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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
Growth Rates of GDP per capita
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
Growth Rates of GDP per capita
7.2 The Impact of Politico-Economic Institutions on 121
Unemployment Rates
7.3 The Impact of Politico-Economic Institutions on the 124
Percentage of the Population Falling below the Poverty Line
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
8.1.1 The Impact of Politico-Economic Institutions on 132
Annual Growth Rates of GDP per capita
x
8.1.2 The Impact of Politico-Economic Institutions on 133
Unemployment Rates
8.1.3 The Impact of Politico-Economic Institutions on the 134
Percentage of the Population Falling below the
Poverty Line
8.1.4 The Impact of Politico-Economic Institutions on 135
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
Growth Rates of GDP per capita
8.2.2 The Impact of Politico-Economic Institutions on 138
Unemployment Rates
8.2.3 The Impact of Politico-Economic Institutions on the 141
Percentage of the Population Falling below the Poverty Line
8.2.4 The Impact of Politico-Economic Institutions on 143
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
Institutions on Annual Growth Rates of GDP per Capita in
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
Institutions on the Percentage of the Population Falling
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
Institutions on Annual Growth Rates of GDP per Capita in
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
Institutions on the Percentage of the Population Falling
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
5.6 Correlation Matrix of the Variables Used to Test the Impact of 93
Politico-Economic Institutions on Unemployment Rates
5.7 Multiple Regression Analysis of the Significant Predictor Variables 95
and Unemployment Rates
5.8 Correlation Matrix of the Variables Used to Test the Impact of 96
Politico-Economic Institutions on the Percentage of the Population
Falling below the Poverty Line
5.9 Multiple Regression Analysis of the Significant Predictor Variables 98
and the Percentage of the Population Falling below the Poverty Line
5.10 Correlation Matrix of the Variables Used to Test the Impact of 99
Politico-Economic Institutions on Income Inequality
5.11 Multiple Regression Analysis of the Significant Predictor Variables 101
and Income Inequality
6.1 Correlation Matrix of the Variables Used to Test the Impact of 103
Politico-Economic Institutions on Annual Growth Rates of GDP
per capita
6.2 Multiple Regression Analysis of the Significant Predictor Variables 104
and Annual Growth Rates of GDP per Capita
6.3 Correlation Matrix of the Variables Used to Test the Impact of 105
Politico-Economic Institutions on Unemployment Rates
6.4 Multiple Regression Analysis of the Significant Predictor Variables 107
and Unemployment Rates
6.5 Correlation Matrix of the Variables Used to Test the Impact of 109
Politico-Economic Institutions on the Percentage of the Population
Falling below the Poverty Line
6.6 Multiple Regression Analysis of the Significant Predictor Variables 111
and the Percentage of Population Falling below the Poverty Line
xvi
6.7 Correlation Matrix of the Variables Used to Test the Impact of 113
Politico-Economic Institutions on Income Inequality
6.8 Multiple Regression Analysis of the Significant Predictor Variables 115
and Income Inequality
7.1 Correlation Matrix of the Variables Used to Test the Impact of 118
Politico-Economic Institutions on Annual Growth Rates of GDP
per capita
7.2 Multiple Regression Analysis of the Significant Predictor Variables 120
and Annual Growth Rates of GDP per capita
7.3 Correlation Matrix of the Variables Used to Test the Impact of 122
Politico-Economic Institutions on Unemployment Rates
7.4 Multiple Regression Analysis of the Significant Predictor Variables 124
and Unemployment Rates
7.5 Correlation Matrix of the Variables Used to Test the Impact of 126
Politico-Economic Institutions on the Percentage of the Population
Falling below the Poverty Line
7.6 Multiple Regression Analysis of the Significant Predictor Variables 128
and the Percentage of the Population Falling below the Poverty Line
7.7 Correlation Matrix of the Variables Used to Test the Impact of 129
Politico-Economic Institutions on Income Inequality
7.8 Multiple Regression Analysis of the Significant Predictor Variables 131
and Income Inequality
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
East Asia and Latin America
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
8.6 The Impact of the Significant Variables on Unemployment Rates in 138
East Asia
8.7 The Impact of the Significant Variables on Unemployment Rates in 139
Latin America
8.8 The Impact of the Significant Variables on the Percentage of the 141
Population Falling below the Poverty Line in East Asia
8.9 The Impact of the Significant Variables on the Percentage of the 142
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
economic performance.
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
economic performance.
H3-1: Population growth rates have a negative effect on annual growth
rates of GDP per capita.
H3-2: Population growth rates have a positive effect on unemployment
rates.
H3-3: Population growth rates have a positive effect on the percentage
of the population falling below the poverty line.
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
economic performance.
H4-1: Life expectancy at birth has a positive effect on annual growth
rates of GDP per capita.
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
economic performance.
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
population falling below the poverty line.
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
of the population falling below the poverty line.
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
population falling below the poverty line.
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
population falling below the poverty line.
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
population falling below the poverty line.
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
and economic performance.
H10-1: Government effectiveness has a positive effect on annual growth
rates of GDP per capita.
H10-2: Government effectiveness has a negative effect on unemployment
rates.
H10-3: Government effectiveness has a negative effect on the percentage
of the population falling below the poverty line.
H10-4: Government effectiveness has a negative effect on income
inequality.
H11: There is a significant relationship between regulatory quality and
economic performance.
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
population falling below the poverty line.
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
population falling below the poverty line.
H12-4: Rule of law has a negative effect on income inequality.
H13: There is a significant relationship between control of corruption and
economic performance.
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
and economic performance.
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
economic performance.
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
population falling below the poverty line.
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
Table 3.2 (Continued)
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
Source: World Bank Database
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)
East Asia 1995 1998 2005 2009 Latin America
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
Table 3.3 (Continued)
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
Source: World Bank Database
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)
East Asia 1995 1998 2005 2009 Latin America
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
Source: Author’s calculations based on World Bank Database
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
Table 3.6 (Continued)
Variable Measurement Source
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
Kaufmann, Kraay, and Mastruzzi
59
Table 3.6 (Continued)
Variable Measurement Source
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.
Kaufmann, Kraay, and Mastruzzi
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.
Kaufmann, Kraay, and Mastruzzi
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).
63
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
65
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
69
Table 4.3 (Continued)
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
71
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)
72
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
East Asia 1990-1999 2000-2009 Latin America 1990-1999 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
Paraguay 23.95 18.66 Peru 20.90 20.22 Uruguay 16.51 17.07 Venezuela 20.50 23.97 Mean 31.52 26.39 Mean 20.98 21.05
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
East Asia 1990-1999 2000-2009 Latin America 1990-1999 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
Paraguay 23.25 18.80 Peru 15.23 19.53 Uruguay 12.97 15.85 Venezuela 22.59 35.20 Mean 32.42 32.41 Mean 16.59 19.25
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:
74
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
East Asia 1990-1999 2000-2009 Latin America 1990-1999 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
Paraguay 2.36 1.92 Peru 1.83 1.30 Uruguay 0.64 0.17 Venezuela 2.13 1.73 Mean 1.74 1.15 Mean 1.87 1.40
Source: Author’s calculations based on World Bank Database
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
75
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
East Asia 1990-1999 2000-2009 Latin America 1990-1999 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
Paraguay 68.9 71.1 Peru 67.8 72.2 Uruguay 73.5 75.4 Venezuela 72.2 73.4 Mean 70.9 74.5 Mean 70.0 73.0
Source: Author’s calculations based on World Bank Database
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
East Asia 1990-1999 2000-2009 Latin America 1990-1999 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
Paraguay 90.3 93.9 Peru 81.9 88.8 Uruguay 95.4 97.7 Venezuela 89.8 94.8 Mean 86.0 91.7 Mean 80.9 89.8
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
East Asia 1990-1999 2000-2009 Latin America 1990-1999 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
Paraguay 56.0 72.1 Peru 77.7 88.0 Uruguay 77.6 89.7 Venezuela 69.8 80.0 Mean 62.0 75.8 Mean 64.6 79.0
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).
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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
economic performance.
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
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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.
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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
Table 5.1 (Continued)
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
Variable Mean Standard Deviation
Minimum Maximum
Annual growth rates of GDP per capita
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
Income inequality 8.4392 1.85472 5.63 12.15
Investment 28.9549 3.47398 25.39 34.75
Gross national savings 32.4163 1.41377 30.03 34.85
Population growth rates 1.4667 .33540 1.08 2.14
Life expectancy at birth 72.5218 2.20627 68.99 75.62
Adult literacy rates 88.4676 3.61968 86.00 96.10
Combined gross enrollment 67.8053 6.13740 61.99 76.24
Political rights 4.1583 .35424 3.67 5.00
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
Protection of property rights 46.0392 6.06733 38.53 54.71
Economic freedom 6.2408 .47865 5.50 6.76
86
Table 5.3 Summary Statistics for Latin America
Variable Mean Standard Deviation
Minimum Maximum
Annual growth rates of GDP per capita
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
Income inequality 18.1240 3.22079 12.19 27.56
Investment 21.0166 1.80840 17.26 24.60
Gross national savings 18.1061 2.05207 15.15 22.41
Population growth rates 1.6419 .25845 1.30 2.03
Life expectancy at birth 71.3606 1.76602 68.35 73.81
Adult literacy rates 84.9547 4.27164 80.88 90.42
Combined gross enrollment 70.6684 6.61011 64.64 80.29
Political rights 2.4882 .19565 2.29 2.88
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
Protection of property rights 58.7333 6.98434 51.00 68.00
Economic freedom 7.0517 .20670 6.76 7.31
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
The correlations between the variables used to test the impact of institutional
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
Note: *Correlation is Significant at 0.05, *P<0.05
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.
To determine the presence of multicollinearity, the collinearity statistics
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.
When considering the regression coefficient of the predictor variables, it was
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
Table 5.7 Multiple Regression Analysis of the Significant Predictor Variables and
Unemployment Rates
Constant / Variable b Std.Er t p-value Collinearity Statistics Tolerance VIF
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
Std.Erest = .260 R = .981 ; R2 = .962 ; Adjusted R Square = .908 ; F = 17.938 ; p-value = .003*
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
The correlations between the variables used to test the impact of institutional
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
Note: *Correlation is Significant at 0.05, *P<0.05
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
Table 5.9 shows the results of the multiple regression analysis of the
significant predictor variables and the dependent variable. Initially, the impact of
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.
To determine the presence of multicollinearity, the collinearity statistics
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.
