and Marwah, 2000).

Due to the importance of institutions as a major factor underlying long-term

economic performance, this study will focus on the role of institutions by providing

empirical evidence regarding how institutions affect economic performance across

81

the two regions. According to the literature review, most empirical studies in the field

of the new institutional economics have overlooked the role of economic institutions

in their impact on long-term economic performance. As pointed out by Boyd (2006),

political institutions have occupied center stage in explaining economic performance

across the new institutional economics research field. Therefore, this study will fill

this research gap by studying the impact of both political and economic institutions

on economic performance.

82

CHAPTER 5

DESCRIPTIVE STATISTICS AND DATA ANALYSIS:

POLITICO-ECONOMIC INSTITUTIONS AND ECONOMIC

PERFORMANCE IN EAST ASIA AND LATIN AMERICA

5.1 Descriptive Statistics

This section provides the descriptive statistics and a discussion of the data

used to test the hypotheses in this study. The data are arrayed as a time-series cross-

section of 27 countries for 20 years (1990-2009). Summary statistics for the data of

East Asian and Latin American countries are presented in Table 5.1.

Table 5.1 Summary Statistics for East Asia and Latin America

Variable Mean Standard Deviation

Maximum Maximum

Annual growth rates of GDP per capita

4.3006 .90136 2.70 5.72

Unemployment rates 6.6311 .84400 5.50 8.05

Percentage of the population falling below the poverty line

34.0211 10.09687 16.68 54.51

Income inequality 16.3538 2.87670 11.81 24.41

Investment 24.9857 2.03499 22.26 28.25

Gross national savings 25.2612 1.52902 23.22 28.39

Population growth rates 1.5543 .28556 1.19 1.97

Life expectancy at birth 71.9412 1.98382 68.67 74.72

Adult literacy rates 86.7830 4.13438 83.45 96.10

Combined gross enrollment 69.2368 6.35865 63.31 78.27

Political rights 3.3233 .23722 3.01 3.75

Civil liberties 3.5164 .38120 2.96 4.18

83


Variable Mean Standard

Deviation Maximum Maximum

Press freedom 46.6015 1.93687 44.00 50.21

Government effectiveness .1043 .04099 .06 .18

Regulatory quality .7136 .03503 .68 .78

Rule of law .8312 .07235 .74 .98

Control of corruption .9534 .09008 .81 1.07

Protection of property rights 52.3863 6.48468 44.76 60.76

Economic freedom 6.6480 .33374 6.13 7.04

Summary statistics for the data of East Asia and those of Latin America are

shown in Table 5.2 and Table 5.3 respectively. According to the statistics, it is

obvious that East Asia has better economic performance than Latin America. East

Asia has higher annual growth rates of GDP per capita, lower unemployment rates

and lower income inequality. As for the percentage of the population falling below

the poverty line, the reason why East Asia has a higher percentage than Latin

America may be that the data of East Asia are incomplete. Only the data on China,

Indonesia, Malaysia, Philippines, Thailand, and Vietnam were available. Some of

these countries (China and Indonesia) have a very high percentage of the population

falling below the poverty line. Therefore, the mean of the percentage of the

population falling below the poverty line in East Asia appears to be higher than that

in Latin America.

With respect to political institutions, East Asian countries have higher mean

scores than Latin American countries in all measures including political rights, civil

liberties, press freedom, government effectiveness, regulatory quality, rule of law,

and control of corruption. On the other hand, Latin America has higher mean scores

than East Asia in both protection of property rights and economic freedom. This

indicates that the political institutions in East Asia are more effective than those in

Latin America, whereas the economic institutions in East Asia are less effective than

those in Latin America.

84

The measures of political institutions show clear weaknesses for Latin

America in comparison with East Asia across virtually all dimensions and

consistently over time. This is because ‘despite international democracy assistance

during the last two decades, the region has only been able to consolidate electoral

democracies, but not a democracy capable of organizing an effective participation of

the citizens’ (Carrillo-Florez and Petri, 2009: 3). Moreover, Latin America has been

characterized by high levels of political instability (Coatsworth, 2008). With respect

to governance, many East Asian countries have been among the top-rated countries in

the world in terms of the quality of their public institutions (i.e. government

effectiveness, regulatory quality, rule of law, and control of corruption), while most

of the Latin American countries have been in the bottom half (Elson, 2006).

The effectiveness of economic institutions in Latin American countries is due

to the trade liberalization and capital market deregulation in the late 1980s. According

to Edwards (1995), after decades of protectionism that had been at the heart of the

region’s development strategy, most Latin American countries began to open up to

the rest of the world in the late 1980s by implementing trade liberalization programs.

The main goal of trade liberalization programs was to reverse the negative

consequences of protectionism and, especially, its anti-export bias. The trade reforms

included supporting an evolving market-based system by putting in place ‘well-

defined and specified property rights that provide incentives for people to be

productive’ (North, 1996: 7). In addition, for many decades, most Latin American

countries imposed tight controls on the financial sector. There were ceilings on

interest rates, credit was allocated to sectors that social planners deemed promising,

and the creation of new financial institutions were often not granted. However, these

policies did not work as expected. Therefore, starting in the late 1980s and early

1990s, many Latin American countries implemented major financial reforms and a

number of significant accomplishments were made (Edwards, 1995; Llosa, 2006).

These financial reforms have significantly increased the effectiveness of economic

institutions in the region.

85

Table 5.2 Summary Statistics for East Asia


Minimum Maximum


5.3990 .94559 3.84 7.10

Unemployment rates 4.5921 .69740 3.54 5.72

Percentage of population falling below the poverty line

49.6346 16.02523 23.94 84.60


Investment 28.9549 3.47398 25.39 34.75







Civil liberties 4.1122 .50371 3.40 5.00

Press freedom 52.1736 1.41451 49.78 54.30

Government effectiveness .9573 .15626 .75 1.20

Regulatory quality .9488 .11963 .75 1.10

Rule of law .9065 .11771 .78 1.12

Control of corruption 1.0210 .11274 .81 1.15



86

Table 5.3 Summary Statistics for Latin America


Minimum Maximum


3.2022 1.19858 1.08 5.19

Unemployment rates 8.6700 1.15003 6.75 10.53

Percentage of population falling below the poverty line

19.8732 5.01505 6.91 27.76


Investment 21.0166 1.80840 17.26 24.60







Civil liberties 2.9206 .26464 2.53 3.35

Press freedom 41.0294 3.05346 36.00 46.12

Government effectiveness .4402 .08321 .36 .65

Regulatory quality .4790 .07041 .36 .61

Rule of law .7559 .07791 .65 .87

Control of corruption .8865 .11558 .68 1.10



87

5.2 Data Analysis

In this study, multiple regression was employed to determine if politico-

economic institutions affected economic performance of East Asia and Latin America.

First, the full sample–data on both East Asia and Latin America – was analyzed. Then

the data on each region were analyzed separately so that the results could be

compared.

The researcher first explored the interrelationships among all of the variables

by finding the correlation coefficients. Subsequently, the researcher analyzed which

factors affected each indicator of economic performance by including the 15

independent variables and the 2 control variables. The independent variables were

entered into the equation using the standard multiple regression method.

The researcher specified the symbols of the variables analyzed in this study as

follows:

Dependent Variables

Y1 = Annual growth rates of GDP per capita

Y2 = Unemployment rates

Y3 = Percentage of the population falling below the poverty line

Y4 = Income inequality

Independent Variables

X1 = Investment rates

X2 = Gross national savings

X3 = Population growth rates

X4 = Life expectancy at birth

X5 = Adult literacy rates

X6 = Combined gross enrollment

X7 = Political rights

X8 = Civil liberties

X9 = Press freedom

X10 = Government effectiveness

X11 = Regulatory quality

88

X12 = Rule of law

X13 = Control of corruption

X14 = Protection of property rights

X15 = Economic freedom

Control Variables

C1 = Population Size

C2 = Trade openness

In the following section, the data of the full sample were analyzed. The impact

of politico-economic institutions on various indicators of economic performance

including annual growth rates of GDP per capita, unemployment rates, percentage of

the population falling below the poverty line, and income inequality in East Asia and

Latin America are presented.

5.2.1 The Impact of Politico-Economic Institutions on Annual Growth

Rates of GDP per Capita

The correlations between the variables used to test the impact of institutional

factors on the annual growth rates of the GDP per capita are shown in Table 5.4. It

was found that the variables, which were positively correlated with the annual growth

rates of the GDP per capita at the significance level of 0.05 include gross national

savings, adult literacy rates, press freedom, regulatory quality, and economic freedom.

The variable which was negatively correlated with annual growth rates of GDP per

capita at the significance level of 0.05 was civil liberties. However, the independent

variable which had the highest correlation with the dependent variable was gross

national savings. The variables with the highest correlation were civil liberties and

trade openness, followed by protection of property rights and trade openness.

82

Table 5.4 Correlation Matrix of the Variables Used to Test the Impact of Politico-Economic Institutions on Annual Growth Rates of

GDP per Capita

Variable Y1 X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 C1 C2 Y1 1.000 X1 .256 1.000 X2 .790* .367 1.000 X3 -.157 .728* -.177 1.000 X4 .366 -.412 .561* -.839* 1.000 X5 .621* -.226 .589* -.669* .842* 1.000 X6 .301 -.405 .527* -.789* .950* .801* 1.000 X7 .005 .747* .228 .728* -.538* -.564* -.572* 1.000 X8 -.479* .379 -.624* .772* -.954* -.872* -.933* .516* 1.000 X9 .689* .153 .838* -.428 .736* .672* .588* -.033 -.70 1.000 X10 .466 .192 .752* -.254 .687* .626* .611* .024 -.635* .825* 1.000 X11 .587* -.055 .605* -.549* .793* .899* .688* -.365 -.775* .765* .702* 1.000 X12 .215 -.323 .445 -.695* .890* .730* .899* -.488* -.874* .606* .685* .735* 1.000 X13 .283 -.016 .610* -.426 .649* .529* .732* -.074 -.750* .459 .494* .489* .765* 1.000 X14 -.361 .397 -.569* .742* -.937* -.829* -.895* .457* .973* -.670* -.662* -.764* -.880* -.742* 1.000 X15 .676* .169 .882* -.369 .654* .552* .648* .049 -.683* .768* .589* .586* .535* .684* -.578* 1.000 C1 .470 -.244 .686* -.725 .943* .770* .826* -.304 -.900* .861* .728* .823* .803* .630* -.891* .765* 1.000 C2 .441 -.302 .660* -.738* .969* .860* .925* -.438* -.979* .762* .708* .816* .869* .724* -.976* .692* .939* 1.000

Note: *Correlation is Significant at 0.05, *P<0.05

89

90

Table 5.5 shows the results of the multiple regression analysis of the

significant predictor variables and the dependent variable. Initially, the impact of

institutional factors on annual growth rates of GDP per capita was analyzed by

entering two groups of the data–15 independent variables and 2 control variables–

into the model with the Enter method. However, the final model to emerge from the

Enter analysis contained 6 predictor variables. The SPSS printout regarding the

impact of institutional factors on annual growth rates of GDP per capita is shown in

Appendix G.

To determine the presence of multicollinearity, the collinearity statistics

needed to be considered. According to the collinearity statistics shown in Table 5.5,

the tolerance value for each predictor variable was not less than .10; therefore, the

multicollinearity assumption was not violated. This is also supported by the VIF

value, which is well below the cut-off of 10.

The final model creates the appropriate equation which affects annual growth

rates of GDP per capita at the significance level of 0.05. The equation has a multiple

correlation coefficient of .928 and can explain about 72.4 percent of the variance in

annual growth rates of GDP per capita, with the standard error of .462.

When considering the regression coefficient of the predictor variables, it was

found that gross national savings had the greatest impact on annual growth rates of

GDP per capita at the significance level of 0.05. The regression coefficient (b) and the

standardized regression coefficient () were .557 and .969 respectively. The following

variable is rule of law with a regression coefficient (b) of -8.758 and a standardized

regression coefficient () of -.695. These indicate that gross national savings and rule

of law make a significant unique contribution to the prediction of annual growth rates

of GDP per capita.

91

Table 5.5 Multiple Regression Analysis of the Significant Predictor Variables and

Annual Growth Rates of GDP per Capita

Constant / Variable b Std.Er t p-value Collinearity Statistics Tolerance VIF

Constant 1.170 6.367 .184 .859 Investment rates -.046 .166 -.094 -.277 .790 .174 5.759 Gross national savings .557 .148 .969 3.769 .007* .300 3.329 Population growth rates .883 1.635 .202 .540 .606 .142 7.041 Political rights -3.091 1.827 -.449 -1.692 .135 .281 3.557 Regulatory quality 7.966 6.459 .306 1.233 .257 .322 3.107 Rule of law -8.758 2.809 -.695 -3.118 .017* .399 2.507

Std.Erest = .462 R = .928 ; R2 = .861 ; Adjusted R Square = .724 ; F = 7.232 ; p-value = .010*

Note: *P<0.05

However, the relationship between rule of law and annual growth rates of

GDP per capita was contradictory with the theoretical predictions and findings of

previous research. According to new institutional theory and prior research, rule of

law–a measure of political institutions–is positively correlated with economic growth.

In other words, improved rule of law is associated with higher economic growth. The

possible explanation for this study’s surprising finding is that the lagged value of rule

of law (approximately five years) may be required. This is because in the short run,

the relationship between rule of law and economic growth is not very strong and

outliers can occur. Indeed, the causal link between these two variables is particularly

robust in the long run (The World Bank, 2012).

The equation which predicts annual growth rates of GDP per capita of East

Asian and Latin American countries can be shown in the form of raw scores as

follows:

Y1 = 0 + .557X2 – 8.758X12

92

5.2.2 The Impact of Politico-Economic Institutions on Unemployment

Rates


factors on unemployment rates are shown in Table 5.6. It was found that there were

no variables which were positively correlated with unemployment rates at the

significance level of 0.05. The variables which were negatively correlated with

unemployment rates at the significance level of 0.05 were investment rates, gross

national savings, political rights, press freedom, government effectiveness, and

economic freedom. However, the independent variable which had the highest

correlation with the dependent variable was investment rates.

90

Table 5.6 Correlation Matrix of the Variables Used to Test the Impact of Politico-Economic Institutions on Unemployment Rates


Variable Y2 X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 C1 C2 Y2 1.000 X1 -.790* 1.000 X2 -.745* .367 1.000 X3 -.268 .728* -.177 1.000 X4 -.141 -.412 .561* -.839* 1.000 X5 -.147 -.226 .589* -.669* .842* 1.000 X6 -.110 -.405 .527* -.789* .950* .801* 1.000 X7 -.556* .747* .228 .728* -.538* -.564* -.572* 1.000 X8 .109 .379 -.624* .772* -.954* -.872* -.933* .516* 1.000 X9 -.622* .153 .838* -.428 .736* .672* .588* -.033 -.70 1.000 X10 -.580* .192 .752* -.254 .687* .626* .611* .024 -.635* .825* 1.000 X11 -.360 -.055 .605* -.549* .793* .899* .688* -.365 -.775* .765* .702* 1.000 X12 -.137 -.323 .445 -.695* .890* .730* .899* -.488* -.874* .606* .685* .735* 1.000 X13 -.309 -.016 .610* -.426 .649* .529* .732* -.074 -.750* .459 .494* .489* .765* 1.000 X14 .058 .397 -.569* .742* -.937* -.829* -.895* .457* .973* -.670* -.662* -.764* -.880* -.742* 1.000 X15 -.675* .169 .882* -.369 .654* .552* .648* .049 -.683* .768* .589* .586* .535* .684* -.578* 1.000 C1 -.333 -.244 .686* -.725 .943* .770* .826* -.304 -.900* .861* .728* .823* .803* .630* -.891* .765* 1.000 C2 -.207 -.302 .660* -.738* .969* .860* .925* -.438* -.979* .762* .708* .816* .869* .724* -.976* .692* .939* 1.000

93

94

Table 5.7 shows the results of the multiple regression analysis of the significant

predictor variables and the dependent variable. Initially, the impact of institutional

factors on unemployment rates was analyzed by entering two groups of data–15

independent variables and 2 control variables–into the model using the Enter method.

However, the final model to emerge from the Enter analysis contained 7 predictor

variables. The SPSS printout regarding the impact of institutional factors on

unemployment rates is shown in Appendix H.


needed to be considered. According to the collinearity statistics shown in Table 5.7,

the tolerance value for each predictor variable was not less than .10; therefore, the

multicollinearity assumption was not violated. This is also supported by the VIF

value, which is well below the cut-off of 10.

The final model creates the appropriate equation which affects unemployment

rates at the significance level of 0.05. The equation had the multiple correlation

coefficient of .981 and could explain about 90.8 percent of the variance in annual

growth rates of GDP per capita, with a standard error of .260.


found that investment rates had the greatest impact on unemployment rates at the

significance level of 0.05. The regression coefficient (b) and the standardized

regression coefficient () were -.293 and -.638 respectively. The following variable is

economic freedom, with a regression coefficient (b) of -3.862 and a standardized

regression coefficient () of -.545. These results point out that investment rates and

economic freedom make a significant and unique contribution to the prediction of

unemployment rates. These findings are congruent with theoretical predictions and

the findings of previous research, which indicate that investment rates and economic

freedom are negatively correlated with unemployment rates. That is, higher

investment rates and more economic freedom lead to lower unemployment rates.

95


Unemployment Rates


Constant 40.483 5.991 6.757 .001* Investment rates -.293 .050 -.638 -5.814 .002* .635 1.575 Press freedom .025 .122 .053 .201 .849 .109 9.206 Government effectiveness -4.846 3.916 -.232 -1.237 .271 .219 4.571 Regulatory quality -.457 3.525 -.019 -.130 .902 .345 2.896 Control of corruption 1.465 1.432 .149 1.023 .353 .363 2.755 Economic freedom -3.862 1.316 -.545 -2.934 .032* .222 4.497 Population size -.014 .042 -.047 -.336 .751 .397 2.521


Note: *P<0.05

The equation which predicts the unemployment rates of East Asian and Latin

American countries can be shown in the form of raw scores as follows:

Y2 = 40.483 - .293X1 – 3.862X15

5.2.3 The Impact of Politico-Economic Institutions on the Percentage of

Population Falling Below the Poverty Line


factors on the percentage of the population falling below the poverty line are shown

in Table 5.8. It was found that the variable which was positively correlated with the

percentage of the population falling below the poverty line at the significance level of

0.05 was political rights. However, there were no variables which were negatively

correlated with the percentage of the population falling below the poverty line at the

significance level of 0.05. Therefore, the independent variable which had the highest

correlation with the dependent variable was political rights.

94

Table 5.8 Correlation Matrix of the Variables Used to Test the Impact of Politico-Economic Institutions on the Percentage of the

Population Falling below the Poverty Line


Variable Y3 X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 C1 C2 Y3 1.000 X1 .105 1.000 X2 .113 .367 1.000 X3 .243 .728*-.177 1.000 X4 -.296 -.412 .561* -.839* 1.000 X5 -.476 -.226 .589* -.669* .842* 1.000 X6 -.343 -.405 .527* -.789* .950* .801* 1.000 X7 .576* .747*.228 .728* -.538* -.564* -.572* 1.000 X8 .235 .379 -.624* .772* -.954* -.872* -.933* .516* 1.000 X9 -.030 .153 .838* -.428 .736* .672* .588* -.033 -.70 1.000 X10 -.190 .192 .752* -.254 .687* .626* .611* .024 -.635* .825* 1.000 X11 -.329 -.055 .605* -.549* .793* .899* .688* -.365 -.775* .765* .702* 1.000 X12 -.261 -.323 .445 -.695* .890* .730* .899* -.488* -.874* .606* .685* .735* 1.000 X13 .027 -.016 .610* -.426 .649* .529* .732* -.074 -.750* .459 .494* .489* .765* 1.000 X14 .253 .397 -.569* .742* -.937* -.829* -.895* .457* .973* -.670* -.662* -.764* -.880* -.742* 1.000 X15 .216 .169 .882* -.369 .654* .552* .648* .049 -.683* .768* .589* .586* .535* .684* -.578* 1.000 C1 -.078 -.244 .686* -.725 .943* .770* .826* -.304 -.900* .861* .728* .823* .803* .630* -.891* .765* 1.000 C2 -.279 -.302 .660* -.738* .969* .860* .925* -.438* -.979* .762* .708* .816* .869* .724* -.976* .692* .939* 1.000

96

97



institutional factors on the percentage of the population falling below the poverty line

was analyzed by entering two groups of data – 15 independent variables and 2 control

variables–into the model using the Enter method. However, the final model to emerge

from the Enter analysis contained 4 predictor variables. The SPSS printout regarding

the impact of institutional factors on the percentage of population falling below the

poverty line is shown in Appendix I.


needed to be considered. According to Table 5.9, the tolerance value for each

independent variable was not less than .10; therefore, the multicollinearity assumption

was not violated. This is also supported by the VIF value, which is well below the

cut-off of 10.

The final model creates the appropriate equation which affects the percentage

of the population falling below the poverty line at the significance level of 0.05. The

equation had a multiple correlation coefficient of .815 and could explain about 49.5

percent of the variance in the percentage of the population falling below the poverty

line, with a standard error of 4.967.


found that regulatory quality had the greatest impact on the percentage of population

falling below the poverty line at the significance level of 0.05. The regression

coefficient (b) and the standardized regression coefficient () were -136.637 and -

.708 respectively. The following variable is population size, with a regression

coefficient (b) of 1.643 and a standardized regression coefficient () of .662. These

indicate that regulatory quality and population size make a significant and unique

contribution to the prediction of the percentage of population falling below the

poverty line. These findings are in line with theoretical predictions and the findings of

preceding research, which indicate that regulatory quality is negatively correlated

with poverty, while population size is positively associated with poverty.

98


the Percentage of the Population Falling below the Poverty Line


Constant -201.609 90.018 -2.240 .055 Investment rates -1.217 .888 -.325 -1.371 .208 .746 1.340 Regulatory quality -136.637 49.766 -.708 -2.746 .025* .632 1.582 Economic freedom 25.323 15.700 .438 1.613 .145 .570 1.753 Population size 1.643 .616 .662 2.665 .029* .681 1.467

Std.Erest = 4.967 R = .815 ; R2 = .664 ; Adjusted R Square = .495 ; F = 3.945 ; p-value = .047*

Note: *P<0.05

The equation which predicts the percentage of the population falling below

the poverty line of East Asian and Latin American countries can be shown in the form

of raw scores as follows:

Y3 = 0 - 136.637X11 + 1.643C1

5.2.4 The Impact of Politico-Economic Institutions on Income Inequality


factors on income inequality are shown in Table 5.10. It was found that there were no

variables which were positively correlated with income inequality at the significance

level of 0.05. The variables which were negatively correlated with income inequality

at the significance level of 0.05 included gross national savings, adult literacy rates,

press freedom, government effectiveness, and regulatory quality. However, the

independent variable which had the highest correlation with the dependent variable

was government effectiveness.

97 Table 5.10 Correlation Matrix of the Variables Used to Test the Impact of Politico-Economic Institutions on Income Inequality

Variable Y3 X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 C1 C2 Y3 1.000 X1 -.417 1.000 X2 -.580* .367 1.000 X3 -.028 .728* -.177 1.000 X4 -.356 -.412 .561* -.839* 1.000 X5 -.565* -.226 .589* -.669* .842* 1.000 X6 -.403 -.405 .527* -.789* .950* .801* 1.000 X7 .067 .747* .228 .728* -.538* -.564* -.572* 1.000 X8 .417 .379 -.624* .772* -.954* -.872* -.933* .516* 1.000 X9 -.523* .153 .838* -.428 .736* .672* .588* -.033 -.70 1.000 X10 -.588* .192 .752* -.254 .687* .626* .611* .024 -.635* .825* 1.000 X11 -.539* -.055 .605* -.549* .793* .899* .688* -.365 -.775* .765* .702* 1.000 X12 -.397 -.323 .445 -.695* .890* .730* .899* -.488* -.874* .606* .685* .735* 1.000 X13 -.341 -.016 .610* -.426 .649* .529* .732* -.074 -.750* .459 .494* .489* .765* 1.000 X14 .401 .397 -.569* .742* -.937* -.829* -.895* .457* .973* -.670* -.662* -.764* -.880* -.742* 1.000 X15 -.366 .169 .882* -.369 .654* .552* .648* .049 -.683* .768* .589* .586* .535* .684* -.578* 1.000 C1 -.335 -.244 .686* -.725 .943* .770* .826* -.304 -.900* .861* .728* .823* .803* .630* -.891* .765* 1.000 C2 -.475 -.302 .660* -.738* .969* .860* .925* -.438* -.979* .762* .708* .816* .869* .724* -.976* .692* .939* 1.000


99

100



institutional factors on income inequality was analyzed by entering two groups of

data – 15 independent variables and 2 control variables – into the model using the

Enter method. However, the final model to emerge from the Enter analysis contained

4 predictor variables. The SPSS printout regarding the impact of institutional factors

on income inequality is shown in Appendix J.





cut-off of 10.

The final model creates the appropriate equation which affects income

inequality at the significance level of 0.05. The equation had the multiple correlation

coefficient of .821 and could explain about 51.2 percent of the variance in income

inequality, with a standard error of 2.116.


found that investment rates had the greatest impact on income inequality at the


regression coefficient () were -1.434 and -.885 respectively. The following variable

was political rights, with a regression coefficient (b) of 16.281 and a standardized

regression coefficient () of .738. These indicate that investment rates and political

rights make a significant and unique contribution to the prediction of income

inequality.

101


Income Inequality


Constant 2.187 43.997 .050 .962 Investment rates -1.434 .510 -.885 -2.812 .023* .411 2.435 Political rights 16.281 6.802 .738 2.393 .044* .427 2.340 Government effectiveness -32.571 18.756 -.440 -1.737 .121 .632 1.583 Economic freedom .162 6.266 .006 .026 .980 .650 1.539


Note: *P<0.05

The relationship between investment rates and income inequality is congruent

with theoretical predictions and previous studies’ findings, which indicate that

investment rates are negatively correlated with income inequality. In other words,

higher investment rates lead to a reduction in income inequality. Nonetheless, the

relationship between political rights and income inequality is in contrast with

theoretical assumptions and the findings of previous research. Many scholars contend

that political rights increase the opportunities for participation in politics, allowing the

poor to demand more equitable income distribution (Reuveny and Li, 2003). A

possible explanation for this study’s surprising finding is that in developing countries,

political rights may have a lagged effect on income inequality.

The equation which predicts income inequality of East Asian and Latin


Y4 = 0 - 1.434X1 + 16.281X7

CHAPTER 6

DATA ANALYSIS: POLITICO-ECONOMIC INSTITUTIONS

AND ECONOMIC PERFORMANCE IN EAST ASIA

In this chapter, the data of East Asian countries will be analyzed. The impact

of politico-economic institutions on various indicators of economic performance,


the population falling below the poverty line, and income inequality in East Asia, will

be presented.

6.1 The Impact of Politico-Economic Institutions on Annual Growth



factors on annual growth rates of GDP per capita are shown in Table 6.1. It was found

that there were no variables which were correlated with annual growth rates of GDP

per capita at the significance level of 0.05. However, the variables with the highest

correlation were protection of property rights and trade openness, followed by civil

liberties and trade openness.

Table 6.1 Correlation Matrix of the Variables Used to Test the Impact of Politico-Economic Institutions on Annual Growth Rates of GDP

per Capita

Variable Y1 X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 C1 C2 Y1 1.000 X1 .203 1.000 X2 .391 .333 1.000 X3 .171 .842* .323 1.000 X4 -.076 -.579* .162 -.661* 1.000 X5 .349 -.230 .402 -.397 .785* 1.000 X6 .025 -.549* .029 -.637* .938* .693* 1.000 X7 -.049 .598* .708* .641* -.235 -.158 -.298 1.000 X8 -.067 .520* -.201 .582* -.955* -.832* -.902* .204 1.000 X9 -.155 .040 .509* -.070 .439 .415 .299 .476 -.469 1.000 X10 .136 -.555* -.140 -.691* .808* .683* .829* -.490* -.855* .316 1.000 X11 .056 -.658* .072 -.700* .901* .828* .845* -.368 -.936* .376 .802* 1.000 X12 .088 -.161 .537* -.197 .754* .750* .621* .191 -.803* .682* .488* .729* 1.000 X13 .205 .067 .171 -.187 .595* .641* .624* .045 -.710* .547* .741 .541* .632* 1.000 X14 .038 .532* -.152 .569* -.948* -.789* -.884* .169 .982* -.542* -.858* -.919* -.792* -.719* 1.000 X15 .296 .242 .799* .242 .127 .334 .011 .610* -.248 .324 -.164 .176 .571* .194 -.167 1.000 C1 -.043 .346 .577* .470 -.352 -.121 -.597* .613* .281 .203 -.587* -.286 .140 -.310 .286 .672* 1.000 C2 -.044 -.559* .155 -.590* .961* .787* .900* -.186 -.985* .505* .849* .920* .761* .673* -.993* .157 -.301 1.000


103

104


significant predictor variables and the dependent variable. The results show that there

is no statistically significant relationship between the variables. The SPSS printout

regarding the impact of institutional factors on annual growth rates of GDP per capita

is shown in Appendix K.




Constant 8.167 16.687 .489 .645 Investment rates -.082 .169 -.289 -.483 .649 .193 5.168 Gross national savings .446 .271 .781 1.645 .161 .308 3.246 Population growth rates

.957 1.759 .339 .544 .610 .179 5.597

Adult literacy rates .073 .118 .350 .617 .565 .215 4.642 Press freedom -.344 .267 -.610 -1.289 .254 .310 3.228 Rule of law -3.707 4.419 -.538 -.839 .440 .169 5.930 Control of corruption 3.293 3.588 .458 .918 .401 .279 3.586 Protection of property rights

-.026 .061 -.188 -.436 .681 .374 2.674

Std.Erest = .770 R = .808 ; R2 = .653 ; Adjusted R Square = .098 ; F = 1.177 ; p-value = .448

6.2 The Impact of Politico-Economic Institutions on Unemployment Rates


factors on unemployment rates are shown in Table 6.3. It was found that the variable

which was positively correlated with unemployment rates at the significance level of

0.05 was government effectiveness. The variables which were negatively correlated

with unemployment rates at the significance level of 0.05 were investment rates,

gross national savings, population growth rates, political rights, and economic

freedom. However, the independent variable which had the highest correlation with

the dependent variable was investment rates.

104


Variable Y2 X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 C1 C2 Y2 1.000 X1 -.870* 1.000 X2 -.625* .333 1.000 X3 -.777* .842* .323 1.000 X4 .322 -.579* .162 -.661* 1.000 X5 .056 -.230 .402 -.397 .785* 1.000 X6 .328 -.549* .029 -.637* .938* .693* 1.000 X7 -.782* .598* .708* .641* -.235 -.158 -.298 1.000 X8 -.321 .520* -.201 .582* -.955* -.832* -.902* .204 1.000 X9 -.171 .040 .509* -.070 .439 .415 .299 .476 -.469 1.000 X10 .577* -.555* -.140 -.691* .808* .683* .829* -.490* -.855* .316 1.000 X11 .461 -.658* .072 -.700* .901* .828* .845* -.368 -.936* .376 .802* 1.000 X12 -.120 -.161 .537* -.197 .754* .750* .621* .191 -.803* .682* .488* .729* 1.000 X13 -.037 .067 .171 -.187 .595* .641* .624* .045 -.710* .547* .741 .541* .632* 1.000 X14 -.346 .532* -.152 .569* -.948* -.789* -.884* .169 .982* -.542* -.858* -.919* -.792* -.719* 1.000 X15 -.511* .242 .799* .242 .127 .334 .011 .610* -.248 .324 -.164 .176 .571* .194 -.167 1.000 C1 -.447 .346 .577* .470 -.352 -.121 -.597* .613* .281 .203 -.587* -.286 .140 -.310 .286 .672* 1.000 C2 .357 -.559* .155 -.590* .961* .787* .900* -.186 -.985* .505* .849* .920* .761* .673* -.993* .157 -.301 1.000


105

106



institutional factors on unemployment rates was analyzed by entering two groups of




on unemployment rates is shown in Appendix L.



independent variable was not less than .10; therefore, the multicollinearity

assumption was not violated. This is also supported by the VIF value, which is well

below the cut-off of 10.


rates at the significance level of 0.05. The equation had a multiple correlation

coefficient of .999 and could explain about 99.1 percent of the variance in annual





regression coefficient () were -.233 and -.947 respectively. The following variables

are political rights, with a regression coefficient (b) of -2.181 and a standardized

regression coefficient () of -.675, rule of law, with a regression coefficient (b) of -

3.425 and a standardized regression coefficient () of -.571, control of corruption,

with a regression coefficient (b) of 2.870 and a standardized regression coefficient ()

of .458, press freedom, with a regression coefficient (b) of .170 and a standardized

regression coefficient () of .346, population size, with a regression coefficient (b)

of .037 and a standardized regression coefficient () of .303, adult literacy rates, with

a regression coefficient (b) of -.039 and a standardized regression coefficient () of -

.217, and population growth rates, with a regression coefficient (b) of .530 and a

standardized regression coefficient () of .216. These indicate that investment rates,

107

political rights, rule of law, control of corruption, press freedom, population size,

adult literacy rates, and population growth rates make a significant, unique

contribution to the prediction of unemployment rates.

The relationship between each of these variables, except for control of

corruption and press freedom, and unemployment rates, was congruent with

theoretical predictions and previous studies’ findings. Instead of having negative

relationships with unemployment rates, control of corruption and press freedom were

positively correlated with unemployment rates. A possible explanation for these

findings is that in developing countries, control of corruption and press freedom may

have lagged effects on income inequality. In the long run, control of corruption can

lower unemployment rates. This is because decreased corruption increases economic

investment, resulting in reduced unemployment. Likewise, press freedom helps to

reduce unemployment since it promotes investment by creating a business-enabling

environment.


Unemployment Rates


Constant 5.933 1.431 4.146 .014* Investment rates -.233 .014 -.947 -16.070 .000* .193 5.169 Gross national savings .022 .034 .043 .639 .558 .145 6.899 Population growth rates .530 .157 .216 3.377 .028* .165 6.065 Adult literacy rates -.039 .013 -.217 -3.099 .036* .137 7.287 Political rights -2.181 .240 -.675 -9.080 .001* .122 8.225 Press freedom .170 .026 .346 6.663 .003* .249 4.015 Rule of law -3.425 .380 -.571 -9.002 .001* .167 5.988 Control of corruption 2.870 .329 .458 8.734 .001* .244 4.096 Population size .037 .005 .303 7.045 .002* .364 2.751


Note: *P<0.05

108

The equation which predicts unemployment rates of East Asian countries can

be shown in the form of raw scores as follows:

Y2 = 5.933 -.233X1 + .530X3 -.039X5 -2.181X7 + .170X9 -3.425X12 +

2.870X13 + .037C1

6.3 The Impact of Politico-Economic Institutions on the Percentage of the




in Table 6.5. It was found that the variables which were positively correlated with the


0.05 were political rights, economic freedom, and population size. However, there

were no variables that were negatively correlated with the percentage of the

population falling below the poverty line at the significance level of 0.05. The


was political rights.

106



Variable Y3 X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 C1 C2 Y3 1.000 X1 -.030 1.000 X2 .287 .362 1.000 X3 .326 .183 -.661* 1.000 X4 -.238 .334 -.411 .811* 1.000 X5 -.431 .080 -.640* .942* .742* 1.000 X6 -.223 .695* .658* -.241 -.231 -.286 1.000 X7 .526* -.173 .586* -.960* -.837* -.921* .237 1.000 X8 .174 .424 -.076 .467 .363 .352 .433 -.463 1.000 X9 -.084 -.187 -.692* .809* .692* .839* -.519* -.855* .316 1.000 X10 -.423 .029 -.704* .906* .833* .862* -.406 -.935* .365 .802* 1.000 X11 -.217 .466 -.209 .795* .731* .688* .126 -.816* .644* .497 .739* 1.000 X12 .026 .063 -.194 .616* .616* .673* -.016 -.711* .506* .752* .536* .602* 1.000 X13 -.301 -.147 .569* -.949* -.804* -.893* .185 .984* -.560* -.858* -.920* -.820* -.732* 1.000 X14 .171 .736* .291 .155 .241 .077 .594* -.230 .170 -.237 .145 .501* .062 -.171 1.000 X15 .599* .294 .179 -.079 .295 .056 -.132 -.037 -.290 .125 .026 -.011 .138 .055 .154 1.000 C1 .496* .178 -.590* .961* .814* .904* -.191 -.991* .539* .851* .925* .804* .697* -.995* .194 -.045 1.000 C2 -.177 .288 .854* -.588* -.287 -.544* .581* .550* -.021 -.575* -.690* -.232 .024 .549* .176 .158 -.566* 1.000


109

110



institutional factors on the percentage of population falling below the poverty line


variables – into the model using the Enter method. However, the final model to

emerge from the Enter analysis contained 8 predictor variables. The SPSS printout

regarding the impact of institutional factors on the percentage of population falling

below the poverty line is shown in Appendix M.





cut-off of 10.


of population falling below the poverty line at the significance level of 0.05. The

equation had the multiple correlation coefficient of .980 and coulf explain about 85.3




found that investment rates had the greatest impact on the percentage of the

population falling below the poverty line at the significance level of 0.05. The

regression coefficient (b) and the standardized regression coefficient () were -3.383

and -1.081 respectively. The following variables include economic freedom, with a

regression coefficient (b) of 77.664 and a standardized regression coefficient ()

of .813 and adult literacy rates, with a regression coefficient (b) of -2.069 and a

standardized regression coefficient () of -.804. These indicate that investment rates,

economic freedom, and adult literacy rates make a significant and unique contribution

to the prediction of the percentage of the population falling below the poverty line.

111


the Percentage of the Population Falling below the Poverty Line


Constant -256.370 112.678 -2.275 .107 Investment rates -3.383 .914 -1.018 -3.702 .034* .177 5.646 Gross national savings .683 1.538 .091 .444 .687 .316 3.164 Population growth rates 23.222 9.417 .699 2.466 .090 .167 5.996 Adult literacy rates -2.069 .549 -.804 -3.767 .033* .294 3.405 Rule of law -10.042 23.798 -.104 -.422 .701 .220 4.540 Control of corruption 25.625 19.446 .301 1.318 .279 .257 3.887 Economic freedom 77.664 19.848 .813 3.913 .030* .310 3.227 Population size -.235 .364 -.131 -.644 .565 .321 3.113


Note: *P<0.05

The relationship between investment rates and the percentage of the

population falling below the poverty line, and that between adult literacy rates and the

percentage of the population falling below the poverty line, correspond to theoretical

predictions and the findings of preceding research. However, the relationship between

economic freedom and the percentage of the population falling below the poverty line

was contradictory with theoretical assumptions and previous studies’ findings.

According to Conners (2011), modern theories of economic growth emphasize the

importance of economic freedom for promoting economic growth and achieving

reductions in poverty. The main reasons are that more economic freedom implies

increased opportunities for the poor to participate in economic activities and that

increased economic freedom reduces barriers that exist in less economically-free

countries, thus unleashing the entrepreneurial spirit of the poor. A potential

explanation for this study’s surprising finding is that economic freedom may have a

lagged effect on poverty reductions. In other words, it takes a long time for economic

freedom to affect the percentage of the population falling below the poverty line.


the poverty line of East Asian countries can be shown in the form of raw scores as

follows:

Y3 = 0 - 3.383X1 - 2.069X5 + 77.664X15

112

6.4 The Impact of Politico-Economic Institutions on Income Inequaltty


factors on income inequality are shown in Table 6.7. It was found that the variables

which were positively correlated with income inequality at the significance level of

0.05 included investment rates, civil liberties, and protection of property rights. The

variables that were negatively correlated with income inequality at the significance

level of 0.05 were life expectancy at birth, adult literacy rates, combined gross

enrollment, regulatory quality, rule of law, and trade openness. However, the


was protection of property rights.

110

Table 6.7 Correlation Matrix of the Variables Used to Test the Impact of Politico-Economic Institutions on Income Inequality

Variable Y4 X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 C1 C2 Y4 1.000 X1 .577* 1.000 X2 -.145 .333 1.000 X3 .321 .842* .323 1.000 X4 -.594* -.579* .162 -.661* 1.000 X5 -.561* -.230 .402 -.397 .785* 1.000 X6 -.553* -.549* .029 -.637* .938* .693* 1.000 X7 .136 .598* .708* .641* -.235 -.158 -.298 1.000 X8 .647* .520* -.201 .582* -.955* -.832* -.902* .204 1.000 X9 -.337 .040 .509* -.070 .439 .415 .299 .476 -.469 1.000 X10 -.391 -.555* -.140 -.691* .808* .683* .829* -.490* -.855* .316 1.000 X11 -.785* -.658* .072 -.700* .901* .828* .845* -.368 -.936* .376 .802* 1.000 X12 -.656* -.161 .537* -.197 .754* .750* .621* .191 -.803* .682* .488* .729* 1.000 X13 -.178 .067 .171 -.187 .595* .641* .624* .045 -.710* .547* .741 .541* .632* 1.000 X14 .663* .532* -.152 .569* -.948* -.789* -.884* .169 .982* -.542* -.858* -.919* -.792* -.719* 1.000 X15 -.287 .242 .799* .242 .127 .334 .011 .610* -.248 .324 -.164 .176 .571* .194 -.167 1.000 C1 .045 .346 .577* .470 -.352 -.121 -.597* .613* .281 .203 -.587* -.286 .140 -.310 .286 .672* 1.000 C2 -.657* -.559* .155 -.590* .961* .787* .900* -.186 -.985* .505* .849* .920* .761* .673* -.993* .157 -.301 1.000


113

114



institutional factors on income inequality was analyzed by entering two groups of




on income inequality is shown in Appendix N.





cut-off of 10.


inequality at the significance level of 0.05. The equation had a multiple correlation

coefficient of .995 and could explain about 95 percent of the variance in income

inequality, with a standard error of .336.


found that population growth rates had the greatest impact on income inequality at the


regression coefficient () were -6.449 and -1.306 respectively. The following

variables were investment rates, with a regression coefficient (b) of .524 and a

standardized regression coefficient () of 1.062, adult literacy rates, with a regression

coefficient (b) of -.384 and a standardized regression coefficient () of -1.006, gross

national savings, with a regression coefficient (b) of 1.070 and a standardized

regression coefficient () of .963, economic freedom, with a regression coefficient

(b) of -12.674 and a standardized regression coefficient () of -.894, control of

corruption, with a regression coefficient (b) of 10.560 and a standardized regression

coefficient () of .834, press freedom, with a regression coefficient (b) of -.946 and a

standardized regression coefficient () of -.824, and population size, with a

regression coefficient (b) of .173 and a standardized regression coefficient () of .652.

