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The effects of goverance corruption on education budgets and income in Central and Eastern Europe

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The effects of goverance corruption on education budgets and income in Central and Eastern Europe
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Hannaway, Tamara Lynn
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Sustainable development ( lcsh )
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Education and state ( fast )
Sustainable development ( fast )
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This thesis addresses economic development in the context of endogenous corruption. We also ask whether economic growth exacerbates poverty or income inequality. The evidence to date is mixed. The thesis examines relationships among and between defined constraints on economic development by offering policy makers a unique method of measuring governance corruption's effects on education budgets and individual income. Governance corruption includes malfeasance, misfeasance, nonfeasance, or perpetrations involving state, non-state, and private sector actors that circumvent, distort, or manipulate the democratic process, and thereby undermine the government's revenue stream. Governance is the official governmental system and institutional `rules of the game' by which a country is governed; state capture, rent seeking, and free riding behaviors corrupt the system. The Shadow Economy, acting as the surrogate for corruption, measures the percent of total productivity unaccounted for in the official GDP. The individual actor is the unit of measure; Central and Eastern European countries are the sample set; individual income is the dependant variable; and the independent variables are the Human Development Index, education expenditures, and the Shadow Economy. The analyses presented suggest clear evidence that as the size of the Shadow Economy increases, the budget for educa-tion expenditures as a percentage of the total national government expenses decreases. The evidence implies that as the education budget decreases, so does the official individual income, and therefore, available measures for economic growth are inadequate to measure income ine-quality, thereby leaving analyses and conclusions regarding the effects of economic growth on the individual actor, wanting. These findings are consistent with New Growth Theory, particularly, that education is critical to a healthy and sustainable economic development, and offer evidence that adding the effects of corruption to current economic growth models provides unique learning about growth's effects on income inequality. The practical application is that education expenditures and individual income are analyzed together and in light of the effect of corruption on them. This evidence may be appreciable to economic development and education policy making.
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Includes bibliographical references.
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by Tamara Lynn Hannaway.

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Full Text
THE EFFECTS OF GOVERNANCE CORRUPTION ON
EDUCATION BUDGETS AND INCOME IN
CENTRAL AND EASTERN EUROPE
by
Tamara Lynn Hannaway
BA, Fort Lewis College, 1984
MBA, Westminster College, 1995
A thesis submitted to the
Faculty of the Graduate School of the
University of Colorado Denver in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy
Public Affairs
2012


2012 by Tamara Lynn Hannaway
All rights reserved.


This thesis for the Doctor of Philosophy degree by
Tamara Lynn Hannaway
has been approved for the
Graduate School of Public Affairs
by
Peter deLeon, Chair
Paul Teske
Robert Reichardt
Christoph Stefes
Date
in


Hannaway, Tamara Lynn (Ph.D., Public Affairs)
The Effects of Governance Corruptions on Education Budgets and Income in
Central and Eastern Europe
Thesis directed by Professor Peter deLeon
ABSTRACT
This thesis addresses economic development in the context of endogenous corruption.
We also ask whether economic growth exacerbates poverty or income inequality. The evidence
to date is mixed. The thesis examines relationships among and between defined constraints on
economic development by offering policy makers a unique method of measuring governance cor-
ruptions effects on education budgets and individual income. Governance corruption includes
malfeasance, misfeasance, nonfeasance, or perpetrations involving state, non-state, and private
sector actors that circumvent, distort, or manipulate the democratic process, and thereby under-
mine the governments revenue stream. Governance is the official governmental system and
institutional rules of the game by which a country is governed; state capture, rent seeking, and
free riding behaviors corrupt the system. The Shadow Economy, acting as the surrogate for cor-
ruption, measures the percent of total productivity unaccounted for in the official GDP. The
individual actor is the unit of measure; Central and Eastern European countries are the sample set;
individual income is the dependant variable; and the independent variables are the Human Devel-
opment Index, education expenditures, and the Shadow Economy. The analyses presented
suggest clear evidence that as the size of the Shadow Economy increases, the budget for educa-
tion expenditures as a percentage of the total national government expenses decreases. The
evidence implies that as the education budget decreases, so does the official individual income,
and therefore, available measures for economic growth are inadequate to measure income ine-
quality, thereby leaving analyses and conclusions regarding the effects of economic growth on the
individual actor, wanting. These findings are consistent with New Growth Theory, particularly,
that education is critical to a healthy and sustainable economic development, and offer evidence
that adding the effects of corruption to current economic growth models provides unique learning
about growths effects on income inequality. The practical application is that education expendi-
tures and individual income are analyzed together and in light of the effect of corruption on them.
This evidence may be appreciable to economic development and education policy making.
Key words: Governance, Corruption, Education Expenditures, Income Inequality, New Growth
Theory, Shadow Economy, Human Development, Economic Growth, Economic Development,
Sustainable Development.
The form and content of this abstract are approved. I recommend its publication.
Approved: Peter deLeon
IV


DEDICATION
I dedicate this work to my daughter Hannah, with a very special thank you for your love,
smile, vibrance, and the joy you bring. For your love, patience, inspiration, and support, thank
you, especially to my mom, dad, grandma, sister, and brother, and also to my extended family,
friends, colleagues, mentors, and students.
Many persons from childhood to today deserve mention and a debt of gratitude. They
have imparted wisdom, contributed some important element, provided inspiration, influence, and
encouragement. The list is many years in the making, and longer than this dissertation; thank
you.
v


ACKNOWLEDGEMENTS
Many thanks to my advisor, Dr. Peter deLeon, for believing in me from the start, for his
guidance, and for his contribution to and support of my research. I also wish to thank all the
members of my committee for their valuable participation and insights.
From before the start of this Ph.D. process, Colorado Christian University has been my
place of full-time employment. The extraordinary grace, understanding, and support extended to
me by the whole of the institution and its employees, my colleagues, the administration, and the
staff, is greatly appreciated. Thank you. A more ardent team of cheerleaders the academy has
never known.
vi


TABLE OF CONTENTS
CHAPTER
I. INTRODUCTION..............................................................1
Research Questions...............................................2
Problem and Research Approach....................................9
Sample Set Rules................................................16
Thesis Overview.................................................17
II. LITERATURE REVIEW........................................................18
Governance......................................................19
Measuring Governance.............................................22
Good Governance and Sustainable Development......................24
Human Development as a Metric for Governance.....................28
Corruption as a Dimension of Governance.........................31
Forms, Size, and Scope of Corruption.............................33
Shadow Economy Rules.............................................37
Causes and Consequences of Corruption............................37
Corruptions Remuneration........................................40
Controlling Corruption...........................................41
Consequences of Corruption on Development........................43
Measuring Corruption Indirectly................................44
Economic Growth.................................................49
The Business Cycle the foundation of economic growth stages....49
Economic Growth Theories.........................................52
Measuring Education Delivery....................................64
Measuring the Quantity of Education (Supply).....................64
vii


Measuring the Value of Education (demand)
64
Governance Corruption in Education...........................65
Summary....................................................71
III. THE DATA AND ME I I IODOEOGY.........................................72
Methodology................................................72
Hypothesis.................................................73
Data.......................................................73
IV. ANALYSIS.............................................................83
Research Question 1........................................83
Research Question 2........................................86
Research Question 3........................................91
Research Question 4........................................94
Hypothesis 4.1...............................................94
Hypothesis 4.2...............................................96
Hypothesis 4.3...............................................98
V. FINDINGS............................................................101
Summary....................................................109
Data Limitations...........................................117
VI. CONSLUSIONS AND FUTURE RESEARCH.....................................122
Conclusions................................................122
Future Research............................................128
APPENDIX A: COUNTRY BRIEFS................................................133
APPENDIX B: DATA RELIABILITY AND VALIDITY.................................187
TABLES AND FIGURES........................................................227
GLOSSARY..................................................................243
REFERENCES................................................................246
viii


LIST OF FIGURES
Figure 1.1 Change in Income per Capita and Change in Human Development Index.84
Figure 1.2 Change in Income per Capita and Change in LEI and EAI.........85
Figure 3.1 Shadow Economys Effects on Future Education Expenditures....................................93
Figure 3.2 Shadow Economys Effects on Current Education Expenditures...................................93
Figure 5 Shadow Economy MIMIC Diagram......................................75,232
Figure 5.1 Change in Income per Capita and the Education Expenditure..............................105
Figure 5.2 Change in Income per Capita and Lagged Education Expenditure.105
Figure 6 Shadow Economy Simultaneous Equations..........................................................232
Figure 6.2 The Kuznets Curve (1966).....................................................................129
Figure 6.3 Educational Kuznets Curve (authors depiction)...............................................130
Figure 7 Data Validation Comparison.....................................................................239
Figure 8 The Policy Problem.............................................................................246
IX


LIST OF TABLES
Table 1 Hypothesis 1 Data.......................................................228
Table 2 Hypothesis 2 Data.......................................................229
Table 3 Hypothesis 3 Data.......................................................230
Table 4.0 Equation 4 Comparison..................................................107
Table 4.1 Correlation Coefficient Matrix.........................................242
Table 4.2 Correlation Coefficient Matrix.........................................242
Table 4.3 Correlation Coefficient Matrix.........................................242
Table 4.4 Correlation Coefficient Matrix.........................................242
Table 5 GDP per Capita Cycle.......................................................233
Table 7.1 Data Validation........................................................234
Table 7.2 Data Validation Equation Analysis......................................240
Table 7.3 Data Sources...........................................................241
Table 7.4 Correlation Coefficient Matrix......................................192, 242
x


PREFACE
This thesis analyzes public policy through an economic development policy lens and
framework. The purpose of this thesis is to inform economic development policy through the ex-
amination of relationships among and between defined influences and constraints on economic
development, and to offer policy makers a unique method of measuring governance corruptions
effects on education budgets and individual income. The approach used is to compare and con-
trast 1990 and 2008 economic development as measured by Gross Domestic Product per Capita,
or Individual Income; the Human Development Index and its component indices; governance cor-
ruption as measured by the Shadow Economy (Schneider et al., 2010, p. 5); and Education
Expenditures as measured by United Nations Educational, Scientific and Cultural Organization
(UNESCO).
The research herein covers broad areas of literature from the social sciences; political sci-
ence, history, corruption, governance, and economics; and from business, accounting, and
education. The unit of measure is the individual. Actors in this thesis may be employees of the
state, of non-state institutions, or work in the private sector. Official income per capita is stated
in US dollars using the year 2000 as a base year. Narrowing the scope of this array of literature
was based on an experience.
While standing in the rubble of what recently was the Berlin Wall in 1989,1 looked east,
then west, the east again. Grey, Color, Grey. Stooped, vibrant, stooped. Battered, flourishing,
battered. A woman, standing behind a smallish grungy table, was selling bits of the wall,
stamped with what she stated was some official seal. I bought one, just in case there was such a
seal, and picked up another from the piles upon which the tourists walked and children played.
The image of dichotomy, contrast, and dissimilarity, in my visual perspective overwhelmed my
senses. I knew a few facts aboui me worn w ai, out the textbooks said nothing of what
my eyes could see. I felt a communal sense of anguish flowing from the east, while the west was
xi


as familiar as my own United States. Some entity or organism, some insidious, living thing lying
to the east, beyond the government or its structures and peace accords, beyond the empty promis-
es believed by the proletariat, was causing the pervasive agony.
Repeated questions tumbled about in my head for a score of years, through several visits
to the same and other places behind what was the Iron Curtain. 1) What festering plaque prohib-
iting citizens from living a life fulfilled? 2) Why could the people not shake it off, loose it,
overcome it, beat it? 3) Who were the guardians of the people; where were these sentries; and
why did they not act on behalf of the millions of downtrodden? 4) How did the physical infra-
structure decay and the economic powerhouse implode? Thus, the variables for this thesis
became: 1) Corruption. 2) Education. 3) Governance. 4) Economic policy.
Xll


CHAPTERI
INTRODUCTION
This thesis endeavors to inform economic development policy so as to encourage healthy
economic growth (Kuznets, 1966, p. 493) without the friction of institutional or political corrup-
tion. We also ask whether economic growth exacerbates poverty. The evidence to date is mixed.
Deceiving in its simplicity, this fundamental question is of paramount importance as factors of
globalization induce growth in the worlds Gross Domestic Product (Levitt, 1983). To inform
development policy toward a more balanced growth, this thesis examines relationships among
and between defined influences and constraints on economic development, and specifically, it of-
fers policy makers a unique method of measuring governance corruptions effects on education
budgets and individual income. Growth, neither culprit nor remedy, is the benign measure of
influences on economic productivity over time (Kuznets, 1973). The terms for economic change,
growth and development in this thesis, follow the definitions advanced by Schumpeter (1939) and
Kuznets (1934, 1940). Economic growth is incremental change, generally measured by change in
Gross Domestic Product (GDP). Economic development is a new steady state. This new level of
development is realized in response to economic growth and the evolution, health, maturation,
and increased capacity of the economy to sustain growth.
This thesis advances and challenges New Growth Theory (Romer, 1990) by investigating
how specific measures of governance, and the corruption within governance, affect growth of in-
dividual income; further, it explores linkages between these factors and the causal relationships
among them (Barro, 2001b; Sen, 1997, 1999). Data from nations under the former Soviet spheres
of influence during the Cold War have extraordinary potential to shed light on governance and
development policy. While international attention focused on the transitions from Soviet rule to
independence, intergovernmental organizations and academics seized the opportunity for re-
1


search, documentation, and data collection. Meanwhile, advances in technology and computing
power grew exponentially. The breadth and depth of data available for analysis on the conse-
quences of growth are unprecedented.
Research Questions
Research Question 1: Are the Human Development Index and Income per Capita highly corre-
lated in the sample set?
Research Question 2: Does governance corruption, as measured by the Shadow Economy, nega-
tively affect Income per Capita?
Research Question 3: Does governance corruption, as measured by the Shadow Economy, nega-
tively affect Education Expenditure?
Research Question 4: Do the pre-test Human Development Index, governance corruption as
measured by the Shadow Economy and Education Expenditure together explain the
change in Income per Capita?
The key hypothesis this thesis tests is: governance corruptions effects on education
through the public resource mechanism, its budget, are direct and negative; succinctly, the higher
the degree of corruption, the lower the relative education budget. Further, the lower the education
budget per capita, the lower the relative individual income.
The evidence varies on whether economic growth exacerbates or alleviates the relative or
absolute income at the national level. Evidence at the level of the individual actor is far more ob-
scure (Galbraith & Kum, 2005). For this reason, the focus of this thesis is the total income to the
individual actor.
A comparison of the multinational evidence indicates the presence of four conceptual
challenges: First, how do we define and measure individual income? Second, when or what pe-
riod should we measure? Third, what in addition to income provides a more thorough picture of
the living standard of the individual? Lastly, which aspects of governance corruption effect indi-
2


vidual income? These are important questions for policy makers, as national (aggregated) figures
mask the effect of policy decisions at the individual level. For example, measured from 1990 to
2007, Uzbekistans GDP increased 1.2 billion (US equivalent dollars) or 1.8 percent, while the
individual income decreased from $3,155 to $2,425, or 30 percent, overthe same period.
The first conceptual challenge stems from inconsistent definitions and measurement
methods, which seem to report contradictory evidence. For example, Uozada describes one side
of the debate: The fierce debates (among)...[a]cademics, journalists, and multilateral organiza-
tions...over economic globalization have focused recently on global poverty and income
inequality...and a general consensus seems to have formed around the proposition that poverty
and inequality are on the rise (2002, p. 5). Maddisons data show that since 1820, the average
yearly world GDP growth is 2.21 percent, while GDP per capita growth is 1.2 percent (2009, p.
4), suggesting that the consensus should be that aggregate and individual incomes levels have di-
verged. On the contrary, Sala-i-Martins data, measured in both absolute and relative terms, show
that, while the world population has steadily increased, fewer people live in poverty today than at
any time in recorded history, and empirical evidence shows converging income levels and an
emerging world middle class (2002, p. 2). Making sense out of what seem to be contradictory
findings would require an exhaustive analysis of the underlying data sets, a justification for and a
comparison of the definitions and measurements of the variables, (Pritchett, 1997, pp. 12-13), ef-
forts beyond the scope of this thesis.
Instead, this thesis employs demographic and economic data produced and shared
through data networks, and the International Comparisons Program (ICP). Scholars, academics,
and professional researchers affiliated with lists of international agencies (e.g., United Nations,
World Bank, International Monetary Fund), intergovernmental organizations (IGO), and non-
governmental organizations (NGO), including abbreviations as used in this thesis, share data (See
detailed list of affiliated organizations in the Glossary). This network of agencies provide data
3


for public use, which usually include detailed methodology, reliability, and validity statistics
(HDR, 2007). Institutes such as Brooking Institution (BI), Transparency International (TI), The
Heritage Foundation (HF), International Comparisons Program (ICP), European Statistics, Data,
and Metadata Exchange (SDMD) (here after, Institutes), feed critical research and data to the
network. The first conceptual challenge is thus met by using data from the same network of
sources.
The second conceptual challenge stems from inconsistent measurement methods that led
to comparing dissimilar periods. Barro (1991) and Solow (1956), among others, claim that in-
comes converge overtime. Generally, scholars who report that incomes converge favor
analyzing the longest time span with the most or best available cross-country data (Barro & Sala-
i-Martin, 1991). Conversely, other scholars, who favor analyzing specific periods, argue that the
development situation or stage of each countrys economic growth matters when measuring ine-
quality (Rostow, 1991). The latter group offer evidence that incomes diverge when tied to certain
circumstances in history. This point, data knitted with situations, is central to the purpose of this
thesis and solving its questions (Matheson, 2008; Pritchett, 1997; Rostow, 1991). Measuring an
economy from one arbitrary date to another based on data availability may invite risk. The risk is
missing vital information about the characteristics of economic growth specific to each country,
its quality and sustainability, and the path, patterns, or cycles of the growth, some of which is
available through historical accounts.
One of the first scholars to trace the paths of income over time was Simon Kuznets.
Kuznets (1966) invested much of his extraordinary career examining questions about income dis-
tribution, economic measurement methods, and growth. He advanced theories that numerous
scholars, including several fellow Nobel Laureates (e.g., Robert Solow, Douglass North,
Amartya Sen, Gordon Tullock, Edmund Phelps, Paul Krugman, Herbert Simon, Milton Fried-
man, and others, plus several whose work is not central to this thesis), have studied, tested,
4


refuted, or confirmed on how and why economies grow. Kuznets argued that the trajectory of in-
come growth depended on the stage of development in a country. He found that the paths of
higher and lower incomes in lesser-developed, more agrarian societies tend to diverge during pe-
riods of economic growth, thus increasing income inequality, while incomes in more developed
societies tend to converge as wealth distributes over a greater percentage of the population. The
second conceptual challenge is met by anchoring the data to a regime change (Rostow, 1991).
Following work by Matheson (2008), Pritchett (1997), and Xu & Li (2008), accounting for the
stage of development (adding the stage as a variable to a development function), creates a frame-
work that invites conditioning variables (e.g., regime, governance, institution), and makes sense
out of the standard economic variables (e.g., GDP, life expectancy, educational attainment, trade
alliances) by anchoring them to a common phase or event (e.g., industrial revolution, inventions,
growth stages, policy stages, regime changes, intrastate armed conflict, treaties, the end of the
Soviet Empire) (Brewer & deLeon, 1983; Rostow, 1991; Xu & Li, 2008).
The third conceptual challenge rests in the defining of living standards by a simple dollar
figure. While the convergence/divergence debate just described persists, Sen (1984) questions its
relevance. He asserts that examining the income level between the richest and poorest in a socie-
ty may be in vain, as income per se may not reflect the reality of human development, yet GDP
per Capita (Income per Capita or Ic) is often used to infer or approximate living standards in the
literature (Deininger & Squire, 1996; Gini, 1921; Kuznets, 1934; Sen, 1984). The third concep-
tual challenge in this thesis is that empirical evidence on income changes over time neglects
evidence of diverging living standards which has been the origin of media coverage, the Mil-
lennial Development Goals, and even armed conflicts. In the Forward to the 2008Millennial
Development Goals Report, Sha Zukang wrote, today ...2.5 billion people, almost half the devel-
oping worlds population, live without improved sanitation; [mjore than one third of the growing
urban population in developing countries live in slum conditions (2008d, p. 4). The third con-
5


ceptual challenge is met by employing the Human Development Index (HDI) as a measure for the
living standard. The work of the Human Development Program, in its 20th year at the United Na-
tions, has provided a center for researching and measuring the living standard (HDR, 2009e).
The last conceptual challenge is in defining governance corruption, and for the purposes
of this thesis, limiting its scope to that which effects economic growth policy, and limiting its
pervasiveness to that which policy may be able or interested to ameliorate. The objective is to
isolate corruption [that] alters the composition of government expenditure (Mauro et al., 2002,
p. 277). Bracketing the span of the state (official), non-state (institutions), and private sectors to
isolate expenditure-altering types of corruption are the bodies of literature on (1) political corrup-
tion and the (2) unofficial economy. Political corruption is, a co-operative form of
unsanctioned, usually condemned, policy influence for some type of significant personal gain, in
which the currency could be economic, political, or ideological remuneration (deLeon, 1993, p.
25). Unofficial influences include .. .those economic activities and the income derived from
them that circumvent or otherwise avoid government regulation, taxation or observation, which
are measured by the Shadow Economy (Schneider et al., 2010, p. 1). This thesis employs the fol-
lowing working definition.
Governance corruption is a co-operative form of unsanctioned, usually condemned, poli-
cy influence that circumvents or otherwise avoids government regulation, taxation, or
observation, and alters the composition of government expenditure for some type of significant
personal gain, in which the currency could be economic, political, or ideological remuneration.
Can multiple scholars with conflicting theories and evidence on economic development
and income levels be right simultaneously? Perhaps. A summary of the conceptual challenges
facing researchers and solutions for this thesis follow.
The first challenge is simultaneous convergence and/or divergence in income and/or liv-
ing standards, which suggest dichotomous definitions and/or uses for these terms and permits
6


dissimilar data and/or measurement. No wonder scholars disagree. Remedy: Utilize International
Comparisons Program data.
The second challenge is simultaneous converging and diverging results, using the same
data and methods, which suggests a problem of arbitrary data periods or date ranges unidentified
with and/or tied to events, endogenous or exogenous. Solution: Tie data to the fall of the Berlin
Wall, the end of the Cold War, and analyze the 30 countries of the former Eastern Bloc (See
Country Briefs).
The third challenge: Income is an insufficient measure of the human condition. Remedy:
The Human Development Index as a proxy for the living standard.
The fourth challenge is to narrow the scope of corruption to that which (1) is found in the
governance process and (2) is measurable and missing from the official GDP. Solution: Employ
the working definition of Governance Corruption, measured by the Shadow Economy as a proxy
for the missing GDP. Adding background information, or context, makes these challenges easier
to understand.
The historical backdrop for this thesiss economic development process follows. Histori-
cally, like today, international trade and the migration of people and resources have driven
national economies; shifting prosperity and poverty, technology adoption, and the intermixing of
cultures (Diamond, 1997; Elisseeff, 1998). Aided by advances in technology and modes of
communication, escalating globalization has fueled a blur of activity resulting in increased inter-
dependence of nations (T. L. Friedman, 2005). Why have some nations struggled while others
flourish? Levitt (1983) argued that A powerful force drives the world toward a converging
commonality and that force is technology... Two vectors shape the world technology and glob-
alization. The first helps determine human preferences; the second, economic realities (p. 1).
Nye (2006) adds that, Globalization has two driving forces: technology and policy. Thus far,
policy has reinforced the... effects of technology (p. 1). Specifically, technology renders dis-
7


tance (from one village or hemisphere to another) progressively less important. Accordingly,
economic development or decay necessarily takes place in the larger context of globalization, in-
duced by technology, through development policy, with public administrators at the reins. The
policy makers steer economic development not unlike stagecoach drivers steer a team of horses.
Public administrators and policy makers are at the reins of development. Their actions and deci-
sions pilot, guide, and encourage or restrict the Economic Horsepower (EH) of an economy.
Rostow (1991) used internationalization rather than globalization to discuss the process
by which economies became interdependent. He brought elements of social overhead capital (in-
frastructure) together with economics in the Stages of Economic Growth, which provided criteria
(e.g., policy, technology adoption, income inequality, external forces) to weigh the readiness and
capacity for aggregate economic growth (1991). Sen (1988, 1997, 1999) added that the states
level of development and thus its capacity to tend to the human development (individual) needs
of its citizens is a key factor in the inequality formula, where capacity is the measure of total po-
tentiality, whether actual or merely possible. North (1994, p. 17) underscores the necessity for
well-informed policy decisions to manage the unanticipated consequences and outcomes of de-
cisions made in the face of uncertainty a condition that is further constrained by the limited
capacity of humans to solve the complex problems (North, 1994, p. 19; H. Simon, 1972). The
research questions are studied in light of globalization, as the force of globalization adds an un-
dercurrent of involuntary activity from seemingly exogenous sources and a theme of necessity to
the balance of the literature. Understanding the elements of this debate is critical to informing
development policies that mitigate growths potentially negative consequences (Sen, 1999).
8


Problem and Research Approach
The IMF reports that [corruption ... diverts public resources to private gains, and away
from needed public spending on education and health.... [and], by reducing tax revenue ... it can
accentuate income inequality (IMF, 201 Id, p. 1). However, gaps exist in the literature on certain
measures of corruptions effects on public education budgets, and on certain measures of the di-
minished budgets on individual incomes. Further, it seems plausible that diminished education
funding affects certain measures of individual income. Kuznets referred to the ability for an indi-
vidual to earn income in part, his education and skill training as the reverse side of income
(1934, p. 7). Yet, supporting evidence of corruptions effects on education budgets and income
has lacked careful attention.
Hence, the overarching hypothesis this thesis tests is that the effects of governance cor-
ruption on education through the public resource mechanism, its budget, are direct and negative;
we posit that the higher the degree of corruption, the lower the relative education budget. Fur-
ther, the lower the education budget per capita, the lower the relative individual income. The
combined weight of just these two effects of corruption on long-run economic growth is poten-
tially debilitating.
The measure or degree of governance corruption in a country and its effect on the func-
tioning of that country is corrupt by its very nature. In order to measure corruptions effect on a
government function such as its development policy, its economy, or human development, a
country must first account for it on its balance sheet. It must measure the extent of the problem of
corruption in dollars.
Rent seeking, according to Tullock (1993, p. 2), is the outlay of resources by individuals
and organizations in the pursuit of rents created by government. By reducing the public resource
pool, the remuneration for corruption could shift toward that which complements the endeavors
9


of those that are corrupt and away from approved, sustainable economic development and mar-
ket-demanded goods and services (S. Gupta et al., 1998).
State capture is any group or social strata, external to the state, that exercises decisive
influence over state institutions and policies for its own interests against the public good (Pesic,
2007, p. 1). Essential to this thesis, IMF scholars Mauro, Abed et al. (2002, p. 278) assert that ex-
torting from education starts before the budget approval, so fewer dollars are allocated to
education, and more are allocated to projects where that extortion is easier to hide.
An initial inquiry into relationships among and between governance, corruption, econom-
ic development, and individual income variables yielded the four conceptual challenges addressed
above. The three problem themes follow.
Problem One includes gaps in the recent literature specifically tying governance corrup-
tion to the mechanisms through which the corrupt affect income inequality (deLeon, 1993; S.
Gupta et al., 1998; S. Gupta et al., 2000; Rose-Ackerman, 1999b). The second problem includes
difficulty measuring governance, corruption, and economic development at the aggregate and in-
dividual levels (Galbraith & Kum, 2005; Schneider et al., 2010a). Solving the mechanism and
measurement problems requires that we add to the working definitions of governance corruption,
an explanation of the GDP and Income variables.
Accounting for governance corruption requires adding Official and Unofficial compensa-
tion. Compensation On the Books adds to a countrys official GDP through National Income
Accounting (Kuznets, 1934). Remuneration On the Ground avoids official ledgers, and creates or
adds to the unofficial economy, or Shadow Economy (SE). The Shadow Economy is defined as
remuneration generated through actions and transactions representing primarily tax, regulation,
and administrative process avoidance (Schneider et al., 2010, p. 5). The Shadow Economy is a
situation where businesses operating outside the tax system and registered businesses conceal
transactions to avoid paying taxes or social security charges, or to avoid the costs associated with
10


legislation on safe working conditions or protection of consumers rights (Russell, 2010, p. 10).
The Shadow Economy includes rent seeking and state capture, which are discussed later.
Governance is restricted to the purview of formal, official governmental institutions at
the national level plus the informal or unofficial institutions (North, 1991a) or the traditions and
institutions by which authority in a country is exercised (Kaufmann, 2006, p. 82). The informal
institutions may include officially recognized entities such as labor unions, and unofficial entities
such as cartels. The scope of the informal institutions include the IGO, NGO, and business com-
munities as they engage in transactions and the democratic process (Mauro et al., 2002). Other
types of governance or management (e.g., corporate governance, business, institutional, or organ-
izational management), while essential, are outside the scope of this thesis except to classify the
productivity of goods or services as official or unofficial. History provides much evidence that
governing regimes produce a spectrum of economic development results, some not so good.
Armstrong (2005) asserts that good governance is a by-product of sound public administration
and strong governmental institutions; it minimizes corruption and reinforces healthy and sustain-
able economic development. Good governance, by definition, requires integrity, transparency,
and accountability in the public sector (pp. 1-2). Conversely, poor or weak governance lacks the-
se characteristics; it mushrooms out of corruption and maladministration, carrying with it
devastating human costs (e.g., poverty, inequality, ill health, illiteracy) and a lack of public trust
that undermines and even destroys political stability (p. 9).
Problem Two is the measuring corruption per se (Kaufmann, 2006, p. 82). However, re-
cent economic and statistical modeling has provided increasingly reliable approximations of its
influence and economic costs through surveys, extensive audits and tracking of markets that are
clandestine, extrapolation aided by increasing statistical capacity, and redundancy overtime (p.
82). The IMF uses the portion of total production attributed to the unofficial economy as a proxy
for the level of governance corruption (Abed & Gupta, 2002b; Russell, 2010; Schneider, 2009;
11


Schneider et al., 2010). This thesis follows the IMFs lead, using the definition and measurement
devised by Schneider & Enste (2000), for the unofficial or Shadow Economy, as the proxy for the
corruption found in governance.
Measuring economic growths effect on the individual income, economic condition, and
living standard requires a standardized measure. Sen addressed these elements by conceiving the
Human Development Index (HDI) and subsequent Human Development Reports (HDR) from
1990 through present. A living standard is characterized, in part, by an individuals insufficient
means to earn a living, particularly by insufficient education or skill and the access to basic needs
(HDR, 2009e). Healthy and sustainable economic growth is a byproduct product of a healthy
economy, which is maturing, growing, tending to the human development and thereby capacity
development needs of its population, and is not likely to exacerbate poverty rates or levels
(Kuznets, 1971; Thomann, 2008).
Measuring economic growth accurately and adequately spotlights limitations Kuznets
knowingly built into the National Income accounting system, still used by researchers and policy
makers throughout the world today to report data used internally by country and externally to in-
ternational agencies.
Economic welfare cannot be adequately measured unless the personal distribu-
tion of income is known. And, no income measurement undertakes to estimate
the reverse side of income, that is, the intensity and unpleasantness of effort go-
ing into the earning of income. The welfare of a nation can, therefore, scarcely
be inferred from a measurement of national income as defined (Kuznets, 1934,
pp. 6-7).
Kuznets intensity and unpleasantness (p. 6) represents a spectrum of types of effort.
Effort ranged from the required physical toil to the dexterity to manage the necessary motivations
and the overall economy. Likewise, the effort ranged from the mental muscle required in policy
learning to the earning of a degree.
Kuznets distribution of personal income is quantitative and the effort going into the
earning of income is qualitative (p. 7). GDP data, together with other economic indicators, pro-
12


vides sufficient material to replicate Kuznets original tests using new data. Yet, even significant
results from testing income distribution in the equation reaches not half of the essence of his as-
sertion. Capturing and measuring the intensity and unpleasantness of effort going into the
earning of income (pp. 6-7) is at the heart of Sens work on human capability and is central to
his human development research (1997, 1999, 2004). It adds valuable context to Kuznets re-
verse side of income (1934, pp. 6-7). The HDI measures economic development holistically by
measuring the development of its citizens capability to earn and live. By doing so, the HDI rec-
ords a measurement by which policy analysts can evaluate development policy (HDR, 2009e;
Alkire, 2005; Mazumdar, 2003).
Problem Three is the scope and limitations of this thesis. Important to note here is that
many variables that are widely used in cross-country analysis on economic growth and individual
income in transition countries are beyond the scope of this thesis. Specifically, future research
would include three important variables. (1) A variable critical to economic productivity would
measure progress toward market liberalization (Sachs & Wemer, 1995). In the 2010 Transition
Report, the EBRD offers a new sector-based approach to measuring transition progress that
provides data on privatization, markets, banking, and infrastructure (p. 3). (2) Progress toward
EU accession, measured by the European Commission (2011b). (3) A variable critical to under-
standing economic growth patterns would mark the history and intensity of armed conflicts
(HIIK, 2010a). Variables that indicate market liberalization, EU accession progress, and periods
of unrest may add explanatory power to the analysis.
Sequence of Reasoning
Step One in the sequence of reasoning is to create a baseline or starting point that
measures the accumulated stock of human development in each country in 1990. The Human
Development Index (HDR, 1990) equally weighs three observations: the Life Expectancy Index
(LEI), the Educational Attainment Index (EAI), and the GDP Index (GDPI). This baseline meas-
13


ure becomes the pre-test, independent variable, HDI1990. Data are available for each of the coun-
tries in the sample set. We test to ensure that the HDI baseline of the sample data are consistent
with widely accepted findings, where the GDP per capita is not a statically significant proxy for
the living standard or human development, consistent with Galbraith & Kum (2005), Sen (Sen,
1984), and Kuznets (1934), among others. Research question one correlates to step one in the
logic sequence.
Research Question 1: Are the Human Development Index and Income per capita highly
correlated? If GDP per Capita is not a sufficient proxy for the stock of human development, we
can move forward with the next step.
Step Two measures governance corruptions effects on GDP per Capita, or Individual In-
come per Capita (Ic). Some scholars argue that all of the accepted income measurements lack
accuracy due in large part to the inaccuracy of the production data going into them (e.g., Gal-
braith & Kum (2005); Schneider & Enste, (2000)). Even the best GDP data report only the
income earned on production of goods and services that are accounted for On the Books in the
National Income Accounting system Kuznets designed (1934, ch. 1). The balance of the remu-
neration moves through the unofficial economies (Abed & Gupta, 2002b; Schneider & Enste,
2000). The notation for aggregate income On the Books, or Official Individual Income is Ic0.
Likewise, estimated corruption remuneration earned On the Ground, the Unofficial Individual In-
come per Capita, is Icv. Adding the two streams of productivity together approximates the total
value of goods and services produced in a country in one year, Total Individual Income, I0 + Iu =
IT (Galbraith & Kum, 2005). (See Data Legend in Appendix). Research question two corre-
sponds to step two in the logic.
Research Question 2: Does governance corruption, as measured by the Shadow Econo-
my, negatively affect Income per Capita?
14


Step Threes purpose in the sequence is to uncover the relationships among and between
the elements of corruption defined by the Shadow Economy and education funding. In the offi-
cial National Income Accounting for GDP is an allocation for public education funding. The of-
ficial governments budget line item, Education Expenditure as a Percentage of Total Govern-
ment Expenditures (EE), reflects an education target set forth in that countrys economic
development policy. In Armenia from 1999 to 2007, for example, the official expenditure on
public education averaged 2.52 percent of the GDP. This translates to 13.2 percent of the total
government expenditure (UNESCO, 2009d,Table 13). Research question three corresponds to
logic step three.
Research Question 3: Does governance corruption, as measured by the Shadow Econo-
my, negatively affect Education Expenditure?
Step Four is the key research question for this thesis. It sets up the reasoning to test the
Ic, HDI, SE, and EE variables simultaneously. Nelson and Phelps write, ...educated people
make good innovators, so that education speeds the process of technical diffusion (1966, p. 70).
According to Romer (1994b), Arrow (1962), Lucas (2009), and others, educations unique contri-
bution to economic growth is increasing returns or knowledge spillovers, which sets educations
contribution apart from other public goods (e.g., infrastructure, health, defense, etc...) and from
other economic growth factors (e.g., consumption, savings and investment, trade balances, tax
consequence, etc...). For a country and its citizens, education is an investment in future econom-
ic sustainability. For a Shadow Economy and its constituents, an educated citizenry may be
threatening (Monas, 1984). Hence, Education Expenditure budget provides a logical line item,
though certainly not the only line item, from which to direct public funds (Freire, 1970). Re-
search question four corresponds to logic step four.
Research Question 4: Do the pre-test Human Development Index, Governance Corrup-
tion, and Education Expenditure together explain the change in Income per capita?
15


Step Five is to define the parameters of the sample data set.
Sample Set Rules
Rule 1: The country was or remains Socialist
Rule 2: Four or more years of Soviet influence (Sachs & Warner, 1992, 1996, 1998), plus a cre-
ated, liberated, or re-gained sovereignty, independence or the ability to trade, travel, and migrate
which began between 1988 and 1992.
Rule 3: Geographically related by inland border, trade or sea-trade route, and western oriented.
Rule 4: Ethnolinguistically interrelated, Economically interdependent
16


Thesis Overview
The purpose of this thesis is to explore the intricacies of the effects of governance corrup-
tion (measured by the Shadow Economy) on individual economic growth (measured by Ic)
through education budgets (measured by Education Expenditures on public education as a per-
centage of GDP). Chapter 2 reviews segments of the major bodies of literature that are
specifically relevant to governance, corruption, and human and economic development to frame
the thesis, starting with broad academic themes and theories, and narrowing to the specific works
upon which this thesis is built. In doing so, the work outside the scope of this thesis is excused
and the reasons for this are discussed. This segment adds context to the data. Chapter 3 explains
and examines these data, describes logical flow of the equations for analysis, and discusses each
step in the econometric methods with which the data are analyzed. Chapter 4 presents the results,
describing complications and resolutions to data or methodological issues. Chapter 5 offers an
analysis of the econometric results and discusses the findings in terms of the framework, specific
theories, and the model employed, as well as necessary caveats and limitations of the data. Chap-
ter 6 describes some potential next steps for the policy community and suggests future research
scholars may undertake to further the thesis findings. The Appendix in includes the Country
Brief, in which is found data on each of the countries in the sample set relevant to governance,
corruption, and human and economic development.
17


CHAPTER 2
LITERATURE REVIEW
The goal of this dissertation is to inform economic development policy to encourage
healthy economic growth (Kuznets, 1966, p. 493; Sen, 1997) using the New Growth Theory that
is widely accepted in cross-country longitudinal analysis (Cortright, 2001b; Romer, 1996). Clear-
ly a proponent, Romer (1994a, p. 21) asserts that, The most important job for economic policy is
to create an institutional environment that supports technological change. The following litera-
ture review takes advantage of New Growth Theorys treatment of development policy and
knowledge capital, which is characterized by increasing returns, and which is required to adopt
and exploit technological advance (Phelps & Nelson, 1966; Romer, 1993). New Growth Theory
bridges the two main theories on economic growth, which lie on a continuum from endogenous
and exogenous drivers. The incentives provided by national governance via the development pol-
icy process bridge endogenous growth motives and exogenous knowledge catalysts. The major
bodies of literature reviewed are governance and corruption as a dimension of governance, eco-
nomic growth and economic development, human development and its components, and theories
about and measurement of education outcomes.
18


Governance
According to the IMF (201 Id, p. 1), [governance is a broad concept covering all as-
pects of the way a country is governed, including its economic policies and regulatory
framework. The IMF definition frames the concept by identifying the level, manner, and system
of authority. This framing permits a separation in the governance literature between dimensions
that inform this thesis and those that do not. Pertinent are the forms of governance that directly
influence national-level development policy specifically through its budget. Further, the influ-
ence on governance through informal authority by any institution or organization is recognized
and included. Corruption is, therefore, included, as it must be given that corruption is informal
and influences governance.
Kooiman & Jentoft (2009, p. 818) assert, [t]he term governance has become a catchword
in the social sciences as well as in the policy world... [and] has different meanings to different
people. It is usually qualified by such terms as good, network, global, natural resource, or pub-
lic, while general theorizing on the concept remains rare. For example, Boviard & Loffler
(2003, p. 316) define public governance to be the ways in which stakeholders interact with each
other in order to influence the outcomes of public policies. Stoker, in Governance as Theory
(1998, p. 18), proposes aspects of governance. For example, governance recognizes the power
to get things done which does not rest on the power of government. The IGOs leading the storm
of research activity on national and institutional governance each define governance slightly dif-
ferently, according to the respective organizations need. The OECD, focuses (mostly) on
governance between the national and sub-national public sector and private firms working within
the public sectors domain (Towards Better Measurement of Government, 2007). The UN works
with institutions at every level through programs worldwide focusing on the human condition and
poverty elimination (1985), and peace keeping. The IMF promotes soundness and transparency
in banking and financial management (201 Id). The World Bank focuses on strengthening the
19


economic development ability of national-level institutions and governments through governance
norms and generating aggregate data for all nations (2009g). The scope, breadth, and depth of
literature on governance are thus, enormous; however, the dimensions of governance central to
this thesis present a narrow spectrum. The following section defines the boundaries between
governance literatures by their specifications to include or excuse them from a role in this thesis.
Norths (1991a) work on governance combines the formal government plus the institu-
tions as actors that command respect in the economy. Institutions, considered broadly, are, the
humanly devised constraints that structure human interactionthe rules of the game (North,
1994, p. 8). Informal institutions permeate the culture in a variety of forms (e.g., a cultural norm,
NGOs, the local PTA, unions, family movie night, gangs, mafias, industry associations, charities,
churches, holidays, lobbyists, and political action committees), and may have positive, neutral, or
negative impacts on governments formal policy objectives. Here, governance is restricted to the
purview of (1) formal authority of governmental institutions and (2) informal authority or influ-
ence by institutions (North, 1991a) that affect national-level government budget, taxation,
spending, fiscal, or monetary policy. The scope includes governance over certain actions and
transactions by official, institutional, and private organizations or their respective individual ac-
tors.
To limit the breadth of governance, forms of governance, or management, which serve to
manage or direct the interw orking of private-sector operations (e.g., business, institution, enter-
prise, company, charity) or Non-Governmental Organizations (NGOs) and associations, are
outside the scope of this thesis (Kettl, 2000).
The depth of governance relates to the level at which governing takes place. Global gov-
ernance, for example, focuses on updating the existing multilateral institutions, and creating an
effective oversight body .. .to bring together national government, multinational public agencies,
and civil society to.. .address global challenges (Boughton & Bradford Jr., 2007). Susan Rose-
20


Ackerman proposes an international tribunal or other form of multinational non-governmental
organization (Rose-Ackerman, 1999a, p. 195). The option of governing from the highest level
seems unlikely to gain traction as global powers such as the US and EU seek to fortify autonomy
through leadership, alliance, and example (Armitage & Nye, 2007; Kaminski, 2005), and have
more readily supported the governance strengthening efforts within a national or regional scope.
Hence, a global perspective is outside the scope of this thesis.
The other extreme limits the scope of governance research to that of only a national gov-
ernments formal influence. This is equally unrealistic as a method to solidify good governance
as this neglects the influence of the private sector, institutions, and the IGO and NGO communi-
ties in particular (North, 1991a). On the contrary, informal authority by any of these entities,
listed above or tacit, is within the scope of this thesis, if the influence affects national-level eco-
nomic development policy (i.e. through variation, adaptation, modification, or transmutation
within the development policy process or within the fiscal or monetary policy processes), eco-
nomic growth, sustainability, or economic outcome.
To limit the depth of governance, levels of governance above the national level (i.e., in-
ternational agencies such as the UN, and intergovernmental organizations such as the European
Union) or beneath the national level (e.g., non-national, regional, territorial, municipal, tribal) that
serve to manage or direct the //r/t'/'w orking of non-national-level governments, are outside the de-
fined scope of this thesis.
An understanding of a countrys governance must capture the method by which the gov-
ernment system governs. Government systems exist on continuum from centralized to
decentralized decision-making and control, adding another dimension to the concept of govern-
ance. Governance literature exposes the relationship between state intervention and societal
autonomy, (and)... different strands of the literature highlight different facets of this continuum.
21


Existing understandings may be classified according to whether they emphasize the politics, poli-
ty or policy dimensions of governance (Treib et al., 2007, p. 4).
To summarize, the word governance refers to the system, structure, and form of govern-
ment including its actors, as well as the act of governing or the method by which a system is
governed. Governance includes the scope and breadth of influence, official or unofficial, that af-
fect the legitimate public sector, institutional, political, and policy systems (North, 1991a;
deLeon, 1993; Mauro, 2004; Kaufmann et al., 2008) and command respect and allegiances in so-
ciety and, therefore, on the economy itself (Rose-Ackerman, 1978, 1999; North, 1991a).
Governance actors include public and private institutions, public officials, and private citizens
transacting in the democratic process (Thompson, 2007, p. 2).
Measuring Governance
Governance scholars seek to understand, to measure and, to measure the impact of both
the formal and the informal authority in a given society if it were to be able to manage its system
for the greater good of the whole nation (Abed & Gupta, 2002b; Kaufmann et al., 2000; Treib et
al., 2007). However, measuring an intangible (such as transparency in governance) is an elusive
matter of perception, and the task, enormous. World Banks governance scholars created a meth-
odology for the World Governance Indicators (WGI) metric (Rose-Ackerman, 2006). The
indicators are based on several hundred individual variables measuring perceptions of govern-
ance, drawn from 35 separate data sources constructed by 32 different organizations from around
the world (Kaufmann et al., 2008, p. 1). The aggregated score, or index, is used as a meter of
governance process.. .capacity... (and) respect (p. 7). The six dimensions of governance are
Voice and Accountability, Political Stability and Absence of Violence, Government Effective-
ness, Regulatory Quality, Rule of Law, and Control of Corruption (Kaufmann et al., 2008).
Similarly, UN agencies such as the United Nations Development Programme, measure
participation, consensus orientation, accountability, transparency, responsiveness, effectiveness
22


and efficiency, equitability, and rule of law (2009f). The UN adds more survey information gath-
ered from the same and other country-level agencies as the WGI to augment the data for a gov-
governance index suitable to UN needs, which reflect sub-national governance factors (What is
Good Governance?, 2009). Therefore, the UN data are not sufficient for this thesis. The IMF
concentrates its research on governance of financial institutions rather than national-level gov-
ernmental governance, and is not appropriate for this thesis (IMF, 201 Id).
Critics are quick to list the limits to and faults in the available governance indices.
Tackling the issue of measuring governance was the premise of a meeting of scholars, data ex-
perts, clients, donors, and policy makers at the Kennedy School of Government, Harvard
University, in May 2003 (Besancon, 2003, p. 1).
The World Banks Worldwide Governance Research Indicators Dataset, the
Global Governance Initiative, the OECDs Participatory Development and Good
Government rankings, Freedom Houses index, and Transparency Intemationals
rating system for governance are all primarily subjective, being based on expert
or informed opinions, systematically gathered and arrayed with or against other
perceptions and surveyed views. So are the majority of the forty-seven data sets
[that are available]... .Data experts inform us that one of the simple reasons for
using subjective data is that no complete cross-country, objective data are availa-
ble, particularly from the underdeveloped nation states.
Answering the critics, efforts to complete objective research are underway. Based on a
successful pilot study, using field researchers collecting data from a sample of countries that pro-
vided information-rich data, Robert Rothberg (2005) asserts that the time has come for a
quantitative measure of governance. Until such a project is complete, he suggests an ordinal
ranking of countries, an index, to bolster the qualitative data, following the lead of the WGI, Hu-
man Development scholars and others. Johnston (2007, pp. 8-9) put forth a benchmarking
strategy that emphasizes integrity in government processes; however, like that of Rothberg, this
effort is also time and resource intensive. Most critics agree that the WGI is the most widely ac-
cepted index, largely due to the funding provided by the World Bank for developing it, and for its
inclusivity of non-Bank scholars, national, and international agencies (Bovaird & Loffler, 2003;
23


Kurtzman et al., 2004). Radelet (2003, p. 33) writes, [t]he most comprehensive set of global
governance indicators has been compiled by the World Bank and combines subjective and objec-
tive attributes. Absent completed objective research, however, and because field research is
limits to a handful of countries, these sources are insufficient. Instead, this thesis employs data
that estimates the budget effects of governance, discussed further in the section Measuring Cor-
ruption (Schneider et al., 2010a).
Moving on, while the international agencies have a unique governance foci (e.g., IMF,
Bank, OECD, and UN), each stress the criticality of good governance to foster sustainable eco-
nomic growth and development, and inclusive prosperity. The distinction made between
governance and the characteristics of good governance is central to this thesis. As Kaufmann,
(2000, p. 1) state, ...there is a strong causal relationship from good governance to better devel-
opment outcomes such as higher per capita incomes, lower infant mortality, and higher literacy.
In this thesis, a two-equation simultaneous linear regression model, the Multiple Indicator
and Multiple Causes (MIMIC) equation to estimate and measure governance as a missing varia-
ble. This equation measures the quality of governance by the percentage of corruption and
underground activity in the economic process simultaneously (Breusch, 2005, p. 5). Given that
GDP is the standard measure for an economy (the argument for this is developed in the literature
review section on economic growth), the estimated percentage of simultaneous corruption and
underground activity missing from the GDP is the inverse of the quality or goodness of govern-
ance (Schneider et al., 2010a).
Good Governance and Sustainable Development
According to Rotberg (2009, p. 113) [governance is the delivery of political goods to
citizens. The better the quality of that delivery and the greater the quantity of the political goods
24


being delivered, the higher the level of governance...Over time, civilizations have thrived,
prospered, deteriorated, and dissolved for many reasons beyond the scope of this thesis. Despite
this waxing and waning of civilizations and nation states, and essential to this thesis, however, is
clear evidence of a general accumulation of capability such as economic and human capability,
commerce, longevity, learning, and technology, all of which were subject to (or because of) the
prevailing system of governance. Grindle (2011) describes a new theme in governance literature.
This common theme suggests a new generation of thinking that emphasizes the im-
portance of knowing the context in which reformed policies, institutions, and processes are to be
introduced, and designing interventions that are appropriate to time, place, historical experience
and local capacity.... Understanding the historical evolution of how countries muddle their way
toward relatively efficient and effective institutions is critical.... (p. 415).
Many scholars have sought to measure the stock of capability, inherited or earned, as an
indicator of the quality or the degree of goodness of governance. For example, Elisseeff (1998)
studied the Silk Road trade routes, which fostered a mixing of races and cultures, as well as the
building of vast realms, such as the Mongol, Roman, and Persian empires. Language, culture,
ethnicity, heritage, beliefs, religions, customs and philosophies, migration patterns, trade routes
and knowledge diffusion, geography and climate, political arrangements and government struc-
tures, and pure blind luck, among other historical human and institutional factors together, affect
and are affected by governance (Diamond, 1997; Elisseeff, 1998; Mauro, 1995). These are ele-
ments of governance, but neglect a measurement of governance, and offer an insufficient metric
for this thesis.
However, to accommodate the diffusion of cultures and institutions, scholars developed a
measurement for the degree of ethnic, linguistic and religious homogeneity, heterogeneity, or
what they term fractionalization, and its effects on economic growth (Alesina et al., 2002). Mau-
ro (1995) used an index of ethnolinguistic fractionalization, as an instrument to account for the
25


homogeneity as an indicator of cultural behavior, language based, and non-language based com-
munication barriers. La Porta & Lopez-De-Silanes (1999, p. 223) used the opposite measure,
ethnolinguistic heterogeneity, to inform theories of determinants of institutional and more spe-
cifically government performance (p. 233), on the importance of economic, political, and
cultural historical factors. They asserted that poor countries, closer to the equator, that use
French or socialist laws, or have high proportions of Catholics or Muslims, exhibit inferior gov-
ernment performance (p. 222). Good government performance, to these scholars, is, good
economic development (p. 223). Ethnolinguistics is important in governance research.
Ethnoliguistically homogenous countries have better governments than heterogeneous ones.
Common Law countries have better governments than French civil laws or socialist law coun-
tries. Predominantly Protestant counties have better governments than [do] either predominantly
Catholic or predominantly Muslim countries (1999, p. 265). Moreover, those with a history of
British rule with Protestant traditions seem to be less corrupt (Serra, 2006; Treisman, 2000).
Fractionalization is adopted in this thesis through the proxy variable for corruption, the
Shadow Economy, as it is one of the inputs employed by Schneider et al. (2010). Wars and holo-
causts greatly affect its index value, as holocausts decrease a countrys fractionalization by
exterminating a segment of ethnic or religious groups (Burleigh, 1996). Following is an example
of the effect a fractured population has on growth (Alesina et al., 2002, p. 9).
In terms of economic magnitudes, the results... suggest that going from complete
ethnic homogeneity (an index of 0) to complete heterogeneity (an index of 1) de-
presses annual growth by 1.9 percentage points. In other words, up to 1.77
percentage points of the deference in annual growth between South Korea and
Uganda can be explained by deferent degrees of ethnic fractionalization.
According to Paulo Mauro (1998a, p. 266), [t]his variable is a good instrument because,
in accordance with Shleifer and Vishny (1993) arguments, more fractionalized countries tend to
have more dishonest bureaucracies. The index of ethnolinguistic fractionalization has a correla-
tion coefficient of .36 (significant at the conventional levels) with the corruption index. Mauro
26


(1998a, p. 266) added a proxy for the degree of state capture by Ades and Di Telia (1994), a
proxy for the degree of rent-seeking following arguments by Sachs and Warner (1995), and
whether the country achieved independence after 1945 (following Taylor and Hudson, 1972).
The correlation coefficients between these and corruption index were .21, .23, and .41, respec-
tively (Shleifer & Vishny, 1993).
Barro & McCleary (2003), following North (1994), measure belief systems as a determi-
nant of governance. Furthering work by Max Weber (1930, pp. 22-27), La Porta et al. (1999) use
religion as a proxy for work ethic, tolerance, trust, and other characteristics of a society that may
be instrumental in shaping its government (p. 224). The key finding as it pertains to this thesis is
this: Statist laws are thus a more robust predictor of poor government performance than inter-
ventionist religions (p. 264) or cultural influences (p. 224). Therefore, a countrys
fractionalization, its political, legal, economic, religious, and cultural history, its age as an inde-
pendent state, and the influence of unofficial institutions through rent-seeking and state capture,
are posited to be critical influences in and on governance, yet taken together are insufficient to
measure governance directly.
Economic geography, which explains concentrations of economic activity based on geo-
graphic space, adds an understanding of the role of global latitude and climate in the economic
growth (Diamond, 1997; Krugman, 1998; Nissan, 1991). Scholars continue to work on modes
and methods of governance delivery in an age of rapid information dissemination to foster an in-
creased understanding of how the power of institutions determines the quality of governance
(Kersley et al., 2008; Kettl, 2000; Lynn, 1998; Treib et al., 2007). Heritage, migration, geograph-
ic proximity, and the diffusion of races and knowledge all inform the notion of governance, but
do not measure it adequately.
27


Human Development as a Metric for Governance
Building off Kenneth Arrows work in Social Choice Theory (Arrow, 1963a, 1963b),
Sens seminal work in welfare economics laid much of the foundation for the body of literature in
human development. Sens Inequality Reexamined (1992), Human Capital and Human Capabil-
ity (1997), and Development as Freedom (1999), provide the framework for using the Capability
Approach as an indicator of prior governance, and to measure governance (Alkire, 2005). Per-
haps most importantly, the human development approach has profoundly affected an entire
generation of policy-makers and development specialists around the world (HDR, 2009e, p. iii).
Working with United Nations Human Development Programme (UNDP), Sen and Huq devised
the Human Development Index (HDI). The HDI is an indexed value for the accumulated store of
a countrys human capital and human capability. This stock of capability is the summation of the
whole of a countrys history; that includes the effects of ethnic, linguistic, and religious fraction-
alization, the geography, travel routes, trade agreements, regime changes and wars, latitude,
climate, plagues, and holocausts, and luck (HDR,1990; Sen, 1997).
The human development index is a composite index that measures the average
achievements in a country in three basic dimensions of human development: a
long and healthy life, as measured by life expectancy at birth; knowledge, as
measured by the adult literacy rate and the combined gross enrollment ratio...
and a decent standard of living, as measured by gross domestic product (GDP)
per capita in purchasing power parity (PPP) US dollars [in 2007 as the base year]
(HDR, 2007, p. 225).
Scholars collaborate and share research and data through networks such as the Interna-
tional Comparison Program (ICP) and Eurostats Statistical Data and Metadata Exchange
(SDMX), delivering data sets for internal and public use. These state-of-the-art measures incor-
porate recent advances in theory and measurement and support the centrality of inequality and
poverty in the human development framework.... with the intention of stimulating reasoned public
debate beyond the traditional focus on aggregates (HDR, 2007, p. 224).
28


Importantly, the HDI normalizes its data across countries in two ways. First, using pur-
chasing power parity, the value is normalized. Second, using GDP per capita shifts the focus to
the individual as the unit of measure, rather than looking at country aggregates. A feature of the
HDI is that its particular component indices capture a sense of community cohesion, the level or
degree of social capital (Carilli et al., 2008), to aid in approximating the countrys bounded poten-
tial, as well as that of the individual (HDR,1990; Sen, 1997; H. A. Simon, 1997).
Well defended as a goal in and of itself, human development directly enhances the capa-
bility of people to lead worthwhile lives (Sen, 1997), so there are immediate gains in what is
ultimately important, while safeguarding similar opportunities for ones neighbor, and for the fu-
ture. A country that enjoys high human development, such as the United States, has nearly
limitless boundaries on its potential for innovation and progress on many fronts from military to
the arts. Citizens who suffer from low literacy rates, poor health, and extreme underemployment,
in a country scoring low on the HDI, are effectively bounded by todays struggle for food and
shelter (Sachs, 2005; H. Simon, 1972). Others utilize similar variables to gauge governance qual-
ity (La Porta et al., 1999). We measure the output of public goods by infant mortality, school
attainment, illiteracy, and [by] an index for infrastructure quality (Anand & Sen, 2000, p. 237).
There is hardly any example in the world of the expansion of education and
health being anything other than monotone: good education and good health
seem to generate powerful demand for these opportunities (and more) for our
children. This is a relationship that goes well beyond the redistribution of in-
come to the poor at a given point of time important though that is. It should also
be noted that any instrumental justification for human development... relates con-
cretely to people's ability to generate for themselves the real opportunities of
good living (p. 237).
Sen wrote the following in the Forward for the 2009 HDR. In 1990 public understand-
ing of development was galvanized by the appearance of the first Human Development
Report... .it had a profound effect on the way policy-makers, public officials and the news media,
as well as economists and other social scientists, view societal advancement (p. iv). While the
concept of human development is much broader than any single composite index can measure,
29


the HDI offers a powerful alternative to income as a summary measure of human well-being
(HDR, 2007, p. 225). For the reasons cited above, and, because the HDI is an amalgam of com-
ponent indices that approximate the entirety of development attained by the respective country
and year (HDR, 1990), the HDI is the best measure available for the stock of development and
governance. For this thesis, these factors are the stock of capability in the pre-test year HDI1990.
To summarize, governance refers to the system, structure, and form of government in-
cluding its actors, as well as the act of governing or the method by which a system is governed.
For the purposes of this thesis, governance is limit to the influence, official or unofficial, that af-
fects the legitimate, national-level, public sector, institutional, political, and policy systems. Its
actors include public and private institutions, public officials, and private citizens transacting in
the democratic process. Since its influence commands respect and allegiances in society and
among its actors, governance affects the economy.
30