When considering the regression coefficient of the predictor variables, it was
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
Table 5.9 Multiple Regression Analysis of the Significant Predictor Variables and
the Percentage of the Population Falling below the Poverty Line
Constant / Variable b Std.Er t p-value Collinearity Statistics Tolerance VIF
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
The correlations between the variables used to test the impact of institutional
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
Note: *Correlation is Significant at 0.05, *P<0.05
99
100
Table 5.11 shows the results of the multiple regression analysis of the
significant predictor variables and the dependent variable. Initially, 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
4 predictor variables. The SPSS printout regarding the impact of institutional factors
on income inequality is shown in Appendix J.
To determine the presence of multicollinearity, the collinearity statistics
needed to be considered. According to Table 5.11, 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 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.
When considering the regression coefficient of the predictor variables, it was
found that investment rates had the greatest impact on income inequality at the
significance level of 0.05. The regression coefficient (b) and the standardized
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
Table 5.11 Multiple Regression Analysis of the Significant Predictor Variables and
Income Inequality
Constant / Variable b Std.Er t p-value Collinearity Statistics Tolerance VIF
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
Std.Erest = 2.116 R = .821 ; R2 = .675 ; Adjusted R Square = .512 ; F = 4.150 ; p-value = .041*
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
American countries can be shown in the form of raw scores as follows:
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,
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, will
be presented.
6.1 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 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
Note: *Correlation is Significant at 0.05, *P<0.05
103
104
Table 6.2 shows the results of the multiple regression analysis of the
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.
Table 6.2 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 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
The correlations between the variables used to test the impact of institutional
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
Table 6.3 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 -.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
Note: *Correlation is Significant at 0.05, *P<0.05
105
106
Table 6.4 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
9 predictor variables. The SPSS printout regarding the impact of institutional factors
on unemployment rates is shown in Appendix L.
To determine the presence of multicollinearity, the collinearity statistics
needed to be considered. According to Table 6.4, 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 unemployment
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
growth rates of GDP per capita, with a standard error of .065.
When considering the regression coefficient of the predictor variables, it was
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 -.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.
Table 6.4 Multiple Regression Analysis of the Significant Predictor Variables and
Unemployment Rates
Constant / Variable b Std.Er t p-value Collinearity Statistics Tolerance VIF
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
Std.Erest = .065 R = .999 ; R2 = .997 ; Adjusted R Square = .991 ; F = 164.976 ; p-value = .000*
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
Population Falling Below the Poverty Line
The correlations between the variables used to test the impact of institutional
factors on the percentage of the population falling below the poverty line are shown
in Table 6.5. It was found that the variables which were positively correlated with the
percentage of the population falling below the poverty line at the significance level of
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
independent variable which had the highest correlation with the dependent variable
was political rights.
106
Table 6.5 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 -.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
Note: *Correlation is Significant at 0.05, *P<0.05
109
110
Table 6.6 shows the results of the multiple regression analysis of the
significant predictor variables and the dependent variable. Initially, the impact of
institutional factors on the percentage of 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 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.
To determine the presence of multicollinearity, the collinearity statistics
needed to be considered. According to Table 6.6, 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 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
percent of the variance in the percentage of the population falling below the poverty
line, with a standard error of 3.870.
When considering the regression coefficient of the predictor variables, it was
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
Table 6.6 Multiple Regression Analysis of the Significant Predictor Variables and
the Percentage of the Population Falling below the Poverty Line
Constant / Variable b Std.Er t p-value Collinearity Statistics Tolerance VIF
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
Std.Erest = 3.870 R = .980 ; R2 = .960 ; Adjusted R Square = .853 ; F = 8.966 ; p-value = .049*
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 equation which predicts the percentage of the population falling below
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
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6.4 The Impact of Politico-Economic Institutions on Income Inequaltty
The correlations between the variables used to test the impact of institutional
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
independent variable which had the highest correlation with the dependent variable
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
Note: *Correlation is Significant at 0.05, *P<0.05
113
114
Table 6.8 shows the results of the multiple regression analysis of the
significant predictor variables and the dependent variable. Initially, 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
9 predictor variables. The SPSS printout regarding the impact of institutional factors
on income inequality is shown in Appendix N.
To determine the presence of multicollinearity, the collinearity statistics
needed to be considered. According to Table 6.8, 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 income
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.
When considering the regression coefficient of the predictor variables, it was
found that population growth rates had the greatest impact on income inequality at the
significance level of 0.05. The regression coefficient (b) and the standardized
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.
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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.
Table 6.8 Multiple Regression Analysis of the Significant Predictor Variables and
Income Inequality
Constant / Variable b Std.Er t p-value Collinearity Statistics Tolerance VIF
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
Std.Erest = .336 R = .995 ; R2 = .991 ; Adjusted R Square = .950 ; F = 4.150 ; p-value = .041*
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,
including annual growth rates of GDP per capita, unemployment rates, percentage of
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
The correlations between the variables used to test the impact of institutional
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
Note: *Correlation is Significant at 0.05, *P<0.05
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.
To determine the presence of multicollinearity, the collinearity statistics
needed to be considered. According to Table 7.2, 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 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.
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 .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
Table 7.2 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 .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
Std.Erest = .572 R = .956 ; R2 = .913 ; Adjusted R Square = .813 ; F = 9.050 ; p-value = .008*
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
American countries can be shown in the form of raw scores as follows:
Y1 = 0 + .322X2 – 4.582X13
121
7.2 The Impact of Politico-Economic Institutions on Unemployment Rates
The correlations between the variables used to test the impact of institutional
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
Table 7.3 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 -.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
Note: *Correlation is Significant at 0.05, *P<0.05
122
123
Table 7.4 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 P.
To determine the presence of multicollinearity, the collinearity statistics
needed to be considered. According to Table 7.4, 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 unemployment
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
growth rates of GDP per capita, with a standard error of .120.
When considering the regression coefficient of the predictor variables, it was
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 -.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.
Table 7.4 Multiple Regression Analysis of the Significant Predictor Variables and
Unemployment Rates
Constant / Variable b Std.Er t p-value Collinearity Statistics Tolerance VIF
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
Std.Erest = .120 R = .998 ; R2 = .995 ; Adjusted R Square = .990 ; F = 184.626 ; p-value = .000*
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
Population Falling Below the Poverty Line
The correlations between the variables used to test the impact of institutional
factors on the percentage of the population falling below the poverty line are shown
in Table 7.5. It was found that the variables that were positively correlated with the
125
percentage of the population falling below the poverty line at the significance level of
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
Table 7.5 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 -.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
Note: *Correlation is Significant at 0.05, *P<0.05
126
127
Table 7.6 shows the results of the multiple regression analysis of the
significant predictor variables and the dependent variable. Initially, the impact of
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 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.
To determine the presence of multicollinearity, the collinearity statistics
needed to be considered. According to Table 7.6, 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 .812 and could explain about 50.7
percent of the variance in the percentage of the population falling below the poverty
line, with a standard error of 3.807.
When considering the regression coefficient of the predictor variables, it was
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
Percentage of the Population Falling below the Poverty Line
Constant / Variable b Std.Er t p-value Collinearity Statistics Tolerance VIF
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
Std.Erest = 3.807 R = .812 ; R2 = .659 ; Adjusted R Square = .507 ; F = 4.347 ; p-value = .031*
Note: *P<0.05
The equation which predicts the percentage of the population falling below
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
The correlations between the variables used to test the impact of institutional
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
independent variable which had the highest correlation with the dependent variable
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
Note: *Correlation is Significant at 0.05, *P<0.05
129
130
Table 7.8 shows the results of the multiple regression analysis of the
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.
To determine the presence of multicollinearity, the collinearity statistics
needed to be considered. According to Table 7.8, 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 income
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.
When considering the regression coefficient of the predictor variables, it was
found that regulatory quality had the greatest impact on income inequality at the
significance level of 0.05. The regression coefficient (b) and the standardized
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
Table 7.8 Multiple Regression Analysis of the Significant Predictor Variables and
Income Inequality
Constant / Variable b Std.Er t p-value Collinearity Statistics Tolerance VIF
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
Std.Erest = 2.430 R = .846 ; R2 = .716 ; Adjusted R Square = .538 ; F = 4.031 ; p-value = .040*
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
8.1.1 The Impact of Politico-Economic Institutions on Annual Growth
Rates of GDP per Capita
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
Annual growth rates of GDP per capita
134
Figure 8.2 The Impact of the Significant Variables on Unemployment Rates in
East Asia and Latin America
8.1.3 The Impact of Politico-Economic Institutions on the Percentage of
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
8.1.4 The Impact of Politico-Economic Institutions on Income Inequality
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
impact on income inequality in East Asia and Latin America is political rights.
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
(-)
8.2.1 The Impact of Politico-Economic Institutions on Annual Growth
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
Annual growth rates of GDP per capita
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.
Figure 8.6 The Impact of the Significant Variables on Unemployment Rates in
East Asia
+
-
+
+
-
-
-
+
Investment rates
Rule of law
Unemployment rates
Political rights
Control of corruption
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.
Figure 8.7 The Impact of the Significant Variables on Unemployment Rates in
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
impact on unemployment rates, while press freedom and control of corruption have
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’.
8.2.3 The Impact of Politico-Economic Institutions on the Percentage of
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.
Figure 8.8 The Impact of the Significant Variables on the Percentage of the
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
Adult literacy rates
The percentage of population falling
below the poverty line Economic freedom
142
Figure 8.9 The Impact of the Significant Variables on the Percentage of the
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.
8.2.4 The Impact of Politico-Economic Institutions on Income Inequality
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
Adult literacy rates
Income inequality
Investment rates
Gross national savings
Economic freedom
Control of corruption
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
impact on income inequality in East Asia and Latin America is political rights.