115

These empirical results indicate that population growth rates, investment rates, adult

literacy rates, gross national savings, economic freedom, control of corruption, press

freedom, and population size make a significant unique contribution to the prediction

of income inequality.


Income Inequality


Constant 95.302 17.015 5.601 .030* Investment rates .524 .080 1.062 6.574 .022* .176 5.691 Gross national savings 1.070 .180 .963 5.936 .027* .174 5.747 Population growth rates -6.449 .877 -1.306 -7.352 .018* .145 6.888 Adult literacy rates -.384 .059 -1.006 -6.560 .022* .195 5.127 Press freedom -.946 .170 -.824 -5.562 .031* .209 4.792 Rule of law .941 2.557 .066 .368 .748 .144 6.937 Control of corruption 10.560 2.039 .834 5.179 .035* .177 5.657 Economic freedom -12.674 2.442 -.894 -5.191 .035* .155 6.465 Population size .173 .038 .652 4.579 .045* .226 4.422


Note: *P<0.05

The effects of adult literacy rates, economic freedom, press freedom, and

population size on income inequality were congruent with theoretical predictions and

the findings of preceding research. However, the effects of population growth rates,

investment rates, gross national savings, and control of corruption on income

inequality were in contrast with theoretical assumptions and the findings of previous

research. A possible explanation regarding population growth rates and control of

corruption is that these two variables may have lagged effects on income inequality.

With respect to investment rates and gross national savings which were economic

variables, this study’s surprising findings may be explained by the problem of non-

linear relationships. According to Li (2010), there is the potential for non-linear

relationships between economic variables (i.e., the relationship between these

variables may not be proportional). Lee, Kim, and Newbold (2004: 2) suggest that it

116

should be assumed that ‘the relationship between many economic variables is better

characterized by a nonlinear specification’.

The equation which predicts income inequality of East Asian countries can be

shown in the form of raw scores as follows:

Y4 = 95.302 + .524X1 + 1.070X2 - 6.449X3 - .384X5 - .946X9 + 10.560X13

- 12.674X15 + .173C1

CHAPTER 7

DATA ANALYSIS: POLITICO-ECONOMIC INSTITUTIONS

AND ECONOMIC PERFORMANCE IN LATIN AMERICA

In this chapter, the data of Latin American countries will be analyzed. The

impact of politico-economic institutions on various indicators of economic performance,


the population falling below the poverty line, and income inequality in Latin America,

will be presented.

7.1 The Impact of Politico-Economic Institutions on Annual Growth

Rates of GDP Per Capita


factors on annual growth rates of GDP per capita are shown in Table 7.1. It was found

that the variables that were positively correlated with annual growth rates of GDP per

capita at the significance level of 0.05 included investment rates, gross national

savings, life expectancy at birth, press freedom, economic freedom, population size,

and trade openness. The variables that were negatively correlated with annual growth

rates of GDP per capita at the significance level of 0.05 were population growth rates,

civil liberties, and rule of law. However, the independent variable which had the

highest correlation with the dependent variable was press freedom. The variables with

the highest correlation were adult literacy rates and population size, followed by

population growth rates and life expectancy at birth.

Table 7.1 Correlation Matrix of the Variables Used to Test the Impact of Politico-Economic Institutions on Annual Growth Rates of

GDP per Capita

Variable Y1 X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 C1 C2 Y1 1.000 X1 .566* 1.000 X2 .766* .360 1.000 X3 -.479* -.126 -.712* 1.000 X4 .532* .135 .733* -.988* 1.000 X5 .309 -.072 .663* -.932* .928* 1.000 X6 .429 .082 .759* -.957* .952* .982* 1.000 X7 -.383 -.124 -.626* .845* -.872* -.916* -.909* 1.000 X8 -.518* .052 -.804* .908* -.927* -.928* -.935* .825* 1.000 X9 .860* .597* .733* -.670* .722* .535* .648* -.644* -.624* 1.000 X10 -.430 -.359 -.365 -.003 -.032 -.086 -.152 .221 .093 -.416 1.000 X11 -.240 .220 -.458 .834* -.793* -.795* -.767* .647* .808* -.358 -.280 1.000 X12 -.506* -.617* -.093 -.364 .347 .591* .448 -.459 -.389 -.274 .308 -.455 1.000 X13 .071 .250 .447 -.553* .451 .471 .540* -.254 -.405 .192 .200 -.469 .203 1.000 X14 -.441 .072 -.762* .887* -.910* -.877* -.877* .773* .956* -.531* -.014 .762* -.409 -.384 1.000 X15 .576* .312 .839* -.888* .875* .871* .941* -.829* -.850* .774* -.285 -.659* .184 .599* -.739* 1.000 C1 .567* .182 .763* -.983* .998* .913* .947* -.863* -.925* .752* -.058 -.765* .301 .458 -.910* .883* 1.000 C2 .739* .443 .915* -.867* .893* .792* .877* -.791* -.865* .854* -.309 -.571* .038 .473 -.829* .911* .917* 1.000


118

119

Table 7.2 shows the results of multiple regression analysis of the significant

predictor variables and the dependent variable. Initially, the impact of institutional

factors on annual growth rates of GDP per capita was analyzed by entering two

groups of data – 15 independent variables and 2 control variables – into the model

using the Enter method. However, the final model to emerge from the Enter analysis

contained 7 predictor variables. The SPSS printout regarding the impact of institutional

factors on annual growth rates of GDP per capita is shown in Appendix O.





cut-off of 10.

The final model creates the appropriate equation which affects annual growth

rates of GDP per capita at the significance level of 0.05. The equation had a multiple

correlation coefficient of .956 and could explain about 81.3 percent of the variance in

annual growth rates of GDP per capita, with a standard error of .572.


found that gross national savings had the greatest impact on annual growth rates of

GDP per capita at the significance level of 0.05. The regression coefficient (b) and the

standardized regression coefficient () were .332 and .530 respectively. The following

variable is control of corruption, with a regression coefficient (b) of -4.582 and a

standardized regression coefficient () of -.400. These indicate that gross national

savings and control of corruption make a significant, unique contribution to the

prediction of annual growth rates of GDP per capita.

120




Constant .704 6.611 .107 .919 Investment rates .166 .147 .208 1.127 .303 .424 2.357 Gross national savings .332 .128 .530 2.586 .041* .343 2.915 Press freedom .085 .078 .200 1.085 .319 .426 2.346 Government effectivenes .507 2.705 .032 .187 .858 .498 2.006 Regulatory quality -5.099 3.488 -.271 -1.462 .194 .419 2.389 Rule of law -6.046 3.248 -.356 -1.861 .112 .394 2.537 Control of corruption -4.582 1.816 -.400 -2.523 .045* .573 1.745


Note: *P<0.05

The relationship between gross national savings and annual growth rates of

GDP per capita was consistent with theoretical predictions and previous research

findings. Nevertheless, the relationship between control of corruption and annual

growth rates of GDP per capita was contradictory with theoretical predictions and the

findings of most previous studies. This study’s findings support the view that

corruption can be efficient for economic development. Supporters of this perspective

contend that corruption greases the wheel of development and through that fosters

economic growth. The general idea is that corruption facilitates beneficial trades that

would otherwise not have taken place. In doing so, it promotes efficiency by allowing

individuals in the private sector to circumvent regulatory and administrative

restrictions as well as pre-existing government failures of various sources (Aidt,

2009). According to this view, therefore, control of corruption may hinder economic

growth.

The equation which predicts annual growth rates of GDP per capita of Latin


Y1 = 0 + .322X2 – 4.582X13

121

7.2 The Impact of Politico-Economic Institutions on Unemployment Rates


factors on unemployment rates are shown in Table 7.3. It was found that the variable

that was positively correlated with unemployment rates at the significance level of

0.05 was rule of law. The variables that were negatively correlated with

unemployment rates at the significance level of 0.05 were investment rates, gross

national savings, press freedom, economic freedom, and trade openness. However,

the independent variable which had the highest correlation with the dependent

variable was investment rates.

119


Variable Y2 X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 C1 C2 Y2 1.000 X1 -.915* 1.000 X2 -.656* .360 1.000 X3 .399 -.126 -.712* 1.000 X4 -.413 .135 .733* -.988* 1.000 X5 -.233 -.072 .663* -.932* .928* 1.000 X6 -.396 .082 .759* -.957* .952* .982* 1.000 X7 .352 -.124 -.626* .845* -.872* -.916* -.909* 1.000 X8 .285 .052 -.804* .908* -.927* -.928* -.935* .825* 1.000 X9 -.787* .597* .733* -.670* .722* .535* .648* -.644* -.624* 1.000 X10 .415 -.359 -.365 -.003 -.032 -.086 -.152 .221 .093 -.416 1.000 X11 -.007 .220 -.458 .834* -.793* -.795* -.767* .647* .808* -.358 -.280 1.000 X12 .478* -.617* -.093 -.364 .347 .591* .448 -.459 -.389 -.274 .308 -.455 1.000 X13 -.426 .250 .447 -.553* .451 .471 .540* -.254 -.405 .192 .200 -.469 .203 1.000 X14 .234 .072 -.762* .887* -.910* -.877* -.877* .773* .956* -.531* -.014 .762* -.409 -.384 1.000 X15 -.612* .312 .839* -.888* .875* .871* .941* -.829* -.850* .774* -.285 -.659* .184 .599* -.739* 1.000 C1 -.463 .182 .763* -.983* .998* .913* .947* -.863* -.925* .752* -.058 -.765* .301 .458 -.910* .883* 1.000 C2 -.712* .443 .915* -.867* .893* .792* .877* -.791* -.865* .854* -.309 -.571* .038 .473 -.829* .911* .917* 1.000


122

123



institutional factors on unemployment rates was analyzed by entering two groups of




on unemployment rates is shown in Appendix P.





cut-off of 10.


rates at the significance level of 0.05. The equation had a multiple correlation

coefficient of .998 and could explain about 99 percent of the variance in annual





regression coefficient () were -.336 and -.461 respectively. The following variables

include press freedom, with a regression coefficient (b) of -.175 and a standardized

regression coefficient () of -.449, control of corruption, with a regression coefficient

(b) of -3.824 and a standardized regression coefficient () of -.336, regulatory quality,

with a regression coefficient (b) of -6.562 and a standardized regression coefficient

() of -.382, and civil liberties, with a regression coefficient (b) of 1.383 and a

standardized regression coefficient () of .254. These indicate that investment rates,

press freedom, control of corruption, regulatory quality, and civil liberties make a

significant and unique contribution to the prediction of unemployment rates.

The effects of all variables, except for civil liberties, on unemployment rates

are congruent with theoretical predictions and previous studies’ findings. However,

this study’s results reveal that there was a positive relationship between civil liberties

124

and unemployment rates. This finding is in contrast with theoretical assumptions and

the findings of previous research which indicate that higher degrees of civil liberties –

a component of democracy – are associated with a reduction in unemployment. This

is because civil liberties provide people with ‘the freedoms to develop views,

institutions and personal autonomy apart from the state’ (Ryan 1993, p.1). This

implies that civil liberties also provide the freedom to pursue employment

opportunities. A possible explanation for this study’s surprising finding is that civil

liberties may have a lagged effect on unemployment rates.


Unemployment Rates


Constant 25.524 1.843 13.846 .000* Investment rates -.336 .031 -.461 -10.757 .000* .419 2.385 Civil liberties 1.383 .410 .254 3.375 .015* .137 7.325 Press freedom -.175 .020 -.449 -8.644 .000* .286 3.501 Government effectiveness -.237 .554 -.016 -.429 .683 .529 1.889 Regulatory quality -6.562 .986 -.382 -6.656 .001* .233 4.283 Rule of law .847 .715 .055 1.185 .281 .362 2.759 Control of corruption -3.824 .367 -.366 -10.432 .000* .627 1.596


Note: *P<0.05

The equation which predicts unemployment rates of Latin American countries

can be shown in the form of raw scores as follows:

Y2 = 25.524 - .336X1 + 1.383X8 - .175X9 - 6.562X11 - 3.824X13

7.3 The Impact of Politico-Economic Institutions on the Percentage of the




in Table 7.5. It was found that the variables that were positively correlated with the

125


0.05 were political rights, civil liberties, government effectiveness, and protection of

property rights. The variables which were negatively correlated with the percentage

of the population falling below the poverty line at the significance level of 0.05

included gross national savings, life expectancy at birth, adult literacy rates,

combined gross enrollment, press freedom, economic freedom, population size, and

trade openness. However, the independent variable which had the highest correlation

with the dependent variable was political rights.

123



Variable Y3 X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 C1 C2 Y3 1.000 X1 -.287 1.000 X2 -.581* .360 1.000 X3 .428 -.126 -.712* 1.000 X4 -.482* .135 .733* -.988* 1.000 X5 -.543* -.072 .663* -.932* .928* 1.000 X6 -.565* .082 .759* -.957* .952* .982* 1.000 X7 .647* -.124 -.626* .845* -.872* -.916* -.909* 1.000 X8 .517* .052 -.804* .908* -.927* -.928* -.935* .825* 1.000 X9 -.486* .597* .733* -.670* .722* .535* .648* -.644* -.624* 1.000 X10 .491* -.359 -.365 -.003 -.032 -.086 -.152 .221 .093 -.416 1.000 X11 .195 .220 -.458 .834* -.793* -.795* -.767* .647* .808* -.358 -.280 1.000 X12 -.191 -.617* -.093 -.364 .347 .591* .448 -.459 -.389 -.274 .308 -.455 1.000 X13 -.103 .250 .447 -.553* .451 .471 .540* -.254 -.405 .192 .200 -.469 .203 1.000 X14 .485* .072 -.762* .887* -.910* -.877* -.877* .773* .956* -.531* -.014 .762* -.409 -.384 1.000 X15 -.578* .312 .839* -.888* .875* .871* .941* -.829* -.850* .774* -.285 -.659* .184 .599* -.739* 1.000 C1 -.500* .182 .763* -.983* .998* .913* .947* -.863* -.925* .752* -.058 -.765* .301 .458 -.910* .883* 1.000 C2 -.638* .443 .915* -.867* .893* .792* .877* -.791* -.865* .854* -.309 -.571* .038 .473 -.829* .911* .917* 1.000


126

127



institutional factors on the percentage of the population falling below the poverty line


variables – into the model employing the Enter method. However, the final model to

emerge from the Enter analysis contained 4 predictor variables. The SPSS printout

regarding the impact of institutional factors on the percentage of population falling

below the poverty line is shown in Appendix Q.





cut-off of 10.


of the population falling below the poverty line at the significance level of 0.05. The

equation had a multiple correlation coefficient of .812 and could explain about 50.7




found that press freedom had the greatest impact on the percentage of the population

falling below the poverty line at the significance level of 0.05. The regression

coefficient (b) and the standardized regression coefficient () were -1.040 and -.596

respectively. The following variable was rule of law, with a regression coefficient (b)

of -39.137 and a standardized regression coefficient () of -.562. These indicate that

press freedom and rule of law make a significant and unique contribution to the

prediction of the percentage of the population falling below the poverty line. These

findings are congruent with theoretical predictions and the findings of previous

research.

128

Table 7.6 Multiple Regression Analysis of the Predictor Variables and the



Constant 102.894 51.045 2.016 .075 Political rights -9.344 13.283 -.182 -.703 .500 .565 1.769 Press freedom -1.040 .437 -.596 -2.384 .041* .606 1.649 Government effectiveness 24.699 14.504 .379 1.703 .123 .766 1.306 Rule of law -39.137 16.129 -.562 -2.427 .038* .706 1.416


Note: *P<0.05


the poverty line of Latin American countries can be shown in the form of raw scores

as follows:

Y3 = 0 - 1.040X9 - 39.137X12

7.4 The Impact of Politico-Economic Institutions on Income Inequality


factors on income inequality are shown in Table 7.7. It was found that the variables

which were positively correlated with income inequality at the significance level of

0.05 included political rights and government effectiveness. The variables that were

negatively correlated with income inequality at the significance level of 0.05 were

gross national savings, economic freedom, and trade openness. However, the


was government effectiveness.

127

Table 7.7 Correlation Matrix of the Variables Used to Test the Impact of Politico-Economic Institutions on Income Inequality

Variable Y4 X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 C1 C2 Y4 1.000 X1 -.360 1.000 X2 -.538* .360 1.000 X3 .244 -.126 -.712* 1.000 X4 -.291 .135 .733* -.988* 1.000 X5 -.408 -.072 .663* -.932* .928* 1.000 X6 -.448 .082 .759* -.957* .952* .982* 1.000 X7 .574* -.124 -.626* .845* -.872* -.916* -.909* 1.000 X8 .379 .052 -.804* .908* -.927* -.928* -.935* .825* 1.000 X9 -.388 .597* .733* -.670* .722* .535* .648* -.644* -.624* 1.000 X10 .603* -.359 -.365 -.003 -.032 -.086 -.152 .221 .093 -.416 1.000 X11 -.042 .220 -.458 .834* -.793* -.795* -.767* .647* .808* -.358 -.280 1.000 X12 -.106 -.617* -.093 -.364 .347 .591* .448 -.459 -.389 -.274 .308 -.455 1.000 X13 -.110 .250 .447 -.553* .451 .471 .540* -.254 -.405 .192 .200 -.469 .203 1.000 X14 .351 .072 -.762* .887* -.910* -.877* -.877* .773* .956* -.531* -.014 .762* -.409 -.384 1.000 X15 -.487* .312 .839* -.888* .875* .871* .941* -.829* -.850* .774* -.285 -.659* .184 .599* -.739* 1.000 C1 -.318 .182 .763* -.983* .998* .913* .947* -.863* -.925* .752* -.058 -.765* .301 .458 -.910* .883* 1.000 C2 -.544* .443 .915* -.867* .893* .792* .877* -.791* -.865* .854* -.309 -.571* .038 .473 -.829* .911* .917* 1.000


129

130


significant predictor variables and the dependent variable. The impact of institutional

factors on income inequality was analyzed by entering two groups of data – 15

independent variables and 2 control variables – into the model using the Enter method.

However, the final model to emerge from the Enter analysis contained 5 predictor

variables. The SPSS printout regarding the impact of institutional factors on income

inequality is shown in Appendix R.





cut-off of 10.


inequality at the significance level of 0.05. The equation had a multiple correlation

coefficient of .846 and could explain about 53.8 percent of the variance in income

inequality, with a standard error of 2.430.


found that regulatory quality had the greatest impact on income inequality at the


regression coefficient () were -56.153 and -1.106 respectively. This indicates that

regulatory quality makes a significant and unique contribution to the prediction of

income inequality. This finding is consistent with theoretical predictions and previous

studies’ findings.

131


Income Inequality


Constant -15.035 20.827 -.722 .491 Political rights 16.965 10.797 .502 1.571 .155 .349 2.869 Civil liberties 11.468 6.377 .710 1.798 .110 .228 4.393 Regulatory quality -56.153 15.577 -1.106 -3.605 .007* .378 2.648 Rule of law -4.767 9.923 -.104 -.480 .644 .760 1.316 Control of corruption -10.451 7.211 -.338 -1.449 .185 .654 1.529


Note: *P<0.05

The equation which predicts income inequality of Latin American countries

can be shown in the form of raw scores as follows:

Y4 = 0 - 56.153X11

CHAPTER 8

DISCUSSIONS OF RESULTS

This chapter presents the discussions of the results regarding the impact of

politico-economic institutions on economic performance in East Asia and Latin

America. Subsequently, the results with regard to the impact of politico-economic

institutions on economic performance in East Asia and that in Latin America are

compared and discussed.

8.1 Discussions of the Impact of Politico-Economic Institutions on

Economic Performance in East Asia and Latin America



In the full sample, the variable which has the greatest impact on annual growth

rates of GDP per capita is gross national savings which are a growth-related factor.

The institutional factor which has a significant impact on annual growth rates of GDP

per capita in East Asia and Latin America is rule of law. However, the relationship is

in a negative way. That is, the greater the regulatory quality, the lower are the annual

growth rates of GDP per capita. This finding is contradictory with the commonly held

belief that countries adhering to law can achieve high economic growth (Higbee and

Schmid, 2004). According to Haggard and Tiede (2010), rule of law can bring about

economic growth in four ways: the mitigation of violence; protection of property

rights; institutional checks on government; and control of biases that distort public

policy, including corruption. However, it is necessary to be aware that empirical

evidence on the impact of institutions on economic growth is sensitive to sample

selection and to the estimation technique used (Butkiewicz and Yanikkaya, 2004).

133

Figure 8.1 shows the impact of significant variables on annual growth rates of GDP

per capita.

Figure 8.1 The Impact of Significant Variables on Annual Growth Rates of GDP per

Capita in East Asia and Latin America

8.1.2 The Impact of Politico-Economic Institutions on Unemployment

Rates

The variable which has the greatest impact on unemployment rates in the full

sample is investment rates, which are a growth-related factor. The institutional factor

which has a significant influence on unemployment rates in East Asia and Latin

America is economic freedom. That is, the greater the economic freedom, the lower

are the unemployment rates. This finding is congruent with the study conducted by

Feldmann (2007) which finds that economic freedom tends to substantially reduce

unemployment, especially among women and young people. According to the

neoclassical theory, the government should not intervene with the market. That is, the

optimal economic structure is a free market. Therefore, the argument that a neoclassical

economist would make about unemployment is that it is often caused by the

government that makes it difficult for employers to fire employees. This causes

employers to hesitate employing new workers, thus resulting to high unemployment

rates. The neoclassical theory is well supported by the finding of this study regarding

the relationship between economic freedom and unemployment rates. Therefore, due

to the large costs of unemployment, governments in developing countries should

consider increasing economic freedom as a means of reducing unemployment. The

impact of the significant variables on unemployment rates is illustrated in Figure 8.2.

-

+ Gross national savings

Rule of law


134

Figure 8.2 The Impact of the Significant Variables on Unemployment Rates in



the Population Falling Below the Poverty Line

The findings of this study show that regulatory quality has a significant and

negative impact on the percentage of the population falling below the poverty line; in

other words, regulatory quality can reduce poverty. This is in line with the study

conducted by Davis (2011) which suggests that policies and efforts aimed at

improving regulatory quality would have a significant impact on poverty reduction

efforts. This is because regulatory quality can create macroeconomic stability and

foster economic growth, thus reducing poverty (Birner, 2009). Another variable

which affects the percentage of population falling below the poverty line in the full

sample is population size. That is, the larger the population size, the higher is the

percentage of the population falling below the poverty line. Figure 8.3 displays the

impact of the significant variables on the percentage of the population falling below

the poverty line.

Figure 8.3 The Impact of the Significant Variables on the Percentage of the

Population Falling below the Poverty Line in East Asia and Latin

America

-

- Investment rates

Economic freedom

Unemployment rates

+

- Regulatory quality

Population size

The percentage of population falling

below the poverty line

135


The variable which has the greatest impact on income inequality is investment

rates, which are a growth-related factor. The finding is that the higher investment

rates, the lower is income inequality. The institutional factor which has a significant


However, the relationship is in a positive way. This does not support the findings of

previous studies. Some of those studies found a negative linear relationship between

democracy and income inequality (e.g. Cutright, 1967, Muller, 1988), whereas others

(e.g. Simpson, 1990) have argued that there is curvilinear relationship between

political democracy and income inequality. However, it is necessary to be aware that

income inequality is affected by numerous factors, including economic growth and

the overall development level of a country, macroeconomic factors, demographic

factors, political factors, as well as historical, cultural, and natural factors (Kaasa,

2003). Figure 8.4 shows the impact of the significant variables on income inequality.

Figure 8.4 The Impact of the Significant Variables on Income Inequality in East

Asia and Latin America

8.2 Comparisons of the Impact of Politico-Economic Institutions on

Economic Performance in East Asia and That in Latin America

This section provides comparisons of the impact of politico-economic

institutions on economic performance in East Asia and that in Latin America. The

comparisons between the two regions can provide meaningful insight into the

differences in the factors which affect economic performance across regions. The

comparisons of East Asia and Latin America are shown in Table 8.1.

+

- Investment rates

Political rights

Income inequality

136

Table 8.1 Comparisons of East Asia and Latin America

Annual growth

rates of GDP

Per capita

Unemployment

Rates

Percentage of

Population

Falling Below

the Poverty Line

Income

Inequality

East Asia -Investment rates

(-)

-Population

growth rates (+)

-Adult literacy

rates (-)

-Political rights (-)

-Investment rates

(-)

-Adult literacy

rates (-)

-Economic

freedom (+)

-Investment rates

(+)

-Gross national

savings (+)

-Population growth

rates (-)

-Adult literacy Rates (-)

-Press freedom (+)

-Rule of law (-)

-Control of

corruption (+)

-Population size (+)

-Press freedom (-)

-Control of

corruption (+)

-Economic

freedom (-)

-Population size (+)

Latin

America

-Gross national

savings (+)

-Control of

corruption (-)

-Investment rates (-)

-Civil liberties (+)

-Press freedom (-)

-Regulatory

quality (-)

-Control of

corruption (-)

-Press freedom (-)

-Rule of law (-)

-Regulatory quality

(-)


Rates of GDP Per capita

In the model of East Asia, there is no variable that has a relationship with

annual growth rates of GDP per capita. In other words, none of the independent

variables of East Asia have an effect on the annual growth rates of GDP per capita.

The model of Latin America is Y1 = 0 + .322X2 – 4.582X13. The factors that

affect annual growth rates of GDP per capita are gross national savings and control of

137

corruption. When gross national savings increase by 1 percent, annual growth rates of

GDP per capita will increase by .322 percent. However, when control of corruption

increases by 1 percent, annual growth rates of GDP per capita will reduce by 4.582

percent. The equation has a cutting point along the Y axis at 0. Figure 8.5 illustrates

the impact of the significant variables on annual growth rates of GDP per capita in

Latin America.

Figure 8.5 The Impact of the Significant Variables on Annual Growth Rates of GDP

Per capita in Latin America

This finding supports the view that corruption has the potential to foster

economic growth. The most prominent advocate of this view is Leff (1964), who

wrote a provocative article called “Economic Development Through Bureaucratic

Corruption.” The general idea of this viewpoint is that ‘corruption facilitates

beneficial trades that would otherwise not have take place. In doing so, it promotes

efficiency by allowing individuals in the private sector to correct or circumvent pre-

existing government failures of various sorts’ (Aidt, 2009: 2). This view has been

supported by many empirical studies (e.g. Egger and Winner, 2005, Lui, 1985).

However, the arguments in favor of the positive effect of corruption depend heavily

on the static and partial perspectives of the context in which corruption is taking place.

Actually, the effects of corruption are multifaceted and not as straightforward as early

authors portrayed (Hodge, Shankar, Rao and Duhs, 2009). Therefore, although

corruption can promote economic growth in Latin America in the short run, ultimately

it is a significant hindrance for genuine wealth and sustainable development.

-

+ Gross national savings

Control of corruption


138

8.2.2 The Impact of Politico-Economic Institutions on Unemployment Rates

The model of East Asia is Y2 = 5.933 -.233X1 + .530X3 -.039X5 -2.181X7

+ .170X9 -3.425X12 + 2.870X13 + .037C1. The factors which affect unemployment

rates include investment rates, population growth rates, adult literacy rates, political

rights, press freedom, rule of law, control of corruption, and population size. When

investment rates increase by 1 percent, unemployment rates will reduce by .233

percent. When population growth rates rise by 1 percent, unemployment rates will

grow by .530 percent. When adult literacy rates increase by 1 percent, unemployment

rates will fall by .039 percent. When political rights rise by 1 percent, unemployment

rates will decrease by 2.181 percent. When press freedom rises by 1 percent,

unemployment rates will grow by .170 percent. When rule of law increases by 1 percent,

unemployment rates will reduce by 3.425 percent. When control of corruption

increases by 1 percent, unemployment rates will rise by 2.870 percent. Moreover,

when population size increases by 1 percent, unemployment rates will grow by .037

percent. The equation has a cutting point along the Y axis at 5.933. The impact of the

significant variables on unemployment rates in East Asia is shown in Figure 8.6.


East Asia

+

-

+

+

-

-

-

+

Investment rates

Rule of law

Unemployment rates

Political rights


Press freedom

Population size

Adult literacy rates

Population growth rates

139

The model of Latin America is Y2 = 25.524 - .336X1 + 1.383X8 - .175X9 -

6.562X11 - 3.824X13. The factors affecting unemployment rates are investment rates,

civil liberties, press freedom, regulatory quality, and control of corruption. When

investment rates increase by 1 percent, unemployment rates will reduce by .336

percent. When civil liberties rise by 1 percent, unemployment rates will increase by

1.383 percent. When press freedom increases by 1 percent, unemployment rates will

decrease by .175 percent. When regulatory quality increases by 1 percent, unemployment

rates will reduce by 6.562 percent. When control of corruption rises by 1 percent,

unemployment rates will decrease by 3.824 percent. The equation has a cutting point

along the Y axis at 25.524. Figure 8.7 displays the impact of the significant variables

on unemployment rates in Latin America.


Latin America

According to the models of these two regions, the factors which affect

unemployment rates are partially different. The institutional factors which have an

impact on unemployment rates in East Asia are political rights, press freedom, rule of

law, and control of corruption. Political rights and rule of law have the expected


an unexpected impact. The findings suggest that enjoyment of political rights is

crucial to the enjoyment of employment opportunities. This is because when

individuals have the rights to participate in the political life of the state without

discrimination, they can push forward their demands regarding employment

+

-

-

-

-

Investment rates

Control of corruption Unemployment rates

Press freedom

Regulatory quality

Civil liberties

140

opportunities. Therefore, East Asian countries should implement political reforms to

increase citizens’ political rights as a means of reducing unemployment rates. The

findings of this study also suggest that the stronger the rule of law, the lower are

unemployment rates. This is because under the rule of law, the government is

prevented from stultifying individual efforts by ad hoc action. Thus, individuals are

free to pursue their personal ends and desires, including employment opportunities.

This implies that strengthening the rule of law is an effective means of lowering

unemployment rates in East Asian countries. Instead of reducing unemployment rates,

press freedom and control of corruption are found to increase unemployment rates in

East Asia. This is contradictory to the literature and previous empirical studies.

According to Bui (2011), serving as a watchdog on the government, a free press can

potentially lead to more stability for the country. This will in turn attract more long-

term investment for the country’s economy, thus reducing unemployment. As for

corruption, its prevalence influences the economic environment through the creation

of significantly-higher levels of risk and uncertainty in economic transactions

(Voskanyan, 2000). This will discourage long-term investment, thus increasing

unemployment. Hence, control of corruption should help reduce unemployment rates.

As for Latin American countries, the institutional factors which affect

unemployment rates include civil liberties, press freedom, regulatory quality, and

control of corruption. Civil liberties have an unexpected impact on unemployment

rates, whereas press freedom, regulatory quality, and control of corruption have the

expected impact. According to Feldmann (2010), strict limits on civil liberties tend to

affect economic performance adversely, possibly increasing unemployment. In other

words, a decrease in civil liberties tends to lead to an increase in unemployment rates.

However, this does not apply to the Latin American case. Press freedom and control

of corruption reduce unemployment rates in Latin America for the reasons explained

above. Therefore, Latin American countries should implement measures to increase

press freedom and corruption control as a way to lower unemployment rates. As for

regulatory quality, there is strong evidence that shortcomings in the level of

regulatory quality prevent an economy from reaching its fullest potential, including

full employment (Ernst, 2007). The findings imply that regulatory reform is a means

for reducing unemployment in Latin American countries. According to Ernst (2007: ii),

regulatory reform ‘seeks to build and reinforce market mechanisms, creating market

141

architectures that support rather than hinder the competitive drive of private

enterprise’.


the Population Falling Below the Poverty Line

The model of East Asia is Y3 = 0 - 3.383X1 - 2.069X5 + 77.664X15. The

factors which impact the percentage of the population falling below the poverty line

are investment rates, adult literacy rates, and economic freedom. When investment

rates rise by 1 percent, the percentage of the population falling below the poverty line

will decrease by 3.383 percent. When adult literacy rates increase by 1 percent, the

percentage of the population falling below the poverty line will drop by 2.069 percent.

When economic freedom increases by 1 percent, the percentage of the population

falling below the poverty line will increase by 77.664 percent. The equation has a

cutting point along the Y axis at 0. Figure 8.8 shows the impact of the significant

variables on poverty in East Asia.


Population Falling Below the Poverty Line in East Asia

The model of Latin America is Y3 = 0 - 1.040X9 - 39.137X12. The factors

which affect the percentage of the population falling below the poverty line are press

freedom and rule of law. When press freedom rises by 1 percent, the percentage of

the population falling below the poverty line will decrease by 1.040 percent. When

rule of law increases by 1 percent, the percentage of the population falling below the

poverty line will drop by 39.137 percent. The equation has a cutting point along the Y

axis at 0. The impact of the significant variables on poverty in Latin America is

illustrated in Figure 8.9.

-

+

- Investment rates


The percentage of population falling

below the poverty line Economic freedom

142


Population Falling Below the Poverty Line in Latin America

According to the models of East Asia and Latin America, the factors which

affect the percentage of the population falling below the poverty line in these two

regions are totally different. The institutional factor which affects the percentage of

the population falling below the poverty line in East Asia is economic freedom. This

finding of this study is contrary to theory and empirical studies regarding the link

between economic freedom and poverty, which suggests that economic freedom is a

key to poverty reduction. According to Hasan, Quibria and Kim (2003: 22), ‘openness to

trade, an important indicator of economic freedom, is robustly associated with

poverty reduction’. Norton and Gwartney (2008) also find that the level of economic

freedom exerts a strong negative impact on the poverty rate. The most prominent way

in which economic freedom may moderate poverty levels is through sustained

economic growth throughout a country. The main reasons for economic growth in

free market economies are that there is more exchange between buyers and sellers

and that there is the high efficiency of price mechanism in allocating resources

(Salleh, 2009). Nevertheless, the finding of this study indicates that economic

freedom does not guarantee poverty reduction. This may be because the poor in free

market systems cannot reap enormous benefits of economic freedom. In other words,

benefits of growth caused by economic freedom have not trickled down to the poor.

In Latin America, the findings of this study suggest that an increase in press

freedom and the rule of law reduces the percentage of population falling below the

poverty line. According to studies conducted by the World Bank, the higher the level

of press freedom in countries, the greater is the control over corruption and thus the

greater focus of scarce resources on priority development issues. This has a positive

influence on poverty reduction. Therefore, a free press is a crucial key in the

-

- Press freedom The percentage of population falling

below the poverty line Rule of law

143

reduction of poverty. The rule of law can also help reduce poverty since it plays an

important role in creating the enabling conditions for the empowerment of the poor.

The findings imply that in order to reduce poverty, developing countries in Latin

America should focus on increasing press freedom and improving the rule of law.

Nonetheless, it is essential to bear in mind that there are many other causes of poverty.

The primary factors that lead to poverty are overpopulation, the unequal distribution

of resources in the world economy, inability to meet high standards of living and

costs of living, inadequate education and employment opportunities, environmental

degradation, certain economic and demographic trends, and welfare incentives. These

factors need to be taken into consideration when developing countries attempt to

reduce poverty.


The model of East Asia is Y4 = 95.302 + .524X1 + 1.070X2 - 6.449X3 -

.384X5 -.946X9 + 10.560X13 - 12.674X15 + .173C1. The factors which impact

income inequality are investment rates, gross national savings, population growth

rates, adult literacy rates, press freedom, control of corruption, economic freedom,

and population size. When investment rates increase by 1 percent, income inequality

will rise by .524 percent. When gross national savings increase by 1 percent, income

inequality will increase by 1.070 percent. When population growth rates rise by 1

percent, income inequality will reduce by 6.449 percent. When adult literacy rates

increase by 1 percent, income inequality will decrease by .384 percent. When press

freedom increases by 1 percent, income inequality will reduce by .946 percent. When

control of corruption rises by 1 percent, income inequality will increase by 10.560

percent. When economic freedom increases by 1 percent, income inequality will drop

by 12.674 percent. When population size rises by 1 percent, income inequality will

increase by .173 percent. The equation has a cutting point along the Y axis at 95.302.

Figure 8.10 portrays the impact of the significant variables on income inequality in

East Asia.

144

Figure 8.10 The Impact of the Significant Variables on Income Inequality in

East Asia

The model of Latin America is Y4 = 0 - 56.153X11. The factor which affects

income inequality is regulatory quality. When regulatory quality increases by 1

percent, income inequality will decrease by 56.153 percent. The equation has a

cutting point along the Y axis at 0. Figure 8.11 shows the impact of the significant

variable on income inequality in Latin America.

Figure 8.11 The Impact of the Significant Variable on Income Inequality in

Latin America

In accordance with the models of these two regions, the institutional factors

which affect income inequality in both regions are totally different. In East Asia,

+

-

+

-

-

+

-

+

Population growth rates


Income inequality

Investment rates

Gross national savings

Economic freedom


Press freedom

Population size

- Regulatory quality Income inequality

145

press freedom and economic freedom have the expected impact on income inequality,

while control of corruption has the unexpected impact. The findings of this study are

in line with the study by UNESCO (2008) which found that there is a negative

relationship between press freedom and income inequality. This is because a free

press can precisely demonstrate the inequalities suffered by a population. The more

those inequalities are exposed, the more the people will become aware of them and be

able to proclaim their rights and demand access to greater freedom. With regard to

economic freedom, most authors agree on the negative relationship between

economic freedom and income inequality. According to Scully (2002), economic

freedom reduces income inequality by increasing the share of market income going to

the lowest income quintile and lowering the share going to the highest income

quintile. As for control of corruption, it is expected to have a negative relationship

with income inequality. This is because it has been found that corruption increases

income inequality significantly (Gupta, Davoodi and Alonso-Terme, 2002). According

to Dincer and Gunalp (2005), corruption increases income inequality because the

benefits from corruption are likely to accrue to those that belong mostly to high-

income groups. Moreover, corruption distorts the redistributive role of government.

Therefore, control of corruption should reduce income inequality. However, this does

not apply to the Latin American case. The findings of this study imply that Latin

American countries should adopt policy to enhance press freedom and economic

freedom as a means of reducing income inequality.

In Latin America, the institutional factor which affects income inequality in a

negative way is regulatory quality. This indicates that when regulatory quality is

improved, income distribution will be more equal. This finding is consistent with

previous studies (Edinaldo and Ramesh, 2010; Saima and Rashida, 2006; Zhuang, de

Dios and Lagman-Martin, 2010) which find that low regulatory quality is associated

with persistently high or worsening inequality. This is because when the poor are not

given protection by a strong regulatory system, their ability to extract rents is inferior

to that of the rich (Chong and Gradstein, 2007). The findings indicate that East Asian

countries should implement regulatory reform since regulatory quality can help lower

income inequality.

CHAPTER 9

CONCLUSIONS

The objective of this study was three-fold: to study political and economic

institutions in East Asia and Latin America; to examine the relationship between

politico-economic institutions and economic performance in selected East Asian and

Latin American countries over the period of 1990-2009; and to help improve policy

decisions with respect to institutional building and economic efficiency in developing

countries. This study employs a time-series, cross-country analysis.

9.1 Major Findings

According to the descriptive statistics, East Asia is ahead of Latin America in

terms of economic performance gauged by economic growth, unemployment, poverty,

and income inequality. As for fundamental socio-economic factors, East Asia has

higher investment rates and gross national savings than Latin America. The

population growth rates in East Asia are lower than those in Latin America. In

addition, East Asian countries perform better than Latin American countries in terms

of life expectancy at birth and adult literacy rates. However, the combined gross

enrollment ratio in Latin America is slightly higher than that in East Asia.

With respect to political institutions, East Asian countries have higher mean

scores than Latin American countries in all measures, including political rights, civil

liberties, press freedom, government effectiveness, regulatory quality, rule of law,

and control of corruption. On the other hand, Latin America has higher mean scores

than East Asia in both protection of property rights and economic freedom. These

findings imply that the political institutions in East Asia are more effective than those

in Latin America, while the economic institutions in East Asia are less effective than

those in Latin America.

147

In the full sample, the institutional factor which has a significant impact on

annual growth rates of GDP per capita is rule of law. However, the relationship is in

an unexpected way. That is, the greater is the regulatory quality, the lower are the

annual growth rates of GDP per capita. The institutional factor which has a significant

influence on unemployment rates in East Asia and Latin America is economic

freedom; that is, the more economic freedom, the lower unemployment rates. It was

also found that that regulatory quality has a significant and negative impact on the

percentage of the population falling below the poverty line. In other words, regulatory

quality can reduce poverty. In addition, the institutional factor which has a significant


However, the relationship is in an unexpected way. That is, the more political rights,

the higher income inequality.

In the case of East Asia, there is no institutional factor that has a significant

impact on annual growth rates of GDP per capita. The institutional factors which have

an impact on unemployment rates in East Asia are political rights, press freedom, rule

of law, and control of corruption. Political rights and rule of law have an expected


the unexpected impact. The institutional factor which affects the percentage of the

population falling below the poverty line in East Asia is economic freedom.