Corruption as a Dimension of Governance
The social sciences literatures qualify corruption by type or characteristic. Moody-Stuart
(1996, p. 19) uses the definition for corruption found in the Encyclopedia of the Social Sciences:
Corruption is the misuse of public power for private profit. He "distingui sh | cs | between grand
corruption, which involves senior officials, ministers, and heads of state, and petty corrup-
tion, ... which is usually about getting routine procedures followed more quickly. Emphasizing
the criticality of the difference, he states the following. But grand corruption can destroy na-
tions: where it is rampant, there is no hope of controlling petty corruption (p. 19). It is the
effects of nation-destroying corruption that we seek to understand and measure in this thesis.
Joseph Nye (1967, p. 419) defines and characterizes corruption in the following way.
Corruption is behavior which deviates from the formal duties of a public role be-
cause of private-regarding (personal, close family, private clique) pecuniary or
status gains; or violates rules against the exercise of certain types of private re-
garding influence. This includes such behavior as bribery (use of a reward to
pervert the judgment of a person in a position of trust); nepotism (bestowal of
patronage by reason of ascriptive relationship rather than merit); and misappro-
priation (illegal appropriation of public resources for private-regarding uses).
Nye continues by stating that corruption may be beneficial to economic development,
governmental capacity, and institutional integration into the political arena. As such, increasing
its transparency and legitimacy, or authorizing those aspects beneficial to society and public wel-
fare may be the way to ameliorate corruption (pp. 419, 427).
While these definitions are widely accepted, each limits the scope of corruption to a polit-
ical realm or public sector, which will not do for the purposes of this thesis. Robert Merton, on
the other hand, suggests that corruption may be a remedy created by society; functional deficien-
cies of the official structure generate an alternative (unofficial) structure to fulfill existing needs
somewhat more efficiently' (1968, pp. 127, emphases in original). We seek to measure the ef-
fects of corrosive, development-limiting corruption in this thesis, whether public or private, if it
31


alters the composition of government expenditure by avoiding the democratic process or by influ-
encing budgets or spending (Mauro et al., 2002, pp. 263-265).
Governance corruption refers to corruption in the system, structure, and form of govern-
ment including its actors, as well as the act of governing or the method by which a system is
governed. Its actors include national-level public and private institutions, or public officials and
private citizens who pursue private interests by circumventing the democratic process (Thomp-
son, 2007, p. 2). Therefore, in this thesis, governance corruption includes the scope and breadth
of influence, official or unofficial, that affect a distortion into the legitimate, national-level, public
sector, institutional, political, and policy systems (North, 1991a; deLeon, 1993; Mauro, 2004;
Kauffnann et al., 2008) that command respect and allegiances in society and, therefore, on the
economy itself (Rose-Ackerman, 1978, 1999; North, 1991a).
Examples of inferior, as opposed to good, governance raise the question about the cause
of the failure in governance. Strictly defining corruption, however, forces bounds on the word as
if corruption is merely a movement in character or form of a thing (or person) from point A to-
ward point B. Instead, corruption is not linear, and it is more than one adjective can describe.
Corruption is a process, thus, it must be viewed in its context. Corruption must be evalu-
ated for its effects; measured, then re-examined for its causes as a cyclical rather than linear
movement. In context, corruption may be temporarily beneficial, as in composting to build nutri-
ent rich soil. In context, corruption may be necessary to reach a mutually desired societal goal
(Rose-Ackerman, 1999a). Corruption in the proper context may fill a societal need or promote
economic development, and outside the proper context, may destroy a nation (Merton, 1968;
Moody-Stuart, 1996; Nye, 1967). According to Nye (1967, pp. 419-422), corruption has poten-
tial development benefit in three major categories: economic development, national integration,
and governmental capacity. If corruption helps promote economic development which is gener-
ally necessary to maintain a capacity to preserve legitimacy in the face of social change, then (by
32


definition) it is beneficial for political development (p. 419). Corruption may further economic
development by the cutting of red tape, through capital formation, incentivizing entrepreneurship,
and overcoming discrimination. Corruption may further governmental capacity, as well. The
capacity of the political structures of many new states to cope with change is frequently limit by
the weakness of their new institutions and (often despite apparent centralization) the fragmenta-
tion of power in a country. Moreover, there is little elasticity of power -i.e., power does not
expand or contract easily with a change of man or situation (p. 421. parentheses in original).
Tanzi (1998, pp. 581-582) echoes Nyes assertions that corruption may benefit a development by
citing Tullock (1996) and Becker and Stigler (1974)...Baumol (1990) and Murphy & Shleifer,
and Vishny (1991).
In Gupta et al. (2000) we read the contradictory claims about the economic benefits of
corruption. Some, consistent with Nye (1967), claim it is beneficial as method to overcome and
overly centralized and overextended bureaucracy, red-tape, and delays (Leff, 1964: Lui, 1985)
(p. 7). On the contrary, citing Pradhan and Compos (1999) and Wei (1997) (p. 8), as well as
(Kaufmann and Wei, 1999)... and the 1997 World Development Report, Gupta et al. suggest
the efficient grease hypothesis is not supported by data (2000, p. 7). While evaluating corrup-
tion further is outside the scope of this thesis, defining and measuring corruption is necessary, and
requires the reader to know its context. Part of corruptions context is its form and the level at
which it operates.
Forms, Size, and Scope of Corruption
Corruption has many meanings in the literature. Corruption is a struggle between public
good and rent-seeking individuals, and is a principal inhibitor of economic growth and economic
development (Abed & Gupta, 2002a; deLeon, 1993; Heidenheimer & Johnston, 2002; Johnston,
2007; Mauro, 1995; Rose-Ackerman, 1999b; Rotberg, 2005). In Thinking About Political Cor-
ruption, deLeon (1993, pp. 23-25) offers several methods to categorize corruption. For example,
33


Lowi differentiates corruption based on its scale, where Corruption, Big C, such as that in the
Iran-Contra affair, is often justifiable by the participants, and requires coordination among
many parties. Spelled with a little C, Lowis corruption is small-scale scandal, that reflects or
contributes to individual moral depravity... [such as] embezzlement, tax evasion, or special privi-
leges of office (as found in deLeon, 1993, pp. 23-25). Heidenheimer colors corruption black,
white, or gray, depending on the probability that a majority consensus would find the acts punish-
able based on principle, tolerable, or meet mixed review based on the class or status, respectively
(quoted in deLeon, 1993, pp. 23-24). Rose-Ackerman (1999b, p. 27) distinguishes corruption by
its scale, as well, where Grand Corruption occurs at the highest levels of government and in-
volves major government programs and projects providing examples such as cartels and
privatization processes rife with bribery (Rose-Ackerman citing Moody-Stewart (sic), 1997).
Dennis Thompson (2007, p. 2) separates the broader concepts of individual and institutional cor-
ruption, stating that institutional corruption is not just the stark land of bribery, extortion, and
simple personal gain, but also the shadowy world of implicit understandings, ambiguous fa-
vors, and political advantage. Thompson follows with, identifying and assessing this kind of
corruption depends critically on understanding the purposes of the institutions in which it takes
place (p. 2). Finally, deLeon & Green (2004, p. 72) move beyond the view of identifying the ac-
tor to add the concept of pervasiveness of corruption; whether or not it is systemic.
Narrowing the literature by the form, level, or size of corruption is fruitless, as corruption
is crosscutting through political, institutional, business-level, and governmental red tape. Big
Corruption, Grand Corruption, and espionage, or little corruption, scandal, barter, and petty thiev-
ery; none of these accounts for the percent of GDP unaccounted for because of corruption, by any
name. Dissecting corruption by form, level, or size is insufficient for this thesis, as the form of
corruption does not necessarily inform the measurement required, nor does it inform the size of
the overall corruption problem as it is relative to the GDP in a country. Broadening the literature,
34


however, to include the scope of corruption, considering the degree to which corruption is sys-
temic is constructive toward understanding and measuring of its impact (deLeon & Green, 2004).
One avenue scholars select to isolate the effects of corruption is to differentiate between
that productivity that is reported and official, versus that which is not, regardless of scope, scale,
color, and size. While this method offers more quantifiable data, it blurs the boundaries of the
conventional definitions of corruption found in public affairs literature. Rent seeking and state
capture are examples of the unofficial productivity.
The seminal author on government privilege-seeking or protection-seeking is Gordon
Tullock (1967), who suggests that tariffs (p. 225), regulation protection (p. 226) monopoly con-
cessions (p. 228), transfers (such as favor by pressure from lobby groups) (pp. 228, 232), and
barriers to entry (pp. 231) are forms of theft from the governments revenue stream. Anne O.
Krueger (1974) penned the term rent seeking, and added as varieties, bribery, corruption, smug-
gling, and black markets (p. 291), import restrictions (p. 298), quotas, license-allocation, fair
trade and minimum wage policies (p. 301), credit rationing and preferential tax treatment (p.
302). Usage of the term rent stems from Smiths (1776) account. Wages, profit, and rent, are
the three original sources of all revenue as well as of all exchangeable value. All other revenue is
ultimately derived from some one or other of these (p. 54). All taxes and the [government]
revenue which is founded upon them... are ultimately derived from some one or other of those
three original sources of revenue... (p. 55). Rent seeking, according to Tullock, is the outlay
of resources by individuals and organizations in the pursuit of rents created by government
(1993, p. 2). Consistent with Krueger (1974), Smith (1776), and Tullock (1967), asserts that rent
seeking causes a net waste of resources in inefficient production (p, 228), it lowers government
revenue, and diminishes official taxable individual and corporate income (p. 229). Krueger
(1974) adds, diminishing returns will reduce the [labor] wage. The domestic price of imports,
the distributive margin, and the [profit] wage of distributors will increase (p. 297).
35


State capture is any group or social strata, external to the state, that exercises decisive in-
fluence over state institutions and policies for its own interests against the public good (Pesic,
2007, p. 1). A form of grand corruption, it includes the ability of domestic or foreign informal in-
stitutions or firms to mold or manipulate state laws, policies, or regulations (Kaufmann, 2003, p.
21). At the highest level, officials setting public policy change or break rules to favor certain
vendors, buy votes, or bargain for power (Chua, 2006). Essential to this thesis, Mauro, Abed, et
al. (2002, p. 278) assert that extorting from education starts before the budget approval, so fewer
dollars are allocated to education, and more are allocated to projects where that extortion is easier
to hide.
La Porta &Shleifer (2008, p. 7) concentrate their research on the difference between for-
mal and informal firm productivity, using 14 African and 14 Latin American countries as the
sample set, and OLS regression with data from informal surveys, educational attainment, and
economic data. Raw materials, production costs, and electricity are the supply-side variables,
while sales, GDP, and output are the demand-side variables. The individual is the unit of meas-
ure, and the variables are normed to this. This method of approximating the size of the informal
economy would have satisfied the needs and purpose of this thesis; however, as of yet, the data
are not available for the sample set of countries in Eastern Europe. La Porta & Shleifer, (2008, p.
7) group the determinants of the size of the unofficial economy into three broad categories: the
cost of becoming formal, the cost of staying formal, and the benefits of being formal. Many of
the costs of becoming and staying in the official economy are measureable, and belong with
state capture and rent seeking, in the shadow economy.
Similarly, to account for the corruptions effects on GDP, Schneider et al. (2010) use the
term Shadow Economy (SE) to separate official and unofficial dealings. Shadow suggests that
the unofficial activity is obscured or hidden, and better defines the productivity this thesis seeks
to measure. The shadow economy is an unobservable economic phenomenon, and no consensus
36


exists as to the definition of the shadow economy (Buehn & Schneider, 2009, p. 5). Consistent
with Dresher, Kotsogiannis et al. (2005), employing a multiple indicator, multiple causes
(MIMIC) structural equation method. Since this is the only data set available that approximates
the size of the informal economy that also covers the countries of interest herein, this thesis uses
the Schneider data set, and the following definition for the shadow economy (Schneider et al.,
2010, p. 3).
Shadow Economy Rules
The shadow economy includes all market-based legal production of goods and
services that are deliberately concealed from public authorities for any of the fol-
lowing reasons:
(1) to avoid payment of income, value added or other taxes,
(2) to avoid payment of social security contributions,
(3) to avoid having to meet certain legal labor market standards, such as mini-
mum wages, maximum working hours, safety standards, etc., and
(4) to avoid complying with certain administrative procedures, such as complet-
ing statistical questionnaires or other administrative forms.
Assuming that either productivity is reported or it is not reported is naive, however, add-
ing that which we know to be reported with that which we can estimate to be unreported is a
closer, though still problematic, approximation of the total GDP per country (Galbraith & Kum,
2005; Schneider et al., 2010a).
Causes and Consequences of Corruption
The states institutional environment is cooperatively managed through the highest-level
authority of its governance processes. Governance performance can be measured by its ability to
control corruption, which is particularly threatening to both aggregate and individual prosperity
(Mauro et al., 2002). Corruption is likely to be a symptom of wider institutional failures...
(Kaufmann, 2006, p. 98), and may hinder the accumulation of knowledge and technical capital
and economic growth (La Porta & Shleifer, 2008).
An overview of corruption around the world shows that many of its most commonly cited
causes and consequences are thought to be economic in nature (Tanzi, 1998, p. 587). Mauro cites
37


causes related to rent-seeking through subsidies, price controls, and trade arbitrage, influence re-
lated to trade restrictions or protectionist tariffs, and incentives or bribery stemming from low
wages of civil servants, and other societal factors such as ethnolinguistic fractionalization and
family ties (Mauro, 2000, pp. 4-6). Informal economic activity that operates outside of the formal
economy has many pseudonyms including, but not limits to, three arenas, which may overlap.
Recall that corruptions equilibrium necessarily allows benefit or gain for a portion of the actors.
(1) The first is exchange of products and services (e.g., unofficial, underground, unobserved, un-
reported, undeclared, non-transparent, informal, hidden, shadow, illegitimate, barter, cash,
parallel, secondary, black, and gray economies or markets, of transactions that are on the ground
versus on the books) (Feige & Urban, 2008; Schneider et al., 2010a, p. 3); Eurostat uses NOE,
non-observed economy (Eurostat201 lc). (2) Second is the trading for some sort of favor (e.g.,
lobby and special interest groups, Political Action Committee[PACs], labor unions, mafias, car-
tels) (North, 1990). This includes State capture, which is the ability of domestic or foreign
informal institutions or firms to mold or manipulate state laws, policies, or regulations) (Kauf-
mann, 2003, p. 21). (3) Thirdly, some gain by (or through) the trading of knowledge (e.g.,
espionage, trade secrets, copyright infringement, scientific breakthrough, or reason). These mar-
kets fall into the broader sphere of corruption of the formal governance system (deLeon, 1993;
Mauro, 2004b; Rose-Ackerman, 1999b; Schneider, 2009).
Werlin (1994, p. 554) states that corruption... arises out of the inadequacy of political
software (persuasive power), particularly the distrust of governmental institutions and, .. .has a
corrosive effect on the requirements for development (2000, p. 182). Weak, formal, legitimate
systems, ineffective political hardware (contracts, procedures) and influential institutions are
symptoms of poor state management, or ineffective governance. Quoting Joseph Nye (1967),
corruption seeps into the social, governmental, and political realms it flourishes with the weak-
ness of social and governmental enforcement mechanisms; and the absence of a strong sense of
38


national community... [and the] weakness of the legitimacy of governmental institutions (p.
418). Regarding the type of change which seems to be occurring in our age (modernization)
... [and to] the capacity of political structures and processes to cope with societal change, Nye
states:
[Modernization in the United States, or its] development (or decay) will mean
growth (or decline) in the capacity of a society's governmental structures and
processes to maintain their legitimacy over time (i.e., presumably in the face of
social change). This allows us to see development as a moving equilibrium and
avoid some of the limitations of equating development and modernization.
As for consequences of corruption in the public sector, businesspersons see bribes as a
form of tax, which increases prices. In Corruption and the Composition of Government Expendi-
ture, Mauro finds ... evidence of a negative, significant, and robust relationship between
corruption and government expenditure on education, which is a reason for concern, since previ-
ous literature has shown that educational attainment is an important determinant of economic
growth (1998b, p. 277). Some forms of corruption have terminal consequences. Under totali-
tarian regimes, corruption is often directly linked to human rights violations, asserts Pope in
Transparency Internationals (TI) Confronting Corruption (2000b, p. ix).
Solving the causes of corruption question is vast beyond the scope of this thesis, and the
consequences are far reaching, equally far outside its scope. Merton (1968, p. 130) asserts that
the demand for services of special privileges are built into the structure of society. As deUeon
reminds readers, It is sown in Corruption (quoting 1 Corinthians 15:42, 1993, p. 3). As such,
where it is rampant, there is no hope of controlling petty corruption (Moody-Stuart, (1996, p.
19). It is the corruption that avoids the democratic process that we seek to mute (Thompson,
2007).
The important point upon which scholars agree is that a layer of activity runs parallel to
the formal system of government, and it attempts to avoid detection. The corruption that is meas-
urably absent from the National Income Accounting revenue steam, and therefore, is missing
39


from official GDP reports, is corruption as a dimension of governance. In this thesis, this layer is
called governance corruption.
Corruptions Remuneration
Corruptions remuneration is some type of significant personal gain, in which the cur-
rency could be economic, social, political, or ideological (deLeon, 1993, p. 25). Its value could
lie in the shadowy world of implicit understandings, ambiguous favors, and political advantage
(Thompson, 2007, p. 2). We should not expect to find a sharp distinction between corruption
and non-corrupt actions. Instead, we will find the gradations of judgment, reflecting a variety of
equivocations, mitigating circumstances, and attributed motives (Johnston, 1986, p. 379). Cor-
ruption may be the act of an individual, or of a group or institution, to gain power, prestige, or
position. The IMF and Good Governance (201 Id, p. 1) factsheet states, .. .corruption thrives in
the presence of excessive government regulation and intervention in the economy; substantial ex-
change and trade restrictions; complex tax laws...tax incentives, zoning laws... and monopoly
rights over exports and imports... (and poor) remuneration of the civil service. Power is a poten-
tial pay-off for corruption (Nye, 1967, p. 421). Institutions gain respect because of the power
they wield (Kooiman & Jentoft, 2009, p. 883; Mauro, 2004b; Schneider & Enste, 2002). Power
is inherent in governance and shapes the capacity of governments and institutions to govern
(Kooiman & Jentoft, 2009, p. 833). Good governance fosters the power of the state, while bad
governance allows corruption to flourish, and nurtures informal activity. Some scholars assert
that certain environments invite corruption, such as where monopoly power is in the hands of of-
ficials, when the risks of getting caught are low and penalties are mild (Klitgaard, 1988; Rose-
Ackerman, 1978). Other scholars assert that corruption can be predicted by patterns of potential
inducements or sanctions... [and the] structure of opportunities and incentives (Sandholtz &
Koetzle, 2000, p. 36).
40


Since corruptions remuneration is some type of significant personal gain, in which the
currency could be economic, social, political, or ideological (deLeon, 1993, p. 25), it is im-
portant to understand how scholars measure corruption in order to measure its impact on econom-
ic growth, living standards, and income inequality.
Controlling Corruption
Interestingly, correcting the imbalances that cause corruption must be as multidimension-
al as the corruption itself. Any realistic strategy must start with an explicit recognition that there
are those who demand acts of corruption on the part of public sector employees and there are
public employees willing for a price to perform these acts. There is thus both a demand for and
supply of corruption (Tanzi, 1998, p. 587), and responsibility for acting illegally or unethically
resides on both sides of corruptions equilibrium, which calls for horizontal accountability
(Kauffnann, 2006). Mauro states, [s]ince much of public corruption can be traced to government
intervention in the economy, policies aimed at liberalization, stabilization, deregulation, and pri-
vatization can sharply reduce the opportunities for rent-seeking behavior and corruption (2000,
p. 6). Rose-Ackerman suggests several successful anti-corruption projects as models for over-
coming corruption of varying types, and case studies substantiate her theory that clean-running
and stable governments are a key precursor to equitable, effective, and efficient economic growth
that target both the supply and demand (Klitgaard, 1988; Rose-Ackerman, 1999b; Shleifer &
Vishny, 1993). Policies aimed at curbing incentives for corrupt activity may be simple; increas-
ing the penalty when caught, and/or increasing the law enforcement to catch it (Boswell & Rose-
Ackerman, 1996), for example. While Thompson (2007) agrees on the mechanisms and that dis-
covery should take place within the institutional form rather than in a criminal court, he asserts
that the institutional complexity of governments must undergo structural reform in order to re-
duce corruption.
41


Increasing the risk of exposure may help, especially as in political corruption. Transpar-
ency International (TI) created the national integrity system to make undertaking corruption a
high risk and low return endeavor (TI, 2000b, p. ix). TI suggests that the best method to con-
trol the cancer of corruption is to increase both enforcement and prevention by using the national
integrity system, which is a system of checks and balances designed to disperse power and limit
situations conducive to corrupt behavior (2000b, p. xiii). Policies that affirm Freedom of Press,
Freedom of Information, civil liberty protection and election oversight are critical when building
a transparent governance (2000b, p. xxv). Transparency is critical in the private sector to reduce
capture and increase foreign investment, calling for policies to further accountability in corpo-
rate ethics and traditional legal and judicial reforms that focus on timely information, auditing,
insider rules, and financial disclosure (Kaufinann, 2003, p. 21).
Corruption levels are determined by the overall level of the benefit available (Boswell
& Rose-Ackerman, 1996, pp. 84, emphasis in original). Decreasing corruptions available benefit
is multidimensional, a task the World Bank took on in its Multi-pronged strategies for Combat
Corruption: Addressing State Capture and Administrative Corruption (2000a, p. 39). The World
Banks framework starts with five major areas of focus listed next. Where novel approaches be-
yond those mentioned above exist, those are listed, as well. (1) Institutional Restraints:
independent prosecution and enforcement. (2) Political Accountability: Transparency in party
financing, asset declaration, and conflict of interest rules. (3) Civil Society Participation: public
hearings in the drafting of laws. (4) Competitive Private Sector: Monopoly regulation. (5) Public
Sector Management: Merit based pay for civil service, customs oversight (ch. 4).
Another vein of literature discusses increasing vertical and horizontal accountability by
both state and non-state actors (UNDP, 2010e). Peer-level whistle-blowing protection and stake-
holder groups are examples of horizontal accountability. From state actors, access-to-information
laws are an example of vertical accountability (Relly, 2011).
42


Consequences of Corruption on Development
Mauro (2000, pp. 4-6) following Nye (1967, pp. 421-423) cites several consequences of
corruption. Corruption lowers investment and retards economic growth to a significant extent,
talent may be misallocated, [it] may reduce the effectiveness of aid flows, bring about loss of tax
revenue. Pertinent to this thesis, Mauro (2000, p. 7) asserts, [corruption may distort the com-
position of government expenditure.... [b]y reducing tax collection or raising the level of public
expenditure, corruption may lead to adverse budgetary consequences, [and] lead to lower quality
of infrastructure and public services.
Citing empirical evidence from the former Soviet Union, Adeb & Gupta found that the
... disintegration of the command structures in the old regimes triggered some of the most chaot-
ic economic, political, and social changes in modem history. Absence of the rule of law and
accountable systems of governance led to rent-seeking, corruption, and outright thievery (Abed
& Gupta, 2002b, p. 2). Kargbo (2006) writes of corruption that has hurt both aggregate (official
production) and individual income in Africa. Corruption leads to the decline in real per capita
incomes, inflation, a widening budget and balance of payment deficits, and declining official pro-
duction and exports (p. 8). He reports, [corruption has also led to massive neglect of the social
sector, which has substantially decreased the quality of human resources in African states over
the years (p. 8). Specific to this thesis, [t]he provision of educational and health opportunities
have been limited, thus impacting negatively on the quality of life, labour, productivity, incomes,
innovativeness, competitiveness, and poverty reduction in Africa States (p. 8). The IMF Fact-
sheet adds to the body of evidence on the relationship between corruption and education funding
(IMF, 201 Id, p. 1).
Corruption can reduce investment and economic growth. It diverts public re-
sources to private gains, and away from needed public spending on education and
health. By reducing tax revenue, corruption can complicate macroeconomic
management, and since it tends to do so in a regressive way, it can accentuate in-
come inequality.
43


Transparency International (TI) (2009b) and Mauro (2000) discuss corruption within the
public education budgets system of allocation. Countries with high levels of corruption invest
less in public services, leaving the education sector under-funded (p. 6). UNESCO reports that
between 10 and 87 percent of non-wage spending on primary education is lost to resource
leakages in the execution of the budget (Hallak & Poisson, 2007, p. 105). Mauro (1998, p. 277)
presents evidence of a negative, significant, and robust relationship between corruption and gov-
ernment expenditure on education. Finally, Mauro (2000, p. 6) states the following about
incentive to divert funds away from education in the budgeting and allocation process:
Corruption may distort the composition of government expenditure. Corruption
may tempt government officials to choose government expenditures less on the
basis of public welfare than on the opportunity they provide for extorting
bribes.... education turns out to be the only component of public spending that
remains significantly associated with corruption when the level of per capita in-
come in 1980 is used as an additional explanatory [control] variable.
Measuring Corruption Indirectly
Vito Tanzi (1998, p. 577) asserts, [w]hile there are no direct ways of measuring corrup-
tion, there are several indirect ways of getting information about its prevalence in a country or in
an institution, including published reports such as newspapers, case-studies, empirical country-
level data, and questionnaire based surveys (emphasis in original). The most prevalent method is
the survey method. The World Bank and European Bank for Reconstruction and Development
(EBRD) created the Business Environment and Enterprise Performance Survey (BEEPS) in 1999,
and it is in its fourth iteration. The 2008 round conducted over 11,000 surveys of business own-
ers and top managers on business climate and corruption. Data are available on 26 countries in
Eastern Europe. While a strength of the BEEPS is in survey data specific to corruption, it is lim-
ited to surveys of firms and employees of those firms about corruption in the business
environment (BEEPS, 2008a). Therefore, it is not sufficient in scope for this thesis. The EBRD
has published thematic Transition Reports yearly since 1994. In 1998, the theme was Ten Years
of Transition (EBRD, 20 lOi), and the report included results from several surveys, and macroe-
44


conomic data for the transition counties backdated to 1989. With the backdated data, the EBRD
dataset for is a sufficient source for eight of the seventeen variables on twenty-three of the thirty
countries on which this thesis focuses. The variables that are not part of the EBRD dataset in-
clude those in the Human Development Index and the component indices, Education Attainment
Index, Life Expectancy Index, and Gross Domestic Product Index, which together make up eight
variables, this poses no problem, as the HDI variables are each available from the HDR for each
country. However, the two variables on which rest the key hypothesis in this thesis are not part of
this data set: (1) Government Expenditures on Education as a percentage of Total Government
Spending, (2) a measure for unofficial GDP per capita as a percentage of total GDP per capita.
Since testing both of these variables requires macroeconomic data consistent and normalized with
that of the World Bank and IMF, the EBRD data cannot be used in this thesis. EBRD in Transi-
tion Report 2010 concludes the following regarding its own measure for progress.
One problem is the subjective nature of the scoring and possible non-transparency of the
demarcation between categories.... This is because [the data] cannot be easily validated external-
ly and creates a risk that a countrys overall economic performance might influence the judgment
about (and scorning for) its transition progress (which, in the extreme, would render regressions
of growth on the transition indicators meaningless) (p. 2).
The EBRD continues describing its fundamental concern with measuring economic pro-
gress using this (or presumably other, similar) datasets.
A more fundamental objection is that, with the exception of the infrastructure indicators,
many of the scores reflect a rather simplistic view that a successful transition is mainly about re-
moving the role of the state and encouraging private ownership and market forces wherever
possible. The problem with this view is that markets cannot function properly unless there are
well-run effective public institutions in place, (p. 2)
45


Several widely used surveys on the perceptions of corruption in the private sector and at
different levels of government are available. Transparency Intemationals (TI) Corruptions Per-
ceptions Index (CPI) is one such survey. The 2010 CPI measures the degree to which public
sector corruption is perceived to exist in 178 countries around the world. It scores countries on a
scale from 10 (very clean) to 0 (highly corrupt). However, the article continues, [g]iven its
methodology, the CPI is not a tool that is suitable for trend analysis or for monitoring changes in
the perceived levels of corruption overtime for all countries (CPI, 2010b, p. 5). TIs Global
Corruption Barometer uses similar methodology (GCB, 2009), and is a wealth of information, by
country, on the scale, type, causes, consequences and true accounts of corruption, but offers no
measurement of it relative to GDP (GDR, 2009c), as that is not its purpose.
The Cost / Benefit Analysis (CBA) allows one to value the implicit and explicit costs of
corruption using empirical and survey data (Sen, 2000). Kauffnann (2006, p. 81) suggests that,
Corruption can, and is being, measured in three broad ways: (by)... gathering the informed
views of relevant stakeholders...tracking countries institutional features (and by)...careful audits
of specific projects. Together, the CBA and national income accounting tools can estimate the
portion of funds that are unavailable domestically due to corruption by budget line item and do so
with statistically significant precision. This precision increases, and costs become increasingly
explicit as more nations practice standardized accounting methods (IMF, 2011, p. 1; D. K. Gupta,
2001; Weimer & Vining, 2004). However, National Income accounting practices are yet incon-
sistent between countries and links between social and professional research are lacking,
requiring us to abandon the CBA method for this thesis (Feige & Urban, 2008). For an example
of the chasm rendering the CBA unproductive for the method herein, deLeon (1998) attempts to
chart a middle course between the positivists and post-positivists representing respectively the
purely quantitative and purely qualitative method extremes (p. 111). To elucidate the division,
deLeon affirms the post-positivists argument that too often the policy analyst is well removed
46


from the political and value conflicts inherent in public policy thereby making difficult the bene-
fit assessment. However, the positivists play an important role in policy analysis, for it is by us-
using many of these quantitative techniques that they can propose with some rigor a clear-cut set
of expectations necessary for prediction purposes (p. 110). Further, in Weimers (Weimer,
1998, p. 118) words, one would be hard-pressed to find any important public policy decisions
that were made solely on the basis of cost-benefit analysis (quoted in deLeon, 1998, p. 108).
Several scholars critique the MIMIC method used by Schneider et al. (2010) asserting
that the variables used in the MIMIC equation are highly correlated with each other (Breusch,
2005; La Porta & Shleifer, 2008, p. 8), possibly exaggerating the estimated size of the informal
economy, and that the benchmarks are subjective. Recall also, that correlation coefficient be-
tween the electricity demand method, currency demand, key perceptions indices and structural
equation methods, is .88 or greater (S. Gupta et al., 1998, p. 12), so the concern runs across the
potential methods. Addressing the subjectivity of the benchmarks, Schneider and other scholars
collaborate with Bruesch to arrive at a calibrated benchmark that holds a proportional relation-
ship between the measurement in different years (Dell'Anno & Schneider, 2006, p. 9), avoiding
overstating the percentage of the Shadow Economy. Schneider et al. (2010a) counter with the
following statement, regarding the MIMIC (Multiple Indicators Multiple Causes) model.
[Tjhis is the first study that applies the same estimation technique and almost the same
data sample to such a large number of shadow economies... [using] the MIMIC estimation meth-
od for all countries, thus creating a unique data set that allows us to compare shadow economy
data. (p. 3)
According to the IMF scholars on corruption estimation, ... little is known about the de-
velopment and the size of the Shadow Economies in developing Eastern European and Central
Asian countries... in the recent past (201 Id, p. 1). The criticism by Bruesch relative to this the-
sis is the suggestion that the MIMIC model may overstate the actual Shadow Economy
47


percentage. However, other scholars assert that corruption accounts for far more of the economic
activity than studies to date have realized or uncovered (Levy, 2007). Since the size and scope of
the MIMIC technique and study of national-level government and institutions is revolutionary
and methodologically unmatched, it therefore, is the measure employed for the methodology in
this thesis (See MIMIC diagram in Appendix).
Theoretically, the relationship between corruption and the Shadow Economy is thus un-
settled. There is, however, reason to believe that the relationship might differ among high and
low income countries (F. G. Schneider, 2009). Others assert that the relationship is complex, de-
pending on the maturity of the government, and the quality of governance (La Porta & Shleifer,
2008). Recent literature asserts that corruption and the Shadow Economy are substitutes when
corruption is high, and complements when corruption is relatively lower, supporting the need to
measure them separately in a simultaneous equation model (Buehn & Schneider, 2009; Dreher,
Kotsogiannis et al., 2005; Russell, 2010).
48


Economic Growth
The business cycle literature is foundational for understanding the effects of corruption
for two reasons. First, much of the business cycle literature precedes the economic growth litera-
ture chronologically, and more importantly, it is referenced frequently in the latter. Second, this
thesis pivots on the political lightning bolt the end of the Cold War. Baumgartner & Jones
(1993) call this non-incremental change a punctuation in societys equilibrium or stasis (p. 23).
A watershed event, this demarcation was a catalyst for a new business cycle and economic
growth stage for many Central and Eastern Europe countries (Rostow, 1991). The economic
growth literature underscores the criticality of education funding in economic policy development
and of this thesis by underscoring required to build strong, healthy, and viable economic devel-
opment today, with tomorrows staying power. In Innovation: The New Pump of Growth, Paul
Romer asserts that it is the application of knowledge through an educated workforce such as high-
ly trained scientists and engineers who are to credit for past economic growth, and that growth
has proven to be unsustainable in countries around the world without sufficient public support
through funding and public policy (Romer, 1998b). Developing this argument begins with an un-
derstanding of the business cycle.
The Business Cycle the foundation of economic growth stages
Business cycle literature falls into two major categories, the how cycles work their
identification and measurement, and, the theories on why or their cause. From here, the litera-
ture divides again into endogenous non-linear and exogenous linear-multiplier, with some helpful
bridges between them. This pattern repeats itself in the economic growth literature. Economic
growths reputation is on the line, as some claim it is economic growth itself that can exacerbate
poverty or income inequality (e.g., Galbraith, 2008; Rothschild, 1986; Pritchett, 1997), while oth-
ers claim economic growth is required to alleviate both (e.g., Sachs & Warner, 1995; Sen, 1999;
Barro 2001; Friedman, 1997). Either way, aggregate growth seems inevitable (Maddison, 2009);
49


furthermore, growth is merely a measure of the change in annual official GDP output, so culpa-
bility or credit belong to the causes of change. Complicating the equation, both sides may still be
right, if given identical data and definitions, the time span pivots on different events or cycles
(Galbraith & Kum, 2005; Pritchett, 1997; Rostow, 1991).
Early work on business cycles focused on identification and measurement and questioned
whether movement equals a cycle or an aberration. What magnitude of change constitutes a sta-
tistical oscillation rather than a cycle? Is it repeated or at least periodic, continuous, intermittent,
or patterned (Mitchell, 1928; Slutsky, 1929)? Regarding the naming of cycle names their dura-
tions, Juglar (1893), working on the credit crisis in France and later in the US, identified 8 to 10
year industrial cycles tied to the issuance of credit. Kitchin is credited for identifying the 3.5-year
business cycle (1923, p. 10). Kitchin, Slutsky (1929), and Wright (1920), considered the short
cycles normal oscillations due to human psychology. Kondratiev identified half-century long-
wave cycles (48 to 60 years) that incorporate several shorter waves (Kondratiev, 1926). Schum-
peter solidified these three time-spans by the authors names Kitchin, Juglar, and Kondratieiv
(1926) into the economic growth literature of today with his theory that each represents an in-
novation of different magnitude, exists simultaneously, and should be additive with revolutionary
change creating a higher steady state (Schumpeter, 1939). Mitchell, in turn, tied a 4-year cycle
to the effects of the political cycle and found two or three of these shorter cycles exist between
Juglar crises (Glasner & Cooley, 1997, p. 347).
In a foundational work to the developing economic growth and governance theories,
Rostow adds that the economic stages of development are logical, rational, and based on exoge-
nous globalization and endogenous governance forces. Rostow (1991) found the pre-
conditioning stages were roughly 15 to 20 years and the take-off stages were about 60 years, con-
sisting of several interwoven growth spurts of varying lengths and magnitudes depending on the
industries involved and the spreading effects of lateral interaction between industries. Kuznets
50


(1940) proposed a 15 20 year cycle consistent with the explanation of interim shorter cycles
within larger growth phases and based on his own national income research. Bums & Mitchell
(1946) developed a definition for the business cycle, which is the duration in months from trough
to trough when measuring the rate of change of productivity; this set the standard for other coun-
tries. Note the identification of a standard: a trough marks a cycle.
After working out the statistical problems with the counter-cyclical (and therefore, the
canceling-out) nature of supply and demand forces within aggregate indexes, from these diver-
gent camps based on theory, come a general agreement that economies do cycle. Given that
economies cycle, one may ask how the economy cycles, referring to the initial date, degree, and
duration of the cycle. This elevates the pertinent question to why why do economies cycle?
Following Keynes (1936) lead on why economies cycle and why they grow, many econ-
omists modeled endogenous causes with some type of oscillator such as income/expenditure
(Samuelson, 1939), income/savings (Kaldor, 1959), inventory (Metzler, 1941), trade (Hicks,
1950), and credit and money supply (Hayek, 1933). Some scholars added non-linear or dynamic
effects (Goodwin, 1951), price signal, or information on intertemporal discoordination (Hayek,
1933), or time lags between acquisition and distribution (Kalecki, 1954), or the idea of a multipli-
er (Howitt et al., 1999). Other scholars, following Schumpeter and Kuznets, worked on
exogenous causes such as structural change and entrepreneurial gains (Schumpeter, 1942),
knowledge accumulation (Romer, 1996), shocks with a ratchet effect (Smithies, 1957), and
technical change (Hicks, 1950; Solow, 1956).
Summarizing the review of business cycle literature economies cycle. Cycles are the
effect; cycles do not materialize out of a void; they have a cause. This critical point refers to
Rostows economic growth stages: events demarcate and catalyze economic growth stages
(Pritchett, 1997; Rostow, 1975, 1991; Xu & Li, 2008). The cause(s) is (are) due, generally, to
some influence or to a combination of endogenous, evolutionary change and exogenous revolu-
51


tionary shifts in the steady state (Ofer, 1987; Schumpeter, 1942). Like the business cycle itself,
The Kuznets curve...emerges as a clear empirical regularity.... (Barro, 2000, p. 32). The esti-
mated relationship may reflect not just the influence of the level of per capita GDP but also the
dynamic effect; whereby, the adoption of each type of new technology has a Kuznets-type dy-
namic effect on the distribution of income (Barro, 2000). Business cycle research was a
foundation for an explosion of attention on economic growth, which offers theories and models
on why and how economies grow.
Economic Growth Theories
A countrys economic growth, defined as a long-term change in capacity to supply in-
creasingly diverse economic goods to its population (Kuznets, 1973, p. 247), is based on
advancing technology and the institutional and ideological adjustments that it demands. Econom-
ic growth, generally, is the change in aggregate producing power, GDP, which is the amount of
goods and services produced in an economy over one year. A negative change is negative
growth. In addition, the realized growth rate is the total growth rate minus inflation. Kuznets
(1940, pp. 259-259) elaborates on Schumpeter (1939), who distinguishes economic growth from
economic development by the degree of change, where growth is incremental, evolutionary, and
continuous and where development is characterized by discontinuity of the steady-state...a dis-
ruption of the static equilibrium leading to an indeterminate future equilibrium (Kuznets, 1940,
p. 259). A consequence of evolving societies, increasing population, production of basic necessi-
ties, and reliance on incentive to spur on economic activity (Phelps, 2008; Schumpeter, 1939,
1942) both fuel and are fueled by inflation; what Rostow (1991) called the compound interest
of an economy (pp. 4-6). The staying power of a new steady state requires constant progress in
the pre-conditions to it, in infrastructure and social capital (Putnam, 2000). Staying power also
requires the foresight to invest in those factors that will be valuable in the future both for con-
52


sumption and demand for exports (Krugman, 2000; Rostow, 1991). It is critical to assessing the
quality of the economic development ex post facto.
After the development has occurred, analysts can see the evidence of healthy or un-
healthy economic growth by an economys ability to meet the needs of its citizens, and its
customers. Citizens produce the inputs to GDP, the goods and services produced in a country in a
year. In addition, citizens (and the additional worlds population) are consumers, or customers, of
that which is produced; if the product is desirable and the price is commensurate with its per-
ceived value. For example, assume Country A and Country B both decided to increase GDP by
pursuing additional shares of the world transportation market: Country A pursues the horse
drawn carriage, and Country B, racecars. Even the finest horse-drawn carriage has a limited ap-
peal in the local or world market, yet the price is relatively low. Likewise, even the latest, high
priced and technologically advanced racecars have limited appeal, yet the horsepower is high.
Both are in the realm of transportation, however, neither is meeting a high demand in the popula-
tion. Neither provides the host country a sustainable industry nor creates a trading advantage. As
indicated above, there must be a desirable product (with high demand) and a commensurate price
and value in the international market to gain economic development strength. Neither the horse-
drawn carriage nor racecar ideas would meet the current or future needs of the citizens or of the
export markets. The sustainable economic development depends on the public officials ability to
forecast, based on foresight, the investment needs in infrastructure, education, training, research
and development, and expertise to produce needed and desirable goods for the times; and to inno-
vate and revolutionize to meet the challenges for tomorrow and for the world market (Nelson &
Phelps, 1966, pp. 70-71).
In review essay of economic growth literature, Klenow & Rodriguez-Clare (1997) chal-
lenge researchers to complete four steps when assessing economic growth: (1) more tightly link
theory and evidence... (2) tie research to business cycles... (3) develop more theories of intema-
53


tional productivity differences... (4) and, collect detailed country data bearing on the process of
technology diffusion (Barro, 2001a; Klenow & Rodriguez-Clare, 1997, p. 597; Klingner &
Sabet, 2005). The rationale for this thesis parallels Klenlow & Rodriquez-Clares four-step chal-
lenge. First, the thesis accomplishes steps one and two by linking governance, economic growth
theories, and individual income with stages of economic growth. Second, this thesis satisfies
steps three and four by analyzing individual income as a measure of education value based on the
staying power of economic growth in a sample of countries. New Growth Theory (NGT) is a
theoretic umbrella over the four challenges above (Barro, 2001b; Klenow & Rodriguez-Clare,
1997). Stated differently, this thesis satisfies the challenges, capitalizing on NGTs treatment of
education as fundamental to successful and sustainable development policy (Romer, 1998a).
Theories on the Causes and Types of Economic Growth
Two theoretical camps divide the economic growth literature based on causes (why), then
further divide based on effects (how). The first camp asserts that exogenous forces cause eco-
nomic growth, the second camp that endogenous change cause economic growth. The literature
further divides, based on the effect of the growth; the two camps divide into four based on how
economies grow. This latter division is centered on the path of growth -the trajectory; either
individual incomes converge (the gap between the richest and poorest shrinks) or diverge (the gap
between the richest and the poorest grows), or move in some combination of these paths over
time. Each of these four major theoretical camps, two on causes (exogenous and endogenous)
and two on effects (convergence and divergence), inform this thesis study on the causes and ef-
fects, the why and how, of economic growth. This large literature is important to understanding
the nature of economic development.
Neoclassical Growth Theory and Exogenous Growth Theory
Solow isolated key determinants of economic growth into the factors of production, tech-
nology, labor, and capital, isolating the growth attributed to each. His work laid the foundation
54


for Neo-Classical Growth Theory. From the literature on technical change grew the burgeoning
literature on the rate of technology and innovation transfer, adoption, and diffusion as a measure
of policy economic development and stability (Klingner & Sabet, 2005). Solow wrote, regarding
the forty years ending in 1949, [g]ross output per man hour doubled over the interval, with 87
per cent of the increase attributable to technical change and the remaining 12 per cent to in-
creased use of capital (1957, p. 320).
Consider the tremendous change and growth in the transportation industry, for instance,
with the aid of automation. Fredrick Taylor (1911, p. 21) predicted this rapid change when ap-
plying the principles of scientific management, which include the scientific education and
scientific skill training of workers toward the needs of the future, or to infuse the workplace with
a scientific knowledge that promotes ingenuity. In scientific knowledge, the worker is quickly
given the very best knowledge of his predecessors; and, provided... with standard implements and
methods which represent the best knowledge of the world up to date, he is able to use his own
originality and ingenuity to make real additions to the world's knowledge, instead of reinventing
things which are old (p. 126). Rostow referred to this shift in economic growth stages, the shift
from the pre-conditions to take off stage, to take-off (1991, p. 5).
Rostow stated, whenever these principles are correctly applied, results must follow
which are truly astounding (p. iii). Clearly, a doubling (or 100 percent change) of the average
persons productivity or output per hour over forty years is astounding, yet it makes sense in light
of the assembly lines coming of age during the same forty years. Solow (1957) attributed 87
percent of that doubling to technical change, one such type of change being automation (p. 320).
Henry Ford produced 11 Model T cars in the first month of its production in 1909. Then, Ford
automated its assembly line, implementing Fredrick Taylors theories on Scientific Management
(1911a). In 1910, 12,000 Model T cars rolled off the assembly line, and by 1925, 2 million Mod-
el Ts rolled off that line (Brinkley, 2003, p. 475).
55


Solow (1957) states that the remaining 12 Vi percent of the doubling of productivity per
working hour over his forty-year study was due to the increased use of capital (p. 320). Ford built
the Highland Park Ford Plant in 1913 to accommodate the Model Ts assembly line (Brinkley,
2003), an example of increased use of capital. Taylor writes on, the same principles can be ap-
plied with equal force to all social activities: to the management of our homes; the management
of our farms; the management of the business of our tradesmen, large and small; of our churches,
our philanthropic institutions our universities, and our governmental departments (1911a, p. 8).
Relying on a Keynesian foundation to apply external stimuli to an otherwise closed sys-
tem, Solows findings revived Keynes work leading some scholars to isolate specific factors,
while others focused on production disincentives (Howitt, 1986; Keynes, 1936; North, 1994).
Working from these strengths, Exogenous Growth Theory specifically employs global technology
advancement as the exogenous agent of growth (Hahn & Solow, 1997; Howitt, 1986, 1997). Ei-
ther way, models of exogenous theories treat growth as the result of some external catalyst,
neglecting part of the evidence, such as cycles inside the economies and additive or compound
growth. Returns on endogenous factors (e.g., interest, inflation, population or compound growth,
incremental knowledge gains over time, or inventions that revolutionize productivity, etc...)
mathematically eliminate the possibility of exogenous growth, arguing for a different growth mo-
tive, a point conceded by many neoclassical theorists (Romer, 1996).
Critics of exogenous growth theories contend that this camp forces illogical conclusions
that neglect variation among countries in technical accumulation, and neglect the effects of hu-
man capital generally, and knowledge capital, specifically. In doing so, this camp disregards vast
literature on the effects of such variables as technology diffusion and adoption (Easterly &
Levine, 2001; Klingner & Sabet, 2005; Romer, 1990; Taylor, 1911a), educational attainment and
quality (UNESCO, 2010d; 2001b; Barro & Lee, 1996), knowledge spillovers (Arrow, 1962),
governance (IMF, 201 Id; Abed & Gupta, 2002b; Kaufmann et al., 2008), and corruption
56


(deLeon, 1993; Rose-Ackerman, 1978; Tanzi, 1998). Therefore, a Keynesian-based theory is in-
sufficient for this thesis.
Endogenous Growth Theory
Endogenous Growth Theory refutes Solows work. Literature from the endogenous
growth camp treats factors such as governance, policy, effects of the national economic and fi-
nancial systems, education and innovation, social capital, and incentives as agents that develop
human and social capital and drive incremental growth from within. These work in conjunction
with technical progress and innovation (Barro et al., 1994; Barro & Sala-i-Martin, 2004; Putnam,
2000; Romer, 1994b). While exogenous growth models require holding technical advancement
constant across countries, endogenous models treat technical change as a variable (Easterly &
Levine, 2001; Romer, 2001). Ironically, advances in computing technology since Solows semi-
nal work on exogenous technical change was its undoing. The ability to manage and calculate
large cross-country longitudinal data sets allowed researchers to treat more variables as variables
rather than constants (Cobb & Douglas, 1928; Sala-i-Martin, 1997). When applied to evidence in
westernized, democratized countries, and/or to isolated and self-reliant regions (e.g., the US,
UK), use of endogenous theory yield strong correlations between sound governance and growth.
This becomes important when considering economic policy options in the US, and/or for non-
westemized communities that rely on endogenous factors for growth (King & Levine, 1993;
Martin & Sunley, 1998).
However, this theory neglects a different part of the evidence: revolutionary shifts, tech-
nology adoption rates, and other effects of external events (Easterly & Levine, 2001; Solow,
1956). Endogenous growth neglects the volume of literature on the stages of economic growth.
Rostow (1991, p. 6) cites that sometimes,[T]he stage of preconditions arise not endogenously,
but by some external intrusion by more advanced societies. These invasions-literal or figurative-
shocked the traditional society and began or hastened its undoing. Therefore, both endogenous
57


and exogenous theories are guilty of remaining true to their precepts at the expense of evidence.
Endogenous Growth Theory neglects external factors such as peace treaties or a neighboring
countys research on solar energy, where Exogenous Growth Theory neglects internal factors
such as education spillovers or positive externalities of a hometown Olympic athlete.
New Growth Theory
New Growth Theory links the roots of endogenous economic factors to technical progress
adoption and diffusion increases that drive economic growth. New Growth Theory builds a
bridge between endogenous and exogenous camps. For example, assume that a new technology
in an era of rapid globalization developed outside the economy in question. Each economy must
choose whether to employ its own resources in order to adopt the exogenous catalyst, or not.
Romer (1998a, p. 2) describes the process thus.
New Growth Theory identifies three specific features that make growth possible. First,
we live in a physical world that is filled with vastly more unexplored possibilities than we can
image, let alone explore. Second. Our ability to cooperate and trade with large numbers people
makes it possible for millions of discoveries and small bits of knowledge to be shared. Third, and
most important, markets create incentive for people to exert effort, make discoveries, and share
information.
Specifically, Romers concept of knowledge as an economic asset (non-rivalrous, partial-
ly excludable, human capital with increasing returns) is fundamental to potential growth. If a
poor nation invests in education and does not destroy the incentives for its citizens to acquire ide-
as from the rest of the world, Romer states, it can rapidly take advantage of the publicly
available part of the worldwide stock of knowledge (2007, p. 3). Romer asserts that living
standards are a direct result of knowledge and technology adoption (Romer, 1993). Phelps (2008,
p. 14) attributes a good economy to education, which produces vitality, and policies that promote
inclusion.
58


New Growth Theory proponents defend good governance as a practical necessity for ed-
ucation and the educated to flourish, drawing from classical economic theorists and new ideas
from the governance literature (Phelps, 2008). North (1992, p. 3) suggests economists treat the
policy development and implementation as a function of governance, . as a critical factor in the
performance of economies, as the source of the diverse performance of economies, and as the ex-
planation for inefficient markets. The assertion that Social Capital is a pre-condition to a long-
wave growth stage from the political economy literature, parallels New Growth Theory, entrepre-
neurial theories, and theories on human capital (Matheson, 2008; Nelson & Phelps, 1966; Phelps,
2008; Putnam, 2000; Rostow, 1991; Schumpeter, 1939; H. A. Simon, 1986; Xu & Li, 2008).
Technology and innovation diffusion and adoption are both precursors and by-products
of the quality and quantity of education, GDP, economic growth and economic development by
country; the speed and degree of its transfer are, in part, a result of the governance system, and
the success of its development policy implementation, specifically, of education policy (Nelson &
Phelps, 1966; North, 1992; Romer, 1993). Innovation diffusion and adoption describes the
spread of new products, values, policies, or processes beyond the locus of their original success.
If viewed purposively, this spread can be described as both organizational learning and
knowledge management (Sabet & Klingner, 1993). This concept of adoption closely resembles
Taylors principles of scientific knowledge, scientific learning, and scientific management
(1911a). Klingner & Sabet (2005) summarize the importance: the true measure of these innova-
tions value lies in the effectiveness of shared information and transferred knowledge to attain
societal goals like sustainable development (p. 206).
Critics argue that education does not produce increasing returns. Instead, education is
like other economic factors experiencing constant returns to the investment in education (Solow,
1957), diminishing marginal utility and rent seeking (North, 1990), crowding out by other activi-
ties such as leisure, and quality or effectiveness challenges (Pritchett, 2001, p. 369). Rather than
59


knowledge spillovers and positive externalities from education, put forth by New Growth Theory,
recent studies provide evidence of negative education externalities and lack of a correlation be-
tween growth and education expansion...and schooling variables (p. 380). Importantly, Pritchett
(p. 382) asserts, [r]ent seeking and directly unproductive activities can be privately remunerative
but socially dysfunctional and reduce overall growth which agrees with North (1990) suggesting
that informal institutions may benefit from education. One key challenge to this argument is con-
sistency in definitions. Returns to education (its funding cost/benefit balance) and returns to
knowledge are different.
For the evidences of and requirements for economic growth, this thesis builds on Romer
(1996), and Easterly & Levine (2001), by modeling dimensions of governance, development pol-
icy, education, and market demand of output. New Growth Theory stands apart from other
economic growth theories for three reasons: it shoulders change regardless of origin, pace, or va-
riety, it allows researchers to treat factors of growth as variables or constants, and it incorporates
education as a variable in economic growth.
Convergence, Divergence, and Bridging Theories
Incomes convergence over time
Robert Barro starts with Solows neoclassical growth model for evidence of automatic
forces that lead to convergence overtime in the levels of per capita income and product (1991,
p. 1). The homogeneity of US data earned credit for much of the statistical strength in his model.
This point is significant (and expanded later) as an indicator of internal, freer market forces tend-
ing to cause converging incomes (Barro, 2000). Convergence Theory maintains credibility for
use in discrete situations, but until recently, had done little to inform economic globalization
(Barro, 2000, 2001a; Olson, Sama et al., 2000; Jeffrey D. Sachs, 2005; Sadik, 2008). Conver-
gence Theory may explain why incomes in Kuznets maturing economies grew increasingly
similar. Clearly, according to Sachs & Warner (1995), it is the policy choices that underlie
60


each countrys economic realities and have allowed countries into the convergence club or
kept it trapped in poverty shy the human capital to raise it up (p. 4).
Incomes divergence over time
Divergence Theory suggests that incomes diverge overtime in certain situations. It
gained widespread recognition as the refutation to the convergence literature (Easterly & Levine,
2001; Matheson, 2008). Substantial scholarly work and research on discrete datasets agree that
there are likely correlations between economic growth and divergent incomes (Baddeley, 2006;
Pritchett, 1997), including Rothschilds recalculation of Kuznets' intersectoral inequality ratio
for the private nonagricultural economy between 1948 and 1982 [which] results in a significant
increase in sectoral inequality (1986, p. 205). Stated otherwise, incomes between the richest and
poorest in this particular data set grew apart. Divergence Theory may explain why incomes in
Kuznets immature economies grew increasingly disparate. Pritchett (1997, p. 15) suggests that
the divergence is a matter of progressiveness or backwardness of the fabric of civic society.
He follows with a prescription for growth policy similar to Sachs & Warner (1995a, p. 10), that
countries must provide an open economy, free of repressive regulations, with relatively low levels
of corruption .
Bridging Theories
Finally, five theories offer a bridge between the convergence and divergence camps.
Knowledge about the difference between camps is the critical issue for many policy analysts and
public administrators and policy makers, as the outcome bears great weight on long-term individ-
ual and aggregate prosperity (North, 1991b). First, poverty trap theories suggest simultaneous
convergence and divergence. For instance, if policy makers neglect to appropriate adequate fund-
ing for education, empirical evidence shows that the poorest sector of the population will grow
relatively poorer while the richest grow relatively richer (Romer & Barro, 1990).
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Second, Schumpeterian Growth Theory asserts that growth comes from quality-
improving research and development innovations. This theory informs the balance of economic
growth theories and adds validity to both camps, as each require innovation regardless of its
origin (Howitt, 1999; Howitt & Mayer-Foulkes, 2005; Phelps, 2008).
Third, in The Stages of Economic Growth, Rostow (1991) offers an historical view of
characteristics of economies in different stages, which, in turn, offers potential bridges between
the four major economic growth camps, allowing each to be right under certain conditions. For-
tunately, this suggests a futility in damning converging or diverging theories, as these mean little
without knowledge of the relative stage and trend of the economy, and less without an accounting
of the variables composition. It is insufficient to ask a question requesting results on income tra-
jectories absent considerable context.
Path dependency theories suggest that the economys behavior may take time to adjust, as
investment in land, labor, and capital, are somewhat directional (North, 1991b). The movement
of individual income inequality may be the result of a combination of factors from legacy eco-
nomic investments, which take time to adjust, and legacy skill sets, which take time to re-train.
Lastly, Kuznets data support both diverging and converging income inequality. He aptly
applied different data scenarios to discover the determiners of inequality, while he admitted that
the data available left much unknown, and part of the credit for the richness of information avail-
able now goes to him for the challenge he presented. Kuznets (1934, pp. 6-7), as the innovator of
National Income accounting, knew and reported the limitations of GDP per capita as a measure-
ment for individual welfare or the reverse side of income.
In summary, the convergence / divergence argument remains a vital and productive field
that tells policy makers and analysts nothing, or, worse, can be skewed to tell them anything, ab-
sent considerable context. For this reason, and because the presence of corruption skews the very
data policy makers need to make development decisions (Schneider et al., 2010), it is critical to
62


inform public administrators and their policy development endeavors that good governance and
control over corruption is important (Tanzi, 1998). Public budgets (e.g., infrastructure, health
care, public works, and courts) suffer, in the presence of corruption. This thesis defends that si-
phoning funds away from education does more harm than siphoning funds from other publically
funded programs as reduction in education budgets has an increasingly detrimental effect on eco-
nomic growth when the others effects are, at best, constant (health care) or decreasing
(depreciating assets) (Mauro, 1997). Mauro asserts corruption lowers expenditure on education
(p. 267).
New Growth Theory is critical to developing the healthy growth policy arguments. Edu-
cation funding as a part of development policy must be protected, as education, and consequently,
societys ability to adopt and use technology effectively, hinges on this protection. This may ex-
plain why healthier, more mature economies with better governance and control over corruption
experience less poverty, less inequality, and converging incomes; however, this is a thought for
future research.
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Measuring Education Delivery
Measuring the Quantity of Education (Supply)
The body of literature on assessing and measuring education is enormous. The literature
surrounding education delivery includes research on quality, quantity, and process measurements.
Quality measures for education include the output-based test scores and literacy rates, and on in-
puts such as materials, facilities, class size, and teacher training (UNESCO, 2005; D. J. Brewer,
Krop et al., 1999, p. 187). Quantity measures for education include matriculation rates, years of
schooling, level of school attainment, and gender parity (Barro & Lee, 1993, pp. 365-368). These
factors are critical for assessing education systems, however, they measure education delivery
based on an externally developed budget. This body of literature, which informs education as an
industry, discipline, or process, is outside the scope of this project. Rather, the focus for this the-
sis is public expenditures budgeted for public education, and the effects of corruption on the
public expenditures for public education. Education funding provides its own body of literature.
However, this thesis focuses on empirical data indicating the level of public funding from official
budgets, or supply-side financing (Patrinos, 2007, p. 1). Therefore literature on other funding
or school choice methods, to include private schools or private funding (M. Friedman, 1997),
voucher programs and school choice (M. Schneider et al., 1997; Teske & Schneider, 2001), bus-
sing, scholarships, grants, foreign aid, for-profit schools, and demand-side mechanism
(Patrinos, 2007, p. 3) is excused for the purposes of this thesis.
Measuring the Value of Education (demand)
Another body of literature, equally vast, measures the value of the schooling provided to
the individual (e.g., living standards, individual income, human development, and contribution to
society) and to society (returns to education, national and international market value of goods and
services). This work is quintessential to the questions in this thesis. Sen integrates these volumes
of work into his work on Human Development (HDR, 2009e; Sen, 1997, 1999, 2004). He syn-
64


thesizes work by other Nobel Laureates into the human development literature by including edu-
cation as a means to economic development (e.g., Kuznets work on accounting for individual
income in National Income accounting [1934, 1955]; Solows work on technical change, [1957];
Arrows work on learning by doing, [1962]; and Simons ideas on bounded rationality, [1997])
Sen added work by noted scholars on the subject, as well (e.g., the work of Sachs &
Warner, on knowledge spillovers and economic policy, [1995]; Romers work on education and
technical change [1990]; proceedings for the World Bank on development through education by
Romer et al. [1992]; as well as Romers work on endogenous change, investment in education,
and New Growth Theory [1994, 1998]; and Barros work in educational attainment and human
capital [1993, 2001, 2001b]). Together with the IGOs research network and the vast amount of
research ongoing for the Millennium Development Project, Sen created an index that asserts to
measure the extent to which each countrys governance system to date has aided, or neglected,
human development; this included the educational institutions it fostered over time. The HDI
provides the pre-test data for this thesis, upon which the research questions are built.
This thesis assumes that education is a public good, which is not universally held to be
true. Education Expenditure data in this thesis does not account for private funding of private or
public education, nor does it account for foreign aid for education. School choice, voucher pro-
grams, charter schools, and lotteries were borne out of a perceived or real shortcoming of the
system in place. Education may be better, more efficiently or more effectively delivered by the
private sector (M. Friedman, 1997).
Governance Corruption in Education
Literacy rates, reporting the percentage of citizens with basic reading, writing, and nu-
meracy skills (UNESCO, 2010d, p. 94), are insufficient to measure the complexity of education
gained during school, and are often self-reported. The rate of graduation is equally insufficient to
measure education earned, if graduates are not proficient readers or writers. Testing high on an
65


exam is an insufficient measure for application of knowledge on the job, or the degree to which
knowledge and training matches the needs of employers (Romer, 1998a). Other factors to include
gender differences (Barro & Lee, 2001), class size (D. J. Brewer et al., 1999), school choice (M.
Schneider et al., 1997), school funding and competition (M. Friedman, 1997), and overall school
quality (GMR, 2005), are neglected in the EAI.
Gupta et al. (1998) report that by reducing the public resource pool, the remuneration for
corruption could shift toward that which complements the endeavors of those that are corrupt and
away from approved, sustainable economic development and market-demanded goods and ser-
vices. Following are three such methods, or privilege-seeking behaviors. Bribes paid to school
officials merely to gain entry into school (p. 10) is one method by which funds are transferred
away from the public resource pool; by virtue of the official position of the bribe taker, this effec-
tively increases the cost of education, creating a daunting financial barrier to free public
education, and this income by-passes taxation. A second possible method is the under-delivery
and over-pricing of supplies and textbooks, thereby increasing the effective cost of schooling and
adversely affecting the demand (p. 11). Under-delivery decreases the quality and quantity of ed-
ucation, with suppliers withholding shipment until sufficient bribes are paid (p. 12). The third
method, because of school officials or other government employees having access to the budget,
is siphoning, using various processes to create a profit center. One such mechanism is under-
invoicing, where part of the taxes collected for public education are pocketed (p. 6); another is
corruption and theft, where the service is charged to the government and either under provided
or not provided to the public at all (p. 5).
Education is measurable by its evidence, it is measured in human capital (Barro, 2001b;
Lucas, 1988), and specifically, knowledge capital. Knowledge as capital, knowledge gains, posi-
tive knowledge externalities, knowledge spillovers, and other adjectives that describe that
transformation from the process of educating to gain a state of knowledge, or increases in intel-
66


lectual muscle, show up in economic growth generally, and individual income, specifically.
Klingner & Sabet (2005) refer to knowledge, adaptation, and innovation as a knowledge spiral
(p. 208). Romer (1990), consistent with Solow (1956, 1957), asserts that human capital, partially
determined by knowledge, determines the rate of economic growth.
It is because of this EAI shortcoming and shortcomings of other widely used measures
for education attainment (e.g., test scores, literacy rates, graduation rates) that this thesis employs
two measures for education. The first measure for education is an input-driven or supply-side
measure, government spending on public education as a percentage of total government expendi-
ture, or Education Expenditure (EE), using the precedent set by the United Nations in its report
Education for All (GMR, 2010d). Many measures for education inputs exist, however, since this
thesis uses GDP per capita as a measure for economic development, consistent with Sen (1997),
La Porta & Shleifer (2008), and Gupta et al. (1998), we must use a derivative of GDP as the
measure for a nations spending on education to maintain data and construct validity across varia-
bles, which the ICP network provides in the public data. The second measure for education is an
outcome-driven measure, or demand-side, Income per capita (Ic), which serves as the proxy for
learning, knowledge, and skill attainment consistent with a widely accepted and standard method
since Kuznets (1934) (Barro & Lee, 1993; Galbraith & Kum, 2005; Gupta et al., 1998; Mauro,
Abed etal., 2002; Schumpeter, 1939; Sen, 1984)
Two scholars help frame debate on the effects of corruption on education. Both scholars
argue that corruption is corrosive to a society; from the quantitative, empirical analysis discipline,
Paulo Mauro, and from the qualitative, social capital discipline is Paulo Freire. Paulo Mauro
(1998) Harvard Ph.D. and Fiscal Operations Division Chief for the IMF, asserts in Corruption
and the Composition of Government Expenditure, .. .there is significant evidence that corruption
is negatively associated with government expenditure on education, and the relationship is robust
to a number of changes in specification. He continues by arguing that the education line item
67


suffers relative to those more lucrative to rent-seekers. The results are consistent with the hy-
pothesis that education provides more limited opportunities for rent-seeking than other items
do and that there is also tentative evidence that the direction of the causal link is at least in
part from corruption to the composition of spending (Mauro et al., 2002). Scholars and econo-
mists with research confirming Mauro include Armstrong (2005) Gupta et al. (2000), Pritchett,
(1997), and Chua, (2006, 1997, 2001).
The Paulo Freire Institute Headquarters is at the UCLA Graduate School for Education
and Information Studies, which also houses the Freire Online, a Journal, dedicated to critical ped-
agogy. Freire was awarded the UNESCO Prize of Education for Peace in 1986, and the
International Development Prize by King Baudouin of Belgium in 1980 for his work in education
and pedagogy. He also served as the Secretary of Education in Sao Paulo, and his work has
been the subject of hundreds of Ph.D dissertations. His work informs public policy through the-
ories of social capital, critical theory, and social networks (Gadotti & Torres, 2005). In the
seminal work on pedagogy in lesser-developed regions, Freire uncovers struggles against the
tides of political, corrupt, philosophical, cultural, and social oppression infused into the educa-
tional systems in communist counties. He asserts that the ineffectual education inhibits personal
freedoms and the ability for the undereducated to be the owner of ones own labor (1970, p.
183). More critical is his assertion about motive. The oppressor knows full well that this inter-
vention [educating the oppressed] would not be to his interest. What is to his interest is for the
people to continue in a state of submersion, impotent in the face of oppressive reality (Freire,
1970, p. 52). Furthermore, I have already affirmed that it would indeed be naive to expect the
oppressor elites to carry out a liberating education (p. 135).
Contrary to Mauro and Freire assertions about education under communist regimes, the
evidence reveals robust education policy and delivery plans from the 1920s and through the
1980s intended to create sustainable economic growth in the former USSR. The Technical and
68