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
impact on unemployment rates, while press freedom and control of corruption have
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
Hong Kong Indonesia Malaysia Philippines Singapore
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
Year Argentina Bolivia Brazil Chile Colombia
Costa Rica Ecuador
El Salvador Guatemala Honduras
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
Year Mexico Nicaragua Panama Paraguay Peru Uruguay Venezuela
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
Hong Kong Indonesia Malaysia Philippines Singapore
South Korea Taiwan Thailand Vietnam
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
Year Argentina Bolivia Brazil Chile Colombia
Costa Rica Ecuador
El Salvador Guatemala Honduras
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
Year Mexico Nicaragua Panama Paraguay Peru Uruguay Venezuela
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
Hong Kong Indonesia Malaysia Philippines Singapore
South Korea Taiwan Thailand Vietnam
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
Year Argentina Bolivia Brazil Chile Colombia
Costa Rica Ecuador
El Salvador Guatemala Honduras
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
Year Mexico Nicaragua Panama Paraguay Peru Uruguay Venezuela
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
Hong Kong Indonesia Malaysia Philippines Singapore
South Korea Taiwan Thailand Vietnam
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
Year Argentina Bolivia Brazil Chile Colombia
Costa Rica Ecuador
El Salvador Guatemala Honduras
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
Year Mexico Nicaragua Panama Paraguay Peru Uruguay Venezuela
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
Hong Kong Indonesia Malaysia Philippines Singapore
South Korea Taiwan Thailand Vietnam
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
Year Argentina Bolivia Brazil Chile Colombia
Costa Rica Ecuador
El Salvador Guatemala Honduras
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
Year Mexico Nicaragua Panama Paraguay Peru Uruguay Venezuela
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
Hong Kong Indonesia Malaysia Philippines Singapore
South Korea Taiwan Thailand Vietnam
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
Year Argentina Bolivia Brazil Chile Colombia
Costa Rica Ecuador
El Salvador Guatemala Honduras
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
Year Mexico Nicaragua Panama Paraguay Peru Uruguay Venezuela
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
Hong Kong Indonesia Malaysia Philippines Singapore
South Korea Taiwan Thailand Vietnam
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
Year Argentina Bolivia Brazil Chile Colombia
Costa Rica Ecuador
El Salvador Guatemala Honduras
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
Year Mexico Nicaragua Panama Paraguay Peru Uruguay Venezuela
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
Hong Kong Indonesia Malaysia Philippines Singapore
South Korea Taiwan Thailand Vietnam
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
Year Argentina Bolivia Brazil Chile Colombia
Costa Rica Ecuador
El Salvador Guatemala Honduras
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
Year Mexico Nicaragua Panama Paraguay Peru Uruguay Venezuela
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
Hong Kong Indonesia Malaysia Philippines Singapore
South Korea Taiwan Thailand Vietnam
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
Year Argentina Bolivia Brazil Chile Colombia
Costa Rica Ecuador
El Salvador Guatemala Honduras
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
Year Mexico Nicaragua Panama Paraguay Peru Uruguay Venezuela
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
Hong Kong Indonesia Malaysia Philippines Singapore
South Korea Taiwan Thailand Vietnam
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
Year Argentina Bolivia Brazil Chile Colombia
Costa Rica Ecuador
El Salvador Guatemala Honduras
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
Year Mexico Nicaragua Panama Paraguay Peru Uruguay Venezuela
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
Hong Kong Indonesia Malaysia Philippines Singapore
South Korea Taiwan Thailand Vietnam
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
Year Argentina Bolivia Brazil Chile Colombia
Costa Rica Ecuador
El Salvador Guatemala Honduras
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
Year Mexico Nicaragua Panama Paraguay Peru Uruguay Venezuela
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
Hong Kong Indonesia Malaysia Philippines Singapore
South Korea Taiwan Thailand Vietnam
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
Year Argentina Bolivia Brazil Chile Colombia
Costa Rica Ecuador
El Salvador Guatemala Honduras
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
Year Mexico Nicaragua Panama Paraguay Peru Uruguay Venezuela
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
Hong Kong Indonesia Malaysia Philippines Singapore
South Korea Taiwan Thailand Vietnam
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
Year Argentina Bolivia Brazil Chile Colombia
Costa Rica Ecuador
El Salvador Guatemala Honduras
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
Year Mexico Nicaragua Panama Paraguay Peru Uruguay Venezuela
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
Hong Kong Indonesia Malaysia Philippines Singapore
South Korea Taiwan Thailand Vietnam
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
Year Argentina Bolivia Brazil Chile Colombia
Costa Rica Ecuador
El Salvador Guatemala Honduras
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
Year Mexico Nicaragua Panama Paraguay Peru Uruguay Venezuela
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
Hong Kong Indonesia Malaysia Philippines Singapore
South Korea Taiwan Thailand Vietnam
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
Year Argentina Bolivia Brazil Chile Colombia
Costa Rica Ecuador
El Salvador Guatemala Honduras
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
Year Mexico Nicaragua Panama Paraguay Peru Uruguay Venezuela
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
Hong Kong Indonesia Malaysia Philippines Singapore
South Korea Taiwan Thailand Vietnam
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
Year Argentina Bolivia Brazil Chile Colombia
Costa Rica Ecuador
El Salvador Guatemala Honduras
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
Year Mexico Nicaragua Panama Paraguay Peru Uruguay Venezuela
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
Hong Kong Indonesia Malaysia Philippines Singapore
South Korea Taiwan Thailand Vietnam
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
Year Argentina Bolivia Brazil Chile Colombia
Costa Rica Ecuador
El Salvador Guatemala Honduras
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
Year Mexico Nicaragua Panama Paraguay Peru Uruguay Venezuela
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
Hong Kong Indonesia Malaysia Philippines Singapore
South Korea Taiwan Thailand Vietnam
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
Year Argentina Bolivia Brazil Chile Colombia
Costa Rica Ecuador
El Salvador Guatemala Honduras
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
Year Mexico Nicaragua Panama Paraguay Peru Uruguay Venezuela
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
SPSS Output for the Impact of Politico-Economic Institutions on
Annual Growth Rates of GDP per Capita in
East Asia and Latin America
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
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 13114.1224 2.81803 13
.9435 .13841 13
Y1X1X2X3X4X5X6X7X8X9X10X11X12X13X14C1C2
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, X11,X2, X3, X7, X5, X9, X12
a.
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, X11, X2, X3, X7, X5, X9, X12a.
Dependent Variable: Y1b.
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
(Constant)X1X2X3X5X7X9X10X11X12X13C1C2
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y1a.
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 13.1012 .04098 13.7146 .03624 13.8197 .06062 13.9444 .08699 13
52.6176 6.51053 13114.1224 2.81803 13
.9435 .13841 13
Y1X1X2X3X4X5X6X7X8X10X11X12X13X14C1C2
Mean Std. Deviation N
255
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C2, C1, X1, X13, X10, X11,X2, X3, X7, X5, X6, X12
a.
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, X11, X2, X3, X7, X5, X6, X12a.
Dependent Variable: Y1b.
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
(Constant)X1X2X3X5X6X7X10X11X12X13C1C2
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y1a.
256
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 13114.1224 2.81803 13
.9435 .13841 13
Y1X1X2X3X4X5X6X7X8X11X12X13X14C1C2
Mean Std. Deviation N
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.
Dependent Variable: Y1b.
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C2, C1, X1, X13, X11, X2, X3, X7, X12, X6, X5, X4a.
Dependent Variable: Y1b.
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
(Constant)X1X2X3X4X5X6X7X11X12X13C1C2
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y1a.
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
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C2, X1, X13, X7, X11, X2, X3, X12, X6, X5, X4, X14a.
Dependent Variable: Y1b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y1a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
260
Model Summary
.990a .981 .753 .45263Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), X14, X7, X11, X13, X2, X3, X12, X5, X1, X6, X4, X8a.
Dependent Variable: Y1b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y1a.
261
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
.989a .979 .863 .33679Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), X14, X7, X11, X2, X3, X12, X1, X5, X6, X4, X8a.
Dependent Variable: Y1b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y1a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
.989a .979 .907 .27763Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), X14, X7, X11, X2, X3, X12, X1, X6, X4, X8a.
Dependent Variable: Y1b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y1a. Regression
Descriptive Statistics
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
Mean Std. Deviation N
264
Model Summary
.985a .971 .904 .28212Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), X14, X7, X11, X2, X3, X12, X1, X6, X4a.
Dependent Variable: Y1b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y1a.
265
Regression Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
.970a .940 .844 .35975Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), X14, X7, X11, X2, X3, X12, X1, X6a.
Dependent Variable: Y1b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y1a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
.942a .888 .757 .44934Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), X14, X7, X11, X2, X3, X12, X1a.
Dependent Variable: Y1b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y1a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
.928a .861 .742 .46282Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), X12, X7, X11, X2, X1, X3a.
268
ANOVAb
9.295 6 1.549 7.232 .010a
1.499 7 .21410.795 13
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), X12, X7, X11, X2, X1, X3a.
Dependent Variable: Y1b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y1a.
APPENDIX H
SPSS Output for the Impact of Politico-Economic
Institutions on Unemployment Rates in East Asia and
Latin America
Appendix H
SPSS Output for the Impact of Politico-Economic Institutions on
Unemployment Rates in East Asia and Latin America
Regression
Descriptive Statistics
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
Y2X1X2X3X4X5X6X7X8X9X10X11X12X13X14X15C1C2
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 . 1.000 . 12 0 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
R SquareChange F Change df1 df2 Sig. F Change
Change Statistics
Predictors: (Constant), C2, C1, X1, X13, X10, X15, X11, X3, X7, X5, X9, X12a.
271
ANOVAb
8.828 12 .736 . .a
.000 0 .8.828 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: Y2b.
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
(Constant)X1X3X5X7X9X10X11X12X13X15C1C2
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y2a.
272
Regression
Descriptive Statistics
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
Y2X1X2X4X5X6X7X8X9X10X11X12X13X14X15C1C2
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 . 1.000 . 12 0 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
R SquareChange F Change df1 df2 Sig. F Change
Change Statistics
Predictors: (Constant), C2, C1, X1, X13, X10, X15, X11, X7, X5, X9, X4, X12a.
ANOVAb
8.828 12 .736 . .a
.000 0 .8.828 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C2, C1, X1, X13, X10, X15, X11, X7, X5, X9, X4, X12a.
Dependent Variable: Y2b.
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
(Constant)X1X4X5X7X9X10X11X12X13X15C1C2
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y2a.
Regression
Descriptive Statistics
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
Y2X1X2X4X5X6X8X9X10X11X12X13X14X15C1C2
Mean Std. Deviation N
274
Model Summary
1.000a 1.000 1.000 . 1.000 . 12 0 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
R SquareChange F Change df1 df2 Sig. F Change
Change Statistics
Predictors: (Constant), C2, C1, X1, X13, X10, X15, X11, X2, X9, X5, X4, X12a.
ANOVAb
8.828 12 .736 . .a
.000 0 .8.828 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C2, C1, X1, X13, X10, X15, X11, X2, X9, X5, X4, X12a.
Dependent Variable: Y2b.
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
(Constant)X1X2X4X5X9X10X11X12X13X15C1C2
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y2a.
275
Regression
Descriptive Statistics
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
Y2X1X2X4X5X8X9X10X11X12X13X14X15C1C2
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 . 1.000 . 12 0 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
R SquareChange F Change df1 df2 Sig. F Change
Change Statistics
Predictors: (Constant), C2, C1, X1, X13, X10, X15, X11, X2, X9, X5, X4, X12a.