Nevertheless, the relationship is in an unexpected way. The finding of this study

indicates that economic freedom does not guarantee poverty reduction. Moreover,

press freedom and economic freedom have an expected impact on income inequality

in East Asia, while control of corruption has an unexpected impact.

As for Latin America, the institutional factor that affects annual growth rates

of GDP per capita in an unexpected way is control of corruption. It was found that the

greater the control of corruption, the lower were the annual growth rates of GDP per

capita. The institutional factors which impact unemployment rates in Latin America

include civil liberties, press freedom, regulatory quality, and control of corruption.

Civil liberties have an unexpected impact on unemployment rates, whereas press

freedom, regulatory quality, and control of corruption have an expected impact. With

regard to poverty, the findings of this study suggest that an increase in press freedom

and the rule of law reduces the percentage of the population falling below the poverty

148

line. Furthermore, the institutional factor which affects income inequality in Latin

America is regulatory quality. The finding indicates that when regulatory quality is

improved, income distribution will be more equal.

9.2 Policy Implications

The institutional framework is vital for sustainable economic development,

along with other policy factors, such as government policies to allocate resources for

alleviating poverty and reducing economic inequality. The results of this study

suggest that a broad strategy that includes improvements in politico-economic

institutions is essential for economic development. Policies aimed at enhancing

economic performance of developing countries should first consider improving

institutions as a pre-requisite for economic development.

Although it cannot be known exactly how to transform ill economies into

successful ones, the findings of this study provide some implications. The following

implications serve as a path to creating policies that could lead to sustainable

economic development. These implications should, therefore, be carefully adopted by

policymakers and policy implementers in the economic development field.

First, politico-economic institutions vary substantially across regions,

accounting for differences in development outcomes. As put forward by North (1990),

institutions vary widely in their impact on economic performance; some economies

develop institutions that produce economic growth and development, whereas others

develop institutions that produce stagnation. The results of this study show that while

East Asian countries should improve both political and economic institutions to

achieve better economic performance, Latin American countries should place strong

emphasis on developing political institutions to fix their economies. The aspects of

institutions that should be improved are also different across the two regions, as

specified in the previous chapter. Since institutional differences are important for

understanding cross-country divergence in economic outcomes, policymakers in

developing countries should focus on considering how politico-economic institutions

in their countries affect economic performance. This will enable them to formulate

concrete and effective policies to increase economic growth, reduce unemployment,

reduce poverty, and lower income inequality.

149

Secondly, political institutions are not the sole influence on economic

performance. Equally as important are economic institutions, particularly property

rights. The results of this study illustrate that one of the main reasons behind Latin

America’s poor economic record compared with East Asia’s is their weak protection

and enforcement of property rights. According to North (2006: 7), the most important

economic institution is ‘well-defined and specified property rights that provide

incentives for people to be productive’. A political system that puts in place a legal

system and a judiciary that will enforce contracts and agreements are also essential.

Therefore, it is imperative for most Latin American countries as well as some East

Asian countries to establish an institutional framework that puts in place efficient

property rights.

Finally, East Asian countries and Latin American countries are very different

in their political institutions, economic institutions, as well as fundamental socio-

economic factors as revealed by this study. Apart from differences across regions,

every country also has its own distinctively historical, religious, and cultural

background. Therefore, a blueprint of institutional development that fits all countries

does not exist (Bloch and Tang, 2004). It is rather possible that the institutions of one

country will function in other ways and with other consequences. As pointed out by

Boyd (2006), the process of imposing uniform institutional blueprints on developing

countries has often produced disappointing results. This implies that there is no fixed

model for institutional design that will work across different local contexts, and that

institutional analysis should incorporate an understanding of local context, including

constraints and opportunities. Therefore, to improve their economic performance,

Latin American countries cannot simply adopt institutions like those of East Asian

countries. In other words, transferring the formal political and economic rules of

successful East Asian economies to Latin American economies is not a sufficient

condition for good economic performance.

For successful reform, policy makers in Latin American countries should take

into account the socio-economic and political context of their countries when making

policies for creating more effective institutions. In addition, it is essential to change

both the institutions and the belief systems because it is the mental models of the

actors (individuals and entrepreneurs of organizations) that will shape choices. This

150

requires that the role of public participation be taken into account. Such participation

is consistent with the principles and values of democracy and free markets, which

could be considered as the basis for effective political and economic institutions.

Since the belief systems and consequent informal constraints – norms of behavior,

conventions, and codes of conduct – can be changed only gradually, institutional reform

must be incremental rather than revolutionary (North, 1993). Moreover, a country’s

institutions are deeply rooted in its history and culture. Therefore, policymakers that

seek to improve their countries’ institutions need to recognize that it can be especially

difficult to elicit institutional change and that institutional change is a complex and

relatively slow process.

9.3 Theoretical Contributions

This study contributes to the existing knowledge of the relationship between

democracy and economic growth as well as the new institutional economics in four

ways. First, it estimates the long run effects of democracy on economic performance

within the institutional framework which also incorporates governance and economic

institutions. This study, therefore, enhances knowledge and understanding of how the

institutional framework impacts economic performance. Specifically, it provides

theoretical contributions in studying the impact of political and economic institutions

on economic performance. Secondly, this study offers empirical results that are based

on developing countries, which are a great laboratory due to their diverse institutions.

Thus, this study complements previous research on the relationship between

democracy and economic growth and on the new institutional economics in developing

countries. Theoretically, this study enriches the empirical findings on the interaction

between politico-economic institutions and economic performance, especially in the

case of developing countries. The third contribution is that this study is one of few

which investigate the impact of institutional factors on various measures of economic

performance. Most previous studies on this issue only used either level of output

(GDP, GDP per capita, GNP per capita, and GDP per worker) or growth of output

(GDP growth, GDP per capita growth, and GDP per worker growth) as the proxy of

economic performance (Efendic, Pugh and Adnett, 2011). By employing many

151

measures, the findings of this study better reflect how institutions affect various

aspects of economic performance. The last contribution of this study is the adoption

of both time-series and cross-country approaches rather than only a cross-country

approach, which is more common. By employing the time-series approach, the

researcher was able to investigate how changes in one variable over time affect

another variable, and thus to address the issue of long-run causality between variables.

By adopting the cross-country approach, the researcher could produce findings which

were complimentary with case studies in advancing our understanding of the growth

process. This is because although case studies can generate novel hypotheses, a claim

based on case studies that are not supported by cross-country regressions requires

close scrutiny (Rodrik, 2002).

9.4 Suggestions for Further Research

Due to the limitations of this study, there are some suggestions for further

research. First, a much larger sample should be required for greater precision. Future

research might draw from a greater number of countries, including both developed

and developing countries, to ensure that the research results can be applied to

countries at different levels of development. Research which incorporates evidence

from both developed and developing countries will make stronger empirical and

theoretical contributions to the field of new institutional economics. Second, since

other institutions such as legal and social institutions might also affect economic

performance, those institutions should be examined in future studies. Future research

which examines other types of institutions, apart from political and economic

institutions, will provide substantial theoretical contributions to the new institutional

economics field. Thirdly, since this study and most existing studies on the relationship

between institutions and economic performance rely on surveys conducted by

Freedom House, future research should utilize other democracy indices such as the

Economist Intelligence Unit’s democracy index to see whether the results are

different from those found in previous studies.

Fourth, future research may utilize data acquired from other sources. This is

because the data used in this study were incomplete (i.e. not available in some years

152

or not available for some countries) for some indicators such as adult literacy rates,

gross enrollment ratio, and economic freedom. The reason is that the organizations

that collect these data did not collect them on an annual basis at the beginning stage.

Therefore, data from other sources that are more complete will provide more accurate

empirical results. Fifth, further research should overcome this study’s limitations of

statistical analysis. It should be assumed that the relationships between economic

variables are better characterized by a nonlinear specification. As suggested by Lee et

al. (2004: 2), in some cases, ‘nonlinear models can provide better economic insights’.

Moreover, future research should extend this study by using a time-lagged regression

analysis. Research with a combination of time lags would produce more accurate

research results. Finally, better indicators of institutions, better instruments, and

different techniques are necessary in order to confirm the robustness of previous

findings as well as this study’s findings. The hope is that further research will not

only create a better understanding of the relationship between existing institutions and

economic performance, but will also help in the design of new institutions conductive

to economic growth, unemployment reduction, poverty reduction, and more equal

income distribution.

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APPENDICES

APPENDIX A-1

Annual Growth Rates of GDP per Capita (%), 1990-2009

175

Appendix A-1

Annual Growth Rates of GDP per Capita (%), 1990-2009

Country

Year China

Hong Kong Indonesia Malaysia Philippines Singapore

South Korea Taiwan Thailand

Vietnam 1990 2.29 3.57 7.2 6 0.57 5.06 7.91 9.67 2.84 1991 7.72 4.82 7.18 6.64 -2.91 3.56 8.38 7.12 3.76 1992 12.81 5.21 5.55 6.09 -1.96 3.19 4.92 6.69 6.47 1993 12.7 4.24 5.63 7.14 -0.18 8.94 5.19 6.93 5.99 1994 11.83 3.65 5.95 6.49 2.08 8.12 7.57 7.78 6.82 1995 9.7 0.29 6.82 7.08 2.4 4.92 7.62 8.19 7.59 1996 8.85 -0.33 6.11 7.23 3.58 3.5 5.98 5.06 7.4 1997 8.19 4.19 3.24 4.63 2.97 4.76 3.67 -2.02 6.49 1998 6.77 -6.81 -14.32 -9.64 -2.64 -4.67 -7.52 -11.06 4.3 1999 6.67 1.58 -0.58 3.63 1.29 6.35 8.71 3.73 3.43 2000 7.55 7 3.51 6.43 3.85 8.17 7.58 5.5 3.87 6.62 2001 7.52 -0.24 2.26 -1.59 -0.23 -5 3.21 6.2 1.12 5.47 2002 8.37 1.39 3.11 3.31 2.45 3.21 6.55 -2.2 4.09 5.68 2003 9.32 3.21 3.41 3.8 2.95 5.02 2.29 3.5 5.83 5.78 2004 9.45 7.62 3.68 4.84 4.39 8.21 4.23 3.2 5.11 6.31 2005 10.65 6.62 4.36 3.44 3.01 10.67 3.74 6 3.55 7.04 2006 12.07 6.33 4.2 3.98 3.4 5.29 4.83 4 4.26 6.9 2007 13.6 5.32 5.06 4.63 5.09 4.11 4.76 4.6 4.19 7.16 2008 9.03 1.41 4.76 2.93 1.86 -3.49 1.98 5.7 1.84 5.01 2009 8.54 -3.12 3.35 -3.34 -0.74 -4.22 -0.09 0.1 -2.79 4.03

176

Country

Year Argentina Bolivia Brazil Chile Colombia

Costa Rica Ecuador

El Salvador Guatemala Honduras

1990 -3.78 2.27 -5.94 1.88 3.98 1.29 0.33 3.39 0.76 -2.69 1991 11.1 2.86 -0.15 6.03 0.32 0.05 2.85 2.06 1.28 0.43 1992 10.41 -0.69 -2.03 10.24 3.06 6.53 -0.69 5.89 2.42 2.79 1993 4.49 1.87 3.07 5.06 0.47 4.85 -1.8 5.73 1.53 3.46 1994 4.44 2.29 3.75 3.88 3.91 2.22 2.64 4.56 1.65 -3.78 1995 -4.11 2.35 2.85 8.83 3.31 1.39 -0.13 5.09 2.56 1.56 1996 4.18 2.1 0.62 5.79 0.24 -1.61 0.64 0.66 0.63 1.22 1997 6.75 2.73 1.83 5.1 1.61 2.95 2.39 3.36 2.02 2.68 1998 2.58 2.85 -1.45 1.87 -1.17 5.73 0.59 3.02 2.62 0.71 1999 -4.51 -1.63 -1.22 -2.01 -5.83 5.65 -7.62 2.83 1.46 -3.93 2000 -1.87 0.42 2.81 3.22 2.65 -0.5 1.42 1.62 1.18 3.58 2001 -5.38 -0.37 -0.11 2.16 0.02 -1.09 3.99 1.25 -0.12 0.64 2002 -11.74 0.44 1.25 1.03 0.87 0.81 2.97 1.94 1.34 1.67 2003 7.84 0.69 -0.2 2.78 2.29 4.37 2.36 1.94 0.01 2.46 2004 8.04 2.16 4.37 4.91 3.71 2.4 6.78 1.51 0.61 4.12 2005 8.17 7.37 1.93 4.45 3.14 4.13 4.84 2.72 0.72 3.95 2006 7.43 2.64 2.8 3.51 5.07 7.11 2.78 3.79 2.8 4.54 2007 7.59 -1.79 5 3.54 5.33 6.27 2.89 4.18 3.72 4.2 2008 5.71 4.29 4.15 2.65 1.24 1.24 6.13 1.98 0.79 1.92 2009 -0.13 1.59 -1.55 -2.49 -0.6 -2.79 -0.7 -4 -1.87 -3.85

177

Country

Year Mexico Nicaragua Panama Paraguay Peru Uruguay Venezuela

1990 3.1 -2.32 5.91 0.41 -7.09 -0.36 4.01 1991 2.3 -2.53 7.2 -0.13 0.13 2.84 7.3 1992 1.74 -2.05 6 0.86 -2.35 7.18 3.69 1993 0.11 -2.81 3.31 1.41 2.8 1.93 -1.92 1994 2.6 0.92 0.77 1.29 10.76 6.5 -4.44 1995 -7.86 3.59 -0.3 3.03 6.67 -2.17 1.78 1996 3.52 4.17 0.75 -1.84 0.73 5.01 -2.23 1997 5.23 1.98 4.35 0.74 5.05 7.86 4.25 1998 3.45 1.86 5.23 -1.57 -2.29 3.96 -1.65 1999 2.44 5.25 1.9 -3.54 -0.69 -2.39 -7.76 2000 5.1 2.47 0.76 -5.33 1.38 -2.29 1.79 2001 -1.19 1.45 -1.31 0 -1.25 -4.06 1.5 2002 -0.19 -0.63 0.35 -2.03 3.55 -7.74 -10.5 2003 0.33 1.18 2.33 1.81 2.63 0.96 -9.39 2004 3 3.97 5.62 2.14 3.61 5.06 16.24 2005 2.16 2.97 5.33 0.94 5.49 7.33 8.45 2006 3.79 2.6 6.68 2.39 6.44 4.05 8.03 2007 2.3 1.85 10.24 4.82 7.6 7.16 6.37 2008 0.47 6.14 8.92 3.94 8.51 8.2 3.09 2009 -7.48 -6.88 0.76 -5.53 -0.28 2.52 -4.82

APPENDIX A-2

Unemployment (% of Total Labor Force), 1990-2009

179

Appendix A-2

Unemployment (% of Total Labor Force), 1990-2009

Country

Year China


South Korea Taiwan Thailand Vietnam

1990 2.5 1.33 2.4 5.06 8.4 1.78 2.46 1.67 3.85 12.33 1991 2.3 1.8 2.6 4.35 10.5 1.75 2.45 1.51 2.1 10.39 1992 2.3 1.96 2.8 3.72 9.8 1.8 2.53 1.51 2.8 11 1993 2.6 1.97 2.8 3.03 9.3 1.7 2.9 1.45 2.6 10.6 1994 2.8 1.92 4.42 2.95 9.48 1.73 2.48 1.56 2.6 10.3 1995 2.9 3.19 8.95 3.14 9.53 1.75 2.07 1.79 1.7 5.82 1996 3 2.77 4.88 2.52 8.53 1.65 2.06 2.6 1.5 5.88 1997 3.1 2.2 4.69 2.45 8.68 1.43 2.62 2.72 1.5 6.01 1998 3.1 4.7 5.39 3.2 10.05 2.5 6.95 2.69 4.4 6.85 1999 3.1 6.25 6.33 3.43 9.75 2.8 6.58 2.92 4.19 6.74 2000 3.1 4.95 6.08 3 11.18 2.68 4.43 2.99 3.59 6.42 2001 3.6 5.1 8.1 3.53 11.13 2.65 4.02 4.57 3.34 6.28 2002 4 7.33 9.1 3.48 11.4 3.55 3.28 5.17 2.41 6.01 2003 4.3 7.93 9.5 3.61 11.4 3.95 3.57 4.99 2.17 5.78 2004 4.2 6.81 9.86 3.54 11.83 3.35 3.68 4.44 2.08 5.6 2005 4.2 5.58 11.24 3.53 11.35 3.13 3.73 4.13 1.84 5.31 2006 4.1 4.78 10.28 3.33 8 2.65 3.47 3.91 1.52 4.82 2007 4 4.02 9.11 3.2 7.33 2.13 3.25 3.91 1.38 4.64 2008 4.2 3.52 8.39 3.3 7.4 2.23 3.18 4.14 1.39 4.7 2009 4.3 5.15 8 3.7 7.48 3.03 3.65 5.85 1.39 6

180

Country


Costa Rica Ecuador


1990 7.6 4.28 7.75 6.64 4.64 6.1 8.1 4.8 1991 6.48 4.83 8.21 6.38 5.5 8.5 8.73 4.7 1992 7.11 5.8 6.68 5.94 4.1 8.9 9.32 3.2 1993 11.72 5.43 6.5 5.04 4.1 8.3 9.94 3 1994 14.41 4.64 7.79 4.91 4.2 7.1 7.67 2.8 1995 18.85 4.65 7.37 5.65 5.2 6.9 7.63 3.2 1996 19.12 5.43 6.48 7.8 6.2 10.4 7.68 4.3 1997 15.95 5.68 6.12 7.9 5.7 9.2 7.98 3.2 1998 14.7 7.6 6.21 9.7 5.6 11.5 7.32 4 1999 16.2 7.6 10.01 13.1 6.02 14.42 6.95 3.8 2000 17.39 11.4 7.1 9.7 13.33 5.19 14.1 6.94 7.5 4 2001 20.71 11.4 11.27 9.87 14.98 6.07 10.42 6.95 7.5 4.2 2002 20.8 7.6 11.67 9.81 15.65 6.4 8.64 6.21 7.5 3.8 2003 14.5 7.6 12.3 9.54 14.16 6.67 9.81 6.9 7.5 5.1 2004 12.1 11.7 11.47 10.02 13.6 6.5 10.97 6.78 7.5 5.9 2005 10.1 9.2 9.82 9.31 11.76 6.63 10.71 7.23 7.5 4 2006 8.7 8 9.97 7.95 12.04 5.96 10.1 6.56 7.5 3.95 2007 7.5 7.8 9.29 7.03 11.15 4.6 8.8 6.33 3.2 3.9 2008 7.3 7.5 7.9 7.41 10.58 4.95 6.9 5.89 3.2 3.94 2009 8.4 7.5 8.1 9.63 12 7.82 8.5 8.88 3.2 4.4

181

Country


1990 2.74 15.5 16.72 6.6 8.3 8.5 11.1 1991 2.69 14.9 15.97 5.1 5.9 8.9 9.5 1992 2.83 14.4 14.67 5.3 9.4 9 7.7 1993 3.43 15 13.26 5.1 9.9 8.3 6.6 1994 3.7 12.2 14 4.4 8.8 9.2 8.7 1995 6.23 16.9 14.02 3.3 7.1 10.3 10.25 1996 5.45 16 14.32 8.2 7.2 11.9 11.75 1997 3.73 14.3 13.37 5 8.6 11.56 11.35 1998 3.16 13.2 13.58 5.8 6.9 10.14 11.2 1999 2.5 10.7 11.75 6.8 9.4 11.19 14.53 2000 2.2 9.8 13.52 7.3 7.85 13.39 14.01 2001 2.76 10.7 14.71 7.6 8.83 15.19 13.36 2002 2.98 10.7 14.11 10.8 9.72 16.74 15.96 2003 3.41 11.7 13.65 8.1 10.3 17.13 18.05 2004 3.92 11 12.35 7.3 9.4 13.32 15.07 2005 3.58 11.5 10.33 5.8 9.6 12.13 12.24 2006 3.59 11.3 9.11 6.7 8.5 10.91 9.96 2007 3.72 11 6.78 5.6 8.42 9.18 8.5 2008 3.96 10.5 5.85 5.6 8.39 7.6 7.36 2009 5.47 9.56 6.93 5.6 8.6 7.32 7.88

APPENDIX A-3

Poverty (% of Population Falling below the Poverty Line),

1990-2009

183

Appendix A-3

Poverty (% of the Population Falling below the Poverty Line), 1990-2009

Country

Year China



1990 84.6 84.6 1991 55.4 1992 82.2 11.2 25.6 1993 78.6 84.6 85.7 1994 78 52.6 1995 74.1 11 1996 65.1 77 17.5 1997 71.1 6.84 43.8 1998 69.6 16.7 78.3 1999 61.4 81.6 20 2000 44.8 20.7 2001 2002 51.2 67 15.1 68.7 2003 43.8 2004 7.81 11.5 52.5 2005 36.3 53.8 2006 62.8 45 48.2 2007 56.6 2008 38.5 2009 50.6 2.27 26.5

184

Country


Costa Rica Ecuador


1990 27.8 13.7 18.8 61.6 1991 17.3 17.4 1992 3.2 24.4 17.9 50.8 1993 24.7 17.2 1994 10.4 28.2 45.4 1995 21.9 23.3 25.2 1996 7.02 22.6 7.8 25.5 15.7 27.7 1997 29.9 23.3 12.3 25.8 29.2 1998 8.9 22.6 7.47 28 11.1 27.7 25.5 29.9 1999 35.6 23 29.7 26.8 2000 5.97 29.1 11.5 22.2 26.8 2001 22.3 10 2002 19.7 34.2 21.3 23.7 29.8 2003 21.7 5.34 26.3 11.5 22.4 25.3 33.4 2004 16 20.9 2005 11.3 30.4 18.3 8.56 20.4 20.5 34.8 2006 8.6 2.38 27.9 2007 24.7 12.8 35.4 2008 10.4 15.2 2009 2.35 9.87 2.43 5.42 13.4

185

Country


1990 19.4 5.21 1991 26.8 1992 30.4 2.04 1993 49.3 10.9 1994 17.2 17 1995 17.8 19.7 21.9 21.1 1996 26.4 21.2 19.9 3 28.1 1997 15.2 21.1 1998 21.5 38.5 30.3 3.09 24 1999 23.7 2000 15.7 20 2.26 2001 37.5 22.7 27.9 1.62 2002 13.5 20 28.1 24.4 2003 4.16 31.7 2004 11 18 2005 31.9 18.4 19.4 4.47 19.8 2006 9.03 17.9 18.5 4.18 10.1 2007 14.2 2008 8.11 13.2 2009 14.7 0.22

APPENDIX A-4

Income Inequality, 1990-2009

187

Appendix A-4

Income Inequality, 1990-2009

Country

Year China



1990 1991 8.59 1992 11.58 9.38 1993 5.63 1994 8.32 1995 12.15 1996 9.65 8.43 1997 12.71 9.76 1998 9.72 4.73 7.75 5.49 1999 8.61 2000 9.74 8.44 2001 2002 7.81 6.11 2003 9.32 2004 6.99 8.07 6.59 2005 8.34 6.61 2006 9 6.42 2007 6.17 2008 6.19 2009 5.88 11.33 15.18

188

Country


Costa Rica Ecuador


1990 27.33 17.35 12.63 21.7 1991 8.71 16.38 1992 11.01 24.53 13.09 19.76 1993 28.15 12.65 1994 17.17 18.23 19.19 1995 26.48 19.93 14.78 1996 13.57 24.85 17.04 20.62 13.25 17.22 1997 29.33 27.53 11.77 15.19 17.38 1998 15.24 26.25 17.91 24.38 13.41 18.68 17.24 19 1999 45.26 26.39 22.86 16.69 2000 16.84 23.47 12.37 19.64 17.57 2001 25.31 14.57 2002 18.03 39.25 25.08 21.18 20.45 2003 23.37 16.07 26.05 16.1 23.8 16.85 17.49 2004 18.42 23.05 2005 15.89 33.73 20.84 12.41 17.56 16.38 25.96 2006 14.69 20.03 13.84 24.64 16.81 23.85 2007 21.94 19.45 12.3 17.21 11.95 30.08 2008 2009 12.28 17.38 3.6 13.24 12.87

189

Country


1990 7.88 8.88 1991 31.54 1992 14.18 9.21 1993 23.04 8.65 1994 14.23 10.33 1995 28.12 27.73 12.17 1996 12.41 27.38 11.67 10.18 14.33 1997 15.25 13.47 1998 13.38 17.86 30.2 11.29 18.39 1999 26.62 2000 14.61 25.13 10.42 2001 13.84 27.01 17.56 10.99 2002 12.82 24.83 27.43 18.69 2003 10.4 16.46 2004 11.38 22.92 2005 15.01 19.08 15.27 10.75 13.96 2006 11.48 23.12 13.91 11.47 9.95 2007 16.89 15.41 12.01 2008 14.45 15.03 2009 16 13.47 8.64

APPENDIX B-1

Investment Rates (% of GDP), 1990-2009

191

Appendix B-1

Investment Rates (% of GDP), 1990-2009

Country

Year China



1990 36.14 27.03 40.65 32.85 23.4 35.05 38.09 24.37 41.07 1.18 1991 36.12 26.79 42.36 37.79 20.37 33.39 40.06 24.69 42.84 8.59 1992 37.46 28.05 39.07 35.36 21.34 34.64 37.08 26.83 39.96 11.22 1993 44.48 27.13 36.92 39.18 24.19 36.47 35.99 27.28 40.01 25.12 1994 42.2 31.22 38.41 41.2 24.06 32.31 36.77 26.55 40.25 25.48 1995 41.9 34.06 39.29 43.64 22.45 33.27 36.94 26.69 42.09 27.14 1996 40.44 31.58 38.16 41.48 24.02 34.25 37.85 23.97 41.82 28.1 1997 37.95 34.01 39.1 42.97 24.78 37.19 35.45 25.08 33.66 28.3 1998 37.1 28.86 25.4 26.68 20.34 30.03 25.04 25.99 20.45 29.05 1999 36.57 24.85 20.61 22.38 18.75 31.27 28.89 24.97 20.5 27.63 2000 35.12 27.46 22.25 26.87 21.17 33.18 30.56 25.68 22.84 29.61 2001 36.27 25.32 22.54 24.4 18.97 26.77 29.16 19.84 24.1 31.17 2002 37.87 22.84 21.4 24.78 17.67 23.77 29.2 19.34 23.8 33.22 2003 41.2 21.92 25.6 22.76 16.83 16.12 29.89 19.91 24.97 35.45 2004 43.26 21.84 24.06 23.05 16.75 21.75 29.93 23.7 26.79 35.47 2005 42.1 20.57 25.08 19.99 14.59 19.98 29.69 22.72 31.44 35.57 2006 42.97 21.73 25.4 20.45 14.51 21.03 29.62 22.68 28.3 36.81 2007 41.74 20.93 24.92 21.56 15.38 21.07 29.43 22.12 26.43 43.13 2008 44.05 20.44 27.82 19.29 15.33 30.2 31.21 22.4 29.12 39.71 2009 48.24 21.31 31 14.49 14.65 26.36 25.92 17.66 21.24 38.13

192

Country


Costa Rica Ecuador


1990 14.01 12.53 16.31 25.39 19.89 27.32 17.65 13.86 15.32 24.87 1991 14.65 15.58 15.99 22.98 17.15 17.94 22.1 15.41 15.43 26.68 1992 16.72 16.7 15.31 24.25 18.46 20.24 21.27 18.53 20.17 28.09 1993 19.7 16.56 16.86 26.9 22.69 20.88 20.29 18.58 19.51 36.31 1994 19.98 14.37 17.91 24.68 27.44 20.05 21.85 19.69 17.56 40.72 1995 18.52 15.24 18.03 26.41 27.56 18.24 21.72 20.04 17.22 25.95 1996 19.64 16.24 17.04 27.38 24.01 15.96 19.92 15.19 15.07 25.98 1997 20.84 19.63 17.43 27.77 22.65 18.08 21.53 15.12 16.65 27.76 1998 20.99 23.61 17.03 26.99 21.34 20.46 25.31 17.55 19.93 30.53 1999 17.85 18.77 16.38 21.05 14.06 17.03 15.17 16.43 20.45 32.34 2000 17.55 18.14 18.25 22.05 14.9 16.91 21.3 16.93 19.91 28.29 2001 15.69 14.27 18.03 22.33 16.03 20.31 24.83 16.67 19.66 26 2002 10.78 16.3 16.2 21.95 17.25 22.62 26.8 16.39 20.55 24.26 2003 14.07 13.23 15.77 21.11 18.68 20.64 20.98 16.98 20.3 25.28 2004 18.68 11.02 17.12 20.04 19.44 23.13 23.3 16.2 20.85 29.67 2005 20.78 14.25 16.21 22.14 20.22 24.35 23.59 15.84 19.74 27.62 2006 22.95 13.87 16.76 20.03 22.4 26.42 23.78 16.99 20.82 28.34 2007 24.13 15.19 18.33 20.43 23.03 24.67 24.29 15.93 20.83 33.22 2008 25.15 17.55 20.69 29.02 23.04 27.58 27.89 14.91 16.4 36.03 2009 21.19 16.97 16.51 23.54 21.94 15.87 23.34 13.12 12.85 19.85

193

Country


1990 21.12 11.72 10.38 22.87 16.46 13.54 10.22 1991 21.27 18.41 14.93 22.86 17.29 16.68 18.68 1992 21.17 16.48 19.68 21.92 17.31 17 23.72 1993 30.56 15.87 24.12 22.69 19.31 17.3 18.75 1994 30.01 20.13 24.52 27.69 22.23 17.53 14.16 1995 27.04 21.12 26.23 26.02 24.8 16.98 18.11 1996 28.02 23.33 26.74 25.43 22.82 16.84 16.56 1997 29.32 27.52 25.68 26.47 24.09 16.85 27.67 1998 27.14 29.53 27.21 22.74 23.61 17.34 30.66 1999 25.34 38.39 25.8 20.81 21.09 15.08 26.52 2000 25.54 31.69 24.14 18.81 20.16 14.46 24.17 2001 22.81 28.48 17.64 18.72 18.77 14.33 27.52 2002 23.54 26.29 15.75 18.69 18.4 13.07 21.16 2003 22.89 26.15 19 20.11 18.43 15.21 15.22 2004 24.85 29.08 18.7 19.23 17.95 17.47 21.8 2005 24.38 31.27 18.36 19.77 17.89 17.7 23 2006 26.18 32 19.46 19.65 20.04 19.35 26.92 2007 26.53 33.4 24.13 18.04 22.94 19.57 29.2 2008 27.11 32.86 26.84 18.05 26.88 22.28 25.89 2009 23.92 24.58 23.86 15.52 20.71 17.21 24.78

APPENDIX B-2

Gross National Savings (% of GDP), 1990-2009

195

Appendix B-2

Gross National Savings (% of GDP), 1990-2009

Country

Year China



1990 39.126 35.662 25.384 30.275 18.65 43.028 37.576 30.997 32.568 10.359 1991 39.366 33.694 26.278 28.74 18.018 44.126 37.683 31.436 35.168 13.254 1992 38.774 33.351 25.083 31.158 19.459 45.954 36.419 30.718 34.399 17.562 1993 42.542 35.331 35.602 34.068 18.386 43.375 36.79 30.316 34.98 13.672 1994 43.573 33.6 36.877 33.144 19.446 47.806 35.966 29.123 34.688 23.913 1995 42.118 31.452 36.245 34.055 17.807 49.625 35.426 28.68 34.068 25.923 1996 41.288 31.658 35.25 37.12 19.355 48.691 33.843 27.758 33.765 19.922 1997 41.827 29.623 37.511 37.136 19.518 52.595 33.91 27.444 32.854 22.618 1998 40.188 30.361 29.191 39.681 22.658 51.783 36.979 27.223 33.309 25.106 1999 38.012 31.125 24.332 38.07 14.979 48.331 34.196 27.642 30.657 31.73 2000 36.831 31.577 27.072 35.917 18.235 44.041 33.336 28.417 30.437 33.158 2001 37.581 30.944 26.835 32.351 16.525 39.648 30.832 26.292 28.525 33.27 2002 40.302 30.414 25.403 32.735 17.302 36.658 30.507 28.026 27.494 31.5 2003 43.999 32.308 29.052 34.747 17.195 38.821 32.309 29.136 28.322 30.564 2004 46.818 31.319 24.665 35.138 18.623 38.713 34.412 29.531 28.506 31.965 2005 49.225 31.921 25.178 35.044 16.592 41.086 31.89 28.449 27.109 34.516 2006 52.307 33.804 28.381 37.006 19.063 45.853 31.102 30.298 29.415 36.541 2007 52.379 33.269 27.348 37.473 20.322 48.408 31.502 31.691 32.783 33.299 2008 53.695 34.129 29.844 36.774 17.511 44.784 31.554 29.104 29.916 27.767 2009 54.198 29.911 33.577 30.986 20.451 45.398 29.859 29.033 29.533 31.564

196

Country


Costa Rica Ecuador


1990 17.299 8.512 17.722 23.582 18.557 19.49 14.113 9.565 12.8 17.052 1991 14.412 10.796 17.788 22.71 19.485 14.812 16.055 11.789 12.825 16.557 1992 13.876 9.455 18.78 22.123 17.631 15.47 19.607 15.536 12.016 16.372 1993 15.652 9.256 18.966 21.709 16.734 13.724 16.615 16.8 12.333 20.718 1994 15.642 10.716 19.998 21.872 19.818 15.186 18.459 18.694 11.583 23.995 1995 15.961 10.238 15.639 24.606 19.612 15.056 17.126 17.476 12.712 17.021 1996 15.601 11.726 14.243 23.059 16.369 13.633 20.28 13.199 11.961 16.866 1997 15.234 12.646 13.932 23.096 14.631 14.502 19.037 14.214 12.816 18.836 1998 15.097 12.814 13.07 21.93 13.945 16.979 15.615 16.618 14.013 21.583 1999 13.807 10.652 12.06 21.156 12.862 12.826 19.736 14.507 14.292 21.298 2000 13.032 11.012 14.49 20.831 15.798 12.416 26.463 13.638 13.807 21.15 2001 12.759 11.23 13.841 20.837 14.884 16.632 21.608 15.583 12.96 19.674 2002 21.003 12.294 14.686 20.995 16.142 17.534 21.993 13.556 14.482 20.637 2003 21.533 13.128 16.527 20.039 17.815 15.62 19.487 12.314 15.648 18.491 2004 20.935 15.857 18.877 22.187 18.818 18.874 21.681 12.139 15.986 21.933 2005 24.071 18.809 17.792 23.338 19.072 19.433 24.555 12.297 15.176 24.622 2006 26.566 26.379 18.008 24.933 20.544 21.878 27.663 12.815 15.783 24.616 2007 26.517 28.56 18.441 24.857 20.169 18.41 27.937 9.937 15.596 24.184 2008 24.658 29.003 18.977 27.407 20.084 18.238 30.111 7.299 12.11 20.644 2009 22.721 22.878 14.985 25.132 19.786 13.906 22.63 11.348 12.838 16.186

197

Country


1990 18.535 42.319 13.327 30.622 11.798 12.859 25.935 1991 17.02 11.354 11.55 25.289 12.9 14.038 21.264 1992 15.062 1.277 15.423 22.288 11.977 13.117 17.543 1993 25.795 1.396 21.958 24.846 12.235 12.691 15.001 1994 24.23 -4.444 24.133 25.642 16.292 12.1 18.51 1995 26.568 1.234 22.824 24.863 16.268 12.954 20.72 1996 27.372 -1.2 24.602 21.296 16.297 12.769 29.252 1997 27.681 -1.977 20.657 18.627 18.395 13.885 32.019 1998 23.856 1.297 17.921 20.634 17.731 14.4 20.99 1999 22.875 0.815 15.678 18.405 18.417 10.923 24.683 2000 22.759 1.599 18.21 16.479 17.264 10.012 35.8 2001 20.313 8.717 16.167 14.055 16.535 10.944 30.852 2002 21.538 6.913 15.052 20.92 16.447 15.346 33.447 2003 21.865 7.573 14.523 22.311 16.812 15.405 32.344 2004 24.179 11.437 11.156 21.377 17.978 17.491 38.824 2005 23.861 14.829 13.448 19.992 19.335 17.942 42.192 2006 25.719 16.987 16.341 20.922 23.155 17.372 41.293 2007 24.722 16.827 16.905 19.552 24.203 18.643 35.118 2008 25.484 9.593 14.959 16.259 22.687 17.527 34.724 2009 23.198 12.657 23.647 16.136 20.874 17.773 27.393

APPENDIX B-3

Population Growth Rates (%), 1990-2009

199

Appendix B-3

Population Growth Rates (%), 1990-2009

Country

Year China



1990 1.47 0.32 1.66 2.8 2.43 3.88 1.15 1.36 2.18 1991 1.36 0.83 1.61 2.69 2.37 2.85 0.93 1.33 2.1 1992 1.23 0.84 1.57 2.6 2.32 3 0.91 1.3 2.03 1993 1.15 1.72 1.53 2.54 2.27 2.53 0.9 1.23 1.95 1994 1.13 2.25 1.49 2.52 2.23 3.13 0.9 1.11 1.87 1995 1.09 1.98 1.46 2.53 2.2 3.04 1.43 0.96 1.8 1996 1.05 4.44 1.43 2.55 2.17 4.06 0.95 0.8 1.79 1997 1.02 0.83 1.41 2.54 2.13 3.36 0.94 0.66 1.55 1998 0.96 0.83 1.38 2.49 2.1 3.4 0.72 0.62 1.39 1999 0.87 0.96 1.37 2.39 2.06 0.8 0.71 0.69 1.29 2000 0.79 0.88 1.36 2.26 2.02 1.73 0.84 0.64 0.84 0.16 2001 0.73 0.74 1.35 2.12 1.98 2.7 0.74 0.59 1.03 1.34 2002 0.67 0.44 1.33 1.99 1.94 0.91 0.56 0.52 1.17 1.32 2003 0.62 -0.2 1.32 1.9 1.91 -1.48 0.5 0.46 1.23 1.46 2004 0.59 0.78 1.3 1.84 1.89 1.25 0.38 0.42 1.16 1.39 2005 0.59 0.44 1.27 1.81 1.87 2.35 0.21 0.38 1.02 1.3 2006 0.56 0.64 1.24 1.78 1.86 3.13 0.33 0.33 0.85 1.23 2007 0.52 1 1.22 1.75 1.84 4.17 0.33 0.3 0.71 1.2 2008 0.51 0.75 1.18 1.71 1.82 5.32 0.31 0.24 0.61 1.23 2009 0.51 0.37 1.15 1.66 1.79 3.02 0.29 0.23 0.56 1.23

200

Country


Costa Rica Ecuador


1990 1.43 2.28 1.73 1.77 1.97 2.55 2.31 1.38 2.3 2.83 1991 1.4 2.31 1.65 1.81 1.93 2.49 2.26 1.48 2.32 2.77 1992 1.38 2.33 1.58 1.83 1.9 2.43 2.19 1.55 2.33 2.72 1993 1.35 2.33 1.53 1.81 1.87 2.41 2.11 1.54 2.33 2.64 1994 1.33 2.3 1.51 1.74 1.84 2.43 1.99 1.42 2.32 2.54 1995 1.3 2.25 1.51 1.64 1.82 2.47 1.86 1.23 2.31 2.43 1996 1.29 2.19 1.51 1.52 1.79 2.51 1.73 1.03 2.29 2.32 1997 1.27 2.14 1.51 1.42 1.77 2.52 1.61 0.86 2.28 2.23 1998 1.23 2.1 1.5 1.33 1.74 2.49 1.51 0.71 2.29 2.15 1999 1.17 2.07 1.47 1.27 1.71 2.41 1.42 0.6 2.32 2.1 2000 1.1 2.06 1.45 1.22 1.68 2.29 1.35 0.53 2.37 2.07 2001 1.02 2.04 1.42 1.18 1.64 2.17 1.29 0.46 2.42 2.05 2002 0.96 2.02 1.39 1.14 1.61 2.05 1.23 0.39 2.47 2.03 2003 0.92 1.99 1.34 1.1 1.58 1.93 1.18 0.35 2.49 2.02 2004 0.91 1.95 1.27 1.07 1.55 1.8 1.14 0.34 2.5 2.01 2005 0.93 1.9 1.2 1.05 1.53 1.68 1.1 0.35 2.49 2 2006 0.96 1.85 1.11 1.04 1.51 1.54 1.07 0.38 2.48 2 2007 0.98 1.81 1.04 1.02 1.49 1.42 1.05 0.41 2.47 2 2008 0.99 1.77 0.97 1 1.46 1.34 1.04 0.44 2.46 2 2009 0.98 1.73 0.91 0.98 1.43 1.32 1.06 0.47 2.46 1.99

201

Country


1990 1.89 2.25 2.05 2.63 2.07 0.66 2.33 1991 1.87 2.37 2.05 2.57 2.01 0.68 2.24 1992 1.84 2.45 2.05 2.5 1.95 0.7 2.26 1993 1.82 2.46 2.05 2.44 1.89 0.72 2.21 1994 1.79 2.37 2.05 2.38 1.84 0.73 2.16 1995 1.77 2.22 2.03 2.32 1.8 0.74 2.11 1996 1.55 2.06 2.02 2.26 1.76 0.54 2.06 1997 1.45 1.93 2.01 2.21 1.71 0.64 2.01 1998 1.4 1.8 1.99 2.16 1.66 0.54 1.96 1999 1.39 1.68 1.96 2.11 1.6 0.46 1.92 2000 1.42 1.58 1.92 2.08 1.53 0.37 1.84 2001 1.04 1.47 1.89 2.04 1.47 0.23 1.85 2002 1.01 1.38 1.85 2.01 1.41 0.01 1.82 2003 1.01 1.31 1.82 1.97 1.36 -0.15 1.78 2004 1.01 1.28 1.78 1.94 1.31 -0.05 1.75 2005 1.01 1.27 1.75 1.9 1.26 0.12 1.71 2006 1.09 1.27 1.72 1.87 1.21 0.26 1.69 2007 1.01 1.27 1.68 1.83 1.17 0.28 1.66 2008 1.01 1.28 1.65 1.8 1.14 0.3 1.63 2009 1.01 1.32 1.61 1.76 1.13 0.33 1.6