Vocational Education in the Union of Soviet Socialist Republics (Movsovic, 1959) written for
UNESCO, reports the education plan than ensures free education, the forms and methods of
which are identical for the whole Soviet Union (p. 14). Where the role of the instructor... in
the educational process is to inculcate into his pupils sound professional knowledge and work
habits and develop creative initiatives and conscious labour discipline in them (p. 19). The duty
of the teaching staff is ... constantly to improve professional and pedagogical qualifications and
to... .make careful preparation for classes... (p. 19). The report continues, .. .it is the function
of every department in an institution of higher education not only to acquaint the student with the
scientific bases of present-day industry but also to provide him with the solid scientific-
theoretical grounding necessary for his future activity (p. 44).
Article 121: Constitution of the USSR establishes the right to education of the
citizens of the Soviet Union. This right is given effect through the system of gen-
eral and compulsory education,... general secondary education,... institutions of
higher education and secondary vocational schools, and...frees technical educa-
tion and training... .With all school education in the pupils mother tongue (p. 4).
Researchers such as Pritchett (2001), Levy (2007), and North (1991), attempt to reconcile
the incongruous evidence. Between the USSRs the enormous sums spent by the Soviet State on
higher education and secondary vocational training (p. 6) and research and development cele-
brated as the hallmark in education planning for sustainable economic development, and its
delivery over sixty years. However, growth waned increasingly toward the end of this period
(Movsovic, 1959; Ofer, 1987). Ofer suggests that four factors contributed to the downward trend
(1987, pg. 1812-1820). First is the inability of central planning to adapt to growing complexities
in the world economy. Second is the increase in relative spending on defense versus technical in-
novation. Third is the weakening of the material incentive system... which in turn has negative
effects on work motivation and efforts, thus further reducing growth (p. 1815). Last is the effect
of corruption. A second economy developing alongside the main, public sector takes another
69


bite from the effectiveness of the public sector (p. 1816). Schneider refers to this economy as
the Shadow Economy (Schneider et al., 2010a).
Paradoxically, New Growth Theory follows both declarations in the Constitution of the
USSR and the work of Mauro, Freire and others, affirming that education is a requirement of de-
velopment, and deserves priority status in the public budgeting process (UNESCO, 2010d;
Cortright, 2001).
According to Mauro, education turns out to be the only component of public spending
that remains significantly associated with corruption when the level of per capita income in 1980
is used as an additional explanatory variable (2000, p. 10). However, underdeveloped countries
have literate citizens. In fact, one of the reasons that the Educational Attainment Index (EAI) is
problematic for the HDI is due to the lack of a better measure of education quality and quantity.
Nearly every country has literacy rates near 99% (HDR, 2007, p. 226) corrupt or not, developed
or not, with centrally planned or market-based economies. This fact alone calls for a different
metric for gauging education broadly and literacy specifically. This fact also begs a different
measure for the value of education as decided by the market for the sum of what a countrys edu-
cation public has thought of, innovated, created, engineered, developed, advanced, manufactured,
and most importantly to the level of Gross Domestic Product per capita, what they have sold.
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Summary
Research Question 1 asks whether the change in Human Development Index accounts for
the change in Income per capita. Hypothesis 1 maintains that the change in the HDI does not
account for the change in Ic. Affirming this hypothesis opens the door to test whether the HDI
together with a variable for the Shadow Economy better explains the change in Ic. Research
Question 2 represents an attempt to inform the body of literature of the effect of corruption in
these areas, specifically. Hypothesis 2 maintains that governance corruption, as measured by the
SE, has a negative effect on Ic. Research Question 3 asks whether governance corruption, as
measured by the Shadow Economy has a negative effect on Education Expenditures. Hypothesis
3 maintains that the variation in EE can be explained by the variation in SE. If the tests affirm
this hypothesis, the door is open to test the last research question. Research Question 4: Do the
pre-test Human Development Index, governance corruption, and education expenditure together
explain the change in Income per Capita? Hypothesis 4 maintains that there is a significant
relationship between the change in Ic, HDI1990 and change in EE.
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CHAPTER 3
THE DATA AND METHODOLOGY
Methodology
New Growth Theory suggests that both endogenous and exogenous factors encourage
economic change, and that knowledge gains that support and invite research, development, tech-
nology advances, and the skills to implement technology adoption are pivotal to creating healthy,
sustainable economic development. The research design for this thesis is quasi-experimental.
Equations 1, 2, and 3 are foundational to Equation 4. Equation 1 tests the correlation coefficient,
or the strength of association (Gujarati & Porter, 2009, p. 20). Equation 2 compares adjusted R2
between two linear regression equations. For Equation 3, the model is a linear regression, and the
method is OLS. The key equation for this thesis is Equation 4, which compare the change in To-
tal Income per Capita, IcT, to the change in education expenditures (EE) given the HDI1990
starting point. The model specified for this research is a Three-Variable Linear Regression using
Ordinary Least Squares method. The model uses IcT as the regressand, which depends on
HDI1990, and EE as regressors (Vj = /?, + P2^2l + @3X31 Ei)- The test for this model using the
Linear Regression Analysis is a hypothesis test to predict the AIc3 per county. The equation run
to predict AIc3 follows (AIcT = Intercept + HDI1990 +AEE3 + error). The approach to test
the hypotheses is the test-of-significance approach. For test purposes, STATA requires the re-
searcher to set the level of significance. The level of significance is set at the 95% confidence
level, or 5% probability level of rejecting a hypothesis that is true. However, each test will identi-
fy the p-value, or the lowest significance level at which a null hypothesis can be rejected
(Gujarati & Porter, 2009, p. 122).
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Hypothesis
The key hypothesis this thesis tests is: governance corruptions effects on education
through the public resource mechanism (Government Expenditure on Public Education as a per-
centage of Total Government Expenditures) are direct and negative; the higher the degree of
corruption, the lower the relative education budget per capita. Further, the lower the education
budget per capita, the lower the relative individual income.
Data
The raw data are non-experimental, pooled, qualitative and quantitative, on a sample of
30 countries occupying the Central and Eastern Europe, out of a current potential population of
between 189 and 194 sovereign countries in the world plus 10 to 13 additional countries in transi-
tional phases (depending on the changing political climate). All of the countries in the world are
the population set. The transitional country pertinent to this study is Kosovo, which is reported as
part of Serbia, as the declaration for separation of these two countries occurred after the 2008
post-test year (See Country Briefs). To test the effects of governance corruption on education
budgets and income per capita, we chose Central and Eastern European countries that offer
unique data availability due to the focus by research scholars from international data agencies on
the unprecedented events of the late 1980s marked by the dissolution of the Soviet Union. The
data used herein have been quantified through indexing or econometric modeling, by the data
purveyor, and were collected and input by hand, imported, or copied in a quantitative format.
Each country has pre-test (1990) and post-test (2008) variables that are a nominal-scale, are dis-
crete, and random. The data are linear (in the parameters, or i.e., not exponential) (Gujarati &
Porter, 2009, p. 38).
The data are secondary data, retrieved from three sources that are each contributors to es-
tablished International Comparisons Program (ICP) (201 le) network of shared data. The first and
73


major source is human development data from the Human Development Project, part of the Unit-
ed Nations Development Programme (UNDP). This programs research produces data, data sets,
fact sheets, reports, and policy recommendations on human development, international and na-
tional-level governance and public administration research. This research work supports the
UNDP state and local governance programs, and other UN programs. The project attempts to
gather data on every country, with success in procuring data for 166 countries. The data are col-
lected through a combination of surveys, institutional data, government data, primary research on
location, and sharing with ICP. The resulting qualitative and quantitative data are indexed from 1
to 100 (HDR, 2008c). The HDI is a composite index composed of three indices; the Educational
Attainment Index (EAI); the Uife Expectancy Index (LEI); and the Gross Domestic Product Index
(GDPI). When GDP is divided by population, the resulting figure is Income per Capita, or Indi-
vidual Income Ic. In other words, the HDI = (1 x LEI) + (1 x EAI) + (1 x Ic). Each component
index is developed from its component data. The composite HDI and these component indices
are the key variables used. The source for each data point used is noted on Table D, found in the
Appendix.
The second source for data is the Shadow Economy (Schneider et al., 2010b) data, which
provides figures measuring that GDP produced and not counted in National Income Accounting.
The sources for data on 162 countries included a combination of surveys, institutional data, gov-
ernment data, primary research, and data shared through the. This data set covers all of the
sample set of countries, missing only three data points, Turkey and Mongolia in 1990, and Turk-
menistan in 2007. These three countries were studied individually, and the data for these is
available using the same methods (Eilat & Zinnes, 2000; Yereli et al., 2007; Zhou, 2007). The
method preferred by Schneider is the MIMIC method (DelfAnno & Schneider, 2006), which is a
simultaneous equation model sets qualitative and quantitative data into an equation as inputs and
74


outputs productivity to derive the percentage of the official economy lost to unofficial productivi-
ty.
Comparisons of nine common methods for calculating national-level governance corrup-
tion (e.g. WGI, TI, CPI, GCB, BPI) are found in Table A (Schneider & Enste, 2000). Many other
methods exist for sub-national, finn level (e.g., BEEPS, EBRD), and other forms of corruption.
DellAnno & Schnieder state, "|11here does not exist any commonly accepted methodology for
estimating the underground economy. The estimates are always subjective and depend on the
quality of the dataset the methods applied and the subjective decisions of the researcher. Shadow
Economy estimates are never very stable and absolute... (2006, p. 16). The authors go on to
support the MIMIC method by asserting that the MIMIC is the better of the known methods for
calculating national-level governance corruption relative to productivity on the books (F. G.
Schneider & Enste, 2000) (see Figure 1. MIMIC Model below).
Figure 5 Shadow Economy MIMIC Model.
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For the purposes of this thesis, in order [t]o get information about the dynamics and size
of the Shadow Economy, the MIMIC model is still one of the best approaches to this purpose (p.
16). Since Schneiders data have the best coverage on the sample set of counties and since it uti-
lized the most appropriate measurement method for our purpose (which is the Shadow Economy
size), Schneiders data is the better choice overall.
The third source of data is the Estates project, for the Education Expenditure data. Es-
tates is a joint international research group for the UN, through UNESCO, the World Bank
Research Group, and the ICP. Yearly data publications such as the Global Education Digest
(GED) provide a catalog of statistics. Data for the 2008 Education Expenditures are found in the
2008 GED, Table 13, Public Expenditure and Expenditure on Education by Nature of Spending
(pp. 167-176). The statistic used in this thesis is the Public Education Expenditure (EE) as a Per-
centage of Total Government Expenditure. In a major advancement, the UN, through its
Statistical Information System on Expenditure in Education (SISEE), requested yearly data pro-
curement as of 1998. From 1970 to 1990, United Nations Childrens Fund (UNICEF) gathered
the official data every five years, and added data to the set in the off years when it met all of the
previous methodology criteria. Researchers and the ICP still use the earlier data and deem it as
reliable (UNECSO, 1998). Data for education statistics deemed reliable based on the new meth-
odology became available in 1998. Important here is the dearth of data that exist from 1986
through 1998; only 54 data points exist for these eight years for the entire world, and only seven
of these are readings for the sample set.
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Reliability and Validity Testing
See Data Reliability and Validity Testing in the Appendix.
Research Question 1
Recall that which Kuznets, Sen, and others assert is Official GDP per capita, Ic, may not
be a sufficient proxy for individual human development, as it lacks variables such as individual
welfare, living standard, or earning capability (HDR, 2008c, p. 225; Kuznets, 1934; Sen, 1984).
Klenow & Rodriguez-Clare (2005, p. 833) and others assert the evidence from many scholars
employing various models are consistent in that less than half of the variation in individual in-
come can be attributed change in human capital and development (Easterly & Levine, 2001;
Klenow & Rodriguez-Clare, 1997; W. K. Wong, 2007a). In addition, the HDI is a widely accept-
ed proxy for the stock of human capital (HDR, 1990; Sen, 1997; W.-K. Wong, 2007b)
Research Question 1.1: Are the HDI and the change in the Total Income per Capita cor-
related at .5 or higher? To test this construct with our data, we can run the correlation coefficient
test. If we reject the null hypothesis, then we can conclude for now, that the correlation between
the Human Development Index from 1990 to 2008 and the change in Total Income per Capita,
(IcT), is less than .5, consistent with the rule used in Wong, (2007b).
Hypothesis 1.1: The correlation coefficient of Ale Tfrom 1990 to 2008 and AH I)/ from
1990 to 2008 is less than .5.
Hypothesis 1.2: The correlation coefficient of ALcTfrom 1990 to 2008 and AHDI compo-
nent indices, ALEI + EAI from 1990 to 2008 is less than .33.
This test of the changes in the independent component indices is important for several
reasons. Sen contends that the sum total of the factors human development over time are cap-
tured in a snap shot in time measured by the HDI composite index made up of three component
indices, EAI, LEI, and GDPI. Using the HDI, then, we respect and factor into the equation the
sum of history for each country, or the proxy human capital and development stock at one point
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in time, consistent with the recent literature (Klenow & Rodriquez-Clare, 2005; Wong, 2007).
Examining the change equalizes the pre-test differences in countries.
Gujarati & Porter, (2009) explain that a properly specified model will yield an intercept
term. The intercept may be statistically equal to zero, which means that it runs through the origin,
or close to it; however, the near-zero intercept is a product of the regression equation. In the case
of 30 Eastern and Central European countries, the regression equation will yield an intercept
term. This point on the Y-axis is the starting point for calculating the effects of governance cor-
ruption on education budgets and income per country. Without very strong a priori
expectation, according to Gujarati & Porter (2009), one would be well advised to stick to the
conventional, intercept-present model (p. 150). One example of a strong a priori expectation of
a zero-intercept model exists here. In equation 4, one would expect that where human develop-
ment is zero, income would also be zero, justifying a zero-intercept model (Gujarati & Porter,
2009, p. 150). In addition, one can check for misspecification of a model after the fact by check-
ing the statistical significance of the constant, to verify that there are no omitted variables (p.
198). For example, the greatest difference in the pre-test HDI, or HDI1990, in the Central and
Eastern Europe is .263 points, from Tajikistan at .636 to Austria at .899. The post-test HDI, or
HDI2007, for these two countries is .688 and .955 respectively, or .267 points apart. Tajikistans
HDI increased .0818 points, while that of Austria increased .0623. Examining the change in HDI
shows that the sum total of the change in economic and demographic data underlying Tajikistans
HDI increased at a faster pace than did Austria, .015% faster.
According to Gujarati & Porter, (2009), analyzing the change in our variables minimizes
the chances of heteroscedasticity, autocorrelation, and multicolinearity naturally present in pooled
(cross-country, time-series) data. Heteroscedasticity is the unequal variances due to errors, outli-
ers, inertia, skewness, or incorrectly specified linear regression (pp. 365-368). Autocorrelation is
correlation between members of a series of observations ordered in time or space... and does not
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exist in the u (p. 413). Multicolinearity is similar linear relationships among some or all ex-
planatory variables (p. 321) naturally present in pooled data. Gujarati Porter, (2009, p. 434)
suggests the Durban Watson d-test cannot be used to detect serial correlation, in the equations
containing SE or EE data, as data points are missing from both sets of data. It cannot be used on
the HDI data or the SE data, as the data have lagged variables.
This examination may inform many policy questions. Two sample policy questions spe-
cific to this thesis are: Which element of the development data had the greatest impact on
Tajikistans change in income per capita? alternatively, What component measure of Moldo-
vas HDI shows the least progress over time, and what can a policy change do to fix it?
The thesis purpose is to measure the effects of corruption on education budgets and in-
come per capita as measured by the capability of an individual to earn income. This brings us to
the second research question.
Research Question 2
Does the corruption, as measured by the Shadow Economy, negatively affect Income per
Capita? This question requires us to add the first new data point, corruption as measured by the
SE. Recall that the SE is stated as a percentage of the official GDP (Schneider et al., 2010b).
The SE set of data is one of several that assert to measure the size of GDP lost to corruption, hid-
den in the underground or unofficial markets. The only such study with adequate coverage of
Central and Eastern Europe was sponsored by the World Bank and published in 2010, Shadow
Economies all over the World: New Estimates for 162 Countries from 1999 to 2008.
We employ an OLS regression to test our second research question. We will regress the
AIc0 and the HDIi990, and then add a variable for explanatory power. If the theoretical construct
is valid, the relationship between AIc0 and HDI1990 will be statistically significant; and the SE
variable will add explanatory power to the regression making it a more robust predictor of AIc0.
Consistent with widely accepted growth regression models, maintaining a constant in a compari-
79


son of equations measuring goodness of fit (adjusted R2) expresses that the pretest starting point
was not zero; this shows the value of the pretest stock of Human Development based on the OLS
regressions (Barro, 2001b; Cobb & Douglas, 1928; Klenow & Rodriquez-Clare, 2005; Mauro,
Abed etal., 2002; Pritchett, 2001).
Hypothesis 2: The adjusted R2 resulting from an OLS regression ofpretest HDI against
the AIc0 is equal to or greater than the adjusted R2 resulting from an OLS regression ofpretest
HDI and SE2oos against the AIc0.
If we reject the null hypothesis, we must conclude for now that the SE20os per country in-
cluded in the regression with the HDI1990, explains more of the variation in the AIc3 than does the
HDI1990 alone. This finding would be consistent with the theory that corruption hinders economic
development, and of the findings of Schneider et al. (2010), Kauffmann et al. (2008), Johnston,
(2007), and other scholars.
Research Question 3
Does a change in the Shadow Economy negatively affect Education Expenditure? To test
the effects of corruption on education budgets, we employ our final new variable, Education Ex-
penditure (EE). If the presence of the SE has no significant effect on the EE, then we find that the
theoretical construct was invalid. The null hypothesis states that AEEc does not equal the ASE.
Gupta et al. (1998, p. 28), and others assert the evidence from many scholars employing various
models are consistent in that less than one third of the variation in individual income can be at-
tributed directly to the change in corruption (Kaufinann, 2003; Pritchett, 2001). In addition, the
HDI is a widely accepted proxy for the stock of human capital (HDR, 1990; Sen, 1997; Wong,
2007)
Hypothesis 3: The variation in the AEEc from 1990 to 2008 is not explained by the varia-
tion in SE2008.
80


If we reject the null hypothesis, that the effects of the SE on EE are statistically signifi-
cant, then we can conclude for now that a relationship exists. If our working theory about
corruptions effects on education budgets prevails, and an increase in the size of the SE results in
a decrease in education funding, we can measure this decrease, and measure its effect, if any, on
income.
Research Question 4.1
Do the pre-test HDI, governance corruption, and education expenditure together explain
the change in Income per capita? The null hypothesis asserts that there is no significant relation-
ship between the Official Income per Capita, AIc0, and the explanatory variables, HDIi990, and the
change in EE per capita from the pretest to the posttest values EEci990 and EEc2oos
Hypothesis 4.1: The variation in the AIc0 from 1990 to 2008 is explained by the varia-
tion in the HDIiggo, and the change from EEc3 in 1990 and EEc3 in 2oo&
If we reject the null hypothesis, we can assume for now that a relationship between the
pre and post-test per capita figures for EE3, and the AIc0 exists.
Research Question 4.2
The null hypothesis asserts that there is no significant relationship between the Unofficial
Income per Capita, AIcu, and the explanatory variables, HDI1990, change from the EE per capita
pretest the and posttest values, AEEc
Hypothesis 4.2: The variation in the AIc3 from 1990 to 2007 is explained by the varia-
tion in the HDI1990, and the change in EEc3 from 1990 to 2008.
Research Question 4.3
The null hypothesis asserts that there is no significant relationship between the AIcu and
the explanatory variables, HDI1990, the change AEE3
Hypothesis 4.3: The variation in the AIcT from 1990 to 2008 is not explained by the variation in
the HDIi990, and the change in EEc from 1990 to 2008.
81


Summary Statistics
Variable | Ob Mean Std. Dev. Min Max
HDI1990 30 .78 .0575506 . 636 .896
HDICh 30 . 0507633 .0283926 - . 0204 . 0918
LEI EAI 30 130.0245 20.85304 103.895 183.504
IcChDc 30 1218.467 1702.272 -1194 6119
SE1990 30 27.42 8.127662 12.2 45.1
SE2008 30 38.95233 11.65943 16.1 68.8
SE2008DC 30 1287.911 1022.685 109 4113
IcTotalChDc 30 1824.1 2225.068 -1382 7862
EEDc2008 30 551.7297 554.2541 46. 02128 2397.184
EEChDc 30 123.391 266.0138 -272 .1115 890.7047
82


CHAPTER 4
ANALYSIS
Research Question 1
Research Question 1.1: Are the Human Development Index and the Change in the In-
come per capita correlated at .5 or higher? To test this construct with our data, we can run the
correlation coefficient test. If we reject the null hypothesis, then we can conclude for now that
the correlation between the Human Development Index from 1990 to 2008 and the change in In
come per Capita is less than .5, consistent with the rule used in Wong, (2007b).
Summary Statistics
Variable | Obs Mean Std. Dev. Min Max
HDICh | 30 . 0507633 . 0283926 -.0204 .0918
LEI EAI | 30 130.0245 20.85304 103.895 183.504
IcChDc | 30 1218.467 1702.272 -1194 6119
Correlation Coefficient
1 IcChDc HDICh LEI EAI
IcChDc | 1. 0000
HDICh | 0. 5005 1.0000
LEI EAI | 0. 6793 0.2639 1.000
Hypothesis 1.1: The correlation coefficient of Ale from 1990 to 2008 and AHDI from
1990 to 2008 is less than .5.
Equation 1.1
Null Hypothesis: H0 : if \t\ > ta_n_2\reject H0
2
Maintained Hypothesis: H]: if \t\ < ta \ fail to reject H0
2
The correlation coefficient is .4599, which is less than the benchmark of .5. On a one-
tailed test, the t-statistic is -.501, well within the acceptance region of < .1697 at 30 degrees of
freedom at the 95% confidence level. For now, we maintain that the correlation between the


change in the variables meets the test requirement, at less than the benchmark. Below, the scatter
graph shows the correlation.
O
O
o
00
o
o
o
CD
O
O
O
o
o
o
(N
-.02
Change in Income per Capita vs
Change in Human Development Index

A
m

m
A
A
m
A
a
0 .02 .04 .06 .08 .1
Human Development Index % Change from 1990 to 2008
a Eastern Bloc, non USSR Former USSR
Correlation Coefficient = .4599
Figure 1.1 Change in Income per Capita and Change in Human Development Index.
To test the correlation between the Life Expectancy Index and the Educational Attain-
ment Index, equally weighted (the weights in the HDI are equally weighted), we take the GDP
index out, and re-run the correlation coefficient.
Hypothesis 1.2: The correlation coefficient of Alefrom 1990 to 2008 and AHDI compo-
nent indices, ALE1 + EAT from 1990 to 2008 is less than .5.
Equation 1.2
Nidi Hypothesis: H0 : if |t| >ta 2: reject H0
2
Maintained Hypothesis: Eh : if \t\ < ta \ fail to reject H0
2
The correlation coefficient is .0552, which is significantly less than the benchmark of .5. On a
one-tailed test, the t-statistic is -.501, well within the acceptance region of < .1697 at 30 degrees
84


of freedom at the 95% confidence level. For now, we maintain that the correlation between the
change in the HDI is very slightly negatively correlated with the change in Ic, at -.0015. Below is
the scatter graph depicting the correlation between the Change in Income per Capita and the life
expectancy and educational attainment indices.
Life Expectancy Index + Educational Attainment Index
o
o
CN
O
lO
O
O
vs Income per Capita


o
LO
^---
-2000
0 2000 4000 6000
Change in Income per Capita from 1990 to 2008
Former USSR Eastern Bloc, non USSR
Correlation Coefficient .0552
Figure 1.2 Change in Income per Capita and Change in LEI and EAI.
85


Research Question 2
Does governance corruption negatively affect Individual Income? (Governance corrup-
tion is measured by the average Shadow Economy from 2000-2008, and Education expenditure is
measured with the proxy EEc. A linear regression comparison of the R2 tests Research Question
2, using the Change in Income per Capitaoflicmi, AIc0, as the dependent and HDIi990 as the inde-
pendent variable. HDE990 is the pre-test or legacy measure, the starting point in human
development measurements, for the sample set of countries.
Hypothesis 2: The adjusted R2 resulting from a linear regression of HDI against the AIc0
is higher than the adjusted R2 resulting from a linear regression of HDI and SE2oos against the
AIco-
Equation 2.1
Null Hypothesis'. H0 : AIc0 j HDI1990
Maintained Hypothesis'. Hi: AIco=HDIi990
Summary Statistics
Variable | Obs Mean Std. Dev. Min Max
HD11990 | 30 .78 . 0575506 . 636 . 896
Icl990 | 30 3010.067 3628.822 426 19428
SE2008 | 30 38.95233 11.65943 16.1 68.8
Correlation Coefficient
I HDI1990 Icl990 SE2008
--------------+-----------------------------
HD11990 | 1.0000
Icl990 | 0.6886 1.0000
SE2008 | -0.5148 -0.5977 1.0000
86


Test: Linear Regression 95% Confidence Level
Regressed dependent variable AIc0 using independent variable HDL990
Source | SS df MS
Model | Residual | 35855179.2 48178968.3 1 28 35855179.2 1720677.44
Total | 84034147.5 29 2897729.22
Number of obs = 30
F( 1, 28) = 20.84
Prob > F = 0.0001
R-squared = 0.4267
Adj R-squared = 0.4062
Root MSE = 1311.7
IcChDc | Coef. Std. Err. t P>|t| [95% Conf. Interval]
HD11990 | 19320.9 4232.54 4.56 0.000 10650.93 27990.86
_cons | -13851.83 3310.056 -4.18 0.000 -20632.17 -7071.489
Post-Estimation Statistics for Regression
White's test for Ho: homoscedasticity
against Ha: unrestricted heteroscedasticity
chi2(2)= 8.62
Prob > chi2= 0.0134
Cameron & Trivedi's decomposition of IM-test
Source | chi 2 df P
Heteroskedasticity | Skewness | Kurtosis | 8.62 4.37 0.58 2 1 1 0.0134 0.0365 0.4452
Total | 13.58 4 0.0088
Ramsey RESET test using powers of the fitted values of IcChDc
Ho: model has no omitted variables
F(3,25)= 5.95
Prob > F = 0.0033
Information Criteria
Model | Obs 11(null) 11(model) df AIC BIC
1 30 -265.2512 -256.9067 2 517.8134 520.6158
The regression output shows an /' -score at 20.84 with 29 degrees of freedom, at most,
40.62 % of the variation in the AIc0 can be explained by the variation in the HDI1990, and the t-
value of the HDI relationship is very significant at 4.56. This test passes the 2-t Rule of
Thumb. The RMSE is 1311.7. The minimum MSE criterion consists in choosing an estimator
whose MSE is the least in a competing set of estimators.. .there is a trade-off involved to obtain
minimum variance, you may have to accept some bias (Gujarati & Porter, 2009, p. 828).
87


Whites test confirms autocorrelation with X2 of 8.62 on 2 degrees of freedom. The IM-test con-
firms left skewed data at 4.37 and a short and fat (platykurtic) kurtosis distribution at .58. The
AIC is 517.8. The analysis suggests rejecting the null hypothesis, confirming a significant rela-
tionship. Next, we compare the R2 values between here and a second equation adding SE2oos as
an explanatory variable.
Equation 2.2
Null Hypothesis:
H0 : R2 regress AIc0 with HDIiggo >R2 regress AIc0 with HDIiggo andSE20os
Maintained Hypothesis:
Hi: A2 regress AIc0 with HDIi990 < R2 regress AIc0 with HDT990 and SE2oos
Summary Statistics
Variable | Obs Mean Std. Dev. Min Max
HD11990 | 30 .78 . 0575506 . 636 . 896
SE2008 | 30 38.95233 11.65943 16.1 68.8
IcChDc | 30 1218.467 1702.272 -1194 6119
Correlation Coefficient
I HDI1990 IcChDc SE2008
HD11990 | 1.0000
IcChDc | 0.6532 1.0000
SE2008 | -0.5148 -0.5981 1.0000
Test: Linear Regression95% Confidence Level
Regressed dependent variable AIc0 using independent variables HDT990 and SE2008-
Source | SS df MS Number of obs = 30
F( 2, 27) = 14. 62
Model | 43690767.5 2 21845383.8 Prob > F = 0.0000
Residual | 40343379.9 27 1494199.26 R-squared = 0.5199
Adj R-squared = Root MSE 0.4844 1222.4
Total | 84034147.5 29 2897729.22
IcChDc | Coef. Std. Err. t P> 1 11 [95% Conf
HD11990 | SE2008 | cons | 13897.18 -52.00247 -7595.717 4600.658 22.70871 4120.428 3.02 -2.29 -1.84 0.005 0.030 0.076 4457.41 -98.5969 -16050.14
Interval
]
23336.95
-5.40805
858.7039
88


Full Text

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THE EFFECTS OF GOVER NANCE CORRUPTION ON EDUCATION BUDGETS AN D INCOME IN CENTRAL AND EASTERN EUROPE by Tamara Lynn Hannaway BA, Fort Lewis College, 1984 MBA, Westminster College, 1995 A thesis sub mit ted to the Faculty of the Graduate School of the University of Colorado Denver in partial fulfillment of the requirements for the degree of Doctor of Philosophy Public Affairs 201 2

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ii 201 2 by Tamara Lynn Hannaway All rights reserved.

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iii This thesis for the Doctor of Philosophy de gree by Tamara Lynn Hannaway has been approved for the Graduate School of Public Affairs b y Peter deLeon Chair Paul Teske Robert Reichardt Christoph Stefes Date ____________________________

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iv Hannaway, Tamara Lynn (Ph.D., Public Aff airs) The Effects of Governance Corruptions on Education Budgets and Income in Central and Eastern Europe Thesis directed by Professor Peter deLeon ABSTRACT This thesis addresses economic development in the context of endogenous corruption We also ask whether economic growth exacerbates poverty or income inequality The evidence to date is mixed. T he thesis examines relationships among and between defined constraints on economic development by offering policy makers a unique method of measuring govern ance co rGovernance corruption includes m alfeasance, misfeasance, nonfeasance o r perpetrations involving state, non state, and private sector actors that circumvent distort, or manipulate the democratic process, and thereby unde rmine the government revenue stream G overnance is the official government al system and institution s tate capture, rent seeking, and free riding behaviors corrupt the system. T he Shadow Economy acting as the surrogate for co rruption measures the percent of total productivity unaccounted for in the official GDP. T he individual actor is the unit of measure ; Central and Eastern Europe an countries are the sample set ; i ndividual income is the dependant variable ; and t he independent variables are the Human Deve lopment Index, education expenditures and the Shadow Economy. T he analyses presented suggest clear evidence that as the size of the Shadow Economy increases, t he budget for educ ation expenditures as a percentage of the total national government expenses decreases. The evidence implies that as the education budget decreases, so does the official individual income and therefore, available measures for economic g rowth are inadequate to measure income in equality, thereby leaving analyses and conclusions regarding the effects of economic growth on the individual actor, wanting. The se findings are consistent with New Growth Theory, particularly, that education is cr itical to a healthy and sustainable economic development and offer ev i dence that adding the effects of corruption to current economic growth models provides unique learning The practical application is that ed ucation expend itures and individual income are analyzed together and in light of the effect of corruption on them. This evidence may be appreciable to economic development and education policy making. Key words: Governance, Corruption, Education Expendi tures, Income Inequality, New Growth Theory, Shadow Economy, Human Development, Economic Growth, Economic Development, Sustainable Development. The form and content of this abstract are approved. I recommend its publication. Approved: Peter deL e on

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v DEDICATION I dedicate this work t o my daughter Hannah with a very special thank you for your love, smile, vibrance, and the joy you bring. For your love, patience, inspirat ion, and support, thank you, especially to my mom, dad, grandma, sister, and brother, and also to my extended family, friends, colleagues, mentors, and students. Many persons from childhood to today deserve mention and a debt of gratitude. They have i m parted wisdom, contributed some important element, provided inspiration, influence, and encouragement. The list is many years in the making and longer than this dissertation ; thank you.

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vi ACKNOWLEDGEMENT S Many thanks to my advisor, Dr. Peter deLeon, for believing in me from the start, for his guidance, and for his contribution to and support of my research. I also wish to thank all the members of my commit tee for their valuable participation and insights. From before the start of this Ph.D. process, Colorado Christian University has been my place of full time employment. The extraordinary grace, understanding, and support extended to me by the whole of the institution and its employees, my colleagues, the administration, and the staff, is greatly ap preciated. Thank you. A more ardent team of cheerleaders the academy has never known.

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vii TABLE OF CONTENTS CHAPTER I. INTRODUCTION ................................ ................................ ................................ .............. 1 Research Questions ................................ ................................ ........................... 2 Problem and Research Approach ................................ ................................ ...... 9 Sample Set Rules ................................ ................................ ............................ 16 Thesis Overview ................................ ................................ ............................. 17 II. LITERATURE REVIEW ................................ ................................ ................................ 18 Governance ................................ ................................ ................................ ..... 19 Measuring Governance ................................ ................................ ..................... 22 Good Governance and Sustainable Development ................................ ............. 24 Human Development as a Metric for Governance ................................ ............ 28 Corruption as a Dimension of Gover nance ................................ ..................... 31 Forms, Size, and Scope of Corruption ................................ .............................. 33 Shadow Economy Rules ................................ ................................ .................... 37 Causes and Consequences of Corruption ................................ .......................... 37 ion ................................ ................................ ............... 40 Controlling Corruption ................................ ................................ ...................... 41 Consequences of Corruption on Development ................................ .................. 43 Measuring Corruption Indirectly ................................ ................................ .... 44 Economic Growth ................................ ................................ ........................... 49 The Business Cycle the foundation of economic growth stages .................... 49 Economic Growth Theories ................................ ................................ .............. 52 Measuring Education Delivery ................................ ................................ ....... 64 Measuring the Quantity of Education (Supply) ................................ ................ 64

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viii Measuring the Value of Education (demand) ................................ .................... 64 Governance Corruption in Education ................................ ................................ 65 Summary ................................ ................................ ................................ ......... 71 III. THE DAT A AND METHODOLOGY ................................ ................................ ............. 72 Methodology ................................ ................................ ................................ ... 72 Hypothesis ................................ ................................ ................................ ....... 73 Data ................................ ................................ ................................ ................. 73 IV. ANALYSIS ................................ ................................ ................................ ....................... 83 Research Question 1 ................................ ................................ ........................ 83 Research Question 2 ................................ ................................ ........................ 86 Research Question 3 ................................ ................................ ........................ 91 Research Question 4 ................................ ................................ ........................ 94 Hypothesis 4.1 ................................ ................................ ................................ ... 94 Hypothesis 4.2 ................................ ................................ ................................ ... 96 Hypothesis 4.3 ................................ ................................ ................................ ... 98 V. FINDINGS ................................ ................................ ................................ ...................... 101 Summary ................................ ................................ ................................ ....... 109 Data Limitations ................................ ................................ ............................ 117 VI. CONSLUSIONS AND FUTU RE RESEARCH ................................ ............................. 122 Conclusions ................................ ................................ ................................ ... 122 Future Research ................................ ................................ ............................. 128 APPENDIX A: COUNTRY BRIEFS ................................ ................................ ......................... 133 APPENDIX B: DATA RE LIABILITY AND VALIDI TY ................................ ........................ 187 TABLES AND FIGURES ................................ ................................ ................................ ........... 227 GLOSSARY ................................ ................................ ................................ ................................ 243 REFERENCES ................................ ................................ ................................ ............................ 246

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ix LIST OF FIGURES Figure 1.1 Change in Income per Capita and Change in Human Development Index. ...... 84 Figure 1.2 Change in Income per Capita and Change in LEI and EAI. .............................. 85 ...................... 93 .................... 93 Figure 5 Shadow E conomy MIMIC Diagram. ................................ ............................ 7 5 232 Figure 5.1 Change in Income per Capita and the Education Expenditure. ....................... 105 Figure 5.2 Change in In come per Capita and Lagged Education Expenditure. ................ 105 Figure 6 Shadow Economy Simultaneous Equations. ................................ ...................... 232 Figure 6.2 The Kuznets Curve (1966). ................................ ................................ ............. 129 ................................ ............ 130 Figure 7 Data Validation Comparison. ................................ ................................ ............. 239 Figure 8 The Policy Problem. ................................ ................................ ........................... 246

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x LIST OF TABLES Table 1 Hypot hesis 1 Data. ................................ ................................ ............................... 228 Table 2 Hypothesis 2 Data. ................................ ................................ ............................... 229 Table 3 Hypothesis 3 Data. ................................ ................................ ............................... 230 Table 4.0 Equation 4 Comparison. ................................ ................................ ................... 107 Table 4.1 C orrelation Coefficient Matrix. ................................ ................................ ........ 242 Table 4.2 Correlation Coefficient Matrix. ................................ ................................ ........ 242 Table 4.3 Correlation Coefficient Matrix. ................................ ................................ ........ 242 Table 4.4 Correlation Coefficient Matrix. ................................ ................................ ........ 242 Table 5 GDP per Capita Cycle. ................................ ................................ ......................... 233 Table 7.1 Data Validation. ................................ ................................ ................................ 234 Table 7.2 Data Validation Equation Analysis. ................................ ................................ .. 240 Tab le 7.3 Data Sources. ................................ ................................ ................................ .... 241 Table 7.4 Correlation Coefficient Matrix. ................................ ................................ 192, 242

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xi PREFACE This thesis analyzes public policy through an economic development policy lens and framework. The purp ose of this thesis is to inform economic development policy through the e xamination of relationships among and between defined influences and constraints on economic development, and to offer policy makers a unique method of measuring go v ernance corruption effects on education budgets and individual income. The approach used is to compare and co ntrast 1990 and 2008 economic development as measured by Gross Domestic Product per Capita, or Individual Income; the Human Development Index and its component in dices ; governance co r(Schneider et al., 2010, p. 5) ; and Education Expenditures as measured by United Nations Educational, Scientific and Cultural Organiz ation (UNESCO). The research herein covers broad areas of literature from the social sciences; political sc ience, history, corruption, governance, and economics ; and from business, accounting, and education. The unit of measure is the individual. Actor s in this thesis may be employees of the state, of non state institutions, or work in the private sector. Official income per c apita is stated in US dollars using the year 2000 as a base year. Narrowing the scope of this array of literature was based on an experience. While standing in the rubble of what recently was the Berlin Wall in 1989, I looked east, then west, the east again. Grey, Color, Grey. Stooped, vibrant, stooped. Battered, flourishing, battered. A woman, standing behind a smallish gr ungy table, was selling bits of the wall, stamped with what she stated was some official seal. I bought one, just in case there was such a seal, and picked up another from the piles upon which the tourists walked and children played. The image of dichoto my, contrast, and dissimilarity, in my visual perspective overwhelmed my senses. I knew a few facts about the Cold War, but the textbooks said nothing of what my eyes could see. I felt a communal sense of anguish flowing from the east, while the west wa s

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xii as familiar as my own United States. Some entity or organism, some insidious, living thing lying to the east, beyond the government or its structures and peace accords, beyond the empty promi ses b e lieved by the proletariat, was causing the pervasive ago ny. Repeated questions tumbled about in my head for a score of years, through several visits to the same and other places behind what was the Iron Curtain. 1) What festering plaque prohi bi t ing citizens from living a life fulfilled? 2) Why could the peop le not shake it off, loose it, overcome it, beat it? 3) Who were the guardians of the people; w here were the se sentries ; and why did they not act on behalf of the m illions of downtrodden? 4) How did the physical infr astructure decay and the economic powe rhouse implode? Thus, the variables for this thesis became: 1) Corruption. 2 ) Education. 3) Governance. 4) Economic policy.

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1 CHAPTER I INTRODUCTION This thesis endeavors to inform economic development policy so as to encourage healthy economic growth (Kuznets, 1966, p. 493) without the friction of institutional or political corru ption We also ask whether economic growth exacerbate s poverty The evidence to date is mixed. Deceiving in its simplicity, this fundamental question is of paramount importance as factors of (Levitt, 1983) To inform development policy toward a more balanced growth th is thesis examines relationships among and between defined influences and constraints on economic development, and specifically, it o ffers policy makers a uniqu budgets and individual income. influences on economic productivity over time (Kuznets, 1973) The terms for economic change, growth and d e velopment in this thesis, follow the definitions advanc ed by Schumpeter (1939) and Kuznets (1934, 1940) Economic growth is incremental change, generally measured by change in Gross Domestic Product (GDP). Economic dev elopment is a new steady state. This new level of development is realized in response to economic growth and the evolution, health, maturation, and increased capacity of the economy to sustain growth. This thesis advances and challenges New Growth Theory (Romer, 1990) by i nvestigating how specific measures of governance, and the corruption within governance, affect growth of i ndividual income; further, it explores linkages between these factors and the causal relationships among them (Barro, 2001b; Sen, 1997, 1999) Data from nations under the former Soviet spheres of influence during the Cold War have extraordinary potential to shed light on governance and development policy. While international attention focused on the transition s from Soviet rule to independence, intergovernmental organizations and academics seized the opportunity for r e-

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2 search, documentation, and data collection. Meanwhile, advances in technology and computing power grew exponentially. The breadth and depth of data available for analysis on the cons equences of growth are u n precedented. Research Questions Research Question 1 : Are the H uman Development Index and Income per C apita highly corr elated in the sample set ? Research Question 2: Does governance corrupt ion, as measured by the Shadow Economy, neg atively affect I ncome per Capita ? Research Question 3: D oes governance corruption, as measured by the Shadow Economy, neg atively affect E ducation E xpenditure? Research Question 4: Do the pre test H uman Developmen t Index governance corruption as measured by the Shadow Economy and E ducation E xpenditure together explain the change in I n come per C apita? through the public resource m echanism, its budget, are direct and negative; succinctly, the higher the degree of corruption, the lower the relative education budget. Further, the lower the ed u cation budget per capita, the lower the relative individual income. The evidence varies on w hether economic growth exacerbates or alleviates the relative or absolute income at the national level. Evidence at the level of the individual actor is far more o bscure (Galbr aith & Kum, 2005) For this reason, t he focus of this thesis is the total i n come to the individual actor A comparison of the multinational evidence indicates the presence of four conceptual challenges: First, how do we define and measure individual i ncome ? Second, when or what p eriod should we measure ? Third, what in addition to income provides a more thorough picture of the living standard of the individual? Lastly, which aspects of governance corruption effect ind i-

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3 vidual income? These are import ant questions for policy makers, as national ( aggregated ) figures mask the effect of policy decisions at the individual level. For e x ample, measured from 1990 to increased 1.2 billion (US equivalent dollars) or 1.8 percent, while th e individual income decreased from $3,155 to $2,425, or 30 percent, over the same period. The first conceptual challenge stems from inconsistent definitions and measurement methods, which seem to report contradictory evidence. For example, Lozada describe s one side of the debate : The fierce debates (among)...[a]cademics, journalists, and multilateral organiz ations...over economic globalization have focused recently on global poverty and income inequality...and a general consensus seems to have formed aro und the proposition that poverty (2002, p. 5) ince 1820, the average yearly world GDP growth is 2.21 percent, while GDP per capita growth is 1.2 percent (2009, p. 4) suggesting that the consensus should be that aggregate and individual incomes levels have d iverged. On the contrary, Sala i measured in both absolute and relative terms, show that, while the world pop ulation has steadily increased, fewer people live in poverty today than at any time in recorded history, and empirical evidence shows converging income levels and an (2002, p. 2) Making sen se out of what seem to be contradictory findings would require an exhaustive analysis of the underlying data sets a justification for and a comparison of the definitions and measurements of the variables, (Pritchett, 1997, pp. 12 13) e fforts beyond the scope of this thesis. Instead, this thesis emp loys demographic and economic data produced and shared through data networks and the International Comparisons Program ( ICP ) S cholars, academics, and professional researchers affiliated with list s of international agencies ( e.g., United Nations, World B ank, International Monetary Fund) intergovernmental organizations (IGO), and non governmental organizations (NGO), including abbreviations as used in this thesis share data (See detailed list of affiliated organizations in the Glossary) This network of agencies provide data

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4 for public use, which usually include detailed methodology, reliability, and validity statistics (HDR, 2007) Institutes such as Brooking Institution (BI), Transparency International (TI), The Heritage Foundation (HF), Intern a tional Comparisons Program (ICP) European Statistics, Data, and Metadata Exchange (SDMD) (here af ter Institutes), feed critical research and data to the network The first conceptual challenge is thus met by using data from the same network of sources. The second conceptual challenge stems from inconsistent measurement methods that led to comparing dissimilar periods. Barro (1991) and Solow (1956) among others, claim that i ncomes converge over time. Generally, scholars who report that incomes conve rge favor analyzing the longest time span with the most or best available cross country data (Barro & Sala i Martin, 1991) Conversely, other scholars, who favor analyzing specific per i ods, argue that the developm ent situation or stage equality (Rostow, 1991) The latter group offer evidence that incomes diverge when tie d to certain circumstances in history. This point, data knitted with situations, is central to the purpose of this thesis and solving its questions (Matheso n, 2008; Pritchett, 1997; Rostow, 1991) Measuring an economy from one arb i trary date to another based on data availability may invite risk The risk is missing vital information about the characteristics of economic growth specific to each country its quality and sustainability and the path pattern s, or cycles of the growth some of which is available through historical accounts. One of the first scholars to trace the paths of income over time was Simon Kuznets. Kuznet s (1966) invested much of his extraordinary career examining questions about income di stribution, economic measurement methods and growth He advanc ed theories that numerous scholars, includin g several fellow Nobel L aureates ( e.g., Robert Solow, Douglass North, Amartya Sen Gordon Tullock, Edmund Phelps, Paul Krugman, Herbert Simon Milton Frie dman, and others, plus several whose work is not central to this thesis ), have studied, tested,

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5 refu ted, or confirmed on how and why economies grow. Kuznets argued that the trajectory of i ncome growth depended on the stage of development in a country. He found that the paths of higher and lower incomes in lesser developed more agrarian societies tend to diverge during p eriods of economic growth, thus increasing income inequality, while incomes in more developed societies tend to converge as wealth distributes over a greater percentage of the population. The second conceptual challenge is met by anchor ing the data to a regime change (Rostow, 1991) Following work by Matheson (2008) Pritchett (1997) and Xu & Li (2008) accounting for the stage of devel opment ( add ing the stage as a variable to a development function ) creates a fram ework that invites conditioning variables ( e.g., regime, governance, institution ), and makes sense out of the standard economic variables ( e.g., GDP, life expectan cy educatio nal attainment trade alliances ) by a n choring them to a common phase or event ( e.g., industrial revolution, inventions, growth stages, policy stages, regime changes, intrastate armed conflict, treaties, the end of the Soviet Empire) ( Brewer & deLeon, 1983; Rostow, 1991; Xu & Li, 2008) The third conceptual challenge rests in the defining of li ving standards by a simple dollar figure. While the convergence/divergence debate just described persists, Sen (1984) question s its relevance. He asserts that examining the income level between the richest and poorest in a soci ety may be in vain as income per se may not reflect th e reality of human development, yet GDP per Capita (Income per Capita or Ic) is often used to infer or approximate living standards in the literature (Deininger & Squire, 1996; Gini, 1921; Kuznets, 1934; Sen, 1984) The third conce ptual challenge in this thesis is that empirical evidence on income changes over time neglects evidence of diverging living standards which has been the origin of media coverage, the Mi llennial Development Goals, and even armed conflict s. In the Forward to the 2008 Millennial Development Goals Report Sha Zukang wrote, t 2.5 billion people, almost half the deve lurban population in de (2008d, p. 4) The third co n-

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6 ceptual challenge is met by employing the Human Development Index (HDI) as a mea s ure for the living standard. The work of the Human Development Program, in its 20 th year at the United N ations, has provided a ce nter for researching and measuring the living standard (HDR, 2009e) The last conceptual challeng e is in defining governance corruption, and for the purposes of this thesis, limiting its scope to that which effects economic growth policy, and limiting its pervasiveness to that which policy may be able or interested to ameliorate. The objective is to isolate [that] (Mauro et al., 2002, p. 277) Brac ket ing the span of the state (offi cial) non state (institutions) and private se c tors to isolate expenditure altering types of corruption are the bodies of literature on (1) p olitical corru ption and the (2) unofficial economy. Political corruption is, a co operative form of unsanctioned usually condemned, policy influence for some type of significant personal gain, in which the currency could be economic, p o litical, or ideological remuneration (deLeon, 1993, p. 25). U nofficial influences include ome derived from them that circumvent or ot h erwise avoid government regulation, taxation or observation, which are measured by the Shadow Economy (Schneider et al., 2010, p. 1) T his thesis employs the fo ll owing wor k ing definition. Governance corruption is a co operative form of unsanctioned, usually condemned, pol icy influence that circumvents or otherwise avoids government regulation, taxation, or observation, and alters the composition of government exp enditure for some type of significant personal gain, in which the currency could be economic, political, or ideological remuneration Can multiple scholars with conflicting theories and evidence on economic development and income levels be right simultaneo usly? Perhaps. A summar y of the conceptual challenges facing researchers and solutions for this thesis follow. The first challenge is simultaneous convergence and/or divergence in income and/or li ving standards, which suggest dichotomous definitions and/ or uses for these terms and permits

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7 dissimilar data and/or measurement. No wonder scholars disagree. Remedy: Utilize I n ternational Comparisons Program data. The second challenge is simultaneous converging and diverging results, using the same data and me thods, which suggests a problem of arbitrary data periods or date ranges unidentified with and/or tied to events, endogenous or exogenous. Solution: Tie data to the fall of the Berlin Wall, the end of the Cold War and analyze the 30 countries of the for mer Eastern Bloc (See Country Briefs) The third challenge : I ncome is an insufficient measure of the human condition. Remedy: T he Human Development Index as a proxy for the living standard. The fourth challenge is to narrow the scope of corruption to t hat which (1) is found in the gover n ance process and (2) is measurable and missing from the official GDP. Solution: Employ the working definition of Governance Corruption, measured by the Shadow Economy as a proxy for the missing GDP. Adding background information or context, makes these challenges easier to understand. The historical backdrop for this economic development process follows Histor ically, like today, international trade and the migration of people and resources have driven natio nal economies; shifting prosperity and poverty technology adoption, and the intermixing of cultures (Diamond, 1997; Elisseeff, 1998) Aided by advances in technology and modes of communication, escalating g lobali zation has fueled a blur of activity resulting in increased inte rdependence of nations (T. L. Friedman, 2005) Why have some nations struggled while others flourish? Levitt (1983) commonality and that force is technology... Two vectors shape the world technology and glo balization. The first helps determine human pr (p. 1) Nye (2006) adds Globalization has two driving forces: technology and policy. Thus far, (p. 1) Specifically, technology renders di s-

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8 tance (from one vil lage or hemisphere to another) progressively less important. Accordingly, economic development or decay necessarily takes place in the larger context of globalization, i nduced by technology, through development policy, with p ublic administrators at the re ins. The policy makers steer economic development not unlike stagecoach drivers steer a team of horses. Public administrators and policy makers are at the reins of development. Their actions and dec isions pilot, guide, and encourage or restrict the E con omic H ors e power (EH ) of an economy. Rostow (1991) used internationalization rather than globalization to discuss the process by which economies became interdependent. He brought elements of social overhead capital (i nfrastructure) together with economics in the Stages of Economic Growth which provided criteria ( e.g., policy, technology adoption, income inequality, external forces) to weigh the readiness and capacity for aggregate economic growth (1991) Sen (1988, 1997, 1999) level of development and thus its capacity to tend to the human development (individual) needs of its citizens is a key factor in the inequality formula, where capacity is the measure of total p otentiality, whether actual or merely possible. North (1994, p. 17) underscores the necessity for well informed policy decisions to mana e(North, 1994, p. 19; H. Simon, 1972) The research questions are studied in light of globaliza tion, as the force of globalization adds an u ndercurrent of involuntary activity from seemingly exogenous sources and a theme of necessity to the balance of the literature. Understanding the elements of this debate is critical to informing development pol (Sen, 1999)