ANOVAb
8.828 12 .736 . .a
.000 0 .8.828 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C2, C1, X1, X13, X10, X15, X11, X2, X9, X5, X4, X12a.
Dependent Variable: Y2b.
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
(Constant)X1X2X4X5X9X10X11X12X13X15C1C2
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y2a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
277
Model Summary
1.000a 1.000 1.000 . 1.000 . 12 0 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
R SquareChange F Change df1 df2 Sig. F Change
Change Statistics
Predictors: (Constant), C2, C1, X1, X13, X10, X15, X11, X2, X9, X5, X4, X12a.
ANOVAb
8.828 12 .736 . .a
.000 0 .8.828 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C2, C1, X1, X13, X10, X15, X11, X2, X9, X5, X4, X12a.
Dependent Variable: Y2b.
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
(Constant)X1X2X4X5X9X10X11X12X13X15C1C2
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y2a.
278
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 . 1.000 . 12 0 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
R SquareChange F Change df1 df2 Sig. F Change
Change Statistics
Predictors: (Constant), C2, C1, X1, X13, X10, X15, X11, X2, X9, X5, X4, X12a.
ANOVAb
8.828 12 .736 . .a
.000 0 .8.828 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C2, C1, X1, X13, X10, X15, X11, X2, X9, X5, X4, X12a.
Dependent Variable: Y2b.
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
(Constant)X1X2X4X5X9X10X11X12X13X15C1C2
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y2a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
280
Model Summary
.999a .999 .985 .10561 .999 71.861 11 1 .092Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
R SquareChange F Change df1 df2 Sig. F Change
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C2, C1, X1, X13, X10, X15, X11, X9, X5, X12, X4a.
Dependent Variable: Y2b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y2a.
281
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
.997a .994 .965 .15969 .994 34.418 10 2 .029Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
R SquareChange F Change df1 df2 Sig. F Change
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C2, C1, X1, X13, X10, X15, X11, X9, X5, X12a.
Dependent Variable: Y2b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y2a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
.997a .993 .974 .13852 .993 50.786 9 3 .004Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
R SquareChange F Change df1 df2 Sig. F Change
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C2, C1, X1, X13, X10, X15, X11, X9, X5a.
Dependent Variable: Y2b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y2a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
284
Model Summary
.997a .993 .980 .12204 .993 73.589 8 4 .000Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
R SquareChange F Change df1 df2 Sig. F Change
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C1, X13, X1, X11, X10, X15, X9, X5a.
Dependent Variable: Y2b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y2a.
285
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
.981a .962 .908 .26002 .962 17.938 7 5 .003Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
R SquareChange F Change df1 df2 Sig. F Change
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C1, X13, X1, X11, X10, X15, X9a.
Dependent Variable: Y2b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y2a.
APPENDIX I
SPSS Output for the Impact of Politico-Economic
Institutions on the Percentage of the Population Falling below the
Poverty Line in East Asia and Latin America
Appendix I
SPSS Output for the Impact of Politico-Economic Institutions on
the Percentage of the Population Falling below the Poverty Line in
East Asia and Latin America
Regression
Descriptive Statistics
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
Y3X1X2X3X4X5X6X7X8X9X10X11X12X13X14X15C1C2
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 . 1.000 . 12 0 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
R SquareChange F Change df1 df2 Sig. F Change
Change Statistics
Predictors: (Constant), C2, C1, X1, X13, X10, X15, X11, X3, X7, X5, X9, X12a.
289
ANOVAb
586.796 12 48.900 . .a
.000 0 .586.796 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: Y3b.
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
(Constant)X1X3X5X7X9X10X11X12X13X15C1C2
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y3a.
290
Regression
Descriptive Statistics
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
Y3X1X3X4X5X6X7X8X9X10X11X12X13X14X15C1C2
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 . 1.000 . 12 0 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
R SquareChange F Change df1 df2 Sig. F Change
Change Statistics
Predictors: (Constant), C2, C1, X1, X13, X10, X15, X11, X3, X7, X5, X9, X12a.
ANOVAb
586.796 12 48.900 . .a
.000 0 .586.796 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: Y3b.
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
(Constant)X1X3X5X7X9X10X11X12X13X15C1C2
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y3a.
Regression
Descriptive Statistics
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
Y3X1X3X5X6X7X8X9X10X11X12X13X14X15C1C2
Mean Std. Deviation N
292
Model Summary
1.000a 1.000 1.000 . 1.000 . 12 0 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
R SquareChange F Change df1 df2 Sig. F Change
Change Statistics
Predictors: (Constant), C2, C1, X1, X13, X10, X15, X11, X3, X7, X5, X9, X12a.
ANOVAb
586.796 12 48.900 . .a
.000 0 .586.796 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: Y3b.
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
(Constant)X1X3X5X7X9X10X11X12X13X15C1C2
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y3a.
293
Regression
Descriptive Statistics
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
Y3X1X3X5X7X8X9X10X11X12X13X14X15C1C2
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 . 1.000 . 12 0 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
R SquareChange F Change df1 df2 Sig. F Change
Change Statistics
Predictors: (Constant), C2, C1, X1, X13, X10, X15, X11, X3, X7, X5, X9, X12a.
ANOVAb
586.796 12 48.900 . .a
.000 0 .586.796 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: Y3b.
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
(Constant)X1X3X5X7X9X10X11X12X13X15C1C2
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y3a.
Regression
Descriptive Statistics
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
Y3X1X3X5X7X9X10X11X12X13X14X15C1C2
Mean Std. Deviation N
295
Model Summary
1.000a 1.000 1.000 . 1.000 . 12 0 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
R SquareChange F Change df1 df2 Sig. F Change
Change Statistics
Predictors: (Constant), C2, C1, X1, X13, X10, X15, X11, X3, X7, X5, X9, X12a.
ANOVAb
586.796 12 48.900 . .a
.000 0 .586.796 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: Y3b.
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
(Constant)X1X3X5X7X9X10X11X12X13X15C1C2
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y3a.
296
Regression
Descriptive Statistics
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
Y3X1X3X5X7X9X10X11X12X13X15C1C2
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 . 1.000 . 12 0 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
R SquareChange F Change df1 df2 Sig. F Change
Change Statistics
Predictors: (Constant), C2, C1, X1, X13, X10, X15, X11, X3, X7, X5, X9, X12a.
ANOVAb
586.796 12 48.900 . .a
.000 0 .586.796 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: Y3b.
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
(Constant)X1X3X5X7X9X10X11X12X13X15C1C2
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y3a.
Regression
Descriptive Statistics
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
Y3X1X3X5X7X9X10X11X13X15C1C2
Mean Std. Deviation N
298
Model Summary
1.000a 1.000 .997 .38006 1.000 369.227 11 1 .041Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
R SquareChange F Change df1 df2 Sig. F Change
Change Statistics
Predictors: (Constant), C2, C1, X1, X13, X10, X15, X11, X3, X7, X5, X9a.
ANOVAb
586.651 11 53.332 369.227 .041a
.144 1 .144586.796 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C2, C1, X1, X13, X10, X15, X11, X3, X7, X5, X9a.
Dependent Variable: Y3b.
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
(Constant)X1X3X5X7X9X10X11X13X15C1C2
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y3a.
299
Regression
Descriptive Statistics
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
Y3X1X3X5X7X10X11X13X15C1C2
Mean Std. Deviation N
Model Summary
.966a .932 .594 4.45529 .932 2.756 10 2 .295Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
R SquareChange F Change df1 df2 Sig. F Change
Change Statistics
Predictors: (Constant), C2, C1, X1, X13, X10, X15, X11, X3, X7, X5a.
ANOVAb
547.097 10 54.710 2.756 .295a
39.699 2 19.850586.796 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C2, C1, X1, X13, X10, X15, X11, X3, X7, X5a.
Dependent Variable: Y3b.
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
(Constant)X1X3X5X7X10X11X13X15C1C2
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y3a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
.931a .867 .469 5.09328 .867 2.180 9 3 .282Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
R SquareChange F Change df1 df2 Sig. F Change
Change Statistics
Predictors: (Constant), C2, C1, X1, X10, X15, X7, X11, X3, X5a.
301
ANOVAb
508.971 9 56.552 2.180 .282a
77.824 3 25.941586.796 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C2, C1, X1, X10, X15, X7, X11, X3, X5a.
Dependent Variable: Y3b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y3a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
302
Model Summary
.929a .863 .588 4.49006 .863 3.138 8 4 .142Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
R SquareChange F Change df1 df2 Sig. F Change
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C2, C1, X1, X10, X15, X7, X11, X3a.
Dependent Variable: Y3b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y3a.
303
Regression Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
.895a .802 .524 4.82458 .802 2.887 7 5 .131Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
R SquareChange F Change df1 df2 Sig. F Change
Change Statistics
Predictors: (Constant), C1, X11, X1, X15, X10, X7, X3a.
ANOVAb
470.413 7 67.202 2.887 .131a
116.383 5 23.277586.796 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C1, X11, X1, X15, X10, X7, X3a.
Dependent Variable: Y3b.
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
(Constant)X1X3X7X10X11X15C1
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y3a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
.881a .776 .553 4.67546 .776 3.474 6 6 .078Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
R SquareChange F Change df1 df2 Sig. F Change
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C1, X11, X1, X15, X7, X3a.
Dependent Variable: Y3b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y3a.
Regression
Descriptive Statistics
31.5344 6.99283 1324.5339 1.86959 133.1929 .13738 13.7146 .03624 13
6.8377 .12094 13114.1224 2.81803 13
Y3X1X7X11X15C1
Mean Std. Deviation N
306
Model Summary
.866a .750 .572 4.57459 .750 4.208 5 7 .044Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
R SquareChange F Change df1 df2 Sig. F Change
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C1, X11, X1, X15, X7a.
Dependent Variable: Y3b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y3a.
Regression
Descriptive Statistics
31.5344 6.99283 1324.5339 1.86959 13
.7146 .03624 136.8377 .12094 13
114.1224 2.81803 13
Y3X1X11X15C1
Mean Std. Deviation N
307
Model Summary
.815a .664 .495 4.96757 .664 3.945 4 8 .047Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
R SquareChange F Change df1 df2 Sig. F Change
Change Statistics
Predictors: (Constant), C1, X11, X1, X15a.
ANOVAb
389.382 4 97.345 3.945 .047a
197.414 8 24.677586.796 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C1, X11, X1, X15a.