APPENDIX C-1

Life Expectancy at Birth (Years), 1990-2009

203

Appendix C-1

Life Expectancy at Birth (Years), 1990-2009

Country

Year China



1990 68.1 77.4 61.6 70.2 65.4 74.3 71.3 69.2 65.5 1991 68.4 77.9 62.1 70.5 65.9 - 71.7 69 66.4 1992 68.7 77.7 62.6 70.7 66.3 74.8 - 68.9 67.3 1993 69 78 63.2 70.9 66.7 - 72.7 68.7 68.1 1994 69.3 78.5 63.8 71.2 67.2 76.3 - 68.5 68.8 1995 69.6 78.7 64.4 71.4 67.6 76.4 73.4 68.4 69.4 1996 70 79.6 65 71.6 68 76.7 - 68.3 70 1997 70.3 80.1 65.7 71.9 68.4 77 74.2 68.2 70.5 1998 70.6 80.1 66.3 72.1 68.7 77.4 - 68.2 71 1999 71 80.4 66.8 72.3 69.1 77.6 75.4 68.2 71.5 2000 71.3 80.9 67.4 72.5 69.5 78.1 75.9 76.4 68.2 71.9 2001 71.6 81.4 67.9 72.8 69.8 78.4 76.3 76.5 68.2 72.4 2002 71.9 81.5 68.3 73 70.1 - 76.8 76.7 68.2 72.8 2003 72.1 81.3 68.8 73.2 70.5 79 77.3 76.9 68.3 73.1 2004 72.4 81.8 69.2 73.5 70.8 79.5 77.8 77.3 68.3 73.4 2005 72.6 81.6 69.7 73.7 71 80 78.4 77.3 68.4 73.7 2006 72.8 82.4 70.1 73.9 71.3 80.1 79 77.4 68.5 73.9 2007 72.9 82.4 70.4 74.2 71.6 80.4 79.3 77.6 68.7 74.2 2008 73.1 82.3 70.8 74.4 71.8 80.7 79.8 77.8 68.9 74.4 2009 73.3 82.7 71.2 74.6 72.1 80.3 80.3 78 69.1 74.6

204

Country


Costa Rica Ecuador


1990 71.5 58.8 66.3 73.6 68.3 75.7 68.9 66 62.2 66.4 1991 71.8 59.3 66.7 73.9 68.5 76 69.4 66.9 62.8 66.8 1992 72 59.8 67.1 74.2 68.6 76.2 69.8 67.6 63.3 67.2 1993 72.2 60.3 67.5 74.4 68.9 76.4 70.3 68.2 63.9 67.7 1994 72.4 60.7 67.9 74.7 69.1 76.6 70.8 68.6 64.4 68.1 1995 72.7 61.1 68.3 75 69.4 76.8 71.2 68.9 65 68.5 1996 72.9 61.5 68.7 75.3 69.8 77 71.7 69.1 65.5 68.9 1997 73.1 61.9 69.1 75.6 70.1 77.2 72.1 69.2 66.1 69.3 1998 73.3 62.2 69.5 76 70.4 77.4 72.6 69.4 66.7 69.7 1999 73.5 62.6 69.8 76.4 70.7 77.6 73 69.5 67.2 70 2000 73.8 63 70.2 76.8 71 77.8 73.4 69.7 67.7 70.3 2001 74 63.3 70.5 77.2 71.3 78 73.7 69.9 68.2 70.6 2002 74.2 63.7 70.8 77.5 71.5 78.1 74 70.1 68.7 70.8 2003 74.4 64 71.1 77.8 71.8 78.3 74.3 70.3 69 71 2004 74.6 64.4 71.4 78.1 72 78.4 74.5 70.5 69.4 71.3 2005 74.8 64.7 71.6 78.2 72.3 78.5 74.7 70.7 69.7 71.5 2006 75 65 71.9 78.4 72.5 78.7 74.8 71 69.9 71.8 2007 75.1 65.4 72.2 78.5 72.7 78.8 75 71.1 70.1 72 2008 75.3 65.7 72.4 78.6 73 78.9 75.1 71.3 70.3 72.2 2009 75.5 66 72.6 78.7 73.2 79 75.3 71.5 70.6 72.4

205

Country


1990 70.9 64.1 72.4 68 65.6 72.6 71.2 1991 71.4 64.9 72.6 68.2 66.1 - - 1992 71.7 65.6 72.8 68.4 66.5 73 71.7 1993 72 66.2 73 68.6 67 - 71.8 1994 72.2 66.8 73.1 68.7 67.5 - 72.1 1995 72.4 67.3 73.3 68.9 68 73.4 72.2 1996 72.8 67.8 73.5 69.1 68.5 73.5 72.4 1997 73.1 68.3 73.7 69.3 69 73.7 72.6 1998 73.4 68.7 73.9 69.6 69.5 74 72.7 1999 73.7 69.2 74.1 69.8 70 74.2 72.9 2000 74 69.7 74.3 70.1 70.5 74.9 73.3 2001 74.2 70.1 74.5 70.3 71 74.9 73.4 2002 74.3 70.6 74.6 70.6 71.4 74.8 73.6 2003 74.4 71.1 74.8 70.8 71.8 74.9 72.8 2004 74.4 71.5 75 71.1 72.2 75.2 73 2005 74.4 72 75.2 71.3 72.5 75.6 73.2 2006 74.5 72.4 75.3 71.5 72.8 75.7 73.4 2007 75 72.8 75.5 71.7 73 75.9 73.6 2008 75.1 73.1 75.7 71.9 73.3 76 73.5 2009 75.3 73.5 75.8 72.1 73.5 76.1 73.7

APPENDIX C-2

Adult Literacy Rates (Total) (% of People Aged 15 and Above),

1990-2009

207

Appendix C-2

Adult Literacy Rates (Total) (% of People Aged 15 and Above), 1990-2009

Country

Year China



1990 77.8 - 81.5 82.9 93.6 89.1 87.6 88 87.6 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 90.9 - 81.5 88.7 92.6 92.5 87.6 86 92.6 90.3 2001 86 2002 86 2003 86 2004 96.1 2005 92.6 - 92 91.2 93.2 93.9 - 96.1 93.7 90.3 2006 93 - 92 91.5 93.3 94.2 - 96.1 93.9 90.3 2007 93.3 - 92 91.9 93.4 94.4 - 96.1 94.1 90.3 2008 93.6 - 92 92.2 93.5 94.7 - 96.1 94.4 90.3 2009 93.9 - 92 92.6 93.6 94.9 - 96.1 94.5 90.3

208

Country


Costa Rica Ecuador


1990 96.1 80 74.6 94.3 80.8 92.6 88.3 74.1 46 56.9 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 97.2 86.7 86.4 95.7 91.2 94.9 91 74.1 69.1 80 2001 2002 2003 2004 2005 97.5 90.7 89.6 96.3 92.8 95.7 91 83.6 71.8 83.6 2006 97.6 90.7 89.6 96.4 92.3 95.8 91 83.6 72.5 83.6 2007 97.6 90.7 90 96.5 92.7 95.9 91 82 73.2 83.6 2008 97.7 90.7 90 96.7 92.7 96.1 91 82 73.9 83.6 2009 97.8 90.7 90 96.8 92.7 96.2 91 82 74.6 83.6

209

Country


1990 87.6 57.5 88.8 90.3 81.9 95.4 89.8 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 90.5 76.7 91.9 90.3 87.2 96.8 93 2001 2002 2003 2004 2005 91.6 78 93.1 94.6 87.9 97.8 95.2 2006 91.7 78 93.2 94.6 88.7 97.8 95.2 2007 92.8 78 93.4 94.6 89.6 97.9 95.2 2008 92.8 78 93.6 94.6 89.6 97.9 95.2 2009 92.8 78 93.7 94.6 89.6 97.9 95.2

APPENDIX C-3

Combined Gross Enrollment (Total) (%), 1990-2009

211

Appendix C-3

Combined Gross Enrollment (Total) (%), 1990-2009

Country

Year China



1990 53.6 69.8 61.4 59.1 75 61.8 78.9 - 49.7 48.6 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 67 - 62.1 68 78.8 - 91.2 - 71.3 63.4 2001 2002 2003 2004 2005 68.5 74.4 67.2 71.5 80.6 - 97.2 - 78.3 62.3 2006 68.7 74.4 68.2 71.5 79.6 85 98.5 - 78 62.3 2007 68.7 74.4 68.2 71.5 79.6 85 98.5 - 78 62.3 2008 68.7 74.4 68.2 71.5 79.6 - 98.5 - 78 62.3 2009 68.7 74.4 68.2 71.5 79.6 - 98.5 - 78 62.3

212

Country


Costa Rica Ecuador


1990 79.5 63.1 66.8 71.3 57.3 64.7 70.2 55.5 43 59.3 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 - 83.7 90.2 77.7 70.7 66.1 - 64.5 58.2 63.5 2001 2002 2003 2004 2005 88.6 86 87.2 82.9 76.1 73 - 72.9 67 74.8 2006 88.6 86 87.2 82.5 77.8 73 - 72.3 67.6 74.8 2007 88.6 86 87.2 82.5 79 73 - 74 70.5 74.8 2008 88.6 86 87.2 82.5 79 73 - 74 70.5 74.8 2009 88.6 86 87.2 82.5 79 73 - 74 70.5 74.8

213

Country


1990 65.3 55.1 66.6 56 77.7 77.6 69.8 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 71.8 68.2 76.4 72.3 88.1 84.2 67.3 2001 2002 2003 2004 2005 79 71.3 79.5 72.1 87.5 90.4 75.4 2006 80.2 72.1 79.7 72.1 88.1 90.9 79.7 2007 80.2 72.1 79.7 72.1 88.1 90.9 85.9 2008 80.2 72.1 79.7 72.1 88.1 90.9 85.9 2009 80.2 72.1 79.7 72.1 88.1 90.9 85.9

APPENDIX D-1

Political Rights, 1990-2009

215

Appendix D-1

Political Rights, 1990-2009

Country

Year China



1990 7 6 5 3 4 2 3 2 7 1991 7 6 5 3 4 2 5 6 7 1992 7 6 5 3 4 2 3 3 7 1993 7 7 4 3 5 2 4 3 7 1994 7 7 4 3 5 2 3 3 7 1995 7 7 4 2 5 2 3 3 7 1996 7 7 4 2 4 2 2 3 7 1997 7 7 4 2 5 2 2 3 7 1998 6 6 5 2 5 2 2 2 7 1999 6 4 5 2 5 2 2 2 7 2000 6 3 5 2 5 2 1 2 7 2001 6 5 3 5 2 5 2 1 2 7 2002 6 5 3 5 2 5 2 2 2 7 2003 6 5 3 5 2 5 2 2 2 7 2004 6 5 3 4 2 5 1 2 2 7 2005 6 5 2 4 3 5 1 1 3 7 2006 6 5 2 4 3 5 1 2 7 7 2007 6 5 2 4 4 5 1 2 6 7 2008 6 5 2 4 4 5 1 2 5 7 2009 6 5 2 4 4 5 1 1 5 7

216

Country


Costa Rica Ecuador


1990 1 2 2 2 3 1 2 3 3 2 1991 1 2 2 2 2 1 2 3 3 2 1992 2 2 2 2 2 1 2 3 4 2 1993 2 2 3 2 2 1 2 3 4 3 1994 2 2 2 2 3 1 2 3 4 3 1995 2 2 2 2 4 1 2 3 4 3 1996 2 2 2 2 4 1 2 3 3 3 1997 2 1 3 2 4 1 3 2 3 2 1998 3 1 3 3 3 1 2 2 3 2 1999 2 1 3 2 4 1 2 2 3 3 2000 1 1 3 2 4 1 3 2 3 3 2001 3 1 3 2 4 1 3 2 3 3 2002 3 2 2 2 4 1 3 2 4 3 2003 2 3 2 1 4 1 3 2 4 3 2004 2 3 2 1 4 1 3 2 4 3 2005 2 3 2 1 3 1 3 2 4 3 2006 2 3 2 1 3 1 3 2 3 3 2007 2 3 2 1 3 1 3 2 3 3 2008 2 3 2 1 3 1 3 2 3 3 2009 2 3 2 1 3 1 3 2 4 3

217

Country


1990 4 3 4 4 3 1 1 1991 4 3 4 3 3 1 1 1992 4 4 4 3 6 1 3 1993 4 4 3 3 5 2 3 1994 4 4 2 4 5 2 3 1995 4 4 2 4 5 2 3 1996 4 3 2 4 4 1 2 1997 3 3 2 4 5 1 2 1998 3 2 2 4 5 1 2 1999 3 3 1 4 5 1 4 2000 2 3 1 4 3 1 3 2001 2 3 1 4 1 1 3 2002 2 3 1 4 2 1 3 2003 2 3 1 3 2 1 3 2004 2 3 1 3 2 1 3 2005 2 3 1 3 2 1 4 2006 2 3 1 3 2 1 4 2007 2 3 1 3 2 1 4 2008 2 3 1 3 2 1 4 2009 2 4 1 3 2 1 5

APPENDIX D-2

Civil Liberties, 1990-2009

219

Appendix D-2

Civil Liberties, 1990-2009

Country

Year China



1990 7 5 4 3 4 3 3 3 7 1991 7 5 4 3 4 3 5 4 7 1992 7 5 4 3 5 3 3 4 7 1993 7 6 5 4 5 2 4 5 7 1994 7 6 5 4 5 2 3 5 7 1995 7 6 5 4 5 2 3 4 7 1996 7 5 5 3 5 2 2 3 7 1997 7 5 5 3 5 2 2 3 7 1998 7 4 5 3 5 2 2 3 7 1999 7 4 5 3 5 2 2 3 7 2000 7 4 5 3 5 2 2 3 6 2001 7 3 4 5 3 5 2 2 3 6 2002 7 3 4 5 3 4 2 2 3 6 2003 7 3 4 4 3 4 2 2 3 6 2004 7 2 4 4 3 4 2 1 3 6 2005 7 2 3 4 3 4 2 1 3 5 2006 7 2 3 4 3 4 2 1 4 5 2007 7 2 3 4 3 4 2 1 4 5 2008 7 2 3 4 3 4 2 1 4 5 2009 7 2 3 4 3 4 2 2 4 5

220

Country


Costa Rica Ecuador


1990 3 3 3 2 4 1 2 4 4 3 1991 3 3 3 2 4 1 3 4 5 3 1992 3 3 3 2 4 1 3 3 5 3 1993 3 3 4 2 4 2 3 3 5 3 1994 3 3 4 2 4 2 3 3 5 3 1995 3 4 4 2 4 2 3 3 5 3 1996 3 3 4 2 4 2 4 3 4 3 1997 3 3 4 2 4 2 3 3 4 3 1998 3 3 4 2 4 2 3 3 4 3 1999 3 3 4 2 4 2 3 3 4 3 2000 2 3 3 2 4 2 3 3 4 3 2001 3 3 3 2 4 2 3 3 4 3 2002 3 3 3 1 4 2 3 3 4 3 2003 2 3 3 1 4 2 3 3 4 3 2004 2 3 3 1 4 1 3 3 4 3 2005 2 3 2 1 3 1 3 3 4 3 2006 2 3 2 1 3 1 3 3 4 3 2007 2 3 2 1 3 1 3 3 4 3 2008 2 3 2 1 4 1 3 3 4 3 2009 2 3 2 1 4 1 3 3 4 3

221

Country


1990 4 3 2 3 4 2 3 1991 4 3 2 3 5 2 3 1992 3 3 3 3 5 2 3 1993 4 5 3 3 5 2 3 1994 4 5 3 3 4 2 3 1995 4 4 3 3 4 2 3 1996 3 3 3 3 3 2 3 1997 4 3 3 3 4 2 3 1998 4 3 3 3 4 2 3 1999 4 3 2 3 4 2 4 2000 3 3 2 3 3 1 5 2001 3 3 2 3 3 1 5 2002 2 3 2 3 3 1 4 2003 2 3 2 3 3 1 4 2004 2 3 2 3 3 1 4 2005 2 3 2 3 3 1 4 2006 3 3 2 3 3 1 4 2007 3 3 2 3 3 1 4 2008 3 3 2 3 3 1 4 2009 3 3 2 3 3 1 4

APPENDIX D-3

Press Freedom, 1990-2009

223

Appendix D-3

Press Freedom, 1990-2009

Country

Year China



1990 1991 1992 1993 1994 89 30 58 58 55 60 29 29 54 71 1995 83 30 71 64 46 65 28 30 49 68 1996 83 30 74 61 46 61 22 30 31 68 1997 83 41 77 61 46 66 25 28 34 69 1998 81 - 77 61 30 66 28 25 31 71 1999 81 - 53 66 30 66 28 25 30 71 2000 80 - 49 70 30 66 27 21 30 75 2001 80 - 47 70 30 68 27 22 29 80 2002 80 - 53 71 30 68 30 21 33 82 2003 80 - 56 71 30 66 29 24 36 82 2004 80 - 55 69 34 64 29 23 39 82 2005 82 28 58 69 35 66 29 21 42 82 2006 83 29 58 65 40 66 30 20 50 79 2007 84 30 54 68 46 69 30 20 59 77 2008 84 30 54 65 45 69 30 20 56 82 2009 85 33 54 65 45 68 30 23 57 83

224

Country


Costa Rica Ecuador


1990 1991 1992 1993 1994 29 20 27 30 49 16 23 41 59 55 1995 29 17 30 30 48 21 41 32 60 45 1996 31 17 30 28 54 18 38 41 56 34 1997 31 20 30 30 55 16 40 53 56 47 1998 36 18 32 27 55 16 40 53 59 47 1999 41 18 35 27 60 16 40 53 60 48 2000 41 22 33 27 59 16 44 40 54 48 2001 33 22 31 27 60 16 40 37 49 45 2002 37 25 32 22 60 17 40 35 49 43 2003 39 30 38 22 63 14 41 38 58 51 2004 35 37 36 23 63 19 42 42 62 52 2005 41 35 40 24 63 19 41 41 58 51 2006 45 33 39 26 61 18 41 43 58 52 2007 49 37 42 30 57 20 41 42 59 51 2008 47 39 42 30 59 19 41 42 58 51 2009 49 42 42 29 59 19 44 42 60 52

225

Country


1990 1991 1992 1993 1994 60 56 27 41 58 23 30 1995 54 53 22 56 57 25 49 1996 52 44 30 52 60 25 31 1997 52 44 30 52 56 29 32 1998 54 40 30 52 59 30 33 1999 54 40 30 47 63 30 33 2000 50 40 30 51 67 29 34 2001 46 40 30 51 54 30 34 2002 40 32 30 51 30 25 44 2003 38 40 34 55 35 30 68 2004 36 37 45 54 34 26 68 2005 42 42 44 56 40 29 72 2006 48 44 43 57 39 28 72 2007 48 42 43 60 42 30 74 2008 51 43 44 60 44 30 74 2009 55 45 44 59 44 26 73

APPENDIX E-1

Government Effectiveness, 1990-2009

227

Appendix E-1

Government Effectiveness, 1990-2009

Country

Year China



1990 1991 1992 1993 1994 1995 1996 0.044 1.123 0.197 0.878 0.038 2.011 0.814 1.232 0.432 -0.22 1997 1998 -0.33 0.896 -0.83 0.557 -0.21 2.027 0.316 0.641 0.088 -0.59 1999 2000 -0.13 1.078 -0.5 0.831 -0.19 2.076 0.749 0.743 0.071 -0.44 2001 2002 -0.05 1.285 -0.53 0.874 -0.14 1.867 0.891 0.837 0.219 -0.44 2003 -0.1 1.498 -0.52 1.035 -0.1 1.918 0.941 0.986 0.346 -0.42 2004 -0.05 1.668 -0.37 1.103 -0.23 2.019 1.017 1.077 0.287 -0.44 2005 -0.21 1.569 -0.45 1.058 -0.07 1.882 1.038 0.892 0.454 -0.22 2006 0.028 1.789 -0.3 1.11 -0.02 2.049 1.104 1.013 0.356 -0.17 2007 0.206 1.833 -0.26 1.217 0.118 2.26 1.222 0.92 0.349 -0.17 2008 0.152 1.786 -0.21 1.135 0.083 2.267 1.119 0.923 0.188 -0.16 2009 0.116 1.757 -0.21 0.989 -0.14 2.194 1.112 1.061 0.152 -0.26

228

Country


Costa Rica Ecuador


1990 1991 1992 1993 1994 1995 1996 0.557 0.07 -0.24 0.985 0.184 0.078 -0.96 -0.55 -0.33 -0.92 1997 1998 0.182 -0.07 -0.1 1.376 -0.47 0.681 -0.49 -0.6 -0.41 -0.68 1999 2000 0.082 -0.28 0.078 1.185 -0.4 0.459 -0.83 -0.56 -0.53 -0.54 2001 2002 -0.4 -0.26 -0.04 1.216 -0.44 0.425 -0.81 -0.48 -0.5 -0.6 2003 -0.16 -0.32 0.21 1.283 -0.19 0.428 -0.71 -0.29 -0.44 -0.54 2004 -0.09 -0.54 0.143 1.265 -0.06 0.364 -0.82 -0.22 -0.61 -0.53 2005 -0.25 -0.77 0.006 1.24 -0.11 0.255 -0.97 -0.31 -0.65 -0.63 2006 -0.08 -0.73 -0.05 1.166 -0.01 0.154 -1.02 -0.25 -0.59 -0.59 2007 -0.12 -0.76 -0.07 1.309 0.034 0.295 -1.01 -0.21 -0.58 -0.57 2008 -0.23 -0.84 0.057 1.258 0.079 0.393 -1.05 -0.2 -0.59 -0.6 2009 -0.42 -0.72 0.076 1.209 0.041 0.431 -0.84 -0.04 -0.69 -0.71

229

Country


1990 1991 1992 1993 1994 1995 1996 -0.03 -1.22 -0.37 -1.13 -0.15 -0.07 -0.91 1997 1998 0.315 -0.42 0.248 -1.01 0.102 0.649 -0.5 1999 2000 0.284 -0.61 0.191 -1.16 -0.11 0.549 -0.76 2001 2002 0.302 -0.73 -0.01 -1.07 -0.32 0.664 -1 2003 0.174 -0.69 -0.02 -0.88 -0.39 0.639 -0.93 2004 0.146 -0.68 0.04 -0.84 -0.45 0.403 -1.02 2005 0.009 -0.8 0.112 -0.79 -0.6 0.541 -0.89 2006 0.154 -0.96 0.126 -0.85 -0.52 0.449 -0.96 2007 0.171 -0.96 0.214 -0.84 -0.43 0.537 -1.08 2008 0.162 -0.95 0.268 -0.88 -0.24 0.576 -1.11 2009 0.168 -1.04 0.246 -0.93 -0.36 0.688 -0.95

APPENDIX E-2

Regulatory Quality, 1990-2009

231

Appendix E-2

Regulatory Quality, 1990-2009

Country

Year China



1990 1991 1992 1993 1994 1995 1996 0.195 1.521 0.398 0.665 0.519 1.636 0.473 0.856 0.455 0.24 1997 1998 -0.26 1.708 -0.27 0.583 0.301 2.026 0.33 1.036 0.162 -0.61 1999 2000 -0.28 1.698 -0.31 0.383 0.148 1.958 0.579 1.13 0.459 -0.68 2001 2002 -0.49 1.655 -0.66 0.487 -0.03 1.881 0.789 1.026 0.183 -0.69 2003 -0.35 1.855 -0.62 0.676 0.009 1.829 0.701 0.991 0.282 -0.55 2004 -0.24 1.935 -0.6 0.495 -0.16 1.81 0.83 1.221 0.282 -0.48 2005 -0.2 1.883 -0.45 0.541 -0.01 1.789 0.835 1.116 0.458 -0.56 2006 -0.28 1.945 -0.28 0.536 -0.08 1.742 0.755 0.957 0.287 -0.57 2007 -0.18 1.958 -0.25 0.566 -0.06 1.858 0.923 1.001 0.164 -0.43 2008 -0.15 1.992 -0.23 0.413 -0.02 1.966 0.714 1.088 0.286 -0.52 2009 -0.2 1.83 -0.28 0.335 0.016 1.835 0.849 1.142 0.367 -0.56

232

Country


Costa Rica Ecuador


1990 1991 1992 1993 1994 1995 1996 0.83 0.688 0.36 1.265 0.533 0.701 0.235 0.523 0.187 -0.37 1997 1998 0.636 0.3 0.296 1.336 0.088 0.878 -0.05 0.301 0.08 -0.12 1999 2000 0.313 0.147 0.354 1.387 0.112 0.656 -0.45 0.169 -0.11 -0.3 2001 2002 -1.08 0.01 0.23 1.483 0.091 0.392 -0.6 0.005 -0.13 -0.4 2003 -0.72 -0.03 0.345 1.533 0 0.488 -0.57 -0.11 -0.28 -0.5 2004 -0.73 -0.12 0.097 1.451 -0.03 0.588 -0.67 0.122 -0.17 -0.33 2005 -0.64 -0.6 0.065 1.474 0.071 0.564 -0.86 0.059 -0.33 -0.47 2006 -0.73 -0.94 -0.02 1.483 0.137 0.353 -1.1 0.082 -0.16 -0.42 2007 -0.8 -1.15 -0.04 1.516 0.243 0.435 -1.11 0.172 -0.15 -0.2 2008 -0.84 -1 0.069 1.587 0.27 0.507 -1.15 0.206 -0.11 -0.2 2009 -0.9 -0.98 0.177 1.502 0.24 0.533 -1.36 0.383 -0.07 -0.24

233

Country


1990 1991 1992 1993 1994 1995 1996 0.646 -0.16 0.715 0.862 0.582 0.98 0.037 1997 1998 0.37 0.006 0.904 -0.67 0.569 0.875 -0.15 1999 2000 0.368 -0.13 0.647 -0.81 0.446 0.752 -0.42 2001 2002 0.532 -0.4 0.522 -0.53 0.141 0.534 -0.56 2003 0.446 -0.34 0.377 -0.65 0.148 0.317 -1.06 2004 0.509 -0.28 0.312 -0.71 0.278 0.296 -1.1 2005 0.366 -0.37 0.248 -0.8 0.131 0.284 -1.17 2006 0.417 -0.46 0.338 -0.65 0.151 0.276 -1.2 2007 0.442 -0.39 0.399 -0.57 0.273 0.163 -1.54 2008 0.41 -0.35 0.614 -0.5 0.365 0.204 -1.49 2009 0.348 -0.39 0.443 -0.41 0.41 0.372 -1.69

APPENDIX E-3

Rule of Law, 1990-2009

235

Appendix E-3

Rule of Law, 1990-2009

Country

Year China



1990 1991 1992 1993 1994 1995 1996 -0.2 0.97 -0.27 0.794 0.109 1.544 0.846 0.901 0.626 -0.48 1997 1998 -0.37 0.947 -0.74 0.455 -0.05 1.347 0.812 0.878 0.506 -0.39 1999 2000 -0.44 0.819 -0.83 0.317 -0.48 1.275 0.847 0.917 0.516 -0.36 2001 2002 -0.34 1.112 -1 0.423 -0.47 1.38 0.91 0.932 0.307 -0.45 2003 -0.43 1.358 -0.95 0.453 -0.54 1.554 0.783 0.965 0.106 -0.38 2004 -0.35 1.444 -0.74 0.567 -0.59 1.678 0.842 0.96 0.072 -0.35 2005 -0.42 1.558 -0.81 0.599 -0.33 1.705 0.963 0.98 0.142 -0.23 2006 -0.52 1.576 -0.71 0.564 -0.37 1.645 0.845 0.781 0.038 -0.4 2007 -0.45 1.552 -0.64 0.561 -0.47 1.656 1.023 0.767 -0.02 -0.41 2008 -0.33 1.521 -0.62 0.493 -0.53 1.655 0.854 0.779 -0.06 -0.38 2009 -0.35 1.491 -0.56 0.547 -0.53 1.611 0.999 0.927 -0.13 -0.43

236

Country


Costa Rica Ecuador


1990 1991 1992 1993 1994 1995 1996 0.192 -0.21 -0.18 1.31 -0.75 0.599 -0.42 -0.93 -1 -0.81 1997 1998 0.11 -0.25 -0.3 1.173 -0.82 0.741 -0.67 -0.62 -1.05 -0.94 1999 2000 -0.02 -0.35 -0.27 1.283 -0.96 0.646 -0.69 -0.74 -0.85 -0.96 2001 2002 -1.05 -0.35 -0.34 1.295 -0.92 0.658 -0.67 -0.55 -0.94 -0.9 2003 -0.7 -0.39 -0.32 1.224 -0.94 0.64 -0.66 -0.53 -1.17 -0.86 2004 -0.79 -0.53 -0.33 1.235 -0.86 0.581 -0.76 -0.42 -1.1 -0.79 2005 -0.57 -0.9 -0.45 1.249 -0.73 0.522 -0.9 -0.46 -1.12 -0.77 2006 -0.57 -0.92 -0.41 1.244 -0.56 0.431 -1.1 -0.6 -1.12 -0.97 2007 -0.61 -0.96 -0.42 1.233 -0.51 0.358 -1.13 -0.66 -1.18 -0.89 2008 -0.66 -1.1 -0.34 1.284 -0.47 0.442 -1.26 -0.73 -1.18 -0.92 2009 -0.66 -1.22 -0.18 1.251 -0.44 0.563 -1.28 -0.78 -1.12 -0.87

237

Country


1990 1991 1992 1993 1994 1995 1996 -0.45 -0.32 -0.19 -0.39 -0.6 0.622 -0.6 1997 1998 -0.51 -0.72 -0.19 -1 -0.68 0.581 -0.7 1999 2000 -0.34 -0.91 -0.15 -1.02 -0.63 0.594 -0.8 2001 2002 -0.26 -0.73 -0.1 -1.19 -0.47 0.648 -1.16 2003 -0.28 -0.62 -0.13 -1.2 -0.53 0.649 -1.25 2004 -0.32 -0.85 -0.11 -1.08 -0.57 0.46 -1.25 2005 -0.4 -0.65 -0.16 -1.04 -0.78 0.45 -1.31 2006 -0.43 -0.8 -0.13 -1.03 -0.76 0.484 -1.4 2007 -0.51 -0.84 -0.18 -1.1 -0.79 0.532 -1.57 2008 -0.68 -0.84 -0.18 -1.04 -0.76 0.562 -1.6 2009 -0.57 -0.83 -0.09 -0.98 -0.66 0.723 -1.59

APPENDIX E-4

Control of Corruption, 1990-2009

239

Appendix E-4

Control of Corruption, 1990-2009

Country

Year China



1990 1991 1992 1993 1994 1995 1996 -0.2 1.409 -0.34 0.548 -0.23 2.174 0.477 0.568 -0.28 -0.3 1997 1998 -0.26 1.1 -1.07 0.617 -0.23 2.12 0.266 0.743 0.031 -0.53 1999 2000 -0.23 1.056 -0.93 0.371 -0.46 2.068 0.265 0.836 -0.18 -0.68 2001 2002 -0.47 1.528 -1.08 0.324 -0.41 2.28 0.469 0.685 -0.29 -0.59 2003 -0.38 1.586 -0.98 0.384 -0.43 2.223 0.424 0.761 -0.18 -0.54 2004 -0.62 1.708 -0.9 0.509 -0.53 2.27 0.439 0.829 -0.17 -0.76 2005 -0.74 1.799 -0.86 0.341 -0.55 2.085 0.703 0.839 -0.01 -0.77 2006 -0.52 1.877 -0.75 0.388 -0.72 2.145 0.423 0.655 -0.21 -0.7 2007 -0.6 1.906 -0.6 0.352 -0.66 2.224 0.575 0.54 -0.29 -0.61 2008 -0.46 1.93 -0.61 0.14 -0.67 2.277 0.441 0.506 -0.39 -0.68 2009 -0.53 1.845 -0.71 0.021 -0.71 2.261 0.522 0.573 -0.23 -0.52

240

Country


Costa Rica Ecuador


1990 1991 1992 1993 1994 1995 1996 -0.24 -1.06 -0.25 1.354 -0.32 0.585 -1.04 -1.03 -1.08 -1.08 1997 1998 -0.07 -0.32 0.054 1.323 -0.66 0.999 -0.91 -0.69 -0.7 -0.73 1999 2000 -0.26 -0.55 0.107 1.535 -0.53 0.952 -0.93 -0.4 -0.54 -0.8 2001 2002 -0.7 -0.94 -0.13 1.563 -0.32 0.764 -1.1 -0.65 -0.55 -0.91 2003 -0.39 -0.84 0.21 1.281 -0.33 0.708 -0.84 -0.43 -0.7 -0.8 2004 -0.37 -0.78 0.106 1.484 -0.13 0.315 -0.75 -0.38 -0.49 -0.74 2005 -0.41 -0.8 -0.18 1.417 -0.16 0.421 -0.79 -0.43 -0.64 -0.74 2006 -0.38 -0.47 -0.15 1.403 -0.09 0.378 -0.84 -0.19 -0.75 -0.79 2007 -0.38 -0.42 -0.15 1.329 -0.19 0.446 -0.93 -0.28 -0.74 -0.7 2008 -0.45 -0.53 -0.05 1.333 -0.21 0.478 -0.85 -0.36 -0.68 -0.84 2009 -0.49 -0.71 -0.07 1.371 -0.29 0.697 -0.92 -0.17 -0.6 -0.89

241

Country


1990 1991 1992 1993 1994 1995 1996 -0.26 -0.25 -0.33 -0.29 -0.23 0.539 -1 1997 1998 -0.43 -0.78 -0.34 -1.39 -0.34 0.869 -0.76 1999 2000 -0.23 -0.91 -0.52 -1.45 -0.29 0.778 -0.56 2001 2002 -0.12 -0.43 -0.35 -1.34 -0.21 0.82 -1.06 2003 -0.01 -0.43 -0.3 -1.51 -0.03 0.917 -1.1 2004 -0.23 -0.36 -0.23 -1.45 -0.33 0.805 -0.96 2005 -0.28 -0.62 -0.4 -1.51 -0.41 1.044 -1.03 2006 -0.23 -0.74 -0.38 -1.31 -0.26 1.006 -0.96 2007 -0.23 -0.83 -0.37 -1.25 -0.3 1.096 -1.04 2008 -0.21 -0.8 -0.14 -1 -0.22 1.196 -1.08 2009 -0.27 -0.76 -0.26 -0.88 -0.36 1.22 -1.2

APPENDIX F-1

Protection of Property Rights, 1990-2009

243

Appendix F-1

Protection of Property Rights, 1990-2009

Country

Year China



1990 1991 1992 1993 1994 1995 30 90 50 70 50 90 90 90 70 10 1996 30 90 50 70 70 90 90 90 90 10 1997 30 90 50 70 70 90 90 90 90 10 1998 30 90 50 70 70 90 90 90 70 10 1999 30 90 50 70 70 90 90 90 70 10 2000 30 90 50 70 70 90 90 90 70 10 2001 30 90 30 50 50 90 90 90 70 10 2002 30 90 30 50 50 90 90 70 70 10 2003 30 90 30 50 50 90 70 70 70 10 2004 30 90 30 50 30 90 70 70 50 10 2005 30 90 30 50 30 90 70 70 50 10 2006 30 90 30 50 30 90 70 70 50 10 2007 20 90 30 50 30 90 70 70 50 10 2008 20 90 30 50 30 90 70 70 50 10 2009 20 90 30 50 30 90 70 70 50 10

244

Country


Costa Rica Ecuador


1990 1991 1992 1993 1994 1995 70 50 50 90 50 50 50 50 50 50 1996 70 50 50 90 50 50 50 50 50 50 1997 70 50 50 90 50 50 50 50 50 50 1998 70 70 50 90 50 50 50 50 50 50 1999 70 50 50 90 50 50 50 70 50 50 2000 70 50 50 90 50 50 30 70 50 50 2001 50 50 50 90 50 50 30 50 50 30 2002 50 30 50 90 30 50 30 50 30 50 2003 30 30 50 90 30 50 30 50 30 50 2004 30 30 50 90 30 50 30 50 30 30 2005 30 30 50 90 30 50 30 50 30 30 2006 30 30 50 90 30 50 30 50 30 30 2007 30 30 50 90 30 50 30 50 30 30 2008 30 25 50 90 40 50 30 50 30 30 2009 20 20 50 90 40 50 25 50 30 30

245

Country


1990 1991 1992 1993 1994 1995 50 30 50 50 50 50 10 1996 50 30 50 50 50 50 50 1997 50 30 50 50 50 70 50 1998 50 30 50 30 50 70 50 1999 50 30 50 30 70 70 50 2000 50 30 50 30 50 70 50 2001 50 30 50 30 50 70 30 2002 50 30 30 30 30 70 30 2003 50 30 30 30 30 70 30 2004 50 30 30 30 30 70 30 2005 50 30 30 30 30 70 30 2006 50 30 30 30 30 70 30 2007 50 30 30 30 40 70 30 2008 50 25 30 35 40 70 10 2009 50 25 30 30 40 70 5

APPENDIX F-2

Economic Freedom, 1990-2009

247

247

Appendix F-2

Economic Freedom, 1990-2009

Country

Year China



1990 4.85 8.22 6.46 7.26 5.77 8.15 6.24 7.05 6.82 - 1991 1992 1993 1994 1995 5.2 9.08 6.42 7.48 7.13 8.8 6.34 7.27 7.1 - 1996 1997 1998 1999 2000 5.73 8.82 6.04 6.72 6.98 8.53 6.58 7.31 6.66 - 2001 5.79 8.76 5.72 6.35 6.81 8.44 6.9 7.19 6.34 - 2002 5.99 8.76 5.92 6.49 6.85 8.68 6.96 7.34 6.77 - 2003 6.06 8.82 6.2 6.58 6.9 8.59 7.05 7.35 6.76 5.7 2004 5.86 8.75 6.13 6.74 6.66 8.6 7.14 7.56 6.79 6.12 2005 6.28 8.98 6.37 6.89 7.09 8.77 7.3 7.6 6.95 6.38 2006 6.33 8.98 6.33 6.94 7.01 8.7 7.46 7.66 7.05 6.5 2007 6.68 9.03 6.41 6.92 6.92 8.74 7.44 7.62 7.07 6.29 2008 6.65 9.05 6.44 6.72 6.77 8.7 7.28 7.48 7.06 6.15 2009

248

248

Country


Costa Rica Ecuador


1990 4.65 5.65 4.29 6.67 5.19 6.89 5.57 4.81 5.79 5.83 1991 1992 1993 1994 1995 6.76 6.73 4.51 7.47 5.35 6.98 6.27 7.04 6.95 6.39 1996 1997 1998 1999 2000 7.19 6.79 5.85 7.28 5.31 7.31 5.69 7.3 6.33 6.51 2001 6.49 6.51 5.83 7.47 5.52 7.17 5.48 7.24 6.34 6.37 2002 6.09 6.39 6 7.59 5.53 7.07 5.92 7.14 6.4 6.65 2003 5.92 6.31 5.88 7.74 5.72 7.33 5.87 7.17 6.52 6.69 2004 6.12 6.25 5.84 7.67 5.72 7.16 5.24 7.24 6.59 6.73 2005 5.92 6.39 6.25 7.97 5.91 7.39 5.8 7.48 7.03 6.98 2006 6.05 6.42 6.11 8 6.09 7.55 5.87 7.44 7.1 7.3 2007 6.25 6.19 6.09 8.12 6.13 7.6 5.84 7.46 7.21 7.47 2008 5.99 6.16 6.18 8.03 6.19 7.45 6.07 7.44 7.1 7.26 2009

249

249

Country


1990 6.18 3.07 6.78 6.21 3.97 6.24 5.63 1991 1992 1993 1994 1995 6.44 5.62 7.65 6.99 6.29 6.18 4.3 1996 1997 1998 1999 2000 6.42 6.5 7.41 6.28 7.07 6.68 5.61 2001 6.26 6.32 7.4 6.29 7.05 6.68 5.5 2002 6.54 6.55 7.38 6.16 7.04 6.95 4.48 2003 6.5 6.66 7.42 6.15 7.05 6.83 4.07 2004 6.63 6.56 7.4 6.09 7.07 6.94 4.53 2005 7.02 6.88 7.58 6.4 7.23 7 4.72 2006 6.98 6.98 7.64 6.46 7.21 6.93 4.81 2007 6.94 7.04 7.63 6.42 7.27 6.95 4.35 2008 6.89 6.91 7.43 6.55 7.39 6.93 4.33 2009

APPENDIX G

SPSS Output for the Impact of Politico-Economic Institutions on

Annual Growth Rates of GDP per Capita in East Asia

and Latin America

Appendix G


Annual Growth Rates of GDP per Capita in


Regression Descriptive Statistics

4.1612 .87129 1324.5339 1.86959 1325.5452 1.63844 131.4266 .20669 13

72.8588 1.20571 1387.9247 4.22604 1371.3189 5.98123 133.1929 .13738 133.3511 .28498 13

46.4191 1.86537 13.1012 .04098 13.7146 .03624 13.8197 .06062 13.9444 .08699 13

52.6176 6.51053 136.8377 .12094 13

114.1224 2.81803 13.9435 .13841 13

Y1X1X2X3X4X5X6X7X8X9X10X11X12X13X14X15C1C2

Mean Std. Deviation N

Model Summary

1.000a 1.000 1.000 .Model1

R R SquareAdjustedR Square

Std. Error ofthe Estimate

Predictors: (Constant), C2, C1, X1, X13, X10, X15,X11, X3, X7, X5, X9, X12

a.