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9 Problem and Research Approach The IMF reports that [c] from needed public s (IMF, 2011d, p. 1) However, gaps exist in the liter a ture on certain iminished budgets on individual incomes. Further, it seems plausib le that diminished education funding affects certain measures of individual income. Kuznets referred to the ability for an ind ividual to earn income in part, his education and skill training (1934, p. 7) Yet, supporting evidence of corruptions effects on education budgets and income has lacked careful atte n tion Hence, the over arching hypothesis this thesis tests is that the effects of governance co rruption on education through the public resource mechanism its budget, are direct and negative; we posit that the higher the degree of corruption, the lower the relative education b udget. Fu rther, the lower the education budget per capita, the lower the relative individual income. The combined weight of just these two effects of corruption on long run economic growth is pote ntia l l y debilitating. The measure or degree of governanc e corruption in a country and its effect on the fun cgovernment function such as its development policy, its economy, or human development, a country must f irst account for it on its balance sheet. It must measure the extent of the problem of corruption in dollars. Rent seeking and o r ganizations in the pursuit of rents created pool, the remuneration for corruption could shift toward that which complements the endeavors

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10 of those that are corrupt and away from approved, sustainable economic development and ma rket demanded goods and services (S. Gupta et al., 1998) State capture is any group or social strata, external to the state, that exercises decisive influence over state institutions and policies for its own interests against the public good (Pesic, 2007, p. 1) Essential to this thesis, IMF scholars Mauro, Abed et al. (2002, p. 278) assert that e xtorting from education starts before the budget approval, so fewer dollars are all ocated to education, and more are allocated to projects where that extortion is eas ier to hide An initial inquiry into relationships among and between governance, corruption, econo mic deve l opment, and individual income variables yielded the four conceptua l challenges addressed above The t hree problem themes follow. Pr oblem O ne includes gaps in the recent literature specifically tying governance corru ption to the mechanisms through which the corrupt affect income inequality (deLeon, 1993; S. Gupta et al., 1998; S. Gupta et al., 2000; Rose Ackerman, 1999b) The second problem i n cludes difficulty measuring governance, corruption, and economic development at the aggregate and i ndividual levels (Galbraith & Kum, 2005; Schneider et a l., 2010a) Solving the mechanism and measurement problems requires that we add to the working definitions of governance corru p tion, an explanation of the GDP and Income variables. Accounting for g overnance corruption requires adding O fficial and U nofficial compens ation. C ompensation On the Books official GDP through National Income A ccounting (Kuznets, 1934) R emuneration On the Ground avoids offic ial ledgers and creat es or add s to the unofficial economy, or S hadow E conomy (SE) The Shadow Economy is defined as remuneration genera ted through actions and transactions representing primarily tax, regulation, and administrative process avoidance (Schneider et al., 2010, p. 5) The Shadow Economy is a operating outside the tax system and registered businesses conceal transactions to avoid paying taxes or social security charges, or to avoid the costs assoc i ated with

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11 (Russell, 2010, p. 10) The Shadow Economy includes rent seeking and state capture which are discussed later Governance is restricted to the purview of formal, official governmental institutions at the natio n al level plus the informal or unofficial institutions (N orth, 1991a) or the traditions and (Kaufmann, 2006, p. 82) The informal institutions may include officially recognized entities such as labor union s and unofficial entities such as cartel s The scope of the informal institutions include t he IGO, NGO, and business co mmunities as they engage in transactions and the democratic process (Mauro et al., 2002). Other types of gover nance or management ( e.g., corporate governance business, institutional, or or ga nizational management ), while essent ial, are outside the scope of this thesis except to classify the productivity of goods or services as official or unofficial History provides much evidence that governing regimes produce a spectrum of economic development results, some not so good. Arms trong (2005) asserts that g ood governance is a by product of sound public administration and strong governmental institutions; it minimizes corruption and reinforces healthy and sustai nable economic development Good gover nance by definition requires integrity, transparency, and accountability in the public sector (pp. 1 2) Conversely, p oor or weak governance lacks th ese characteristics ; it mushrooms out of corruption and maladministration carrying with it devastating human costs ( e.g., (p. 9) Problem T wo is the m easuring corruption per se (Kaufmann, 2006, p. 82) However, r ecent economic and statistical modeling has provide d increasingly reliable approximations of its influence and economic costs through surveys, extensive audits and tracking of ma rkets that are clandestine, extrapolation aided by increasing statistical capacity, and redundancy over time (p. 82) The IMF uses the portion of total production attributed to the unoff i cial economy as a proxy for the level of governance corruption (Abed & Gupta, 2002b; Russell, 2010; Schneider, 2009;

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12 Schneider et al., 2010) e me nt devised by Schneider & Enste ( 20 0 0 ) for the unofficial or S hadow E conomy as the proxy for the corruption found in gover n ance living standard requires a standardized measure. Sen addressed these elements by conceiving the Huma n Development Index (HDI) and subsequent Human Development Reports (HDR) from 1990 through present. A living standard i s characterized, in part, by an i n means to earn a living, particularly by insufficient education or skill and th e access to basic needs (HDR, 2009e) Healthy and sustainable economic growth is a byproduc t product of a healthy economy, which is maturing, growing, tending to the human development and thereby capacity development needs of its population, and is not likely to exacerbate poverty rates or levels (Kuznets, 1971; Thomann, 2008) Measuring economic growth accurately and adequately spotlights limitations Kuznets knowingly built into the National Income accounting system, still used by researchers and policy makers throughout the world today to report data used internally by country and externally to i nternational agencies. Economic welfare cannot be adequately measured unless the personal distrib ution of income is known. And, no income measurement undertakes to estimate the reverse side of income, that is, the intensity and unpleas antness of effort g oing into the earning of income. The welfare of a nation can, therefore, scarcely be inferred from a measurement of national income as defined (Kuznets, 1934, pp. 6 7) types of effort. Effort ranged from the required physical toil to the dexterity to manage the necessary moti v a tions a nd the overall economy Likewise, the effort ranged from the mental muscle required in policy learning to the earning of a degree (p. 7) GDP data, together with other economic indicators, pr o-

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13 results from testing income distribution in the equation reaches not half of the essence of his a sintensity and unpleasantness of effort going into the (pp. 6 7) his human development research (1997, 1999, 2004) e(1934, pp. 6 7) The H DI measures economic development holistically by cords a measurement by which policy analysts can evalu ate development policy (HDR, 2009e; Alkire, 2005; Mazumdar, 2003) Problem Three is the scope and limitations of this thesis. Important to note here is that many va r iables that are widely used in cross country a nalysis on economic growth and individual income in transition countries are beyond the scope of this thesis. Specifically, future research would include three important variables. (1) A variable critical to economic productivity would measure progress t oward market liberalization (Sachs & Werner, 1995) In the 2010 Transition that provides data on privatization, markets, banking, and infrastructure (p. 3) (2) Progress toward EU accession, measured by the European Com mission (2011b) (3) A variable critical to unde rstanding economic growth patterns would mark the history and intensity of armed conflicts (HIIK, 2010a) Variables that indicate market liberalization, EU accession progress, and periods of unrest may add e x planatory power to the analysis. Sequence of Reasonin g Step One i n the sequence of reasoning is to c reat e a baseline or starting point that measures the accumulated stock of human development in each country in 1990. The Human Deve l opment Index (HDR, 1990) equally weighs three obse rvations: the Life Expectancy Index (LEI), the Education al Attainment Index (EAI), and the GDP I ndex (GDPI) This baseline mea s-

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14 ure becomes the pre test, independent variable, HDI 1990 Dat a are available for each of the cou ntries in the sample set. We te st to e nsure that the HDI baseline of the sample data are co n sistent with widely accepted findings, where the GDP per capita is not a statically significant proxy for the living standard or human development, consistent with Galbraith & Kum ( 2005 ) Sen (Sen, 1984) and Kuznet s ( 1934 ) among o thers. Re search question one correlates to step one in the logic sequence. Research Question 1: Are the H uman Development Index and Income per capita highly correla t ed? If GDP per Capita is not a sufficient proxy for the stock of human development, we can move forward with the next step Step T wo measure s governance GDP per Capita, or I ndividual I ncome per Capita (Ic) Some scholars argue that all of the accepted income measurements lack accuracy due in large part to the inaccuracy of the production data going into them ( e.g., Ga lbraith & Kum (2005) ; Schneider & Enste, (2000) ). Even the best GDP data report only the income earned on produ ction of goods and services that are accounted for O n the B ooks in the National Income A ccounting system Kuznets designed (1934, ch. 1) The balance of the rem uneration moves through the unofficial economies (Abed & Gupta, 2002b; Schneider & Enste, 2000) The notation for aggregate income O n the B ook s or Official Individual I ncome is Ic O Likewise, estimated corruption remuneration earned O n the G round the Unofficial Individual I ncome per Capita, is Ic U Adding the two streams of productivity together approximates the total value of go ods and services pr o duced in a country in one year Total I ndividual I ncome, I O + I U = I T (Galbraith & Kum, 2005) (See Data Legend in Appendix). R esearch question two corr esponds to step two in the logic. Research Question 2: Does governance corruption, as measured by the Shadow Econ omy, neg a tively affect I ncome per Capita ?

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15 Step T hree in the sequence is to uncover the relationships among and between the elements of corruption defined by the Shadow Economy and education funding I n the off icial National Income A ccounting for GDP is an allocation for public education funding. T he o fline item, E ducation Expenditure as a Percentage of Tot al Gover nment Expenditures (EE), development policy. In Armenia from 1999 to 2007, for example, the official expenditure on public education averaged 2.52 percent of the GDP. This translat es to 13.2 percent of the total government expenditure (UNESCO, 2009d,Table 13) Research question three corresponds to logic step three. Research Question 3: Does governance corruption, as measured by the Shadow Econ omy, neg a tively affe ct E ducation E xpenditure? Step Four is the key research question for this thesis. It sets up the reasoning to test the Ic, HDI, SE, and EE variables simultaneously. make good innovators, so that education speeds the process of technical diffusion (1966, p. 70) Accordin g to Romer (1994b) Arrow (1962) Lucas (2009) and ibution to economic growth is increasing returns or knowledge spillovers, which sets contribution apart from other public goods ( e.g., and from other economic growth factors ( e.g., consumption, savings and investment, trade balances, tax For a country and its citi zens, education is an investment i n future econo mic sustainability. For a Shadow Economy and its constituents, an educated citizenry may be threatening (Monas, 1984) Hence, E ducation Expenditure budget provide s a logical line item though certainly not the only line item, from whic h to direct public funds (Freire, 1970) R esearch question four corresponds to logic step f our Research Question 4: Do the pre test H uman Development Index G overnance C orru ption, and E ducation E xpenditure together explain the change in Income per capita?

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16 Step Five is to define the parameters of the sample data set. Sample Set Rules Rule 1: The country was or remains Socialist Rule 2: Four or more years of Soviet influence (Sachs & Warner, 1992, 1996, 1998), plus a cr eated, liberated, or re gained sovereignty, independence or the ability to trade, travel, and migrate which began between 1988 and 1992. Rule 3: Geographically related by inland border, trade or sea trade route and western orien t ed. Rule 4: Ethnoli n guistically interrelated, Economically interdependent

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17 Thesis Overview The purpose of this thesis is to explore the intricacies of the effects of governance corru ption (measured by the Shadow Economy) on individual economic growth (measured by Ic) through education budgets (measured by Education Expenditures on public education as a pe rcentage of GDP ). Chapter 2 reviews segments o f the major bodies of literature that are specifically relevant to governance, corruption, and h uman and economic develo p ment to frame the thesis, starting with broad academic themes and theories, and narrowing to the specific works upon which this thesis is built. In doing so, the work outside the scope of this thesis is excused and the reasons for this are discussed. This segment adds context to the data. Chapter 3 explains and examines these data, describes logical flow of the equations for analysis, and discusses each step in the econometric methods with which the data are analyzed. Chapter 4 presents the results, describing complications and resolutions to data or methodological issues. Chapter 5 offers an analysis of the econometric results and di scusses the findings in terms of the framework, sp e cific theories, and the model employed, as well as necessary caveats and limitations of the data. Cha pter 6 describes some potential next steps for the policy community and suggests future research schola rs may undertake to further the thesis findings The Appendix in includes the Country Brief, in which is found data on each of the countries in the sample set relevant to governance, corruption, and human and economic develo p ment.

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18 C HAPTER 2 LITERATURE REVIEW The goal of this dissertation is to inform economic development policy to encourage healthy ec o nomic growth (Kuznets, 1966, p. 493; Sen, 1997) using the New Growth Theory that is widely accepted in cross country longitudinal analysis (Cortright, 2001b; Romer, 1996) Clea rly a proponent, Romer (1994a, p. 21) asserts that, o nomic policy is to create an institutional environment that supports technological change ature review knowledge capital, which is characterized by increasing returns and which is required to adopt and exploit technological advance (Phelps & Nelson, 1966; Romer, 1993) New Growth Theory bridges the two main theories on eco nomic growth, which lie on a continuu m from endogenous and exogenous drivers. The incentives provided by national governance via the development po licy process bridge endogenous growth motives and exogenous knowledge catalysts. The major bodies of literature reviewed are governance and corr uption as a dimension of governance ec onomic growth and economic development, human development and its components, and theories about and measurement of education outcomes

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19 Governance According to the IMF (2011d, p. 1) overnance is a broad concept covering all a spects of the way a country is governed, including its economic policies and regula tory framework T h e IMF definition frame s the concept by iden tifying the level, manner, and system of authority. This framing permits a separation in the governance literature between dime n sions that inform this thesis and those that do not. Pertinent are the forms of governance that directly influence national level development policy specifically through its budget Further, the infl uence on governance through informal authority by any institution or organization is recognized and included. Corrupti on is, therefore, included, as it must be given that corruption is informal and influences governance. Kooiman & Jentoft (2009, p. 818) assert, in the social sciences as well as in the policy w orld [and] has different meanings to different people I t is usually qualified by s uch terms as good, network, global, natural resource, or pu bFor example, Boviard & Loffler (2003, p. 316) de Stoker, in Governance as Theory (1998, p. 18) proposes aspects of governance. For example, go v ernance recognizes the power to get things done which does not rest on the power of government of research activity on national and institutional governance each define governance slightly di f(mostly) on governance between the national and sub national public sector and private firms working within o main ( Towards Better Measurement of Government 2007) The UN works with institutions at every level through programs worldwide focusing on the human condition and poverty elimination (1985) and peace keeping. The IMF promotes soundness and transparency in banking and financial management (2011d) The World Bank focuses on strengthening the

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20 economic development ability of national level institutions and go v ernments through governance norms and gene rating aggregate data for all nations (2009g) The scope, breadth, and depth of liter ature on governance are thus, enormous; however, the dimensions of governance central to this thesis present a narrow spectrum. Th e following section define s the boundaries between governance literature s by their specifications to include or excuse them f rom a role in this thesis utions as a c humanly devised constraints that structure huma n interaction -1994, p. 8). Informal institutions permeate the culture in a variety of forms ( e.g., a cultural norm, NGOs, the local PTA, unions, family movie night, gangs, mafias, indu s try associations, charities, churche s, holidays, lobbyists, and political action committees), and may have positive, neutral, or c tives. Here, governance is restricted to the purview of ( 1) formal authority of governmental institutions and ( 2) informal authority or infl uence by institutions (North, 1991a) that affect national level government budget, taxation spending fiscal, or monetary policy The scope include s governance over certain actions and transactions by official, institutional and private organizations or their respective individual a ctors. To limit the breadth of governance, forms of go v ernance, or management, which serve to manage or direct the inter working of private sector operations ( e.g., business, institution, ente rpr ise, company, charity) or Non Governmental Organizations (NGOs) and associations, are outside the scope of this thesis (Kettl, 2000) The depth of governance relates to the level at which governing takes place. Global go vernance for example, tions, and creating an (Boughton & Bradford Jr., 2007) Susan Rose

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21 l tinational non governmental organization (Rose Ac kerman, 1999a, p. 195) The option of governing from the highest level seems unlikely to gain traction as global powers such as the US and EU seek to fortify autonomy through leadership, alliance, and example (Ar mit age & Nye, 2007; Kaminski, 2005) and have more readily supported the gover n ance strengthening efforts within a national or regional scope. Hence, a global perspective is outside the scope of this thesis. The other extreme limits the s cope of governance research to that of only a national go vThis is equally unrealistic as a method to solidify good governance as this neglects the influence of the private sector, institutions, and the IGO and NGO commun ities i n particular (North, 1991a) On the contrary, informal authority by any of these entities, listed above or tacit, is within the scope of this thesis, if the influence affects national level ec onomic deve lopment policy (i.e. through variation, adaptation, modification, or transmutation within the development policy process or within the fiscal or monetary policy processes), ec onomic growth, sustainability, or economic outcome. To limit the depth of gover nance, levels of governance above the national level ( i.e., i nternational agencies such as the UN, and intergovernmental organizations such as the European Union) or beneath the national level ( e.g., non national, regional, territorial, municipal, tribal) that serve to manage or direct the inter working of non national level governments, are outside the d efined scope of this thesis. An understanding of governance must capture the method by which the go vernment system governs Government system s exist on continuum from centralized to decentralized decision making and control, adding another dimension to the concept of gover nance Governance literature exposes rands of the literature highlight different facets of this continuum.

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22 Existing understandings may be classified according to whether they emphasize the politics, pol iet al., 2007, p. 4). To summarize the wo rd governance refers to the system, structure, and form of gover nment including its actors, as well as the act of governing or the method by which a system is govern ed Governance includes the scope and breadth of influence, official or unofficial, that a ffect the legitimate public sector, institutional, political, and policy systems (North, 1991a; deLeon, 1993; Mauro, 2004; Kaufmann et al., 2008) and command respect and allegiances in s ociety and, therefore, on the economy itself (Rose Ackerman, 1978, 199 9; North, 1991a). Governance actors include public and private institutions, public officials, and private citizens transacting in the democratic process (Thompson, 2007, p. 2) Measuring Governance Governance scholars seek to understand, to measure and to measure the impact of both the fo r mal and the informal authority in a given society if it were to be able to manage its system for the greater good of the whole nation (Abed & Gupta, 2002 b; Kaufmann et al., 2000; Treib et al., 2007) However, m easuring an intangible ( such as transparency in governance) is a n elusive matter of perception, and the task, enormous. Worl scholars created a met hodology for the World Governa nce Indicators (WGI) metric (Rose Ackerman, 2006) indicators are based on several hundred individual variables measuring perceptions of gover nance, drawn from 35 separate data sources constructed by 32 diffe rent organizations from around (Kaufmann et al., 2008, p. 1) The aggregated score, or index, is used as a meter of (p. 7) T he six dimensions of governance are Voice and Accountability, Political Stability and Absence of Violence, Government Effectiv eness, Regulatory Quality, Rule of L aw, and C ontrol of Corruption (Kaufmann et al., 2008) Similarly, U N agencies such as the United Nations Development Pro gramme, measure partici pation, consensus orientation, accountability, transparency, responsiveness, effectiveness

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23 and efficiency, equitability, and rule of law (2009f) T he UN adds more survey information gat hered from the same and other country level agencie s as the WGI to augment the data for a go vgo v ernance index suitable to UN needs, which reflect sub national governance factors ( What is Good Governance? 2009) Therefore, the UN data are not sufficient for this thesis. The IMF concentrates its research on governance of financial institu tions rather than national level go vernmental governance, and is not appropriate for this thesis (IMF, 2011d) Critics are quick to list the limits to and faults in the available governance indices. xperts, clients, donors, and policy makers at the Kennedy School of Government, Harvard University, in May 2003 (Besancon, 2003, p. 1) rating system for governance a re all primarily subjective, being based on expert or informed opinions, systematically gathered and arrayed with or against other perceptions and surveyed views. So are the majority of the forty seven data sets s that one of the simple reasons for using subjective data is that no complete cross country, objective data are avail able, particularly from t he underdeveloped nation states Answering the critics, e fforts to complete objective research are underway. Ba sed on a successful pilot study, using field researchers collecting data from a sample of countries that pr ovided information rich data, Robert Rothberg (2005) asserts that the time has come for a quantitative measure of governance. Until such a project i s complete, he suggests an ordinal ranking of countries, an index, to bolster the qualitative data, following the lead of the WGI, H uman Development scholars and others Johnston (2007, pp. 8 9) put forth a benchmarking strategy that emphasizes integrity in government processes; however, like that of Rothberg, this effort is also time and resource intensive. Most critics agree that the WGI is t he most widely a ccepted index, largely due to the funding provided by the World Bank for developing it, and for its inclusivity of non Bank scholars, national, and international agencies (Bovaird & Loffler, 2003;

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24 Ku rtzman et al., 2004) Radelet (2003, p. 33) writes, [t] he most comprehensive set of glo bal governance indicators has been compiled by the World Bank and combines subjective and obje ctive attributes Absent completed objective research, however, and because field research is limits to a handful of countries, these sources are insufficient. Instead, t his thesis employs data that estimates the budget effects of governance, discussed further in the section Measuring Co rruption (Schneider et al., 2010a) Moving on, while the international agencies have a unique governance foci ( e.g., IMF, Bank, OECD, and UN ) each stress the criticality of good governance to foster sustainable ec onomic growth and development and inclusive prosperity. The distinction made between governance and the characteristics of good gover nance is central to this thesis. As Kaufmann, (2000, p. 1) lopment outcomes such as higher per capita incomes, lower infant mortality, and higher literacy In this thesis, a two equation simultaneous linear regression model, the Multiple Indicator and Multiple Causes (MIMIC) equation to estimate and measure governance as a missing vari able This equation measures the quality of governance by the percentage of corruption and underground activity in the economic process simultaneously (Breusch, 2005, p. 5) Given that GDP is the standard measure for an economy (the argument for this is deve l oped in the literature review section on economic growth), t he estimated percentage of simultaneous corruption and underground activity miss nance (Schneider et al., 2010a) Good Governance and Sustainable Development According to Rotberg (2009, p. 113) [g] overnance is the delivery of political goods to citizens. The better the quality of that delivery and the greater the quantity of the political goods

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25 Over time, civil i zations have thrived, prospered, deteriorated, and dissolved for many reasons beyond the scope of this thesis. Despite this waxing and waning of civilizations and nation states, and essential to this thesis, however, is clear evidence of a general accumu lation of capability such as economic and human capability, commerce, longevity, learning, and technology, all of which were subject to (or because of) the prevailing system of governance. Grindle (2011) describes a new theme in governance literature. This common theme suggests a new generation of thinking that emphasizes the i mportance of knowing the context in which reformed policies, institutions, and processes are to be introduced, and design ing interventions that are appropriate to time, place, historical experience and local capacity Understanding the histo r ical evolution of how countries muddle their way (p. 415). Many scholars have sought to measure the stock of capability inherited or earned, as an indicator of the quality or the degree of goodness of governance. For example, Elisseeff (1998) studied the Silk Road trade routes, which fostered a mixing of races and cultures, as well as the building of vast realms, such as the Mongol, Roman, and Persian empires. Language, culture, ethnicity, heritage, beliefs, religions, customs and philosophies, migration patterns, trade routes and knowledge diffusion, geography and climate, political arrangements and government stru ctures, and pure blind luck, among other historical human and institutional factors together, affect and are affec ted by governance (Diamond, 1997; Elisseeff, 1998; Mauro, 1995) These are el ements of governance, but neglect a mea s urement of governance, and offer an insuff icient metric for this thesis. However, to accommodat e the diffusion of cultures and institutions, s cholars developed a measurement for the degree of ethnic, linguistic and religious homogeneity heterogeneity or what they term fractionaliz ation and its effects on economic growth (Alesina et al., 2002) Ma uro (1995) used an index of ethnolinguistic fractionalization as an instr ument to account for the

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26 homogeneity as an indicator of cultural behavior, language based, and non language based co mmunication barriers L a Porta & Lopez De Silanes (1999, p. 223) used the opposite measure, and more sp ecifically government performance French or socialist laws, or have high proportions of Catholics or Muslims, exhibit inferior go vernm (p. 223) E thnolinguistics is important in governance research. s ones. Common Law countries have better governments tha n French civil laws or socialist law cou ntries. Predominantly Protestant counties have better governments than [do] either predom i nantly (1999, p. 265) Moreover, those with a history of British rule with Protestant traditions seem to b e less corrupt (Serra, 2006; Treisman, 2000) Fractionalization is adopted in this thesis through the proxy variable for corruption, the Shadow Economy, as it is one of the inputs employed by Schneider et al. (2010 ). W ars and hol ocausts greatly affect its index value as holocausts decrease fractionalization by exterminating a segment of ethnic or religious groups (Burleigh, 1996) Following is an example of the effect a fractured population has on growth (Alesina et al., 2002, p. 9) In terms of economic magnitudes, the result s suggest that going from complete ethnic homogeneity (an index of 0) to complete heterogeneity (an index of 1) d epresses annual growth by 1.9 percentage points In other words, up to 1.77 percentage points of the deference in annual growth between South Korea and Uganda can be explained by deferent degree s of ethnic fractionalization. According to Paulo Mauro (1998a, p. 266) in accordance with Shleifer and Vishny (1993) arguments, more fractionalized countries tend to have more dish onest bureaucracies. The index of ethnolinguistic fractionalization has a correl ation c o efficient of .36 (significant at the conventional levels) with the corruption index Mauro

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27 (1998a, p. 266) added a proxy for the degree of state capture by Ades and Di Tella (1994) a proxy f or the degree of rent seeking and the country achieved independence after 1945 (fo l lowing Taylor and Hudson, 1972) The correlation coefficients between these and c orruption index were 21, .23, and 41 respe ctiv e ly (Shleifer & Vishny, 1993) Barro & McCleary (2003) following North (1994) measure belief systems as a determ inant of governance Furthering work by Max Weber (1930, pp. 22 27) La Porta et al. (1999) us e r istics of a society that may ins to this thesis i s this more robust predictor of poor government performance than inte rve n f ractionalization, its political legal, economic, religious, and cultural history, its age as an ind epen dent state and the influence of unofficial institutions through rent seeking and state capture, are posited to be critical influence s in and on governance, yet taken together are insufficient to measure governance directly Economic geography which exp lains concentrations of economic activity based on ge ographic space, adds an understanding of the role of global latitude and climate in the economic growth (Diamond, 1997; Krugman, 1998; Nissan, 1991) Scholars c ontinue to work on modes and methods of governance delivery in an age of rapid information dissemination to foster an i ncreased understanding of how the power of institutions determines the quality of governance (Ke rsley et al., 2008; Kettl, 2000; Lynn, 1998; Treib et al., 2007) Heritage, migration, geograp hic proximity, and the diffusion of races and knowledge all inform the notion of governance, but do not measure it adequately.

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28 Human Development as a Metric f or Governance (Arrow, 1963a, 1963b) i nal work in welfare economics laid much of the foundation for the body of literature in human de velopment. Inequality Reexamined (1992), Human Capital and Human Capabi lity (199 7) and Development as Freedom (1999) provide the framework for u s ing the Capability Approach as an indicator of prior governance, and to measure governance (Alkire, 2005) rhaps most importantly, the human development approach has profoundly affected an entire g eneration of policy (HDR, 20 09e, p. iii) Working with United Nations Human Development Programme (UNDP), Sen and Huq devised the Human Development Index (HDI). The HDI is an indexed value for the accumulated store of p ital and human capability. This stock of capability is the summation of the that includes the effects of ethnic, linguistic, and religious fractio nalization, the geography, travel routes, trade agreements, regime changes and wars, latitude, climate, plagues and holo causts, and luck (HDR,1990; Sen, 1997) The human development index is a composite index that measures the average ach ievements in a country in three basic dimensions of human development: a long and healthy life, as measured by life expectancy at birth; knowledge, as and a decent standard of liv ing, as measured by gross domestic product (GDP) per capita in purchasing power parity (PPP) US dollars [in 2007 as the base year] (HDR, 2007, p. 225) Scholars collaborate and share research and data through networks such as the Intern ational Comparison Program (ICP) (SDMX) deli ver ing of the art measures inco rporate recent advances in theory and measurement and support the centrality of inequality and reasoned public debate beyond the traditional focus on aggregates (HDR, 2007, p 224)

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29 Importantly, t he HDI normalizes its data across countries in two ways. First, using pu rchasing power parity, the value is normalized. Second, using GDP per capita shift s the focus to the individual as the unit of measure, rather than looking a t country aggregates. A feature of the HDI is that its particular component indices capture a sense of community coh e sion, the level or degree of social capital (Carilli et al., 2008) bounded pote ntial as well as that of the individual (HDR,1990; Sen, 1997; H. A. Simon, 1997 ) Well defended as a goal in and of itself, h uman development directly enhances the cap ability of people to lead worthwhile live s (Sen, 1997) so there are immediate gains in what is uture. A country that enjoys high human development, such as the United States, has nearly the arts. Citizens who suffer from low literacy rates, poor health, and e x treme underemployment, in a country scoring low on the HDI, are effect ively bounded shelter (Sachs, 2005; H. Simon, 1972) Others utilize similar variables to gauge governance qua lity (La Porta et al., 1999) e the output of public goods by infant mortality, school (Anand & Sen, 2000, p. 237) There is hardly any example in the world of the expansion of education and health being anything other than monotone: good education and good h ealth seem to generate powerful demand for these opportunities (and more) for our children. This is a relationship that goes well beyond the redistribution of i ncome to the poor at a given point of time important though that is. It should also be noted t ncretely to people's ability to generate for themselves the real opportunities of good living (p. 237) Sen wrote the following in the Forward ding of dev elopment was galvanized by the appearance of the first Human Development makers, public officials and the news media, (p. iv) concept of human development is much broader than any single composi te index can measure,

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30 the HDI offers a powerful alternative to income as a summary measure of human well being (HDR, 2007, p. 225) For the reasons cited above, and, because the HDI is an amalgam of co mponent indices that approximate the entirety of development attained by the respective country and year (HDR, 1990) the HDI is the best measure available for the stock of development and governance For this thesis, th e se factors are the stock of capability in the pre test year HDI 1990 To summarize, governance refers to the system, st ructure, and form of government i ncluding its actors, as well as the act of governing or the method by which a system is governed. For the purposes of this thesis, governance is limit to the influence, official or unofficial, that a ffects the legitimate, national level, public sector, institutional, political, and policy systems. Its actors include public and private institutions, public officials, and private citizens transacting in the democratic process. Since its influence command s respect and allegi ances in society and among its actors, governance affects the economy

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31 Corruption as a Dimension of Governance The social sciences literatures qualify corruption by type or characteristic Moody Stuart (1996, p. 19) uses the definition for corruption f c es: p, s usually about getting routine procedures fo l lowed more quickly the criticality of the difference, he states the following a(p. 19) It is the that we seek to understand and measure in this thesis. Joseph Nye (1967, p. 419) defines and characterizes corruption in the following way. Corruption is behavior which deviates from the formal duties of a public role b ecause of private regarding (personal close family, private clique) pecuniary or status gains; or violates rules against the exercise of certain types of private r egarding influence This includes such behavior as bribery (use of a reward to pervert the judgment of a person in a position of trust); nepotism (bestowal of patronage by reason of ascriptive relationship rather than merit); and misappr opriation (illegal appropriation of public resources for private regarding uses). Nye continues by stating that corruption may be beneficial to economic development, gover n mental capacity, and institutional integration into the political arena. As such, increasing its transparency and legitimacy, or authorizing those aspects beneficial to society and public we lfare may be the way to ameliorate co rruption (pp. 419, 427) While these definitions are widely accepted, each limits the scope of corruption to a poli tical realm or public sector, which will not do for the purposes of this thesis. Robert Merton on the other hand, sugge functional deficie ncies of the official structure generate an altern a tive (unofficial) structure to fulfill existing needs somewhat more efficiently (1968, pp. 127, emphas e s in original) We seek to measure the e ffects of corrosive, development limiting corruption in this thesis, whether public or private, if it

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32 al ters the composition of government expenditure by avoiding the democratic process or by infl uencing budgets or spending (Mauro et al., 2002, pp 263 265) G overnance corruption refers to corruption in the system, structure, and form of gover nment including its actors, as well as the act of governing or the method by which a system is governed Its actors include national level public and privat i cials and pson, 2007, p. 2). Therefore, in this thesis, g overnance corruption i n cludes the scope and breadth of influence, official or unofficial, that affect a distortion into the legitimate national level, public sector, institutional, political, and policy systems (North, 1991a; deLeon, 1993; Mauro, 2004; Kaufmann et al., 2008) that command respect and allegiances in society and, t herefore, on the economy itself (Rose Ackerman, 1978, 1999; North, 1991a) Examples of inferior as opposed to good, governance raise the question about the caus e of the failure in governance. Strictly defining corruption, however, forces bounds on the word as if corruption is merely a movement in character or form of a thing (or person) from point A t oward point B. Instead, corruption is not linear, and it is more than one adje c tive can describe. Corruption is a process, thus, it must be viewed in its context. Corruption must be eval uated for its effects; measured, then re examined for its causes as a cyclical rather than linear movement. In context, corruption may be temporarily beneficial, as in composting to build nutr ient rich soil In context corruption may be necessary to reach a mutually desired societal goal (Rose Ackerman, 1999a) Corruption in the proper context may fill a societal need or promote economic development and outside the proper context, may destroy a nation (Merton, 1968; Moody Stuart, 1996; Nye, 1967) A ccording to Nye (1967, pp. 419 422) corruption has pote ntial development benefit in three major categories: economic development, national integration, and governmental capacity. rally necess ary to maintain a capac i ty to preserve legitimacy in the face of social change, then (by

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33 definition) it is beneficial for political development (p. 419) Corruption may further e conomic development by the cutting of red tape, through c apital formation incentivizing e ntrepreneurship and overcoming discrimination Corruption may further governmental capacity, as well. capacity of the political structures of many new states to cope with change is fr e quently limit by the weakness of their new institutions and (often despite apparent centralization) the fragment ation of power in a country. i.e., power does not (p. 421. parentheses in original) Tanzi (1998, pp. 581 582) s sertions that corruption may benefit a development by & Shleifer, and Vishny (1991) In Gupta et al. (2000) w e read the contradictory claims about the economic benefits of corruption. Some, consistent overly centralized and overextended bureaucracy, red ( p. 7). On the contrary, c (p. 8) Gupta et al. suggest (2000, p. 7) While e valuating corru ption further is outsid e the scope of this thesis defining and measuring corruption is nece s sary, and requires the reader which it ope r ates. Forms, Size, and Scope of Corruption Corruption has ma ny meanings in the literature Corruption is a struggle between public good and rent seeking individuals and i s a principal inhibitor of economic growth and ec o nomic development (Abed & Gupta, 2002a; deLeon, 1993; Heidenheimer & Johnston, 2002; Johnston, 2007; Mauro, 1995; Rose Ackerman, 1999b; Rotberg, 2005) In Thinking About Political Co rruption deLeon (1993, pp. 23 25) offers several methods to categorize corruption. For example,

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34 Lowi differentiates corruption based on its scale, where Corruption Iran ants, and requires coordination among corruption is small scale scandal i(as found in deLeon, 1993, pp. 23 25) Heidenheimer colors corruption black, white, or gray, depending on the probabil ity that a majority consensus would find the acts punis hable based on principle, tolerable, or meet mixed review based on the class or status, respectively (quoted in deLeon, 1993, pp. 23 24) Rose Ackerman (1999b, p. 27) disti n guishes corruption by nprivatizat ion processes rife with bribery (Rose Ackerman citing Moody Stewart (sic), 1997) Dennis Thompson (2007, p. 2) separates the broader concepts of individual and institutional co rf bribery, extortion, and acorruption depends critically on understand ing the purposes of the institutions in which it takes (p. 2) Finally deLeon & Green (2004, p. 72) move beyond the view of identif ying the a ctor to add the concept of pervasiveness of corruption; whether or not it is systemic Narrowing the literature by the form, level, or siz e o f corruption is fruitless, as corruption is crosscutting through political, institutional, business level, and governmental red tape. B ig Corruption, Grand Corruption, and espionage, or little corruption, scandal, barter, and petty thie very ; n one of th ese accounts for the percent of GDP unaccounted for because of corruption by any name Dissecting corruption by form, level, or size is insufficient for this thesis, as the form of corruption does not necessarily inform the measurement required, n or does it inform the size of the overall corruption problem as it is relative to the GDP in a country. Broadening the literature

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35 however, to include the scope of corruption, c onsidering the degree to which corruption is sy stemic is constructive toward unde r sta n ding and measuring of its impact (deLeon & Green, 2004) One avenue scholars select to isolate the effects of corruption is to differentiate between that productivity that is reported and official, versus that which is not, regardles s of scope, scale, color, and size. Whi le this method offers more quantifiable data, it blurs the boundaries of the conventional definitions of corruption found in public affairs literature. Rent seeking and state capture are examples of the unofficial productivity. The seminal author on gover nment privilege seeking or protection seeking is Gordon Tullock (1967), who suggests that tariffs (p. 225), regulation protection (p. 226) monopoly co ncessions (p. 228), transfers (such as favor by pressure from lobby groups) (pp. 228, 232), and barriers t Krueger (1974) penned the term rent seeking gallocation, fair trade and minimum wage policies ( p. 301), credit rationing and preferential tax treatment (p. 302). the three original sources of all revenue as well as of all exchangeable value. All other reve nue is (1993, p. 2). Consistent with Krueger (1974), Smith (1776), and Tullock (1967), asserts that rent see k revenue, and diminishes official taxable individual and corporate income (p. 229). Krueger ts,

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36 State capture is any group or social strata, external to the state, that exercises decisive i nfluence over state institutions and policies for its own interests ag 2007, p. 1). A form of grand corruption, it includes the ability of domestic or foreign informal i nstitutions or firms to mold or manipulate state laws, policies, or regulations (Kaufmann, 2003, p. 21). At the highest level officials setting public policy change or break rules to favor certain vendors, buy votes, or bargain for power (Chua, 2006). E s sential to this thesis, Mauro, Abed, et al. (2002, p. 278) assert that extorting from education starts before the budget appr oval, so fewer dollars are allocated to education, and more are allocated to projects where that extortion is easier to hide. La P orta &Shleifer (2008, p. 7) concentrate their research on the difference between fo rmal and informal firm productivity, using 14 African and 14 Latin Am erican countries as the sample set and OLS regression with data from informal surveys education al attainment and economic data. Raw materials, production costs, and electricity are the supply side variables, while sales, GDP, and output are the demand side variables T he individual is the unit of mea sure and the variables are normed to this This method of approximating the size of the informal economy would have satisfied the needs and purpose of this thesis; however, as of yet, the data are not ava ilable for the sample set of countries in Eastern Europe. La Porta & Shleifer (2008, p. 7) cost of becoming formal, the cost of staying formal, and the benefits of being formal Many of th e costs of becoming and staying in the official economy are measureable, and belong with state capture and rent seeking in the shadow economy. Similarly, t et al. (2010) use the term Shadow Economy Shadow su g gests that the unofficial activity is obscured or hidden, and better defines the productivity this thesis seeks and no co n sensus

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37 Co n sistent with Dresher Kotsogiannis et al. (2005) employing a multiple indicator, multiple causes (MIMIC) structural equation method. Since this is the only data set available that approximates the size of the informal economy that also covers the countries of interest herein, t his thesis uses the Schneider data set, and the following definition for the shadow economy (Schneider et al., 2010, p. 3) Shadow Eco nomy Rules The shadow economy includes all market based legal production of goods and services t hat are deliberately concealed from public authorities for any of the fo llowing reasons: (1) to avoid payment of income, value added or other taxes, (2) to av oid payment of social security contributions, (3) to avoid having to meet certain legal labor market standards, such as min imum wages, max i mum working hours, safety standards, etc., and (4) to avoid complying with certain administrative procedures, such as comple ting statistical que s tionnaires or other administrative forms. Assuming that either productivity is reported or it is not reported is naive however, ad ding that which we know to be reported with that which we can estima te to be unreported is a c loser, though still problematic, approximation of the total GDP per country (Galbraith & Kum, 2005; Schneider et al., 2010a) Causes and Consequences of Corruption itutional environment is cooperatively managed through the highest level authority of its governance processes. G overnance performance can be measured by its ability to control corruption, which is particularly threatening to both aggre gate and individual prosperity (Mauro et al., 2002) (Kaufmann, 2006, p. 98), and may hinder the accumulation of knowledge and technical capital and economic growth (La Porta & Shleifer, 2008) An overview of corruption around the world shows that many of its most commonly cited causes and consequences are thought to be economic in nature (Tanzi, 1998, p. 587) Mauro cites

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38 causes related to rent seeking through subsidies price controls, and trade arbitrage, influence r elated to trade restrictions or protectionist tariffs, and incentives or bribery stemming from low wages of civil servants, and other societal factors such as ethnolinguistic fractionalization and family ti es (Mauro, 2000, pp. 4 6) Informal ec o nomic activity that operates outside of the formal economy has many pseudonyms including, but not limits to, three arenas, which may overlap. (1) The first is exchange of products and services ( e.g., unofficial, undergrou nd, unobserved, u nreported, undeclared, non transparent, informal, hidden, shadow, illegitimate, barter, cash, parallel, secondary, black and gray economies or markets of transactions that are on the ground versus on the books ) (Feige & Urban, 2008; Schneider et al., 2010a, p. 3) ; Eurostat uses NOE, non observed economy (Eurostat2011c) (2) Second is the trading for some sort of favor ( e.g., lobby and special interest groups, Political Action Committee[PACs], labor unions, mafias, ca rtels) (North, 1990). Thi c informal institutions or firms to mold or manipulate state laws, policies, or regulations) (Kau fmann, 2003, p. 21) (3) Thirdly some gain by (or through) the trading of knowledge ( e. g., espionage, trade secrets, copy right infringement, scientific breakthrough, or reason). These ma rkets fall into the broader sphere of corruption of the formal governance system (deLeon, 1993; Mauro, 2004b; Rose Ackerman, 1999b; Schneider, 2009) Werlin (1994, p. 554) states that c (2000, p. 182) Weak, formal, l e gitimate systems, ineffective political hardware (contracts, procedures) and influential institutions are symptoms of poor state ma nagement, or ineffective governance. Quoting Joseph Nye (1967) corruption seeps into the social, governmental, and political realms kness of social and governmental enforcement mechanisms; and the absence of a strong sense of

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39 national community (p. 418) and to] the capacity of political structures and processes to cope with societal change states: [Modernization in the United States, or its] development (or decay) will mean growth (or decline) in the capacity of a society's governmental structures and processes to maintain their legitimacy over time ( i.e., presumably i n the face of social change). This allows us to see development as a moving equilibrium and avoid some of the limitations of equa t ing development and modernization. As f or consequences of corruption in the public sector, businesspersons see bribes as a f orm of tax, which increases prices. In Corruption and the Composition of Government Expend iture corruption and government expenditure on education, which is a reason for c oncern, since prev ious literature has shown that educational attainment is an important determinant of economic (1998b, p. 277) Some forms of corruption have t itarian regimes, corruption is often directly linked to human rights violations asserts Pope in Co n fronting Corruption (2000b, p. ix) S olving the causes of corruption question is vast beyond the scope of this thesis, and the consequences are far reaching, equally far outside its scope Merton (1968, p. 130) asserts that A s deLeon 993, p. 3). As such, Stuart, (1996, p. 19). It is the corruption that avoids the democratic process that we seek to mute (Thompson, 2007) The important point upon which scholars agree is that a layer of activity runs parallel to the formal system of government, and it attempts to avoid detection. The corruption that is mea surably absent from the National Income Accounting revenue steam, and therefore, is missing

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40 from official GDP reports, is corrupti on as a dimension of governance In this thesis, this layer is called governance corruption rrency could be economic, social, political, or i Its value could lie in d (Thompson, 2007, p. 2) and non corrupt action s. Instead, we will find the gradations of judgment, reflecting a variety of (Johnston, 1986, p. 379) Co rruption may be the act of an individual, or of a group or institution to gain power, prestige, or position The IMF and Good Governance (2011d, p. 1) factsheet states, the presence of excessive government regulation and in tervention in the economy; substantial e x. P ower i s a pote ntial pay off for corruption (Nye, 1967, p. 421) I nstitutions gain respect because of the power they wield (Kooiman & Jentoft, 2009, p. 883; Mauro, 2004b; Schneider & Enste, 2002) w er is inherent in g overnance f governments and institutions to govern (Kooiman & Jentoft, 2009, p. 833) G ood governance fosters the power of the state while bad governance allows corruption to flourish, and nurtu res informal activity. Som e scholars assert that certain environments invite corruption, such as where monopoly power is in the hands of o fficials, when the risks of getting caught are low and penalties are mild (Klitgaard, 1988; Rose Ackerman, 1978) Other scholars a s sert that corruption can be predicted by (Sandholtz & Koetzle, 2000, p. 36)

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41 remuneration pe of significant personal gain, in which the (deLeon, 1993, p. 25) it is i mportant to understand how scholars measure corruption in order to measure its impact on econo mic growth, living standards, and income inequality. Controlling Corruption Interestingly, correcting the imbalances that cause corruption must be as multidimensio nal as t are those who demand acts of corruption on the part of public sector employees and there are pu b lic employees willing for a price to perform these acts. Ther e is thus both a demand for and which calls for horizontal accountability (Kaufmann, 2006) intervention in the economy, policies aimed at liberalization, stabilization, deregulation, and pr ivatization can sharply reduce the opportunities for rent (2000, p. 6) Rose Ackerman suggests several successful anti corruption projects as models for ove rcoming corruption of varying types, and case studies sub stantiate her theory that clean running and stable governments are a key precursor to equitable, effective, and efficient economic growth that target both the supply and demand (Klitgaard, 1988; Rose Ackerman, 1999b ; Shleifer & Vishny, 1993) Po licies aimed at curbing incentives for corrupt activity may be simple; increa sing the penalty when caught, and/or increasing the law enforcement to catch it (Boswell & Rose Ackerman, 1996) for example. While Thomps on (2007) agrees on the mechanisms and that di scovery should take place within the institutional form rather than in a criminal court, he asserts that the institutional complexity of governments must undergo structural reform in order to r educe corruption.

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42 Incre asing the risk of exposure may help especially as in political corruption. Transpa rency International (TI) created the national integrity system to make undertaking corruption a endeavor (TI, 2000b, p. ix) nystem of checks and balances designed to disperse power and limit (2000b, p. xiii) Policies that affirm Freedom of Press, Freedo m of Information, c ivil liberty protection and election oversight are critical when building a transparent governance (2000b, p. xxv) T ransparency is critical in the priva te sector to reduce calling for policies to further accountability in corp orate ethics and traditional legal and judicial reforms that focus on timely information, auditing, insider rules, and financial disclosure (Kaufmann, 2003, p. 21) level of the benefit (Boswell & Rose Ackerman, 1996, pp. 84, emphasis in original) Decreasing corruption available benefit is multidimensional, a task the World Bank t ook on pronged strategies for Combat Corruption: Addressing State Capture and Administr (2000a, p. 39) The World areas of focus listed next. Where novel approaches b eyond those m entioned above exist those are listed, as well. (1) Institutional Restraints: independent prosecution and enforcement. (2) Political Accountability: Transparency in party financing, asset declaration, and conflict of interest rules. (3) Civil Society Participation: public hearings in the drafting of laws. (4) Co m petitive Private Sector: Monopoly regulation (5) Public Sector Management: Merit based pay for civil service, customs oversight (ch. 4) Another vein of literature discusses increasing vertical and horizontal accountability by both state and non state actors (UNDP, 2010e) Peer level whistle blowing protection and stak eholder groups are e xample s of horizontal accountab ility. From state actors, access to information laws are an example of vertical accountability (Relly, 2011)

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43 C onsequences of Corruption on Development Mauro (2000, pp. 4 6) following Nye (1967, pp. 421 423) cites several consequences of talent may be misallocated [ it] may reduce the effectiveness o f aid flows bring about loss of tax revenue Pertinent to this thesis, Mauro (2000, p. 7) a mposition of government expend i expenditure, corruption may lead to adverse budgetary consequences, [and] lead to lower quality of infrastru c ture and Citing empirical evidence from the former Soviet Union, Adeb & Gupta found that the tic economic, political, and social changes in modern histo ry. Absence of the rule of law and accountable systems of governance led to rent (Abed & Gupta, 2002b, p. 2) Kargbo (2006) writes of corruption that has hurt both aggregate (official the decline in real per capita incomes, inflation, a widening budge t and balance of payment deficits, and declining official pr oe sector, which has substantially decreased the quality of human resources in African states ov er have been limited, thus impacting negatively on the quality of life, labour, productivity, incomes, innovativeness, competitiveness, and poverty reduct (p. 8) The IMF Fac tsheet adds to the body of evidence on the relationship b e tween corruption and education f unding (IMF, 2011d, p. 1) Corruption can reduce investment and econ omic growth. I t diverts public r esources to private gains, and away from needed public spending on edu cation and health B y reducing tax revenue, corruption can complicate macroeconomic management, and since it tends to do so in a regressive way, it can accen tuate i nco me inequality.

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44 Transparency International (TI) (2009b) and Mauro (2000) discuss corruption within the ies with high levels of corruption invest less in public services, leaving the education sector under (p. 6) UNESCO r e ports that (Hallak & Poisson, 2007, p. 105) Mauro (1998, p. 277) pre vFinally, Mauro (2000, p. 6) states the following about incentive to divert funds away from education in the budgeting and allocation process: Corruption may distort the composition of government expenditure Corr uption may tempt government officials to choose government expenditures less on the basis of public welfare than on the opportunity th ey provide for extorting remains significan tly associated with corruption when the level of per capita i ncome in 1980 is used as an additional explanatory [control] variable. Measuring Corruption Indirectly Vito Tanzi (1998, p. 577) hile there are no direct ways of me asuring corru ption, there are several indirect ways of getting information about its prevalence in a country or in studies, empirical country level data and questionnaire based surveys (emphasis in original). The most prevalent method is the survey method. The World Bank and European Bank for Reconstruction and Development (EBRD) created the Business Environment and Enterprise Performance Survey (BEEPS) in 1999, and it is in its fourth iteration. The 2008 round conducted over 11,000 surveys of business ow ners and top managers on business climate and corruption. Data are available on 26 countries in Eastern Europe. While a strength of the BEEPS is in survey data specific to corruption it is li mited to surveys of firms and employees of those firms about corruption in the business environment (BEEPS, 2008a) Therefore, it is not sufficient in scope for this thesis. The EBRD has published thematic Transition Report s yearly since 199 4 In 1998 (EBRD, 2010i) and the report included results from several surveys, and macro e-

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45 conomic data for the transition counties backdated to 1989. With the backda ted data, the EBRD dataset for is a sufficient source for eight of the seventeen variables on twenty three of the thirty countries on which this thesis focuses. The variables that are not part of the EBRD dataset i nclude those in the Human Development Index and the component ind i ces, Education Attainment Index, Life E xpectancy Index, and Gross Domestic Product Index which together make up eight v ariables, this poses no problem, as the HDI vari a bles are each available from the HDR for each country. However, the two variables on which rest the key hypothesis in this th esis are not part of this data set: (1) Government Expenditures on Education as a percentage of Total Government Spending, (2) a measure for unofficial GDP per capita as a percentage of total GDP per capita Since testing both of these variables requires macroeconomic data consistent and normalized with that of the World Bank and IMF, the EBRD data cannot be used in this thesis EBRD in Trans ition Report 2010 concludes the following regarding its own measure for progress One problem is the subjective nature of the scoring and possible non transparency of the This is because [the data] cannot be easily validated externa lu ence the judgment abou t (an d scorning for) its transition progress (which, in the extreme, would render regressions of growth on the transition indicators meaningless) (p. 2) The EBRD continues describing its fundamental concern with measuring economic pr ogress u s ing this (or presumably other similar) datasets. A more fundamental objection is that, with the exception of the infrastru cture i n dicators, many of the scores reflect a rather simplistic view that a successful transition is mainly about r emoving the role of the state and encouraging private ownership and market forces wherever possible. The problem with this view is that mar kets cannot function properly unless there are well run effective public institutions in place (p. 2)

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46 Several widely used surveys on the perceptions of corruption in the private sector and at different levels of government are available. Transparency Int (TI) Corruptions Pe rch public sector corruption is perceived to exist in 178 countries around the world It scores countries on a scale from 10 (very clean) to 0 (highly corrupt) methodology, the CPI is not a tool that is suitable for trend anal y sis or for monitoring changes in (CPI, 2010b, p. 5) Corruption Barometer uses similar methodo l ogy (GCB, 2009) and is a wealth of information, by country, on the scale, type, causes, co nsequences and true accounts of corruption, but offers no measurement of it relative to GDP (GDR, 2009c) as that is not its purpose The Cost / Benefit Analysis (CBA) allows one to value the implicit and explicit costs of corruption using empirical and survey data (Sen, 2000) Kaufmann (2006, p. 81) suggests that, gathering the informed features (and by)...careful audits Together, the CBA and national income accounting tools can estimate the portion of funds that are unavailable domestically due to corruption by budget line item and do so with statistically significant precision. This precision increases, and costs become increasingly explicit as more nations practice standardized accounting methods (IMF, 2011, p. 1; D. K. Gupta, 2001; Weimer & Vining, 2004) However, National Income accounting practices are yet inco nsistent between countries and links between social and professional research are lacking requiring us to abandon the CBA method for this thesis (Feige & Urban, 2008) For an example of the chasm rendering the CBA unproductive for the method herein, de L eon (1998) a t tempts to positivists representing respectively the purely quantitative and purely qualitative method extremes (p. 111). To elucidate the division, deLeon affirms the post

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47 efit assessment H ysis, for it is by u su s ing many of these quantitative tec h niques that they can propose with some rigor a clear cut set Further, pressed to find any important public policy decisions that were made sol e ly on the basis of cost (quoted in deLeon, 1998, p. 108) Several scholars critique the MIMIC method used by Schneider et al. (2010) asserting that the va (Breusch, 2005; La Porta & Shleifer, 2008, p. 8) possibly exaggerating the estimated size of the informal economy, and that the benchmarks are subjective. Recall also tha t correlation coefficient b etween the electricity demand method, currency demand, key perceptions indices and structural equation methods, is .88 or greater (S. Gupta et al., 1998, p. 12) so the concer n runs across the potential methods. Addressing the su b jectivity of the benchmarks, Schneider and other scholars collaborate with Bruesch to arrive at a calibrated benchmark nship between the measurement in different year (Dell'Anno & Schneider, 2006, p. 9) avoiding overstating the percentage of the Shadow Economy S chneider et al. ( 2010a) counter with the following statement, regarding the MIMIC (Multiple Indicators Multiple Cau s es) model. [T] his is the first study that applies the same estimation technique and almost the same data sample to such a large number of shado [using] the MIMIC estimation met hod for all countries, thus creating a unique data set that allows us to compare shadow economy data ( p. 3) little is known about the d evelopment and the size of the Shadow Economies in developing Eastern European and Central Asian countries (20 11d, p. 1) The criticism by Bruesch relative to this th esis is the suggestion that the MIMIC model may overstate the actual Shadow Economy

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48 percen t age However, other scholars assert that corruption a ccounts for far more of the economic activity than st udies to date have realized or uncovered (Levy, 2007) Since t he size and scope of the MIMIC technique and study of national level government and institutions is revolutionary and methodologically unmatched, it therefore, is the measure employed for the methodology in this thesis (See MIMIC diagram in A p pendix). Theoretically, the r elationship between corruption and the S hadow E conomy is thus u nsettled. There is, however, reason to believe that the relationship might differ among high and low income countries (F. G. Schneider, 2009) Others assert that the relationship is complex, d epending on the maturity of the government, and the quality of governance (La Porta & Shleifer, 2008) Recent literature asserts that cor ruption and the Shadow Economy are substitutes when corruption is high, and complements when corruption is relatively lower, supporting the need to measure them separately in a simultaneous equation model (Buehn & Schneider, 2009; Dreher, Kotsogia nnis et al., 2005; Russell, 2010)

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49 Economic Growth The business cycle literature is foundation al for understanding the effects of corruption for two reasons. First, much of the business cycle literature precedes the economic growth liter ature chronol ogically, and more importantly, it is referenced frequently in the latter. Second, this thesis pivots on the political lightning bolt the end of the Cold War Baumgartner & Jones (1993) call this non incremental change a punctuat ion in soci e equilibrium or stasis (p. 23) A watershed event this demarcation was a catalyst for a new business cycle and economic growth stage f or many Central and Eastern Europe countries (Rostow, 1991) The economic growth literature underscores the critical ity of education funding in economic policy development and of this thesis by underscoring required to build strong, healthy, and viable economic deve lIn Innovation: The New Pump of Growth Paul Romer asserts that it is the application of knowledge through an educated workforce such as hig hly trained scientists and engineers who are to credit for past economic growth and that growth has proven to be unsustainable in countries around the world without sufficien t public support through funding and public policy (Romer, 1998b) Developing this argument begins with an u nderstanding of the business cycle. The Business Cycle the foundation of economic growth stages Business cycle literature falls into two major categories, the how cycles work -their identification and measurement, and, the theo ries on why -or their cause. From here, the liter ature divides again into endogenous non linear and exogenous linear multiplier, with some helpful bridges between them. This pattern repeats itself in the economic growth literature. Economic eputation is on the line, as some claim it is economic growth itself that can exacerbate poverty or income inequality ( e.g., Galbraith 2008; Rothschild 1986; Pritchett 1997 ), while ot hers claim economic growth is required to alleviate both (e.g., Sachs & W a rner 1995; Sen 1999; Barro 2001; Friedman 1997 ). Either way, aggregate growth seems inevitable (Maddison, 2009) ;

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50 furthermore, growth is merely a measure of the change in annual official GDP output, so culp abi l ity or credit belong to the causes of change. Complicating the equation, both sides may still be right, if given identical data and definitions, the time span pivots on different events or cycles (Galbraith & Kum, 2005; Pritchett, 1997; Rostow, 1991) Early work on business cycles focuse d on identification and measurement and questioned whether movement equals a cycle or an aberration. What magnitude of change constitutes a st atistical os cillation rather than a cycle ? Is it repeated or at least periodic, continuous, intermittent, or patterned (Mitchell, 1928; Slutsky, 1929) ? Regarding the naming of cycle names their dur ations, Juglar (1893) working on the credit crisis in France and later in the US, identified 8 to 10 year industrial cycles tied to the issuance of credit. Kitchin is credited for identify ing the 3.5 year business cycle (1923, p. 10) Kitchin, Slutsky (1929) and Wright (1920) co nsidered the short incorporate several shorter waves (Kondratiev, 1926) Schu mpeter solidified these three time s -Kitchin, Juglar, and Kondratieiv (1926) -into the economic growth literature of today with his theory that each represents an i nnovation of different magnitude, exists simultaneously, and should b e additive with r evolutionary (Schumpeter, 1939) Mitchell, in turn, tied a 4 year cycle to the effects of the political cycle and found two or three of these shorter cycles exist between Juglar crises (Glasner & Cooley 1997, p. 347) In a foundational work to the developing economic growth and governance theories, Rostow adds that the economic stages of development are logical, rational, and based on exog enous globalization and endogenous governance forces. Rostow (1991) found the pre conditioning stages were roughly 15 to 20 years and the take off stages were about 60 years, co nsisting of several interwoven growth spurts of varying lengths and magnitudes depending on the e tween industries. Kuznets