Dependent Variable: Y3b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y3a.
APPENDIX J
SPSS Output for the Impact of Politico-Economic
Institutions on Income Inequality in East Asia and Latin America
309
Appendix J
SPSS Output for the Impact of Politico-Economic Institutions on
Income Inequality in East Asia and Latin America
Regression Descriptive Statistics
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
Y4X1X2X3X4X5X6X7X8X9X10X11X12X13X14X15C1C2
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.
310
ANOVAb
110.160 12 9.180 . .a
.000 0 .110.160 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: Y4b.
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
(Constant)X1X3X5X7X9X10X11X12X13X15C1C2
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y4a.
311
Regression
Descriptive Statistics
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
Y4X1X2X3X4X5X6X7X8X9X10X11X13X14X15C1C2
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, X2
a.
ANOVAb
110.160 12 9.180 . .a
.000 0 .110.160 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C2, C1, X1, X13, X10, X15, X11, X3, X7, X5, X9, X2a.
Dependent Variable: Y4b.
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
(Constant)X1X2X3X5X7X9X10X11X13X15C1C2
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y4a.
Regression
Descriptive Statistics
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
Y4X1X3X4X5X6X7X8X9X10X11X13X14X15C1C2
Mean Std. Deviation N
313
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, X4
a.
ANOVAb
110.160 12 9.180 . .a
.000 0 .110.160 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C2, C1, X1, X13, X10, X15, X11, X3, X7, X5, X9, X4a.
Dependent Variable: Y4b.
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
(Constant)X1X3X4X5X7X9X10X11X13X15C1C2
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y4a.
314
Regression
Descriptive Statistics
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
Y4X1X3X4X5X7X8X9X10X11X13X14X15C1C2
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, X4
a.
ANOVAb
110.160 12 9.180 . .a
.000 0 .110.160 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C2, C1, X1, X13, X10, X15, X11, X3, X7, X5, X9, X4a.
Dependent Variable: Y4b.
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
(Constant)X1X3X4X5X7X9X10X11X13X15C1C2
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y4a.
Regression
Descriptive Statistics
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
Y4X1X3X4X5X7X9X10X11X13X14X15C1C2
Mean Std. Deviation N
316
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, X4
a.
ANOVAb
110.160 12 9.180 . .a
.000 0 .110.160 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C2, C1, X1, X13, X10, X15, X11, X3, X7, X5, X9, X4a.
Dependent Variable: Y4b.
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
(Constant)X1X3X4X5X7X9X10X11X13X15C1C2
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y4a.
317
Regression
Descriptive Statistics
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
Y4X1X3X4X5X7X9X10X11X13X15C1C2
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, X4
a.
ANOVAb
110.160 12 9.180 . .a
.000 0 .110.160 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C2, C1, X1, X13, X10, X15, X11, X3, X7, X5, X9, X4a.
Dependent Variable: Y4b.
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
(Constant)X1X3X4X5X7X9X10X11X13X15C1C2
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y4a.
Regression
Descriptive Statistics
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
Y4X1X3X5X7X9X10X11X13X15C1C2
Mean Std. Deviation N
319
Model Summary
.958a .917 .006 3.02061Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C2, C1, X1, X13, X10, X15, X11, X3, X7, X5, X9a.
Dependent Variable: Y4b.
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
(Constant)X1X3X5X7X9X10X11X13X15C1C2
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y4a.
320
Regression Descriptive Statistics
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
Y4X1X3X5X7X10X11X13X15C1C2
Mean Std. Deviation N
Model Summary
.934a .872 .231 2.65765Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C2, C1, X1, X13, X10, X15, X11, X3, X7, X5a.
Dependent Variable: Y4b.
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
(Constant)X1X3X5X7X10X11X13X15C1C2
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y4a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
.867a .751 .006 3.02147Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C1, X13, X3, X10, X11, X15, X1, X7, X5a.
Dependent Variable: Y4b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y4a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
323
Model Summary
.851a .724 .171 2.75798Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C1, X13, X3, X10, X11, X15, X1, X7a.
Dependent Variable: Y4b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y4a.
324
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
.841a .707 .296 2.54236Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C1, X13, X3, X10, X15, X1, X7a.
ANOVAb
77.841 7 11.120 1.720 .285a
32.318 5 6.464110.160 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C1, X13, X3, X10, X15, X1, X7a.
Dependent Variable: Y4b.
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
(Constant)X1X3X7X10X13X15C1
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y4a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
.838a .702 .403 2.34090Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C1, X13, X1, X10, X7, X15a.
326
ANOVAb
77.281 6 12.880 2.350 .161a
32.879 6 5.480110.160 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C1, X13, X1, X10, X7, X15a.
Dependent Variable: Y4b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y4a.
Regression
Descriptive Statistics
16.8087 3.02984 1324.5339 1.86959 133.1929 .13738 13.1012 .04098 13
6.8377 .12094 13114.1224 2.81803 13
Y4X1X7X10X15C1
Mean Std. Deviation N
Model Summary
.837a .700 .486 2.17287Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C1, X10, X1, X15, X7a.
327
ANOVAb
77.110 5 15.422 3.266 .077a
33.050 7 4.721110.160 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C1, X10, X1, X15, X7a.
Dependent Variable: Y4b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y4a.
Regression
Descriptive Statistics
16.8087 3.02984 1324.5339 1.86959 133.1929 .13738 13.1012 .04098 13
6.8377 .12094 13
Y4X1X7X10X15
Mean Std. Deviation N
Model Summary
.821a .675 .512 2.11622Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), X15, X7, X10, X1a.
328
ANOVAb
74.332 4 18.583 4.150 .041a
35.827 8 4.478110.160 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), X15, X7, X10, X1a.
Dependent Variable: Y4b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y4a.
APPENDIX K
SPSS Output for the Impact of Politico-Economic
Institutions on Annual Growth Rates of GDP per Capita in
East Asia
Appendix K
SPSS Output for the Impact of Politico-Economic Institutions on
Annual Growth Rates of GDP per Capita in East Asia
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C21, X21, X91, C11, X31, X131, X51, X121, X151, X71,X1_1, X41
a.
Dependent Variable: Y11b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y11a.
332
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C21, X21, X91, C11, X31,X131, X51, X121, X151, X71, X1_1, X41
a.
ANOVAb
7.390 12 .616 . .a
.000 0 .7.390 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C21, X21, X91, C11, X31, X131, X51, X121, X151, X71,X1_1, X41
a.
Dependent Variable: Y11b.
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
(Constant)X1_1X21X31X41X51X71X91X121X131X151C11C21
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y11a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
334
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C21, X21, X91, C11, X31,X131, X51, X121, X151, X71, X1_1, X41
a.
ANOVAb
7.390 12 .616 . .a
.000 0 .7.390 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C21, X21, X91, C11, X31, X131, X51, X121, X151, X71,X1_1, X41
a.
Dependent Variable: Y11b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y11a.
335
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C21, X21, X91, C11, X31,X131, X51, X121, X151, X71, X1_1, X41
a.
ANOVAb
7.390 12 .616 . .a
.000 0 .7.390 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C21, X21, X91, C11, X31, X131, X51, X121, X151, X71,X1_1, X41
a.
Dependent Variable: Y11b.
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
(Constant)X1_1X21X31X41X51X71X91X121X131X151C11C21
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y11a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
337
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C21, X21, X91, C11, X31,X131, X51, X121, X151, X71, X1_1, X41
a.
ANOVAb
7.390 12 .616 . .a
.000 0 .7.390 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C21, X21, X91, C11, X31, X131, X51, X121, X151, X71,X1_1, X41
a.
Dependent Variable: Y11b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y11a.
338
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C21, X21, X91, C11, X31,X131, X51, X121, X151, X71, X1_1, X41
a.
ANOVAb
7.390 12 .616 . .a
.000 0 .7.390 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C21, X21, X91, C11, X31, X131, X51, X121, X151, X71,X1_1, X41
a.
Dependent Variable: Y11b.
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
(Constant)X1_1X21X31X41X51X71X91X121X131X151C11C21
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y11a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
.974a .948 .376 .61993Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C21, X21, X91, C11, X31, X131, X51, X121, X151, X71,X1_1
a.
Dependent Variable: Y11b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y11a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
341
Model Summary
.920a .846 .077 .75383Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C11, X51, X1_1, X91, X151, X131, X31, X21, X121, X71a.
Dependent Variable: Y11b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y11a.
342
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
.808a .653 .098 .77014Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C11, X51, X1_1, X91, X21, X131, X31, X121a.
Dependent Variable: Y11b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y11a.
APPENDIX L
SPSS Output for the Impact of Politico-Economic
Institutions on Unemployment Rates in East Asia
Appendix L
SPSS Output for the Impact of Politico-Economic Institutions on
Unemployment Rates in East Asia
Regression
Descriptive Statistics
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
Y21X1_1X21X31X41X51X61X71X81X91X101X111X121X131X141X151C11C21
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C21, X21, X91, C11, X31,X131, X51, X121, X151, X71, X1_1, X41
a.
346
ANOVAb
6.483 12 .540 . .a
.000 0 .6.483 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C21, X21, X91, C11, X31, X131, X51, X121, X151, X71,X1_1, X41
a.
Dependent Variable: Y21b.
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
(Constant)X1_1X21X31X41X51X71X91X121X131X151C11C21
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y21a.
347
Regression
Descriptive Statistics
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
Y21X1_1X21X31X41X51X71X81X91X101X111X121X131X141X151C11C21
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C21, X21, X91, C11, X31,X131, X51, X121, X151, X71, X1_1, X41
a.
ANOVAb
6.483 12 .540 . .a
.000 0 .6.483 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C21, X21, X91, C11, X31, X131, X51, X121, X151, X71,X1_1, X41
a.
Dependent Variable: Y21b.
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
(Constant)X1_1X21X31X41X51X71X91X121X131X151C11C21
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y21a.
Regression
Descriptive Statistics
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
Y21X1_1X21X31X41X51X71X91X101X111X121X131X141X151C11C21
Mean Std. Deviation N
349
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C21, X21, X91, C11, X31,X131, X51, X121, X151, X71, X1_1, X41
a.
ANOVAb
6.483 12 .540 . .a
.000 0 .6.483 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C21, X21, X91, C11, X31, X131, X51, X121, X151, X71,X1_1, X41
a.
Dependent Variable: Y21b.
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
(Constant)X1_1X21X31X41X51X71X91X121X131X151C11C21
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y21a.
350
Regression
Descriptive Statistics
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
Y21X1_1X21X31X41X51X71X91X111X121X131X141X151C11C21
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C21, X21, X91, C11, X31,X131, X51, X121, X151, X71, X1_1, X41
a.