252

ANOVAb

9.110 12 .759 . .a

.000 0 .9.110 12

RegressionResidualTotal

Model1

Sum ofSquares df Mean Square F Sig.

Predictors: (Constant), C2, C1, X1, X13, X10, X15, X11, X3, X7, X5, X9, X12a.

Dependent Variable: Y1b.

Coefficientsa

19.608 .000 . .-.914 .000 -1.961 . . .030 33.5595.637 .000 1.337 . . .068 14.774-.288 .000 -1.398 . . .005 202.550

-7.952 .000 -1.254 . . .013 77.6941.470 .000 3.148 . . .013 78.916

15.829 .000 .744 . . .017 57.43361.135 .000 2.543 . . .006 161.583

-54.980 .000 -3.825 . . .003 319.66428.067 .000 2.802 . . .006 156.4501.749 .000 .243 . . .154 6.478-.376 .000 -1.217 . . .034 29.277

-15.205 .000 -2.415 . . .022 45.170

(Constant)X1X3X5X7X9X10X11X12X13X15C1C2

Model1

B Std. Error

UnstandardizedCoefficients

Beta

StandardizedCoefficients

t Sig. Tolerance VIFCollinearity Statistics

Dependent Variable: Y1a.

253


4.1612 .87129 1324.5339 1.86959 1325.5452 1.63844 131.4266 .20669 13

72.8588 1.20571 1387.9247 4.22604 1371.3189 5.98123 133.1929 .13738 133.3511 .28498 13

46.4191 1.86537 13.1012 .04098 13.7146 .03624 13.8197 .06062 13.9444 .08699 13

52.6176 6.51053 13114.1224 2.81803 13

.9435 .13841 13

Y1X1X2X3X4X5X6X7X8X9X10X11X12X13X14C1C2


Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

9.110 12 .759 . .a

.000 0 .9.110 12


Model1




254

Coefficientsa

29.794 .000 . .-.799 .000 -1.714 . . .020 49.120.290 .000 .546 . . .031 32.730

4.601 .000 1.091 . . .044 22.799-.302 .000 -1.465 . . .005 194.143

-9.266 .000 -1.461 . . .016 63.3031.209 .000 2.587 . . .006 161.300

15.402 .000 .724 . . .017 58.87756.906 .000 2.367 . . .005 184.343

-48.987 .000 -3.408 . . .002 417.11225.725 .000 2.568 . . .005 196.940

-.284 .000 -.918 . . .021 46.882-14.779 .000 -2.348 . . .021 48.678


Model1

B Std. Error


Beta





4.1612 .87129 1324.5339 1.86959 1325.5452 1.63844 131.4266 .20669 13

72.8588 1.20571 1387.9247 4.22604 1371.3189 5.98123 133.1929 .13738 133.3511 .28498 13.1012 .04098 13.7146 .03624 13.8197 .06062 13.9444 .08699 13

52.6176 6.51053 13114.1224 2.81803 13

.9435 .13841 13

Y1X1X2X3X4X5X6X7X8X10X11X12X13X14C1C2


255

Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

9.110 12 .759 . .a

.000 0 .9.110 12


Model1




Coefficientsa

77.020 .000 . .-.257 .000 -.550 . . .118 8.4471.294 .000 2.433 . . .028 35.525

-1.365 .000 -.324 . . .056 17.855-.408 .000 -1.980 . . .004 252.641-.282 .000 -1.933 . . .011 90.014

-14.619 .000 -2.305 . . .009 115.87221.886 .000 1.029 . . .013 74.51540.474 .000 1.683 . . .008 119.423

-23.500 .000 -1.635 . . .005 186.39313.899 .000 1.388 . . .011 87.746

-.135 .000 -.435 . . .045 22.222-4.937 .000 -.784 . . .033 29.876


Model1

B Std. Error


Beta




256


4.1612 .87129 1324.5339 1.86959 1325.5452 1.63844 131.4266 .20669 13

72.8588 1.20571 1387.9247 4.22604 1371.3189 5.98123 133.1929 .13738 133.3511 .28498 13.7146 .03624 13.8197 .06062 13.9444 .08699 13

52.6176 6.51053 13114.1224 2.81803 13

.9435 .13841 13

Y1X1X2X3X4X5X6X7X8X11X12X13X14C1C2


Variables Entered/Removedb

C2, C1,X1, X13,X11, X2,X3, X7,X12, X6,X5, X4

a

. Enter

Model1

VariablesEntered

VariablesRemoved Method

Tolerance = .000 limits reached.a.


Model Summary

1.000a 1.000 1.000 .Model1



Predictors: (Constant), C2, C1, X1, X13, X11, X2, X3,X7, X12, X6, X5, X4

a.

257

ANOVAb

9.110 12 .759 . .a

.000 0 .9.110 12


Model1




Coefficientsa

-118.136 .000 . .-.300 .000 -.644 . . .091 10.9961.807 .000 3.397 . . .006 154.1183.326 .000 .789 . . .019 51.4463.414 .000 4.725 . . .001 1570.169-.520 .000 -2.520 . . .002 400.310-.665 .000 -4.566 . . .001 867.581

-14.139 .000 -2.229 . . .010 103.55949.210 .000 2.047 . . .005 186.097

-21.426 .000 -1.491 . . .006 156.05414.653 .000 1.463 . . .010 99.581

-.447 .000 -1.446 . . .007 145.598-12.865 .000 -2.044 . . .007 139.463


Model1

B Std. Error


Beta




258

Regression

Descriptive Statistics

4.1612 .87129 1324.5339 1.86959 1325.5452 1.63844 131.4266 .20669 13

72.8588 1.20571 1387.9247 4.22604 1371.3189 5.98123 133.1929 .13738 133.3511 .28498 13.7146 .03624 13.8197 .06062 13.9444 .08699 13

52.6176 6.51053 13.9435 .13841 13

Y1X1X2X3X4X5X6X7X8X11X12X13X14C2


Model Summary

1.000a 1.000 1.000 .Model1



Predictors: (Constant), C2, X1, X13, X7, X11, X2, X3,X12, X6, X5, X4, X14

a.

ANOVAb

9.110 12 .759 . .a

.000 0 .9.110 12


Model1


Predictors: (Constant), C2, X1, X13, X7, X11, X2, X3, X12, X6, X5, X4, X14a.


259

Coefficientsa

78.614 .000 . ..639 .000 1.370 . . .004 229.024

1.665 .000 3.130 . . .009 105.990-1.957 .000 -.464 . . .024 41.8361.041 .000 1.441 . . .004 248.810-.731 .000 -3.546 . . .001 806.447.209 .000 1.433 . . .002 485.086

-18.492 .000 -2.916 . . .004 243.37362.213 .000 2.588 . . .003 324.508

-35.148 .000 -2.445 . . .002 406.410.195 .000 .019 . . .049 20.501

-1.024 .000 -7.651 . . .000 4074.843-62.780 .000 -9.973 . . .000 5715.362

(Constant)X1X2X3X4X5X6X7X11X12X13X14C2

Model1

B Std. Error


Beta




Regression


4.0650 .91123 1424.4119 1.85338 1425.5960 1.58564 141.4097 .20839 14

72.9914 1.26026 1488.2126 4.20073 1471.7760 5.99573 143.1960 .13247 143.3348 .28047 14.7136 .03503 14.8312 .07235 14.9534 .09008 14

52.0567 6.59783 14

Y1X1X2X3X4X5X6X7X8X11X12X13X14


260

Model Summary

.990a .981 .753 .45263Model1



Predictors: (Constant), X14, X7, X11, X13, X2, X3,X12, X5, X1, X6, X4, X8

a.

ANOVAb

10.590 12 .882 4.307 .361a

.205 1 .20510.795 13


Model1


Predictors: (Constant), X14, X7, X11, X13, X2, X3, X12, X5, X1, X6, X4, X8a.


Coefficientsa

-19.731 227.299 -.087 .945-.350 .617 -.712 -.567 .671 .012 83.0291.516 2.175 2.638 .697 .613 .001 754.6361.273 6.115 .291 .208 .869 .010 103.043.358 3.211 .496 .112 .929 .001 1038.995

-.115 .322 -.531 -.358 .781 .009 115.928-.253 .452 -1.664 -.559 .675 .002 466.585

-13.428 29.216 -1.952 -.460 .726 .001 950.4756.741 29.349 2.075 .230 .856 .000 4299.701

14.920 13.935 .574 1.071 .478 .066 15.120-3.331 15.662 -.264 -.213 .867 .012 81.4839.696 29.605 .959 .328 .799 .002 451.314-.053 .508 -.385 -.105 .934 .001 712.699

(Constant)X1X2X3X4X5X6X7X8X11X12X13X14

Model1

B Std. Error


Beta




261

Regression


4.0650 .91123 1424.4119 1.85338 1425.5960 1.58564 141.4097 .20839 14

72.9914 1.26026 1488.2126 4.20073 1471.7760 5.99573 143.1960 .13247 143.3348 .28047 14.7136 .03503 14.8312 .07235 14

52.0567 6.59783 14

Y1X1X2X3X4X5X6X7X8X11X12X14


Model Summary

.989a .979 .863 .33679Model1



Predictors: (Constant), X14, X7, X11, X2, X3, X12,X1, X5, X6, X4, X8

a.

ANOVAb

10.568 11 .961 8.470 .110a

.227 2 .11310.795 13


Model1


Predictors: (Constant), X14, X7, X11, X2, X3, X12, X1, X5, X6, X4, X8a.


262

Coefficientsa

53.181 34.138 1.558 .260-.159 .152 -.324 -1.051 .403 .111 9.048.822 .365 1.430 2.254 .153 .026 38.316

-.595 1.645 -.136 -.362 .752 .074 13.470-.670 .502 -.926 -1.334 .314 .022 45.862-.017 .087 -.079 -.197 .862 .065 15.456-.108 .070 -.711 -1.543 .263 .049 20.209

-4.006 3.792 -.582 -1.057 .401 .035 28.917-2.696 4.142 -.830 -.651 .582 .006 154.70911.267 6.215 .433 1.813 .212 .184 5.4331.455 4.194 .116 .347 .762 .095 10.551.101 .139 .735 .729 .542 .010 96.715

(Constant)X1X2X3X4X5X6X7X8X11X12X14

Model1

B Std. Error


Beta




Regression


4.0650 .91123 1424.4119 1.85338 1425.5960 1.58564 141.4097 .20839 14

72.9914 1.26026 1471.7760 5.99573 143.1960 .13247 143.3348 .28047 14.7136 .03503 14.8312 .07235 14

52.0567 6.59783 14

Y1X1X2X3X4X6X7X8X11X12X14


Model Summary

.989a .979 .907 .27763Model1



Predictors: (Constant), X14, X7, X11, X2, X3, X12,X1, X6, X4, X8

a.

263

ANOVAb

10.563 10 1.056 13.705 .027a

.231 3 .07710.795 13


Model1


Predictors: (Constant), X14, X7, X11, X2, X3, X12, X1, X6, X4, X8a.


Coefficientsa

50.879 26.434 1.925 .150-.163 .124 -.331 -1.309 .282 .112 8.939.774 .223 1.346 3.473 .040 .048 21.039

-.509 1.308 -.116 -.389 .723 .080 12.521-.654 .408 -.904 -1.601 .208 .022 44.680-.105 .056 -.689 -1.870 .158 .053 19.009

-3.465 2.150 -.504 -1.612 .205 .073 13.675-3.115 2.930 -.959 -1.063 .366 .009 113.87010.926 4.920 .420 2.221 .113 .200 5.0101.112 3.143 .088 .354 .747 .115 8.721.116 .096 .842 1.207 .314 .015 68.150

(Constant)X1X2X3X4X6X7X8X11X12X14

Model1

B Std. Error


Beta



Dependent Variable: Y1a. Regression


4.0650 .91123 1424.4119 1.85338 1425.5960 1.58564 141.4097 .20839 14

72.9914 1.26026 1471.7760 5.99573 143.1960 .13247 14.7136 .03503 14.8312 .07235 14

52.0567 6.59783 14

Y1X1X2X3X4X6X7X11X12X14


264

Model Summary

.985a .971 .904 .28212Model1



Predictors: (Constant), X14, X7, X11, X2, X3, X12,X1, X6, X4

a.

ANOVAb

10.476 9 1.164 14.625 .010a

.318 4 .08010.795 13


Model1


Predictors: (Constant), X14, X7, X11, X2, X3, X12, X1, X6, X4a.


Coefficientsa

55.986 26.414 2.120 .101-.163 .126 -.331 -1.289 .267 .112 8.939.959 .141 1.669 6.818 .002 .123 8.127

-.938 1.264 -.214 -.742 .499 .088 11.329-.797 .392 -1.102 -2.032 .112 .025 39.855-.089 .055 -.588 -1.626 .179 .056 17.761

-5.151 1.474 -.749 -3.494 .025 .160 6.23110.343 4.968 .398 2.082 .106 .202 4.9471.401 3.182 .111 .440 .683 .116 8.656.025 .044 .181 .564 .603 .071 14.015

(Constant)X1X2X3X4X6X7X11X12X14

Model1

B Std. Error


Beta




265


4.0650 .91123 1424.4119 1.85338 1425.5960 1.58564 141.4097 .20839 14

71.7760 5.99573 143.1960 .13247 14.7136 .03503 14.8312 .07235 14

52.0567 6.59783 14

Y1X1X2X3X6X7X11X12X14


Model Summary

.970a .940 .844 .35975Model1



Predictors: (Constant), X14, X7, X11, X2, X3, X12,X1, X6

a.

ANOVAb

10.147 8 1.268 9.801 .011a

.647 5 .12910.795 13


Model1


Predictors: (Constant), X14, X7, X11, X2, X3, X12, X1, X6a.


266

Coefficientsa

5.129 10.784 .476 .654-.141 .160 -.288 -.881 .419 .113 8.878.886 .173 1.542 5.109 .004 .132 7.596.522 1.326 .119 .394 .710 .130 7.668

-.134 .064 -.883 -2.088 .091 .067 14.913-5.569 1.862 -.810 -2.991 .030 .164 6.1107.366 6.054 .283 1.217 .278 .221 4.517

-1.609 3.591 -.128 -.448 .673 .147 6.781.048 .055 .345 .870 .424 .076 13.130

(Constant)X1X2X3X6X7X11X12X14

Model1

B Std. Error


Beta




Regression


4.0650 .91123 1424.4119 1.85338 1425.5960 1.58564 141.4097 .20839 143.1960 .13247 14.7136 .03503 14.8312 .07235 14

52.0567 6.59783 14

Y1X1X2X3X7X11X12X14


Model Summary

.942a .888 .757 .44934Model1



Predictors: (Constant), X14, X7, X11, X2, X3, X12, X1a.

267

ANOVAb

9.583 7 1.369 6.781 .016a

1.211 6 .20210.795 13


Model1




Coefficientsa

-8.971 10.502 -.854 .426-.184 .199 -.375 -.929 .389 .115 8.731.693 .183 1.205 3.782 .009 .184 5.431

1.188 1.607 .272 .739 .488 .138 7.224-3.056 1.774 -.444 -1.722 .136 .281 3.55811.860 7.067 .456 1.678 .144 .253 3.946-5.606 3.795 -.445 -1.477 .190 .206 4.854

.079 .066 .570 1.194 .277 .082 12.167

(Constant)X1X2X3X7X11X12X14

Model1

B Std. Error


Beta




Regression


4.0650 .91123 1424.4119 1.85338 1425.5960 1.58564 141.4097 .20839 143.1960 .13247 14.7136 .03503 14.8312 .07235 14

Y1X1X2X3X7X11X12


Model Summary

.928a .861 .742 .46282Model1



Predictors: (Constant), X12, X7, X11, X2, X1, X3a.

268

ANOVAb

9.295 6 1.549 7.232 .010a

1.499 7 .21410.795 13


Model1




Coefficientsa

1.170 6.367 .184 .859-.046 .166 -.094 -.277 .790 .174 5.759.557 .148 .969 3.769 .007 .300 3.329.883 1.635 .202 .540 .606 .142 7.041

-3.091 1.827 -.449 -1.692 .135 .281 3.5577.966 6.459 .306 1.233 .257 .322 3.107

-8.758 2.809 -.695 -3.118 .017 .399 2.507

(Constant)X1X2X3X7X11X12

Model1

B Std. Error


Beta




APPENDIX H

SPSS Output for the Impact of Politico-Economic

Institutions on Unemployment Rates in East Asia and

Latin America

Appendix H


Unemployment Rates in East Asia and Latin America

Regression


6.9767 .85771 1324.5339 1.86959 1325.5452 1.63844 131.4266 .20669 13

72.8588 1.20571 1387.9247 4.22604 1371.3189 5.98123 133.1929 .13738 133.3511 .28498 13

46.4191 1.86537 13.1012 .04098 13.7146 .03624 13.8197 .06062 13.9444 .08699 13

52.6176 6.51053 136.8377 .12094 13

114.1224 2.81803 13.9435 .13841 13



Model Summary

1.000a 1.000 1.000 . 1.000 . 12 0 .Model1



R SquareChange F Change df1 df2 Sig. F Change

Change Statistics


271

ANOVAb

8.828 12 .736 . .a

.000 0 .8.828 12


Model1




Coefficientsa

42.484 .000 . .-.415 .000 -.906 . . .030 33.5591.200 .000 .289 . . .068 14.774.061 .000 .300 . . .005 202.550

-1.541 .000 -.247 . . .013 77.694.113 .000 .247 . . .013 78.916

-1.233 .000 -.059 . . .017 57.433-3.770 .000 -.159 . . .006 161.583-5.705 .000 -.403 . . .003 319.6645.487 .000 .557 . . .006 156.450

-4.578 .000 -.646 . . .154 6.478.021 .000 .068 . . .034 29.277

-1.569 .000 -.253 . . .022 45.170


Model1

B Std. Error


Beta




272

Regression


6.9767 .85771 1324.5339 1.86959 1325.5452 1.63844 1372.8588 1.20571 1387.9247 4.22604 1371.3189 5.98123 133.1929 .13738 133.3511 .28498 13

46.4191 1.86537 13.1012 .04098 13.7146 .03624 13.8197 .06062 13.9444 .08699 13

52.6176 6.51053 136.8377 .12094 13

114.1224 2.81803 13.9435 .13841 13



Model Summary

1.000a 1.000 1.000 . 1.000 . 12 0 .Model1




Change Statistics


ANOVAb

8.828 12 .736 . .a

.000 0 .8.828 12


Model1




273

Coefficientsa

70.673 .000 . .-.361 .000 -.787 . . .043 23.119-.419 .000 -.590 . . .016 61.421.062 .000 .304 . . .005 202.033

-2.021 .000 -.324 . . .012 82.898.006 .000 .013 . . .019 53.927

2.695 .000 .129 . . .015 66.045-6.064 .000 -.256 . . .007 150.465-3.863 .000 -.273 . . .003 303.3654.263 .000 .432 . . .007 144.558

-3.946 .000 -.556 . . .134 7.460.051 .000 .167 . . .042 23.948.898 .000 .145 . . .021 47.458


Model1

B Std. Error


Beta




Regression


6.9767 .85771 1324.5339 1.86959 1325.5452 1.63844 1372.8588 1.20571 1387.9247 4.22604 1371.3189 5.98123 133.3511 .28498 13

46.4191 1.86537 13.1012 .04098 13.7146 .03624 13.8197 .06062 13.9444 .08699 13

52.6176 6.51053 136.8377 .12094 13

114.1224 2.81803 13.9435 .13841 13



274

Model Summary

1.000a 1.000 1.000 . 1.000 . 12 0 .Model1




Change Statistics


ANOVAb

8.828 12 .736 . .a

.000 0 .8.828 12


Model1




Coefficientsa

102.479 .000 . .-.514 .000 -1.121 . . .006 174.312-.652 .000 -1.246 . . .001 1228.868

-1.233 .000 -1.734 . . .001 1266.711.095 .000 .466 . . .009 106.047.386 .000 .839 . . .001 830.410

11.277 .000 .539 . . .003 376.837-1.007 .000 -.043 . . .003 310.589

-13.757 .000 -.972 . . .001 1317.8597.149 .000 .725 . . .003 397.0051.213 .000 .171 . . .003 391.377-.099 .000 -.324 . . .004 285.0244.728 .000 .763 . . .004 248.922


Model1

B Std. Error


Beta




275

Regression


6.9767 .85771 1324.5339 1.86959 1325.5452 1.63844 1372.8588 1.20571 1387.9247 4.22604 133.3511 .28498 13

46.4191 1.86537 13.1012 .04098 13.7146 .03624 13.8197 .06062 13.9444 .08699 13

52.6176 6.51053 136.8377 .12094 13

114.1224 2.81803 13.9435 .13841 13



Model Summary

1.000a 1.000 1.000 . 1.000 . 12 0 .Model1




Change Statistics


ANOVAb

8.828 12 .736 . .a

.000 0 .8.828 12


Model1




276

Coefficientsa

102.479 .000 . .-.514 .000 -1.121 . . .006 174.312-.652 .000 -1.246 . . .001 1228.868

-1.233 .000 -1.734 . . .001 1266.711.095 .000 .466 . . .009 106.047.386 .000 .839 . . .001 830.410

11.277 .000 .539 . . .003 376.837-1.007 .000 -.043 . . .003 310.589

-13.757 .000 -.972 . . .001 1317.8597.149 .000 .725 . . .003 397.0051.213 .000 .171 . . .003 391.377-.099 .000 -.324 . . .004 285.0244.728 .000 .763 . . .004 248.922


Model1

B Std. Error


Beta




Regression


6.9767 .85771 1324.5339 1.86959 1325.5452 1.63844 1372.8588 1.20571 1387.9247 4.22604 1346.4191 1.86537 13

.1012 .04098 13

.7146 .03624 13

.8197 .06062 13

.9444 .08699 1352.6176 6.51053 136.8377 .12094 13

114.1224 2.81803 13.9435 .13841 13

Y2X1X2X4X5X9X10X11X12X13X14X15C1C2


277

Model Summary

1.000a 1.000 1.000 . 1.000 . 12 0 .Model1




Change Statistics


ANOVAb

8.828 12 .736 . .a

.000 0 .8.828 12


Model1




Coefficientsa

102.479 .000 . .-.514 .000 -1.121 . . .006 174.312-.652 .000 -1.246 . . .001 1228.868

-1.233 .000 -1.734 . . .001 1266.711.095 .000 .466 . . .009 106.047.386 .000 .839 . . .001 830.410

11.277 .000 .539 . . .003 376.837-1.007 .000 -.043 . . .003 310.589

-13.757 .000 -.972 . . .001 1317.8597.149 .000 .725 . . .003 397.0051.213 .000 .171 . . .003 391.377-.099 .000 -.324 . . .004 285.0244.728 .000 .763 . . .004 248.922


Model1

B Std. Error


Beta




278

Regression


6.9767 .85771 1324.5339 1.86959 1325.5452 1.63844 1372.8588 1.20571 1387.9247 4.22604 1346.4191 1.86537 13

.1012 .04098 13

.7146 .03624 13

.8197 .06062 13

.9444 .08699 136.8377 .12094 13

114.1224 2.81803 13.9435 .13841 13

Y2X1X2X4X5X9X10X11X12X13X15C1C2


Model Summary

1.000a 1.000 1.000 . 1.000 . 12 0 .Model1




Change Statistics


ANOVAb

8.828 12 .736 . .a

.000 0 .8.828 12


Model1




279

Coefficientsa

102.479 .000 . .-.514 .000 -1.121 . . .006 174.312-.652 .000 -1.246 . . .001 1228.868

-1.233 .000 -1.734 . . .001 1266.711.095 .000 .466 . . .009 106.047.386 .000 .839 . . .001 830.410

11.277 .000 .539 . . .003 376.837-1.007 .000 -.043 . . .003 310.589

-13.757 .000 -.972 . . .001 1317.8597.149 .000 .725 . . .003 397.0051.213 .000 .171 . . .003 391.377-.099 .000 -.324 . . .004 285.0244.728 .000 .763 . . .004 248.922


Model1

B Std. Error


Beta




Regression


6.9767 .85771 1324.5339 1.86959 1372.8588 1.20571 1387.9247 4.22604 1346.4191 1.86537 13

.1012 .04098 13

.7146 .03624 13

.8197 .06062 13

.9444 .08699 136.8377 .12094 13

114.1224 2.81803 13.9435 .13841 13

Y2X1X4X5X9X10X11X12X13X15C1C2


280

Model Summary

.999a .999 .985 .10561 .999 71.861 11 1 .092Model1




Change Statistics

Predictors: (Constant), C2, C1, X1, X13, X10, X15, X11, X9, X5, X12, X4a.

ANOVAb

8.817 11 .802 71.861 .092a

.011 1 .0118.828 12


Model1




Coefficientsa

52.463 11.887 4.413 .142-.306 .056 -.668 -5.453 .115 .084 11.865-.353 .187 -.496 -1.890 .310 .018 54.495.154 .044 .759 3.469 .179 .026 37.927

-.077 .087 -.167 -.886 .538 .035 28.197-3.038 1.922 -.145 -1.581 .359 .150 6.675

-14.743 5.582 -.623 -2.641 .230 .023 44.0214.158 3.523 .294 1.180 .447 .020 49.063.335 1.526 .034 .219 .862 .053 18.961

-3.731 .654 -.526 -5.704 .110 .148 6.734.078 .045 .257 1.730 .334 .057 17.496

1.537 1.376 .248 1.117 .465 .026 39.034

(Constant)X1X4X5X9X10X11X12X13X15C1C2

Model1

B Std. Error


Beta




281

Regression


6.9767 .85771 1324.5339 1.86959 1387.9247 4.22604 1346.4191 1.86537 13

.1012 .04098 13

.7146 .03624 13

.8197 .06062 13

.9444 .08699 136.8377 .12094 13

114.1224 2.81803 13.9435 .13841 13

Y2X1X5X9X10X11X12X13X15C1C2


Model Summary

.997a .994 .965 .15969 .994 34.418 10 2 .029Model1




Change Statistics

Predictors: (Constant), C2, C1, X1, X13, X10, X15, X11, X9, X5, X12a.

ANOVAb

8.777 10 .878 34.418 .029a

.051 2 .0268.828 12


Model1




282

Coefficientsa

32.391 8.077 4.010 .057-.261 .077 -.568 -3.399 .077 .103 9.682.155 .067 .764 2.308 .147 .026 37.923

-.102 .130 -.222 -.786 .514 .036 27.544-3.615 2.870 -.173 -1.260 .335 .154 6.507

-15.094 8.436 -.638 -1.789 .215 .023 43.9732.630 5.185 .186 .507 .662 .022 46.4811.030 2.240 .104 .460 .691 .056 17.860

-4.385 .839 -.618 -5.224 .035 .206 4.850.090 .068 .295 1.324 .317 .058 17.182

-.159 1.578 -.026 -.101 .929 .045 22.443

(Constant)X1X5X9X10X11X12X13X15C1C2

Model1

B Std. Error


Beta




Regression


6.9767 .85771 1324.5339 1.86959 1387.9247 4.22604 1346.4191 1.86537 13

.1012 .04098 13

.7146 .03624 13

.9444 .08699 136.8377 .12094 13

114.1224 2.81803 13.9435 .13841 13

Y2X1X5X9X10X11X13X15C1C2


Model Summary

.997a .993 .974 .13852 .993 50.786 9 3 .004Model1




Change Statistics

Predictors: (Constant), C2, C1, X1, X13, X10, X15, X11, X9, X5a.

283

ANOVAb

8.770 9 .974 50.786 .004a

.058 3 .0198.828 12


Model1




Coefficientsa

35.875 3.689 9.723 .002-.292 .040 -.636 -7.271 .005 .284 3.521.127 .034 .627 3.764 .033 .078 12.769

-.054 .076 -.117 -.702 .533 .079 12.700-2.926 2.193 -.140 -1.334 .274 .198 5.050

-11.332 3.489 -.479 -3.248 .048 .100 9.9971.999 1.014 .203 1.972 .143 .206 4.863

-4.446 .721 -.627 -6.170 .009 .211 4.750.060 .030 .198 2.007 .138 .224 4.463

-.419 1.294 -.068 -.324 .767 .050 20.071

(Constant)X1X5X9X10X11X13X15C1C2

Model1

B Std. Error


Beta




Regression


6.9767 .85771 1324.5339 1.86959 1387.9247 4.22604 1346.4191 1.86537 13

.1012 .04098 13

.7146 .03624 13

.9444 .08699 136.8377 .12094 13

114.1224 2.81803 13

Y2X1X5X9X10X11X13X15C1


284

Model Summary

.997a .993 .980 .12204 .993 73.589 8 4 .000Model1




Change Statistics

Predictors: (Constant), C1, X13, X1, X11, X10, X15, X9, X5a.

ANOVAb

8.768 8 1.096 73.589 .000a

.060 4 .0158.828 12


Model1




Coefficientsa

36.361 2.969 12.246 .000-.282 .024 -.615 -11.869 .000 .628 1.592.124 .029 .613 4.324 .012 .084 11.915

-.064 .061 -.140 -1.054 .351 .096 10.386-3.086 1.883 -.147 -1.639 .177 .209 4.795

-11.495 3.042 -.486 -3.779 .019 .102 9.7891.785 .676 .181 2.639 .058 .359 2.789

-4.463 .633 -.629 -7.048 .002 .212 4.725.061 .026 .200 2.306 .082 .225 4.444

(Constant)X1X5X9X10X11X13X15C1

Model1

B Std. Error


Beta




285

Regression


6.9767 .85771 1324.5339 1.86959 1346.4191 1.86537 13

.1012 .04098 13

.7146 .03624 13

.9444 .08699 136.8377 .12094 13

114.1224 2.81803 13

Y2X1X9X10X11X13X15C1


Model Summary

.981a .962 .908 .26002 .962 17.938 7 5 .003Model1




Change Statistics

Predictors: (Constant), C1, X13, X1, X11, X10, X15, X9a.

ANOVAb

8.490 7 1.213 17.938 .003a

.338 5 .0688.828 12


Model1




286

Coefficientsa

40.483 5.991 6.757 .001-.293 .050 -.638 -5.814 .002 .635 1.575.025 .122 .053 .201 .849 .109 9.206

-4.846 3.916 -.232 -1.237 .271 .219 4.571-.457 3.525 -.019 -.130 .902 .345 2.8961.465 1.432 .149 1.023 .353 .363 2.755

-3.862 1.316 -.545 -2.934 .032 .222 4.497-.014 .042 -.047 -.336 .751 .397 2.521

(Constant)X1X9X10X11X13X15C1

Model1

B Std. Error


Beta




APPENDIX I


Institutions on the Percentage of the Population Falling below the

Poverty Line in East Asia and Latin America

Appendix I


the Percentage of the Population Falling below the Poverty Line in


Regression


31.5344 6.99283 1324.5339 1.86959 1325.5452 1.63844 131.4266 .20669 13

72.8588 1.20571 1387.9247 4.22604 1371.3189 5.98123 133.1929 .13738 133.3511 .28498 13

46.4191 1.86537 13.1012 .04098 13.7146 .03624 13.8197 .06062 13.9444 .08699 13

52.6176 6.51053 136.8377 .12094 13

114.1224 2.81803 13.9435 .13841 13



Model Summary

1.000a 1.000 1.000 . 1.000 . 12 0 .Model1




Change Statistics


289

ANOVAb

586.796 12 48.900 . .a

.000 0 .586.796 12


Model1




Coefficientsa

-478.472 .000 . .-6.748 .000 -1.804 . . .030 33.55932.748 .000 .968 . . .068 14.774

.093 .000 .056 . . .005 202.55041.365 .000 .813 . . .013 77.6944.740 .000 1.264 . . .013 78.916

-90.737 .000 -.532 . . .017 57.43367.438 .000 .349 . . .006 161.58332.360 .000 .281 . . .003 319.66448.232 .000 .600 . . .006 156.45036.350 .000 .629 . . .154 6.478

-.051 .000 -.020 . . .034 29.277-90.355 .000 -1.788 . . .022 45.170


Model1

B Std. Error


Beta




290

Regression


31.5344 6.99283 1324.5339 1.86959 131.4266 .20669 13

72.8588 1.20571 1387.9247 4.22604 1371.3189 5.98123 133.1929 .13738 133.3511 .28498 13

46.4191 1.86537 13.1012 .04098 13.7146 .03624 13.8197 .06062 13.9444 .08699 13

52.6176 6.51053 136.8377 .12094 13

114.1224 2.81803 13.9435 .13841 13



Model Summary

1.000a 1.000 1.000 . 1.000 . 12 0 .Model1




Change Statistics


ANOVAb

586.796 12 48.900 . .a

.000 0 .586.796 12


Model1




291

Coefficientsa

-478.472 .000 . .-6.748 .000 -1.804 . . .030 33.55932.748 .000 .968 . . .068 14.774

.093 .000 .056 . . .005 202.55041.365 .000 .813 . . .013 77.6944.740 .000 1.264 . . .013 78.916

-90.737 .000 -.532 . . .017 57.43367.438 .000 .349 . . .006 161.58332.360 .000 .281 . . .003 319.66448.232 .000 .600 . . .006 156.45036.350 .000 .629 . . .154 6.478

-.051 .000 -.020 . . .034 29.277-90.355 .000 -1.788 . . .022 45.170


Model1

B Std. Error


Beta




Regression


31.5344 6.99283 1324.5339 1.86959 131.4266 .20669 13

87.9247 4.22604 1371.3189 5.98123 133.1929 .13738 133.3511 .28498 13

46.4191 1.86537 13.1012 .04098 13.7146 .03624 13.8197 .06062 13.9444 .08699 13

52.6176 6.51053 136.8377 .12094 13

114.1224 2.81803 13.9435 .13841 13



292

Model Summary

1.000a 1.000 1.000 . 1.000 . 12 0 .Model1




Change Statistics


ANOVAb

586.796 12 48.900 . .a

.000 0 .586.796 12


Model1




Coefficientsa

-478.472 .000 . .-6.748 .000 -1.804 . . .030 33.55932.748 .000 .968 . . .068 14.774

.093 .000 .056 . . .005 202.55041.365 .000 .813 . . .013 77.6944.740 .000 1.264 . . .013 78.916

-90.737 .000 -.532 . . .017 57.43367.438 .000 .349 . . .006 161.58332.360 .000 .281 . . .003 319.66448.232 .000 .600 . . .006 156.45036.350 .000 .629 . . .154 6.478

-.051 .000 -.020 . . .034 29.277-90.355 .000 -1.788 . . .022 45.170


Model1

B Std. Error


Beta




293

Regression


31.5344 6.99283 1324.5339 1.86959 131.4266 .20669 13

87.9247 4.22604 133.1929 .13738 133.3511 .28498 13

46.4191 1.86537 13.1012 .04098 13.7146 .03624 13.8197 .06062 13.9444 .08699 13

52.6176 6.51053 136.8377 .12094 13

114.1224 2.81803 13.9435 .13841 13



Model Summary

1.000a 1.000 1.000 . 1.000 . 12 0 .Model1




Change Statistics


ANOVAb

586.796 12 48.900 . .a

.000 0 .586.796 12


Model1




294

Coefficientsa

-478.472 .000 . .-6.748 .000 -1.804 . . .030 33.55932.748 .000 .968 . . .068 14.774

.093 .000 .056 . . .005 202.55041.365 .000 .813 . . .013 77.6944.740 .000 1.264 . . .013 78.916

-90.737 .000 -.532 . . .017 57.43367.438 .000 .349 . . .006 161.58332.360 .000 .281 . . .003 319.66448.232 .000 .600 . . .006 156.45036.350 .000 .629 . . .154 6.478

-.051 .000 -.020 . . .034 29.277-90.355 .000 -1.788 . . .022 45.170


Model1

B Std. Error


Beta




Regression


31.5344 6.99283 1324.5339 1.86959 131.4266 .20669 13

87.9247 4.22604 133.1929 .13738 13

46.4191 1.86537 13.1012 .04098 13.7146 .03624 13.8197 .06062 13.9444 .08699 13

52.6176 6.51053 136.8377 .12094 13

114.1224 2.81803 13.9435 .13841 13



295

Model Summary

1.000a 1.000 1.000 . 1.000 . 12 0 .Model1




Change Statistics


ANOVAb

586.796 12 48.900 . .a

.000 0 .586.796 12


Model1




Coefficientsa

-478.472 .000 . .-6.748 .000 -1.804 . . .030 33.55932.748 .000 .968 . . .068 14.774

.093 .000 .056 . . .005 202.55041.365 .000 .813 . . .013 77.6944.740 .000 1.264 . . .013 78.916

-90.737 .000 -.532 . . .017 57.43367.438 .000 .349 . . .006 161.58332.360 .000 .281 . . .003 319.66448.232 .000 .600 . . .006 156.45036.350 .000 .629 . . .154 6.478

-.051 .000 -.020 . . .034 29.277-90.355 .000 -1.788 . . .022 45.170


Model1

B Std. Error


Beta




296

Regression


31.5344 6.99283 1324.5339 1.86959 131.4266 .20669 13

87.9247 4.22604 133.1929 .13738 13

46.4191 1.86537 13.1012 .04098 13.7146 .03624 13.8197 .06062 13.9444 .08699 13

6.8377 .12094 13114.1224 2.81803 13

.9435 .13841 13



Model Summary

1.000a 1.000 1.000 . 1.000 . 12 0 .Model1




Change Statistics


ANOVAb

586.796 12 48.900 . .a

.000 0 .586.796 12


Model1




297

Coefficientsa

-478.472 .000 . .-6.748 .000 -1.804 . . .030 33.55932.748 .000 .968 . . .068 14.774

.093 .000 .056 . . .005 202.55041.365 .000 .813 . . .013 77.6944.740 .000 1.264 . . .013 78.916

-90.737 .000 -.532 . . .017 57.43367.438 .000 .349 . . .006 161.58332.360 .000 .281 . . .003 319.66448.232 .000 .600 . . .006 156.45036.350 .000 .629 . . .154 6.478

-.051 .000 -.020 . . .034 29.277-90.355 .000 -1.788 . . .022 45.170


Model1

B Std. Error


Beta




Regression


31.5344 6.99283 1324.5339 1.86959 131.4266 .20669 13

87.9247 4.22604 133.1929 .13738 13

46.4191 1.86537 13.1012 .04098 13.7146 .03624 13.9444 .08699 13

6.8377 .12094 13114.1224 2.81803 13

.9435 .13841 13



298

Model Summary

1.000a 1.000 .997 .38006 1.000 369.227 11 1 .041Model1




Change Statistics


ANOVAb

586.651 11 53.332 369.227 .041a

.144 1 .144586.796 12


Model1




Coefficientsa

-426.553 12.867 -33.151 .019-7.014 .211 -1.875 -33.238 .019 .077 12.93233.384 1.939 .987 17.220 .037 .075 13.339

-.265 .093 -.160 -2.848 .215 .078 12.84534.897 2.779 .686 12.557 .051 .083 12.1105.160 .312 1.376 16.548 .038 .036 28.102

-72.513 8.923 -.425 -8.126 .078 .090 11.107104.259 11.193 .540 9.315 .068 .073 13.66863.679 3.198 .792 19.911 .032 .156 6.43135.193 1.998 .609 17.615 .036 .206 4.851

-.211 .137 -.085 -1.544 .366 .081 12.328-94.251 3.634 -1.865 -25.934 .025 .048 21.020


Model1

B Std. Error


Beta




299

Regression


31.5344 6.99283 1324.5339 1.86959 131.4266 .20669 13

87.9247 4.22604 133.1929 .13738 13.1012 .04098 13.7146 .03624 13.9444 .08699 13

6.8377 .12094 13114.1224 2.81803 13

.9435 .13841 13



Model Summary

.966a .932 .594 4.45529 .932 2.756 10 2 .295Model1




Change Statistics


ANOVAb

547.097 10 54.710 2.756 .295a

39.699 2 19.850586.796 12


Model1




300

Coefficientsa

-346.167 139.675 -2.478 .131-4.576 1.771 -1.224 -2.584 .123 .151 6.63012.037 16.965 .356 .710 .552 .135 7.433

-.070 1.082 -.042 -.064 .954 .079 12.63915.614 29.577 .307 .528 .650 .100 9.98138.864 68.682 .228 .566 .629 .209 4.78825.750 118.841 .133 .217 .849 .089 11.21250.263 36.268 .625 1.386 .300 .166 6.01738.766 23.284 .670 1.665 .238 .209 4.7941.405 1.121 .566 1.254 .337 .166 6.033

-70.138 39.030 -1.388 -1.797 .214 .057 17.641


Model1

B Std. Error


Beta




Regression


31.5344 6.99283 1324.5339 1.86959 131.4266 .20669 13

87.9247 4.22604 133.1929 .13738 13.1012 .04098 13.7146 .03624 13

6.8377 .12094 13114.1224 2.81803 13

.9435 .13841 13

Y3X1X3X5X7X10X11X15C1C2


Model Summary

.931a .867 .469 5.09328 .867 2.180 9 3 .282Model1




Change Statistics


301

ANOVAb

508.971 9 56.552 2.180 .282a

77.824 3 25.941586.796 12


Model1




Coefficientsa

-367.149 158.735 -2.313 .104-4.229 2.005 -1.131 -2.110 .125 .154 6.4989.301 19.263 .275 .483 .662 .136 7.332-.398 1.207 -.240 -.330 .763 .083 12.034

38.413 28.100 .755 1.367 .265 .145 6.893-5.411 69.507 -.032 -.078 .943 .267 3.75266.051 131.729 .342 .501 .651 .095 10.54151.548 24.441 .892 2.109 .125 .247 4.042

.335 .929 .135 .360 .742 .316 3.167-37.958 35.863 -.751 -1.058 .368 .088 11.397

(Constant)X1X3X5X7X10X11X15C1C2

Model1

B Std. Error


Beta




Regression


31.5344 6.99283 1324.5339 1.86959 131.4266 .20669 133.1929 .13738 13.1012 .04098 13.7146 .03624 13

6.8377 .12094 13114.1224 2.81803 13

.9435 .13841 13

Y3X1X3X7X10X11X15C1C2


302

Model Summary

.929a .863 .588 4.49006 .863 3.138 8 4 .142Model1




Change Statistics

Predictors: (Constant), C2, C1, X1, X10, X15, X7, X11, X3a.