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51 (1940) proposed a 15 20 year cycle consistent with the explanation of interim shorter cycles within larger growth phases and based on his own national income research. Burns & Mitchell (1946) developed a definition for the bus i ness cycle, which is the duration in months from trough to trough when measuring the rate of change of productivity; this set the standard for other cou ntries. Note the identif i cation of a standard: a trough marks a cycle After working out the statistical problems with the counter cyclical ( and therefore, the canceling out ) nature of supply and demand forces within aggregate indexes, from the se dive rgent camps based on theory, come a general agreement that economies do cycle. Given that economies cycle, one may ask how the economy cycles, referring to the initial date, degree, and duration of the cycle. This elevates the pertinent question t o why why do economies cycle? (1936) lead on why economies cycle and why they grow, many eco nomists modeled endogenous causes with some type of oscillator such as inc ome/expenditure (Samuelson, 1939), income/savings (Kaldor, 1959) inventory (Metzler, 1941) trade (Hicks, 1950) and credit and money supply (Hayek, 1933) Some scholars added non (Goodwin, 1951) price signal or information on intertemporal discoordination (Hayek, 1933) or time lags between acquisition and distribution (Kalecki, 1954) or the idea of a multipl ier (Howitt et al., 1999) Other scholars, following Schumpeter and Kuznets, worked on exogenous causes such as structural change and entrepreneurial gains (Schumpeter, 1942) knowledge accumulation (Romer, 1996) technical change (Hicks, 1950; Solow, 1956) Summarizing the review of business cycle literature economies cycle C ycles are the e f fect ; cycles do not materiali ze out of a void; they have a cause. This critical point refers to : e vents demarcate and catalyze economic growth stages (Pritchett, 1997; Rostow, 1975, 1991; Xu & Li, 2008) The c ause(s) is (are) due, generally, to some influence or to a combination of endogenous, evolutionary change and exogenous revol u-

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52 tionary shifts in the steady state (Ofer, 1987 ; Schumpeter, 1942) Like the business cycle itself, (Barro, 2000, p. 32) The est imated relationship may reflect not just the influence of the level of per capita GDP but also the dynamic effect; whereby, the adoption of each type of new technology has a Kuznets type d ynamic effect on the distribution of income (Barro, 2000) Business cycle research was a foundation for an explosion of attention on economic growth, which offers theories and models on why and how economies grow. Economic Growth Theories term change in capacity to supply i ncreasingly diverse economic goods to its population (Kuznets, 1973, p. 247) is based on advancing technology and the institutional and ideological adjustments that it demands. Econo mic growth, generally, is the change in aggregate producing power GDP, which is the amount of goods and services produced in an econo my over one year A negative change is negative (1940, pp. 259 259) elaborates on Schumpeter (19 39) who disti n guishes economic growth from economic development by the degree of change, where growth is incremental, evolutionary, and state...a di sruption of the static e (Kuznets, 1940, p. 259) A consequence of evolving societies, increasing population, production of basic necess it ies, and reliance on incentive to spur on economic activity (Phelps, 2008; Schumpeter, 1939, 1942) of an economy (pp. 4 6) The staying power of a new steady state requires constant progress in the pre conditions to it, in infrastructure and social capital (Putnam, 2000) Staying power also requires the foresight to invest in those factors that will be valuable in the future both for co n-

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53 sumption and demand for exports (Krugman, 2000; Rostow, 1991) I t is cri t ical to assessing the quality of the economic development ex post facto. After the development has occurred, analysts can see the evidence of healthy or u ncustomers. Citizens produce the inputs to GDP, the goods and services produced in a country in a ) are consumers, or cu s tomers, of that which is produced; if the product is desirable and the price is commensurate with its pe rceived value. For example, assume Country A and Country B both decided to increase GDP by pursuing additional shares of the wor ld transportation market: Country A pursues the horse drawn carriage, and Country B, racecars. Even the finest horse drawn carriage has a limited a ppeal in the local or world market, yet the price is relatively low. Likewise, even the latest, high price d and technologically advanced racecars have limited appeal, yet the horsepower is high. Both are in the realm of transportation, however, neither is meeting a high demand in the popul ation. Neither provides the host country a sustainable industry nor cr eates a trading advantage. As indicated above, there must be a desirable product (with high demand ) and a commensurate price and value in the international market to gain economic development strength. Neither the horse drawn carriage nor racecar ideas w ould meet the current or future needs of the citizens or of the export markets. The sustainable forecast, based on foresight, the investment needs in infrastru c ture, education, training, res earch and development, and expertise to produce needed and desirable goods for the times ; a nd to inn ovate and revolutionize to meet the challenges for tomorrow and for the world market (Nelson & Phelps, 1966, pp. 70 71) In review essay of economic growth liter ature, Klenow & Rodriguez Clare (1997) cha llenge r e searchers to complete four steps when assessing economic growth: (1) theory and evidence... ( 2 ) tie research to business cycles... (3) develop more theories of intern a-

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54 tional productiv ity differences... (4) and collect detailed country data bearing on the process of (Barro, 2001a; Klenow & Rodrguez Clare, 1997, p. 597; Klingner & Sabet, 2005) The rational e for this the sis parallels Klenlow & Rodriquez Clare four step cha llenge. First, the thesis accomplishes steps one and two by linking governance, economic growth theories, and individual income with stages of economic growth. Second, this thesis satisfies steps thr ee and four by analyzing individual income as a measure of education value based on the staying power of economic growth in a sample of countries New Growth Theory (NGT) is a theoretic umbrella over the four challenges above (Barro, 2001b; Klenow & Rodrguez Clare, 1997) Stated diffe r ently, this thesis satisfies the challenge s capitalizing education as fundamental to successful and sustainable development policy (Romer, 1998a) Theories on the Causes and Types of Economic Growth Two theoretical camps divide the economic growth literature based on causes (why), then further divide based on effects (how). The first camp asserts that exogenous forces cause ec onomic growth the second camp that endogenous change cause economic growth The literature further di vides based on the effect of the growth ; t he two camps divide into four based on how economies grow This latter division is centered on the pa th of growth the trajectory ; either individual incomes converge (the gap between the richest and poorest shrinks) or diverge (the gap between the richest and the poorest grows) or move in some combination of these paths over time. Each of these four m ajor theoretical camps, two on causes (exogenous and endogenous) and two on effects (convergence and divergence) the causes and e ffects, the why and how of economic growth This large literature is important to understanding the nature of economic development Neoclassical Growth Theory and Exogenous Growth Theory Solow isolated key determinants of economic growth into the factors of production, tec hnology, labor, and capital, isolat ing the growth attributed to each. His w ork laid the foundation

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55 for Neo Classical Growth Theory. From the literature on technical change grew the bu r geoning literature on the rate of technology and innovation transfer, adoption, and diffusion as a measure of policy economic development and stab ility (Klingner & Sabet, 2005) Solow wrote, regarding 87 per cent of the increase att ributable to technical change and the remaining 12 per cent to i n(1957, p. 320) Consider the tremendous change and growth in the transportation industry, for instance, with the aid of automation. Fredrick Taylor (1911 p. 21 ) pre dicted this rapid change when a ppl y scientific skill training of workers toward the needs of the future, or to infuse the wor k place with given the very best knowledge of his predecessors; and, provi d ed methods which represent the best knowledge of the world up to date, he is able to use his own originality and ingenuity to make real add i tions to the world's knowledge, instead of reinventing things which are old (p. 126) Rostow referred to this shift in economic growth stages, the shift (1991, p. 5) (p. iii) Clearly, a doubling (or 100 percent change) of the average it makes sense in light t tributed 87 percent of that doubling to technical change, one such type of change being automation (p. 320) Henry Ford produced 11 Mo del T cars in the first month of its production in 1909. Then, Ford automated its assembly line (1911a) In 1910, 12,000 Model T cars rolled off the assembly line, and by 1925, 2 million Mo del Ts rolled off that line (Brinkley, 2003, p. 475)

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56 Solow (1957) states that the remaining 12 percent of the doubling of productivity per working hour over his forty year study was due to the increased use of capital (p. 320) Ford built the Highland Pa rk Ford Plant in 1913 to accommodate the Model Ts assembly line (Brinkley, 2003) can be a pplied with equal force to all social activities: to the management of our homes; the management of our farms; the management of the business of our trade s men, large and small; of our churches, our philanthropic institutions our universities, and (1911a, p. 8) Relying on a Keynesian foundation to apply external stimuli to an otherwise closed sy swhile others focused on production disincentives (Howitt, 1986; Keynes, 1936; North, 1994) Working from these strengths, Exogenous Growth Theory specifically employs global technology advancement as the exogenous agent of growth (Hahn & Solow, 1997; Howitt, 1986, 1997) E ither way, models of exogenous theories treat growth as the result of some external catalyst, neglecting part of the evidence, such as cycles inside the economies and additive or compound growth. R eturns on endogenous factors ( e.g., interest, inflation, population or compound growth, increment al knowledge gains over time, or inventions that revolutionize productivity, mathematically eliminate the possibility of exogenous growth, arguing for a different growth m otive, a point conceded by many neoclassical theorists (Romer, 1996) Critics of exogenous growth theories contend that th is camp forces illogical conclusions that neglect variation among countries in technical accumulation, and neglect the effects of h uman capital generally, and knowledge capital, specifically. In doing so, th is camp di s regard s vast literature on the effects of such variables as technology diffusion and adoption (Easterly & Levine, 2001; Klingner & Sabet, 2005; Romer, 199 0; Taylor, 1911a) educational attainment and quality (UNESCO, 2010d; 2001b; Barro & Lee, 1996) knowledge spillovers (Arrow, 1962) governance (IMF, 2011d; Abed & Gupta, 2002b ; Kaufmann et al., 2008) and corruption

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57 (deLeon, 1993; Rose Ackerman, 1978; Tanzi, 1998) Therefore a Keynesian based theory is i nsufficient for this thesis. Endogenous Growth Theory Endogenous Growth Theory re Literature from the e ndogenous g rowth camp treats factors such as governance, policy, effects of the national economic and f inancial systems, education and innovation, social capital, and incentives as agents that develop human and soc ial capital and drive incremental growth from within. These work in co n junction with technical progress and innovation (Barro et al., 1994; Barro & Sala i Martin, 2004; Putnam, 2000; Romer, 1994b) While exogenou s growth models require holding technical advancement constant across countries, endogenous models treat technical change as a variable (Easterly & Levine, 2001; Romer, 2001) Ironically, advances in computing tec inal work on exogenous technical change was its undoing. The ability to manage and calculate large cross country longitudinal data sets allowed researchers to treat more vari a bles as variables rather than constants (Cobb & Douglas, 1928; Sala i Martin, 1997) W hen applied to evidence in westernized, democratized countries, and/or to isolated and self reliant regions ( e.g., the US, UK), use of endogenous theory yield strong correlations between sound governance and growth. This become s important when considering economic policy options in the US, and/or for non westernized communities that rely on endogenous factors fo r growth (King & Levine, 1993; Martin & Sunley, 1998) However, this theory neglects a different part of the evidence: revolutionary shifts, tec hnology adoption rates, and other effects of external events (Easterly & Levine, 2001; Solow, 1956) Endogenous growth neglects the volume of literature on the stages of economic growth. Rostow (1991, p. 6) but by some external intrusion by more advanced societies. These inv a sions l iteral or figurative shocked the traditional society and began or hastened its undoing

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58 and exogenous theories are guilty of remaining true to their precepts at the expense of evidence. Endogenous Growth Theory neglects extern al factors such as peace treaties or a neighboring where E xogenous Growth Theory neglects internal factors such as education spillovers or positive externalities of a hometown Olympic athlete New Growth Theory New Growth Theory links the roots of endogenous economic factors to technical progress adoption and diffusion increases that drive economic growth. New Growth Theory builds a bridge be tween endogenous and exogenous camps For example, assume that a new technology in an era of rapid globalization developed outside the economy in question. Each economy must choose whether to employ its own resources in order to adopt the exogenous catalyst, or not Romer (1998a, p. 2) descr ibes the process thus New Growth Theory identifies three specific features that make growth possible. First, we live in a physical world that is filled with vastly more unexplored possibilities than we can image, let alone explore. Second. Our ability to cooperate and trade with large numbers people makes it possible for millions of discoveries and small bits of knowledge to be shared. Third, and most important, markets create incentive for people to exert effort, make discoveries, and share info r mati on. concept of knowledge as an economic asset (non rivalrous, partia lpoor nation invests in education and does not destroy the incentives for its citizens to acquire id e(2007, p. 3) Romer asserts that living standards are a direct result of knowledge and technology adoption (Romer, 1993) Phelps (2008, p. 14) attributes a good economy to education, which produces v i tality, and policies that promote inclusion

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59 New Growth Theory proponents defend good governance as a practical necessity for e ducation and the educated to flourish, drawing from classical economic theorists and new ideas from the governance literature (Phelps, 2008) North (1992, p. 3) suggests economists treat the policy dev elopment and implementation a critical factor in the performance of economies, as the source of the diverse performance of economies, and as the e xplanation for inefficient markets The assertion that Social Capital is a pre condition to a long wave growth stage from the political economy literature, parallels New Growth Theory, entrepr eneurial theories, and theories on human capital (Matheson, 2008; Nelson & Phelps, 1966; Phelps, 2008; Putnam, 2000; Rostow, 1991; Schumpeter, 1939; H. A. Simon, 1986; Xu & Li, 2008) Technology and innovation diffusion and adoption are both precursors and by products of the quality and quantity of education, GDP, economic growth and economic devel opment by country; the speed and degree of its transfer are, in part, a result of the governance system, and the success of its development policy implementation, specifically, of education policy (Nelson & Phelps, 1966; North, 1992; Romer, 1993) spread of new products, values, policies, or processes beyond the locus of their original success. If viewed purposively, this spread can be described as both organization al learning and (Sabet & Klingner, 1993) This concept of adoption closely resembles (1911a) Klingner & Sabet a(p. 206) Critics argue that education does not produce increasing returns. Instead, education is like other economic factors experienci ng constant returns to the investment in education (S olow, 1957) diminishing marginal utility and rent seeking (North, 1990) crowding out by other activ ities such as leisure and quality or effectiveness challenges (Pritchett, 2001, p. 369) Rather than

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60 knowledge spillovers and positive externalities from education, put forth by New Growth Theory, recent studies provide evidence of negative education ei ables (p. 380) Importantly, Pritchett (p. 382) asser ts, but socially dysfunctional and r e duce overall growth that informal institutions may benefit from education. One key challenge to this argument is co nsistency in definitions. Returns to education (its funding cost/benefit balance) and returns to knowledge are different. For the evidences of and requirements for economic growth, this thesis builds on Romer (1996) and Easterly & Levine (2001) by modeling dimensions of governance, development po licy, education, and market demand of output. New Growth Theory stands ap art from other economic growth theories for three reasons : it shoulders change regardless of origin, pace, or v ariety, it allows researchers to treat factors of growth as variables or constants, and it incorp o rates education as a variable in economic growt h. Convergence, Divergence, and Bridging Theories Incomes c onvergence over time (1991, p. 1) The homogeneity of US data earned credit for much of the statistical strength in his model. This point is significant (and expanded later) as an indicator of internal, freer market forces ten ding t o cause converging incomes (Barro, 2000) Convergence Theory mainta ins credibility for use in discrete situations, but until recently, had done little to inform economic globalization (Barro, 2000, 2001a; Olson, Sarna et al., 2000; Jeffrey D. Sachs, 2005; Sadik, 2008) Conve rgenc simila r. Clearly, according to Sachs & Warner (1995)

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61 kept it trapped in poverty shy the h u man capital to raise it up (p. 4) Incomes d ivergence over time Divergence Theory suggests that incomes diverge over time in certain situations. It gained wid e spread recognition as the refutation to the convergence literat ure (Easterly & Levine, 2001; Matheson, 2008) Substantial scholarly work and research on discrete datasets agree that there are likely correlations between economic growth and divergent incomes (Baddeley, 2006; Pritchett, 1997) for the private nonagricultural economy between 1948 and 1982 [which] r esults in a significant (1986, p. 205) Sta t ed otherwise, incomes between the richest and poorest in this p articular data set grew apart. Divergence Theory may explain why incomes in ngly disparate. Pritchett (1997, p 15) suggests that the divergence is a ma t He follows with a prescription for growth policy similar to Sachs & Warner (1995a, p. 10) that countries must provide an open economy, free of repressive regulations, with relatively low levels of corruption Bridging Theories Finally, five theories offer a bridge between the convergence and divergence camps. Knowledge about the difference between camps is the critical issue for many policy analysts and public administra tors and policy makers, as the outcome bears great weight on long term indivi dual and aggregate prosperity (North, 1991b) First, poverty trap theories suggest simultaneous convergence and divergence For instance, if policy makers neglect to appropriate adequate fun ding for education, empirical evidence shows that the poorest sector of the population will grow relatively poorer while the richest grow relatively ric h er (Romer & Barro, 1990)

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62 Second Schumpeterian Growth Theory asserts that growth comes from quality improving research and development innovations. This theory i nforms the balance of economic growth theories and adds validity to both camps, as each require innovation regardless of its origin (Howitt, 1999; Howitt & Mayer Foulkes, 2005; Phelps, 2008) Third in The Stages of Economic Growth Rostow (1991) offers an historical view of characteristics of economies in different stages, which, in turn, offers potential bridges between the four major economic growth camps, allowing each to be right under certain conditions. Fo rtunately, this suggests a futility in damning converging or diverging theories, as these mean little without knowledge of the relative stage and trend of the economy, and less without an accounting aject o ries absent considerable context. Path d epende ncy investment in land, labor, and capital, are somewhat directional (North, 1991b) The movement of individual income inequality may be the result of a combination of factor s from l egacy ec onomic investments, which take time to adjust, and legacy skill sets, which take time to re train. He aptly applied different data scenarios to discover the determiners of inequality, while he admitted that the data available left much unknown, and part of the credit for the richness of information avai lable now goes to him for the challenge he presented. Kuznets (1934, pp. 6 7) as the innovator of National Income accounting, knew and reported the limitations of GDP per capita as a measur eIn summary, the convergence / divergence argument remains a vital and productive field that tells policy makers and analysts nothing, or, worse, can be skewed to tell them anything a bsent considerable context For this reason, an d because the presence of corruption skews the very data policy makers need to make development decisions (Schneider et al., 2010) it is critical to

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63 inform public administrators and their policy development e ndeavors that good governance and control over corruption is important (Tanzi, 1998) Public budgets ( e.g., infrastructure, health care, public works, and courts) suffer, in the presence of corruption. This thesis defends that s iphon ing funds away from education does more harm than siphon ing funds from other pu b lically funded programs as reduction in education budgets has an increasingly detrimental effect on ec onomic grow decreasing (depreciating assets) (Mauro, 1997) M auro asserts (p. 267) New Growth Theory is critical to developing the healthy growth policy arguments. Ed ucation funding as a part of development policy must be protected, as education, and consequen t ly, s ability to adopt and use technology effectively, hinges on this protection. This may e xplain why healthier, more mature economies with better governance and co n trol over corruption experience less poverty less inequality and c onverging incomes; howeve r, this is a thought for future research.

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64 Measuring Education Delivery Measuring the Quantity of Education (Supply) The body of literature on assessing and measuring education is enormous. The literature su r rounding e ducation delivery includes research on quality, quantity, and process measurements Quality measures for education include the output based test scores and literacy rates, and on i nputs such as materials, facilities, class size, and teacher training (UNESCO, 2005; D. J. Brewer, Krop et al., 1999, p. 187) Quantity measures for educ a tion include matriculation rates, years of schooling, level of school attainment, and gender parity (Barro & Lee, 1993, pp. 365 368) These factors are critical for assessing education system s, however, they measure education delivery based on an externally d e veloped budget. This body of literature, which informs e ducation as an industry, discipline or process, is outside the scope of this project. Rather, the focus for this th esis is public expenditures budgeted for public education, and the effects of corruption on the public expenditures for public education. E ducation funding provide s its own body of literature. However, this thesis focuses on empirical data indicating the level of public funding from official budgets, or (Patrinos, 2007, p. 1) Ther e fore literatur e on other funding or school cho ice methods, to include private schools or private funding (M. Friedman, 1997) voucher programs and school choice (M. Schneider et al., 1997; Teske & Schneider, 2001) bu ssing, scholarship s grants, foreign aid, for profit schools, (Patrinos, 2007, p. 3) is excused for the purposes of this thesis. Me asuring the Value of Education (demand) Another body of literature, equally vast, measures the value of the schooling provided to the ind i vidual ( e.g., living standards, individual income, human development, and contribution to society) and to society (re turns to education, national and international market value of goods and services). This work is quintessential to the questions in this thesis. Sen int e grates these volumes of work into his work on Human Development (HDR, 2009e; Sen, 1997, 1999, 2004) He sy n-

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65 thesizes work by other Nobel Laureates into the human development literatur e by including ed ucation as a means to economic development ( e.g., income in National Inc Sen added work by noted scholars on th e subject, as well ( e.g., the work of Sachs & Warner on knowledge spillovers and economic policy [1995] on education and technical change [1990]; proceedings for the World Bank on development thr ough educ a tion by Romer et al. [1992]; education, and New Growth Theory [1994, 1998]; capital [1993, 2001, research network and the vast amount of research ongoing for the Millennium Development Project, Sen created an index that asserts to measure the extent to which human development; this included the educational insti tutions it fostered over time. The HDI provides the pre test data for this thesis, upon which the research questions are built. This thesis assumes that education is a public good, which is not universally hel d to be true. Ed u cation Expenditure data in this thesis do es not account for private funding of private or public educat ion nor does it account for foreign aid for education. School choice, voucher pr ograms, charter schools, and lotteries were borne out of a perceived or real shortcoming of the system in place. Education may be better, more efficiently or more effectivel y delivered by the private sector (M. Friedman, 1997) Governance Corruption in Education Literac ugained during school, and are often self reported. The rate of graduation is e qually insu f ficient to measure education earned, if graduates are not proficient readers or writers. Testing high on an

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66 exam is an insufficient measure for application of knowledge on the job, or the degree to which knowledge and training matches the need s of employers (Romer, 1998a). Other factors to include gender differences (Barro & Lee, 2001), class size (D. J. Brewer et al., 1999), school choice (M. Schneider et al., 1997), school funding and competition (M. Friedman, 1997), and overall school quali ty (GMR, 2005), are neglected in the EAI. Gupta et al. (1998 ) report that by reducing the public resource pool, the remuneration for corruption could shift toward that which complements the endeavors of those that are corrupt and away from approved, sust ainable economic development and market demanded goods and se rvices. Following are three such methods, or privilege seeking behaviors. Bribes paid to school officials merely to gain entry into school (p. 10) is one method by which funds are transferred a way from the public resource pool; by virtue of the official position of the bribe taker, this effe ceducation, and this income by passes taxation. A second poss ible method is the under delivery and over pricing of supplies and textbooks, thereby increasing the effective cost of schooling and adversely affecting the demand (p. 11). Under delivery decreases the quality and quantity of e ducation, with suppliers wit hholding shipment until sufficient bribes are paid (p. 12). The third method, because of school officials or other government employees having access to the budget, nder or not provided to the public at all (p. 5). Education is measurable by its evidence, it is measured in human capital (Barro, 2001b; Lucas, 1988), and specifically, knowledge capital. Knowledge as capital, knowledge gains, pos itive knowledge externalities, knowledge spillovers, and other adjectives that descr ibe that transformation from the process of educating to gain a state of knowledge, or increases in inte l-

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67 le c tual muscle, show up in economic growth generally, and individual income, specifically. Klingner & Sabet (2005) refer to knowledge, adaptation, and inn o (p. 208). Romer (1990), consistent with Solow (1956, 1957), asserts that human capital, partially determined by knowledge, determines the rate of economic growth. It is because of this EAI shortcoming and shortcomings of other widely used measures for educ a tion attainment ( e.g., test scores, literacy rates, graduation rates) that this thesis employs two measures for education. The first measure for education is an input driven or supply side measure, government spendi ng on public education as a percentage of total government expend iture, or Education Expenditure (EE), using the precedent set by the United Nations in its report Education for All (GMR, 2010d). Many measures for education i n puts exist, however, since thi s thesis uses GDP per capita as a measure for economic development, consistent with Sen (1997), La Porta & Shleifer (2008), and Gupta et al. (1998), we must use a derivative of GDP as the nstruct validity across vari ables, which the ICP network provides in the public data. The second measure for education is an outcome driven measure, or demand side, Income per capita (Ic), which serves as the proxy for learning, knowledge, and skill attai nment consistent with a widely accepted and standard method since Kuznets (1934) (Barro & Lee, 1993; Galbraith & Kum, 2005; Gupta et al., 1998; Mauro, Abed et al., 2002; Schumpeter, 1939; Sen, 1984) Two scholars help frame debate on the effects of corrupti on on education. Both scholars argue that corruption is corrosive to a society; from the quantitative, empirical analysis disc i pline, Paulo Mauro, and from the qualitative, social capital discipline is Paulo Freire. Paulo Mauro (1998) Harvard Ph.D. and F iscal Operations Division Chief for the IMF, asserts in Corruption and the Composition of Government Expenditure is negatively associated with government expenditure on education, and the relationship is rob ust

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68 suffers relative to those more lucrative to rent seekers. ypothesis that education provides more limited opportunitie s for rent seeking than other items (Mauro et al., 2002) S cholars and econ omists with research confirming Mauro include Armstrong (2005) Gupta et al. (2000) Pritchett (1997), and Chua, (2006, 1997, 2001). The Paulo Freire Institute Headquarters is at the UCLA Graduate School fo r Education and Information Studies, which also houses the Freire Online a Journal, dedicated to critical pe dagogy. Freire was awarded the UNESCO Prize of Education for Peace in 1986, and the International Development Prize by King Baudouin of Belgium in 1980 for his work in education and pedagogy He also served as the Secretary of Education in Sa o Paulo been the subject of hundreds of Ph D dissertations th eories of social capital critical th eory, and social networks (Gadotti & Torres, 2005) In the seminal work on pedagogy in lesser developed regions, Freire uncovers struggles against the tides of political, corrupt, philosophical, cultural, and social oppression infused into the educ ational systems in communist counties He asserts that th e ineffectual education inhibits personal (1970, p. 183) More critical rvention [educating the oppressed] would not be to his interest. What is to his interest is for the (Freire, 1970, p. 52) Furthermore, (p. 135) Contrary to Mauro and Freire assertions about education under communist regimes the evidence reveals robust education policy and delivery plans from the 1920s and through the 1980s intended to create sustainable economic growth in the former USSR. The Technical and

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69 Vocational Education in the Union of Soviet Socialist Republics (Movsovic, 1959) written for and methods of (p. 14). the educational process is to inculcate into his pupils sound professional knowledge and work habits and develop creative initiat ive s and conscio us labour discipline in them (p. 1 9 ). of every depar tment in an institution of higher education not only to acquaint the student with the scientific bases of present day industry but also to provide him with the solid scientific theoretical groun d Article 121 : Constitution of the USSR establishes the right to education of the citizens of the Soviet Union. This right is given effect through the system of ge ne ral and compulsory education, higher education and seconda ation and training With all school education in the mother tongue (p. 4) Researchers such as Pritchett (2001), Levy (2007) and North (1991), attempt to reconcile the i n congruous evidence. Between the the high research and d evelopment c el ebrated as the hallmark in education planning for sustainable economic development, and its delivery over sixty years H owever, growth waned increasingly toward the end of this period (Movsovic, 1959; Ofer, 1987) Ofer suggests that four factors contributed to the downward trend (1987, pg. 1812 1820) First is the inability of central planning to adapt to growing complexities in the world economy. Second is the increase in relative spending on defense versus technical i nnovation Third is the f the material incentive system of corruption. e

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70 b Schneider refers to this economy as the Shadow Economy (Schneider et al., 2010a) Paradoxically, New Growth Theory follows both declarations in the Constitutio n of the USSR and the work of Mauro, Freire and others, affirm ing that education is a requirement of d evelopment, and deserves priority status in the public budgeting process (UNESCO, 2010d; Cortright, 2001) that r e mains significantly associated with corruption when the level of per capita income in 1980 (2000, p. 10) However, underdeve l ope d countries ha ve literate citizens. In fact, o ne of the reasons that the Educational Attainment Index (EAI) is problematic for the HDI is due to the lack of a better measure of education quality and quantity Nearly every country has literacy rates near 99% (HDR, 2007, p. 226) corrupt or not developed or not, with centrally planned or market based economies This fact alone calls for a different metric for gauging education broadly and literacy specifically This fact also begs a different ed ucation public has thought of, innovated, created, engineered, developed, advanced, manufactured, and most importantly to the level of Gross Domest ic Product pe r capita, what they have sold.

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71 Summary Research Question 1 asks whether the change in Hum an Development Index accounts for the change in Income per capita. H ypothesis 1 maintains that the change in the HDI does not account for the change in Ic. Affirming this hypothesis opens the door to test whether the HDI together with a variable for the Shadow Economy better explains the change in Ic. Research Question 2 represents an attempt to inform the body of literature of the effect of corruption in these areas, specifically. H ypothesis 2 maintains that governance corruption, as measured by the SE has a negative effect on I c Research Question 3 asks whether governance corruption, as measured by the S hadow E conomy has a negative effect on Education Expenditures. Hypothesis 3 maintains that the variation in EE can be explained by the variation in SE. If the tests affirm this hypothesis, the door is open to test the last research question. Research Question 4: Do the pre test H uman Development Index governance corruption, and education expenditure together explain the change in Income per C apit a? H ypothesis 4 maintains that there is a significant relationship between the change in Ic HDI 1990 and change in EE

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72 C HAPTER 3 THE DATA AND METHODOLOGY Methodology New Growth Theory suggests that both endogenous and exogenous factors encourage economi c change, and that knowledge gains that support and invite research, development, tec hno l ogy advances, and the skills to implement technology adoption are pivotal to creating healthy, sustainable economic development. The research design for this thesis i s quasi experimental Equations 1, 2, and 3 are foundational to Equation 4. Equation 1 test s the correlation coefficien t or the strength of association (Guj arati & Porter, 2009, p. 20) Equation 2 compares adjusted R 2 between two linear regression equations For Equation 3 the model is a linear regression, and the method is OLS. The key equation for this thesis is Equation 4, which compar e the change in T otal I ncome per Capita I c T to the change in education expenditures (EE) given the HDI 1990 starting point. The model speci fied for this research is a Three Variable Linear Regression using Ordinary Least Squares method. The model us es I c T as the regres sand, which depends on HDI 1990 and EE as regressors ( ). The test for this model using the Linear R e gression Analysis is a hypothesis test to predict the Ic 3 per county The equation run to predict Ic 3 follows ( The approach to test the hypotheses is the test of significance approach. For test purposes, STATA requires the r esearcher to set the level of significance. The level of significance is set at the 95% confidence level or 5% probabil ity level of rejecting a hypothesis that is true However, each test will ident ify the p (Gujarati & Porter, 2009, p. 122)

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73 Hypothesis The key hypothesis this thesis tests is: governance through the public resource mechanism (Government Expenditure on Public Education as a pe rcentage of T otal Government Expenditures) are direct and negative; the higher the degree of corruption, the lower the relative education budget per capita Further the lower the education budget per capita, the lower the relative individual income. Data The raw data are non experimental, pooled, qualitative and quantitative, on a sample of 3 0 countries occupying the Central and Eastern Europe out of a current potential population of between 189 and 194 sovereign countries in the world plus 10 to 13 additional countr ies in trans itional phases (depending on the changing political climate) All of the countries in t he world are the population set The transitional country pertinent to this study is K o sovo, which is reported as part of Serbia, as the declaration for se paration of these two countries occurred after the 200 8 post test year (See Country Briefs). T o test the effects of governance corruption on education budgets and income per capita, we chose Central and Eastern European countries that offer unique data av ailability due to the focus by r e search scholars from international data agencies on the unprecedented events of the late 1980s marked by the dissolution of the Soviet Union. The data used herein have been quantified through indexing or econometric modeli ng, by the data purveyor, and were collected and input by hand, imported, or copied in a quantitative format Each country has pre test (1990) and post test (200 8 ) variables that are a nominal scale, are di screte, and random. The data are linear ( in the parameters or i.e., not exponential ) (Gujarati & Porter, 2009, p. 38) The data are secondary data, retrieved from three sources th at are each contribut ors to e stablished International Comparisons Program (ICP) (2011e) network of shared data The first and

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74 major source is human development data from the Human Development Project, part of the Uni ted c es data, data sets, fact sheets, reports, and policy recommendations on human development, international and n ational level governance and public administration research. This research w ork supports the UNDP state and local governance programs, and other UN programs. The project attempts to gather data on every country, with success in procuring data for 166 countries. The data are co llected through a combination of su r veys, institution al data, government data, primary research on location, and sharing with ICP The resulting qualitative and quantitative data are indexed from 1 to 100 (HDR, 2008c) The HDI is a composite index composed of three indices ; the Educational A t tainment Index (EAI) ; the Life Expectancy Index (LEI) ; and the Gross Domestic Product Index (GDPI) When GDP is divided by population, the resulting figure is Income per Capita, or Ind ividual Income Ic. In other words, the HDI = (1 x LEI) + (1 x EAI) + (1 x Ic). Each component index is developed from its component data. The composite HDI and these componen t indices are the key variables used. The source for each data point used is noted on Table D, found in the A ppendix. The second source for data is the Shadow Economy (Schneider et al., 2010b) data, which provides figures measuring that GDP produced and not counted in N ational I ncome A c counting. The sources for data on 162 countries included a combination of surveys, institutional data, go vernment data, primary research, and data shared through the This data set covers all of the sample set of countries, missing only three data points, Turkey and Mongolia in 1990, and Tur kmenistan in 2007. These three countries were studied individually, and the data for these is available using the same methods (Eilat & Zinnes, 2000; Yereli et al., 2007; Zhou, 2007) The method preferred by Schneider is the MIMIC method (Dell'Anno & Schneider, 2006) which is a s imultaneous equation model sets qualitative and quantitative data into an equ a tion as inputs and

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75 outputs productivity to derive the percentage of the official economy lost to unofficial productiv ity. Comparisons of nine common methods for calculating nati onal level governance corru ption (e.g. WGI, TI, CPI GCB, BPI ) are found in Table A (Schneider & Enste, 2000) Many other methods exist for sub national, firm level ( e.g., BEEPS EBRD ), and other forms of corru p tion. & Schnieder t any commonly accepted methodology for e s timating the underground economy. The estimates are always subjective and depend on the quality of the dataset the methods applied and the subjective decisions of the researcher. Shadow Economy estimates are neve (2006, p. 16) The authors go on to support the MIMIC method by asserting that the MIMIC is the better of the known me thods for calculating national level governance corruption relative to productivity on the books (F. G. Schneider & Enste, 2000) (see Figure 1. MIMIC M odel below). Figure 5 Shadow Economy MIMIC Model.

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76 For the purposes of this thesis, in order rmation about the dynamics and size (p. 1 6) sample set of counties and since it ut ilized the most appropriate measurement method for our purpose (which is the Shadow Economy size), The third sou rce of data is the Estates project, for the Education Expenditure data. E states is a joint international research group for the UN, through UNESCO, the World Bank Research Group, and the ICP Yearly data publications such as the Global Education Digest ( GED) provide a catalog of statistics. Data for the 200 8 Education Expenditures are found in the 200 8 GED, Table 13, Public Expenditure and Expenditure on Education by Nature of Spending (pp. 167 176) The statistic used in this thesis is the Public Education Expenditure (EE) as a Pe rcentage of Total Government Expenditure. In a major advancement, the UN, through its Sta tistical Information System on Expenditure in Education ( SISEE ), requested yearly data pr othe official data every five years, and added data to the set in the off yea rs when it met all of the previous methodology criteria. Researchers and the ICP still use the earlier data and deem it as reliable (UNECSO, 1998) Data for education statistics deemed reliable based on the new met hodo logy became available in 1998. Important here is the dearth of data that exist from 1986 through 1998; only 5 4 data points exist for these eight years for the entire world, and only seven of these are readings for the sample set

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77 Reliability and Validity Testing See Data Reliability and Validity Testing in the Appendix. Research Question 1 Recall that wh ich Kuznets, Sen, and others assert is Official GDP per capita, Ic, may not be a sufficient proxy for individual human development, as it lacks variables such as individual welfare, living standard, or earning capability (HDR, 2008c, p. 225; Kuznets, 1934; Sen, 1984) Klenow & Rodriguez Clare (2005, p. 833) and others assert the evidence from many scholars employing various models are consistent in that less than half of the variation in individual i ncome can be at tributed change in human capital and development (Easterly & Levine, 2001; Klenow & Rodrguez Clare, 1997; W. K. Wong, 2007a) In addition, the HDI is a widely accep ted proxy for the stock of human capital (HDR, 1990; Sen, 1997; W. K. Wong, 2007b) Research Question 1.1: Are the HDI and the change in the Total Income per C apita co rrelated at 5 or higher ? To test this construct with our data, we can run the correlation co efficient test If we reject the null hypothesis, then we can conclude for now, that the correlation between the Human Development Index from 1990 to 2008 and the change in Total I ncome per C apita, (Ic T ) is less than 5 consistent with the rule used in Wong, (2007b) Hypothesis 1.1 : The correlation co efficient of Ic T from 1990 to 200 8 and HDI from 1990 to 200 8 is less than 5 Hypothesis 1.2 : The correlation coefficient of Ic T from 1990 to 2008 and HDI comp onent i n dices, LEI + EAI from 1990 to 2008 is less than 33 This test of the changes in t he independent component indices is important for several reasons. Sen contends that the sum total of the factors human development over time are ca ptured in a snap shot in time measured by the HDI composite index made up of three component indices, EAI, LEI, and GDPI. Using the HDI, then, we respect and factor into the equation the sum of history for each country or the proxy human capital and development stock at one point

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78 in time consistent with the recent literature (Klenow & Rodriquez Clare, 2005; Wong, 2007) Examining the change equalizes the p re test differences in countries. Gujarati & Porter, (2009) explain that a properly specified model will yield an intercept term. The intercept may be st atistically equal to zero, which means that it runs through the origin, or close to it ; however, the near zero intercept is a product of the regression equ a tion. In the case of 3 0 Eastern and Central European countries, the regression equation will yield an intercept term. This point on the Y axis is the starting point for calculating the effects of governance co rruption on education budgets and income per country. W expectation according to Gujarati & Porter (2009) conventional, intercept present model a zero intercept model exists here. In equation 4, one would expect that where human develo pment is zero, income would also be zero, justifying a zero intercept model (Gujarati & Porter, 2009, p. 150) In addition, one can check for misspecification of a model after the fact by chec king the statistical significance of the constant, to verify that there are no o mit ted variables (p. 198). For example, the greatest difference in th e pre test HDI, or HDI 1990 in the Central and Eastern Europe is .263 points, from Tajikistan at .636 to Austria at .899. The post test HDI, or HDI 2007 HDI increas ed .0818 points, while that of Austria increased .0623. Examining the change in HDI HDI increased at a faster pace than did Austria, .015 % faster According to Gujarati & Porter, (2009) analyzing the change in our variables minimizes the chances of heteroscedasticity, autocorrelation, and multicolinearity natural ly present in pooled (cross country, time series) data. Heteroscedasticity is the unequal variances due to errors, outl iers, inertia, skewness, or incorrectly specified linear regression (pp. 365 368) Autocorrelation is r

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79 exist in the u i (p. 413) x(p. 321) naturally present in pooled data. Gujarati Porter, (2009 p. 434) suggests the Durban Watson d test cannot be used to detect serial correlation, in the equations containing SE or EE data, as data points are missing from both sets of data. It cannot be used on the HDI data or the SE data, as the data have lagg ed variables. T his examination may inform many policy questions. Two sample policy questions sp ecific to this thesis are: development data had the greatest impact on component measure of Mold oT h e purpose is to measure the effects of corruption on education budgets and i ncome per capita as measured by the capability of an individual to earn income. This brings us to the second research question. Research Question 2 Does the corruption, as measured by the Shadow Economy negatively affect Income per Capita ? This question requires us to add the first new data point, corru ption as measured by the SE. Recall that the SE is stated as a percentage of the official GDP (Schneider et al., 2010b) The SE set of data is one of several that assert to measure the size of GDP lost to corrupt ion, hi dden in the underground or unofficial markets. The only such study with adequate coverage of Central and Eastern Europe was sponsored by the World Bank and published in 2010, Shadow Economies all over the World: New Estimates for 162 Countries from 1999 to 200 8 We employ a n OLS regression to test our second research question. We will regress the Ic O and the HDI 1990 and then add a variable for explanatory power. If the theoretical construct is valid, the relationship between Ic O and HDI 1990 will be statistically significant; and the SE va r iable will add explanatory power to the regression making it a more robust predi c tor of Ic O Consistent with widely accepted growth regression models, maintaining a constant in a compar i-

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80 son of equations m easuring goodness of fit (adjusted R 2 ) expresses that the pretest starting point was not zero; this shows the value of the pretest stock of Human Development based on the OLS regressions (Barro, 2001b; Cobb & Dougla s, 1928; Klenow & Rodriquez Clare, 2005; Mauro, Abed et al., 2002; Pritchett, 2001) Hypothesis 2 : The adjusted R 2 resulting from a n OLS regression of pretest HDI against the Ic O is equal to or greater than the adjusted R 2 resulting from an OLS regr ession of pretest HDI and SE 200 8 against the Ic O If we reject the null hypothesis, we must conclude for now that the SE 200 8 per country i ncluded in the regression with the HDI 1990 explains more of the variation in the Ic 3 than does the HDI 1990 alone. This finding would be consistent with the theory that corruption hinders economic development, and of th e findings of Schneider et al. (2010), Kauffmann et al. (2008), Johnston, (2007), and other scholars. Research Question 3 Does a change in the Shado w Economy negatively affect Education Expenditure? To test the effects of corruption on education budgets, we employ our final new variable, Education E xpenditure ( EE ) If the presence of the SE has no significant effect on the EE, then we find that the theoretical construct was invalid. The null hypothesis states that EEc does not equal the SE. Gupta et al. (1998, p. 28) and others assert the evidence from many scholars employing various models are consistent in that less than one third of the variation in individual income can be a ttributed directly to the change in corruption (Kaufmann, 2003; Pritchett, 2001) In add i tion, the HDI is a widely accepted proxy for the stock of human capital (HDR, 1990; Sen, 1997; Wong, 2007) Hypothesis 3: The variation in the EEc from 1990 to 2 00 8 is not explained by the vari ation in SE200 8

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81 If we reject the null hypothesis, that the effects of the SE on EE are statistically signif icant, then we can conclude for now that a relationship exists. If our working theory about education budgets prevails, and an increase in the size of the SE results in a decrease in education funding, we can measure this decrease, and measure its effect, if any, on income. Research Question 4 .1 Do the pre test HDI, governance corruption, and education expenditure together explain the change in Income per capita? The null hypothesis asserts that there is no significant relatio nship between the Official Income per Capita, Ic O and the explanatory variables, HDI 1990 and the change in EE per ca pita from the pretest to the posttest values EEc 1990 and EEc 2008. Hypothesis 4.1 : The variation in the Ic O from 1990 to 200 8 is explained by the vari ation in the HDI 1990 and the change from EEc 3 in 1990 and EEc 3 in 200 8 If we reject the null hypothes is, we can assume for now that a relationship between the pre and post test per capita figures for EE 3 and the Ic O exists. Research Question 4.2 The null hypothesis asserts that there is no significant relationship between the Unofficial Income per Capi ta, Ic U a nd the explanatory variables, HDI 1990 change from the EE per capita pretest the and posttest values EEc Hypothesis 4.2 : The variation in the Ic3 from 1990 to 2007 is explained by the vari ation in the HDI1990, and the change in EEc3 from 199 0 to 200 8 Research Question 4.3 The null hypothesis asserts that there is no significant relationship between the Ic U and the e x planatory variables, HDI 1990 the change EE 3 Hypothesis 4.3 : The variation in the Ic T from 1990 to 200 8 is not explained b y the variation in the HDI 1990 and the change in EEc from 1990 to 200 8

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82 Summary Statistics Variable | Ob Mean Std. Dev. Min Max ------------+ ------------------------------------------------HDI1990 | 30 .78 .0 575506 .636 .896 HDICh | 30 .0507633 .0283926 .0204 .0918 LEI_EAI | 30 130.0245 20.85304 103.895 183.504 IcChDc | 30 1218.467 1702.272 1194 6119 SE1990 | 30 27.42 8. 127662 12.2 45.1 SE2008 | 30 38.95233 11.65943 16.1 68.8 SE2008Dc | 30 1287.911 1022.685 109 4113 IcTotalChDc | 30 1824.1 2225.068 1382 7862 EEDc2008 | 30 551.7297 55 4.2541 46.02128 2397.184 EEChDc | 30 123.391 266.0138 272.1115 890.7047

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83 CHAPTER 4 ANALYSIS Research Question 1 Research Question 1.1: Are the H uman D evelopment Index and the C hange in the I ncome per capita correlated at .5 or higher ? To test this construct with our data, we can run the correlation coefficient test. If we reject the null hypothesis, then we can conclude for now that the correlation between the Human Development Index from 1990 to 2008 and the change in I ncome per C a pita is less than .5, consistent with the rule used in Wong, (2007b) S ummar y Statistics Variable | Obs Mean Std. Dev. Min Max ------------+ -------------------------------------------------------HDICh | 30 .0507633 .0283926 .0204 .0918 LEI_EAI | 30 130.0245 20.85304 103.895 183.504 IcChDc | 30 1218.467 1702.272 1194 6119 Correlation Coefficient | IcChDc HDICh LEI EAI ------------+ --------------------------Ic ChDc | 1.0000 HDICh | 0.5005 1.0000 LEI_ EAI | 0.6793 0.2639 1.000 Hypothesis 1.1 : The correlation coefficient of Ic from 1990 to 2008 and HDI from 1990 to 2008 is less than .5. Equation 1.1 Null Hypothesis : H O : Maintained Hypothesis : H 1 : The correlation coefficient is 4599 which is less than the benchmark of .5. On a one tailed test, the t statistic is .501, w ell within the acceptance region of < .1697 at 30 degrees of freedom at the 95% confidence level. For now, we maintain that the correlation between the

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84 change in the variables meets the test requirement, at less than the benchmark Below, the scatter gra ph shows the correlation. Figure 1 .1 Change in Income per Capita and Change in Human Development Index To test the correlation between the Life Expectancy Index and the Educational Attai nment Index, equally weighted (the weights in the HDI are equall y weighted), we take the GDP index out, and re run the correlation coefficient. Hypothesis 1.2 : The correlation coefficient of Ic from 1990 to 2008 and HDI comp onent ind i ces, LEI + EAI from 1990 to 2008 is less than .5. Equation 1.2 Null Hypothesis : H O : Maintained Hypothesis : H 1 : The correlation coefficient is .0 552 which is significantly less than the benchmark of .5. On a one tailed test, the t statistic is .501, well within the acceptance region of < .1697 at 30 degrees

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85 of freedom at the 95% confidence level. For now, we maintain that the correlation between the change in the HDI is very slightly negatively correlated with the change in Ic, at .0015. Below is the scatter graph depicting the correlation between the Change in Income per Capita and the life expectancy and educational attainment indices. Figure 1.2 Change in Income per Capita and Change in LEI and EAI

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86 Research Qu estion 2 Does governance corruption negatively affect Individual Income? (Governance corru ption is measured by the average Shadow Economy from 2000 200 8 and Education expenditure is measured with the proxy EEc. A linear regression comparison of the R 2 t ests R esearch Q uestion 2, using the Change in Income per Capita Official Ic O as the dependent and HDI 1990 as the ind ependent variable. HDI 1990 is the pre test or legacy measure, the starting point in human development meas urements, for the sample set o f countries Hypothesis 2: The adjusted R 2 resulting from a linear regression of HDI against the Ic O is higher than the adjusted R 2 resulting from a linear regression of HDI and SE 200 8 against the Ic O Equation 2 .1 Null Hypothesis : H O : Ic O H DI 1990 Maintained Hypothesis : H 1 : Ic O = HDI 1990 Summary Statistics Variable | Obs Mean Std. Dev. Min Max ------------+ -------------------------------------------------------HDI1990 | 30 .78 .05 75506 .636 .896 Ic1990 | 30 3010.067 3628.822 426 19428 SE2008 | 30 38.95233 11.65943 16.1 68.8 Correlation Coefficient | HDI1990 Ic1990 SE2008 ------------+ -------------------------HDI1990 | 1.0000 Ic1990 | 0.6886 1.0000 SE2008 | 0.5148 0.5977 1.0000

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87 Test: Linear Regression 95% Confidence Level Regressed dependent variable Ic O using independent variable HDI 1990 Source | SS df MS Number of obs = 30 -------+ -----------------------------F( 1, 28) = 20.84 Model | 35855179.2 1 35855179.2 Prob > F = 0.0001 Residual | 48178968.3 28 1720677.44 R squared = 0.4267 --------+ -----------------------------Adj R squared = 0.4062 Total | 84034147.5 29 2897729.22 Root MSE = 1311.7 ------------------------------------------------------------------------IcChDc | Coef. Std. Err. t P>|t| [95% Conf. Inte r val] --------+ ---------------------------------------------------------------HDI1990 | 19320.9 4232.54 4.56 0.000 10650.93 27990.86 _cons | 13851.83 3310.05 6 4.18 0.000 20632.17 7071.489 Post Estimation Statistics for Regression White's test for Ho: homoscedasticity against Ha: unrestricted heteroscedasticity chi2(2) = 8.62 Prob > chi2 = 0. 0134 Cameron & Trivedi's decomposition of IM test Source | chi2 df p --------------------+ ----------------------------Heteroskedasticity | 8.62 2 0.0134 Skewness | 4.37 1 0.0365 Kurtosis | 0.58 1 0.4452 --------------------+ ----------------------------Total | 13.58 4 0.0088 Ramsey RESET test using powers of the fitted values of IcChDc Ho: model has no o mit ted variables F(3, 25) = 5.95 Prob > F = 0.00 33 Information Criteria Model | Obs ll(null) ll(model) df AIC BIC ------------+ --------------------------------------------------------. | 30 26 5.2512 256.9067 2 517.8134 520.6158 The regression output shows a n F score at 20.84 with 29 degrees of freedom, at most, 40.62 % of the variation in the Ic O can be explained by the variation in the HDI 1990 and the t value of the HDI rela tionship is very significant at 4.56 2 t Rule of Thumb The RMSE is 1311.7 trade off involved to o btain (Gujarati & Porter, 2009, p. 828)

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88 X 2 of 8.62 on 2 d egrees of freedom. The IM test co nfirms left skewed data at 4.37 and a short and fat (platykurtic) kurtosis distribution at 58 The AIC is 517.8 The analysis suggests rejecting the null hypothesis, confirming a significant rel ationship. N ext we comp ar e the R 2 values between here and a second equation adding SE 200 8 as an explanatory variable. Equation 2 .2 Null Hypothesis : H O : R 2 regress Ic O with HDI 1990 2 regress Ic O with HDI 1990 and SE 200 8 Maintained Hypothesis : H 1 : R 2 regress Ic O wi th HDI 1990 < R 2 regress Ic O with HDI 1990 and SE 200 8 Summary Statistics Variable | Obs Mean Std. Dev. Min Max HDI1990 | 30 .78 .0575506 .636 .896 SE2008 | 30 38.95233 11.65943 16.1 68.8 IcChDc | 30 1218.467 1702.272 1194 6119 Correlation Coefficient | HDI1990 IcChDc SE2008 ------------+ --------------------------HDI1990 | 1.0000 IcChDc | 0.6532 1.0000 SE2008 | 0.5148 0.5981 1.0000 Test: Linear Regression 95% Confidence Level Regressed dependent variable Ic O using independent variables HDI 1990 and SE 200 8 Source | SS df MS Number of obs = 30 F( 2, 27) = 14.62 Model | 43690767.5 2 21845383.8 Prob > F = 0.0000 Residual | 40343379.9 27 1494199.26 R squared = 0.5199 ------------+ -----------------------------Adj R squared = 0.4844 Total | 84034147.5 29 2897729.22 Root MSE = 1222.4 -----------------------------------------------------------------------------IcChDc | Coef. Std. Err. t P>|t| [95% Conf. Interval ] ------------+ ---------------------------------------------------------------HDI1990 | 13897.18 4600.658 3.02 0.005 4457.41 23336.95 SE2008 | 52.00247 22.70871 2.29 0.030 98.5969 5.40805 _cons | 7595.717 4120.428 1.84 0.076 16050.14 858.7039

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89 Post Estimation Statistics for Regression White's test for Ho: homoscedasticity against Ha: unrestricted heteroscedasticity chi2(5) = 6.43 Prob > chi2 = 0. 2670 Cameron & Tri vedi's decomposition of IM test Source | chi2 df p --------------------+ ----------------------------Heteroskedasticity | 6.43 5 0.2670 Skewness | 2.08 2 0.3536 Kurtosis | 2.56 1 0.1097 --------------------+ ----------------------------Total | 11.06 8 0.1981 Ramsey RESET test using powers of the fitted values of IcChDc Ho: model has no o mit ted variables F(3, 24) = 3.44 Prob > F = 0.0328 Model | Obs ll(null) ll(model) df AIC BIC -----+ -------------------------------------------------------------. | 30 265.2512 254.2443 3 514.4885 518.6921 ----------------------------------------------------------------------The regression output shows a lower yet still significant F score at 14.62 with 29 d egrees of freedom At m ost, 48.44 % of the variation in the Ic O can be explained by the variation in the pre test HDI, and both t values of the HDI 1990 and SE 200 8 variables are high and significant at 3. 02 6 an d 2.29 This test passes the 2 t Rule of Thumb The RMSE is lower at 1222.4 X 2 of 6.43 on 5 degrees of freedom. The IM test co nfirms a left skewed data at 2.08 and less platykurtic at 2.56 The AIC is lower, at 514.4885 which is pr e ferred to the higher in Equation 2.1 of AI C 517.8134 (p. 494) The analysis suggests rejecting the null hypothesis, confirming a signif icant relationship on the second equation. A comparison of the R 2 test suggests rejecting the null hypothesis, and confirming for now that the R 2 of the augmented second equation is higher from 40. 62 % to 48.44 % In add ition, the entire equation is more robust with a lower RMSE, lower AIC, less skewness, and no

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90 autocorrelation. The F score, which is lower yet still high, explains that the shap e of the distrib ution is flatter. The rejected hypothesis suggests a temporary conclusion in favor of the SE 200 8 per country included in the regression with the HDI 1990 explains more of the variation in Ic O than does the HDI 1990 alone. This finding wou l d be consistent with the theory that corru p tion hinders economic development, and of the findings of Schneider et al. (2010) Kauffmann et al. (2008) Johnston (2007) Mauro (1998b) and ot her scholars.