ANOVAb
6.483 12 .540 . .a
.000 0 .6.483 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C21, X21, X91, C11, X31, X131, X51, X121, X151, X71,X1_1, X41
a.
Dependent Variable: Y21b.
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
(Constant)X1_1X21X31X41X51X71X91X121X131X151C11C21
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y21a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
352
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C21, X21, X91, C11, X31,X131, X51, X121, X151, X71, X1_1, X41
a.
ANOVAb
6.483 12 .540 . .a
.000 0 .6.483 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C21, X21, X91, C11, X31, X131, X51, X121, X151, X71,X1_1, X41
a.
Dependent Variable: Y21b.
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
(Constant)X1_1X21X31X41X51X71X91X121X131X151C11C21
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y21a.
353
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C21, X21, X91, C11, X31,X131, X51, X121, X151, X71, X1_1, X41
a.
ANOVAb
6.483 12 .540 . .a
.000 0 .6.483 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C21, X21, X91, C11, X31, X131, X51, X121, X151, X71,X1_1, X41
a.
Dependent Variable: Y21b.
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
(Constant)X1_1X21X31X41X51X71X91X121X131X151C11C21
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y21a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
355
Model Summary
1.000a 1.000 .999 .02518Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C21, X21, X91, C11, X31, X131, X51, X121, X151, X71,X1_1
a.
Dependent Variable: Y21b.
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
(Constant)X1_1X21X31X51X71X91X121X131X151C11C21
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y21a.
356
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
1.000a 1.000 .999 .01781Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C11, X51, X1_1, X91, X151,X131, X31, X21, X121, X71
a.
ANOVAb
6.482 10 .648 2044.105 .000a
.001 2 .0006.483 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C11, X51, X1_1, X91, X151, X131, X31, X21, X121, X71a.
Dependent Variable: Y21b.
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
(Constant)X1_1X21X31X51X71X91X121X131X151C11
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y21a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
.999a .997 .991 .06599Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C11, X51, X1_1, X91, X21, X131, X31, X121, X71a.
Dependent Variable: Y21b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y21a.
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
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
Descriptive Statistics
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
Y31X1_1X21X31X41X51X61X71X81X91X101X111X121X131X141X151C11C21
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C21, X21, X91, C11, X1_1, X151, X51, X121, X31,X131, X71
a.
Dependent Variable: Y31b.
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
(Constant)X1_1X21X31X51X71X91X121X131X151C11C21
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y31a.
362
Regression
Descriptive Statistics
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
Y31X1_1X21X31X51X61X71X81X91X101X111X121X131X141X151C11C21
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C21, X21, X91, C11, X1_1,X151, X51, X121, X31, X131, X71
a.
ANOVAb
1119.350 11 101.759 . .a
.000 0 .1119.350 11
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C21, X21, X91, C11, X1_1, X151, X51, X121, X31,X131, X71
a.
Dependent Variable: Y31b.
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
(Constant)X1_1X21X31X51X71X91X121X131X151C11C21
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y31a.
Regression
Descriptive Statistics
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
Y31X1_1X21X31X51X71X81X91X101X111X121X131X141X151C11C21
Mean Std. Deviation N
364
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C21, X21, X91, C11, X1_1,X151, X51, X121, X31, X131, X71
a.
ANOVAb
1119.350 11 101.759 . .a
.000 0 .1119.350 11
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C21, X21, X91, C11, X1_1, X151, X51, X121, X31,X131, X71
a.
Dependent Variable: Y31b.
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
(Constant)X1_1X21X31X51X71X91X121X131X151C11C21
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y31a.
365
Regression
Descriptive Statistics
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
Y31X1_1X21X31X51X71X91X101X111X121X131X141X151C11C21
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C21, X21, X91, C11, X1_1,X151, X51, X121, X31, X131, X71
a.
ANOVAb
1119.350 11 101.759 . .a
.000 0 .1119.350 11
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C21, X21, X91, C11, X1_1, X151, X51, X121, X31,X131, X71
a.
Dependent Variable: Y31b.
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
(Constant)X1_1X21X31X51X71X91X121X131X151C11C21
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y31a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
367
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C21, X21, X91, C11, X1_1,X151, X51, X121, X31, X131, X71
a.
ANOVAb
1119.350 11 101.759 . .a
.000 0 .1119.350 11
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C21, X21, X91, C11, X1_1, X151, X51, X121, X31,X131, X71
a.
Dependent Variable: Y31b.
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
(Constant)X1_1X21X31X51X71X91X121X131X151C11C21
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y31a.
368
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C21, X21, X91, C11, X1_1,X151, X51, X121, X31, X131, X71
a.
ANOVAb
1119.350 11 101.759 . .a
.000 0 .1119.350 11
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C21, X21, X91, C11, X1_1, X151, X51, X121, X31,X131, X71
a.
Dependent Variable: Y31b.
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
(Constant)X1_1X21X31X51X71X91X121X131X151C11C21
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y31a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
370
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C21, X21, X91, C11, X1_1,X151, X51, X121, X31, X131, X71
a.
ANOVAb
1119.350 11 101.759 . .a
.000 0 .1119.350 11
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C21, X21, X91, C11, X1_1, X151, X51, X121, X31,X131, X71
a.
Dependent Variable: Y31b.
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
(Constant)X1_1X21X31X51X71X91X121X131X151C11C21
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y31a.
371
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
.985a .970 .675 5.74672Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C11, X121, X1_1, X91, X21,X51, X131, X151, X31, X71
a.
ANOVAb
1086.325 10 108.633 3.289 .407a
33.025 1 33.0251119.350 11
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C11, X121, X1_1, X91, X21, X51, X131, X151, X31, X71a.
Dependent Variable: Y31b.
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
(Constant)X1_1X21X31X51X71X91X121X131X151C11
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y31a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
.980a .960 .783 4.70333Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C11, X121, X1_1, X91, X21,X51, X131, X151, X31
a.
373
ANOVAb
1075.107 9 119.456 5.400 .166a
44.243 2 22.1211119.350 11
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C11, X121, X1_1, X91, X21, X51, X131, X151, X31a.
Dependent Variable: Y31b.
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
(Constant)X1_1X21X31X51X91X121X131X151C11
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y31a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
374
Model Summary
.980a .960 .853 3.87019Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C11, X121, X1_1, X21, X51, X151, X131, X31a.
Dependent Variable: Y31b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y31a.
APPENDIX N
SPSS Output for the Impact of Politico-Economic
Institutions on Income Inequality in East Asia
Appendix N
SPSS Output for the Impact of Politico-Economic Institutions on
Income Inequality in East Asia
Regression
Descriptive Statistics
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
Y41X1_1X21X31X41X51X61X71X81X91X101X111X121X131X141X151C11C21
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C21, X21, X91, C11, X1_1,X151, X51, X121, X31, X131, X71
a.
377
ANOVAb
24.690 11 2.245 . .a
.000 0 .24.690 11
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C21, X21, X91, C11, X1_1, X151, X51, X121, X31,X131, X71
a.
Dependent Variable: Y41b.
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
(Constant)X1_1X21X31X51X71X91X121X131X151C11C21
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y41a.
378
Regression
Descriptive Statistics
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
Y41X1_1X21X31X51X61X71X81X91X101X111X121X131X141X151C11C21
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C21, X21, X91, C11, X1_1,X151, X51, X121, X31, X131, X71
a.
ANOVAb
24.690 11 2.245 . .a
.000 0 .24.690 11
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C21, X21, X91, C11, X1_1, X151, X51, X121, X31,X131, X71
a.
Dependent Variable: Y41b.
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
(Constant)X1_1X21X31X51X71X91X121X131X151C11C21
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y41a.
Regression
Descriptive Statistics
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
Y41X1_1X21X31X51X71X81X91X101X111X121X131X141X151C11C21
Mean Std. Deviation N
380
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C21, X21, X91, C11, X1_1,X151, X51, X121, X31, X131, X71
a.
ANOVAb
24.690 11 2.245 . .a
.000 0 .24.690 11
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C21, X21, X91, C11, X1_1, X151, X51, X121, X31,X131, X71
a.
Dependent Variable: Y41b.
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
(Constant)X1_1X21X31X51X71X91X121X131X151C11C21
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y41a.
381
Regression
Descriptive Statistics
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
Y41X1_1X21X31X51X71X91X101X111X121X131X141X151C11C21
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C21, X21, X91, C11, X1_1,X151, X51, X121, X31, X131, X71
a.
ANOVAb
24.690 11 2.245 . .a
.000 0 .24.690 11
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C21, X21, X91, C11, X1_1, X151, X51, X121, X31,X131, X71
a.
Dependent Variable: Y41b.
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
(Constant)X1_1X21X31X51X71X91X121X131X151C11C21
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y41a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
383
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C21, X21, X91, C11, X1_1,X151, X51, X121, X31, X131, X71
a.
ANOVAb
24.690 11 2.245 . .a
.000 0 .24.690 11
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C21, X21, X91, C11, X1_1, X151, X51, X121, X31,X131, X71
a.
Dependent Variable: Y41b.
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
(Constant)X1_1X21X31X51X71X91X121X131X151C11C21
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y41a.
384
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C21, X21, X91, C11, X1_1,X151, X51, X121, X31, X131, X71
a.
ANOVAb
24.690 11 2.245 . .a
.000 0 .24.690 11
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C21, X21, X91, C11, X1_1, X151, X51, X121, X31,X131, X71
a.
Dependent Variable: Y41b.
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
(Constant)X1_1X21X31X51X71X91X121X131X151C11C21
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y41a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
386
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C21, X21, X91, C11, X1_1,X151, X51, X121, X31, X131, X71
a.
ANOVAb
24.690 11 2.245 . .a
.000 0 .24.690 11
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C21, X21, X91, C11, X1_1, X151, X51, X121, X31,X131, X71
a.
Dependent Variable: Y41b.
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
(Constant)X1_1X21X31X51X71X91X121X131X151C11C21
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y41a.
387
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
.996a .992 .912 .44346Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C11, X121, X1_1, X91, X21,X51, X131, X151, X31, X71
a.
ANOVAb
24.493 10 2.449 12.455 .217a
.197 1 .19724.690 11
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C11, X121, X1_1, X91, X21, X51, X131, X151, X31, X71a.
Dependent Variable: Y41b.
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
(Constant)X1_1X21X31X51X71X91X121X131X151C11
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y41a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
.995a .991 .950 .33641Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C11, X121, X1_1, X91, X21,X51, X131, X151, X31
a.