ANOVAb

506.153 8 63.269 3.138 .142a

80.643 4 20.161586.796 12


Model1


Predictors: (Constant), C2, C1, X1, X10, X15, X7, X11, X3a.


Coefficientsa

-389.443 126.594 -3.076 .037-4.348 1.738 -1.162 -2.501 .067 .159 6.2879.545 16.969 .282 .563 .604 .137 7.322

41.246 23.585 .810 1.749 .155 .160 6.248-5.473 61.275 -.032 -.089 .933 .267 3.75238.650 90.081 .200 .429 .690 .158 6.34350.490 21.360 .873 2.364 .077 .252 3.972

.426 .782 .171 .544 .615 .346 2.887-40.832 30.667 -.808 -1.331 .254 .093 10.723

(Constant)X1X3X7X10X11X15C1C2

Model1

B Std. Error


Beta




303


31.5344 6.99283 1324.5339 1.86959 131.4266 .20669 133.1929 .13738 13.1012 .04098 13.7146 .03624 13

6.8377 .12094 13114.1224 2.81803 13

Y3X1X3X7X10X11X15C1


Model Summary

.895a .802 .524 4.82458 .802 2.887 7 5 .131Model1




Change Statistics


ANOVAb

470.413 7 67.202 2.887 .131a

116.383 5 23.277586.796 12


Model1




304

Coefficientsa

-324.100 125.388 -2.585 .049-3.586 1.764 -.959 -2.033 .098 .178 5.60514.372 17.812 .425 .807 .456 .143 6.98738.461 25.242 .756 1.524 .188 .161 6.199

-45.655 57.300 -.268 -.797 .462 .352 2.842-15.245 86.469 -.079 -.176 .867 .198 5.06234.399 18.924 .595 1.818 .129 .370 2.701

.706 .809 .285 .873 .422 .374 2.677


Model1

B Std. Error


Beta




Regression


31.5344 6.99283 1324.5339 1.86959 131.4266 .20669 133.1929 .13738 13.7146 .03624 13

6.8377 .12094 13114.1224 2.81803 13

Y3X1X3X7X11X15C1


Model Summary

.881a .776 .553 4.67546 .776 3.474 6 6 .078Model1




Change Statistics

Predictors: (Constant), C1, X11, X1, X15, X7, X3a.

305

ANOVAb

455.636 6 75.939 3.474 .078a

131.160 6 21.860586.796 12


Model1




Coefficientsa

-278.339 108.016 -2.577 .042-3.521 1.707 -.941 -2.063 .085 .179 5.59314.454 17.261 .427 .837 .434 .143 6.98730.462 22.444 .598 1.357 .224 .192 5.219

-58.774 64.953 -.305 -.905 .400 .329 3.04131.279 17.943 .541 1.743 .132 .387 2.585

.933 .733 .376 1.272 .250 .426 2.345

(Constant)X1X3X7X11X15C1

Model1

B Std. Error


Beta




Regression


31.5344 6.99283 1324.5339 1.86959 133.1929 .13738 13.7146 .03624 13

6.8377 .12094 13114.1224 2.81803 13

Y3X1X7X11X15C1


306

Model Summary

.866a .750 .572 4.57459 .750 4.208 5 7 .044Model1




Change Statistics

Predictors: (Constant), C1, X11, X1, X15, X7a.

ANOVAb

440.308 5 88.062 4.208 .044a

146.488 7 20.927586.796 12


Model1




Coefficientsa

-223.537 84.080 -2.659 .033-2.488 1.154 -.665 -2.156 .068 .375 2.67033.734 21.625 .663 1.560 .163 .198 5.060

-80.428 58.297 -.417 -1.380 .210 .391 2.55922.864 14.544 .395 1.572 .160 .564 1.774

.960 .717 .387 1.339 .223 .427 2.341

(Constant)X1X7X11X15C1

Model1

B Std. Error


Beta




Regression


31.5344 6.99283 1324.5339 1.86959 13

.7146 .03624 136.8377 .12094 13

114.1224 2.81803 13

Y3X1X11X15C1


307

Model Summary

.815a .664 .495 4.96757 .664 3.945 4 8 .047Model1




Change Statistics

Predictors: (Constant), C1, X11, X1, X15a.

ANOVAb

389.382 4 97.345 3.945 .047a

197.414 8 24.677586.796 12


Model1


Predictors: (Constant), C1, X11, X1, X15a.


Coefficientsa

-201.609 90.018 -2.240 .055-1.217 .888 -.325 -1.371 .208 .746 1.340

-136.637 49.766 -.708 -2.746 .025 .632 1.58225.323 15.700 .438 1.613 .145 .570 1.7531.643 .616 .662 2.665 .029 .681 1.467

(Constant)X1X11X15C1

Model1

B Std. Error


Beta




APPENDIX J


Institutions on Income Inequality in East Asia and Latin America

309

Appendix J


Income Inequality in East Asia and Latin America


16.8087 3.02984 1324.5339 1.86959 1325.5452 1.63844 131.4266 .20669 13

72.8588 1.20571 1387.9247 4.22604 1371.3189 5.98123 133.1929 .13738 133.3511 .28498 13

46.4191 1.86537 13.1012 .04098 13.7146 .03624 13.8197 .06062 13.9444 .08699 13

52.6176 6.51053 136.8377 .12094 13

114.1224 2.81803 13.9435 .13841 13



Model Summary

1.000a 1.000 1.000 .Model1




a.

310

ANOVAb

110.160 12 9.180 . .a

.000 0 .110.160 12


Model1




Coefficientsa

445.508 .000 . .-2.774 .000 -1.712 . . .030 33.559-8.193 .000 -.559 . . .068 14.774-2.890 .000 -4.030 . . .005 202.550

-56.509 .000 -2.562 . . .013 77.6941.497 .000 .922 . . .013 78.916

199.495 .000 2.698 . . .017 57.433269.795 .000 3.227 . . .006 161.583

-257.193 .000 -5.146 . . .003 319.664134.874 .000 3.872 . . .006 156.450

-5.871 .000 -.234 . . .154 6.478-.194 .000 -.180 . . .034 29.277

-54.288 .000 -2.480 . . .022 45.170


Model1

B Std. Error


Beta




311

Regression


16.8087 3.02984 1324.5339 1.86959 1325.5452 1.63844 131.4266 .20669 13

72.8588 1.20571 1387.9247 4.22604 1371.3189 5.98123 133.1929 .13738 133.3511 .28498 13

46.4191 1.86537 13.1012 .04098 13.7146 .03624 13.9444 .08699 13

52.6176 6.51053 136.8377 .12094 13

114.1224 2.81803 13.9435 .13841 13



Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

110.160 12 9.180 . .a

.000 0 .110.160 12


Model1




312

Coefficientsa

882.669 .000 . .2.168 .000 1.337 . . .020 49.580

12.454 .000 6.735 . . .002 547.583-52.647 .000 -3.591 . . .010 100.557-3.484 .000 -4.859 . . .003 290.196

-112.867 .000 -5.117 . . .003 300.337-9.742 .000 -5.998 . . .003 314.216

181.168 .000 2.450 . . .022 46.45188.302 .000 1.056 . . .029 35.00734.388 .000 .987 . . .088 11.374

-80.929 .000 -3.230 . . .007 141.4223.770 .000 3.507 . . .011 87.934

-36.027 .000 -1.646 . . .040 25.083


Model1

B Std. Error


Beta




Regression


16.8087 3.02984 1324.5339 1.86959 131.4266 .20669 13

72.8588 1.20571 1387.9247 4.22604 1371.3189 5.98123 133.1929 .13738 133.3511 .28498 13

46.4191 1.86537 13.1012 .04098 13.7146 .03624 13.9444 .08699 13

52.6176 6.51053 136.8377 .12094 13

114.1224 2.81803 13.9435 .13841 13



313

Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

110.160 12 9.180 . .a

.000 0 .110.160 12


Model1




Coefficientsa

4379.557 .000 . .4.827 .000 2.979 . . .007 151.144

-175.640 .000 -11.982 . . .001 1495.022-58.527 .000 -23.291 . . .000 6549.290-2.764 .000 -3.855 . . .005 186.176

-123.480 .000 -5.599 . . .003 359.908-13.509 .000 -8.317 . . .002 651.807747.643 .000 10.111 . . .001 1071.567-50.236 .000 -.601 . . .067 14.963-36.043 .000 -1.035 . . .034 29.49982.389 .000 3.289 . . .008 125.1043.989 .000 3.711 . . .010 100.749

289.977 .000 13.246 . . .000 2494.062


Model1

B Std. Error


Beta




314

Regression


16.8087 3.02984 1324.5339 1.86959 131.4266 .20669 13

72.8588 1.20571 1387.9247 4.22604 133.1929 .13738 133.3511 .28498 13

46.4191 1.86537 13.1012 .04098 13.7146 .03624 13.9444 .08699 13

52.6176 6.51053 136.8377 .12094 13

114.1224 2.81803 13.9435 .13841 13



Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

110.160 12 9.180 . .a

.000 0 .110.160 12


Model1




315

Coefficientsa

4379.557 .000 . .4.827 .000 2.979 . . .007 151.144

-175.640 .000 -11.982 . . .001 1495.022-58.527 .000 -23.291 . . .000 6549.290-2.764 .000 -3.855 . . .005 186.176

-123.480 .000 -5.599 . . .003 359.908-13.509 .000 -8.317 . . .002 651.807747.643 .000 10.111 . . .001 1071.567-50.236 .000 -.601 . . .067 14.963-36.043 .000 -1.035 . . .034 29.49982.389 .000 3.289 . . .008 125.1043.989 .000 3.711 . . .010 100.749

289.977 .000 13.246 . . .000 2494.062


Model1

B Std. Error


Beta




Regression


16.8087 3.02984 1324.5339 1.86959 131.4266 .20669 13

72.8588 1.20571 1387.9247 4.22604 133.1929 .13738 13

46.4191 1.86537 13.1012 .04098 13.7146 .03624 13.9444 .08699 13

52.6176 6.51053 136.8377 .12094 13

114.1224 2.81803 13.9435 .13841 13



316

Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

110.160 12 9.180 . .a

.000 0 .110.160 12


Model1




Coefficientsa

4379.557 .000 . .4.827 .000 2.979 . . .007 151.144

-175.640 .000 -11.982 . . .001 1495.022-58.527 .000 -23.291 . . .000 6549.290-2.764 .000 -3.855 . . .005 186.176

-123.480 .000 -5.599 . . .003 359.908-13.509 .000 -8.317 . . .002 651.807747.643 .000 10.111 . . .001 1071.567-50.236 .000 -.601 . . .067 14.963-36.043 .000 -1.035 . . .034 29.49982.389 .000 3.289 . . .008 125.1043.989 .000 3.711 . . .010 100.749

289.977 .000 13.246 . . .000 2494.062


Model1

B Std. Error


Beta




317

Regression


16.8087 3.02984 1324.5339 1.86959 131.4266 .20669 13

72.8588 1.20571 1387.9247 4.22604 133.1929 .13738 13

46.4191 1.86537 13.1012 .04098 13.7146 .03624 13.9444 .08699 13

6.8377 .12094 13114.1224 2.81803 13

.9435 .13841 13



Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

110.160 12 9.180 . .a

.000 0 .110.160 12


Model1




318

Coefficientsa

4379.557 .000 . .4.827 .000 2.979 . . .007 151.144

-175.640 .000 -11.982 . . .001 1495.022-58.527 .000 -23.291 . . .000 6549.290-2.764 .000 -3.855 . . .005 186.176

-123.480 .000 -5.599 . . .003 359.908-13.509 .000 -8.317 . . .002 651.807747.643 .000 10.111 . . .001 1071.567-50.236 .000 -.601 . . .067 14.963-36.043 .000 -1.035 . . .034 29.49982.389 .000 3.289 . . .008 125.1043.989 .000 3.711 . . .010 100.749

289.977 .000 13.246 . . .000 2494.062


Model1

B Std. Error


Beta




Regression


16.8087 3.02984 1324.5339 1.86959 131.4266 .20669 13

87.9247 4.22604 133.1929 .13738 13

46.4191 1.86537 13.1012 .04098 13.7146 .03624 13.9444 .08699 13

6.8377 .12094 13114.1224 2.81803 13

.9435 .13841 13



319

Model Summary

.958a .917 .006 3.02061Model1



Predictors: (Constant), C2, C1, X1, X13, X10, X15,X11, X3, X7, X5, X9

a.

ANOVAb

101.035 11 9.185 1.007 .660a

9.124 1 9.124110.160 12


Model1




Coefficientsa

32.860 102.265 .321 .802-.656 1.677 -.405 -.391 .763 .077 12.932

-13.247 15.408 -.904 -.860 .548 .075 13.339-.048 .740 -.066 -.064 .959 .078 12.845

-5.105 22.088 -.231 -.231 .855 .083 12.110-1.835 2.478 -1.130 -.740 .594 .036 28.10254.655 70.921 .739 .771 .582 .090 11.107

-22.852 88.959 -.273 -.257 .840 .073 13.66812.100 25.419 .347 .476 .717 .156 6.4313.327 15.879 .133 .209 .869 .206 4.8511.080 1.086 1.004 .994 .502 .081 12.328

-23.328 28.885 -1.066 -.808 .568 .048 21.020


Model1

B Std. Error


Beta




320


16.8087 3.02984 1324.5339 1.86959 131.4266 .20669 13

87.9247 4.22604 133.1929 .13738 13.1012 .04098 13.7146 .03624 13.9444 .08699 13

6.8377 .12094 13114.1224 2.81803 13

.9435 .13841 13



Model Summary

.934a .872 .231 2.65765Model1



Predictors: (Constant), C2, C1, X1, X13, X10, X15,X11, X3, X7, X5

a.

ANOVAb

96.033 10 9.603 1.360 .496a

14.126 2 7.063110.160 12


Model1




321

Coefficientsa

4.274 83.318 .051 .964-1.523 1.057 -.940 -1.441 .286 .151 6.630-5.656 10.120 -.386 -.559 .632 .135 7.433-.117 .645 -.163 -.181 .873 .079 12.6391.752 17.643 .079 .099 .930 .100 9.981

15.048 40.970 .204 .367 .749 .209 4.7885.067 70.890 .061 .071 .950 .089 11.212

16.871 21.634 .484 .780 .517 .166 6.0172.056 13.889 .082 .148 .896 .209 4.794.505 .669 .470 .755 .529 .166 6.033

-31.902 23.282 -1.457 -1.370 .304 .057 17.641


Model1

B Std. Error


Beta




Regression


16.8087 3.02984 1324.5339 1.86959 131.4266 .20669 13

87.9247 4.22604 133.1929 .13738 13.1012 .04098 13.7146 .03624 13.9444 .08699 13

6.8377 .12094 13114.1224 2.81803 13

Y4X1X3X5X7X10X11X13X15C1


Model Summary

.867a .751 .006 3.02147Model1



Predictors: (Constant), C1, X13, X3, X10, X11, X15,X1, X7, X5

a.

322

ANOVAb

82.772 9 9.197 1.007 .561a

27.388 3 9.129110.160 12


Model1


Predictors: (Constant), C1, X13, X3, X10, X11, X15, X1, X7, X5a.


Coefficientsa

37.398 90.650 .413 .708-.988 1.116 -.610 -.885 .441 .175 5.727

-4.427 11.460 -.302 -.386 .725 .136 7.375-.401 .695 -.559 -.577 .604 .088 11.3377.228 19.537 .328 .370 .736 .106 9.469

-19.544 36.686 -.264 -.533 .631 .337 2.9705.229 80.595 .063 .065 .952 .089 11.212-.765 19.770 -.022 -.039 .972 .257 3.888-.653 15.630 -.026 -.042 .969 .213 4.697.224 .724 .208 .310 .777 .183 5.466

(Constant)X1X3X5X7X10X11X13X15C1

Model1

B Std. Error


Beta




Regression


16.8087 3.02984 1324.5339 1.86959 131.4266 .20669 133.1929 .13738 13.1012 .04098 13.7146 .03624 13.9444 .08699 13

6.8377 .12094 13114.1224 2.81803 13

Y4X1X3X7X10X11X13X15C1


323

Model Summary

.851a .724 .171 2.75798Model1



Predictors: (Constant), C1, X13, X3, X10, X11, X15,X1, X7

a.

ANOVAb

79.734 8 9.967 1.310 .422a

30.426 4 7.606110.160 12


Model1




Coefficientsa

20.289 78.191 .259 .808-1.052 1.014 -.649 -1.037 .358 .176 5.672-3.788 10.412 -.258 -.364 .734 .137 7.3069.660 17.413 .438 .555 .609 .111 9.028

-22.332 33.195 -.302 -.673 .538 .343 2.919-26.683 53.500 -.319 -.499 .644 .169 5.930

-.347 18.033 -.010 -.019 .986 .258 3.882-3.057 13.750 -.122 -.222 .835 .229 4.363

.346 .632 .321 .547 .613 .200 5.002

(Constant)X1X3X7X10X11X13X15C1

Model1

B Std. Error


Beta




324

Regression


16.8087 3.02984 1324.5339 1.86959 131.4266 .20669 133.1929 .13738 13.1012 .04098 13.9444 .08699 13

6.8377 .12094 13114.1224 2.81803 13

Y4X1X3X7X10X13X15C1


Model Summary

.841a .707 .296 2.54236Model1




ANOVAb

77.841 7 11.120 1.720 .285a

32.318 5 6.464110.160 12


Model1




325

Coefficientsa

.900 62.539 .014 .989-1.267 .846 -.782 -1.498 .194 .215 4.645-2.772 9.412 -.189 -.295 .780 .142 7.02615.503 11.876 .703 1.305 .249 .202 4.941

-32.895 23.562 -.445 -1.396 .222 .578 1.731-3.788 15.359 -.109 -.247 .815 .302 3.314-1.438 12.317 -.057 -.117 .912 .243 4.120

.159 .470 .148 .339 .748 .307 3.255


Model1

B Std. Error


Beta




Regression


16.8087 3.02984 1324.5339 1.86959 133.1929 .13738 13.1012 .04098 13.9444 .08699 13

6.8377 .12094 13114.1224 2.81803 13

Y4X1X7X10X13X15C1


Model Summary

.838a .702 .403 2.34090Model1




326

ANOVAb

77.281 6 12.880 2.350 .161a

32.879 6 5.480110.160 12


Model1




Coefficientsa

-5.589 53.891 -.104 .921-1.434 .577 -.885 -2.485 .047 .392 2.55113.672 9.317 .620 1.467 .193 .279 3.587

-31.386 21.176 -.424 -1.482 .189 .607 1.649-2.372 13.431 -.068 -.177 .866 .335 2.989-.503 10.958 -.020 -.046 .965 .260 3.846.200 .414 .186 .483 .646 .336 2.979

(Constant)X1X7X10X13X15C1

Model1

B Std. Error


Beta




Regression


16.8087 3.02984 1324.5339 1.86959 133.1929 .13738 13.1012 .04098 13

6.8377 .12094 13114.1224 2.81803 13

Y4X1X7X10X15C1


Model Summary

.837a .700 .486 2.17287Model1




327

ANOVAb

77.110 5 15.422 3.266 .077a

33.050 7 4.721110.160 12


Model1




Coefficientsa

-1.612 45.446 -.035 .973-1.413 .524 -.872 -2.696 .031 .410 2.44213.106 8.120 .594 1.614 .151 .316 3.162

-32.126 19.267 -.434 -1.667 .139 .631 1.584-1.911 6.978 -.076 -.274 .792 .552 1.810

.242 .315 .225 .767 .468 .499 2.002

(ConstantX1X7X10X15C1

Model1

B Std. Error


Beta




Regression


16.8087 3.02984 1324.5339 1.86959 133.1929 .13738 13.1012 .04098 13

6.8377 .12094 13

Y4X1X7X10X15


Model Summary

.821a .675 .512 2.11622Model1



Predictors: (Constant), X15, X7, X10, X1a.

328

ANOVAb

74.332 4 18.583 4.150 .041a

35.827 8 4.478110.160 12


Model1




Coefficientsa

2.187 43.997 .050 .962-1.434 .510 -.885 -2.812 .023 .411 2.43516.281 6.802 .738 2.393 .044 .427 2.340

-32.571 18.756 -.440 -1.737 .121 .632 1.583.162 6.266 .006 .026 .980 .650 1.539

(Constant)X1X7X10X15

Model1

B Std. Error


Beta




APPENDIX K



East Asia

Appendix K


Annual Growth Rates of GDP per Capita in East Asia

Regression


5.1221 .78476 1327.5804 2.95080 1332.5068 1.45472 131.3353 .29053 13

73.5257 1.39435 1389.2145 3.87645 1369.9248 5.77606 133.9786 .22737 133.8786 .34903 13

51.8291 1.32837 13.9498 .16002 13.9518 .12398 13.8904 .10539 13

1.0192 .11713 1358.9231 7.04109 137.1532 .12265 13

199.3204 5.86254 131.4349 .23745 13

Y11X1_1X21X31X41X51X61X71X81X91X101X111X121X131X141X151C11C21


Model Summary

1.000a 1.000 1.000 .Model1



Predictors: (Constant), C21, X21, X91, C11, X31,X131, X51, X121, X151, X71, X1_1, X41

a.

331

ANOVAb

7.390 12 .616 . .a

.000 0 .7.390 12


Model1


Predictors: (Constant), C21, X21, X91, C11, X31, X131, X51, X121, X151, X71,X1_1, X41

a.


Coefficientsa

172.307 .000 . .-.087 .000 -.326 . . .030 33.700.922 .000 1.710 . . .062 16.198

-1.777 .000 -.658 . . .015 65.186-1.829 .000 -3.249 . . .005 202.999

.053 .000 .263 . . .080 12.521-.374 .000 -.108 . . .036 27.636-.615 .000 -1.041 . . .028 36.2728.955 .000 1.203 . . .014 71.2443.399 .000 .507 . . .073 13.623

-3.909 .000 -.611 . . .026 38.193-.092 .000 -.689 . . .172 5.8173.870 .000 1.171 . . .006 153.958

(Constant)X1_1X21X31X41X51X71X91X121X131X151C11C21

Model1

B Std. Error


Beta




332

Regression


5.1221 .78476 1327.5804 2.95080 1332.5068 1.45472 131.3353 .29053 13

73.5257 1.39435 1389.2145 3.87645 133.9786 .22737 133.8786 .34903 13

51.8291 1.32837 13.9498 .16002 13.9518 .12398 13.8904 .10539 13

1.0192 .11713 1358.9231 7.04109 137.1532 .12265 13

199.3204 5.86254 131.4349 .23745 13

Y11X1_1X21X31X41X51X71X81X91X101X111X121X131X141X151C11C21


Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

7.390 12 .616 . .a

.000 0 .7.390 12


Model1



a.


333

Coefficientsa

172.307 .000 . .-.087 .000 -.326 . . .030 33.700.922 .000 1.710 . . .062 16.198

-1.777 .000 -.658 . . .015 65.186-1.829 .000 -3.249 . . .005 202.999

.053 .000 .263 . . .080 12.521-.374 .000 -.108 . . .036 27.636-.615 .000 -1.041 . . .028 36.2728.955 .000 1.203 . . .014 71.2443.399 .000 .507 . . .073 13.623

-3.909 .000 -.611 . . .026 38.193-.092 .000 -.689 . . .172 5.8173.870 .000 1.171 . . .006 153.958


Model1

B Std. Error


Beta




Regression


5.1221 .78476 1327.5804 2.95080 1332.5068 1.45472 131.3353 .29053 13

73.5257 1.39435 1389.2145 3.87645 133.9786 .22737 13

51.8291 1.32837 13.9498 .16002 13.9518 .12398 13.8904 .10539 13

1.0192 .11713 1358.9231 7.04109 137.1532 .12265 13

199.3204 5.86254 131.4349 .23745 13

Y11X1_1X21X31X41X51X71X91X101X111X121X131X141X151C11C21


334

Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

7.390 12 .616 . .a

.000 0 .7.390 12


Model1



a.


Coefficientsa

172.307 .000 . .-.087 .000 -.326 . . .030 33.700.922 .000 1.710 . . .062 16.198

-1.777 .000 -.658 . . .015 65.186-1.829 .000 -3.249 . . .005 202.999

.053 .000 .263 . . .080 12.521-.374 .000 -.108 . . .036 27.636-.615 .000 -1.041 . . .028 36.2728.955 .000 1.203 . . .014 71.2443.399 .000 .507 . . .073 13.623

-3.909 .000 -.611 . . .026 38.193-.092 .000 -.689 . . .172 5.8173.870 .000 1.171 . . .006 153.958


Model1

B Std. Error


Beta




335

Regression


5.1221 .78476 1327.5804 2.95080 1332.5068 1.45472 131.3353 .29053 13

73.5257 1.39435 1389.2145 3.87645 133.9786 .22737 13

51.8291 1.32837 13.9518 .12398 13.8904 .10539 13

1.0192 .11713 1358.9231 7.04109 137.1532 .12265 13

199.3204 5.86254 131.4349 .23745 13

Y11X1_1X21X31X41X51X71X91X111X121X131X141X151C11C21


Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

7.390 12 .616 . .a

.000 0 .7.390 12


Model1



a.


336

Coefficientsa

172.307 .000 . .-.087 .000 -.326 . . .030 33.700.922 .000 1.710 . . .062 16.198

-1.777 .000 -.658 . . .015 65.186-1.829 .000 -3.249 . . .005 202.999

.053 .000 .263 . . .080 12.521-.374 .000 -.108 . . .036 27.636-.615 .000 -1.041 . . .028 36.2728.955 .000 1.203 . . .014 71.2443.399 .000 .507 . . .073 13.623

-3.909 .000 -.611 . . .026 38.193-.092 .000 -.689 . . .172 5.8173.870 .000 1.171 . . .006 153.958


Model1

B Std. Error


Beta




Regression


5.1221 .78476 1327.5804 2.95080 1332.5068 1.45472 131.3353 .29053 13

73.5257 1.39435 1389.2145 3.87645 133.9786 .22737 13

51.8291 1.32837 13.8904 .10539 13

1.0192 .11713 1358.9231 7.04109 137.1532 .12265 13

199.3204 5.86254 131.4349 .23745 13

Y11X1_1X21X31X41X51X71X91X121X131X141X151C11C21


337

Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

7.390 12 .616 . .a

.000 0 .7.390 12


Model1



a.


Coefficientsa

172.307 .000 . .-.087 .000 -.326 . . .030 33.700.922 .000 1.710 . . .062 16.198

-1.777 .000 -.658 . . .015 65.186-1.829 .000 -3.249 . . .005 202.999

.053 .000 .263 . . .080 12.521-.374 .000 -.108 . . .036 27.636-.615 .000 -1.041 . . .028 36.2728.955 .000 1.203 . . .014 71.2443.399 .000 .507 . . .073 13.623

-3.909 .000 -.611 . . .026 38.193-.092 .000 -.689 . . .172 5.8173.870 .000 1.171 . . .006 153.958


Model1

B Std. Error


Beta




338

Regression


5.1221 .78476 1327.5804 2.95080 1332.5068 1.45472 131.3353 .29053 13

73.5257 1.39435 1389.2145 3.87645 133.9786 .22737 13

51.8291 1.32837 13.8904 .10539 13

1.0192 .11713 137.1532 .12265 13

199.3204 5.86254 131.4349 .23745 13

Y11X1_1X21X31X41X51X71X91X121X131X151C11C21


Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

7.390 12 .616 . .a

.000 0 .7.390 12


Model1



a.


339

Coefficientsa

172.307 .000 . .-.087 .000 -.326 . . .030 33.700.922 .000 1.710 . . .062 16.198

-1.777 .000 -.658 . . .015 65.186-1.829 .000 -3.249 . . .005 202.999

.053 .000 .263 . . .080 12.521-.374 .000 -.108 . . .036 27.636-.615 .000 -1.041 . . .028 36.2728.955 .000 1.203 . . .014 71.2443.399 .000 .507 . . .073 13.623

-3.909 .000 -.611 . . .026 38.193-.092 .000 -.689 . . .172 5.8173.870 .000 1.171 . . .006 153.958


Model1

B Std. Error


Beta




Regression


5.1221 .78476 1327.5804 2.95080 1332.5068 1.45472 131.3353 .29053 13

89.2145 3.87645 133.9786 .22737 13

51.8291 1.32837 13.8904 .10539 13

1.0192 .11713 137.1532 .12265 13

199.3204 5.86254 131.4349 .23745 13

Y11X1_1X21X31X51X71X91X121X131X151C11C21


Model Summary

.974a .948 .376 .61993Model1



Predictors: (Constant), C21, X21, X91, C11, X31,X131, X51, X121, X151, X71, X1_1

a.

340

ANOVAb

7.006 11 .637 1.657 .546a

.384 1 .3847.390 12


Model1


Predictors: (Constant), C21, X21, X91, C11, X31, X131, X51, X121, X151, X71,X1_1

a.


Coefficientsa

-16.755 32.640 -.513 .698-.351 .232 -1.322 -1.515 .371 .068 14.631.590 .367 1.094 1.607 .354 .112 8.916

2.887 1.726 1.069 1.673 .343 .127 7.847.100 .156 .496 .643 .636 .087 11.473

-3.231 2.992 -.936 -1.080 .476 .069 14.453.095 .393 .161 .242 .849 .117 8.514

-4.341 5.353 -.583 -.811 .566 .101 9.9396.832 4.474 1.020 1.527 .369 .117 8.5733.575 5.031 .559 .711 .607 .084 11.887-.071 .071 -.534 -1.012 .496 .187 5.357

-4.823 3.448 -1.459 -1.399 .395 .048 20.925

(Constant)X1_1X21X31X51X71X91X121X131X151C11C21

Model1

B Std. Error


Beta




Regression


5.1221 .78476 1327.5804 2.95080 1332.5068 1.45472 131.3353 .29053 13

89.2145 3.87645 133.9786 .22737 13

51.8291 1.32837 13.8904 .10539 13

1.0192 .11713 137.1532 .12265 13

199.3204 5.86254 13

Y11X1_1X21X31X51X71X91X121X131X151C11


341

Model Summary

.920a .846 .077 .75383Model1



Predictors: (Constant), C11, X51, X1_1, X91, X151,X131, X31, X21, X121, X71

a.

ANOVAb

6.254 10 .625 1.100 .566a

1.137 2 .5687.390 12


Model1


Predictors: (Constant), C11, X51, X1_1, X91, X151, X131, X31, X21, X121, X71a.


Coefficientsa

-25.373 38.976 -.651 .582-.098 .176 -.367 -.555 .635 .176 5.674.700 .436 1.297 1.603 .250 .117 8.513

2.587 2.082 .958 1.243 .340 .129 7.726-.038 .147 -.188 -.259 .820 .145 6.873

-5.485 3.067 -1.589 -1.789 .216 .097 10.266.193 .470 .326 .410 .722 .121 8.244

-7.373 5.952 -.990 -1.239 .341 .120 8.3103.402 4.550 .508 .748 .533 .167 5.9985.402 5.907 .844 .914 .457 .090 11.086-.067 .086 -.500 -.779 .517 .187 5.346

(Constant)X1_1X21X31X51X71X91X121X131X151C11

Model1

B Std. Error


Beta




342

Regression


5.0423 .81093 1427.4604 2.87039 1432.5742 1.42025 141.3171 .28729 14

89.5197 3.89553 1452.0056 1.43699 14

.9065 .11771 141.0210 .11274 14

199.6269 5.74810 14

Y11X1_1X21X31X51X91X121X131C11


Model Summary

.808a .653 .098 .77014Model1



Predictors: (Constant), C11, X51, X1_1, X91, X21,X131, X31, X121

a.

ANOVAb

5.583 8 .698 1.177 .448a

2.966 5 .5938.549 13


Model1


Predictors: (Constant), C11, X51, X1_1, X91, X21, X131, X31, X121a.


343

Coefficientsa

8.167 16.687 .489 .645-.082 .169 -.289 -.483 .649 .193 5.168.446 .271 .781 1.645 .161 .308 3.246.957 1.759 .339 .544 .610 .179 5.597.073 .118 .350 .617 .565 .215 4.642

-.344 .267 -.610 -1.289 .254 .310 3.228-3.707 4.419 -.538 -.839 .440 .169 5.9303.293 3.588 .458 .918 .401 .279 3.586-.026 .061 -.188 -.436 .681 .374 2.674

(Constant)X1_1X21X31X51X91X121X131C11

Model1

B Std. Error


Beta




APPENDIX L


Institutions on Unemployment Rates in East Asia

Appendix L


Unemployment Rates in East Asia

Regression


4.8315 .73501 1327.5804 2.95080 1332.5068 1.45472 131.3353 .29053 13

73.5257 1.39435 1389.2145 3.87645 1369.9248 5.77606 133.9786 .22737 133.8786 .34903 13

51.8291 1.32837 13.9498 .16002 13.9518 .12398 13.8904 .10539 13

1.0192 .11713 1358.9231 7.04109 137.1532 .12265 13

199.3204 5.86254 131.4349 .23745 13



Model Summary

1.000a 1.000 1.000 .Model1




a.

346

ANOVAb

6.483 12 .540 . .a

.000 0 .6.483 12


Model1



a.


Coefficientsa

-5.157 .000 . .-.253 .000 -1.017 . . .030 33.700-.002 .000 -.004 . . .062 16.198.875 .000 .346 . . .015 65.186.074 .000 .141 . . .005 202.999

-.035 .000 -.183 . . .080 12.521-2.428 .000 -.751 . . .036 27.636

.228 .000 .411 . . .028 36.272-4.610 .000 -.661 . . .014 71.2443.040 .000 .484 . . .073 13.623.907 .000 .151 . . .026 38.193.032 .000 .256 . . .172 5.817

-.355 .000 -.115 . . .006 153.958


Model1

B Std. Error


Beta




347

Regression


4.8315 .73501 1327.5804 2.95080 1332.5068 1.45472 131.3353 .29053 13

73.5257 1.39435 1389.2145 3.87645 133.9786 .22737 133.8786 .34903 13

51.8291 1.32837 13.9498 .16002 13.9518 .12398 13.8904 .10539 13

1.0192 .11713 1358.9231 7.04109 137.1532 .12265 13

199.3204 5.86254 131.4349 .23745 13



Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

6.483 12 .540 . .a

.000 0 .6.483 12


Model1



a.


348

Coefficientsa

-5.157 .000 . .-.253 .000 -1.017 . . .030 33.700-.002 .000 -.004 . . .062 16.198.875 .000 .346 . . .015 65.186.074 .000 .141 . . .005 202.999

-.035 .000 -.183 . . .080 12.521-2.428 .000 -.751 . . .036 27.636

.228 .000 .411 . . .028 36.272-4.610 .000 -.661 . . .014 71.2443.040 .000 .484 . . .073 13.623.907 .000 .151 . . .026 38.193.032 .000 .256 . . .172 5.817

-.355 .000 -.115 . . .006 153.958


Model1

B Std. Error


Beta




Regression


4.8315 .73501 1327.5804 2.95080 1332.5068 1.45472 131.3353 .29053 13

73.5257 1.39435 1389.2145 3.87645 133.9786 .22737 13

51.8291 1.32837 13.9498 .16002 13.9518 .12398 13.8904 .10539 13

1.0192 .11713 1358.9231 7.04109 137.1532 .12265 13

199.3204 5.86254 131.4349 .23745 13



349

Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

6.483 12 .540 . .a

.000 0 .6.483 12


Model1



a.


Coefficientsa

-5.157 .000 . .-.253 .000 -1.017 . . .030 33.700-.002 .000 -.004 . . .062 16.198.875 .000 .346 . . .015 65.186.074 .000 .141 . . .005 202.999

-.035 .000 -.183 . . .080 12.521-2.428 .000 -.751 . . .036 27.636

.228 .000 .411 . . .028 36.272-4.610 .000 -.661 . . .014 71.2443.040 .000 .484 . . .073 13.623.907 .000 .151 . . .026 38.193.032 .000 .256 . . .172 5.817

-.355 .000 -.115 . . .006 153.958


Model1

B Std. Error


Beta




350

Regression


4.8315 .73501 1327.5804 2.95080 1332.5068 1.45472 131.3353 .29053 13

73.5257 1.39435 1389.2145 3.87645 133.9786 .22737 13

51.8291 1.32837 13.9518 .12398 13.8904 .10539 13

1.0192 .11713 1358.9231 7.04109 137.1532 .12265 13

199.3204 5.86254 131.4349 .23745 13



Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

6.483 12 .540 . .a

.000 0 .6.483 12


Model1



a.


351

Coefficientsa

-5.157 .000 . .-.253 .000 -1.017 . . .030 33.700-.002 .000 -.004 . . .062 16.198.875 .000 .346 . . .015 65.186.074 .000 .141 . . .005 202.999

-.035 .000 -.183 . . .080 12.521-2.428 .000 -.751 . . .036 27.636

.228 .000 .411 . . .028 36.272-4.610 .000 -.661 . . .014 71.2443.040 .000 .484 . . .073 13.623.907 .000 .151 . . .026 38.193.032 .000 .256 . . .172 5.817

-.355 .000 -.115 . . .006 153.958


Model1

B Std. Error


Beta




Regression


4.8315 .73501 1327.5804 2.95080 1332.5068 1.45472 131.3353 .29053 13

73.5257 1.39435 1389.2145 3.87645 133.9786 .22737 13

51.8291 1.32837 13.8904 .10539 13

1.0192 .11713 1358.9231 7.04109 137.1532 .12265 13

199.3204 5.86254 131.4349 .23745 13

Y21X1_1X21X31X41X51X71X91X121X131X141X151C11C21


352

Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

6.483 12 .540 . .a

.000 0 .6.483 12


Model1



a.


Coefficientsa

-5.157 .000 . .-.253 .000 -1.017 . . .030 33.700-.002 .000 -.004 . . .062 16.198.875 .000 .346 . . .015 65.186.074 .000 .141 . . .005 202.999

-.035 .000 -.183 . . .080 12.521-2.428 .000 -.751 . . .036 27.636

.228 .000 .411 . . .028 36.272-4.610 .000 -.661 . . .014 71.2443.040 .000 .484 . . .073 13.623.907 .000 .151 . . .026 38.193.032 .000 .256 . . .172 5.817

-.355 .000 -.115 . . .006 153.958


Model1

B Std. Error


Beta




353

Regression


4.8315 .73501 1327.5804 2.95080 1332.5068 1.45472 131.3353 .29053 13

73.5257 1.39435 1389.2145 3.87645 133.9786 .22737 13

51.8291 1.32837 13.8904 .10539 13

1.0192 .11713 137.1532 .12265 13

199.3204 5.86254 131.4349 .23745 13

Y21X1_1X21X31X41X51X71X91X121X131X151C11C21


Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

6.483 12 .540 . .a

.000 0 .6.483 12


Model1



a.


354

Coefficientsa

-5.157 .000 . .-.253 .000 -1.017 . . .030 33.700-.002 .000 -.004 . . .062 16.198.875 .000 .346 . . .015 65.186.074 .000 .141 . . .005 202.999

-.035 .000 -.183 . . .080 12.521-2.428 .000 -.751 . . .036 27.636

.228 .000 .411 . . .028 36.272-4.610 .000 -.661 . . .014 71.2443.040 .000 .484 . . .073 13.623.907 .000 .151 . . .026 38.193.032 .000 .256 . . .172 5.817

-.355 .000 -.115 . . .006 153.958


Model1

B Std. Error


Beta




Regression


4.8315 .73501 1327.5804 2.95080 1332.5068 1.45472 131.3353 .29053 13

89.2145 3.87645 133.9786 .22737 13

51.8291 1.32837 13.8904 .10539 13

1.0192 .11713 137.1532 .12265 13

199.3204 5.86254 131.4349 .23745 13

Y21X1_1X21X31X51X71X91X121X131X151C11C21


355

Model Summary

1.000a 1.000 .999 .02518Model1



Predictors: (Constant), C21, X21, X91, C11, X31,X131, X51, X121, X151, X71, X1_1

a.

ANOVAb

6.482 11 .589 929.377 .026a

.001 1 .0016.483 12


Model1


Predictors: (Constant), C21, X21, X91, C11, X31, X131, X51, X121, X151, X71,X1_1

a.


Coefficientsa

2.523 1.326 1.903 .308-.243 .009 -.974 -25.749 .025 .068 14.631.012 .015 .023 .777 .580 .112 8.916.686 .070 .271 9.784 .065 .127 7.847

-.037 .006 -.193 -5.771 .109 .087 11.473-2.312 .122 -.715 -19.023 .033 .069 14.453

.199 .016 .359 12.451 .051 .117 8.514-4.070 .217 -.584 -18.718 .034 .101 9.9392.901 .182 .462 15.963 .040 .117 8.573.603 .204 .101 2.952 .208 .084 11.887.031 .003 .249 10.890 .058 .187 5.357

-.002 .140 -.001 -.016 .990 .048 20.925


Model1

B Std. Error


Beta




356

Regression


4.8315 .73501 1327.5804 2.95080 1332.5068 1.45472 131.3353 .29053 13

89.2145 3.87645 133.9786 .22737 13

51.8291 1.32837 13.8904 .10539 13

1.0192 .11713 137.1532 .12265 13

199.3204 5.86254 13

Y21X1_1X21X31X51X71X91X121X131X151C11


Model Summary

1.000a 1.000 .999 .01781Model1




a.