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91 Research Question 3 D oes governance corruption negatively affect future Education Expenditure ? (Gover nance corruption is measured by the average Shadow Economy from 1990 1999 and Education E xpenditure is the average from 2000 2008 ,(EE$c 20 08 ) ). A linear regression tests the effects of corruption on EE $ c 2008 by setting the EE $ c 2008 as the dependent variable and the Shadow Eco nomy in 1990 SE 1990 as the dependent variable. Hypothesis 3: The variation in the EE$c 2008 is not explained by t he variation in SE 1990 Equation 3 Null Hypothesis : H O : EE$c 2008 1990 Maintained Hypothesis : H 1 : EE$c 2008 = SE 1990 Test: Linear Re gression 95% Confidence Level Summary Statistics Variable | Obs Mean Std. Dev. Min Max ------------+ -------------------------------------------------------SE1990 | 30 27.42 8.127662 12.2 45.1 SE2008 | 30 38.95233 11.65943 16.1 68.8 EEDc2008 | 30 551.7297 554.2541 46.02128 2397.184 Correlation Coefficient | SE1990 SE2008 EEDc2008 ------------+ --------------------------SE1990 | 1.0000 SE2008 | 0.8359 1.0000 EEDc2008 | 0.4450 0.5807 1.0000 Regressed depen dent variable EEc 1990 using independent variable SE 1990 Source | SS df MS Number of obs = 30 ------------+ -----------------------------F( 1, 28) = 6.91 Model | 1764361.29 1 1764 361.29 Prob > F = 0.0137 Residual | 7144369.59 28 255156.057 R squared = 0.1980 ------------+ -----------------------------Adj R squared = 0.1694 Total | 8908730.88 29 307197.616 Ro ot MSE = 505.13 -----------------------------------------------------------------------------EE$c 2008 | Coef. Std. Err. t P>|t| [95% Conf. Interval] ------------+ --------------------------------------------------------------SE1990 | 30.34793 11.54086 2.63 0.014 53.98832 6.707546 _cons | 1383.87 329.6151 4.20 0.000 708.6841 2059.056

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92 Does governance corruption negatively affect current Education Expenditure? (Gover nance c orruption is measured by the average Shadow Economy from 2000 2008, and Education Expenditure is the average from 2000 2008,(EE$c 2008 )). A linear regression tests the effects of corruption on EE$c 2008 by setting the EE$c 2008 as the dependent variable and the SE 2008 as the d ependent variable. Hypothesis 3: The variation in the EE$c 2008 is not explained by the variation in SE 2008 Equation 3 Null Hypothesis : H O : EE$c 2008 2008 Maintained Hypothesis : H 1 : EE$c 2008 = SE 2008 Test: Linear Regression 95% Confidence Level R e gressed dependent variable EEc 1990 using independent variable SE 2008 Source | SS df MS Number of obs = 30 -----------+ -----------------------------F( 1, 28) = 14.24 Model | 3003721.27 1 3003721.27 Prob > F = 0.0008 Residual | 5905009.61 28 210893.2 R squared = 0.3372 ------------+ ----------------------------Adj R squared = 0.3135 Total | 8908730.88 29 307197.616 Root MSE = 459.23 -----------------------------------------------------------------------------EEDc2008 | Coef. Std. Err. t P>|t| [95% Conf. Interval] ------------+ ---------------------------------------------------------------SE2008 | 27.60283 7.314002 3.77 0.001 42.58489 12.62078 _cons | 1626.924 296.9787 5.48 0.000 1018 .591 2235.258 The test results suggest rejecting the null hypothesis and concluding for now that the e ffects of the Shadow Economy on the Education Expenditures per person stated in dollars, SE 200 8 on EE $ c 2008 are statistically significant In additi on 31.35 % of the variation in Education E xpenditures can be explained by variation in the Shadow Economy The F score is 14.24 with 29 degrees of freedom and the t value is 3. 77 for SE 200 8 The RMSE is low at 459.23 This test 2 t Rule of Figure 3 .1 shows the effects of the Shadow Economy on f u ture Education Expenditures. Figure 3.2 shows the effects of the Shadow Economy on current Educ ation Expenditures

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93 Figure Figure Current Education Expenditures

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94 Research Question 4 Do the pre test HDI, governance corruption, and education expenditure explain the c hange in Income per Capita? The question is tested in three ways. (4.1 ) do these variables e xplain Official Income per C apita? (4.2) Do the pre test HDI, governance corruption, and education expenditure explain the change in Unofficial Income per Capita? (4.3) Do the pre test HDI, governance corruption, and education expen diture explain the change in Total Income per Capita? ( Corruption is measured by the average Shad ow Economy as a percent of official GDP from 2000 200 8 and e ducation expenditure is measured with the proxy EE c ). Hypothesis 4.1 The null hypothesis asserts that there is no relationship between the change in Official I ncome per Capita, Ic O and two explanatory variables, (1) the change in Education Expenditure Dollars per Capita between the EEc pretest and the posttest values, EEc 1990 and EEc 200 8 Gujarat i and Porter (2009) ex plain and supports the practice of adding variables to seek higher degrees of significance and better over all fit (pp. 474 475) Hypothesis 4.1 : The variation in the Ic O from 1990 to 200 8 is not explained by the va riation in the HDI 1990 the EEc from 1990 to 200 8 Equation 4.1 Null Hypothesis : H O : Ic O 1990 + EEc 1990 200 8 Maintained Hypothesis : H 1 : Ic O = HDI 1990 + EEc 1990 2008 Summary Statistics Variable | Mean Std. Dev. Min Max -----------+ ----------------------------------------------Ic O | 1218 .467 1702.272 1194 6119 HDI 1990 | .78 .0575506 .636 .896 EEc | 123.391 266.0138 272.112 890.705

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95 Correlation Coefficients | HDI1990 EEChDc IcChDc ------------+ -------------------------HDI1990 | 1.0000 EEChDc | 0.4165 1.0000 IcChDc | 0.6532 0.4723 1.0000 Test: Linear Regression 95% Confidence Level Regress dependent Ic O with independent variables HDI 1990 EE $/ c 1990 200 8 Source | SS df MS Number of obs = 30 ---------+ -----------------------------F( 2, 28) = 14.83 Model | 66140725.8 2 33070362.9 Prob > F = 0.0000 Residual | 62433252.2 28 2229759.01 R squared = 0.5144 ----------+ -----------------------------Adj R squared = 0.4797 Total | 128573978 30 4285799.27 Root MSE = 1493.2 -------------------------------------------------------------------------IcO | Coef. Std. Err. t P>|t| [95% Conf. I n terval] ----------+ ---------------------------------------------------------------HDI1990 | 1182.643 390.9868 3.02 0.005 381.7431 1983.543 EE ChD c | 2.821598 1.057342 2.67 0.013 .6557301 4.987465 Post Estimation Statistics for Regression White's test for Ho: homoscedasticity against Ha: unrestricted heteroscedasticity chi2(5) = 14.3 6 Prob > chi2 = 0.0135 Cameron & Trivedi's decomposition of IM test Source | chi2 df p --------------------+ ----------------------------Heteroskedasticity | 14.36 5 0.0135 Skewness | 3.12 2 0.2099 Kurtosis | 0.34 1 0.5590 --------------------+ ----------------------------Total | 17.83 8 0.0226 Model | Obs ll(nu ll) ll(model) df AIC BIC -----+ --------------------------------------------------------------. | 30 260.7943 2 525.5886 528.391 The regression output shows an F score of 14.83 with 30 degrees of freedom At most, 47.97 % of the variation in the dollar change in total income per capita can be explained by the vari a tion in the independent variables. The t values are significant. This test pass es 2 t Rule both the HDI and E ducati on E xpenditure t score s are greater than 2.0 (Gujarati &

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96 Porter, 2009) The RMSE is 1493.2 critical X 2 value of 14.36 which exceeds the X 2 score of 5 degrees of freedom and which means heteroscedasticity exists (p. 387) The IM tes t confirms slightly left skewed data at 3. 12 and a slightly platykurtic at 34 The AIC is 525.5886 The analysis of the equation suggests rejecting the null hypothesis, confirming for now that a statistically significant relationship exists. The resu lts of this test suggest that the change in Official I ncome per P erson over the 18 year test period is a function of the human development starting point in 1990 (HDI 1990 ), and the change in percentage of the total expenditure budget set aside per person f or public ed u cation in the pretest and posttest years (EEc 1990 and EEc 2008 ). However interesting these results, the change in Official Income per Capita, Ic O does not account for the change in Unofficial, or Shadow Economy I ncome per C apit a, which may yi eld more informative results, (Gujarati & Porter, 2009). (See Table: 4.1 in the Appendix). Testing this equation using the change in the official dollars earned, however, will tend to provide a skew in the results that captures bigness in the available of ficial income, and not the distribution of that income to the individual, only the average distribution of official income per capita. This can be seen in Table 4, on the graphic comparison of these equations. (See Table: 4.1 in the Appendix). Hypothesis 4.2 The null hypothesis asserts that there is no relationship between the change in Unofficial Income per Capita, Ic U and two explanatory variables, (1) the change in Education Expenditure Dollars per Capita between the EEc pretest and the posttest valu es, EEc 1990 and EEc 2008 Gujarati and Porter (2009) explain and supports the practice of adding variables to seek higher degrees of significance and better over all fit (pp. 474 475). Hypothesis 4.2 : The variation in the Ic U from 1990 to 2008 is not ex plained by the va riation in the HDI 1990 the EEc from 1990 to 2008

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97 Equation 4.1 Null Hypothesis: H O : Ic U 1990 + EEc 1990 2008 Maintained Hypothesis: H1 : Ic U = HDI 1990 + EEc 1990 2008 Summary Statistics Variable | Mean Std. Dev. Min Max ------------+ ----------------------------------------------IcChDc | 121 8.467 1702.272 1194 6119 HDI1990 | .78 .0575506 .636 .896 EEChDc | 123.391 266.0138 272.112 890.705 C orrelation Coefficients | HDI1990 EEChDc IcChDc ------------+ -------------------------HDI1990 | 1.0000 EEChDc | 0.4165 1.0000 IcChDc | 0.6532 0.4723 1.0000 Test: Linear Regression 95% Confidence Level Regressed dependent variable Ic U using independent variables HDI 1990 and EEc So urce | SS df MS Number of obs = 30 ------------+ -----------------------------F( 2, 28) = 42.64 Model | 60294328.5 2 30147164.3 Prob > F = 0.0000 Residual | 19797817.4 28 707064.907 R squared = 0.7528 ------------+ -----------------------------Adj R squared = 0.7352 Total | 80092145.9 30 2669738.2 Root MSE = 840.87 ----------------------------------------------------------------------------Ic U | Coef. Std. Err. t P>|t| [95% Conf. I n terval] ------------+ ---------------------------------------------------------------HDI1990 | 1411.18 220.1724 6.41 0.000 960.1775 1862.183 EEc | 1.801061 .5954103 3.02 0.005 .5814186 3.020704 -----------------------------------------------------------------------------Post Estimation Statistics for Regression White's test for Ho: homoscedasticity against Ha: unrestricted heteroscedasticity chi2(5) = 13.69 Prob > chi2 = 0.0177 Cameron & Trivedi's decomposition of IM test Source | chi2 df p --------------------+ ---------------------------Heteroskedasticity | 13.69 5 0.0177 Skewness | 13.34 2 0.0013 Kurtosis | 0.12 1 0.7240 Total | 27.16 8 0.0007

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98 Model | Obs ll(null) ll(model) df AIC BIC -----+ --------------------------------------------------------------. | 30 243.5664 2 491.1329 493.9353 The regression output shows an F score o f 42.64 with 30 degrees of freedom ; at most, 73.52% of the variation in the dollar change in total income per capita can be explained by the variation in the independent variables. The t 2 t Rule of Thumb scores are greater than 2.0 (Gujarati a critical X 2 value of 13.69 which exceeds the X 2 score of 5 d e grees of freed om, and which means heteroscedasticity exists (p. 387). The IM test confirms left skewed data at 13.34, and a slightly platykurtic at .12. The AIC is 491.1329. The analysis of the equation suggests rejecting the null hypothesis, confirming for now that a statistically significant relationship exists. As anticipated, this equation yielded a higher (p. 386) b etween the variation in the change in income and the variation in the independent variables. Testing this equation using the change in the percent of income from the unofficial economy however, will tend to provide a skew in the results that fa ils to capture magnitude of change that is cancelled out due large swings in the opposing variable and not necessar i ly a better picture of the goodness of fit. Table 4 shows a graphic comparison of these equations ( See Appendix: T able : 4 .2 ) Hypothesis 4.3 The null hypothesis asserts that there is no relationship between the change in Total I ncome per Capita, Ic T and two explanatory variables, (1) the change in Education Expenditure Dollars per Capita between the EEc pretest and the posttest values, EE c 1990 and EEc 2008 Gujarati and Porter (2009) explain and support the practice of adding variables to seek higher degrees of significance and better over all fit (pp. 474 475). This equation is a summation of the coeff icients from the Official Income pe r Capita and the Unofficial I n come per Capita equations.

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99 Hypothesis 4.3 : The variation in the Ic T from 1990 to 2008 is not explained by the var iation in the HDI 1990 the EEc from 1990 to 2008. Equation 4.1 Null Hypothesis : H O : Ic T 1990 + EEc 1990 2008 Maintained Hypothesis : H1 : Ic T = HDI 1990 + EEc 1990 2008 Summary Statistics Variable | Mean Std. Dev. Min Max ------------+ ----------------------------------------------IcTotalChDc | 1824.1 2225.068 1382 7862 HDI1990 | .78 .0575506 .636 .896 EEChDc | 123.391 266.0138 272.112 890.705 Correlation Coefficient | HDI1990 IcTota~c EEChDc ------------+ -------------------------HDI1990 | 1.0000 IcTotalChDc | 0.6673 1.0000 EEChDc | 0.4165 0.4811 1.0000 Test: Linear Regression 95% Confidence Level Regressed dependent variable Ic T using independent variables HDI 1990 and EEc So urce | SS df MS Number of obs = 30 ------------+ --------------------------F( 2, 28) = 18.63 Model | 138981667 2 69490833.5 Prob > F = 0.0000 Residual | 104415522 28 3729125.79 R squared = 0.5710 -----------+ ------------------------Adj R squared = 0.5404 Total | 243397189 30 8113239.63 Root MSE = 1931.1 Ic T | Coef. Std. Err. t P>|t| [95% Conf. I n terval] -----------+ ----------------------------------------------------------HDI1990 | 1836.063 505.6345 3.63 0.001 800.3174 2871.808 EEc | 3.734703 1.367383 2.73 0.011 .9337462 6.535661 Post Estimation Statistics for Re gression White's test for Ho: homoscedasticity against Ha: unrestricted heteroscedasticity chi2(5) = 11.18 Prob > chi2 = 0.0479 Cameron & Trivedi's decomposition of IM test Source | chi2 df p --------------------+ ----------------------------Heteroskedasticity | 11.18 5 0.0479 Skewness | 3.15 2 0.2069 Kurtosis | 0.15 1 0.6965 --------------------+ ---------------------------Total | 14.48 8 0.0700

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100 Model | Obs ll(null) ll(model) df AIC BIC -----+ --------------------------------------------------------------. | 30 268.5085 2 541.0171 543.8195 The regression output shows an F score of 18.63 with 30 degrees of freedom ; at most, 54.04% of the variation in the dollar change in total income per capita can be explained by the vari a ti on in the independent variables. The t 2 t Rule E ducation E xpenditure t scores are greater than 2.0 (Gujarati & Heteroscedasticity reports a critical X 2 value of 11.18 which exceeds the X 2 score of 5 degrees of freedom, and which means heteroscedasticity exists (p. 387). The IM test confirms left skewed data at 3 15/2 and a platykurtic at .15/1 The AIC is 541.17 1 The analysis of the equation suggests rejecting the null hypothesis, confirming for now that a statistically significant relationship exists. Testing this equation using the change in the total dollars earned, however will tend to provide a skew in the results that captures bigness in the available income and not necessarily a better picture of the equality of its distribution Table 4 .3 shows a graphic comparison of these equations. (See Appendix: Table : 4 .3 )

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101 CHAPTER 5 FINDINGS This chapter r eports the findings of the data analysis and provides a summary of key points. Conclusions relative to the research questions follow, and last is a description of some d ata li mit ations. First, before we review the findings, recall that t he object of this thesis is to isolate the effect that g overnance corruption has on the public education budget, as found on the National Income A ccounting reports as the line item, Education Expenditures as a Percentage of Total Government Expenditures (EE). The measure chosen for c orruption in g overnance is that used by the International Monetary Fund (Russell, 2010) in what has been referred to as the Shadow Economy. All of the results were tested at the 95% confidence level, unless stated otherwise, and the results reported below were stati s tically significant. The purpose for Research Question 1 is to set the following foundation. The first step r equired for testing the sample s et of countries against what Kuznets (1934), Sen (1984) and others predicted : data using Gross Domestic Product per C apita (Income per Capita, or Ic) d o not reveal in them the work, education, and training going into the earning of the income, neither does it reflect well a measure for a standard of living. Working from Research Question 1 the results of Equation 1.1 show a correlation of 4599 b e twee n the Human Development Index and the change in individual income in the country set occupying Central and Eastern Europe. The 4599 correlation coefficient is less than .5, the widely accepted benchmark in r esearch that measures effects of corruption on income (Wong, (2007). This result is consistent with expectations, and prompts one to look within the Human Develop ment Index (HDI) to is olate possible relationships among and between the components of the HDI, the Educational Attainment Index (EAI), and the Life Expectancy Index (LEI). Notice that this equation purpos e-

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102 fully leaves out the GDP Index (GDPI), to isolate the correlation b e tween just the life expectancy and education variables. The correlation coefficient for Equation 1.2 between the change in total income and the change in the HDI index components LEI and EAI is very low .0015. These r esults are consistent with Kuznets (1934, pp. 6 7) work on factors unaccounted for in National Income Accounting Sen Living Standard (1984) and Sen and H uman Development Programme (1990) This second result again prompts a search for another explanation for the change in income. The next step was to regress the HDI in 1990 (HDI 1990 ) against the change in Ic to set up the propose d theory that the human development earned or attained in a country at the start of the test period has significant bearing on individual income. In fact, the linear regression for Equ ation 2.1 showed 40.62 % of the variation in change in individual income ( Ic O ) was explained by the variation in the pretest, HDI 1990 by country. In other words, nearly 60% of the change in earning power of the individual, as reported in the official GDP reports, was due to some factor lopment in 1990. At this point, the Shadow Economy, or the corruption variable, was added to the equ ation. Given that, 40.62 % of the variation in the Ic O could be attributed to variation in HDI 1990 could we isolate any variation past 40.62 % and attrib ute to corruption? B y comparing the R 2 va lues of linear regression 2.1 to the R 2 value of a linear regression that regressed the Ic 3 against two factors: the HDI 1990 and the Shadow Economy in 2008, linear regression 2.2. Linear regre ssion 2.2 showed tha t 48.44 % of the variation in the Ic O could be attributed to the variation in both the HDI 1990 and the Shadow Economy in 2008. A comparison of the R 2 shows that adding the corruption variable to the human develo pment va r iable per country provides a more robust explanation for the Ic O per country than does the HDI 1990 alone from 40.62 % to 48.44 %. In addition, the data is less skewed. The F score in the second equation is lower, yet still very high, which explains that the shape of the distribution

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103 is flatter. The increased goodness of fit between individual income and human development alone to human development with the shadow economy suggests that underground activity affects findings are consistent with works by F. G. Schneider & Enste (2000) on shadow economies Sen (1997) on human c apability and Simon, (1997) on bounded r a tionality among others. Research Question 3, using Equation 3 adds the variable for Education Expendi ture, tes ting along with the Shadow Economy, S E. A l inear regression regressed EE against the Shadow Economy in 2008. The result showed that 19.94 % of the variation in change in Education E xpenditure was explained by the variation in the Shadow Economy a s a % of GDP in 2008 (SE 2008 ). The high t score of 2 .63 and 98.6 % level of confidence suggests that there is a strong negative relationship between the level of Shadow Economy in the country and the amount of the actual Education Expenditure as a percent age of the total GDP, per capita. This result points to the education function in a country consistent with Mauro (1998), Tanzi (1998), Pritchett (2001) and others. Since the corruption variable seems to have affected both the change in Education E xpenditure and in individual income in this analysis, it would further the work on New Growth Th eory to show evidence that a test of the hypotheses in this thesis affirmed its aim. The idea is to isolate the change in individual income from 1990 to 2008 due to the Shadow Eco n omy and its ability to siphon assets from public funds, and in particular, the funds going into the budget for publicly funded education. This point, the motive may need further review. Recall the following. Freire wrote b(1970, p. 135) non (Hallak &Poisson

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104 of public spending that remains significantly associated wi th corruption when the level of per ca p (1997, p. 10) Second, the playing field of education funding is rife with opportunity, in some ways greater opportunity than the budgets for other public goods according to Mauro et al., 2002 Rent seeking and state capture by the Shadow Economy are particularly harmful to the allocation of education funds (S. Gupta et al., 2000; Mauro et al., 2002) Under invoicing, collecting tax for an un provided scholastic good service, adding user fees, siphoning f unds for text books or mat erials, and vendor kick backs are just a few cited examples of the strategies played to a b scond with public funds dedicated for education (Chua, 2006; S. Gupta et al., 2000, pp. 6 9) Third, educ ation may enjoy increasing returns (society gets back more than the dollars it put in). Stated otherwise, the funds invested on education, efficiently and effectively, show signs of positive externalities ( e.g., knowledge spillovers, technology diffusion and adoption, learning organizations, specialization and division of labor, and learning by doing). Figure 5.1 below shows the relationship between the Education Expenditure as a percentage of total government expenditure from 1990 to 2008, on average per citizen, and the Change in Total Income per Cap ita on average from 1990 to 2007 The two are correlated at .9013, where the greater the education expenditures the greater the positive change in total income, on average. These results are consistent wit h other studies on cross country economic growth and determinants of econo mic development (Barro, 2001b; Barro & Lee, 2001; Romer, 1986; UNECSO, 1998)

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105 Figure 5.1 Change in Income per Capita and the Education E xpenditure Based on the p e r centage of total government expenditure from 2000 to 2008 Figure 5.2 Change in Income per Capita and Lagged Education Expenditure Based on the percen t age of total government expenditure from 1990 to 1999

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106 In the final st age of the analysis, we test aspect s of New Growth Theory, start ing with Ic O (Off i cial GDP/Capita, or Ic) on the left side of the equation. Then, using the HDI 1990 as the pretest variable on the right side of the equation, we added to it change in Educat ion E x penditure (EE); ceteris paribus, the analysis tested three different applications of change in the EE variable. The first application was in equation 4.1, where the change in Official Income per Capita (Ic O ) is the dependent variable The second a pplication was in equation 4.2, where the dependent vari able was the Unofficial Income per Capita (Ic U ) The third application was in equation 4.3, which the dependent variable was Total Income per Capita (Ic T ) The reader will find a complete r eview of the results on the equation co m parison chart on the Table 4.0 in the Appendix. Hypothesis 4 test s the effects of governance corruption on education budgets and income for the countries in Central and Eastern Europe. The prior analyses show that where cor ruption is a higher, two findings are clear. First from Hypothesis 2, as more of a s actions are unofficial, O n the G round rather than official, O n the B ooks the lower the change in income per person since the fall of the Berlin Wall (the H DI 1990 variable equalizes the starting position of each country). Second, as more of the productivity moves through unofficial than official cha nnels less is budgeted for education as a percentage of government spending (Hypothesis 3). This finding is c onsistent with by Mauro (1998), Tanzi (1998), Pritchett (2001) and others. Last, H ypothesis 4 combines the effects of corruption and education spend ing on individual i n come The correl ation coefficient between the change in Official Income per C apita and the HDI in 1990 is 0. 7 260 with the change in e ducation e xpenditure is 0. 7763, and with the Shadow Economy is 0.8882. The correlation coefficient between the change in U no fficial Incom e per Capita and the HDI in 1990 is 0. 7994 with the change in education expenditure is 0.7 129 The correlation coefficient between the change in Total Income per Capita and the HDI in 1990 is 0.7359, with the change in education expenditure is 0.7 875, an d the Shadow Economy is already accounted for in the total income

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107 Variable | IcO IcU IcT HDI1990 EE $c ------------+ --------------------------------------------IcO | 1.0000 IcU | 0.8882 1.0000 IcT | 0.9971 0.9070 1.0000 HDI1990 | 0.7260 0.7994 0.7359 1.0000 EE $ c | 0.7763 0.7129 0.7875 0.5704 1.0000 The linear regression results find a clear relationship between (the independent variables) higher rates of corruption and lower percentages on education spending, and (dependent vari a ble) lower rate of change in the growth of income per capita from 1990 to 2008. Table 4.0 Equation 4 Comparison Comparing the test results for Research Question 4 by equation highlights several i mportant statistics relative to the Economic Horsepower of an economy Consistent with Maruo (1997, 1998, 2000), the public expenditure on public education suffers in the presents of corru ption. Consistent with findings in several major articles on eco nomic growth (S. Gupta et al., 1998; Romer, 1994b, 1996, 1998a; Solow, 1956) education funding predicts the variation in cu rrent income (31.35 %) with 99% level of confidence. Past education funding predicted 16. 64% of current individual income (lagging the 1990 1998 education funding average off of the 2008 income). The combined weight of corruption in the governance function on tax revenues, gene rally, and specifically on the education budget suppresses growth in aggregate and individual income. Worse, however, is the long run effect of this corruption on human development, i ncome, and social capital over time. Since knowledge externalities have a unique ability to

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108 flourish and produce a co m pounding effect on economic growth (Cortright, 2001a) suppressing funds to the education plant is specifically harmful to technology and innovation diffusion and ado p tion (Klingner & Sabet, 2005) This evidence is generalizable to the population of countries, and provides policy makers data on the effects of governance corruption on individual income, not ava ilable heretofore. The analysis shows a highly significant relationship between increased corruption and decreased i ncome, even after accounting for the income earned, and not counted in the National Income A ccounting books earned O n the ground In ord er for development policy makers to create su stainable economic growth, corruption at all levels of state, non state, and private sectors must be minimized. Minimizing corruption in government and political corruption what Moody Stuart calls Grand Corrup tion (Moody Stuart, 1996) (deLeon, 1993) private sector corruption. For the purposes of this thesis, it is more important to categorize the corruption by its e f fect on social capital, on tax revenue, human development, education funding, or other items that may be more or less measureable.

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109 Summary This thesis addresses economic development policy so as to encourage healthy economic growth (Kuznets, 1966, p. 493) without the friction of institutional corruption; to inform deve lopment policy toward a more balanced growth, by offering policy makers a unique method of Growth Theory is the foundation for this work. Maddison (2009) provided evidence that econ oaverage yearly world GDP growth is 2.21 percent (2009, p. 4) Further, he showed that the pace of growth increased approximately at the time of the industrial revolutions in Europe and North America. The IMF is projecting a 4% global growth for 2011 and 20 12, with advanced econ omies growing 1.5% to 2% (UPI 2011, p. 1) Other evidence (Lozada, 2002, p. 5) leaving some to believe that a direct and positive correlation between growth and increased poverty and inequality exists. It does not necessarily follow, ho wever, that the growth is res ponsible for increasing poverty or widening the gap of inequality (Gujarati & Porter, 2009) Yet, in some popular media and political circles, economic growth carries with it a stigma poverty worsens in the wake of economic progress. The distinction between correlation and causation in this debate is critical. Policy adv isors must make the distinction when forming policy decisions regarding economic development in ligh t of reports that seem to link the two. This thesis asserts that linking economic growth d irectly to individual income as a measure of individual prosperity is too simplistic, and that governance corruption erodes, among other public goods, education. Fu rther, it asserts that ce rtain factors of history, governance, and education effect ind i vidual income. Recall the four conceptual challenges, which concern inconsistent definitions and mea surement methods for (1) economic growth, (2) the period analyzed, (3) living standards, (4) corruption. Also, recall the three problem themes. (1) There are gaps in the literature specifically

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110 tying corruption to a mechanism that reduces Income per Capita. (2) Measuring governance, co rruption in governance, (aggregat e) economic development, and individual income (Income per Capita) levels is difficult. (3) The scope of this thesis necessarily excludes important variables, which serve as a launching point for future r e search. The key hypothesis this thesis tested an on education through the public resource mechanism (Government Expenditure on Public Educ ation as a percentage of Total Government Expenditures) are direct and negative; the higher the degree of corruption, the lower the relative education budget per capita. Further, the lower the education budget per capita, the lower the relative individual i n come. In Governance Matters (Kaufmann et al., 2008), authors writing on behalf of the World Bank explain the critic ality of good governance, sound public administration, and effe c tive public policy. The World Bank embarked on the massive project to understand matters of go v ernance in the early 1990s, in order define its dimensions and measure its effectiveness for fut ure gener ations; understanding governance to measure it became an international quest. The IMF, World Bank, UN agencies and other IGOs set a course toward understanding the factors that e n courage measuring governance was the pre mise of a meeting of scholars, data experts, clients, donors, and policy makers at the Kennedy human and ec o nomic development depen ds on good governance (Sen, 1999). Good governance supports sound public policy, transparent governmental operations, and its fiscal discipline; pu blic expenditures on public goods, including that which is spent on public education, depend on good gov erna nce (S. Gupta et al., 2000) Knowledge about and literature on the links between governance and corruption are not new. Quoting Suetonius (110, p. 82) good governance and less corruption, between weak governance and high levels of corruption,

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111 has become increasingly mor e frequently published in public policy literature since the 1970s (Rose Ackerman, 1978, 1999; Abed et al., 2002). The vast body of literature on corruption is i nextricable from that of governance, yet yields its own avenues of study. However, grand or p etty, political or private sector, rewarded by profit or r e venge, malicious or beneficial to the engine of development, governance corruption reduces tax revenue and is therefore, detrimental to the budget available to fu nd public education (deLeon, 1993; Pritchett, 2001; Heidenheimer et al., 2002; Johnston, 2005). Meas uring the effects of corruption and specifically budget, is a discipline aided by the advances in technology and inte rcommunication between counties in the flattening world (T. L. Friedman, 2005) and it is crucial if policy makers are to create and protect budgets with sound evidence that the budgets may be at risk, and how. Several studies have produced da ta on the size of the Shadow Economy as a gauge for the degree of co rruption in a country. The studies use shared or similar data, and si m ilar ( or different ) methods to isolate that productivity which goes unreported to the government (Tanzi, 1998; Dell'A nno et al., 2006; La Porta & Shleifer, 2008). While the test results published from these studies report a di fference in the magnitude of the Shadow Economy the variance in the results is relatively small, and is highly correlated across methods. The re sults are also highly correlated with published d aBusiness Environment and Ente r prise Survey Corruptions Perceptions Index (CPI, 2010a). The only study with adequate coverage of Central and Eastern Europe was sponsored by the World Bank and published in 2010, Shadow Economies all over the World: New Estimates for 162 Countries from 1999 to 2008 In this study, the country with smallest e stima ted Shadow Economy is Switzerland at 8.6%, and the hig h est is the country of Georgia, at 68.8% (Schneider et al., 2010, p. 27). The size of the Shadow Economy (GDP U ) is added to the size of the formal economy (GDP O ) for the total Gross Domestic Produ ct per country (GDP T ). (Each

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112 GDP figure is normalized by dividing by population for Income per Capita [Ic O Ic U and Ic T ]). This particular treatment of GPD per capita is different from any treatment of the size of the Shadow Economy uncovered to date in the available bodies of literature and research. The body of literature around income inequality and converging or diverging incomes has fueled many a debate in the media and between scholars (Kuznets, 1940) However, the data are (at best) misleading without considerable attention paid to context. According to La Porta & Shleifer (2008) o fficial GDP is only 91.4% accurate in the best case scenario, and on average, 70% accurate. These auth ors assert [t] he various estimates thus suggest that, in the average country, roughly 30% of th the former Soviet countries, corruption is systemic (deLeon & Green, 2004) and accounts for far more of the economi c activity than studies to date have realized or uncovered (Stefes, 1997) E stimates on inequality can be no more accurate than the data going in leaving policy makers potentially misinformed. In an effort to produce the most accurate account of individ ual income, adding that which is O n the Books of National Income Accounting t o that which is known to be O n the G round is a step toward accuracy in the convergence / divergence debate. By adding the two streams of income, the data reveal more about the ef fects of the Shadow Economy on educ ation expenditures than did the data absent the Shadow Economy productivity results from testing Research Question 3 and Hypothesis 3 tell us that 31.25 % of the variation in the change in Education Expenditures (EEc) ca n be explained by the Shadow Economy (SE 2008 ), at 95% level of significance on this sample set New Growth Theory stands apart from other economic growth theories for three reasons. First, NGT shoulders change in that the theory allow for changing retur ns to scale Regardless of its origin, regardless of its pace, regardless of its variety, NGT is an equal opportunity theory a requisite advantage in a flattening world (Friedman, 2005). Second, models and equations testing NGT do not force technical a ccumulation, or any other variable, to be held constant across the

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113 sample set or time (Cortright, 2001) Logically it holds that when adoption is voluntary, if po ssessing the education required to adopt technology pr e cedes its adoption, and gaining knowle dge from the technical adoption follows its adoption, then education is inextricable from the process of technical change. Technol o gy adoption may not be voluntary. For example, analog television signals were phased out in the US, and one invention, the radar gun, shaped the art of pitching baseballs and catching speeding automobiles. When adoption is not voluntary, its knowledge may pr e cede it, may be simultaneous to it, or may lag its arrival, and knowledge gained by it still follows may be at a simi lar or different pace. Pace matters. The pace of adoption and diffusion, the pace of education and knowledge gains, and the pace of other factors of economic growth are each important pieces of the growth puzzle (Maddison, 2009) NGT acknowledges the individuality by country and its met h ods and models allow for variations in data across countries and over time aided by advances in comp uting technology and statistical software that allow for the processing of large sets of cross country longitudinal data, such as STATA (2007), used to process the data. Economies cycle cle r equires discipline from trough to trough (Burns & Mit chell, 1946; Kuznets, 1940; Schumpeter, 1939) and each country has its own rhythm. Analyzing economic data using arbitrary dates or dates uncoupled from economic cycles, may yield exaggerated results, too high or t oo low. An example of this common practice would be measuring GDP growth from 1990 to 2000, or worse, the same GDP growth across countries. Policy makers would be better informed if economic data were reported in light of the cycle pos i tion. Economies c ycle. Gross Domestic Product per capita is an economic factor that follows this rule. GDP per capita cycles, and to inform policy makers with consistent and reliable info r mation, the cycle is measured from trough to trough. Economies cycle while they gro w at an average rate of 2.21% since 1820, and at least 1.4% since the time of Christ (Maddison, 2009, p. 4) Econ omies grow. Economies may shrink

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114 for a time, they may even feel the effects of a long depression, but on average for over 2,000 years, they grow. Economies oscillate by the rhythm of the business cycle (about 4 years) (Kitchin, 1923; Mit chell, 1928) Economies cycle in short waves with the rhythm of between 2 and 5 oscillations (about 8 to 20 years) (Jugl ar, 1893; Rostow, 1975; Schumpeter, 1939) Economies cycle in long waves by the rhythm of 3 to 4 short waves (about 35 to 60 years) (Kondrati ev, 1926; Rostow, 1991) and the point in the cycle where a measurement starts or stops is important. Absent info rmation on the phase of econom ic cycles, it is possible to measure Country A starting at its triple trough (the lowest point in a compound bu siness cycle) against Country B starting at its triple peak (the highest point in a compound business cycle) yielding an accurate comparison. Su ppose the ending data recorded the GDP in the opposite phase of the cycle for both countries. Further, supp ose US policy makers are voting on foreign aid for these two countries. Country scenario by supposing that Country B is the Country of Georgia, where 68.8% of t he total GDP is in the hidden economy. According to Rostow (1991) h, such faculties as infr astructure and fiscal preparedness, lay a foundation for economies to enter a growth stage. Events or political upheaval may trigger a trough ( e.g., h quake, or acts of aggression such as may spark long wave growth ( e.g., the industrial revolution, or the personal computer). The poli tical upheaval in 1989 was the dissolution of the Soviet Empire, the fall of the Berlin Wall, the and the subsequent governance and policy challenges facing re born states with still open scars of the Cold War. Shock waves of change hit the neighboring states, and radiated outward to those states bordering the Eastern Bloc. After the political shock, each of the economies suffered. As

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115 seen in Appendix B, Fi gure 6 each country experienced at least one trough as measured in I ncome per Capita. Most coun tries show evidences of more than one cycle, and some a double dip trough. Many economic factors that affect the GDP and economic cycles of Eastern and Central European Countries are outside the scope of this thesis The slice of enormous body of literatu re on measuring education pertinent to this thesis measures the Government Expenditure on Public Education as a Percentage of Total Spending Global Education Digest (2010, Table 13), and the 2011 World Development Indicators stat istical database (HDR, 2010f) T he process of educating is outside the scope of this thesis. The budget figure EE is normalized by dividing by pop u lation (EEc). Central and Eastern Europe, as (Kornai, 2005) at le ast in part due to the geography, but also due to its changing authorities since the fall of the Roman Empire in 1453 (Robinson, 1902, p. 356) It was Winston Churchill in his Sinews of Peace Speech, who effectively drew the map for the land beyond the Iron Curtain. From Stettin in the Baltic to Trieste in the Adriatic, an iron curtain had descended across the continent. Behind that line lie all the capitals of the anc ient states of Central and Eastern E urope. Warsaw, Berlin, Prague, Vienna, Budapest, Belgrade, Bucharest, and Sofia, all these famous cities and the populations around them lie in what I must call the Soviet Sphere, and all are subject in one form or anot her, not only to Soviet influence, but to a very high and, in some cases, increasing measure of control from Moscow (Churchill, 1946). name. It is the effect of Soci alism, governance, corruption, and the effect of the Shadow Econ omy that must be measured to provide policy makers with the fodder they need to make critical development funding decisions. Finally, the data provide evidence to inform th e development poli cy debate in several critical areas : economic development, fiscal policy, education funding, and corruption in gover n-

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116 ance. The application of the method used to measure total income and education funding is di stinctive to this thesis while the findings are consistent with published research Mauro (1998) mechanism are direct and negative; the higher the percentage of corruption, the lower the relative education b udget per capita, affirming prior work by Mauro (1998), Tanzi (1998), Pritchett (2001) and others. Further, the higher the percentage of corruption, the lower the relative ind ividual inc ome, affirming New Growth Theory (Romer, 1998b)

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117 Data Li mit ations The concepts of governance, corruption, parallel economies, and government account ability, are not easily measured nor are they naturally calibrated to each other. For purposes of comparison, testing, and regression, a common denominator or an index simplifies the equati ons and helps the researcher make sense out of the test results. This requisite for c es the difficult step of calibration to come first. In an international endeavor to accommodate the needs of the r esearch community, the leading international agencies ha ve each launched divisions dedicated to statistical capacity building and data colle c tion. The rapid advances in computing technology and software has enabled and stimulated quicker analysis, new methods, and more robust testing, e xamples of which follow. However, the added statistical capacity meets with frustration for some researc h ers. (WGI) most of the data are survey based. The most widely accepted barometers for corr uption are the Global Corruption Barometer, and the Corruption Perceptions Index, which are both su rvey based. The European Bank for Reconstruction and Development (EBRD) issues the Business Environment and Enterprise Performance Survey (BEEPS) every five years. A robust survey of over 15,000 professionals, the BEEPS scores offer a broad look at the corruption in the business sector, or between business and government. While the BEEPS would inform the rent seeking reports, it does not measure the rent se eking in a dollar figure. The Shadow Economy is estima telectricity demand, consumption demand, arbitrage, and tax gaps. These gaps purport to measure that which is missing from or avoids the official economy. These methods presume to measure that is, by filling in the gaps in information found by auditing.

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118 Research is underway to codify factors of governance, c orruption, and transactions mis sing on official registers. Today, however, each of the estimating methods lacks precision, so do ground, creating better measures a nd methods for estimating the efficiency and effectiveness of inequality and government effectiveness in 2005, to the exper i ence collecting the same data toda y is a challenge; very little about the experiences are the same. Following are three disciplines with significant overlap that may inform future research. (1) Technology improvements in hardware, software, and computing capability. (2) Digital commun ications, and digital library resource catalogs, and resource availabi l ity. (3) Increased collaboration between international agencies, data availability, and data consiste ncy. In six years, the change is extraordinary in every way, which serves as the basis for this first caveat. Another year would likely produce more robust results and additional insights. For now, dual income in the Eastern and Central Eur ope is the MIMIC method on the Shadow Economy data produced by Schneider et al. (2010). Next, UNICEF was the agency responsible for collecting data on education expenditures by country every five years from 1965 until 1988. Submissions of national account s data on ed ucation were voluntary and inconsistent. UNESCO undertook the task of standardizing education statistical data and its collection, collecting its first round of data in 1998. Data representing ed ucation expenditures as a percentage of total g overnment expenditures is li mit until 1988, scant from 1988 to 1998, and nearly 100% from 1998 to 2008 on the sample of countries in this thesis. esearchers in similar want of good education data is the basis for this second caveat. Based on this

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119 Therefore, the acquisition of additional or more robust measurements of education funding will remain elusive, unless the education data may become appa r ent through backfilling. The global push by the international agencies for better data and statistical capability through the implementation of international accounting stan dards is underway at the IMF and World Bank. This step will provide data to complete country to country gap analyses. The au gmented data can feed consistent methodologies, primary governance research on the ground, and many other initiatives, which will likely induce extraordinary change in the next six years as it did in the past six. The Shadow Economy as measured by the MIMIC method is one of many methods used in recent research to estimate the scope or size of the unofficial economy in a country. W hile the methods ar e highly correlated, each has limit ations, and the Shadow Economy may overstate or understate the actual unofficial economy. Prudence may suggest that policy analysts employ more than one measure and method to estimate the extent of the unofficial economy in a given country. Thirdly and income is generalizable and scalable to the balance of the 194 sovereign countries recognized in the world today. Analyzing t he balance of the countries may be beneficial to policy ma k ers in many ways. While data advance, measuring the effects of governance where data is available may highlight new results or different relationships. The sample set of cou n tries analyzed herein represent all of the former USSR (15 countries in Group 1) and its satellite states (15 countries as of 2008 in Group 2). However, a third group of only six countries in Central Europe fall into or could fall under the rules set out to isolate Soviet inf luence. (See Appendix: Country Briefs, Group 3). Recall the following is the list of rules for i nclusion in the Sample Set of Countries.

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120 Rule 1: The country was or remains Socialist Rule 2: Four or more years of Soviet influence (Sachs & Warner, 1992 1996, 1998), plus a cr eated, liberated, or re gained sovereignty, independence or the ability to trade, travel, and migrate which began between 1988 and 1992. Rule 3: Geographically related by inland border, trade route, or sea trade route Rule 4: Ethnol i nguistically interrelated, Economically interdependent Using different rules or relaxing these rules, including additional countries in Europe to increase the sample size, or analyzing different country groups based on these or other criteria may yield different findings. For example, the OECD nations, the balance of Europe, and many other countries have data on the Education Expenditure and Shadow Economy variables. Analy zing these additional countries may expose findings specific to the Central and Eastern European data set that are due to the particular countries in the set. Exposing findings peculiar to the data set used in this thesis necessarily requires comparison against other sets of countries. A fourth and a major li mit ation to this data i s its narrow scope. Many variables that are widely used in cross country analysis on economic growth and individual income in transition countries are beyond the scope of this thesis. Specifically, future research on this sample set would include three i mportant variables. (1) A variable critical to economic produ c tivity would measure progress toward market liberalization. In the 2010 Transition Report, the EBRD offers a n privatiz ation, markets, banking, and infrastructure (p. 3). (2) Progress toward EU accession, measured by the European Commission (2011b). (3) A variable critical to understanding economic growth patterns would mark the history and intensity of armed c onflicts (HIIK, 2010a). Other variables that are nonspecific to transition countries are left for future research. Specifically missing are (1) the variables for public expenditure on public goods other than ed uc a tion, and the policies that surround tho se goods, (2) variables that measure the ed u cation plant

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121 agov ernance (Kaufmann et al., 2008) interplay with education and its funding. Isolating educ a tion is as fragile as the ceteris paribus assumption that all else remains unchanged is permanent. Fifth, the elementary level of economet rics performed on models may leave inside much of the information possible to extract from this data, would an econometrician be at the co n trols. Further, the more robust tests, better analysis or more intricate modeling may uncover different findings tha t support either better or worse, the hypothesis herein. Lastly, and possibly most importantly, the data do not have in them the ability to predict beyond the forecasting which is based on imperfect empirical data and extrapolation and i nclined to huma n error The ceteris paribus assumption creates risk for the policy analyst, and builds error into any equation. In turn, the policy makers are at risk of ill informed policy.

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122 CHAPTER 6 C ONSLUSIONS AND FUTUR E RESEARCH Conclusions The analyses presented above suggest clear evidence that as the size of the Shadow Economy increases, the budget for Education Expenditures as a percentage of the total gover nment expenses decreases. There is also evidence that as the Shadow Economy increases and Education Exp enditures decrease, individual income decreases as well. These findings are co nsistent with New Growth Theory, which posits that the quality and quantity of education specifically, provided as a public good, is critical to a healthy and sustainable econom ic develo pment. The practical application of this evidence is different, in that education expenditures and individual income are analyzed together and in light of the effect of corruption on them This process requires that we compare the results of thr ee equations: (1) Official Income per Capita, (2) Unofficial Income per Capita, and (3) Total Income per Capita Minimizing corruption, t he ideas case studies and me thods form a large body of liter ature, much of which is outside the scope of this thesi s, but the motivation for policy makers to min i mize corruption may be greater in light of this new analysis. Two points with the potential to affect the policy agenda deserve repeating. (1) Data do not support as a sustainable solution the allowing of pe tty corruption as a means to grease the wheel of the economy (Gupta et al., 2000, p. 9). (2) Corruption is still corrupt, and there is no evidence in this analysis that small scale, or pays the average individual well over the long term (Rose Ackerman, 1999a, pp. 16, 26). Eventually, society corrodes, school quality decays, infrastructure suffers, budgets for research and development shrink, and the economy implodes onto itself, not unl ike that described in Levy (2007) since the end of the

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123 Cold War, and not unlike the implosion of the Soviet Union itself. The IMF describes the pr o cess in The IMF and Good Governance (2011d) Corruption can reduce investment and economic growth; divert public resources to pr iBy reducing tax reve nue, corruption can complicate macroeconomic management, and since it tends to do so in a regressive way, it can accentuate income inequality (IMF, 2011d, p. 1). D eLeon asserts that ri dding a society o f its c nformed policies may dissuade some of it. To do so, Rose Ackerman (1999a, p. 4) suggests reforms in governance, especially i n the rule of law. Reforms can reduce the incentives for bribery and increase the risks of corruption. The goal is not to eliminat e corruption, but to improve the overall efficiency, fairness, and legitimacy of the state. Hopes for total elimination of corruption will never be worthwhile, but steps can be taken to li mit its reach and reduce the harm it causes. Focus on policies that promote vertical and horizontal accountability in both state and non state institutions may dissuade corruption. These ma y include policies intended toward whi s tle blower protection, incentives, enforceable penalties, and more significant fines (Relly, 2011, p. 5; Rose Ackerman, 2008). A summary of the applicability of governance corruption in the process of education po licy making, c ites four major ways in which corruption targets public budgets; through rent seeking, state capture, control, and bid rigging. Education may be an attractive budget to target, as the effects may be hard to detect, or help to realize a private o r cooperative m o tivation. (1) Rent seeking that is easy to hide from public view. Education will likely continue with old books, buildings, and technology, thus, its funding is easier to reduce without much

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124 more attention than, say, a bridge. Actions suc h as this are rent sector under (2) State capture, in the form of collusion, that is difficult to identify and even harder to trace to its source. Rose Ackerman ( 1999a, pp. 26, 32 ) asserts th at e ducation facilities will likely remain without new desks, thus, its funding is easy to divert to provide remuner a tion for a cor rupt act. (3) Control over the masses by controlling the quality, quantity, or content of educ a tion. One method may be cens orship. Some scholars assert the possibility that education of the general population is not in the best interest of a corrupt ruling party. A corrupt individual or powerful institution may advance the type of education that furthers a specific agenda (F reire, 1970, pp. 55, 81, 135). (4) Bid rigging (no bid or sole source contracts, contract favoritism) assures that contracts are awarded to a particular supplier or firm and clientelism, cronyism, patronage, or nepotism may sway contract decisions (Rose Ackerman, 1999a, p. 27). According to Mauro (2002, p 278), ntracts for school supplies are small scale relative to defense or transportation contracts. (b) S o ward, for example, defense contracts, would be one way to manipulate public budgets. ( e.g., Romer, 1986 p. 40 ; Arrow, 1962; Solow, 195 7) that education spills over to other lear ners and to other disciplines, and therefore, has an increasing return on investment, the evidence is mixed. The question becomes, how or why does the money spent on ed u cation not spillover, take root, and bloo m into new knowledge and a more educated society? If, in fact, the money is

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125 reac h ing the level of the student, or to the extent that it does, what is happening to the value ed ucation passes on? Does it evaporate? The answer may be in accordance with Ofe r (1997), that the unofficial ec onomy is realizing the gains. Another debate is whether knowledge spillovers mete out different results, since knowledge is owned by the individual actor rather while educ ation is merely available and to a questionable degr ee. However well intended the Constitution of the Soviet Union, however well intended the leaders, human development and a broadness of education among other factors, suffered in the hands of the Soviet Empire. Moldova, Tajikistan, Kyrgyzstan, Ukraine, and Serbia have yet to realize the GDP per capita known to each country prior to the dissolution of the USSR. Certai nly, other factors are involved, but of those, are any very far removed from corruption? The average Shadow Economy in these five countri es is over 50%. The a v erage GDP per capita $762 per year and including that gained in on the ground is $939 (stated in US 2000 dollars) (Schneider et al., 2010). These GDP per capita levels are similar to those found in the lowest earning regions of Afri ca. I t seems illogical that a corrupt individual would willingly advance the type of educ ation that others could use to unseat him or her as a matter of simple self preservation (Freire, 1970, pp. 55, 81, 135); Monas (1984) provides the following account Institutional censorship plays an especially harsh, continuous, wide, and deep that power had to be protected from irreverent attacks or underminings, in the i nterests of s tability. Of course, the interests of stability are generally the interests emphasis in original ). Systemic, pervasive corruption does not pay off for the average individual in the long run. Research is ongoing by the IMF and the World Bank to separate or isolate the effects of governance corruption in the funding of different public goods efforts that may inform hypoth esis in this thesis Economies cycle; economies oscillate from trough to peak to trough to peak. Measuring a cycle requires discipline GDP cycles start at the bottom of a trough and end at the bottom of

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126 the next trough (Burns & Mit chell, 1946; Kuznets, 1940; Schumpeter, 1939), and each country has its own rhythm. Gross Dom estic Product per C apita is an economic factor that follows this rule. GDP per C apita cycles. Encouraging policy analysis to weigh the economic cycle when producing formulas, models, and equations is responsible intell i gent analysis. This thesis prov ides a strong case for transparency in National Income A ccounting. As more countries embrace transparency, more countries that share data may uncover more ev i dence of the Shadow Economy ability to avoid detection Backfilling the missing information av ail able on one side of the transaction will take time and research, w ork that is ongoing through international agencies such as Transparency International and the IMF I ncreasing sanctions for lack of transparency are increasing, as well Sustainable de velopment depends on many factors, one of which is an educated populace, who are capable of adopt ing the technology that drives economies to a new steady state (Schu mpeter, 1939) Incentive to produce within the governance system must outweigh the i n centi ves to produce O ff the books As Tanzi (1998) noted there is both a supply of and a demand for corru ption. As incentives to engage in corruption decrease, as the r e muneration dries up, the demand to engage in corrupt behaviors should decrease. Proper a udits, oversight, policies, law, and cons equences may reduce incentives to supply or allow the means or the remuneration for corru p tion. The next step for research in this area is to provide policy makers evidence to support protecting the education budg ets, specifically. Milton Friedman (1997) arguing against publicly funded educati on, foresaw this education funding dilemma, asserting very generically that where the public money goes, so goes the corruption. Possible research areas would be methods of funding for education that protect the funding stream, such as conditional block g rants or matc hing grants. Research in this vein would assist in the education policy planning and education budgeting sta g es.

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1 27 Another vein for future research would inform policy implementation. If corrupt instit uti ons are part of the problem in the de livery of the education product then an international agency, consultant, or a funding source such as US AID that will provide funding conditional on also providing the policy planning and work force to implement it (deLeon & Green 2002) may best handle implementation Research on i mplementa tion s trategies may inform policy makers about approaches that protect the funding stream and ensure that funds progress to the level of the schools or better, to the level of the student If the funds are secure d to the level of the school, if education is made avai lable and the avail a bility is monitored by outside agencies new data may be collected to further knowledge about the value of education on human development and economic growth. It is likely, however, that changing the funding mechanism and level would bring about its own challenges, as corruption follows the available reward to its new source. Susan Rose Ackerman (1999) suggested an international tribunal could govern intern ational corruption issues. However, if corruption is s o wn in to the fabric of our being and therefore of our society, the budget for thwarting it globally may be out of reach (deLeon, 1993, p. 3 quoting 1 Corinthians 15:42 ) Accepting that some corruption has and will a l ways be part of the polity theoretically, suggest s the need for a sieve through which petty corruption passes, expo sing the bigger crimes. This would necessitate a rule of law that invites and protects whistle blowing. Protecting education budgets from stat e capture, rent seeking, and bid rigging starts with a c knowledging that education budgets are susceptible even likely targets. Further, protection must continue through the policy process. Several suggestions for this protection are found in Lester S (2002) Tools of Government and Rose Corruption in Government (2008, p. 340)

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128 Future Research The first priority in effects on education budgets and individual income, where it left off. Many studies o n economic growth use cross country analyses and many use conditioning and dummy variables with robus t econometrics T ransition countr y studies, in particular, generally employ three important vari ables : (1) P rogress toward market liberalization. In the 2010 Transition Report, the EBRD offers based approach to measuring transition progres ization, markets, banking, and infrastructure (p. 3). (2) Progress toward EU accession, measured by the European Commission (2011b). (3) Accounting for the history and intensity of armed co nflicts (HIIK, 2010a). Another f uture research project would include additional variables that are nonspecific to transition coun tries, but are common to cross country regression analysis of time series data A partial list of these variables would include data on : (1) P ublic expenditu re on public goods and infrastructure (in addition to education ). (2) The policy implications central to the budgeting and delivery of those goods ( 3 ) Education funding research that isolates effectiveness and efficiency in the education plant. ( 4 ) T he remaining dimensions of governance (in addition to public goods) (Kaufmann et al., 2008) (5) I nterplay between education and other infrastructure or public good funding and the composition of government revenues T he method used to test governa education budgets and i ncome is ge n eralizable and scalable to the balance of the 194 sovereign countries recognized in the world today. Analyzing the balance of the countries may be beneficial to policy makers in many ways. Wh ile data advance, measuring the effects of governance where data are available may highlight new results or dif ferent relationships. The countries bordering the Eastern Bloc, along the Iron Curtain, are likely to be effected by its creation and demolition (Diamond, 1997; Elisseeff, 1997) (See Appendix: Data Validity and Reliability ) Then, the countries with regional

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129 trading ties and ethnolinguistic similarities woul d make another logical grouping, followed by adding the Western European countries to the sample set Rather than geographical groupings, one might sort the 194 sovereign cou n tries based on many factors, including the number of years of governance stability Communist rule, centralized planning, democratic rule or armed conflict. ec onomic paradigm was the genesis of this thesis, which can be translated to a Kuznets Curve for education not unlike the Kuznetsian curve for the environment (Stern, 2003) His original curve contrasted Income per Capita against Income Inequality over time, where d agra r ian population, moving toward a po p ulation concentrated near industrial centers and a more specialized work force population. The Kuznets curve and the cycle that societies move through, a Ku z nets Cycle (Kuz nets, 1934, 1940, 1966) are represented in Figure 6.2 T he basic Kuznets Curve show s i ncome inequality increasing and the decreasing over the cycle. Figure 6.2 The Kuznets Curve (1966) Applying the general principles of the original and the environmental Kuznets curve to a model similar to that found in equations 4.1 4 3 in this thesis may yield important policy info rmation for analysis and administrators alike. This model would graph education funding over the

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130 matur ation cycle of a governance regime With information on the effects of the Shadow Econ omy on education funding, one could employ a MIMIC equation used in Schneider et al. (2010) to create t he Educational Kuznets Curve seen in Figure 6.3 which shows the eff ects of governance co r ruption on education funding in the instance of a newly independent state. The Educational Kuznets Curve shows a divergence in the funding of education as a country fr om when it embarks on a path toward a on a campaign toward good governance. Du ring this period, and relative to the degree of good governance, the hidden economy prospers. As governance in the new state matures, and as the rule of law becomes more enforc eable, as deve lopment policy matures, more productivity moves into the official economy (Kornai, 2005; Mauro, 2004a) We can a ssum e t his moveme nt of productivity to the official economy increases

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131 available funds for public goods, including education spending. The education inequality would decrease over the latter half of new state governance impl e mentation. T his idea has important implication s in development and sustainability. Rather than a new state falling victim to a full cycle of governance maturity proactive development planning could arrest the cycle, or prevent it. Following is the sequence of logic. Mauro (1998), Barro (2001), and Solow (1956) among others assert that education is fundamental to economic growth. The correlation coefficient between EE (lagged variable that averages from t 10 to t 18 years ) and Ic is .8667. According to Mauro (1998, 2000, 2002), Tanzi (1998), an d Pritchett (2001), education funding suffers in the presence of corruption. In this thesis, a comparison of the R 2 test between two OLS regressions was consistent with these authors. The dependent vari able is Ic, the HDI 1990 is the independent variable for the first equation and the Shadow Economy is added to the second equation. The R 2 of the augmented, second equation is higher, from 40.62% to 48.44%. Schneider et al., (2010), Russell (2010), and others assert that unofficial ec onomic activity, incl uding those specific to the Shadow Econo my, diverts income from the o f ficial GDP and toward unofficial economic activities (p. 5) The average Shadow Economy size for the Last, variation in the HDI, and EEc, t ogether a ccount for 54.04% of the variation in Ic Total Education Inequality is especially li mit ing in developing nations as research shows the direct and positive relationship between good education and positive economic growth (La Porta & Shleifer, 2008) Beyond the education funding issue, education inequa lity affects nations through li mitin g access. Studies show diverging education between the wealthier and poorer while the governance system matures (TI, 2009b) Would a country be better off if education were a protected economic asset? Consider the long term detrimental effects of i nadequate education on economic development (Barro & Lee, 2001; Romer, 1986; Sen, 1984, 1997, 2004; Tanzi, 1998) One might compare the R 2 values

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132 of the change in income per capita of two sets of transition econom ies that gained independence during the dissolution of the USSR. The Group A Countries adopted a democratic political sy stem, embraced good governance and transparent government, and prioritized education in the spending on public goods. The Group B Coun tries did not. Hypothesis 1 is that the change in I ncome Per Capita is higher in Group A Hypothesis 2 is that duration of the Educational Kuznets Curve would be shortened, and hopefully, the divergence in education equality shorter. Measured by the rat e of increase in economic growth between the two groups, the time period for econo m ic recovery after the trough sparked by a regime or political shift (Rostow, 1991) Group A would outpace Group B Additional work furthering the idea of the Educational Kuznets Curve is crit ical. Future research that conjoins the official ly and unofficially earned income would pr omote better informed policymaking at every level of government. Research that marries the income sources with planning in the policy budgeting process, policy analysis, stages of econo mic growth, and economic cycle position would promote better informed policy making, as well. Lastly, adding to the economic development planning process reminders that ed u may be a target for corruption, may produce increa sing returns is vital to informed, responsible policy

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133 APPENDIX A : COUNTRY BRIEFS Region Geographically, the sample set of countries lay in Central and Eastern Europe. The cou ntries chosen for this analysis where selected in part for their commonality, to minimize the scope of determining factors on governance. The countries occupy the former Eastern Bloc, the Balkan ahich reigned 1946 ) may be different by physical magnitude or psycholog ical impact, or, may be the same; the force field went up, and came down, on both sides of the veil. Likewise, construction, patrol, and de molition occurred on both sides of the Berlin Wall (Ofer, 1987). The sample set of countries, based on geography, a re those most affected by the Soviet radiation outward from Moscow in conce ntric ci r cles, and from its Satellite States. The sample countries are, and were, economically inter dependent. Centuries of trade r elatio n ships and routes preceded these new al liances (Elisseeff, 1998), and the same or new routes opened after the dissolution of the former USSR (WTO, 2010f). (See Country Briefs). Similarly, migration routes and shifting empires facilitated the intermixing of nationalities, ethnicities, rel igion s, languages, customs, and disease (Diamond, 1997; Alesina et al. 2002). Thus, the sample set of countries share, in var y ing degrees, similar ethnolinguistic heritage. Socialist Countries in Central and Eastern Europe The goal in this section is to defin e Socialist as used in this thesis. According to Jonas Kornai (1993), literature and the media intertwine terms and confuse meanings for the political systems in Central and Eastern Europe. The term socialism by the pr e-

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134 (p. 10) [He co n (p. 10) nconcerned with a traditional placement on a left right or liberal conservative spe ctrum. According to Kornai, a Socialist political economy centralizes the authority of planning the eco nency (1992, p. 4) For this thesis, Sta t ism, and Fascism are both socialist, as are Socialism, Communism Collectivism, Nazism, Marxism, Stalinism and Leninism. Inclusion in the Sample Set of Countries Rule 1: The country was or remains Socialist Sachs & Warner ( 1995) and Kornai (1993) among others deliberately include in data sets, party declared it was Socialist (Kornai, 1992, p. 4) For this thesis, it is paramount that the cou ntries endured at least several years of extraordinarily influence by the Sovie t Empire, and has since endured the development effects of its demise, either positively or negatively. These effects may be cultural, shifts in population densities, related to trade relations before, during, or after the Cold War, shifts in economic str engths, political and governance systems, or a multitude of other factors either undiscovered or outside the scope of this thesis. Rule 2: Four or more years of Soviet influence, plus newly created, liberated, or re gained sove reignty, independence or t he ability to trade, travel, and migrate Other scholars use different criteria to include or exclude in studies counties that occupy Central and Eastern Europe. Dividing the former USSR states into those that joined certain all iances such as the Commonw ealth of Independent States (CIS), for example, or the later, the Collective Security Treaty Organization (CTSO), or the Eurasian Economic Comm u nity (EAEC),

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135 is not workable over time, as each of these organizations has shifting membership. In addition, no t one has always included all of the former USSR countries, and at no time has Hungary, Y ugosl a via, the Czech Republic, or Albania joined the organizations, according to the World Trade Organization (WTO, 2010h) O ther methods to separate or group countries exist, for example, countries were dissected by factors such as ethnolinguistic homogeneity (Alesina et al., 2002) i nstability by number of coups (Barro, 1991) and religious affili a tion (Barro & McCleary, 2003) Rule 3: Geographically related by inland border, trade route, or sea trade route Geographically, the sample set of countries lay in Central and Easte rn Europe. The cou ntries chosen for this analysis where selected in part for their commonality, to minimize the scope of determining factors on governance. The countries occupy the former Eastern Bloc or Soviet Sphere of Influence (Hirsch et al., 2002) o r they border (significantly) counties that do. This thesis does not include China or other Asian countries as inclusion would require r esearch and data for dummy variables on other geographic and cultural issues beyond the scope of this thesis. This co untry grouping is consistent with the Schneider et al. (2010). For the same reason, former or current communist countries that are geographically distant from the former USSR, such as North Korea are not included. Rule 4: Ethnoli n guistically interrelated Economically interdependent The Central and Eastern European countries are ethnoli n guistically and economically linked, trade among and between these countries survived the Iron Curtain (Ofer, 1987), or was revived at its crumbling; each country was pa rt of the Warsaw Pact, or of NATO, or the country was economically affected by the division between them. Two countries offer examples show e x treme reasons for necessary inclusion to the sample set. The first example is Czechoslovakia in 1968. Unwillin g to be part of the security all iance, it i n vaded by the Warsaw Pact members (save Romania), and inclusion in the Warsaw Pact was forced upon it. For several reasons including this, Czechoslovakia must be included in the

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136 sample set (DOS, 2010b, p. Czechos lovakia ). The second country is Italy, providing two exa mples of ethnolinguistic fractionalization. Before and during WWII fifteen hundred Italian men went to work for Volkswagen in Germany as expatriates, and were not allowed to return home. In fact, o ver seventy percent of the labor force of Germany consisted of foreigners, mostly from Poland and Italy, and Soviet prisoners of War (Burleigh, 1996, p. 43). Over Seventy five hu ndred Italian Jews were victims of the Holocaust alongside the two million So viet Jews and two million ethnic Poles (DOS, Russia, p. People). Prior to the Berlin wall closing migration, Ge rAlbanians migrated to Italy to join family, gain em ployment, or seek asylum from the Albanian government (Bcker et al., 1998, p. 232). Millions of people migrated west after the fall of the Berlin Wall, creating or re creating trading ties with the dawning of a new era in Central and Eastern Europe in th e late 1980s and early 1990s (pp. 259 260). The eco n omy of each county in the sample set was affected greatly by the dissolution of the USSR. The ethnic, cultural, rel igious, or linguistic history differs by country which effects economic growth (Ales ina et al., People), while over 92% of Hungarians claim Hungarian ethnicity (p. Hungary, people). Incl usion in the sample set required certain levels of Soviet in fluence, as well. Soviet influence, degree of heterogeneity, and economic interdependence provide a reasonable grouping to study (Alesina et al. 2002). Trade relations link these countries before and after the fall of the Berlin Wall. From a primarily c losed economy in 1989, as of 2007, Russia is the thirteenth largest exporter and nin eteen largest importer of goods in the world, with trade relations between countries from both the NATO and Warsaw Pact trading alliances, and from both sides of the Iron C urtain. Netherlands buys 10.62% of Russian exports, Italy buys 6.46%, Germany 6.24%, China 5.69%, Turkey 4.3%, and Ukraine purchases 4.01% of the $303 billion in total exports. In 2008, Russia imported $191

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137 billion in goods, Germany provided 14.39%, Chin a 13.98%, Ukraine 5.48%, Italy 4.84%, and the US sent 4.46% of the total imports (CIA, 2009, p. Russia). The dissolution of the former USSR disrupted the economic equilibrium of Eastern E urope. [There] has been the steep collapse of trade among the cou ntries of the former Council for Mutual Economic Assistance (CMEA). In part, the collapse has r esulted from a decline in Russian sales of oil and gas to Eastern Europe. In part, 's exports to the CMEA countries declined steeply, from an estimated $40.1 billion in 1990 to $15.9 billion in 1991 (Lipton et al., 1992, p. 225). The breakup of the former USSR also brought on one of the most profound and far reaching transformations of the twentieth century. The disintegration of the command structures in the old regimes triggered some of the most chaotic economic, political, and social changes in modern history Abed et al. (2002a).