389
ANOVAb
24.464 9 2.718 24.019 .041a
.226 2 .11324.690 11
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C11, X121, X1_1, X91, X21, X51, X131, X151, X31a.
Dependent Variable: Y41b.
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
(Constant)X1_1X21X31X51X91X121X131X151C11
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y41a.
APPENDIX O
SPSS Output for the Impact of Politico-Economic
Institutions on Annual Growth Rates of GDP per Capita
in Latin America
Appendix O
SPSS Output for the Impact of Politico-Economic Institutions on
Annual Growth Rates of GDP per Capita in Latin America
Regression
Descriptive Statistics
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
Y12X1_2X22X32X42X52X62X72X82X92X102X112X122X132X142X152C12C22
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C22, X122, X102, X132, X112, X1_2, X92, X72, X32,X22, X152, X82
a.
Dependent Variable: Y12b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y12a.
393
Regression
Descriptive Statistics
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
Y12X1_2X22X32X52X62X72X82X92X102X112X122X132X142X152C12C22
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C22, X122, X102, X132, X112,X1_2, X92, X72, X32, X22, X152, X82
a.
ANOVAb
20.452 12 1.704 . .a
.000 0 .20.452 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C22, X122, X102, X132, X112, X1_2, X92, X72, X32,X22, X152, X82
a.
Dependent Variable: Y12b.
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
(Constant)X1_2X22X32X72X82X92X102X112X122X132X152C22
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y12a.
Regression
Descriptive Statistics
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
Y12X1_2X22X32X62X72X82X92X102X112X122X132X142X152C12C22
Mean Std. Deviation N
395
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C22, X122, X102, X132, X112,X1_2, X92, X72, X32, X22, X152, X82
a.
ANOVAb
20.452 12 1.704 . .a
.000 0 .20.452 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C22, X122, X102, X132, X112, X1_2, X92, X72, X32,X22, X152, X82
a.
Dependent Variable: Y12b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y12a.
396
Regression
Descriptive Statistics
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
Y12X1_2X22X32X72X82X92X102X112X122X132X142X152C12C22
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C22, X122, X102, X132, X112,X1_2, X92, X72, X32, X22, X152, X82
a.
ANOVAb
20.452 12 1.704 . .a
.000 0 .20.452 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C22, X122, X102, X132, X112, X1_2, X92, X72, X32,X22, X152, X82
a.
Dependent Variable: Y12b.
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
(Constant)X1_2X22X32X72X82X92X102X112X122X132X152C22
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y12a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
398
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C22, X122, X102, X132, X112,X1_2, X92, X72, X32, X22, X152, X82
a.
ANOVAb
20.452 12 1.704 . .a
.000 0 .20.452 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C22, X122, X102, X132, X112, X1_2, X92, X72, X32,X22, X152, X82
a.
Dependent Variable: Y12b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y12a.
399
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C22, X122, X102, X132, X112,X1_2, X92, X72, X32, X22, X152, X82
a.
ANOVAb
20.452 12 1.704 . .a
.000 0 .20.452 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C22, X122, X102, X132, X112, X1_2, X92, X72, X32,X22, X152, X82
a.
Dependent Variable: Y12b.
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
(Constant)X1_2X22X32X72X82X92X102X112X122X132X152C22
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y12a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
401
Model Summary
.971a .943 .316 1.07988Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), X152, X122, X102, X132, X1_2, X22, X112, X92, X32,X72, X82
a.
Dependent Variable: Y12b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y12a.
402
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
.964a .929 .692 .73443Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), X132, X1_2, X72, X102, X92, X22, X112, X122, X32,X82
a.
Dependent Variable: Y12b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y12a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
.963a .928 .767 .63925Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), X132, X1_2, X72, X102, X92, X22, X112, X122, X32a.
Dependent Variable: Y12b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y12a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
405
Model Summary
.963a .927 .811 .57468Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), X132, X1_2, X72, X102, X92, X22, X112, X122a.
Dependent Variable: Y12b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y12a.
406
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
.956a .913 .813 .57288Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), X132, X1_2, X102, X112, X92, X122, X22a.
Dependent Variable: Y12b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y12a.
APPENDIX P
SPSS Output for the Impact of Politico-Economic
Institutions on Unemployment Rates in Latin America
Appendix P
SPSS Output for the Impact of Politico-Economic Institutions on
Unemployment Rates in Latin America
Regression
Descriptive Statistics
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
Y22X1_2X22X32X42X52X62X72X82X92X102X112X122X132X142X152C12C22
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C22, X122, X102, X132, X112,X1_2, X92, X72, X32, X22, X152, X82
a.
410
ANOVAb
16.932 12 1.411 . .a
.000 0 .16.932 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C22, X122, X102, X132, X112, X1_2, X92, X72, X32,X22, X152, X82
a.
Dependent Variable: Y22b.
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
(Constant)X1_2X22X32X72X82X92X102X112X122X132X152C22
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y22a.
411
Regression
Descriptive Statistics
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
Y22X1_2X22X32X52X62X72X82X92X102X112X122X132X142X152C12C22
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C22, X122, X102, X132, X112,X1_2, X92, X72, X32, X22, X152, X82
a.
ANOVAb
16.932 12 1.411 . .a
.000 0 .16.932 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C22, X122, X102, X132, X112, X1_2, X92, X72, X32,X22, X152, X82
a.
Dependent Variable: Y22b.
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
(Constant)X1_2X22X32X72X82X92X102X112X122X132X152C22
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y22a.
Regression
Descriptive Statistics
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
Y22X1_2X22X32X62X72X82X92X102X112X122X132X142X152C12C22
Mean Std. Deviation N
413
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C22, X122, X102, X132, X112,X1_2, X92, X72, X32, X22, X152, X82
a.
ANOVAb
16.932 12 1.411 . .a
.000 0 .16.932 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C22, X122, X102, X132, X112, X1_2, X92, X72, X32,X22, X152, X82
a.
Dependent Variable: Y22b.
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
(Constant)X1_2X22X32X72X82X92X102X112X122X132X152C22
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y22a.
414
Regression
Descriptive Statistics
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
Y22X1_2X22X32X72X82X92X102X112X122X132X142X152C12C22
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C22, X122, X102, X132, X112,X1_2, X92, X72, X32, X22, X152, X82
a.
ANOVAb
16.932 12 1.411 . .a
.000 0 .16.932 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C22, X122, X102, X132, X112, X1_2, X92, X72, X32,X22, X152, X82
a.
Dependent Variable: Y22b.
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
(Constant)X1_2X22X32X72X82X92X102X112X122X132X152C22
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y22a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
416
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C22, X122, X102, X132, X112,X1_2, X92, X72, X32, X22, X152, X82
a.
ANOVAb
16.932 12 1.411 . .a
.000 0 .16.932 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C22, X122, X102, X132, X112, X1_2, X92, X72, X32,X22, X152, X82
a.
Dependent Variable: Y22b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y22a.
417
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C12, X102, X1_2, X132, X122, X112, X22, X92, X72,X152, X82, X32
a.
Dependent Variable: Y22b.
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
(Constant)X1_2X22X32X72X82X92X102X112X122X132X152C12
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y22a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
419
Model Summary
1.000a 1.000 .996 .07642Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), X152, X122, X102, X132,X1_2, X22, X112, X92, X32, X72, X82
a.
ANOVAb
16.926 11 1.539 263.510 .048a
.006 1 .00616.932 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), X152, X122, X102, X132, X1_2, X22, X112, X92, X32,X72, X82
a.
Dependent Variable: Y22b.
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
(Constant)X1_2X22X32X72X82X92X102X112X122X132X152
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y22a.
420
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
.998a .996 .982 .16101Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), X132, X1_2, X72, X102, X92,X22, X112, X122, X32, X82
a.
ANOVAb
18.908 10 1.891 72.940 .002a
.078 3 .02618.986 13
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), X132, X1_2, X72, X102, X92, X22, X112, X122, X32,X82
a.
Dependent Variable: Y22b.
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
(Constant)X1_2X22X32X72X82X92X102X112X122X132
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y22a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
.998a .996 .987 .13960Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), X132, X1_2, X72, X102, X92,X112, X122, X82, X32
a.
422
ANOVAb
18.908 9 2.101 107.804 .000a
.078 4 .01918.986 13
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), X132, X1_2, X72, X102, X92, X112, X122, X82, X32a.
Dependent Variable: Y22b.
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
(Constant)X1_2X32X72X82X92X102X112X122X132
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y22a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
423
Model Summary
.998a .996 .989 .12666Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), X132, X1_2, X72, X102, X92,X112, X122, X82
a.
ANOVAb
18.906 8 2.363 147.318 .000a
.080 5 .01618.986 13
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), X132, X1_2, X72, X102, X92, X112, X122, X82a.
Dependent Variable: Y22b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y22a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
424
Model Summary
.998a .995 .990 .12093Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), X132, X1_2, X102, X112, X92,X122, X82
a.
ANOVAb
18.898 7 2.700 184.626 .000a
.088 6 .01518.986 13
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), X132, X1_2, X102, X112, X92, X122, X82a.
Dependent Variable: Y22b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y22a.
APPENDIX Q
SPSS Output for the Impact of Politico-Economic
Institutions on the Percentage of the Population Falling
below the Poverty Line in Latin America
Appendix Q
SPSS Output for the Impact of Politico-Economic Institutions on
the Percentage of the Population Falling below the Poverty Line in
Latin America
Regression Descriptive Statistics
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
Y32X1_2X22X32X42X52X62X72X82X92X102X112X122X132X142X152C12C22
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C22, X122, X102, X132, X112,X1_2, X92, X72, X32, X22, X152, X82
a.
427
ANOVAb
235.523 12 19.627 . .a
.000 0 .235.523 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C22, X122, X102, X132, X112, X1_2, X92, X72, X32,X22, X152, X82
a.
Dependent Variable: Y32b.
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
(Constant)X1_2X22X32X72X82X92X102X112X122X132X152C22
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y32a.
428
Regression
Descriptive Statistics
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
Y32X1_2X22X32X42X52X62X72X92X102X112X122X132X142X152C12C22
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C22, X122, X102, X132, X112,X1_2, X92, X72, X32, X22, X152, X52
a.
ANOVAb
235.523 12 19.627 . .a
.000 0 .235.523 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C22, X122, X102, X132, X112, X1_2, X92, X72, X32,X22, X152, X52
a.
Dependent Variable: Y32b.
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
(Constant)X1_2X22X32X52X72X92X102X112X122X132X152C22
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y32a.