ANOVAb

6.482 10 .648 2044.105 .000a

.001 2 .0006.483 12


Model1




357

Coefficientsa

2.519 .921 2.736 .112-.243 .004 -.974 -58.441 .000 .176 5.674.012 .010 .023 1.129 .376 .117 8.513.686 .049 .271 13.940 .005 .129 7.726

-.037 .003 -.194 -10.562 .009 .145 6.873-2.313 .072 -.716 -31.932 .001 .097 10.266

.199 .011 .359 17.895 .003 .121 8.244-4.072 .141 -.584 -28.956 .001 .120 8.3102.899 .107 .462 26.971 .001 .167 5.998.604 .140 .101 4.329 .049 .090 11.086.031 .002 .249 15.416 .004 .187 5.346


Model1

B Std. Error


Beta




Regression


4.8331 .70620 1427.4604 2.87039 1432.5742 1.42025 141.3171 .28729 14

89.5197 3.89553 143.9802 .21852 14

52.0056 1.43699 14.9065 .11771 14

1.0210 .11274 14199.6269 5.74810 14

Y21X1_1X21X31X51X71X91X121X131C11


Model Summary

.999a .997 .991 .06599Model1



Predictors: (Constant), C11, X51, X1_1, X91, X21,X131, X31, X121, X71

a.

358

ANOVAb

6.466 9 .718 164.976 .000a

.017 4 .0046.483 13


Model1


Predictors: (Constant), C11, X51, X1_1, X91, X21, X131, X31, X121, X71a.


Coefficientsa

5.933 1.431 4.146 .014-.233 .014 -.947 -16.070 .000 .193 5.169.022 .034 .043 .639 .558 .145 6.899.530 .157 .216 3.377 .028 .165 6.065

-.039 .013 -.217 -3.099 .036 .137 7.287-2.181 .240 -.675 -9.080 .001 .122 8.225

.170 .026 .346 6.663 .003 .249 4.015-3.425 .380 -.571 -9.002 .001 .167 5.9882.870 .329 .458 8.734 .001 .244 4.096.037 .005 .303 7.045 .002 .364 2.751

(Constant)X1_1X21X31X51X71X91X121X131C11

Model1

B Std. Error


Beta




APPENDIX M

SPSS Output for the Impact of Politico-Economic Institutions on the


in East Asia

Appendix M

SPSS Output for the Impact of Politico-Economic Institutions on the

Percentage of the Population Falling below the Poverty Line in

East Asia

Regression


45.5097 10.08757 1227.7243 3.03402 1232.6923 1.34922 121.3355 .30345 12

73.5254 1.45635 1289.4824 3.92114 1269.7781 6.00754 123.9935 .23077 123.8685 .36255 12

51.9537 1.30563 12.9523 .16687 12.9552 .12883 12.8996 .10448 12

1.0275 .11831 1258.8333 7.34641 127.1725 .10562 12

199.9434 5.65593 121.4345 .24800 12



Model Summary

1.000a 1.000 1.000 .Model1



Predictors: (Constant), C21, X21, X91, C11, X1_1,X151, X51, X121, X31, X131, X71

a.

361

ANOVAb

1119.350 11 101.759 . .a

.000 0 .1119.350 11


Model1


Predictors: (Constant), C21, X21, X91, C11, X1_1, X151, X51, X121, X31,X131, X71

a.


Coefficientsa

-155.397 .000 . .-5.226 .000 -1.572 . . .064 15.712-.234 .000 -.031 . . .138 7.225

21.455 .000 .645 . . .127 7.863-.931 .000 -.362 . . .092 10.881

24.204 .000 .554 . . .065 15.397-1.684 .000 -.218 . . .107 9.37718.802 .000 .195 . . .107 9.34755.687 .000 .653 . . .099 10.06957.015 .000 .597 . . .097 10.269

-.153 .000 -.086 . . .219 4.575-37.375 .000 -.919 . . .035 28.618


Model1

B Std. Error


Beta




362

Regression


45.5097 10.08757 1227.7243 3.03402 1232.6923 1.34922 121.3355 .30345 12

89.4824 3.92114 1269.7781 6.00754 123.9935 .23077 123.8685 .36255 12

51.9537 1.30563 12.9523 .16687 12.9552 .12883 12.8996 .10448 12

1.0275 .11831 1258.8333 7.34641 127.1725 .10562 12

199.9434 5.65593 121.4345 .24800 12



Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

1119.350 11 101.759 . .a

.000 0 .1119.350 11


Model1



a.


363

Coefficientsa

-155.397 .000 . .-5.226 .000 -1.572 . . .064 15.712-.234 .000 -.031 . . .138 7.225

21.455 .000 .645 . . .127 7.863-.931 .000 -.362 . . .092 10.881

24.204 .000 .554 . . .065 15.397-1.684 .000 -.218 . . .107 9.37718.802 .000 .195 . . .107 9.34755.687 .000 .653 . . .099 10.06957.015 .000 .597 . . .097 10.269

-.153 .000 -.086 . . .219 4.575-37.375 .000 -.919 . . .035 28.618


Model1

B Std. Error


Beta




Regression


45.5097 10.08757 1227.7243 3.03402 1232.6923 1.34922 121.3355 .30345 12

89.4824 3.92114 123.9935 .23077 123.8685 .36255 12

51.9537 1.30563 12.9523 .16687 12.9552 .12883 12.8996 .10448 12

1.0275 .11831 1258.8333 7.34641 127.1725 .10562 12

199.9434 5.65593 121.4345 .24800 12



364

Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

1119.350 11 101.759 . .a

.000 0 .1119.350 11


Model1



a.


Coefficientsa

-155.397 .000 . .-5.226 .000 -1.572 . . .064 15.712-.234 .000 -.031 . . .138 7.225

21.455 .000 .645 . . .127 7.863-.931 .000 -.362 . . .092 10.881

24.204 .000 .554 . . .065 15.397-1.684 .000 -.218 . . .107 9.37718.802 .000 .195 . . .107 9.34755.687 .000 .653 . . .099 10.06957.015 .000 .597 . . .097 10.269

-.153 .000 -.086 . . .219 4.575-37.375 .000 -.919 . . .035 28.618


Model1

B Std. Error


Beta




365

Regression


45.5097 10.08757 1227.7243 3.03402 1232.6923 1.34922 121.3355 .30345 12

89.4824 3.92114 123.9935 .23077 12

51.9537 1.30563 12.9523 .16687 12.9552 .12883 12.8996 .10448 12

1.0275 .11831 1258.8333 7.34641 127.1725 .10562 12

199.9434 5.65593 121.4345 .24800 12



Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

1119.350 11 101.759 . .a

.000 0 .1119.350 11


Model1



a.


366

Coefficientsa

-155.397 .000 . .-5.226 .000 -1.572 . . .064 15.712-.234 .000 -.031 . . .138 7.225

21.455 .000 .645 . . .127 7.863-.931 .000 -.362 . . .092 10.881

24.204 .000 .554 . . .065 15.397-1.684 .000 -.218 . . .107 9.37718.802 .000 .195 . . .107 9.34755.687 .000 .653 . . .099 10.06957.015 .000 .597 . . .097 10.269

-.153 .000 -.086 . . .219 4.575-37.375 .000 -.919 . . .035 28.618


Model1

B Std. Error


Beta




Regression


45.5097 10.08757 1227.7243 3.03402 1232.6923 1.34922 121.3355 .30345 12

89.4824 3.92114 123.9935 .23077 12

51.9537 1.30563 12.9552 .12883 12.8996 .10448 12

1.0275 .11831 1258.8333 7.34641 127.1725 .10562 12

199.9434 5.65593 121.4345 .24800 12

Y31X1_1X21X31X51X71X91X111X121X131X141X151C11C21


367

Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

1119.350 11 101.759 . .a

.000 0 .1119.350 11


Model1



a.


Coefficientsa

-155.397 .000 . .-5.226 .000 -1.572 . . .064 15.712-.234 .000 -.031 . . .138 7.225

21.455 .000 .645 . . .127 7.863-.931 .000 -.362 . . .092 10.881

24.204 .000 .554 . . .065 15.397-1.684 .000 -.218 . . .107 9.37718.802 .000 .195 . . .107 9.34755.687 .000 .653 . . .099 10.06957.015 .000 .597 . . .097 10.269

-.153 .000 -.086 . . .219 4.575-37.375 .000 -.919 . . .035 28.618


Model1

B Std. Error


Beta




368

Regression


45.5097 10.08757 1227.7243 3.03402 1232.6923 1.34922 121.3355 .30345 12

89.4824 3.92114 123.9935 .23077 12

51.9537 1.30563 12.8996 .10448 12

1.0275 .11831 1258.8333 7.34641 127.1725 .10562 12

199.9434 5.65593 121.4345 .24800 12

Y31X1_1X21X31X51X71X91X121X131X141X151C11C21


Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

1119.350 11 101.759 . .a

.000 0 .1119.350 11


Model1



a.


369

Coefficientsa

-155.397 .000 . .-5.226 .000 -1.572 . . .064 15.712-.234 .000 -.031 . . .138 7.225

21.455 .000 .645 . . .127 7.863-.931 .000 -.362 . . .092 10.881

24.204 .000 .554 . . .065 15.397-1.684 .000 -.218 . . .107 9.37718.802 .000 .195 . . .107 9.34755.687 .000 .653 . . .099 10.06957.015 .000 .597 . . .097 10.269

-.153 .000 -.086 . . .219 4.575-37.375 .000 -.919 . . .035 28.618


Model1

B Std. Error


Beta




Regression


45.5097 10.08757 1227.7243 3.03402 1232.6923 1.34922 121.3355 .30345 12

89.4824 3.92114 123.9935 .23077 12

51.9537 1.30563 12.8996 .10448 12

1.0275 .11831 127.1725 .10562 12

199.9434 5.65593 121.4345 .24800 12

Y31X1_1X21X31X51X71X91X121X131X151C11C21


370

Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

1119.350 11 101.759 . .a

.000 0 .1119.350 11


Model1



a.


Coefficientsa

-155.397 .000 . .-5.226 .000 -1.572 . . .064 15.712-.234 .000 -.031 . . .138 7.225

21.455 .000 .645 . . .127 7.863-.931 .000 -.362 . . .092 10.881

24.204 .000 .554 . . .065 15.397-1.684 .000 -.218 . . .107 9.37718.802 .000 .195 . . .107 9.34755.687 .000 .653 . . .099 10.06957.015 .000 .597 . . .097 10.269

-.153 .000 -.086 . . .219 4.575-37.375 .000 -.919 . . .035 28.618


Model1

B Std. Error


Beta




371

Regression


45.5097 10.08757 1227.7243 3.03402 1232.6923 1.34922 121.3355 .30345 12

89.4824 3.92114 123.9935 .23077 12

51.9537 1.30563 12.8996 .10448 12

1.0275 .11831 127.1725 .10562 12

199.9434 5.65593 12

Y31X1_1X21X31X51X71X91X121X131X151C11


Model Summary

.985a .970 .675 5.74672Model1




a.

ANOVAb

1086.325 10 108.633 3.289 .407a

33.025 1 33.0251119.350 11


Model1




372

Coefficientsa

-88.948 369.538 -.241 .850-3.420 1.366 -1.029 -2.505 .242 .175 5.718

.092 3.437 .012 .027 .983 .140 7.16119.384 15.877 .583 1.221 .437 .129 7.732-1.795 1.174 -.698 -1.529 .369 .142 7.05616.588 28.461 .379 .583 .664 .070 14.368-2.062 4.046 -.267 -.510 .700 .108 9.2967.018 49.315 .073 .142 .910 .113 8.842

25.378 35.229 .298 .720 .603 .173 5.78654.788 52.521 .574 1.043 .487 .098 10.250

-.117 .654 -.066 -.179 .887 .219 4.561


Model1

B Std. Error


Beta




Regression


45.5097 10.08757 1227.7243 3.03402 1232.6923 1.34922 121.3355 .30345 12

89.4824 3.92114 1251.9537 1.30563 12

.8996 .10448 121.0275 .11831 127.1725 .10562 12

199.9434 5.65593 12

Y31X1_1X21X31X51X91X121X131X151C11


Model Summary

.980a .960 .783 4.70333Model1




a.

373

ANOVAb

1075.107 9 119.456 5.400 .166a

44.243 2 22.1211119.350 11


Model1




Coefficientsa

-221.952 237.881 -.933 .449-3.366 1.115 -1.012 -3.018 .094 .176 5.691

.982 2.520 .131 .390 .734 .174 5.74722.441 12.265 .675 1.830 .209 .145 6.888-2.153 .819 -.837 -2.629 .119 .195 5.127-.421 2.378 -.054 -.177 .876 .209 4.792

-6.324 35.750 -.065 -.177 .876 .144 6.93728.446 28.509 .334 .998 .423 .177 5.65773.390 34.137 .768 2.150 .165 .155 6.465

-.184 .527 -.103 -.349 .761 .226 4.422


Model1

B Std. Error


Beta




Regression


45.5097 10.08757 1227.7243 3.03402 1232.6923 1.34922 121.3355 .30345 12

89.4824 3.92114 12.8996 .10448 12

1.0275 .11831 127.1725 .10562 12

199.9434 5.65593 12

Y31X1_1X21X31X51X121X131X151C11


374

Model Summary

.980a .960 .853 3.87019Model1



Predictors: (Constant), C11, X121, X1_1, X21, X51,X151, X131, X31

a.

ANOVAb

1074.415 8 134.302 8.966 .049a

44.935 3 14.9781119.350 11


Model1


Predictors: (Constant), C11, X121, X1_1, X21, X51, X151, X131, X31a.


Coefficientsa

-256.370 112.678 -2.275 .107-3.383 .914 -1.018 -3.702 .034 .177 5.646

.683 1.538 .091 .444 .687 .316 3.16423.222 9.417 .699 2.466 .090 .167 5.996-2.069 .549 -.804 -3.767 .033 .294 3.405

-10.042 23.798 -.104 -.422 .701 .220 4.54025.625 19.446 .301 1.318 .279 .257 3.88777.664 19.848 .813 3.913 .030 .310 3.227

-.235 .364 -.131 -.644 .565 .321 3.113

(ConstantX1_1X21X31X51X121X131X151C11

Model1

B Std. Error


Beta




APPENDIX N


Institutions on Income Inequality in East Asia

Appendix N


Income Inequality in East Asia

Regression


7.9949 1.49818 1227.7243 3.03402 1232.6923 1.34922 121.3355 .30345 12

73.5254 1.45635 1289.4824 3.92114 1269.7781 6.00754 123.9935 .23077 123.8685 .36255 12

51.9537 1.30563 12.9523 .16687 12.9552 .12883 12.8996 .10448 12

1.0275 .11831 1258.8333 7.34641 127.1725 .10562 12

199.9434 5.65593 121.4345 .24800 12



Model Summary

1.000a 1.000 1.000 .Model1




a.

377

ANOVAb

24.690 11 2.245 . .a

.000 0 .24.690 11


Model1



a.


Coefficientsa

93.587 .000 . ..666 .000 1.350 . . .064 15.712

1.141 .000 1.027 . . .138 7.225-6.452 .000 -1.307 . . .127 7.863-.469 .000 -1.228 . . .092 10.881

-1.441 .000 -.222 . . .065 15.397-.891 .000 -.776 . . .107 9.377-.654 .000 -.046 . . .107 9.3478.379 .000 .662 . . .099 10.069

-11.889 .000 -.838 . . .097 10.269.172 .000 .649 . . .219 4.575

2.884 .000 .477 . . .035 28.618


Model1

B Std. Error


Beta




378

Regression


7.9949 1.49818 1227.7243 3.03402 1232.6923 1.34922 121.3355 .30345 12

89.4824 3.92114 1269.7781 6.00754 123.9935 .23077 123.8685 .36255 12

51.9537 1.30563 12.9523 .16687 12.9552 .12883 12.8996 .10448 12

1.0275 .11831 1258.8333 7.34641 127.1725 .10562 12

199.9434 5.65593 121.4345 .24800 12



Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

24.690 11 2.245 . .a

.000 0 .24.690 11


Model1



a.


379

Coefficientsa

93.587 .000 . ..666 .000 1.350 . . .064 15.712

1.141 .000 1.027 . . .138 7.225-6.452 .000 -1.307 . . .127 7.863-.469 .000 -1.228 . . .092 10.881

-1.441 .000 -.222 . . .065 15.397-.891 .000 -.776 . . .107 9.377-.654 .000 -.046 . . .107 9.3478.379 .000 .662 . . .099 10.069

-11.889 .000 -.838 . . .097 10.269.172 .000 .649 . . .219 4.575

2.884 .000 .477 . . .035 28.618


Model1

B Std. Error


Beta




Regression


7.9949 1.49818 1227.7243 3.03402 1232.6923 1.34922 121.3355 .30345 12

89.4824 3.92114 123.9935 .23077 123.8685 .36255 12

51.9537 1.30563 12.9523 .16687 12.9552 .12883 12.8996 .10448 12

1.0275 .11831 1258.8333 7.34641 127.1725 .10562 12

199.9434 5.65593 121.4345 .24800 12



380

Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

24.690 11 2.245 . .a

.000 0 .24.690 11


Model1



a.


Coefficientsa

93.587 .000 . ..666 .000 1.350 . . .064 15.712

1.141 .000 1.027 . . .138 7.225-6.452 .000 -1.307 . . .127 7.863-.469 .000 -1.228 . . .092 10.881

-1.441 .000 -.222 . . .065 15.397-.891 .000 -.776 . . .107 9.377-.654 .000 -.046 . . .107 9.3478.379 .000 .662 . . .099 10.069

-11.889 .000 -.838 . . .097 10.269.172 .000 .649 . . .219 4.575

2.884 .000 .477 . . .035 28.618


Model1

B Std. Error


Beta




381

Regression


7.9949 1.49818 1227.7243 3.03402 1232.6923 1.34922 121.3355 .30345 12

89.4824 3.92114 123.9935 .23077 12

51.9537 1.30563 12.9523 .16687 12.9552 .12883 12.8996 .10448 12

1.0275 .11831 1258.8333 7.34641 127.1725 .10562 12

199.9434 5.65593 121.4345 .24800 12



Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

24.690 11 2.245 . .a

.000 0 .24.690 11


Model1



a.


382

Coefficientsa

93.587 .000 . ..666 .000 1.350 . . .064 15.712

1.141 .000 1.027 . . .138 7.225-6.452 .000 -1.307 . . .127 7.863-.469 .000 -1.228 . . .092 10.881

-1.441 .000 -.222 . . .065 15.397-.891 .000 -.776 . . .107 9.377-.654 .000 -.046 . . .107 9.3478.379 .000 .662 . . .099 10.069

-11.889 .000 -.838 . . .097 10.269.172 .000 .649 . . .219 4.575

2.884 .000 .477 . . .035 28.618


Model1

B Std. Error


Beta




Regression


7.9949 1.49818 1227.7243 3.03402 1232.6923 1.34922 121.3355 .30345 12

89.4824 3.92114 123.9935 .23077 12

51.9537 1.30563 12.9552 .12883 12.8996 .10448 12

1.0275 .11831 1258.8333 7.34641 127.1725 .10562 12

199.9434 5.65593 121.4345 .24800 12

Y41X1_1X21X31X51X71X91X111X121X131X141X151C11C21


383

Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

24.690 11 2.245 . .a

.000 0 .24.690 11


Model1



a.


Coefficientsa

93.587 .000 . ..666 .000 1.350 . . .064 15.712

1.141 .000 1.027 . . .138 7.225-6.452 .000 -1.307 . . .127 7.863-.469 .000 -1.228 . . .092 10.881

-1.441 .000 -.222 . . .065 15.397-.891 .000 -.776 . . .107 9.377-.654 .000 -.046 . . .107 9.3478.379 .000 .662 . . .099 10.069

-11.889 .000 -.838 . . .097 10.269.172 .000 .649 . . .219 4.575

2.884 .000 .477 . . .035 28.618


Model1

B Std. Error


Beta




384

Regression


7.9949 1.49818 1227.7243 3.03402 1232.6923 1.34922 121.3355 .30345 12

89.4824 3.92114 123.9935 .23077 12

51.9537 1.30563 12.8996 .10448 12

1.0275 .11831 1258.8333 7.34641 127.1725 .10562 12

199.9434 5.65593 121.4345 .24800 12

Y41X1_1X21X31X51X71X91X121X131X141X151C11C21


Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

24.690 11 2.245 . .a

.000 0 .24.690 11


Model1



a.


385

Coefficientsa

93.587 .000 . ..666 .000 1.350 . . .064 15.712

1.141 .000 1.027 . . .138 7.225-6.452 .000 -1.307 . . .127 7.863-.469 .000 -1.228 . . .092 10.881

-1.441 .000 -.222 . . .065 15.397-.891 .000 -.776 . . .107 9.377-.654 .000 -.046 . . .107 9.3478.379 .000 .662 . . .099 10.069

-11.889 .000 -.838 . . .097 10.269.172 .000 .649 . . .219 4.575

2.884 .000 .477 . . .035 28.618


Model1

B Std. Error


Beta




Regression


7.9949 1.49818 1227.7243 3.03402 1232.6923 1.34922 121.3355 .30345 12

89.4824 3.92114 123.9935 .23077 12

51.9537 1.30563 12.8996 .10448 12

1.0275 .11831 127.1725 .10562 12

199.9434 5.65593 121.4345 .24800 12

Y41X1_1X21X31X51X71X91X121X131X151C11C21


386

Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

24.690 11 2.245 . .a

.000 0 .24.690 11


Model1



a.


Coefficientsa

93.587 .000 . ..666 .000 1.350 . . .064 15.712

1.141 .000 1.027 . . .138 7.225-6.452 .000 -1.307 . . .127 7.863-.469 .000 -1.228 . . .092 10.881

-1.441 .000 -.222 . . .065 15.397-.891 .000 -.776 . . .107 9.377-.654 .000 -.046 . . .107 9.3478.379 .000 .662 . . .099 10.069

-11.889 .000 -.838 . . .097 10.269.172 .000 .649 . . .219 4.575

2.884 .000 .477 . . .035 28.618


Model1

B Std. Error


Beta




387

Regression


7.9949 1.49818 1227.7243 3.03402 1232.6923 1.34922 121.3355 .30345 12

89.4824 3.92114 123.9935 .23077 12

51.9537 1.30563 12.8996 .10448 12

1.0275 .11831 127.1725 .10562 12

199.9434 5.65593 12

Y41X1_1X21X31X51X71X91X121X131X151C11


Model Summary

.996a .992 .912 .44346Model1




a.

ANOVAb

24.493 10 2.449 12.455 .217a

.197 1 .19724.690 11


Model1




388

Coefficientsa

88.460 28.516 3.102 .199.527 .105 1.067 5.002 .126 .175 5.718

1.115 .265 1.005 4.206 .149 .140 7.161-6.292 1.225 -1.274 -5.136 .122 .129 7.732-.403 .091 -1.054 -4.445 .141 .142 7.056-.853 2.196 -.131 -.389 .764 .070 14.368-.861 .312 -.751 -2.759 .221 .108 9.296.255 3.805 .018 .067 .957 .113 8.842

10.718 2.718 .846 3.943 .158 .173 5.786-11.717 4.053 -.826 -2.891 .212 .098 10.250

.169 .050 .639 3.353 .185 .219 4.561


Model1

B Std. Error


Beta




Regression


7.9949 1.49818 1227.7243 3.03402 1232.6923 1.34922 121.3355 .30345 12

89.4824 3.92114 1251.9537 1.30563 12

.8996 .10448 121.0275 .11831 127.1725 .10562 12

199.9434 5.65593 12

Y41X1_1X21X31X51X91X121X131X151C11


Model Summary

.995a .991 .950 .33641Model1




a.

389

ANOVAb

24.464 9 2.718 24.019 .041a

.226 2 .11324.690 11


Model1




Coefficientsa

95.302 17.015 5.601 .030.524 .080 1.062 6.574 .022 .176 5.691

1.070 .180 .963 5.936 .027 .174 5.747-6.449 .877 -1.306 -7.352 .018 .145 6.888-.384 .059 -1.006 -6.560 .022 .195 5.127-.946 .170 -.824 -5.562 .031 .209 4.792.941 2.557 .066 .368 .748 .144 6.937

10.560 2.039 .834 5.179 .035 .177 5.657-12.674 2.442 -.894 -5.191 .035 .155 6.465

.173 .038 .652 4.579 .045 .226 4.422


Model1

B Std. Error


Beta




APPENDIX O


Institutions on Annual Growth Rates of GDP per Capita

in Latin America

Appendix O


Annual Growth Rates of GDP per Capita in Latin America

Regression


3.2002 1.30552 1321.4875 1.65785 1318.5836 2.19615 131.5179 .15765 13

72.1919 1.02220 1386.4136 3.97503 1372.7129 6.22440 132.4072 .10863 132.8235 .22528 13

41.0090 2.90466 13.4426 .08608 13.4782 .07321 13.7490 .07650 13.8704 .10262 13

46.3122 6.07125 136.5193 .16457 13

28.9244 1.51780 13.6697 .06716 13



Model Summary

1.000a 1.000 1.000 .Model1



Predictors: (Constant), C22, X122, X102, X132, X112,X1_2, X92, X72, X32, X22, X152, X82

a.

392

ANOVAb

20.452 12 1.704 . .a

.000 0 .20.452 12


Model1


Predictors: (Constant), C22, X122, X102, X132, X112, X1_2, X92, X72, X32,X22, X152, X82

a.


Coefficientsa

68.731 .000 . .1.621 .000 2.058 . . .015 65.949.856 .000 1.440 . . .025 39.957

-10.746 .000 -1.298 . . .025 40.57310.216 .000 .850 . . .025 39.572

-17.475 .000 -3.015 . . .004 236.073.374 .000 .832 . . .032 31.284

-4.569 .000 -.301 . . .228 4.3902.571 .000 .144 . . .050 20.0246.722 .000 .394 . . .039 25.702

-5.235 .000 -.411 . . .041 24.216-4.758 .000 -.600 . . .015 68.569

-88.397 .000 -4.548 . . .003 362.716

(Constant)X1_2X22X32X72X82X92X102X112X122X132X152C22

Model1

B Std. Error


Beta




393

Regression


3.2002 1.30552 1321.4875 1.65785 1318.5836 2.19615 131.5179 .15765 13

86.4136 3.97503 1372.7129 6.22440 132.4072 .10863 132.8235 .22528 13

41.0090 2.90466 13.4426 .08608 13.4782 .07321 13.7490 .07650 13.8704 .10262 13

46.3122 6.07125 136.5193 .16457 13

28.9244 1.51780 13.6697 .06716 13



Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

20.452 12 1.704 . .a

.000 0 .20.452 12


Model1



a.


394

Coefficientsa

68.731 .000 . .1.621 .000 2.058 . . .015 65.949.856 .000 1.440 . . .025 39.957

-10.746 .000 -1.298 . . .025 40.57310.216 .000 .850 . . .025 39.572

-17.475 .000 -3.015 . . .004 236.073.374 .000 .832 . . .032 31.284

-4.569 .000 -.301 . . .228 4.3902.571 .000 .144 . . .050 20.0246.722 .000 .394 . . .039 25.702

-5.235 .000 -.411 . . .041 24.216-4.758 .000 -.600 . . .015 68.569

-88.397 .000 -4.548 . . .003 362.716


Model1

B Std. Error


Beta




Regression


3.2002 1.30552 1321.4875 1.65785 1318.5836 2.19615 131.5179 .15765 13

72.7129 6.22440 132.4072 .10863 132.8235 .22528 13

41.0090 2.90466 13.4426 .08608 13.4782 .07321 13.7490 .07650 13.8704 .10262 13

46.3122 6.07125 136.5193 .16457 13

28.9244 1.51780 13.6697 .06716 13



395

Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

20.452 12 1.704 . .a

.000 0 .20.452 12


Model1



a.


Coefficientsa

68.731 .000 . .1.621 .000 2.058 . . .015 65.949.856 .000 1.440 . . .025 39.957

-10.746 .000 -1.298 . . .025 40.57310.216 .000 .850 . . .025 39.572

-17.475 .000 -3.015 . . .004 236.073.374 .000 .832 . . .032 31.284

-4.569 .000 -.301 . . .228 4.3902.571 .000 .144 . . .050 20.0246.722 .000 .394 . . .039 25.702

-5.235 .000 -.411 . . .041 24.216-4.758 .000 -.600 . . .015 68.569

-88.397 .000 -4.548 . . .003 362.716


Model1

B Std. Error


Beta




396

Regression


3.2002 1.30552 1321.4875 1.65785 1318.5836 2.19615 131.5179 .15765 132.4072 .10863 132.8235 .22528 13

41.0090 2.90466 13.4426 .08608 13.4782 .07321 13.7490 .07650 13.8704 .10262 13

46.3122 6.07125 136.5193 .16457 13

28.9244 1.51780 13.6697 .06716 13



Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

20.452 12 1.704 . .a

.000 0 .20.452 12


Model1



a.


397

Coefficientsa

68.731 .000 . .1.621 .000 2.058 . . .015 65.949.856 .000 1.440 . . .025 39.957

-10.746 .000 -1.298 . . .025 40.57310.216 .000 .850 . . .025 39.572

-17.475 .000 -3.015 . . .004 236.073.374 .000 .832 . . .032 31.284

-4.569 .000 -.301 . . .228 4.3902.571 .000 .144 . . .050 20.0246.722 .000 .394 . . .039 25.702

-5.235 .000 -.411 . . .041 24.216-4.758 .000 -.600 . . .015 68.569

-88.397 .000 -4.548 . . .003 362.716


Model1

B Std. Error


Beta




Regression


3.2002 1.30552 1321.4875 1.65785 1318.5836 2.19615 131.5179 .15765 132.4072 .10863 132.8235 .22528 13

41.0090 2.90466 13.4426 .08608 13.4782 .07321 13.7490 .07650 13.8704 .10262 13

6.5193 .16457 1328.9244 1.51780 13

.6697 .06716 13

Y12X1_2X22X32X72X82X92X102X112X122X132X152C12C22


398

Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

20.452 12 1.704 . .a

.000 0 .20.452 12


Model1



a.


Coefficientsa

68.731 .000 . .1.621 .000 2.058 . . .015 65.949.856 .000 1.440 . . .025 39.957

-10.746 .000 -1.298 . . .025 40.57310.216 .000 .850 . . .025 39.572

-17.475 .000 -3.015 . . .004 236.073.374 .000 .832 . . .032 31.284

-4.569 .000 -.301 . . .228 4.3902.571 .000 .144 . . .050 20.0246.722 .000 .394 . . .039 25.702

-5.235 .000 -.411 . . .041 24.216-4.758 .000 -.600 . . .015 68.569

-88.397 .000 -4.548 . . .003 362.716


Model1

B Std. Error


Beta




399

Regression


3.2002 1.30552 1321.4875 1.65785 1318.5836 2.19615 131.5179 .15765 132.4072 .10863 132.8235 .22528 13

41.0090 2.90466 13.4426 .08608 13.4782 .07321 13.7490 .07650 13.8704 .10262 13

6.5193 .16457 13.6697 .06716 13

Y12X1_2X22X32X72X82X92X102X112X122X132X152C22


Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

20.452 12 1.704 . .a

.000 0 .20.452 12


Model1



a.


400

Coefficientsa

68.731 .000 . .1.621 .000 2.058 . . .015 65.949.856 .000 1.440 . . .025 39.957

-10.746 .000 -1.298 . . .025 40.57310.216 .000 .850 . . .025 39.572

-17.475 .000 -3.015 . . .004 236.073.374 .000 .832 . . .032 31.284

-4.569 .000 -.301 . . .228 4.3902.571 .000 .144 . . .050 20.0246.722 .000 .394 . . .039 25.702

-5.235 .000 -.411 . . .041 24.216-4.758 .000 -.600 . . .015 68.569

-88.397 .000 -4.548 . . .003 362.716


Model1

B Std. Error


Beta




Regression


3.2002 1.30552 1321.4875 1.65785 1318.5836 2.19615 131.5179 .15765 132.4072 .10863 132.8235 .22528 13

41.0090 2.90466 13.4426 .08608 13.4782 .07321 13.7490 .07650 13.8704 .10262 13

6.5193 .16457 13

Y12X1_2X22X32X72X82X92X102X112X122X132X152


401

Model Summary

.971a .943 .316 1.07988Model1



Predictors: (Constant), X152, X122, X102, X132,X1_2, X22, X112, X92, X32, X72, X82

a.

ANOVAb

19.286 11 1.753 1.504 .568a

1.166 1 1.16620.452 12


Model1


Predictors: (Constant), X152, X122, X102, X132, X1_2, X22, X112, X92, X32,X72, X82

a.


Coefficientsa

-34.056 127.917 -.266 .834.303 .771 .384 .393 .762 .060 16.803.432 .791 .727 .547 .682 .032 31.043

-1.797 8.864 -.217 -.203 .873 .050 20.0958.876 18.003 .739 .493 .708 .025 39.354.685 11.057 .118 .062 .961 .016 63.842.221 .580 .491 .380 .769 .034 29.250

-.806 6.589 -.053 -.122 .923 .302 3.310-6.897 16.535 -.387 -.417 .748 .066 15.0803.201 20.356 .188 .157 .901 .040 24.956

-6.880 14.858 -.541 -.463 .724 .042 23.923.053 14.930 .007 .004 .998 .016 62.120

(Constant)X1_2X22X32X72X82X92X102X112X122X132X152

Model1

B Std. Error


Beta




402

Regression


3.0876 1.32320 1421.3634 1.65907 1418.6179 2.11389 141.5023 .16233 142.4118 .10573 142.8109 .22152 14

41.3739 3.10679 14.4402 .08321 14.4790 .07041 14.7559 .07791 14.8865 .11558 14

Y12X1_2X22X32X72X82X92X102X112X122X132


Model Summary

.964a .929 .692 .73443Model1



Predictors: (Constant), X132, X1_2, X72, X102, X92,X22, X112, X122, X32, X82

a.

ANOVAb

21.143 10 2.114 3.920 .144a

1.618 3 .53922.761 13


Model1


Predictors: (Constant), X132, X1_2, X72, X102, X92, X22, X112, X122, X32,X82

a.


403

Coefficientsa

-12.800 49.010 -.261 .811.417 .371 .523 1.124 .343 .110 9.132.349 .502 .558 .696 .537 .037 27.112

-.840 5.849 -.103 -.144 .895 .046 21.7305.701 8.084 .456 .705 .532 .057 17.608

-1.206 6.913 -.202 -.175 .873 .018 56.521.057 .210 .134 .273 .803 .098 10.243

-.462 3.695 -.029 -.125 .908 .439 2.279-7.239 11.056 -.385 -.655 .559 .068 14.606-1.169 12.850 -.069 -.091 .933 .041 24.158-7.260 5.465 -.634 -1.329 .276 .104 9.614

(Constant)X1_2X22X32X72X82X92X102X112X122X132

Model1

B Std. Error


Beta




Regression


3.0876 1.32320 1421.3634 1.65907 1418.6179 2.11389 141.5023 .16233 142.4118 .10573 14

41.3739 3.10679 14.4402 .08321 14.4790 .07041 14.7559 .07791 14.8865 .11558 14

Y12X1_2X22X32X72X92X102X112X122X132


Model Summary

.963a .928 .767 .63925Model1



Predictors: (Constant), X132, X1_2, X72, X102, X92,X22, X112, X122, X32

a.

404

ANOVAb

21.127 9 2.347 5.744 .054a

1.635 4 .40922.761 13


Model1


Predictors: (Constant), X132, X1_2, X72, X102, X92, X22, X112, X122, X32a.


Coefficientsa

-19.078 28.972 -.658 .546.411 .322 .516 1.279 .270 .110 9.055.428 .191 .684 2.240 .089 .193 5.188

-1.014 5.017 -.124 -.202 .850 .047 21.0996.096 6.754 .487 .903 .418 .062 16.225.078 .150 .183 .520 .631 .144 6.931

-.458 3.216 -.029 -.143 .894 .439 2.279-8.556 7.033 -.455 -1.217 .291 .128 7.801

.225 8.760 .013 .026 .981 .067 14.817-7.746 4.091 -.677 -1.894 .131 .141 7.111

(Constant)X1_2X22X32X72X92X102X112X122X132

Model1

B Std. Error


Beta




Regression


3.0876 1.32320 1421.3634 1.65907 1418.6179 2.11389 142.4118 .10573 14

41.3739 3.10679 14.4402 .08321 14.4790 .07041 14.7559 .07791 14.8865 .11558 14

Y12X1_2X22X72X92X102X112X122X132


405

Model Summary

.963a .927 .811 .57468Model1



Predictors: (Constant), X132, X1_2, X72, X102, X92,X22, X112, X122

a.

ANOVAb

21.110 8 2.639 7.990 .017a

1.651 5 .33022.761 13


Model1


Predictors: (Constant), X132, X1_2, X72, X102, X92, X22, X112, X122a.


Coefficientsa

-21.543 23.625 -.912 .404.409 .289 .513 1.417 .216 .111 9.049.437 .167 .698 2.610 .048 .203 4.923

5.887 6.001 .470 .981 .372 .063 15.845.102 .081 .241 1.272 .259 .406 2.466

-.349 2.850 -.022 -.122 .907 .452 2.214-9.258 5.497 -.493 -1.684 .153 .170 5.896

.689 7.599 .041 .091 .931 .072 13.798-7.464 3.456 -.652 -2.160 .083 .159 6.281

(ConstantX1_2X22X72X92X102X112X122X132

Model1

B Std. Error


Beta




406

Regression


3.0876 1.32320 1421.3634 1.65907 1418.6179 2.11389 1441.3739 3.10679 14

.4402 .08321 14

.4790 .07041 14

.7559 .07791 14

.8865 .11558 14

Y12X1_2X22X92X102X112X122X132


Model Summary

.956a .913 .813 .57288Model1



Predictors: (Constant), X132, X1_2, X102, X112, X92,X122, X22

a.

ANOVAb

20.792 7 2.970 9.050 .008a

1.969 6 .32822.761 13


Model1


Predictors: (Constant), X132, X1_2, X102, X112, X92, X122, X22a.


407

Coefficientsa

.704 6.611 .107 .919

.166 .147 .208 1.127 .303 .424 2.357

.332 .128 .530 2.586 .041 .343 2.915

.085 .078 .200 1.085 .319 .426 2.346

.507 2.705 .032 .187 .858 .498 2.006-5.099 3.488 -.271 -1.462 .194 .419 2.389-6.046 3.248 -.356 -1.861 .112 .394 2.537-4.582 1.816 -.400 -2.523 .045 .573 1.745

(ConstantX1_2X22X92X102X112X122X132

Model1

B Std. Error


Beta




APPENDIX P


Institutions on Unemployment Rates in Latin America

Appendix P


Unemployment Rates in Latin America

Regression


9.1220 1.18786 1321.4875 1.65785 1318.5836 2.19615 131.5179 .15765 13

72.1919 1.02220 1386.4136 3.97503 1372.7129 6.22440 132.4072 .10863 132.8235 .22528 13

41.0090 2.90466 13.4426 .08608 13.4782 .07321 13.7490 .07650 13.8704 .10262 13

46.3122 6.07125 136.5193 .16457 13

28.9244 1.51780 13.6697 .06716 13



Model Summary

1.000a 1.000 1.000 .Model1




a.

410

ANOVAb

16.932 12 1.411 . .a

.000 0 .16.932 12


Model1



a.


Coefficientsa

11.814 .000 . .-.452 .000 -.631 . . .015 65.949.001 .000 .001 . . .025 39.957

-.077 .000 -.010 . . .025 40.5731.644 .000 .150 . . .025 39.5723.266 .000 .619 . . .004 236.073-.120 .000 -.293 . . .032 31.284-.417 .000 -.030 . . .228 4.390

-7.287 .000 -.449 . . .050 20.0242.707 .000 .174 . . .039 25.702

-3.843 .000 -.332 . . .041 24.216-.052 .000 -.007 . . .015 68.5696.255 .000 .354 . . .003 362.716


Model1

B Std. Error


Beta




411

Regression


9.1220 1.18786 1321.4875 1.65785 1318.5836 2.19615 131.5179 .15765 13

86.4136 3.97503 1372.7129 6.22440 132.4072 .10863 132.8235 .22528 13

41.0090 2.90466 13.4426 .08608 13.4782 .07321 13.7490 .07650 13.8704 .10262 13

46.3122 6.07125 136.5193 .16457 13

28.9244 1.51780 13.6697 .06716 13



Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

16.932 12 1.411 . .a

.000 0 .16.932 12


Model1



a.


412

Coefficientsa

11.814 .000 . .-.452 .000 -.631 . . .015 65.949.001 .000 .001 . . .025 39.957

-.077 .000 -.010 . . .025 40.5731.644 .000 .150 . . .025 39.5723.266 .000 .619 . . .004 236.073-.120 .000 -.293 . . .032 31.284-.417 .000 -.030 . . .228 4.390

-7.287 .000 -.449 . . .050 20.0242.707 .000 .174 . . .039 25.702

-3.843 .000 -.332 . . .041 24.216-.052 .000 -.007 . . .015 68.5696.255 .000 .354 . . .003 362.716


Model1

B Std. Error


Beta




Regression


9.1220 1.18786 1321.4875 1.65785 1318.5836 2.19615 131.5179 .15765 13

72.7129 6.22440 132.4072 .10863 132.8235 .22528 13

41.0090 2.90466 13.4426 .08608 13.4782 .07321 13.7490 .07650 13.8704 .10262 13

46.3122 6.07125 136.5193 .16457 13

28.9244 1.51780 13.6697 .06716 13



413

Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

16.932 12 1.411 . .a

.000 0 .16.932 12


Model1



a.