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138 Country Briefs The following Country Briefs present evidence and data relevant to this thesis on three groups of sovereign countries as of 2008. Group I consists of fifteen countries that occupy the geographic area of the former United Soviet Socialist Republics (USSR or SSR for an individual Republic) as of 1989. Group II consists of fifteen Eastern Bloc countries du r ing and after World War II until 1998 (Beissinger, 2006) former communist states of Eastern Europe, including Yugoslavia and Albania, as well as the (Hirsch et al., 2002, p. 316) Group III is for data validation in this thesis. It consists of six countries extraordinarily influenced by the Soviet E mpire, that maintain a communistic or socialistic authority, were occ ustate (sometimes referred to as a client state) is a political term that refers to a cou ntry that is fo rmally independent, but under heavy political and economic influence or control by another (Hirsch et al., 2002, p. 316) Following is the rule of thumb for inclusion in Group III. The country: 1) is socialist, 2) was occupied by or allied with communist rulers through a satellite relationship, 3) is situated within Eastern Europe, Central Eu rope, or Central Asia and bordered the USSR, 4) shared strong ethnolinguistic, migration, and economic history (Elisseeff, 1998) 5) was not part of the USSR, and 6) maintains strong trade relationships with countries in Groups I and II (CIA, 2009). The countries in Group III are Austria, Finland, Greece, Turkey, Italy, and Cyprus. Unless otherwise noted, the six sources for corruption, governance, historical, and dem ographic data and information follow. 1. The States Department of State (DOS) electronic public library, Countries and Regions: Background Notes by country (DOS, 2010c) 2. The Europa World Year Book (Europa) by country (Maher, 2008) 3. The 2006 United States Agency for I nternational Development (USAID) Anti Corruption Final Report (2006) the US Library of

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139 Congress (2010), and (CIA2009a) Sc hneider e t al. provide the Shadow Economy figures (Schneider et al., 2010a) unless noted othe r wise. The Country Briefs assume 1990 for the pre test year (HDI 1990 ), and 2007 for the post test (HDI 2007 ), unless otherwise note d. In each case, if 1990 and 2008 data are not available, the data reported are the closest available to the test dates and are noted. Human development and economic statistics data originate from the Human D e velopment Report (HDR) statistical database f ound in the 2010 report, or in prior year HDR reports as noted by the report year (2010). The 1993 HDR report provides HDI 1990 unless 1990 data are unavai lable, in which case this section cites the report where the data are available for the earliest pos sible year. However, this citation does not hold for the Human Development Index data. The HDI researchers made an adjustment in the methodology inversed the ranking order so that high rankings are equivalent to high human development levels after 1995, when the inverse was the standard in the initial years of the HDR. The Annex to the 2009 report, HDI Trends and Indic ators (1980 2007), provide the HDI 1990 (p. Annex) Table 1 (1993) reports the legacy Ed u cational Attainment value, and Table H (2009) reports the HDI 2007 value. Table GER reports the co mbined primary, secondary, a nd tertiary ratio, or the Gross Enrollment Ratio (HDR, 2009, p. Table GER) In the Country Briefs section, the GDP data are stated in terms of Purchasing Power Par ity (ppp), standardized to the United States dollar in 2000, as this is the methodology used b y the HDR. The 2010 Human Development Report provides the demographic and economic data in its statistical database (HDR, 2010f) The United Nations Educational, Scientific, and Cultural O rga nisation (UNESCO) provide the data on government expenditure on public education (2010). Pre test Shadow Economy data are calculations for the average Shadow Economy size in the years 1990 1998, while the posttest Shadow Economy d a ta are calculations fo r the average from 1999 through 2008, as consistent with the method used in creating the Shadow Economy data set (Schneider et al., 2010a)

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140 Group #1 Countries of the former USSR The Baltics: Estonia, Latvia, Lithuania D uke Midaugas unified the Baltic tribes from 1236 to 1263 in current day Lithuania. Grand Duke Gediminas ruled until 1341, and stretched Gediminas Dynasty from the Baltic to the Black Sea, spreading Christianity. By 1864 and until WWI, the Russian Empire had taken co ntrol over the Baltics from Austria and Prussia. Lithuania and Latvia declared independence after WWI, and Estonia won independence in 1918 with the Peace Treaty of Tartu German and Sov iet forces occupied each during the interwar years. The Ge r man and Soviet non Aggression Pact of 1939 brought the Baltics into the USSR. During the Cold War, the economies of the Baltic States were reorganized to benefit the Soviet needs into an urbanized industrial workforce buil ding military equipment. Wit h the demise of the Soviet Empire, the Baltic States gained independence again in September 1991 (DOS, 2010, p. Balkans, History). Estonia Europa reports that Russian annexation of Estonia from Swedish rule in 1721 established By the end of 1949, most Estonian farmers had been forced to join collective farms. I n vestment concentrated on electricity generation and the chemicals sector expanded heavy industry St ru ctural change in the economy was accompanied by increased political r e (Maher, 2008, p. 1585) s perity during the Soviet period, the collapse of the USSR and its internal economic system resulted in serious economic difficulties. The annual (p. 1586) August 20, 1990, marks the re independence of the Republic of Estonia as a democratic state (CIA, p. Estonia) literate The combined gross enrollmen t was 81.5%. Of government expenditures, 25.5% were

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141 dedicated to public education. The education index was 2.66. The Life Expectancy index was .740 life expectancy at birth was 69.4 years. The GDP was $5.99 billion $3,822 per capita, for a GDP index value of .781. The GDP per capita hit its low in 1993, registering $2,744, rebound ing by 2001 to exceed levels prior to the USSR breakup. The average annual growth GDP per capita rate from 1989 to 2008 was 8.9%. The HDI in 1990 was .817, and the Shadow Economy equaled 34.3% of the total GDP. In 2008, the population of Estonia decreased to 1.34 million. On average, 99.8% of those ages 15 and above were literate in the years from 1999 2007, with combined gross enrollment of 91.2%. The education index was 2008 government e x penditure was on education. The Life Expectancy Index was .799 with expectancy of 72.9 years. GDP I ndex was .887, at $9.53 billion and $7,114 per capita. The HDI increased to .883 or 40th in the world. translates to $3.8 billion in 2008, bringing the gross GDP to $13.8 billion and GDP per capita $9,980 per person. Latvia According to DOS, Latvia did not enjoy a time of terr itorial sovereignty prior to Nove mto the USSR until August 21, 1991 (p. Latvia, History). During the Cold War, Latvia maintained some economic viability as a t rade route to the north via the Baltic Sea, and capitalized on its i ndigenous resources of timber and agriculture products. Latvia e m braced free market reforms and transparency since reestablishing its independence (p. Economy). Latvia lost a third of it s pop ul a tion to the Holocaust (p. History). literate, with a combined gross enrollment of 73.7%. Of government expenditures, 16.79% were dedicated to public education. The education index was 2.66. The Life Expectancy index was

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142 capita, for a GDP index value of .771. The HDI in 1990 was .803. The HDI in 1990 was .817, and the Shadow Economy equaled 25.7% of the total GDP. The average annual growth of the GDP per capita from 1989 to 2008 was 6.7%. The GDP per capita hit its low in 1993, registering $2,271, and rebounded by 2004 to exceed levels prior to the USSR breakup. I n 2008, the population of Latvia increased to 2.27 million. On average, 99.8% of those ages 15 and above were literate in the years from 1999 2008, with combined gross enrollment of 2008 govern ment expenditure was on education. The Life Expectancy Index was .788 with expectancy of 72.3 years. GDP I ndex was .851, at $13.67 billion and $6,036 per capita. The HDI increased to .866 or 48th in the m 1999 to 2008 averaged 41.7%, which translates to $5.7 billion in 2008, bringing the gross GDP to $19.38 billion and income per capita, $8,553 per person. Lithuania According to DOS, Lithuania regained independence on February 4, 1991 from the USSR. The Lithuanians posted the greatest relative population loss to the Holocaust. As a R ethe USSR demise, the inefficient infrastructure could not compete with world m anufa c turing, and Lithuania relied on the former USSR for 90% of its exports. By 1997, only 47% of the exports shipped to the Soviet States, and the market reforms toward private enterprise had begun to work. Lithuania has an ice free seaport, which ferr ies goods and traffic to Swedish, Danish, and German ports. As of 2008, Lithuania has strong growth in the technology and service sectors along with a record of democratic voting (p. Lith u ania, History). 6.0% of those ages 15 and above were literate, with a combined gross enrollment of 74.6%. Of government expenditures, 21.8% were

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143 dedicated to public education. The education index was 2.66. The Life Expectancy index was .763 and life expectancy at birth per capita, for a GDP index value of .816. The HDI in 1990 was .828, and the Shadow Economy equaled 26% of the total GDP. The GDP per capita hit its low in 1993, registering $2,435, and rebo unded by 2003 to exceed levels prior to the USSR breakup. The average annual growth rate in GDP per capita from 1989 to 2008 was 4.3%. In 2008, the population of Lithuania decreased to 2.27 million. On average, 99.7% of those ages 15 and above were lit erate in the years from 1999 2007, with combined gross enrol l2008 government expenditure was on education. The Life Expectancy Index was .780 with expectancy of 71.8 years. GDP Inde x was .863, at $20.25 billion and $5,154 per capita in US 2000 constant dollars. from 1999 to 2008 averaged 31.9%, which translates to $6.48 bi l lion in 2008, bringi ng the gross GDP to $26.72 billion and income per capita, $6,798 per person. Central Asia: Kazakhstan and Turkestan Tajikistan, Turkmenistan, Kyrgyzstan, and Uzbekistan The vast area of Turkestan covers the geographic boundaries of Uzbekistan, Turkmen ist an, Taji k istan, and Kyrgyzstan, Afghanistan, and Mongolia (Humboldt, 1843). Characterizing the region are nomadic peoples and the Silk Road overland travel route connecting Asia with E urope. The region fell under the rule of Genghis Khan by 1227 and rema ined occupied by various Mongolian rulers until the Russian Empire took control of all but Mongolia (Elisseeff, 1998) In 1727, Russia and Manchu China concluded the Treaty of Kha iaka deta i l i ng the border between China and Mongolia that exits in large part today (Dos, 2010, p. Mongolia). Mongolia and K an der odel for its communist government was the Soviet model. From 1920 until the 1980s, Mongolia aligned i t self with the Soviet Union for continued military

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144 assistance against China ( p. Mongolia ). Since its 1990 independence, Mongolia has shifted t oward a mar ket economy, electing non communist leaders starting in the 1993 elections, and has increased foreign relations with China, the US, and other industrialized nations ( p. Mongolia ). The Shadow Economy of the Kazakhstan and Turkestan regions grew from 25.88% of the total GDP in 1990 to 40.57% in 2008 (Schneider, 2010, Tables 1 3). Tajikistan 999); however, the Mongol and then Russian Empires honored no boundaries. In 1920, the are a came under Soviet rule as part of Uzbekistan. Tajikistan gained autonomy as a Soviet Socialist Repu blic in 1929. From 1992 through 1997, Tajikistan suffered ongoing civil war, decimating its economic infrastructure, and remains the poorest country of t he former Soviet Union. By 2008, interference in the economy and massive corruption stifle economic growth and private inves tliterate, with a combined gross enrollment of 77.5%. Of government expenditures, 29.3% were dedicated to public education. The education index was 2.25. The Life Ex pectancy index was per capita, for a GDP index value of .581. The HDI in 1990 was .636, and the Shadow Economy figure was 25.88% of the total GDP. The GDP per capita hit its low in 1996, registering $122, where it remained through 1997. Tajikistan is one of five countries in the sample set with a GDP per capita that, as of the data gathered for 2008, has yet to rebound to levels seen prior to the USSR breakup. Moldova, Kyrgyzstan, the Ukraine, and Serbia are the other countries. The ave rage annual growth rate in GDP per capita from 1989 to 2008 was 2.8%.

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145 In 2008, the population of Tajikistan increased 6.84 million. On average, 99.6% of those ages 15 and ab ove were literate in the years from 1999 2007, with combined gross enrollment of 2008 government e x penditure was on education. The Life Expectancy Index was .691 with expectancy of 66.4 year s. GDP I ndex was .474, decreased 26% to $1.67 billion and $245 per capita. The HDI increased to .688 or 44.3%, which translates to $742 million in 2008, bringing t he gross GDP to $2.42 billion and i ncome per capita, a meager $353 per person, the lowest income per person in the sample set. Turkmenistan th century until it broke down in the late 12 th century. The seven centuries depleted the strength of the Turkmen. As the Ru s the Soviet Empire overtook Turkmenistan by 1924 ( p. History ). Although its economy has pro mise due to vast natural gas reserves, it is corrupt, and deeply connected to centralized planning. Researchers are unable to quantify the degree of corruption in Turkmenistan. The Red Cross and international organizations maintain a presence in Turkmenistan to thwart ongoing human rights and political viol a tions (p. Political Conditions). were li t erate with no gross enrollment data available. Of government expenditures, 24.5% were dedicated to public education. The E ducation I ndex was 2.25 Life Expectancy index was 629; capita, with no GDP index value available. The 1990 HDI for Turkmen i stan is .73. The GDP per capita hit its low in 1997, regis tering $455, and rebounded by 2004 to exceed levels prior to the

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146 USSR breakup. The average annual growth rate in GDP per capita from 1990 to 2008 was 2.65%. The HDI in 1990 was .817, and the Shadow Economy equaled 24 % of the total GDP. In 2008, the popu lation of Turkmenistan increased to 5.4 million. On average, 99.5% of those ages 15 and above were literate in the years from 1999 2007, with combined gross enrol l2008 government expenditur e on education averaged 24.5% of total expenditures. The Life Expectancy Index was .661 with expectancy of 64.6 years. GDP Index was .651. GDP rose to 8.64 billion and $1,714 nd economy e stimation was 36%, and the gross GDP estimation was $11.76 billion in US constant year 2000 dollars, which translates to a gross figure of $2,331 Total per capita income Uzbekistan in 1924 from the territ oDOS, p. People). This territory is 90% Sunni Muslim and about 80% Uzbek. The economy of Uzbekistan relies on natural resources cotton for exports and manufacturing for the Russian market. The 2007 GDP/c is 25% less than 1990 figures. The economic depression is a result of tight gover nprovides for separation of powers, freedom of speech, and re p resentative government. In reality, 2008] elections or referenda were deemed free or a tion for Security and Co operation in Europe (OSCE) ( p. Political Conditions ). were li t erate, with a combined gross enrollment of 75.6%. Of government expenditures, 22.84% were dedicated to public education. The education index was 2.25. The Life Expectancy index

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147 $685 per capita, for a GD P index value of .510. The HDI in 1990 was .687. GDP per capita hit its low in 1996, registering $499, and rebounded in 2006 to exceed levels prior to the USSR breakup its average annual growth rate from was 1.08%. The Shadow Economy equaled 22.1 % of the total GDP or $3.1 billion in US equivalent dollars in 2000. In 2008, the population of Uzbekistan increased to 27.31 million. On average, 96.9% of those ages 15 and above were literate in the years from 1999 2008, with combined gross enrol lment of 72. 2008 government expenditure on education was 22.84% of total government expenditures. The Life E x pectancy Index was .711 with expectancy of 67.6 years. GDP Index was .532, the GDP increased to $22.93 b illion; ho wever, and the GDP per person to $840. The HDI increased to .710 or 119th in the world. slates to $8.69 billion in 2008 bringing the gross GDP to $37.93 bi llion and income per capita, $1,158 per person. Kazakhstan According to DOS, Kazakhstan provided the coal for the USSR, which moved its indu strial se c tors closer to Kazakhstan for efficiency. This changed the ethnic makeup of the country when the Kazakhst an Kazakh people (renamed Kyrgyz by the Soviets so they would have the same name as the Kyrgyz people in Kyrgyzstan) became a minority ethnic group to Russians who were displaced to work and oversee coal production. This is the only former Soviet state wh ere the indigenous population became the minority (p. Economy) I t is the largest land locked cou ntry in the world, sharing with Russia its northern border, over 6,800 kilometers. As of 2005, the elections did not meet the OSCE standards. The economy is growing and healthy, with energy its leading sector ( p. Economy ).

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148 were li t erate, with a combined gross enrollment of 80.0%. Of government expenditures, 18.88% were dedi cated to public education. The education index was 2.25. The Life Expectancy index $1,612 per capita, for a GDP index value of .721. The HDI in 1990 was .778. In 2008, the population of Kazakhstan increased to 37.3 million. On average, 99.6% of those ages 15 and above were literate in the years from 1999 2007, with combined gross enrol l2008 government expenditure was on education. The Life Expectancy Index was .667 with expectancy of 64.9 years. GDP Index was .782, increased an average of 2% to $37.3 bi l lion and $2,380 per capita. The HDI increased to .804 or 82th in the world. Kazak from 1999 to 2008 averaged 45.3%, which translates to $16.8 billion in 2008, bringing the gross GDP to $54.2 billion and i n come per capita, $3,458 per person. Kyrgyzstan e Soviets in 1926 are roughly the bo rders of the territories inhabited by the Kyrgyz people as of the 16 th century (DOS, 2010, p. p. History ) of the USSR. In 1 lture and coal (p. Economy). Corruption has plagued politics in Kyrgyzstan, with the 2005 election improved but not acceptable by election commission of the OSCE community (p. Go vernment). I were literate, with a combined gross enrollment of 77.5%. Of government expenditures, 23.1% were dedicated to public education. The education index was 2.25. The Life Ex pectancy index

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149 $465 per capita, for a GDP index value of .547. The HDI in 1990 was .687. In 2008, the population of Kyrgyzstan increased to 5.28 million. On av erage, 99.3% of those ages 15 and above were literate in the years from 1999 2007, with combined gross enrol l2008 government expenditure was on education. The Life Expectancy Index was .710 with expectancy of 67.6 years. GDP Index was .500, decreased an average of 1.07% per year to $2.0 billion and $379 per capita. The HDI increased to .710 or 120th in the world. The estimated underground economy from 1999 to 2008 averaged 42.0%, which tran s lates to $804 million in 2008, bringing the gross GDP to $2.4 billion and income per capita, $538 per person. Eastern Europe: Belarus, Moldova, Poland, Romania, Russian Federation, and Ukraine Belarus approximately those created in 1939 with land seized during the Soviet invasion of Poland unified with the Belorussian SSR (Maher, 2004, p. 713) e ty of consumer goods available than other republics ( p. 713 ). Belarus was an original member of the USSR in 1922. The following is from DOS (2010c, p. Belerus) Occupied by the Russian empire from the end of the 18th century until 1918, Belarus declared its short lived National Republic on March 25, 1918, only to be declare d its sovereignty on July 27, 1990, and independence from the Soviet U nion on August 25, 1991, and independence from the Soviet Union on August 25, 1991. aownturn was due in part to the lack of an independent economic infrastructure and trade lost to neighboring countries with dissolution of the Soviet economic system, and was minimized in part by private market forces as operating successfully as black mark ets to the communist economy (Maher, 2004) By 200 8, elections failed to meet OSCE standards follo w-

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150 12 terms that had been stipulated by the European Commission in November 2006 as being co nditional to Belarus's access to greater aid and trade co operation within the European (DOS, 2010c, p. Political Conditions) literate, with a combined gross enrollment of 80.2%. Government expenditures on education were 17.1% of total government expenditures. The education index was 2.47. The Life Expe cbillion $1,410 per capita, for a GD P index value of .705. The HDI in 1990 was .795. GDP per capita hit its low in 1995, registering $920, and rebounded in 2002 to exceed levels prior to the USSR breakup. The average annual growth rate in GDP per capita from 1990 to 2008 was 3.09%. The S hadow Economy equaled 35.6% of the total GDP or $5.1 billion in US equivalent dollars in 2000. In 2008, the population of Belarus decreased to 9.68 million. On average, 99.7% of those ages 15 and above were literate in the years from 1999 2007, with combi ned gross enrollment of 2008 government expenditure was on education. The Life Expectancy Index was .733 with expectancy of 69.0 years. GDP I ndex was .782, increased to 24.34 billion and $2,5 15 per capita. The HDI increased to .828 or 68th which translates to $12.12 billion in 2 007, bringing the gross GDP to $36.47 billion and income per capita, $3,767 pe r person. Moldova ndepen d ence in 1991. The boundaries are consistent with those formed in 1940 by the USSR and roughly those of Bessarabia since the 13 th century Moldova wa s formerly part of the Mongol and

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151 Ottoman Empires, offering a southern overland passage for the Silk Road (p. History). The GDP in 2007 was 56% of its 1990 height. Moldova is one of the poorest countries in Europe, having lost much of its market for its main export to the USSR, wine. Corruption, state sponsored m edia, and ineffective law enforcement plague Moldova, and the elections through 2008 were unacceptable to the Operation for Security and Co Operation in Europe (OSCE) (p. Government). In 1990, Mo literate, with a combined gross enrollment of 69.9%. Government expenditures on education were 22.9% of total government expenditures. The education index was 2.38. The Life Expe cbillion or $980 per capita, for a GDP index value of .619. The HDI in 1990 was .735. GDP per capita hit its low in 1999, registering $346, and has yet to rebound to exceed levels prior to the USSR breakup. In 2008, the population of Moldova decreased to 3.63 million. On average, 99.2% of those ages 15 and above were literate in the years from 1999 2007, with combined gross enrol lment of 71.6%. The education ind ex was .899; 20.2% of the 2000 2008 government expend i ture was on education. The Life Expectancy Index was .722 with expectancy of 68.3 years. GDP I ndex was .541; its income per capita fell to $2.11 billion and $591 per capita, one of the worst performin g economies coming out of the former USSR. The HDI decreased to .720 or 117th in the world. The estimated underground economy from 1999 to 2008 averaged 45.8%, which translates to $967 million in 2008, bringing the gross GDP to $3.08 billion and income p er capita, $862 per person, an average annual growth of 2.6%. Poland Poland was reconstructed in 1918, during the Treaty of Versailles, to roughly the bound aries of the laid out by King Mieszko I in 966. Poland united with Lithuania and together occupied

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152 Poland was partitioned between Austria, Prussia, and Russia from 1795 to 1918. From 1918 to 1939, Poland was independent, but was split between Germany and USSR in 1939, as a result of the Molotov Ribbentrop Pact, the non aggression pact between the Soviet Union and Germany following WWII. From 1945 to 1989, Poland was part of the Soviet Empire. In 2007, 98% of iverse, and growing, and as of 1996, its election processes pass the OCSE standards (p. Economy). literate, with a combined gross enrollment of 75.5%. Government expenditures on education were 12.2% of total government expenditures. The education index was 2.57. The Life Expe ctancy index was .769 and life expectancy at birth was 71.1 years. The GDP was $118 billion or $3,097 per capita, for a GDP index value of .738. The HDI in 1990 was .806. The GDP per cap ita low was in 1992, at $2,936, and rebounded in 1994 to exceed levels prior to the USSR breakup. In 2008, the population of Poland increased slightly to 38.2 million. Th e loss of roughly two million ethnic Poles to the holocaust partly offset p opulation. On average, 99.3% of those ages 15 and above were literate in the years from 1999 2007, with combined gross e n rollment of 87.7. The education index was .952; 12.12% of 2008 government expenditure was on education. The Life Expectancy Index was .842 with expectancy of 75.5 years. GDP I ndex was .847; the GDP was $237 million and $6,228. The HDI increased to .880 or 41st in the derground economy from 1999 to 2008 averaged 28%, which tran slates to $66 billion in 2008 bringing the gross GDP to $303 billion and income per capita, $7,972 per person, an average a n nual increase of 3.7%. Romania According to DOS, the Treaty of Berlin c 1881. Today, 89% of its population are ethnic Romanians and affiliate themselves with the R o

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153 claims as some of its values, human dignity, civil rights and freedoms, and justice (p. Government). were literate, with a combined gross enrollment of 66.4%. Government expenditures on educ ation were 13.6% of total government expenditures. The education index was 2.47. The Life $43.98 billion or $1,896 per capita, for a GDP index v alue of .752. The HDI in 1990 was .786. GDP per capita hit its low in 1992, registering $1,553, and rebounded in 2004 to exceed levels prior to the USSR breakup. In 2008, the population of Romania decreased to 21.51 million. On average, 97.6% of those ages 15 and above were literate in the years from 1999 2007, with combined gross enrol l2008 government expenditure was on education. The Life Expectancy Index was .792 with expectancy of 72.5 years. GDP Index was .804, growing to $61.19 billion and $2,845 per capita, an a n nual average rground economy from 1999 to 2008 averaged 36.3%, which trans lates to $22.2 billion in 2008, bringing the gross GDP to $83.41 billion and income per capita, $3,877 per person. Russian Federation than 100 ethnic groups speaking six la nguages and many dialects (p. People). Russia continues to vernment) From 1994 through 2008, Russian forces fought two wars and many skirmishes with the Chechens, for the territory of Chechnya, which threatened to recede along with the rest of the Caucasian states (p. Russian Federation). This ge o graphic area was of int erest to the new Russian

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154 Federation for its vast oil reserves. Chechnya, having not been a separate state of the USSR, did not qualify for separate n a tion status under the new Soviet constitution (p. Russian Federation). The market reforms toward a freer market system in the wake of the demise of the USSR have not taken hold. Tight control over industry, corruption, and high tariffs helped cause several years of hyperinflation. Recent increases in oil revenues after a tax collection overhaul have s olidi fied the economy on surer ground (p. Economy). literate, with a combined gross enrollment of 83.7%. The education index was 2.61. The Life Expectancy index was .714 an $385.9 billion or $2,602 per capita, for a GDP index value of .817. The HDI in 1990 was .821, and the Shadow Economy equaled 27.8% of the total GDP. The GDP per capita hit its low in 1998, regi stering $1,511, and rebounded by 2007 to exceed levels prior to the USSR breakup. The average annual growth rate in GDP per capita from 1990 to 2008 was .083%. In 2008, the population of Russia decreased to 141.95 million. On average, 99.5% of those ages 15 and above were literate from 1999 2007, with combined gross enrollment of 81.9. The E ducation I 2008 government expenditure was on education. The Life Expectancy Index was .686 with expectancy of 66.2 years. GDP Index was .833, with GDP at $432 billion and $2,602 per person. The HDI d e creased to .817 or 71st in the 2008 averaged 48.6%, translat ing to $209 billion in 2008, bringing the gross GDP to $641 bill ion and income per capita, $ 4,523 per person. Ukraine 1654 are now the same, after the 1939 reunification of Galicia Volhynia, and the 1954 of transfer of Crimea from the USSR back to U kraine. It was one of the founding republics of the Soviet Union in 1922.

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155 From 1961 through 1989, Ukraine produced about 25% of the total agricultural output of the USSR, and lead Europe in technology research, and industrial and steel manufacturing for the arms, mining and transportation industries. The population is over 75% ethnic Ukrainian. Many of the Soviet officials came from the Ukraine, including Leonid Brezhnev, leader of the Soviet party from 1964 1982, and the ruling clans r e main powerful (USAI D, 2006; CIA, 2009a) The Chernobyl Nuclear Power Plant explosion in 1986 marked the start of a deep economic trough. e pendence, on August 24, 1991 by a then newly elected parliament, marked the start of Ukraine as a democratic state its 1990 levels, due in part to the economic infrastructure and trade lost to neighboring cou n tries to emb race a transparent, market based system. By 2008, the political process had begun to stab ilize, after public scrutiny for election fraud and corruption leading up to the 2004 elections (CIA,2009a) literate, with a combined gross enrollment of 77.9%. Government expenditures on education were 24.35% of total governme nt expenditures. The education index was 2.30. The Life Expe cbillion or $1,387 per capita, for a GDP index value of .742. The HDI in 1990 was .754, and the Shado w Economy equaled 29.4 % of the total GDP. The GDP per capita hit its low in 1998, $590, and has not yet rebounded to its levels prior to the USSR breakup. The average annual growth rate in GDP per capita from 1990 to 2008 was .095%. In 2008, the popula tion of Ukraine decreased to 46.26 million. On average, 99.7% of those ages 15 and above were literate in the years from 1999 2007, with combined gross enrol l2008 government expenditure wa s on education. The Life Expectancy Index was .720 with expectancy of 68.2

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156 years. GDP Index was .707; the GDP was $53.46 billion and $1,156 per capita. The HDI i n2008 averaged 53.9%, which translates to $28.8 billion in 2008, bringing the gross GDP to $82 billion and income per capita, $1,794 per person. Transcaucasia: Armenia, Azerbaijan, and Georgia Armenia boundaries today are those created in 1828 by the Russian Empire. Armenia signed its original Declaration of Independence from the Ottoman Turks on May 28, 1918. In 1920, the Soviet Red Army declared Armenia a Soviet Republic. Armenia reclaimed her sov ereignty from the USSR on August 23, 1990. Indu srun economy, as the second most densely populated region provided the needed labor, leaving relatively few acres available for agricultural production; o nly twenty percent of 1989 GDP was due to agricultural production. By 1993, A rwith dissolution of the Soviet economic system, the lack of a self sufficien cy du e to reliance on imports for food and exports for GDP, and, being land locked, reliance on relationships with neighboring countries for trade and travel. Azerbaijani and Turkish forces blocked rail traffic in 1992, nearly bringing its economy to a standst unwillingness to embrace a transparent, market based system (p. Economy). Through 2008, the political process remained unstable after public scrutiny for election fraud and corruption leading up to the e arly 2008 elections (p. Armenia) was 3.54 million, 93.0% of those ages 15 and above were literate, with a combined gross enrollment of 74.1%. Government expenditures on education were 20.50% of total government expenditures. The education index was 2.25. The Life Expe ctancy index was

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157 billion or $709 per capita, for a GDP index value of .574. The HDI in 1990 was .731, and the Shadow Economy equaled 40.3% of the total GDP. The GDP per capita hit its low in 1992, re gistering $392, and rebounded by 2002 to exceed levels prior to the USSR breakup. The average annual growth rate in GDP per capita from 1990 to 2008 was 3.2%. By 2008, the population of Armenia decreased by 440,000 to 3.08 million. On average, 99.5% of those ages 15 and above were literate in the years from 1999 2007, with combined 2008 go vernment expenditure was on education. The Life Expectancy Index was .810 with expect ancy of 73.6 years. GDP Index was .675, increased to $4.67 billion and $1,299 per capita. The HDI i n2008 averaged 48.7%, which translates to $2.27 billion in 2008 bringing the gross GDP to $7.82 billion and income per capita, $2,487 per person. Azerbaijan According to documents at the DOS, the Russian Empire created Azerbaijan Democratic s original Declar ation of Independence from the Ottoman Turks on May 28, 1918; in 1920, the Soviet Red Army declared Azerbaijan a Soviet Republic. Azerbaijan reclaimed her sovereignty from the USSR on August 30, 1990. Unlike many former Soviet states, Az erbaijan is economically viable, po ssessing two important advantages: vast oil reserves, and a coastline on the Caspian Sea. economic infrastructure and trade lost to neighboring countries following the dissolution of the Soviet economic system. As a Soviet Republic, it provided mostly agricultural products to the USSR to the detriment strengthening its industrial sector. Heavy industries are state owned and p rbated the turmoil and hyperinflation accompanied the rapid expansion into the oil industry. By

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158 2008, the elections largely conformed to the Organization for Sec urity and Cooper a tion in Europe (OSCE) standards (p. Azerbai jan) literate, with a combined gross enrollment of 72.1%. Government expenditures on education were 26.6% of total government expenditures. The education index was 2 .25. The Life Expe cbillion or $978 per capita, for a GDP index value of .654. The HDI in 1990 was .755, and the Shadow Economy equaled 36.3% of the total GDP. The GDP per capita hit its low in 1995, re gistering $488, and rebounded by 2006 to exceed levels prior to the USSR breakup. The average annual growth rate in GDP per capita from 1990 to 2008 was 2.8%. By 2008, the population of Azerbaijan grew to 8.68 mil lion. On average, 99.5% of those ages 15 and above were literate in the years from 1999 2007, with combined gross enrollment of 2008 government e x penditure was on education. The Life Expect ancy Index was .751 with expectancy of 70 years. GDP Index was .728, GDP increased to $5.73 billion and $1,825 per person. The HDI increased to .787 or 63.3%, which translates to $11.7 billion in 2008, bringing the gross GDP to $30.21 billion and i ncome per capita, $3,840 per person. Georgia sAfter 2,200 years of occupation and shifting e m pires, the population remains cohesive with over 82% of its population being ethnic South Caucasians and Georgian Orthodox ( p. History ). From 1961 through 1989, Georgia was one of the most prosperous states a

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159 E conomy). Georgia exported 100% of some fruits and vegetables to the USSR until 1990. Turkey is now its biggest trading partner. Georgia has the highest rate of Shadow Economy in the world, at 68%, with a government p. Economy ) New reforms and a flat tax structure increased tax collection from 17.8% in 2004 to 22.2% in 2008. literate, with a combined gross enrollment of 78.3%. Government exp enditures on education were 6.9% of total government expenditures. The education index was 2.25. The Life Expecta nor $1,572 per capita, for a GDP index value of .675. The HDI in 1990 was .739, and the Shadow Economy equaled 45.1% of the total GDP. The GDP per capita hit its low in 1994, registering $458, and as of the 2008 data collection, had yet to rebound to meet levels prior to the USSR breakup. The aver age annual growth rate in GDP per capita from 1990 to 2008 was .012%. In 2008, the population of Georgia decreased to 4.31 million. On average, 99.0% of those ages 15 and above were literate in the years from 1999 2007, with combined gross enrol lment of 2008 government expenditure was on education. The Life Expectancy Index was .777 with expectancy of 71.6 years. GDP Index was .641, GDP decreased to $5.47 billion and $1,249 per capita. The HDI i n2008 is the highest in the world, averaging 68.8%, which translates to $3.76 billion in 2008, bringing the gross GDP to $9.24 billion and income per capita, $ 2,108 per person.

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160 Group #2 Non USSR Soviet Influenced States Bulgaria Bulgaria first won its independence in 1908. Bulgaria sided with the Axis powers during Histor y ). Due to the B y] October 2002 o3.0% of those ages 15 and above were literate, with a combined gross enrollment of 72.6%. Government expenditures on education were 8.54%. The education index was 2.42. The Life Expectancy index was .770 and life expe ctancy at birth was 71.2 years. Bul index value of .732. The HDI in 1990 was .803. GDP per capita hit its low in 1997, registering $1,373, and rebounded in 2003 to exceed levels prior to the USSR breakup. By 2008, the populat ion of Bulgaria fell to 7.62 million. On average, 98.3% of those a ges 15 and above were literate in the years from 1999 2007, with combined gross enrollment of 2008 government expenditure was o n education. The Life Expectancy Index was .802 with expectancy of 73.1 years. GDP I ndex was .788, with GDP at $20.28 billion and $2,661 per capita. The HDI increased to .788 or 007 averaged 37.5%, which translates to $7.6 billion in 2007, bringing the gross GDP to $27.9 billion and i ncome per capita, $3,659 per person, an annual average of 2.4%. Germany Ge rman p rics, fiefdoms and independent cities and towns. In 962, the territories were part of the Holy Roman

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161 Empire until the Congress of Vienna in 1815, which created the German Confederation made up of 38 independent states. The German Empire began in 1871, because of the Franco Prussian war, and ended with the Treaty of Versailles in 1919, with the loss of the Alsace territory to s western border was re drawn partitioning traditionally Slavic territory to Poland in 1945. Soviet forces maintained its occupation including the western part of Germany starting from West Berlin (p. History). During the 1950s, East German citizens fle d to the West by the millions. The Soviets p. History ). As the Sov e. O n November 9, 1989, the wall was open to free travel ( p. History ). The economy in Germany increased by 126% from 1990 to 2007 yet remains plagued by high unemployment and high infrastructure costs in the western region (p. Economy). in 1990 with very high literacy 99%, and co mbined gross enrollment of 75.8%. G overnment expenditures on education were 9.2% of total government expenditures. The education index was 2.90. The Life Expectancy index was .842 capita, for a GD P index value of .932. The HDI in 1990 was .896. GDP per capita hit its low in 1993, registering $20,268, and rebounded in 1994 to exceed le v els prior to the USSR breakup. In 2008, the population of Germany increased to 82.1 million. On average, 99% o f those ages 15 and above were literate in the years from 1999 2007, with combined gross enrollment of 2008 government e x penditure was on education. The Life Expectancy Index was .913 with expe ctancy of 79.8 years. GDP I ndex was .975, and increased to $2.097 billion and $25,547 per capita. The HDI increased to .947

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162 16.1%, which translates to $337 billion in 2008, bringing the gross GDP to $2.435 billion and i ncome per capita, $29,660 per person. Former Czechoslovakia: Czech Republic, Slovakia According to DOS, the Czech and Slovak ethnic groups from the Hungarian Empire t ogether are considered the largest ethnic group in Southeastern Europe, and were identified by their religious affiliations, where up to 69% of whom where Roman Catholic depending on the area. Forty percent were atheist. The Jewish population was 120,000 in 1948. In 1989, 3,000 Jews rema ined, the balance lost to concentration camps (p. People). The Slovaks came from the Great Moravian Empire, while the Czechs came from the Hapsburg Empire becoming one cou ntry, Czechoslovakia, on October 28, 1918. Despite the ethnic and cultural differen ces, world leaders kept the two states together in the Pittsburg Agreement, in May 1918, signed by Czech oslovakian Prime Minister Thomas Masaryk, in an attempt to bridge the educational and economic inequality between the two regions. Josip Broz Tito, a C atholic Priest, lead the church and its Czechoslovakia through the medium of the Catholic Church. The Communist Party took over the country by force in February Du b p. History ). Dubcek was removed due to a sluggish economy, under the Warsaw Pact, in 1968 (p. Economy), however, it remained stagnate through the 1980s. The Ve l vet Revolution, prote sting 250 human rights violations by the government, started the demise of the communist strong hold, which ended December 1989 (p. History). The two countries, led by Klaus for the Czechs and Merciar for the Slovak people, formally separated in 1993. Cze ch Republic eated in October 1918 out of the Czech lands of the Austrian Empire and Slovakia, from the

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163 Hungarian Empire. The two were again split on December 16, 1992 into separate sovereign n aled Eastern European bloc, joining the Council for Mutual Economic Assistance (CMEA) and the Warsaw Pact. [The g]overnment fo lrecovery began in January of 1991, with large influx of funds from the IMF. By 1995, the economy was 96% off 1990 levels, due in part to the economic downturn in neighboring countries with dissolution of the Soviet economic sy stem. Through 2008, the pol itical situation remained consistent, with democratic elections in a market economic system ( p. 3775 Shadow Economy is 19.8. above were literate, wit h a combined gross enrollment of 71.7%. Of government expenditures, 16.9% were dedicated to public education. The education index was 2.68. The Life E x pectancy billion or $5,336 per capita, for a GDP index value of .859. The HDI in 1990 was .847, and the Shadow Economy equaled 13.1% of the total GDP. The GDP per capita hit its low in 1993, re gistering $4,710, and rebounded by 2000 to exceed levels prior to the USSR breakup. The average annual growth rate in GDP per capita from 1990 to 2008 was 2.2%. In 2008, the population of Czech Republic increased slightly to 10.42 million. On ave rage, 99.0% of those ages 15 and above were literate in the years from 1999 2007, with co m bined 2008 government expenditure was on education. The Life Expectancy Index was .856 with e xpectancy of 76.4 years. GDP Index was .916, at $79.15 bi llion and $5,336 per capita. The HDI increased to .903 or 36th in the world. Czech Repu b 1999 to 2008 averaged 19.8%, which translates to $15.67 billion in 2008, bringing the gross GDP up to $94.83 billion, or $9,0 97 per person.

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164 Slovakia According to DOS, Slovakia is still economically challenged due in part to the depen dence on exports of oil to the USSR during the Cold War at the expense of developing a diversified economy and world trading partners. Yugoslavia is surrounded on three sides by four nCrony Capitalism under Prime Mi 85% of its exports, with little infrastructure for sustainable economic ( p. Economy ). opulation was 5.28 million, 97.0% of those ages 15 and above were literate, with a combined gross enrollment of 71.7%. Of government expenditures, 16.9% were dedicated to public education. The education index was 2.72. The Life Expectancy index was .776 per capita, for a GDP index value of .811. The HDI in 1990 was .827, and the Shadow Economy equaled 15.1% of the total GDP. The GDP per capita hit its low in 1993 registering $3,967, and rebounded by 2001 to exceed levels prior to the USSR breakup. The average annual growth rate in GDP per capita from 1989 to 2008 was 6.4%. In 2008, the population of Slovakia increased slightly to 5.41 million. On average, 99. 0% of those ages 15 and above were literate in the years from 1999 2007, with combined 2008 go vernment expenditure was on education. The Life Expectancy Index was .827 with expectancy of 74.6 years. GDP Index was .885, at $46.45 billion and $8,591 per capita. The HDI increased to raged 19.7%, which translates to $9.1 billion in 2008, bringing the gross GDP to $55.6 billion and income per capita, $10,284 per person.

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165 The Balkans: Albania, Former Yugoslavia, and Hungary The Balkan countries that make up the southernmost peninsula in central Europe, include former Yugoslavia, Albania, T urkey, Cyprus, and Greece. Nine countries (as of 2007) have coastlines on this peninsula, used for trade and travel routes to most of Europe and Eurasia i ncluding the Soviet Empire. The nine countries are Croatia, Bosnia and Herzegovina, Slovenia, Albani a, Greece, Turkey, Cyprus, Bulgaria, and Montenegro (Elisseeff, 1998) Part of the Mo ngol Empire until overtaken in the late 1300s by the Ottoman Empire that ruled until 1923, the c ountries in this region were influenced by the Soviet Empire, contiguous with the Soviet Empire, and were under communist rule during a portion of the period from the beginning from WWI to 1990. Albania was the only Balkan country to be absorbed by the US SR (DOS, 2010, p. Ba lkans). Sachs & Warner (1995a, p. 5) include this region as Socialistic in a study economic policy. Albania created in 1385 by the Ottoman Empire and confirmed in 1912 in the Vlore Proclamation decla r(p. Albania) through occupations by Italy and Germany during WWII, and withdrew from the Wa r saw Pact in 1968 to isolate itself further from trade dependence on progressive nations ( p. Albania last of the C entral and Eastern European Countries to embark on democratic and free market r ep. Albania ) resulting in positive economic growth by the late 1990s. The economy still struggles with a negative trade balance, about 4:1; its economic health depends on tourism flows from neighboring countries and the economic health of the EU, its major trading partner, which ntered into a joint pledge for democratic elections and economic reforms. According to the OCSE,

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166 the 2003 and 2005 elections were improved over past elections, yet subject to voting and ca mpaign fraud (p. Albania) literate, with a combined gross enrollment of 68.8%. Of government ex penditures, 10.87% were dedicated to public education. The education index was 2.41. The Life Expectancy index was capita, for a GDP index value of .620. The HDI in 1990 was .784, and the Shadow Economy equaled 31% of the total GDP. The GDP per capita hit its low in 1992, registering $644, and r ebounded by 1999 to exceed levels prior to the USSR breakup. The average annual growth rate in GDP per capita from 1990 to 2008 was 3.34%. In 2008, the population of Albania shrank to 3.14 million. On average, 99.0% of those ages 15 and above were literate in the years from 1999 2007, with combined gross enrollment r atio of 67.8%. The education index was .886; 8.43 2008 government expenditure was on education. The Life Expectancy Index was .858 with expectancy of 76.5 years. GDP Index was .710, at $5.73 Billion and $987 per person. The HDI i n creased to .818 or mated underground economy from 1999 to 2008 averaged 36.3%, which translates to $2.1 billion in 2008, or $7.82 billion and Income per capita, $1,825. Former Yugoslavia The Yugoslavia Country Brief (DOS, 2010) reports the following on the People and P olitic al Highlights page. The Ottoman and Hapsburg Empires ruled over the geographic r e gion of Austria s granted the Kingdom of Y ugoslavia sovereignty, until the Axis powers occ u war saw the establishment of a Communist, federal Yugoslavia under the wartime leader, Josip p. Political Highlights

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167 1948, after Tito made several foreign policy decisions without consulting Moscow, Yugoslavia government i cp. Serbia terms of economic growth, and the 1999 NATO bombings further devastated its ec onomic infr astructure (p. Economy). Yugoslavia lies between the Adriatic Sea and three fo r mer Soviet states, Hungary, Romania, and Bulgaria. Five Yugoslav republics have coastlines on the Adriatic co ntrolling trade traffic from the east to the USSR (p. E conomy ). Disaggregated data on the percentage of government expenditures dedicated to education were not available for Y u goslavia. Bosnia and Herzegovina between ethnic groups, damaged or destroyed much of the economic infrastructure killing tho usands ( p. Economy ). EU troops remained deployed there through 2008, to aid progress toward transparency in politics and banking (p. History). and above were liter ate, with a combined gross enrollment of 65.8%. The education index was 2.34. The Life Expectancy index was .696, and life expectancy at birth was 66.7 years. Bosnia .753. The HDI in 1990 was .803, and the Shadow Economy equaled 28% of the total GDP. The GDP per capita hit its low in 1993, registering $388, and rebounded by 1994 to exceed levels prior to the USSR breakup. The average annual growth rate in GDP per ca pita from 1993 to 2008 was .94%.

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168 In 2008, the population decreased to 3.77 million. On average, 96.7% of those ages 15 and above were literate in the years from 1999 2007, with combined gross enrollment of 69.0%. The education index was .874; 15.60 % o 2008 government e xpenditure was on education. The Life Expectancy Index was .834 with expectancy of 75.1 years. GDP Index was .726, at $8.38 billion and $2,162 per capita. The HDI increased to .812 or 76th in the world. The estimated underground economy, from 1999 to 2008, averaged 34.6%, which translates to $2.9 billion in 2008, bringing the gross GDP t o $11.29 billion and income per capita, $2,910 per person. Serbia According to the DOS, Serbia, with its current geograp hic boundaries, became a princ ipality u n der Russian protection in 1829, and gained its first independence in 1878 at the Congress of Berlin. The communist party invaded Yugoslavia in 1941, under the direction of Josip Broz Tito, creating a satellite state for the party and a strong economy, able to remain sovereign during the Cold War year. During the communist years, the Serbian region was a regional military and economic power. Serbia maintained its continuity with Montenegro as one state until October 2006, with a peaceful split of the traditional geographic regions (p. Serbia). literate, with a combined gross enrollment of 65.8%. The education index was 2.34. The L ife $10.95 billion or $1,445 per capita, for a GDP index value of .814. The HDI in 1990 was .797. The Shadow Economy equaled 23.6% of the total GDP. The GDP per ca pita hit its low in 1993, registering $650, and has not rebounded to levels prior to the USSR breakup as of the 2008 data reporting period. The average annual growth rate in GDP per capita from 1990 to 2008 was .007%.

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169 In 2008, the population of Serbia increased to 9.15 million. From 1999 2007 96.4% of those a g es 15 and above were literate, with combined gross enrollment of 74.5%. The education 2008 government expenditure was on education. The Life Expectancy I ndex was .816 with expectancy of 73.9 years. GDP Index was .773, at $1.19 underground economy from 1999 to 2008 averaged 39.67%, which translates to $1.19 billion in 2008, bringing the gross GDP to $4.19 billion and i n come per capita, $1,763 per person. Montenegro Montenegro gained its independence as a principality at the Congress of Berlin in 1878. Montenegro was unified with Serbia while being occupied b y Austrian forces until WWI. Mo ntenegro gained its independence from Serbia on June 3, 2006. The elections of 2006 were the Adriatic, agricultural trade and t ourism have become primary economic industries ( p. Mont enegro ). population was 590,000, 92.3% of those ages 15 and above was literate, with a combined gross enrollment of 65.8%. The E ducation I ndex was 2.34. The Life Expectancy inde $848 thousand or $1,445 per capita combined with Serbia, for a GDP index value of .753. The HDI in 1990 was .815, and the Shadow Economy equaled 23.6% of the total GDP. The GD P per capita hit its low in 1999, registering $1,449, as a sovereign nation, rebound ing by 2003 to exceed levels prior to the USSR breakup. The GDP per capita average annual growth rate from 1997 to 2008 was 2.1%. In 2008, the population of Montenegro g rew to 620 thousand. On average, 96.4% of those ages 15 and above were literate in the years from 1999 2007, with combined gross enrol l2008 government

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170 expenditure was on education. The Life Expectancy Index was .817 with expectancy of 74 years. GDP Index was .795, at $1.45 billion and $2,335 per capita. The HDI increased to .834 or 65th 39.67%, whi ch translates to $570 thousand in 2008, bringing the gross GDP to $2.03 billion and income per capita, $3,261 per person. Croatia In 1868, the Archduke of Hungary assumed control over Croatia, to protect it from Tur kish control. Croatia enjoyed domestic autonomy until the end of WWI, when it joined the Kingdom of Yugoslavia. Internal conflicts from 1990 through 1999 cost Croatia heavily in the industrial sectors and crushed its tourism industry. The 1999 bombings by NATO forces further destroyed infrast ructure and trade throughout Yugoslavia. Its GDP in 1993 was only 60% of its on, 92.7% of those ages 15 and above were literate, with a combined gross enrollment of 65.3%. The education index was 2.34. The Life $25.1 billion or $5,285 per c apita, for a GDP index value of .805. The HDI in 1990 was .817, and the Shadow Economy equaled 24.6% of the total GDP. The GDP per capita hit its low in 1993, registering $3,469 and rebounded by 2002 to exceed levels prior to the USSR breakup. The avera ge annual growth rate in GDP per capita from 1990 to 2008 was 1.3%. In 2008, the population of Croatia decreased slightly to 4.43 million. On average, 98.7% of those ages 15 and above were literate in the years from 1999 2007, with combined gross e nrollm 2008 government expenditure was on education. The Life Expectancy Index was .850 with expectancy of 76 years. GDP Index was .847, at $30.18 billion, the per capita figure to $6,707. Th e HDI increased to

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171 raged 34.7%, which translates to $10.47 billion in 2008, bringing the gross GDP to $40.65 billion and income per capita, $9,168 per pe r son. Macedoni a The Treaty of Versailles laid out the geographic area of Macedonia, which partitioned parts of the country off to Bulgaria and Greece. Its constitution as an independent country took effect November 20, 1991. By 2008, Macedonia had met the criteria for membership to NATO. The economy is plagued with dated industrial infrastructure, and out migration of the skilled l abor. Civil war between ethnic Albanians in 2001 slowed economic progress (DOS, 2010, p. Macedonia). 1.91 million, 92.7% of those ages 15 and above were literate, with a combined gross enrollment of 65.8%. The education index was 2.34. The Life Expectancy index was .773, and life expectancy at birth was 71.4 years. Maced o was $3.93 billion or $1,919 per capita, for a GDP index value of .753. The HDI in 1990 was .782, and the Shadow Economy equaled 35.6 % of the total GDP. The GDP per capita hit its low in 1994, registering $1,578, and rebounded by 2006 to exceed levels prior to the USSR brea kup. The average annual growth rate in GDP per capita from 1990 to 2008 was .062%. In 2008, the population of Macedonia was 2.04 million. On average, 97% of those ages 15 and above were literate from 1999 2007, with combined gross enrollment of 70.1%. The ed u2008 government expenditure was on education. The Life Expectancy Index was .819 with expectancy of 74.1 years. GDP Index was .753, at $4.43 billion and $2,158 per capita. The HDI increased to .81 7 or 72 nd in the world. slates to $1.6 billion in 2008, bringing the gross GDP to $6.5 billion and income per capita, $2,940 per person.

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172 Slovenia According to DOS, part of the Hapsburg Empire until 1918, Slovenia joined other Slavic states to form Yugoslavia in 1929. Axis powers Hungary, Italy, and Germany occupied the area most nliterate, with a combined gross enrollment of 72.7%. The education index was 2.34. The Life $16.61 billion or $8,317per capita, for a GDP index value of .854. The HDI in 1990 was .853, and the Shadow Economy equaled 22.9% of the total GDP. The GDP per capita hit its low in 1992, registering $7,168, and rebounded by 1996 to exceed levels prior to the USSR breakup. The average annual growth rate in GDP per capita from 1990 to 2008 was 2.69%. I n 2008, the population of Slovenia increased to 2.02 million. On average, 99.7% of those ages 15 and above were literate in the years from 1999 2007, with combined gross enrol l2008 go vernment expenditure was on education. The Life Expectancy Index was .886 with expectancy of 78.2 years. GDP Index was .933, at $16.61 billion and $13,789 per capita. The HDI i n creased to .929 omy from 1999 to 2008 averaged 28%, which translates to $7.8 billion in 2008, bringing the gross GDP to $35.68 billion and i ncome per capita, $17,650 per person. Hungary The Treaty of Trianon created the Hungar ian borders in 1920, which divided the Austro Hungarian Empire into the Czechoslovakia, Yugoslavia, Austria, Hungary, and small parts o f P oland (DOS, 2010, p. Hungary ). The territory, excluding Austria, had remained constant from 895

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173 p. People). Hungary lost over 50% of its export market when the Soviet Union collapsed It is plagued with corruption, and its state owned industries cannot yet compete with free market competitors ( p. People ). In 1990, the population was 10.37 milli on, 97% of those above 15 were literate The combined gross enrollment was 67.7%. Government expenditures on education were 5.27%. The education index was 2.73. The Life Expectancy index was .740, life expectancy at birth was was $43.98 billion, $4,240 per capita GDP index was .812. The HDI in 1990 was .812 T he Shadow Economy equaled 22.3% of the total GDP. The GDP per capita low was $3,606 in 1993, rebounding in 1999 to exceed levels prior to the USSR breakup. The aver age growth rate in GDP per capita from 1990 to 2008 was 2.03%. In 2008, the population of Hungary had declined to 10.04 million. Average literacy of those 15 and above was 98.9% from 1999 2007, with combined gross enrollment of 90.2%. The education index was .960; 11.50% of Hungary 2008 government expenditure was on ed ucation. The Life Expectancy Index was .805 Life expectancy was 73.3 years. GDP Index was .874, with GDP at $62.39 billion and $6,216 per capita The HDI i n creased to .879 or 43rd in the 2008 averaged 25.8%, which translates to $16.1 billion in 2008, bringing the gross GDP to $78.49 billion and income per cap ita, $7,820 per person. Mongolia According to DOS, Chinggis (Geng on n o on military assistance from the Russian and Soviet Empires 1727 through 1989. The government began its transit ion to a democracy after three centuries of co m munist rule in 2008. Mongolia is the second largest land eters. With vast mineral deposits and a well developed agricultural sector, the ec onomy started to

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174 rebound after civil unrest du r ing the 2006 attempt to convert to a transparent market economy (p. Economy). Mongolia is nearly ethnically homogeneous : 94.9% Mongol, 5% Kazakh (CIA, 2010, p. People). million, 93.0% of those ages 15 and above were literate, with a combined gross enrollment of 65.5%. Of government expenditures, 17.96% were dedicated to public education. The education index was 2.42. The Life Expectancy index was .596 and life expecta capita, for a GDP index value of .35. The HDI in 1990 was .676. In 2008, the population of Mongolia increased to 2.64 million. On average, 97.3% of those ages 15 and above were literate in the years from 1999 2007, with combined gross enrol l2008 government expenditure was on education. The Life Expectancy Index was .687 with expectancy of 66.2 years. GDP In dex was .580. The GDP growth rate was 2.09% to $1.94 billion and $735 per cap i economy from 1999 to 2008 averaged 37.9% (Zhou, 2007, p. 23), which translates to $736 mi llion in 2008, bringing the gross GDP to $2.68 billion and income per capita, $1,014 per person.