Regression
Descriptive Statistics
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
Y32X1_2X22X32X52X62X72X92X102X112X122X132X142X152C12C22
Mean Std. Deviation N
430
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C22, X122, X102, X132, X112,X1_2, X92, X72, X32, X22, X152, X52
a.
ANOVAb
235.523 12 19.627 . .a
.000 0 .235.523 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C22, X122, X102, X132, X112, X1_2, X92, X72, X32,X22, X152, X52
a.
Dependent Variable: Y32b.
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
(Constant)X1_2X22X32X52X72X92X102X112X122X132X152C22
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y32a.
431
Regression
Descriptive Statistics
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
Y32X1_2X22X32X52X72X92X102X112X122X132X142X152C12C22
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C22, X122, X102, X132, X112,X1_2, X92, X72, X32, X22, X152, X52
a.
ANOVAb
235.523 12 19.627 . .a
.000 0 .235.523 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C22, X122, X102, X132, X112, X1_2, X92, X72, X32,X22, X152, X52
a.
Dependent Variable: Y32b.
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
(Constant)X1_2X22X32X52X72X92X102X112X122X132X152C22
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y32a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
433
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C22, X122, X102, X132, X112,X1_2, X92, X72, X32, X22, X152, X52
a.
ANOVAb
235.523 12 19.627 . .a
.000 0 .235.523 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C22, X122, X102, X132, X112, X1_2, X92, X72, X32,X22, X152, X52
a.
Dependent Variable: Y32b.
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
(Constant)X1_2X22X32X52X72X92X102X112X122X132X152C22
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y32a.
434
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C22, X122, X102, X132, X112,X1_2, X92, X72, X32, X22, X152, X52
a.
ANOVAb
235.523 12 19.627 . .a
.000 0 .235.523 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C22, X122, X102, X132, X112, X1_2, X92, X72, X32,X22, X152, X52
a.
Dependent Variable: Y32b.
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
(Constant)X1_2X22X32X52X72X92X102X112X122X132X152C22
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y32a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
436
Model Summary
.906a .821 -1.149 6.49372Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C22, X122, X102, X132, X112, X1_2, X92, X72, X32,X22, X152
a.
Dependent Variable: Y32b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y32a.
437
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
.882a .777 -.335 5.11972Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), X152, X122, X102, X132, X1_2, X22, X112, X92, X32,X72
a.
Dependent Variable: Y32b.
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
(Constant)X1_2X22X32X72X92X102X112X122X132X152
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y32a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
.872a .760 .041 4.33735Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), X152, X122, X102, X132, X1_2, X22, X112, X92, X72a.
Dependent Variable: Y32b.
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
(Constant)X1_2X22X72X92X102X112X122X132X152
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y32a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
440
Model Summary
.857a .734 .309 4.50807Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), X132, X1_2, X72, X102, X92,X22, X112, X122
a.
ANOVAb
280.946 8 35.118 1.728 .283a
101.614 5 20.323382.559 13
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), X132, X1_2, X72, X102, X92, X22, X112, X122a.
Dependent Variable: Y32b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y32a.
441
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
.857a .734 .424 4.11609Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), X132, X1_2, X72, X102, X92,X22, X122
a.
ANOVAb
280.906 7 40.129 2.369 .157a
101.653 6 16.942382.559 13
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), X132, X1_2, X72, X102, X92, X22, X122a.
Dependent Variable: Y32b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y32a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
.841a .707 .455 4.00323Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), X122, X22, X102, X92, X1_2, X72a.
Dependent Variable: Y32b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y32a.
Regression
Descriptive Statistics
18.5976 5.42472 1418.6179 2.11389 142.4118 .10573 14
41.3739 3.10679 14.4402 .08321 14.7559 .07791 14
Y32X22X72X92X102X122
Mean Std. Deviation N
Model Summary
.825a .681 .481 3.90851Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), X122, X22, X102, X92, X72a.
444
ANOVAb
260.348 5 52.070 3.408 .060a
122.211 8 15.276382.559 13
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), X122, X22, X102, X92, X72a.
Dependent Variable: Y32b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y32a.
Regression
Descriptive Statistics
18.5976 5.42472 142.4118 .10573 14
41.3739 3.10679 14.4402 .08321 14.7559 .07791 14
Y32X72X92X102X122
Mean Std. Deviation N
Model Summary
.812a .659 .507 3.80745Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), X122, X92, X102, X72a.
445
ANOVAb
252.089 4 63.022 4.347 .031a
130.470 9 14.497382.559 13
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), X122, X92, X102, X72a.
Dependent Variable: Y32b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFollinearity Statistic
Dependent Variable: Y32a.
APPENDIX R
SPSS Output for the Impact of Politico-Economic
Institutions on Income Inequality in Latin America
Appendix R
SPSS Output for the Impact of Politico-Economic Institutions on
Income Inequality in Latin America
Regression
Descriptive Statistics
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
Y42X1_2X22X32X42X52X62X72X82X92X102X112X122X132X142X152C12C22
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C22, X122, X102, X132, X112,X1_2, X92, X72, X32, X22, X152, X82
a.
448
ANOVAb
125.377 12 10.448 . .a
.000 0 .125.377 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C22, X122, X102, X132, X112, X1_2, X92, X72, X32,X22, X152, X82
a.
Dependent Variable: Y42b.
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
(Constant)X1_2X22X32X72X82X92X102X112X122X132X152C22
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y42a.
449
Regression
Descriptive Statistics
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
Y42X1_2X22X32X52X62X72X82X92X102X112X122X132X142X152C12C22
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C22, X122, X102, X132, X112,X1_2, X92, X72, X32, X22, X152, X82
a.
ANOVAb
125.377 12 10.448 . .a
.000 0 .125.377 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C22, X122, X102, X132, X112, X1_2, X92, X72, X32,X22, X152, X82
a.
Dependent Variable: Y42b.
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
(Constant)X1_2X22X32X72X82X92X102X112X122X132X152C22
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y42a.
Regression
Descriptive Statistics
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
Y42X1_2X22X32X62X72X82X92X102X112X122X132X142X152C12C22
Mean Std. Deviation N
451
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C22, X122, X102, X132, X112,X1_2, X92, X72, X32, X22, X152, X82
a.
ANOVAb
125.377 12 10.448 . .a
.000 0 .125.377 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C22, X122, X102, X132, X112, X1_2, X92, X72, X32,X22, X152, X82
a.
Dependent Variable: Y42b.
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
(Constant)X1_2X22X32X72X82X92X102X112X122X132X152C22
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y42a.
452
Regression
Descriptive Statistics
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
Y42X1_2X22X32X72X82X92X102X112X122X132X142X152C12C22
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C22, X122, X102, X132, X112,X1_2, X92, X72, X32, X22, X152, X82
a.
ANOVAb
125.377 12 10.448 . .a
.000 0 .125.377 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C22, X122, X102, X132, X112, X1_2, X92, X72, X32,X22, X152, X82
a.
Dependent Variable: Y42b.
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
(Constant)X1_2X22X32X72X82X92X102X112X122X132X152C22
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y42a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
454
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C22, X122, X102, X132, X112,X1_2, X92, X72, X32, X22, X152, X82
a.
ANOVAb
125.377 12 10.448 . .a
.000 0 .125.377 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C22, X122, X102, X132, X112, X1_2, X92, X72, X32,X22, X152, X82
a.
Dependent Variable: Y42b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y42a.
455
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
1.000a 1.000 1.000 .Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), C22, X122, X102, X132, X112,X1_2, X92, X72, X32, X22, X152, X82
a.
ANOVAb
125.377 12 10.448 . .a
.000 0 .125.377 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), C22, X122, X102, X132, X112, X1_2, X92, X72, X32,X22, X152, X82
a.
Dependent Variable: Y42b.
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
(Constant)X1_2X22X32X72X82X92X102X112X122X132X152C22
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y42a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
457
Model Summary
.971a .943 .311 2.68301Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), X152, X122, X102, X132,X1_2, X22, X112, X92, X32, X72, X82
a.
ANOVAb
118.178 11 10.743 1.492 .570a
7.199 1 7.199125.377 12
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), X152, X122, X102, X132, X1_2, X22, X112, X92, X32,X72, X82
a.
Dependent Variable: Y42b.
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
(Constant)X1_2X22X32X72X82X92X102X112X122X132X152
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y42a.
458
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
.898a .807 .162 3.27339Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), X132, X1_2, X72, X102, X92,X22, X112, X122, X32, X82
a.
ANOVAb
134.122 10 13.412 1.252 .478a
32.145 3 10.715166.267 13
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), X132, X1_2, X72, X102, X92, X22, X112, X122, X32,X82
a.
Dependent Variable: Y42b.
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
(Constant)X1_2X22X32X72X82X92X102X112X122X132
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y42a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
.898a .807 .371 2.83525Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), X132, X1_2, X72, X102, X92,X112, X122, X82, X32
a.
460
ANOVAb
134.113 9 14.901 1.854 .289a
32.155 4 8.039166.267 13
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), X132, X1_2, X72, X102, X92, X112, X122, X82, X32a.
Dependent Variable: Y42b.
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
(Constant)X1_2X32X72X82X92X102X112X122X132
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y42a.
Regression
Descriptive Statistics
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
Mean Std. Deviation N
461
Model Summary
.898a .807 .497 2.53600Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), X132, X102, X72, X122, X92, X112, X82, X32a.
Dependent Variable: Y42b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y42a.
462
Regression
Descriptive Statistics
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
Mean Std. Deviation N
Model Summary
.871a .759 .477 2.58571Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), X132, X102, X72, X122, X92, X112, X82a.
Dependent Variable: Y42b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y42a.
Regression
Descriptive Statistics
18.3470 3.57628 142.4118 .10573 142.8109 .22152 14.4402 .08321 14.4790 .07041 14.7559 .07791 14.8865 .11558 14
Y42X72X82X102X112X122X132
Mean Std. Deviation N
Model Summary
.871a .758 .550 2.39773Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), X132, X102, X72, X122, X112, X82a.
Dependent Variable: Y42b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y42a.
Regression
Descriptive Statistics
18.3470 3.57628 142.4118 .10573 142.8109 .22152 14.4790 .07041 14.7559 .07791 14.8865 .11558 14
Y42X72X82X112X122X132
Mean Std. Deviation N
Model Summary
.846a .716 .538 2.43007Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), X132, X72, X122, X112, X82a.
465
ANOVAb
119.025 5 23.805 4.031 .040a
47.242 8 5.905166.267 13
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), X132, X72, X122, X112, X82a.
Dependent Variable: Y42b.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: Y42a.
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