Coefficientsa

11.814 .000 . .-.452 .000 -.631 . . .015 65.949.001 .000 .001 . . .025 39.957

-.077 .000 -.010 . . .025 40.5731.644 .000 .150 . . .025 39.5723.266 .000 .619 . . .004 236.073-.120 .000 -.293 . . .032 31.284-.417 .000 -.030 . . .228 4.390

-7.287 .000 -.449 . . .050 20.0242.707 .000 .174 . . .039 25.702

-3.843 .000 -.332 . . .041 24.216-.052 .000 -.007 . . .015 68.5696.255 .000 .354 . . .003 362.716


Model1

B Std. Error


Beta




414

Regression


9.1220 1.18786 1321.4875 1.65785 1318.5836 2.19615 131.5179 .15765 132.4072 .10863 132.8235 .22528 13

41.0090 2.90466 13.4426 .08608 13.4782 .07321 13.7490 .07650 13.8704 .10262 13

46.3122 6.07125 136.5193 .16457 13

28.9244 1.51780 13.6697 .06716 13



Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

16.932 12 1.411 . .a

.000 0 .16.932 12


Model1



a.


415

Coefficientsa

11.814 .000 . .-.452 .000 -.631 . . .015 65.949.001 .000 .001 . . .025 39.957

-.077 .000 -.010 . . .025 40.5731.644 .000 .150 . . .025 39.5723.266 .000 .619 . . .004 236.073-.120 .000 -.293 . . .032 31.284-.417 .000 -.030 . . .228 4.390

-7.287 .000 -.449 . . .050 20.0242.707 .000 .174 . . .039 25.702

-3.843 .000 -.332 . . .041 24.216-.052 .000 -.007 . . .015 68.5696.255 .000 .354 . . .003 362.716


Model1

B Std. Error


Beta




Regression


9.1220 1.18786 1321.4875 1.65785 1318.5836 2.19615 131.5179 .15765 132.4072 .10863 132.8235 .22528 13

41.0090 2.90466 13.4426 .08608 13.4782 .07321 13.7490 .07650 13.8704 .10262 13

6.5193 .16457 1328.9244 1.51780 13

.6697 .06716 13

Y22X1_2X22X32X72X82X92X102X112X122X132X152C12C22


416

Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

16.932 12 1.411 . .a

.000 0 .16.932 12


Model1



a.


Coefficientsa

11.814 .000 . .-.452 .000 -.631 . . .015 65.949.001 .000 .001 . . .025 39.957

-.077 .000 -.010 . . .025 40.5731.644 .000 .150 . . .025 39.5723.266 .000 .619 . . .004 236.073-.120 .000 -.293 . . .032 31.284-.417 .000 -.030 . . .228 4.390

-7.287 .000 -.449 . . .050 20.0242.707 .000 .174 . . .039 25.702

-3.843 .000 -.332 . . .041 24.216-.052 .000 -.007 . . .015 68.5696.255 .000 .354 . . .003 362.716

(ConstantX1_2X22X32X72X82X92X102X112X122X132X152C22

Model1

B Std. Error


Beta




417

Regression


9.1220 1.18786 1321.4875 1.65785 1318.5836 2.19615 131.5179 .15765 132.4072 .10863 132.8235 .22528 13

41.0090 2.90466 13.4426 .08608 13.4782 .07321 13.7490 .07650 13.8704 .10262 13

6.5193 .16457 1328.9244 1.51780 13

Y22X1_2X22X32X72X82X92X102X112X122X132X152C12


Model Summary

1.000a 1.000 1.000 .Model1



Predictors: (Constant), C12, X102, X1_2, X132, X122,X112, X22, X92, X72, X152, X82, X32

a.

ANOVAb

16.932 12 1.411 . .a

.000 0 .16.932 12


Model1


Predictors: (Constant), C12, X102, X1_2, X132, X122, X112, X22, X92, X72,X152, X82, X32

a.


418

Coefficientsa

-1.535 .000 . .-.390 .000 -.544 . . .045 22.149.014 .000 .026 . . .030 33.811

3.440 .000 .456 . . .001 899.5001.426 .000 .130 . . .024 41.7182.738 .000 .519 . . .008 123.616-.146 .000 -.356 . . .019 52.880

-1.270 .000 -.092 . . .117 8.549-8.163 .000 -.503 . . .024 41.4102.215 .000 .143 . . .032 31.558

-3.064 .000 -.265 . . .030 33.413-.014 .000 -.002 . . .014 70.114.482 .000 .615 . . .001 1098.165


Model1

B Std. Error


Beta




Regression


9.1220 1.18786 1321.4875 1.65785 1318.5836 2.19615 131.5179 .15765 132.4072 .10863 132.8235 .22528 13

41.0090 2.90466 13.4426 .08608 13.4782 .07321 13.7490 .07650 13.8704 .10262 13

6.5193 .16457 13

Y22X1_2X22X32X72X82X92X102X112X122X132X152


419

Model Summary

1.000a 1.000 .996 .07642Model1




a.

ANOVAb

16.926 11 1.539 263.510 .048a

.006 1 .00616.932 12


Model1



a.


Coefficientsa

19.088 9.052 2.109 .282-.359 .055 -.501 -6.583 .096 .060 16.803.031 .056 .057 .548 .681 .032 31.043

-.710 .627 -.094 -1.132 .461 .050 20.0951.738 1.274 .159 1.365 .403 .025 39.3541.981 .782 .376 2.532 .239 .016 63.842-.109 .041 -.266 -2.651 .230 .034 29.250-.683 .466 -.049 -1.465 .381 .302 3.310

-6.617 1.170 -.408 -5.655 .111 .066 15.0802.956 1.440 .190 2.052 .289 .040 24.956

-3.726 1.051 -.322 -3.544 .175 .042 23.923-.393 1.056 -.054 -.372 .773 .016 62.120


Model1

B Std. Error


Beta




420

Regression


9.0158 1.20850 1421.3634 1.65907 1418.6179 2.11389 141.5023 .16233 142.4118 .10573 142.8109 .22152 14

41.3739 3.10679 14.4402 .08321 14.4790 .07041 14.7559 .07791 14.8865 .11558 14

Y22X1_2X22X32X72X82X92X102X112X122X132


Model Summary

.998a .996 .982 .16101Model1




a.

ANOVAb

18.908 10 1.891 72.940 .002a

.078 3 .02618.986 13


Model1



a.


421

Coefficientsa

24.743 10.744 2.303 .105-.299 .081 -.411 -3.681 .035 .110 9.132-.009 .110 -.016 -.084 .938 .037 27.112-.372 1.282 -.050 -.290 .791 .046 21.730.827 1.772 .072 .467 .673 .057 17.608

1.145 1.516 .210 .755 .505 .018 56.521-.185 .046 -.476 -4.026 .028 .098 10.243-.444 .810 -.031 -.549 .621 .439 2.279

-6.670 2.424 -.389 -2.752 .071 .068 14.6061.302 2.817 .084 .462 .675 .041 24.158

-4.223 1.198 -.404 -3.525 .039 .104 9.614


Model1

B Std. Error


Beta




Regression


9.0158 1.20850 1421.3634 1.65907 141.5023 .16233 142.4118 .10573 142.8109 .22152 14

41.3739 3.10679 14.4402 .08321 14.4790 .07041 14.7559 .07791 14.8865 .11558 14

Y22X1_2X32X72X82X92X102X112X122X132


Model Summary

.998a .996 .987 .13960Model1




a.

422

ANOVAb

18.908 9 2.101 107.804 .000a

.078 4 .01918.986 13


Model1




Coefficientsa

23.956 4.576 5.235 .006-.298 .070 -.410 -4.270 .013 .111 8.972-.378 1.110 -.051 -.340 .751 .046 21.663.901 1.335 .079 .675 .537 .075 13.282

1.259 .575 .231 2.191 .094 .092 10.816-.183 .033 -.471 -5.543 .005 .142 7.020-.440 .701 -.030 -.628 .564 .441 2.268

-6.816 1.474 -.397 -4.624 .010 .139 7.1861.488 1.512 .096 .984 .381 .108 9.257

-4.289 .782 -.410 -5.483 .005 .183 5.453


Model1

B Std. Error


Beta




Regression


9.0158 1.20850 1421.3634 1.65907 142.4118 .10573 142.8109 .22152 14

41.3739 3.10679 14.4402 .08321 14.4790 .07041 14.7559 .07791 14.8865 .11558 14

Y22X1_2X72X82X92X102X112X122X132


423

Model Summary

.998a .996 .989 .12666Model1




a.

ANOVAb

18.906 8 2.363 147.318 .000a

.080 5 .01618.986 13


Model1




Coefficientsa

23.304 3.771 6.180 .002-.299 .063 -.410 -4.708 .005 .111 8.972.814 1.189 .071 .685 .524 .078 12.801

1.206 .501 .221 2.405 .061 .100 9.995-.175 .021 -.450 -8.271 .000 .285 3.504-.399 .626 -.027 -.637 .552 .454 2.202

-7.016 1.226 -.409 -5.720 .002 .165 6.0431.605 1.336 .103 1.201 .283 .114 8.780

-4.169 .633 -.399 -6.585 .001 .230 4.339

(Constant)X1_2X72X82X92X102X112X122X132

Model1

B Std. Error


Beta




Regression


9.0158 1.20850 1421.3634 1.65907 142.8109 .22152 14

41.3739 3.10679 14.4402 .08321 14.4790 .07041 14.7559 .07791 14.8865 .11558 14

Y22X1_2X82X92X102X112X122X132


424

Model Summary

.998a .995 .990 .12093Model1




a.

ANOVAb

18.898 7 2.700 184.626 .000a

.088 6 .01518.986 13


Model1




Coefficientsa

25.524 1.843 13.846 .000-.336 .031 -.461 -10.757 .000 .419 2.3851.383 .410 .254 3.375 .015 .137 7.325-.175 .020 -.449 -8.644 .000 .286 3.501-.237 .554 -.016 -.429 .683 .529 1.889

-6.562 .986 -.382 -6.656 .001 .233 4.283.847 .715 .055 1.185 .281 .362 2.759

-3.824 .367 -.366 -10.432 .000 .627 1.596

(Constant)X1_2X82X92X102X112X122X132

Model1

B Std. Error


Beta




APPENDIX Q



below the Poverty Line in Latin America

Appendix Q


the Percentage of the Population Falling below the Poverty Line in

Latin America


19.4965 4.43022 1321.4875 1.65785 1318.5836 2.19615 131.5179 .15765 13

72.1919 1.02220 1386.4136 3.97503 1372.7129 6.22440 132.4072 .10863 132.8235 .22528 13

41.0090 2.90466 13.4426 .08608 13.4782 .07321 13.7490 .07650 13.8704 .10262 13

46.3122 6.07125 136.5193 .16457 13

28.9244 1.51780 13.6697 .06716 13



Model Summary

1.000a 1.000 1.000 .Model1




a.

427

ANOVAb

235.523 12 19.627 . .a

.000 0 .235.523 12


Model1



a.


Coefficientsa

1183.210 .000 . .5.180 .000 1.938 . . .015 65.949.739 .000 .366 . . .025 39.957

-75.212 .000 -2.676 . . .025 40.573-38.943 .000 -.955 . . .025 39.572

-127.853 .000 -6.501 . . .004 236.0731.493 .000 .979 . . .032 31.284

-18.403 .000 -.358 . . .228 4.390103.510 .000 1.711 . . .050 20.024-73.457 .000 -1.268 . . .039 25.70266.274 .000 1.535 . . .041 24.216

-65.916 .000 -2.449 . . .015 68.569-590.351 .000 -8.950 . . .003 362.716


Model1

B Std. Error


Beta




428

Regression


19.4965 4.43022 1321.4875 1.65785 1318.5836 2.19615 131.5179 .15765 13

72.1919 1.02220 1386.4136 3.97503 1372.7129 6.22440 132.4072 .10863 13

41.0090 2.90466 13.4426 .08608 13.4782 .07321 13.7490 .07650 13.8704 .10262 13

46.3122 6.07125 136.5193 .16457 13

28.9244 1.51780 13.6697 .06716 13



Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

235.523 12 19.627 . .a

.000 0 .235.523 12


Model1



a.


429

Coefficientsa

-77.622 .000 . .2.407 .000 .901 . . .025 39.3253.013 .000 1.493 . . .021 46.635

115.761 .000 4.119 . . .005 185.77217.447 .000 15.655 . . .001 1368.80213.773 .000 .338 . . .020 50.3025.676 .000 3.721 . . .015 67.064

26.244 .000 .510 . . .177 5.63819.879 .000 .329 . . .104 9.600

-275.685 .000 -4.761 . . .009 106.760125.825 .000 2.915 . . .026 37.956

-243.192 .000 -9.034 . . .003 342.561-445.874 .000 -6.760 . . .005 221.099


Model1

B Std. Error


Beta




Regression


19.4965 4.43022 1321.4875 1.65785 1318.5836 2.19615 131.5179 .15765 13

86.4136 3.97503 1372.7129 6.22440 132.4072 .10863 13

41.0090 2.90466 13.4426 .08608 13.4782 .07321 13.7490 .07650 13.8704 .10262 13

46.3122 6.07125 136.5193 .16457 13

28.9244 1.51780 13.6697 .06716 13



430

Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

235.523 12 19.627 . .a

.000 0 .235.523 12


Model1



a.


Coefficientsa

-77.622 .000 . .2.407 .000 .901 . . .025 39.3253.013 .000 1.493 . . .021 46.635

115.761 .000 4.119 . . .005 185.77217.447 .000 15.655 . . .001 1368.80213.773 .000 .338 . . .020 50.3025.676 .000 3.721 . . .015 67.064

26.244 .000 .510 . . .177 5.63819.879 .000 .329 . . .104 9.600

-275.685 .000 -4.761 . . .009 106.760125.825 .000 2.915 . . .026 37.956

-243.192 .000 -9.034 . . .003 342.561-445.874 .000 -6.760 . . .005 221.099


Model1

B Std. Error


Beta




431

Regression


19.4965 4.43022 1321.4875 1.65785 1318.5836 2.19615 131.5179 .15765 13

86.4136 3.97503 132.4072 .10863 13

41.0090 2.90466 13.4426 .08608 13.4782 .07321 13.7490 .07650 13.8704 .10262 13

46.3122 6.07125 136.5193 .16457 13

28.9244 1.51780 13.6697 .06716 13



Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

235.523 12 19.627 . .a

.000 0 .235.523 12


Model1



a.


432

Coefficientsa

-77.622 .000 . .2.407 .000 .901 . . .025 39.3253.013 .000 1.493 . . .021 46.635

115.761 .000 4.119 . . .005 185.77217.447 .000 15.655 . . .001 1368.80213.773 .000 .338 . . .020 50.3025.676 .000 3.721 . . .015 67.064

26.244 .000 .510 . . .177 5.63819.879 .000 .329 . . .104 9.600

-275.685 .000 -4.761 . . .009 106.760125.825 .000 2.915 . . .026 37.956

-243.192 .000 -9.034 . . .003 342.561-445.874 .000 -6.760 . . .005 221.099


Model1

B Std. Error


Beta




Regression


19.4965 4.43022 1321.4875 1.65785 1318.5836 2.19615 131.5179 .15765 13

86.4136 3.97503 132.4072 .10863 13

41.0090 2.90466 13.4426 .08608 13.4782 .07321 13.7490 .07650 13.8704 .10262 13

6.5193 .16457 1328.9244 1.51780 13

.6697 .06716 13

Y32X1_2X22X32X52X72X92X102X112X122X132X152C12C22


433

Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

235.523 12 19.627 . .a

.000 0 .235.523 12


Model1



a.


Coefficientsa

-77.622 .000 . .2.407 .000 .901 . . .025 39.3253.013 .000 1.493 . . .021 46.635

115.761 .000 4.119 . . .005 185.77217.447 .000 15.655 . . .001 1368.80213.773 .000 .338 . . .020 50.3025.676 .000 3.721 . . .015 67.064

26.244 .000 .510 . . .177 5.63819.879 .000 .329 . . .104 9.600

-275.685 .000 -4.761 . . .009 106.760125.825 .000 2.915 . . .026 37.956

-243.192 .000 -9.034 . . .003 342.561-445.874 .000 -6.760 . . .005 221.099


Model1

B Std. Error


Beta




434

Regression


19.4965 4.43022 1321.4875 1.65785 1318.5836 2.19615 131.5179 .15765 13

86.4136 3.97503 132.4072 .10863 13

41.0090 2.90466 13.4426 .08608 13.4782 .07321 13.7490 .07650 13.8704 .10262 13

6.5193 .16457 13.6697 .06716 13

Y32X1_2X22X32X52X72X92X102X112X122X132X152C22


Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

235.523 12 19.627 . .a

.000 0 .235.523 12


Model1



a.


435

Coefficientsa

-77.622 .000 . .2.407 .000 .901 . . .025 39.3253.013 .000 1.493 . . .021 46.635

115.761 .000 4.119 . . .005 185.77217.447 .000 15.655 . . .001 1368.80213.773 .000 .338 . . .020 50.3025.676 .000 3.721 . . .015 67.064

26.244 .000 .510 . . .177 5.63819.879 .000 .329 . . .104 9.600

-275.685 .000 -4.761 . . .009 106.760125.825 .000 2.915 . . .026 37.956

-243.192 .000 -9.034 . . .003 342.561-445.874 .000 -6.760 . . .005 221.099


Model1

B Std. Error


Beta




Regression


19.4965 4.43022 1321.4875 1.65785 1318.5836 2.19615 131.5179 .15765 132.4072 .10863 13

41.0090 2.90466 13.4426 .08608 13.4782 .07321 13.7490 .07650 13.8704 .10262 13

6.5193 .16457 13.6697 .06716 13

Y32X1_2X22X32X72X92X102X112X122X132X152C22


436

Model Summary

.906a .821 -1.149 6.49372Model1



Predictors: (Constant), C22, X122, X102, X132, X112,X1_2, X92, X72, X32, X22, X152

a.

ANOVAb

193.354 11 17.578 .417 .850a

42.168 1 42.168235.523 12


Model1


Predictors: (Constant), C22, X122, X102, X132, X112, X1_2, X92, X72, X32,X22, X152

a.


Coefficientsa

561.932 766.646 .733 .597-2.344 5.264 -.877 -.445 .733 .046 21.676

.806 5.395 .399 .149 .906 .025 39.951-33.474 63.201 -1.191 -.530 .690 .035 28.252-42.893 108.484 -1.052 -.395 .760 .025 39.520

1.803 3.596 1.182 .501 .704 .032 31.054-2.709 42.844 -.053 -.063 .960 .258 3.87120.840 79.327 .344 .263 .836 .104 9.599

-54.237 122.731 -.937 -.442 .735 .040 25.08757.556 89.467 1.333 .643 .636 .042 23.989

-54.281 93.605 -2.016 -.580 .665 .015 67.526-136.318 276.432 -2.067 -.493 .708 .010 98.091

(Constant)X1_2X22X32X72X92X102X112X122X132X152C22

Model1

B Std. Error


Beta




437

Regression


19.4965 4.43022 1321.4875 1.65785 1318.5836 2.19615 131.5179 .15765 132.4072 .10863 13

41.0090 2.90466 13.4426 .08608 13.4782 .07321 13.7490 .07650 13.8704 .10262 13

6.5193 .16457 13

Y32X1_2X22X32X72X92X102X112X122X132X152


Model Summary

.882a .777 -.335 5.11972Model1



Predictors: (Constant), X152, X122, X102, X132,X1_2, X22, X112, X92, X32, X72

a.

ANOVAb

183.100 10 18.310 .699 .716a

52.423 2 26.212235.523 12


Model1


Predictors: (Constant), X152, X122, X102, X132, X1_2, X22, X112, X92, X32,X72

a.


438

Coefficientsa

479.019 589.717 .812 .502-3.767 3.471 -1.410 -1.085 .391 .066 15.163-1.665 1.577 -.825 -1.056 .402 .182 5.494

-16.255 41.534 -.578 -.391 .733 .051 19.629-47.338 85.234 -1.161 -.555 .634 .025 39.247

.679 2.194 .445 .310 .786 .054 18.5856.038 30.748 .117 .196 .862 .312 3.207

33.815 59.002 .559 .573 .624 .117 8.543-89.882 78.201 -1.552 -1.149 .369 .061 16.38555.237 70.439 1.280 .784 .515 .042 23.922

-36.273 67.952 -1.347 -.534 .647 .017 57.249


Model1

B Std. Error


Beta




Regression


19.4965 4.43022 1321.4875 1.65785 1318.5836 2.19615 132.4072 .10863 13

41.0090 2.90466 13.4426 .08608 13.4782 .07321 13.7490 .07650 13.8704 .10262 13

6.5193 .16457 13

Y32X1_2X22X72X92X102X112X122X132X152


Model Summary

.872a .760 .041 4.33735Model1



Predictors: (Constant), X152, X122, X102, X132,X1_2, X22, X112, X92, X72

a.

439

ANOVAb

179.085 9 19.898 1.058 .541a

56.438 3 18.813235.523 12


Model1


Predictors: (Constant), X152, X122, X102, X132, X1_2, X22, X112, X92, X72a.


Coefficientsa

492.706 498.720 .988 .396-3.949 2.914 -1.478 -1.355 .268 .067 14.890-1.499 1.287 -.743 -1.165 .328 .196 5.095

-56.863 69.203 -1.394 -.822 .471 .028 36.0471.137 1.573 .745 .723 .522 .075 13.3096.357 26.040 .124 .244 .823 .312 3.205

22.493 43.564 .372 .516 .641 .154 6.489-86.342 65.806 -1.491 -1.312 .281 .062 16.16664.475 56.224 1.494 1.147 .335 .047 21.236

-42.221 56.109 -1.568 -.752 .506 .018 54.385


Model1

B Std. Error


Beta




Regression


18.5976 5.42472 1421.3634 1.65907 1418.6179 2.11389 142.4118 .10573 14

41.3739 3.10679 14.4402 .08321 14.4790 .07041 14.7559 .07791 14.8865 .11558 14

Y32X1_2X22X72X92X102X112X122X132


440

Model Summary

.857a .734 .309 4.50807Model1




a.

ANOVAb

280.946 8 35.118 1.728 .283a

101.614 5 20.323382.559 13


Model1




Coefficientsa

252.208 185.326 1.361 .232-1.684 2.267 -.515 -.743 .491 .111 9.049-1.313 1.312 -.512 -1.000 .363 .203 4.923

-38.697 47.071 -.754 -.822 .448 .063 15.845-.946 .632 -.542 -1.497 .195 .406 2.466

19.722 22.356 .303 .882 .418 .452 2.2141.906 43.118 .025 .044 .966 .170 5.896

-85.442 59.612 -1.227 -1.433 .211 .072 13.79816.075 27.112 .342 .593 .579 .159 6.281

(ConstantX1_2X22X72X92X102X112X122X132

Model1

B Std. Error


Beta




441

Regression


18.5976 5.42472 1421.3634 1.65907 1418.6179 2.11389 142.4118 .10573 14

41.3739 3.10679 14.4402 .08321 14.7559 .07791 14.8865 .11558 14

Y32X1_2X22X72X92X102X122X132


Model Summary

.857a .734 .424 4.11609Model1




a.

ANOVAb

280.906 7 40.129 2.369 .157a

101.653 6 16.942382.559 13


Model1




442

Coefficientsa

247.126 132.702 1.862 .112-1.613 1.473 -.493 -1.095 .315 .218 4.583-1.299 1.165 -.506 -1.115 .307 .215 4.654

-37.093 27.357 -.723 -1.356 .224 .156 6.420-.945 .577 -.541 -1.638 .152 .406 2.463

19.172 16.955 .294 1.131 .301 .655 1.527-83.818 42.856 -1.204 -1.956 .098 .117 8.55515.334 19.453 .327 .788 .461 .258 3.878

(Constant)X1_2X22X72X92X102X122X132

Model1

B Std. Error


Beta




Regression


18.5976 5.42472 1421.3634 1.65907 1418.6179 2.11389 142.4118 .10573 14

41.3739 3.10679 14.4402 .08321 14.7559 .07791 14

Y32X1_2X22X72X92X102X122


Model Summary

.841a .707 .455 4.00323Model1



Predictors: (Constant), X122, X22, X102, X92, X1_2,X72

a.

443

ANOVAb

270.378 6 45.063 2.812 .101a

112.181 7 16.026382.559 13


Model1


Predictors: (Constant), X122, X22, X102, X92, X1_2, X72a.


Coefficientsa

165.907 81.337 2.040 .081-.874 1.105 -.267 -.791 .455 .367 2.728-.677 .833 -.264 -.812 .443 .398 2.515

-21.019 17.737 -.410 -1.185 .275 .351 2.853-.787 .526 -.451 -1.496 .178 .461 2.167

24.159 15.299 .371 1.579 .158 .761 1.315-57.417 26.006 -.825 -2.208 .063 .300 3.330

(Constant)X1_2X22X72X92X102X122

Model1

B Std. Error


Beta




Regression


18.5976 5.42472 1418.6179 2.11389 142.4118 .10573 14

41.3739 3.10679 14.4402 .08321 14.7559 .07791 14

Y32X22X72X92X102X122


Model Summary

.825a .681 .481 3.90851Model1



Predictors: (Constant), X122, X22, X102, X92, X72a.

444

ANOVAb

260.348 5 52.070 3.408 .060a

122.211 8 15.276382.559 13


Model1




Coefficientsa

122.842 59.007 2.082 .071-.593 .807 -.231 -.735 .483 .404 2.475

-14.796 15.521 -.288 -.953 .368 .436 2.292-.873 .503 -.500 -1.738 .120 .482 2.074

23.858 14.932 .366 1.598 .149 .761 1.314-42.184 17.068 -.606 -2.472 .039 .665 1.505

(Constant)X22X72X92X102X122

Model1

B Std. Error


Beta




Regression


18.5976 5.42472 142.4118 .10573 14

41.3739 3.10679 14.4402 .08321 14.7559 .07791 14

Y32X72X92X102X122


Model Summary

.812a .659 .507 3.80745Model1




445

ANOVAb

252.089 4 63.022 4.347 .031a

130.470 9 14.497382.559 13


Model1




Coefficientsa

102.894 51.045 2.016 .075-9.344 13.283 -.182 -.703 .500 .565 1.769-1.040 .437 -.596 -2.384 .041 .606 1.64924.699 14.504 .379 1.703 .123 .766 1.306

-39.137 16.129 -.562 -2.427 .038 .706 1.416

(ConstanX72X92X102X122

Mode1

B Std. Error


Beta


t Sig. Tolerance VIFollinearity Statistic


APPENDIX R


Institutions on Income Inequality in Latin America

Appendix R


Income Inequality in Latin America

Regression


18.8210 3.23235 1321.4875 1.65785 1318.5836 2.19615 131.5179 .15765 13

72.1919 1.02220 1386.4136 3.97503 1372.7129 6.22440 132.4072 .10863 132.8235 .22528 13

41.0090 2.90466 13.4426 .08608 13.4782 .07321 13.7490 .07650 13.8704 .10262 13

46.3122 6.07125 136.5193 .16457 13

28.9244 1.51780 13.6697 .06716 13



Model Summary

1.000a 1.000 1.000 .Model1




a.

448

ANOVAb

125.377 12 10.448 . .a

.000 0 .125.377 12


Model1



a.


Coefficientsa

67.329 .000 . .2.755 .000 1.413 . . .015 65.9491.564 .000 1.063 . . .025 39.957

-52.733 .000 -2.572 . . .025 40.57352.664 .000 1.770 . . .025 39.572

-21.448 .000 -1.495 . . .004 236.073.795 .000 .714 . . .032 31.284.511 .000 .014 . . .228 4.390

-2.278 .000 -.052 . . .050 20.02426.881 .000 .636 . . .039 25.702

-18.839 .000 -.598 . . .041 24.216-1.736 .000 -.088 . . .015 68.569

-219.626 .000 -4.563 . . .003 362.716


Model1

B Std. Error


Beta




449

Regression


18.8210 3.23235 1321.4875 1.65785 1318.5836 2.19615 131.5179 .15765 13

86.4136 3.97503 1372.7129 6.22440 132.4072 .10863 132.8235 .22528 13

41.0090 2.90466 13.4426 .08608 13.4782 .07321 13.7490 .07650 13.8704 .10262 13

46.3122 6.07125 136.5193 .16457 13

28.9244 1.51780 13.6697 .06716 13



Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

125.377 12 10.448 . .a

.000 0 .125.377 12


Model1



a.


450

Coefficientsa

67.329 .000 . .2.755 .000 1.413 . . .015 65.9491.564 .000 1.063 . . .025 39.957

-52.733 .000 -2.572 . . .025 40.57352.664 .000 1.770 . . .025 39.572

-21.448 .000 -1.495 . . .004 236.073.795 .000 .714 . . .032 31.284.511 .000 .014 . . .228 4.390

-2.278 .000 -.052 . . .050 20.02426.881 .000 .636 . . .039 25.702

-18.839 .000 -.598 . . .041 24.216-1.736 .000 -.088 . . .015 68.569

-219.626 .000 -4.563 . . .003 362.716


Model1

B Std. Error


Beta




Regression


18.8210 3.23235 1321.4875 1.65785 1318.5836 2.19615 131.5179 .15765 13

72.7129 6.22440 132.4072 .10863 132.8235 .22528 13

41.0090 2.90466 13.4426 .08608 13.4782 .07321 13.7490 .07650 13.8704 .10262 13

46.3122 6.07125 136.5193 .16457 13

28.9244 1.51780 13.6697 .06716 13



451

Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

125.377 12 10.448 . .a

.000 0 .125.377 12


Model1



a.


Coefficientsa

67.329 .000 . .2.755 .000 1.413 . . .015 65.9491.564 .000 1.063 . . .025 39.957

-52.733 .000 -2.572 . . .025 40.57352.664 .000 1.770 . . .025 39.572

-21.448 .000 -1.495 . . .004 236.073.795 .000 .714 . . .032 31.284.511 .000 .014 . . .228 4.390

-2.278 .000 -.052 . . .050 20.02426.881 .000 .636 . . .039 25.702

-18.839 .000 -.598 . . .041 24.216-1.736 .000 -.088 . . .015 68.569

-219.626 .000 -4.563 . . .003 362.716


Model1

B Std. Error


Beta




452

Regression


18.8210 3.23235 1321.4875 1.65785 1318.5836 2.19615 131.5179 .15765 132.4072 .10863 132.8235 .22528 13

41.0090 2.90466 13.4426 .08608 13.4782 .07321 13.7490 .07650 13.8704 .10262 13

46.3122 6.07125 136.5193 .16457 13

28.9244 1.51780 13.6697 .06716 13



Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

125.377 12 10.448 . .a

.000 0 .125.377 12


Model1



a.


453

Coefficientsa

67.329 .000 . .2.755 .000 1.413 . . .015 65.9491.564 .000 1.063 . . .025 39.957

-52.733 .000 -2.572 . . .025 40.57352.664 .000 1.770 . . .025 39.572

-21.448 .000 -1.495 . . .004 236.073.795 .000 .714 . . .032 31.284.511 .000 .014 . . .228 4.390

-2.278 .000 -.052 . . .050 20.02426.881 .000 .636 . . .039 25.702

-18.839 .000 -.598 . . .041 24.216-1.736 .000 -.088 . . .015 68.569

-219.626 .000 -4.563 . . .003 362.716


Model1

B Std. Error


Beta




Regression


18.8210 3.23235 1321.4875 1.65785 1318.5836 2.19615 131.5179 .15765 132.4072 .10863 132.8235 .22528 13

41.0090 2.90466 13.4426 .08608 13.4782 .07321 13.7490 .07650 13.8704 .10262 13

6.5193 .16457 1328.9244 1.51780 13

.6697 .06716 13

Y42X1_2X22X32X72X82X92X102X112X122X132X152C12C22


454

Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

125.377 12 10.448 . .a

.000 0 .125.377 12


Model1



a.


Coefficientsa

67.329 .000 . .2.755 .000 1.413 . . .015 65.9491.564 .000 1.063 . . .025 39.957

-52.733 .000 -2.572 . . .025 40.57352.664 .000 1.770 . . .025 39.572

-21.448 .000 -1.495 . . .004 236.073.795 .000 .714 . . .032 31.284.511 .000 .014 . . .228 4.390

-2.278 .000 -.052 . . .050 20.02426.881 .000 .636 . . .039 25.702

-18.839 .000 -.598 . . .041 24.216-1.736 .000 -.088 . . .015 68.569

219.626 .000 -4.563 . . .003 362.716

(ConstantX1_2X22X32X72X82X92X102X112X122X132X152C22

Model1

B Std. Error


Beta




455

Regression


18.8210 3.23235 1321.4875 1.65785 1318.5836 2.19615 131.5179 .15765 132.4072 .10863 132.8235 .22528 13

41.0090 2.90466 13.4426 .08608 13.4782 .07321 13.7490 .07650 13.8704 .10262 13

6.5193 .16457 13.6697 .06716 13

Y42X1_2X22X32X72X82X92X102X112X122X132X152C22


Model Summary

1.000a 1.000 1.000 .Model1




a.

ANOVAb

125.377 12 10.448 . .a

.000 0 .125.377 12


Model1



a.


456

Coefficientsa

67.329 .000 . .2.755 .000 1.413 . . .015 65.9491.564 .000 1.063 . . .025 39.957

-52.733 .000 -2.572 . . .025 40.57352.664 .000 1.770 . . .025 39.572

-21.448 .000 -1.495 . . .004 236.073.795 .000 .714 . . .032 31.284.511 .000 .014 . . .228 4.390

-2.278 .000 -.052 . . .050 20.02426.881 .000 .636 . . .039 25.702

-18.839 .000 -.598 . . .041 24.216-1.736 .000 -.088 . . .015 68.569

-219.626 .000 -4.563 . . .003 362.716


Model1

B Std. Error


Beta




Regression


18.8210 3.23235 1321.4875 1.65785 1318.5836 2.19615 131.5179 .15765 132.4072 .10863 132.8235 .22528 13

41.0090 2.90466 13.4426 .08608 13.4782 .07321 13.7490 .07650 13.8704 .10262 13

6.5193 .16457 13

Y42X1_2X22X32X72X82X92X102X112X122X132X152


457

Model Summary

.971a .943 .311 2.68301Model1




a.

ANOVAb

118.178 11 10.743 1.492 .570a

7.199 1 7.199125.377 12


Model1



a.


Coefficientsa

-188.050 317.815 -.592 .660-.521 1.915 -.267 -.272 .831 .060 16.803.511 1.965 .347 .260 .838 .032 31.043

-30.501 22.023 -1.488 -1.385 .398 .050 20.09549.333 44.728 1.658 1.103 .469 .025 39.35423.672 27.471 1.650 .862 .547 .016 63.842

.415 1.442 .373 .287 .822 .034 29.2509.861 16.370 .263 .602 .655 .302 3.310

-25.799 41.081 -.584 -.628 .643 .066 15.08018.133 50.576 .429 .359 .781 .040 24.956

-22.927 36.915 -.728 -.621 .646 .042 23.92310.217 37.094 .520 .275 .829 .016 62.120


Model1

B Std. Error


Beta




458

Regression


18.3470 3.57628 1421.3634 1.65907 1418.6179 2.11389 141.5023 .16233 142.4118 .10573 142.8109 .22152 14

41.3739 3.10679 14.4402 .08321 14.4790 .07041 14.7559 .07791 14.8865 .11558 14

Y42X1_2X22X32X72X82X92X102X112X122X132


Model Summary

.898a .807 .162 3.27339Model1




a.

ANOVAb

134.122 10 13.412 1.252 .478a

32.145 3 10.715166.267 13


Model1



a.


459

Coefficientsa

34.103 218.441 .156 .886-.015 1.654 -.007 -.009 .993 .110 9.132.066 2.236 .039 .030 .978 .037 27.112

-22.475 26.071 -1.020 -.862 .452 .046 21.73017.744 36.031 .525 .492 .656 .057 17.60811.811 30.812 .732 .383 .727 .018 56.521

-.514 .935 -.446 -.549 .621 .098 10.2439.964 16.470 .232 .605 .588 .439 2.279

-30.269 49.278 -.596 -.614 .582 .068 14.606-15.879 57.274 -.346 -.277 .800 .041 24.158-17.504 24.356 -.566 -.719 .524 .104 9.614


Model1

B Std. Error


Beta




Regression


18.3470 3.57628 1421.3634 1.65907 141.5023 .16233 142.4118 .10573 142.8109 .22152 14

41.3739 3.10679 14.4402 .08321 14.4790 .07041 14.7559 .07791 14.8865 .11558 14

Y42X1_2X32X72X82X92X102X112X122X132


Model Summary

.898a .807 .371 2.83525Model1




a.

460

ANOVAb

134.113 9 14.901 1.854 .289a

32.155 4 8.039166.267 13


Model1




Coefficientsa

39.727 92.947 .427 .691-.022 1.420 -.010 -.015 .989 .111 8.972

-22.432 22.546 -1.018 -.995 .376 .046 21.66317.216 27.104 .509 .635 .560 .075 13.28210.991 11.675 .681 .941 .400 .092 10.816

-.529 .671 -.460 -.789 .474 .142 7.0209.930 14.231 .231 .698 .524 .441 2.268

-29.230 29.938 -.575 -.976 .384 .139 7.186-17.209 30.708 -.375 -.560 .605 .108 9.257-17.031 15.888 -.550 -1.072 .344 .183 5.453


Model1

B Std. Error


Beta




Regression


18.3470 3.57628 141.5023 .16233 142.4118 .10573 142.8109 .22152 14

41.3739 3.10679 14.4402 .08321 14.4790 .07041 14.7559 .07791 14.8865 .11558 14

Y42X32X72X82X92X102X112X122X132


461

Model Summary

.898a .807 .497 2.53600Model1



Predictors: (Constant), X132, X102, X72, X122, X92,X112, X82, X32

a.

ANOVAb

134.111 8 16.764 2.607 .153a

32.157 5 6.431166.267 13


Model1


Predictors: (Constant), X132, X102, X72, X122, X92, X112, X82, X32a.


Coefficientsa

38.690 56.450 .685 .524-22.434 20.166 -1.018 -1.112 .317 .046 21.66217.563 13.080 .519 1.343 .237 .259 3.86610.905 9.130 .675 1.195 .286 .121 8.267

-.531 .595 -.461 -.892 .413 .145 6.8999.871 12.242 .230 .806 .457 .477 2.097

-29.412 24.551 -.579 -1.198 .285 .166 6.040-16.814 14.636 -.366 -1.149 .303 .380 2.628-17.196 10.374 -.556 -1.658 .158 .344 2.906

(ConstantX32X72X82X92X102X112X122X132

Model1

B Std. Error


Beta




462

Regression


18.3470 3.57628 142.4118 .10573 142.8109 .22152 14

41.3739 3.10679 14.4402 .08321 14.4790 .07041 14.7559 .07791 14.8865 .11558 14

Y42X72X82X92X102X112X122X132


Model Summary

.871a .759 .477 2.58571Model1



Predictors: (Constant), X132, X102, X72, X122, X92,X112, X82

a.

ANOVAb

126.152 7 18.022 2.695 .124a

40.116 6 6.686166.267 13


Model1




463

Coefficientsa

-.287 45.128 -.006 .99512.529 12.512 .370 1.001 .355 .294 3.4037.683 8.828 .476 .870 .418 .134 7.435-.059 .425 -.051 -.138 .894 .295 3.389

12.262 12.288 .285 .998 .357 .492 2.033-41.339 22.519 -.814 -1.836 .116 .205 4.888-9.776 13.457 -.213 -.726 .495 .468 2.137

-10.095 8.339 -.326 -1.211 .272 .554 1.806

(Constant)X72X82X92X102X112X122X132

Model1

B Std. Error


Beta




Regression


18.3470 3.57628 142.4118 .10573 142.8109 .22152 14.4402 .08321 14.4790 .07041 14.7559 .07791 14.8865 .11558 14

Y42X72X82X102X112X122X132


Model Summary

.871a .758 .550 2.39773Model1



Predictors: (Constant), X132, X102, X72, X122, X112,X82

a.

464

ANOVAb

126.024 6 21.004 3.653 .057a

40.244 7 5.749166.267 13


Model1




Coefficientsa

-5.576 22.267 -.250 .80912.947 11.259 .383 1.150 .288 .312 3.2048.340 6.901 .517 1.209 .266 .189 5.285

12.474 11.306 .290 1.103 .306 .500 2.001-42.311 19.840 -.833 -2.133 .070 .227 4.413-8.755 10.437 -.191 -.839 .429 .669 1.495

-10.547 7.115 -.341 -1.482 .182 .654 1.529

(ConstantX72X82X102X112X122X132

Model1

B Std. Error


Beta




Regression


18.3470 3.57628 142.4118 .10573 142.8109 .22152 14.4790 .07041 14.7559 .07791 14.8865 .11558 14

Y42X72X82X112X122X132


Model Summary

.846a .716 .538 2.43007Model1




465

ANOVAb

119.025 5 23.805 4.031 .040a

47.242 8 5.905166.267 13


Model1




Coefficientsa

-15.035 20.827 -.722 .49116.965 10.797 .502 1.571 .155 .349 2.86911.468 6.377 .710 1.798 .110 .228 4.393

-56.153 15.577 -1.106 -3.605 .007 .378 2.648-4.767 9.923 -.104 -.480 .644 .760 1.316

-10.451 7.211 -.338 -1.449 .185 .654 1.529

(ConstantX72X82X112X122X132

Model1

B Std. Error


Beta




BIOGRAPHY

NAME Ms. Pananda Chansukree

ACADEMIC BACKGROUND Master of International Business (International

Business), 2004

University of Sydney, Sydney, Australia

Bachelor of Arts (English), 2003

Chulalongkorn University, Bangkok,

Thailand

ACADEMIC EXPERIENCE Visiting Scholar

Indiana University Bloomington (School of

Public and Environmental Affairs)

WORK EXPERIENCE Part-time Lecturer

Assumption University (Faculty of Business

Administration)

Rangsit International College (Department of

Philosophy, Politics and Economics)

Dhurakij Pundit University (Faculty of Public

Administration)

Government and Industry Affairs Officer

TT&T Public Company Limited