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175 Group #3 Non USSR Central and Eastern Europe Neighboring Socialist Countries: Austria, Cyprus, Finland, Greece, Italy, Turkey Austria According to DOS, Th e Treaty of St. Germain granted independence to the Austrian R epublic in 1919, after over 700 years in the Hapsburg and Austrian Empires. Austria was annexed by Germany in 1938. It was free and independent again on October 25, 1955, after the signing of Political Conditions). According to the WTO, as of 2007, Austria was actively part of trade agreements with ten other countries in the sample set of countie s in this thesis (WTO, 2010g). Austria shares borders, over 95% of its borders, with former communist countries, and all of these are still socialist countries. liter ate, with a combined gross enrollment of 77.5%. Of government expenditures, 8.13% were dedicated to public education. The education index was 2.90. The Life Expectancy index was billion or $19,324 Shadow Eco nomy equaled 7.0% of the total GDP, the lowest percentage in the sample set. The GDP per capita hit its low in 1992, registering $19,861, and re bounded by 2004 to exceed levels prior to the USSR breakup. The average annual growth rate in GDP per capita from 1990 2008 was 1.8%. In 2008, the population of Austria increased to 8.34 million. On average, 99.0% of those ages 15 and above were litera te in the years from 1999 2007, with combined gross enrollment of 2008 government e x penditure was on education. The Life Expectancy Index was .915 with expectancy of 79.9 years. GDP I ndex was .989, GDP rose to $227.1 billion and $27,251 per capita. The HDI increased to .955 or n omy from 1999 2008 averaged 14.6%,

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176 which translates to billion in 2008, bringing the gross GDP to $260.4 billion and income per cap ita, $31,229 per person. Greece The Roman Empire conquered Greece in 146 BC, in the Battle of Corinth, and Consta ntinople (Istanbul, Turkey) was the capital of the Roman Empire until 1453 (Robinson, 1902, p. 356) Greece expanded its territory to include southern parts of present day Macedonia, Albania, and Bulgaria (DOS, 2010, p. History). Greece entered WWII fighting alongside the Allies, and then wa 1944. With the aid of the 1947 Truman Doctrine that pledged US support for Turkey and Greece against Soviet threats, Greece did not become a communist country and it remains in the territory of a n cient Greece (DOS, 2010, p. History). As of 2007, Greece exported over half of its total exported goods to countries in Central and Eastern Europe. Germany received 11.11% of its total, and 11.05% shipped to Italy, 7.28% shipped to Cyprus, and 6.74% to Bulgaria, and Turkey received 4.23%. Germany provided to Greece 13.73% of its imports, Italy 12.71% (p. Economy). Greece Panhellenic Socialist Movement (PASOK) prevailed in securing a socialistic government for Greece in 1981 and again in 2009 (p. Government). According to the WTO (2010h) as of 2007, Greece was actively part of trade agreements with eight members other counties in the SET. Greece is bounded substantially on its i n land borde rs, with Macedonia and Bulgaria. literate, with a combined gross enrollment of 77.4%. Of government expenditures, 6.95% were dedicated to public education. The educat ion index was 2.41. The Life Expectancy index was capita, for a GDP index value of .872. The HDI in 1990 was also .872, and the Shadow Economy equaled 23.7% of the total GDP. The GDP per capita hit its low in 1993, registering $9,723, and

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177 rebounded by 1995 to exceed levels prior to the USSR breakup. The average annual growth rate in GDP per capita from 1990 2008 was 2.3%. In 2008, the population of Greece grew to 11.24 million. On average, 97.1% of those a ges 15 and above were literate in the years from 1999 2007, with combined gross enrollment of 2008 government expenditure was on e ducation. The Life Expectancy Index was .902 with expectancy of 79.1 years. GDP Index was .944, at $170.83 billion and $15,203. The HDI increased to .942 or 25th whi ch translates to $51 billion in 2008, bringing the gross GDP to $221.92 billion and income per capita, $19,749 per person. Italy Italy was a Constitutional Monarchy from 1870 through 1922 after centuries of rule by the Holy Roman Empire. Italy joined the Triple Alliance with Austria Hungary and Germany from 1882 through 1914. Italy entered WWI on the side of the Allies in an attempt to regain te rr i tory lost to Austria. Mussolini began his quest for leadership in 1917, after serving in the infantry in th came to power and, over the next few years, eliminated political parties, curtailed personal libe rties, and installed a fascist dictatorship termed the Corp o ople and History), allied 1943, then had its economic infrastru c ture decimated as the battlefield of the Italian Campaign (p. History). Before WWII, thousa nds of unemployed Italians went to work in Germany in factories and shops to back fill the skill drain from German workers into the military. The Volkswagen plant alone employed fifteen hundred Italian men, who were then denied repatriation into Italy at the start of WWII, as the plant began producing armaments (Burleigh, 1996, p. 43) Italy has been a constitutional republic since 1942 (DOS, 2010 p. Government). Italy claimed itself a Pr o-

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178 tectorate State over Albania from June 23, 1917 until 1920, and over Montenegro from 1941 any sends 16.0% and Russia (2010g) as of 2007, Italy was actively part of trade agreements with nine sample set counties in on Curtain. Italy borders Slovenia and Austria on its inland border and is less than one hundred miles across the Adriatic Sea from six communist countries and trading partners: Croatia, Bosnia and Herzegovina, Montenegro, Macedonia, Albania, and Greece. terate The combined gross enrollment of 77.8%. Of government expenditures, 9.64% were dedicated to public education. The education index was 2.54. The Life Expectan cy index was .864 L capita, for a GDP index value of .923. The HDI in 1990 was .889 T he Shadow Economy equaled 23.4% of the total GDP. The GDP per capita hit its lo w in 1993, at $16,730, and r ebounded by 1994 to exceed levels prior to the USSR breakup. The average annual growth rate in GDP per capita from 1990 to 2008 was .8%. In 2008, the population of Italy grew to 59.83 million. On average, 98.9% of those ages 15 and above were literate in the years from 1999 2007, with combined gross enrollment of 2008 government expend i ture was on education. The Life Expectancy Index was .935 with expectancy of 81.1 years. GDP Index was .954, at $1,171.8 billion and $19,586 per capita. The HDI increased to .951 or 18th in the 2008 averaged 27.2%, or $318.7 bi llion in 2008, bringing the gross GDP to $1,490.6 bi llion and in come per capita $24,914 per person.

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179 Turkey The Republic of Turkey emerged in 1923 after the collapse of the Ottoman Empire. emands by the Soviet Union Turkey was not a communist country. Trade relations are strong with the Central and Easter E urail traffic in 1992, closing its borders to Armenia in 1993. As of 2009, Germany was the largest Russia was the largest export market at 14%; Germany was seco nd at 10%, and Italy fifth largest at 5.4%. In 2005, Turkey opened its new oil and gas pipeline with Azerbaijan and Georgia to transport up to a million barrels of Ca s pian Sea oil a day (p. Economy). A s of 2007, Turkey was actively part of trade agreemen ts with ten SET countries (WTO, 2010g). literate, with a combined gross enrollment of 55.0%. Government expenditures on education were 15.55%. The education index w as 1.82. The Life Expectancy index was .660 and life e xGDP index value of .742. The HDI in 1990 was .705, and the Shadow Economy equaled 20.5% of the total GDP (Yereli et al., 2007, p. 89) The GDP per capita hit its low in 1991, registering $3,293, and rebounded by 1992 to exceed levels prior to the USSR breakup. The average annual growth rate in GDP per capita from 1990 to 2008 was 2.2%. In 2008, the population of Turkey grew to 73.91 million. On average, 88.7% of those a ges 15 and above were literate in the years from 1999 2007, with combined gross enrollment of 2008 government expenditure on education was 14.74% of total government expenditures. The Life Expectancy Index was .779 with expe ctancy of 71.7 years. GDP Index was .812, GDP increased to $375 billion and $19,586 per capita.

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180 The HDI increased to .806 or 79th in the w i mated underground economy from 1999 2008 averaged 32.9%, which translates to $123 billion in 2008, bringing the gross GDP to $498.47 and income per capita $6,744 per person. Cyprus Cyprus is located in the Mediterranean Sea, near the B alkan Peninsula, 47 miles south of Turkey. Cyprus was under the protection of Great Britain as a Crown Colony since 1878, after centuries in the Ottoman Empire. The Zurich and London Agreement between the United Kin gdom, Greece and Turkey on August 16, 1 was taken by force by Turkish troops. As of 2006, Cyprus had a democratically elected co mmunist government and it remains a trading partner with much of Europe. As of 2007, the largest export mark ets for Cyprus were the UK, Greece, and Russia. The largest import markets are Greece, Italy, and Germany (DOS, 2010, p. Pe o ple and History). According to the U.S. Library of Congress (1991), in 1960, the census reported seventy seven percent of the popu lation of C yprus was Greek Cypriots, and twenty three percent Turkish Cypriots. In the five years after the invasion by Turkish troops, 65,000 Cypriots emigrated, more than a third of whom went to Greece and Britain. Although the government declared itse lf a democratic republic in 1964, it vernment controlled areas of Cyprus (p. Population) the early 1990s to protect native industries (p. Economy) According to the WTO, as of 2007, Cyprus was actively part of trade agreements with ten members SET counties (WTO, 2010g). literate, with a combined gross e nrollment of 62.4%. Government expenditures on education were 13.17% of total government expenditures. The education index was 2.27. The Life Expe cbillion or $10,6 84 per capita, for a GDP index value of .850. The HDI in 1990 was .849, and the

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181 Shadow Economy equaled 21% of the total GDP. The GDP per capita hit its low in 1991, regi stering $10,488, and rebounded by 1994 to exceed levels prior to the USSR breakup. T he average annual growth rate in GDP per capita from 1990 2008 was 1.9%. In 2008, the population of Cyprus grew to .86 million. On average, 97.7% of those ages 15 and above were literate from 1999 2007, with combined gross enrollment of 77.6. The educ ati 2008 government expenditure was on education. The Life Expectancy Index was .910 with expectancy of 79.6 years. GDP I n dex was .920; GDP doubled to $12.3 billion and $15,510. The HDI increased to .914 or 32nd in estimated underground economy from 1999 to 2008 averaged 29.4%, which translates to $3.6 bi llion in 2008, bringing the gross GDP to $15.92 billion and income per capita, $20,069 per person. Finland The geography occupied by Finland to day has been roughly the same f or about 900 years. From 1154 1809 subject to the Kingdom of Sweden, as the Grand Duchy of Finland, su bject to the Russian Empire until 1917, and the Independent Finnish Republic since. However, Finland was a pawn in the Mo lotov Ribbentrop non aggression pact between Germany and the the Soviet Union in the Winder War of 1939 Conti nuation War (1941 1944), Finland was a co p. History ) against ( p. History ). Finland was neutral after WWII, under the Finno Soviet Pact of Friendship that lasted from 1948 to 1992 ( p. History ). 62.4%, and a 99.6% adult literacy rate. Government expenditures on education were 11.9% of t otal government e xpenditures. The E ducation I ndex was 2.25. The Life Expectancy index was $19,916 per

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182 capita GDP index was .904. The HDI in 1990 was .959, and the Shadow Economy equale d 14.5% of the total GDP. The GDP per capita hit its low in 1993, registering $17,638, and r ebounded by 1997 to exceed levels prior to the USSR breakup. GDP per capita average annual growth rate from 1990 to 2008 was 1.9%. In 2008, the population of Finl and grew to 5.31 million. On average, 99% of those ages 15 and above were literate in the years from 1999 2007, with combined gross enrollment of 77.6. 2008 government expenditure was on education. The Life Expectancy Index was .77 with expectancy of 71.6 years. GDP Index was .920; GDP increased to $153.77 billion and $28,941 per person. The HDI increased to .959. Fi naverage underground economy from 1999 2008 was 18.5%, which tran slates to $28.44 billion in 2008, bringing the gross GDP to $182.23 billion and income per capita, $34,295 per person. Countries excluded from the Sample Set China China passes the first three rules of thumb: geographic, ethnolinguistic, and trade; but was communist with its own force of power. Unlike Mongolia, China operated a sovereign s oe more moderate Sov i(LOC, 2011a, p. Russia) Sweden, Switzerland, The Netherlands These countries pass the first t hree rules of thumb. According to the DOS (2011), Sw eden was officially neutral during both WWI and WWII ; it did support the war efforts on both Switzerland re mained neutral during the 20 th century, though it did defend its own air space du r-

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183 u trality at the start of both used heav ily for its trade routes. German forces invaded The Netherlands in 1940 which remained occupied until 1945, after which it became a foun ding member of NATO ( p. History ). Vietnam, Cuba, North Korea These countries are not included in the sample set. Whil e politically and economically aligned with the former USSR, none pass the geographic rule of thumb.

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184 Country Brief Summary The Sample Set of countries analyzed follows the grouping used by Schneider et al. (2010). However, the groupings between Centra l, Southern, and Eastern Europe, and Central and Western Asia vary greatly depending on the rise and decline of empires, armed conflict, and p oli t ical agenda. Countries of both the former Czechoslovakia and former Yugosl a via are included in the sample set as disaggregated data are widely available, and the standard for cross country analysis (HDR, 2009). The German Democratic Republic (GDR) is included the sample set in Group II, as post German reunification data aggregate GDR with West Germany (FDR). I nclu ding Mongolia in the sample set captures the ethnolinguistic strength of the Mongolian Empire that began in centered in Mongolia and spread outward toward Central and Easter Europe and south gnificant in Central and Easter E urope, while the Chinese emigration west is scant (DOS, 2010b, p. Mongolia). Group III is a challenge in the sample set. For all of the empirical data driven reasons stated above, and these few to follow, this group of cou ntries is substantially similar in that the end of the Cold War marked the end of an economic era, and an economic low point or trough. However, including these countries may add ambiguity to the results, or, it may make the results clearer. Countries th at were influenced by the Cold War but not part of the former USSR or one of its satellite states may have had fewer of the symptoms of economic growth handcuffs endured by communistic countries. In Group III, Austria, Greece, Italy, Turkey, and Cyprus o ccupy the area of the Roman Empire, and Finland was part of the Sweden after about 800 and the Swedish Empire after 1611. Austria became part of the Hapsburg Empire and Greece, Turkey, Italy and Cyprus r e mained part of the Eastern Roman Empire until 1453. Through WWI, the region experiences successive wars. Austria was occupied by a communist regime for 20 years, and is included in the sample set. Greece was occupied for four years by a communist regime is on the Balkan Peninsula, and has a

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185 socialist g overnment. Italian forces invaded Greece in the Greco Italian War in 1940 (DOS, 2010, p. Greece). Greece occupied p arts of Albania, Macedonia, Bulgaria, and Turkey during its expansionary years between 1913 and 1923 (p. History). Italy allied itself wit h a communist r egime for seventeen years, is geographically contiguous to two Socialist states, and is fewer than one hundred miles by sea from five more. Turkey is in the middle of the Balkan Peninsula and Cyprus is 47 miles south in the Mediterranean; e ach remain key trading partners and with former Soviet countries, and have strong geopolitical ties with Greece and Germany. Turkey occupied Cyprus in 1974, and has an elected communist government. As time goes on the hindsight becomes clearer, history will inform the community of r esearchers if this trough marked the start of a grand, 50 or 60 old blocks and resistances to steady growth are finally overcome. The forces making for econo mconditions for Take feature is a new sense of nationalism, and major changes in social values and a shift in the eco nomy (Rostow, 1991, p. 7). Empirical data are clear; the economic depression hit every Central and Eastern European country, and many others around the world, while t he economic dust se ttled along with that at the crumbled Berlin Wall. There are risks in including or excluding Groups II and III. Including both groups, the number in the sample set grows from fifteen to thirty six countries. In Basic Economics Guj ar a ti & Porter (2009, p. 345) ttenuate the collinearity problem... R 2 because the addition of unnecessary variables will lead to a loss in the efficiency of the estimato rs and (p.

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186 474) Given tha t the countries in Groups II and III exhibit the symptoms of an economic depre ssion (Burns & Mit chell, 1946) at the end of the Cold War and are Socialist by this thes stan d ards, they belong in the sample group. Given that these same countries endured extraord inary infl u ence by the Soviet Empire during the Cold War, and share borders, and to some degree, nationalities, religions, heritages, cultures, and histories, they should be included in the sample group. Lastly, since these countries are now, are again, or have continued to be trading partners and therefore, are linked economically, these counties belong in the sample group. Under fitting the model, o mit ting a relevant variable, essentially trades less precision for greater bias (p. 473). itute specif ication bias (p. 344)

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187 APPENDIX B: DATA RELIABILITY AND VALIDITY The overarching theory employed herein is contends that both endogenous and exogenous factors influence growth of an economy. Reliabi lthe research object, or the research goal. Romer (1994b, p. 21) suggests important policy impl ications of this theory. In addition to Romer (1986, 1990, 1994b, 1998b, 2007), the work of many scholars supports NGT. Recall that exogenous sources include among other factors, exo genous technological advancement, and knowledge spillovers from ou t side (Solow, 1957; North, 1994), and radical shaping events ( e.g., armed conflict, regime change, famine) (Rostow, 1991). Endo genous sources of growth include inertia (North, 1991a; Cortri ght, 2001), technology diffusion and adoption rates (Nelson et al., 1966, p. 71). Education (Sen, 1997; Barro et al., 2001), specializ ation, and knowledge externalities are endogenous with increasing returns (Arrow, 1962; North, 1991a; Phelps & Nelson 19 95). Finally, the national level government (Galbraith, 1951; Rostow, 1991; Barro, 2001a), governance, institutions, and the policies set for national level budgets and factors of development, education, and control over corruption are endogenous fa ctors (Rose Ackerman, 1978; Klitgaard, 1988; deLeon, 1993; Mauro, 1995; Johnston, 2005; Kaufmann, 2006). The major source of data is the Human Development Reports. Before we begin describing the data analysis method, we must test the data reliability, and the validity of both the construct and of the SET data. HDR data for the Central and Eastern European SET From the start of the Human Development project in 1990, the Human Development R eport for years 1990 to 2007 includes only that data which are deemed rel iable, timely, verifiable e.g., United Nations, IMF, World Bank, EuroStat, UNESCO, WHO, OECD) (2008c, p. 225) Therefore, as iterative r esearch uncovers new data, more countries are added, more indicators are added to the total

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188 DI, making it increasingly more reliable over time (HDR, 1990, pp. 111 112, 2008c, pp. 223 227) While the underlying data was beco ming more robust, the relativity of the component indices (used herein) has remained constant. In several circumstances, the HDR adopted a new methodology and provided tables reflecting the old and new da ta (p. 227) Test Equation 1.1: HD I Data Reliability First we will test the re liability of the Human Development Index in 1990 (HDI 1990 ) for the SET countries versus the population of countries following Gujarati & Porter (2009) We wi ll run a linear regression using the HDI component indices as independent variables, pr e dicting the overall HDI 1990 on SET. We will do the same for the HDI 2007 Null Hypothesis : H 0 : HDI 1990 1990 + LEI 1990 + EAI 1990 Maintained Hypothesis : H 1 : HD I 1990 = GDPI 1990 + LEI 1990 + EAI 1990 Test Equation 1.2: HD I Data Reliability Null Hypothesis : H 0 : HDI 2007 2007 + LEI 2007 + EAI 2007 Maintained Hypothesis : H 1 : HDI 2007 = GDPI 2007 + LEI 2007 + EAI 2007 If the SET data yield statistically significan t and similar results to the population set (POP) then the SET data are reliable, and the SET data reflects the variations identified in POP If the variation in the change in HDI from 1990 to 2007 is largely explained by the change in the HDI component indices from 1990 to 2007, my research construct was invalid, and we need not explain the any balance of variation with other variables (W eim er, 1998). In other words, if the 1990 to 2007 change in the GDPI, the LEI, and EAI, equally weighted, offer suff ior education spending to further explain the HDI change in the SET. If the change in HDI does not equal the change in the component indices, then we will reje ct the null hypothesis and accept

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189 for now that at least one other variable e x plains the variation in the change in HDI over our date range. We will run a linear regression for this test. Test Equation 2.1: HD I Construct Validity Testing Null Hypothesis : H 0 Maintained Hypothesis : H 1 EAI Lastly, we will run a linear regression using the Ic as the dependent variable, and the e, adding co nfidence to the internal validity of the data (Gujarati & Porter, 2009) Test Equation 2.2: HD I Construct Validity Testing Null Hypothesis : H O : Ic = HDI Maintained Hypothesis : H 1 : Ic HDI If we reject the null hypotheses, then we can conclude for now that the variation in the Ic from 1990 to 2007 cannot be explained by the HDI alone. Assuming the null hypotheses are rejected, this concludes the construct validity testing for the H DR variables. From this point, we can test the HDI data being confident that SET represents the population, and that the test methods are generalizable for the entire population set, and the data are e x ternally valid. Shadow Economy Data for the Centra l and Eastern Europe SET Schneider, 2010, provides the statistics on the SE data. To test the reliability of Sample SET countries, we will run a paired t test to compare the average SE for each year for the SET to the average for each year of the Populati on ( POP ) set (pp. 44 45) Test Equation 3.1: SE Data Reliability Null Hypothesis : H O Maintained Hypothesis : H 1 : Mean Yearly Ave of Set = Mean Yearly Ave of SET If we reject the null hypothesis, we can conclude temporarily that SET average represents the POP average for each year of the study at 95% confidence level. To test the validity of the

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190 construct, we will use a paired t test to test the average SE on the SET countries using the two sets of sampling specifications found in Schneider, 2010, looking for equality in the averages. a particular type of a structural equations model (SEM) to analyze and estimate the shadow economies of 162 cou n tries around the world (p. 10) Test Equation 3.2: SE Data Reliability and Validity Null Hypothesis : H O Maintained Hypothesis : H 1 : Mean Ave SE / Country MIMIC6 = Mean Ave SE / Country MIMIC7 If we reject the null hypothesis, we can conclude te mporarily (given both the MIMIC6 and MIMIC7 data sets are reliable for the population of countries (p. 17) ) that the SET data are valid, and we can test the size of the Shadow Economy as specified in the models used by Schneider. Educational Expenditure data for the SET To test the reliability of the SET of EE data, we start with the education data set for the entire population of countries EdStats, provided by UN ESCO (United Nations Education, Sc ience and Culture Organization) and the data methodology from the technical reference manual (1998) EdStats Reports for years 1970 to 2007 includes only that data which are deemed reli able, timely, verifiable and follow the Statistical Information System on E x penditure in Educati on (SISEE) methodology protocols (1998) Therefore, as it erative research uncovers new data, more countries are added, more indicators are added to the total report, and more data points are added In 1970, UNICEF gathered EE data on 7 1 countries data, 102 countries in 1975, and for the de c

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191 of those data points were from SET; too scant for statistical reliability. From 1980 to 1988, UNICEF gathered da ta on 152 countries and 294 data points, 19 countries from the SET reported 39 data points. From 1998 to 2007, UNESCO gathered 156 country reports on 785 EE data points, 29 countries from the SET reported 172 data points. The pre test EE uses the average EE from 1980 to 1988, and the post test EE uses the average EE from 1998 to 2007. To test the data validity of the SET, we will use the un paired t test for both the pre test and post test data. If we reject the null hypothesis, we can conclude for n ow that the SET for EE data is reliable and valid, and we can proceed with testing our theory. Test Equation 4: EE Data Validity Null Hypothesis: H O : Mean Ave EE of Data Sample SET Maintained : H 1 : Mean Ave EE of Data Set = Mean Ave EE Sample SET Assuming that the data are reliable, and the tests for construct validity confirm that the data gathered allow me to test my research object, then the construct validity is finished and we can move on to validating the data New Variables The last step in defining the data is to create new variables by including the factor of the SE on variables calculated using GDP. Recall, that subscript 1( 1 ) denotes an Official figure as stated by the country in National Income Accounting, subscript 2 ( 2 ) denotes the unofficial fi gures (Schneide r et al., 2010b) and subscript 3( 3 ) denotes the sum of official and unofficial figures (own calculations). In addition, the subscript for the year may be necessary on some data. Total GDP = GDP 3 the Shadow Economy 2 and the Official GDP = GDP O The equation, then, is GDP 1 + GDP 2 = GDP 3 and Ic 1 + Ic 2 = Ic 3 Each of the component measures of the HDI that include or are derived from the official GDP must be enhanced to i nclude the effects of that GDP in the Shadow Economy

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192 For example, the Official GDP 1990 in Ukraine was $243.35 billion in US equivalent do llars. The 16.3% SE 1990 was $39.66 billion, for a Total GDP 1990 of $283.01 billion. The e quation for Ic 3 1990 is $4,716 Ic 1 1990 + $769 Ic 2 1990 = $5,485 in equivalent US dollars per capita. Stated otherwise, the official data report that the Ukrainian people earn $4,716 US equi v alent dollars per person, but the SE makes up 16.3% of the total economy. Therefore, Ukrainians actually earn $5,485 US equivalent dollars on average. The second set of variables is the education funds that are a percentage of GDP 1 The o fficial Education Expenditure = EE 1 The SE effective reduction of EE = EE 2 T percentage of actual Government Expenditures = EE 3 Example: EE 1 Ukraine + EE 2 Ukraine = EE 3 Ukraine % of GE Alternate Equation, in US $Billions: Otherwise stated, the official figure for EE is 15.95% of government spending. However, s ince the SE effectively keeps 16.3% of the potential government revenue out of the gover n ment budgets, the official number is overstated, and education actually realizes, all else equal, 13.7148% of the Government Expenditure budget. The SE is keeping for its use about $750 per Ukrainian citizen per year, given the policy is to invest 15.95% of its expenditure budget into public education.

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193 Data and Construct Reliability and Validity Test Analysis Table 7. 4 Correlation Coefficient Matrix Correla te the change in Total Income per Capita, change in Total Education Expend itures, pre test Human Development Index, pre test Shadow Economy, change in Life Expectancy Index, change in Education Attainment Index, post test Shadow Economy, change in Shadow E conomy, Country Group Test : C orrelate $ Ic3, $ EEc3, HDI 1990 SE 2008 LEI, EAI, SE 1990 SE, Group (obs=36) Variable $ Ic3 $ EEc3 HDI 1990 SE 2008 LEI EAI SE 1990 SE Group $ Ic3 1.0000 $ EEc3 0.8619 1.000 0 HDI 1990 0.7710 0.5549 1.0000 SE 2008 0.6876 0.5285 0.6040 1.0000 LEI 0.0807 0.1046 0.1254 0.1996 1.0000 EAI 0.2757 0.2394 0.4848 0.2350 0.0567 1.0000 SE 1990 0.5863 0.5108 0.4 409 0.8660 0.0844 0.2308 1.0000 SE 0.1260 0.0618 0.3071 0.1641 0.1256 0.0313 0.3163 1.0000 Group 0.6549 0.5661 0.5648 0.6958 0.3222 0.0434 0.6077 0.0898 1.0000 A linear regression tested data reliability of the Central and Eastern Europe sample set (SET) from HDI 1990 as the dependent variable, versus the entire data set of countries. The HDI component indices are the independent variables, predicting the overall HDI 1990 on the sample countries.

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194 Test Equation 1.1: HD I Data Reliability Null Hypothesis : H 0 : HDI 1990 1990 + LEI 1990 + EAI 1990 Maintained Hypothesis : H 1 : HDI 1990 = GDPI 1990 + LEI 1990 + EAI 1990 Test: Linear Regression 95% Confidence Level Regress dependent variable HDI1990 against independent variables GDPI1990, LEI1990, and EAI1990 Source | SS df MS Number of obs = 34 ------------+ -----------------------------F( 3, 30) = 119.18 Model | .124129608 3 .041376536 Prob > F = 0.0000 Residual | .010415456 30 .000347182 R squared = 0.9226 ------------+ -----------------------------Adj R squared = 0.9148 Total | .134545064 33 .004077123 Root MSE = .01863 ----------------------------------------------------------------------------HDI1990 | Coef. Std. Err. t P>|t| [95% Conf. Interval] ------------+ ---------------------------------------------------------------GDPI1990 | .2874717 .0488198 5.89 0.000 .1877685 .3871749 LEI1990 | .3869957 .0748015 5.17 0.000 .2342306 .5397607 EAI1990 | .0525218 .0176434 2.98 0.006 .0164893 .0885544 _cons | .1577265 .0490298 3.22 0.003 .0575943 .2578586 The regression showed with 95% certainty that variation in the HDI component indices for the sample set explained 91.48% of the variation HDI 1990 Each of the independent variables proved to be significant, as well, with a high F score on 33 degrees o f freedom. Gujarati et al. (2009) 2 t y sis is p value is defined as the lowest signif i cance level at which a null hypothesis can p value is for EAI, .006, or EAI would be rejected at a 99.95% confidence level. The high F score and high adju sted R 2 scores are mostly a product of the ind ependent variables being components of the HDI. The rejected null hypothesis allows for temporarily concluding that HDI 1990 components of the sample set are a reliable predictor of the composite of the sample set HDI 1990 The same regression for the HDI 2007 tested the 2007 data.

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195 Test Equation 1.2 Null Hypothesis : H 0 : HDI 2007 2007 + LEI 2007 + EAI 2007 Maintained Hypothesis : H 1 : HDI 2007 = GDPI 2007 + LEI 2007 + EAI 2007 Test: Linear Regression 95% Con fidence Level Regress dependent variable HDI 2007 using independent variables GDPI 2007 LEI 2007 and EAI 2007 Source | SS df MS Number of obs = 35 ------------+ -----------------------------F( 3, 31) =44499.97 Model | .182026005 3 .060675335 Prob > F = 0.0000 Residual | .000042268 31 1.3635e 06 R squared = 0.9998 ------------+ -----------------------------Adj R squared = 0.9997 T otal | .182068273 34 .005354949 Root MSE = .00117 -----------------------------------------------------------------------------HDI2007 | Coef. Std. Err. t P>|t| [95% Conf. Interval] ------------+ --------------------------------------------------------------GDPI2007 | .3358158 .0025453 131.93 0.000 .3306245 .3410071 LEI2007 | .3296197 .00406 81.19 0.000 .3213392 .3379002 EAI2007 | .3338652 .0067689 49.32 0.000 .3200599 .3476706 _cons | .0001469 .0064856 0.02 0.982 .0130806 .0133744 The results of this 2007 test are more significant, as the data are a product of 16 iterations of HDI Reports, and with more data points on mor e countries by the international research age ncies. The three component indices on 36 countries in the Central and Eastern Europe are each significant past the 99.99% level, and the overall regression describes 99.97% of the variation in the HDI 2007 The rejected null hypothesis allows for temporarily concluding that the components contribute to the variance in the composite, and the data in the sa m ple set are reliable. Wainer, et al. 1998 ( in Golafshani) ask to identify validity, whether the data and re search (2003. p. 2) The on education budgets and individual income. In the prior two tes ts the data are, with 95% certainty or greater, valid and reliable for the sample set in 1990 HDI. For the 2007 data sample set, the HDI comp o2007 at over 99.9% level of certainty.

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196 Con struct Validity Testing indices, a rejected the null hypothesis allows for temporarily accepting that at least one other va rTest Equ ation 2.1: HD I Construct Validity Testing Null Hypothesis : H 0 Maintained Hypothesis : H 1 Test: Linear Regression 95% Confidence Level R egress dependent variable HDI using independent variables GDP I, LEI, and EAI Source | SS df MS Number of obs = 34 ------------+ -----------------------------F( 3, 30) = 7.23 Model | .013050195 3 .004350065 Prob > F = 0.0009 Residual | .018060088 30 .000602003 R squared = 0.4195 ------------+ -----------------------------Adj R squared = 0.3614 Total | .031110282 33 .000942736 Root MSE = .02454 ----------------------------------------------------------------------------HDI | Coef. Std. Err. t P>|t| [95% Conf. I n terval] ------------+ ---------------------------------------------------------------GDPI | .1223597 .0547086 2.24 0.033 .0106298 .2340895 LEI | .2706155 .0781373 3.46 0.002 .1110379 .4301931 EAI | .0006193 .0005106 1.21 0.235 .0004234 .0016621 _cons | .0340377 .0065947 5.16 0.000 .0205 696 .0475058 The data show that with 95% certainty, 36% of the variation in the change in the HDI from 1990 to 2007 is attributable to the change in the equally weighted component indices. Of these results, the change in the Educational At tainment Index (EAI) produced the least signif i cant result. This result suggests that the effect of the EAI on the variation in the HDI for this sample of countries is unsure. This result also supports testing alternative education measures that may add explanatory power to the equation. To test whether Ic is a sufficient proxy for individual human development, as part of the co n struct validity testing with the sample data, a linear regression analysis substituting Ic as the dependent variable, and the

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197 Test Equation 2.2: HD I Construct Validity Testing Null Hypothesis : H O : Ic = HDI Maintained Hypothesis : H 1 : Ic HDI Test: Linear Regression 95% Confidence Level Regress dependent variable Ic with independ ent variable HDI Source | SS df MS Number of obs = 32 ------------+ -----------------------------F( 1, 30) = 8.19 Model | 23.1670932 1 23.1670932 Prob > F = 0.0076 Residual | 84.9092157 30 2.83030719 R squared = 0.2144 ------------+ -----------------------------Adj R squared = 0.1882 Total | 108.076309 31 3.48633254 Root MSE = 1.6824 ----------------------------------------------------------------------------Ic | Coef. Std. Err. t P>|t| [95% Conf. Interval] ------------+ ---------------------------------------------------------------HDI | 31.72859 11.09 2.86 0.008 9.079775 54.3774 _cons | .8248046 .6674307 1.24 0.226 .5382707 2.18788 A rejected null hypothesis allows us to conclud e temporarily that the variation in Ic from 1990 to 2007 cannot b e explained by HDI from 1990 to 2007 alone. This result concludes the construct validity testing. With a t value of 2.86, an F score of 8.19 and 31 degrees of fre edom, this test passes the 2 t Rule of Thumb. Now we can test the HDI sample d a ta confide nt that the SET represents the population the test methods are generalizable for the population of cou ntries and the data are externally valid.

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198 Shadow Economy Data for the Central and Eastern European SET To test the reliability of the SET, we will run a paired t test to compare the average SE for each year for the Sample Set to the average for each year of the Set Test Equation 3.1: SE Data Reliability Null Hypothesis : H O : Mean Yearly Ave of SE Set = Mean Ave of SE SET Maintained Hypothesis : H 1 : Test: Two Sample Unpaired t test 99.9% Confidence Level Compare the Mean of the SE Set to SE SET. Variable | Obs Mean Std. Err. Std. Dev. [99.9% Conf. Interval] --------+ ------------------------------------------------------------------AVE70SET | 29 12.94213 .7020142 3.780462 10.363 15.52127 AVE07set| 156 15.25357 .3839153 4.7951 13.96576 16.54138 --------+ ------------------------------------------------------------------combined | 185 14.89124 .3468313 4.717416 13.73137 16.05111 --------+ -------------------------------------------------------------------diff | 2.311439 .9411754 5.459185 .8363066 -----------------------------------------------------------------------------diff = mean(AVE07SET) mean(AVE07Set) t = 2.4559 Ho: diff = 0 degrees of freedom = 183 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.0075 Pr(|T| > |t|) = 0.0150 Pr(T > t) = 0.9925 In this case, the null hypothesis is maintained. The report above maintain that there is a .992% probabil ity that the true average of the Set is greater than the average of the Sample Set, yet still remains in the acceptance area for the null hypothesis, we can conclude temp o rarily that the sample set average represents the POP average for each at 99.9% confi dence level. To test the validity of the construct, an un paired t test compares the average SE on the same countries on the same years using the two sets of sampling specifications found in Schne ider, 2010, looking for equality in the averages between t wo sets of specifications.

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199 Test Equation 3.2: SE Data Reliability and Validity Null Hypothesis : H O Maintained Hypothesis : H 1 : Mean Ave SE / Country MIMIC6 = Mean Ave SE / Country MIMIC7 Te st: Two Sample Paired t test 95% Confidence Level Compare the Mean of the Ave SE MIMIC6 to Mean Ave SE MIMIC7 set. Variable | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] --------+ ------------------------------------------------------------------MIMIC7 | 23 33.56957 2.517664 12.07429 28.34825 38.79088 MIMIC6 | 23 38.67391 2.421586 11.61352 33.65185 43.69598 --------+ ------------------------------------------------------------------diff | 23 5.104348 1.050569 5.038351 7.283094 2.925602 -----------------------------------------------------------------------------mean(diff) = mean(MIMIC7 MIMIC6) t = 4.8587 Ho: mean(diff) = 0 degrees of freedom = 22 Ha: mean(diff) < 0 Ha: mean(diff) != 0 Ha: mean(diff) > 0 Pr(T < t) = 0.0000 Pr(|T| > |t|) = 0.0001 Pr(T > t) = 1.0000 In the case above, the null hypot hesis is maintained. The report above maintains that there is a 1 % probability that the true average of the MIMIC6 of the Sample Set is greater than the average of MIMIC7 of the Sample Set, well past the rejection area for the null hypothesis. The tempor ary conclusion is that the Sample Set average MIMIC6 is lower than the Sample Set Average for MIMIC7 (given both the MIMIC6 and MIMIC7 data sets are reliable for the popul ation (p. 17) ). Further, that the sample set of data are valid, and test the size of the SE as specified in the models used by Schneider. To test the construct that the SE affects the Ic, the linear regression sets Ic as the d ependent variable a nd the pre and posttest SE as the independent variable. Test Equation 3.3 : Shadow Economy variables. Null Hypothesis: H O : Ic = SE 1990 + SE 200 8 Maintained Hypothesis: H 1 : Ic 1990 + SE 200 8

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200 Test: Linear Regression 95% Confidence Level Regres s dependent variable Ic with independent variables SE 1990 and SE 200 8 Source | SS df MS Number of obs = 36 ------------+ -----------------------------F( 2, 33) = 14.82 Model | 157229888 2 78614943.8 Prob > F = 0.0000 Residual | 175083260 33 5305553.32 R squared = 0.4731 ------------+ -----------------------------Adj R squared = 0.4412 Total | 332313147 35 9494661.35 Root MSE = 2303.4 Dch3Ic | Coef. Std. Err. t P>|t| [95% Conf. Interval] ------------+ ---------------------------------------------------------------SE90 | 13.29645 91.45118 0.15 0.885 172.76 24 199.3553 SE08 | 183.3104 64.37775 2.85 0.008 314.2879 52.33289 _cons | 9196.633 1283.722 7.16 0.000 6584.882 11808.39 Rejection of the null hypothesis supports the construct and suggests a stronger re latio nship between the 200 8 Shadow Economy figures than the 1990 figures. Only 44 .12 % of the variation in the dependent variable is explained with the two SE variables. Regressing each sep arately as suggested by Gujarati et al. (2009) shows the following results. Test Equation 3.4 : Shadow Economy variables. Null Hypothesis : H O : Ic = SE 1990 Maintained Hypothesis : H 1 : Ic 1990 Test: Linear Regression 95% Confidence Level Regress dependent variable Ic with independent variable SE 1990 Source | SS df MS Number of obs = 36 ------------+ -----------------------------F( 1, 34) = 17.80 Model | 114213558 1 114213558 Prob > F = 0.0002 Residual | 218099590 34 6414693.81 R squared = 0.3437 ------------+ -----------------------------Adj R squared = 0 .3244 Total | 332313147 35 9494661.35 Root MSE = 2532.7 Dch3Ic | Coef. Std. Err. t P>|t| [95% Conf. Interval] ------------+ ---------------------------------------------------------------SE90 | 212.2003 50.28924 4.22 0.000 314.4003 110.0002 _cons | 8312.048 1369.584 6.07 0.000 5528.718 11095.38 In this test, the null hypothesis is maintained. The SE 1990 explains about 32.4% of the variation i n the Ic. The results are statistically significant at 95% level of certainty.

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201 Test Equation 3.5 : Shadow Economy variables. Null Hypothesis : H O : Ic = SE 200 8 Maintained Hypothesis : H 1 : Ic 200 8 Test: Linear Regression 95% Confidence Level Regress dependent variable Ic with independent variable SE 200 8 Source | SS df MS Number of obs = 36 ------------+ -----------------------------F( 1, 34) = 30.49 Model | 157117731 1 157117731 Prob > F = 0.0000 Residual | 175195416 34 5152806.35 R squared = 0.4728 ------------+ -----------------------------Adj R squared = 0. 4573 Total | 332313147 35 9494661.35 Root MSE = 2270 Dch3Ic | Coef. Std. Err. t P>|t| [95% Conf. Interval] ------------+ ---------------------------------------------------------------SE08 | 175.2049 31.72893 5.52 0.000 239.6858 110.7239 _cons | 9243.678 1224.261 7.55 0.000 6755.681 11731.68 The null hypothesis is rejected, and the temporary conclusion is that 45. 73 % of the vari ation in the Ic can be attributed to the variations in the SE 200 8 With a t value of 5.52 an F score of 30.49 and 3 5 degrees of freedom, this test passes the 2 t Rule of Thumb and the negative sign is the anticipated correct sign

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202 Educational Expenditure data for the SE T Test Equation 4 : EE Data Validity ; Comparing the means of the Set to the Mean of the Sample Set Recall that pre test EE uses the average EE from 1980 to 1988, and the post test EE uses the a v erage EE from 1998 to 2007. An un paired t test compares the variances using a ratio. Null Hypothesis : H O : Maintained Hypothesis : H 1 : Mean Ave EE of set = Mean Ave EE SET Test: Two Sample Unpaired t test 99.9% Confidence Level Compare the Mean of the Ave EE Set to EE SET. Variable | Obs Mean Std. Err. Std. Dev. [99.9% Conf. Interval] --------+ -------------------------------------------------------------------AVE EE SET| 19 15.65927 1.678358 7.315792 9.077346 22.2412 AVE EE Set 152 14.07912 .4289228 5.288115 12.63 96 15.51863 --------+ -------------------------------------------------------------------combined | 171 14.25469 .4240829 5.545604 12.83457 15.67481 --------+ -------------------------------------------------------------------diff | 1.580154 1.347941 2.934134 6.094442 -----------------------------------------------------------------------------diff = mean(AVE EE SET) mean(AVE EE Set) t = 1.1723 Ho: diff = 0 degrees of freedom = 169 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.8786 Pr(|T| > |t|) = 0.2427 Pr(T > t) = 0.1214 In this case, the null hypothesis is maint ained. The report above maintain that there is a .87% probability that the true average of the EE for the SET et is less than the Total Set, and .1214% chance that it is greater than the average of the Total Set, yet still, it remains well within the acc eptance area for the null hypothesis. The temporary conclusion is that the SET variance average represents the Total Set variance average for each at 99.9% conf i dence level (given that the Educational Expenditure of the Total Set is reliable (EdStats, 2010j; 17) ). Further, that the sample set of data from the Central and Eastern European countries are valid, and that we can a ttempt to measure the degree of change in the Ed u cation Expenditures.

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203 New Variables Equation: Ic 1 + Ic 2 = Ic 3 For example, the Official GDP 1990 in Ukraine was $243.35 billion in US equivalent do llars. The 16.3% SE 1990 was $39.66 billion, for a Total GDP 1990 of $283.01 billion. The equation for Ic 3 1990 is $4,716 Ic 1 1990 + $769 Ic 2 1990 = $5,485 in equivalent US dollars per capita. Stated otherwise, the official data say that the Ukrainian people earn $4,716 US equivalent dollars per person, but the SE makes up 16.3% of the total economy. Therefore, Ukrainians actually earn $5,485 US equivalent dollars. Equation: Example: EE 1 Ukraine + EE 2 Ukraine = EE 3 Ukraine % of GE Alternate Equation, in US $Billions: Otherwise stated, the official figure for EE is 15.95% of government spending. However, since the SE effectively keeps 16.3% of the potential government revenue out of the gover n ment budgets, the official number is oversta ted, and education actually gets to spend, all else equal, 13.7148% of the GE budget. The SE is keeping for its use about $750 per Ukrainian citizen per year, given the policy is to invest 15.95% of its expenditure budget into public education. Regional Data Comparison for Sample Set Validity Research Question 1 Research Question 1.1: Are the HDI and the change in the Income per capita correlated at .5 or higher? To test this construct with our data, we can run the correlation coefficient test. If we r eject the null hypothesis, then we can conclude for now, that the correlation between the Human Development Index from 1990 to 2008 and the change in income per capita, adjusted for SE, (Ic 3 ), is less than .5, consistent with the rule used in Wong, (2007b)

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204 Hypothesis 1. 1: The correlation coefficient of Ic 3 from 1990 to 2008 and HDI from 1990 to 2008 is less than .5. Equation 1.1 Null Hypothesis: H O : Maintained Hypothesis: H 1 : The correlation coefficient is .2495, which is significantly less than the benchmark of .5. On a one tailed test, the t statistic is .501, well within the acceptance region of < .1697 at 30 d egrees of freedom at the 95% confidence level. For now, we maintain that the correl a tion between the change in the HDI is correlated with the change in Ic3, but at .2495, the d egree of association is weak. Below, the scatter graph shows the weak correlation. To test the correlation between the Life Expectancy Index and the Educational Attai nment Index, equally weighted (the weights in the HDI are equally weighted), we take the GDP index out, and re run the correlation coefficient. Hypothesis 1.2 : The correlation coefficient of Ic 3 from 1990 to 2008 and HDI co mponent i n dices, LEI + EAI from 1990 to 2008 is less than .5. Equation 1.2 Null Hypothesis: H O : Maintained Hypothesis : H 1 : The correlation coefficient is .0015, which is significantly less than the benchmark of .5. On a one tailed test, the t statisti c is .501, well within the acceptance region of < .1697 at 30 d egrees of freedom at the 95% confidence level. For now, we maintain that the correl a tion between the change in the HDI is very slightly negatively correlated with the change in Ic3, at .0015 B elow is the scatter graph depicting the correlation between the Change in Income per Capita and the life expectancy and educational attainment indices.

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205 Research Question 2 Does governance corruption negatively affect Individual Income? (Governance c orru ption is measured by the average Shadow Economy from 2000 2008, and Education expenditure is measured with the proxy EEc 3 which includes the effect of the shadow economy on the total government expenditures). A linear regression comparison of the R 2 tests Research Question 2, using Ic 3 as the dependent and HDI 1990 as the independent variable. HDI 1990 is the pre test or legacy measure, the starting point in human development m easurements, for the Sample Set Hypothesis 2: The adjusted R 2 resulting from a linear regression of HDI against the Ic 3 is higher than the adjusted R 2 resulting from a linear regression of HDI and SE 2008 against the Ic 3 Equation 2.1 Null Hypothesis : H O : Ic 3 HDI 1990 Maintained Hypothesis : H 1 : Ic 3 =HDI 1990 Test: L inear Regression 95% Confidence Level Regressed dependent variable Ic 3 using independent variable HDI 1990 Source | SS df MS Number of obs = 36 ------------+ -----------------------------F( 1, 34) = 49.83 Model | 197539878 1 197539878 Prob > F = 0.0000 Residual | 134773269 34 3963919.67 R squared = 0.5944 ------------+ -----------------------------Adj R squared = 0.5825 T otal | 332313147 35 9494661.35 Root MSE = 1991 -----------------------------------------------------------------------------ch3tic | Coef. Std. Err. t P>|t| [95% Conf. Interval] ------------+ --------------------------------------------------------------hdi90 | 36596.02 5184.047 7.06 0.000 26060.77 47131.27 _cons | 26219.57 4126.185 6.35 0.000 34604.99 17834.15 Post Estimation Statistics for Regression Wh ite's test for Ho: homoscedasticity against Ha: unrestricted heteroscedasticity chi2(2) = 12.49 Prob > chi2 = 0.0019

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206 Cameron & Trivedi's decomposition of IM test Source | chi2 df p --------------------+ ----------------------------Heteroskedasticity | 12.49 2 0.0019 Skewness | 7.16 1 0.0075 Kurtosis | 1.35 1 0.2460 --------------------+ ---------------------------Total | 21.00 4 0.0003 Information Criteria Model | Obs ll(null) ll(model) df AIC BIC -----+ --------------------------------------------------------------. | 36 339.767 32 3.5223 2 651.0447 654.2117 The regression output shows a high F score at 49.83 with 35 degrees of freedom, at most, 58.25% of the variation in the Ic 3 can be explained by the variation in the pre test HDI, and the t value of the HDI relations 2 t Rule of off involved to obtain m (Gujarati & Porter, 2009, p. 828) X 2 of 12.49 on 2 degrees of freedom. The IM test co nfirms highly left skewed data at 7.16 and a slightly short and fat (platykurtic) kurtosis distribution at 1.35. The AIC is 651.0447. The anal y sis suggests rejecting the null hypothesis, confirming a significant relationship. The next test is a comparison of the R 2 values between this equation and a second equation adding SE 2008 as an explanatory variable. Equation 2.2 Null Hypothesis: H O : R 2 regress Ic 3 with HDI 1990 R 2 regress Ic 3 with HDI 1990 and SE 2008 Maintained Hypothesis: H 1 : R 2 regress Ic 3 with HDI 1990 < R 2 regress Ic 3 with HDI 1990 SE 2008

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207 Test: Linear Regression 95% Confidence Level Regressed dependent variable Ic 3 using independent variables HDI 1990 and SE 2008 Source | SS df MS Number of obs = 36 ------------+ -----------------------------F( 2, 33) = 33.80 Model | 223306328 2 111653164 Prob > F = 0.0 000 Residual | 109006819 33 3303236.93 R squared = 0.6720 ------------+ -----------------------------Adj R squared = 0.6521 Total | 332313147 35 9494661.35 Root MSE = 1817.5 ----------------------------------------------------------------------------ch3tic | Coef. Std. Err. t P>|t| [95% Conf. Interval] ------------+ ---------------------------------------------------------------hdi90 | 26579.5 5937. 794 4.48 0.000 14498.97 38660.03 se08 | 89.02451 31.87514 2.79 0.009 153.875 24.17404 _cons | 15005.97 5505.278 2.73 0.010 26206.54 3805.401 Post Estim ation Statistics for Regression White's test for Ho: homoscedasticity against Ha: unrestricted heteroscedasticity chi2(5) = 12.09 Prob > chi2 = 0.0336 Cameron & Trivedi's decomposition of IM test Source | chi2 df p --------------------+ ---------------------------Heteroskedasticity | 12.09 5 0.0336 Skewness | 6.49 2 0.0389 Kurtosis | 2.01 1 0.1560 --------------------+ ----------------------------Total | 2 0.59 8 0.0083 Model | Obs ll(null) ll(model) df AIC BIC -----+ --------------------------------------------------------------. | 36 339.767 319.703 3 645.406 650.1566 The regression output shows a lower yet still very high F score at 33.80 with 35 degrees of freedom, at most, 65.21% of the variation in the Ic 3 can be explained by the variation in the pre test HDI, and both t values HDI 199 0 and SE 2008 variables are high and significant at 4.48 and 2.79. This test passes the 2 t autocorrelation with X 2 of 12.09 on 5 degrees of freedom. The IM test confirms a left skewed d ata at 6.49 and less platykurtic at 2.41. The AIC is lower, at 645.406, which is preferred to the

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208 (p. 494) The analysis suggests rejecting the null hypothesis, confirming a significant relationship on the second equation. A comparison of the R 2 test suggests rejecting the null hypothesis, and confirming for now that the R 2 of the augmented, second equation is higher, from 58.25% to 65.21%. In add ition, the entire equation is more robust with a low er RMSE, lower AIC, less skewness, and no autocorrelation. The F score, which is lower yet still very high, explains that the shape of the di stribution is flatter. The rejected hypothesis suggests a temporary conclusion in favor of the SE 2008 per country included in the regression with the HDI 1990 explains more of the variation in Ic 3 than does the HDI 1990 alone. This finding would be consistent with the theory that corruption hinders economic development, and of the findings of Schne i der, et al. (2010 ), Kauffmann, et al. (2008), Johnston, (2007), and other scholars.

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209 Research Question 3 Does governance corruption negatively affect Education Expenditure? (Governance co rruption is measured by the average Shadow Economy from 2000 2008, and Education exp enditure is measured with the proxy EE 3 ). A linear regression tests the effects of corruption on EEc 3 by setting the change in EEc 3 ( EEc 3 ) as the dependent variable and the Shadow Eco nomy in 2008, SE 2008 as the dependent variable. Hypothesis 3: The variation in the EEc 3 from 1990 to 2008 is not explained by the va riation in SE 2008 Equation 3 Null Hypothesis : H O : EEc 3 2008 Maintained Hypothesis : H 1 : EEc 3 = SE 2008 Test: Linear Regression 95% Confidence Level Regressed dependent variable $ EEc 3 using independent variable SE 2008 Source | SS df MS Number of obs = 36 ------------+ -----------------------------F( 1, 34) = 13. 17 Model | 2374199.26 1 2374199.26 Prob > F = 0.0009 Residual | 6127530.8 34 180221.494 R squared = 0.2793 ------------+ -----------------------------Adj R squared = 0.2581 Total | 8501730.06 35 242906.573 Root MSE = 4 24.53 -----------------------------------------------------------------------------$ EEc | Coef. Std. Err. t P>|t| [95% Conf. Interval] ------------+ ---------------------------------------------------------------se08 | 21.53734 5.933854 3.63 0.001 33.59639 9.478302 _cons | 1080.751 228.9578 4.72 0.000 615.4524 1546.049 The test results suggest rejecting the null hypothesis, maintaining that the effects of the Shadow Economy on the change in Education Expenditures per person stated in dollars, SE 2008 on $ EEc 3 are statistically significant. In addition, 25.81% of the variation in the change in Educ ation Expenditures can be explained by variation in the Shadow Economy. The F sco re is 13.17 with 35 degrees of freedom and the t value is 3.63 for SE 2008 The RMSE is 424.53. This test ation E x penditures. Figure 4.4 shows the effects of the Shadow Economy on Income per Capita.

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210 Research Question 4 Do the pre test HDI, governance corruption, and education expenditure explain the change in Income per capita? (Corruption is measured by the average Shadow Economy from 2000 2008, and Education ex penditure is measured with the proxy EEc 3 which includes the e ffect of the shadow economy on the total government expenditures). The null hypothesis asserts that there is no relationship between the $ Ic 3 and the expla natory variables, SE 2008 and the per centage of change between the EEc 3 pretest and the posttest values, EEc3 1990 and EEc 3 2008 Gujarati, et al. explains and supports the practice of adding vari ables to seek higher degrees of significance and better over all fit (2009, pp. 474 475) Hypothesis 4.1 : The variation in the $ Ic 3 from 1990 to 2008 is not explained by the va riation in the HDI 1990 the SE 2008 and the EEc 3 in 1 990 and the EEc 3 in 2008 Equation 4.1 Null Hypothesis : H O : Ic 3 1990 + SE 2008 + EEc 3 1990 + EEc 3 2008 Maintained Hypothesis : H 1 : Ic 3 = HDI 1990 + SE 2008 + EEc 3 1990 + EEc 3 2008 Test: Linear Regression 95% Confidence Level Regress dependent variable $ Ic3 with independent variables HDI1990, SE2008, EEc31990, EEc3 2008. Source | SS df MS Number of obs = 36 ------------+ -----------------------------F( 4, 31) = 18.88 Model | 235595046 4 58898761.6 Prob > F = 0.0000 Residual | 96718101 31 3119938.74 R squared = 0.7090 ------------+ -----------------------------Adj R squared = 0.6714 Total | 332313147 35 94 94661.35 Root MSE = 1766.3 -----------------------------------------------------------------------------$ 3Ic | Coef. Std. Err. t P>|t| [95% Conf. I n terval] ------------+ --------------------------------------------------------------HDI1990 | 30198.41 6916.631 4.37 0.000 16091.84 44304.97 SE08 | 69.00913 32.80825 2.10 0.044 135.922 2.096266 EE31990 | 105.0454 79.86971 1.32 0.198 267.9408 57.8 4992 EE32008 | 286.9724 149.392 1.92 0.064 17.71452 591.6593 _cons | 20081.12 7128.202 2.82 0.008 34619.18 5543.054

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211 Post Estimation Statistics for Regression White's test for Ho: homoscedasticity against Ha: unrestricted heteroscedasticity chi2(14) = 19.48 Prob > chi2 = 0.1475 Cameron & Trivedi's decomposition of IM test Source | chi2 df p --------------------+ ---------------------------Heteroskedasticity | 19.48 14 0.1475 Skewness | 5.04 4 0.2830 Kurtosis | 2.41 1 0.1205 --------------------+ ----------------------------Total | 26.93 1 9 0.1063 Ramsey RESET test using powers of the fitted values of 3Ic Ho: model has no omit ted variables F(3, 14) = 6.08 Prob > F = 0.0025 Model | Obs ll(null) ll(model) df AIC BIC -----+ --------------------------------------------------------------. | 36 339.767 317.55 5 645.1001 653.0177 The regression output shows an F score of 18.88 with 35 degrees of freedom, at most, 67.14% of the variation in the dollar change in total income per capita can be explained by the vari a tion in the independent variables. The t values are all significant. This test does not pass the 2 t t scores are less than 2.0 (Gujarati & Porter, 2009) critical X 2 value of 19.48 which exce eds the X 2 score of 14, and which means heteroscedasticity exists (p. 387) The IM test confirms highly left skewed data at 5.04, and a slightly platykurtic at 2.41. The AIC is 645.1001. The anal y sis of the equation suggests rejecting the null hypothesis, confirming for now that a statistically significant relationship exists. The results of this test suggest that after accounting for corruption in the figures for each country, the change in income per person over the 18 year test period is a function of the deve lopment starting point in 1990 (HDI 1990 ), the average level of corruption from 2000 to 2008

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212 (SE 2008 ), and the p ercentage of the total expenditure budget set aside per person for public educ ation in the pretest and posttest years (EEc 1990 and EEc 2008 ). However interesting these results, substituting the change in EEc 3 over the test period, may yield more significan t results, as this method equalizes the pretest or starting point by country, and is consistent with the treatment of the Income variable (Gujarati & Porter, 2009) (See T a bl e: 4.1 in the Appendix). Hypothesis 4.2 : The variation in the $ Ic 3 from 1990 to 2008 is not explained by the va riation in the HDI 1990 the SE 2008 and the percent change in EEc 3 from 1990 to 2008. Equation 4.2 Null Hypothesis : H O : $ Ic 3 1990 + SE 2008 + % EEc 3 Maintained Hypothesis : H 1 : $ Ic 3 = HDI 1990 + SE 2008 + % EEc 3 Test: Linear Regression 95% Confidence Level Regressed dependent variable $ Ic 3 using independent variables HDI 1990 SE 2008 and % EEc 3 Source | SS df MS Number of obs = 36 ------------+ -----------------------------F( 3, 32) = 24.66 Model | 231974312 3 77324770.5 Prob > F = 0.0000 Residual | 100338836 32 3135588.62 R squared = 0.6981 ------------+ -----------------------------Adj R squared = 0.6698 Total | 332313147 35 9494661.35 Root MSE = 1770.8 ----------------------------------------------------------------------------Dch3Ic | Coef. Std. Err. t P>|t| [95% Conf. Interval] ------------+ ---------------------------------------------------------------HDI1990 | 25815.11 5803.391 4.45 0.000 13993.99 37636.23 SE08 | 79.11753 31.6222 2.50 0.018 143.5298 14.70522 CH3EEc | 889.5726 535.0346 1.66 0.106 200.2573 1979.402 _cons | 15091.7 5364.002 2.81 0.008 26017.81 4165.58 Post Estimation Statistics for Reg ression White's test for Ho: homoscedasticity against Ha: unrestricted heteroscedasticity chi2(9) = 14.27 Prob > chi2 = 0.8034 Cameron & Trivedi's decomposition of IM test Source | chi2 df p --------------------+ ----------------------------Heteroskedasticity | 14.27 9 0.1130 Skewness | 7.98 3 0.0464 Kurtosis | 0.52 1 0.4702 --------------------+ ---------------------------Total | 22.77 13 0.0445

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213 Ramsey RESET test using powers of the fitted values of 3Ic Ho: model has no omit ted variables F(3, 15) = 5.50 Prob > F = 0.0041 Model | O bs ll(null) ll(model) df AIC BIC ------------+ ------------------------------------------------------. | 36 339.767 318.2116 4 644.4232 650.7572 The regression output shows an F score of 24.66 with 3 5 degrees of freedom. At most, 66.98% of the variation in the change in the dollars per capita, $ Ic 3 can be explained by the va riation in the independent variables. The t values are all significant, however this test does not 2 t Rule of Thumb EEc t score is less than 2 at 1.66. The RMSE is 1770.8. X 2 value of 14.27 which is greater than the X 2 score of 9, and which means heteroscedasticity is d e tected (Gujarati & Porter, 2009, pp. 386 397) The IM test confirms highly left skewed data at 7.98 and a platykurtic at .52. The AIC is 644. The analysis suggests reje cting the null hypothesis, confirming for now, a stati stically significant relationship. As anticipated, 4.2 (p. 386) between the va riation in the change in income and the variation in the independent variables. Testing 4.2 using the change in the percent of spending on education per capita, however, will tend to provide a skew in th e results that captures bigness in the change due to the medium sized econ o mies ability to adopt change, and not necessarily a better picture of the goodness of fit. This can be seen in Table 4, on the graphic comparison of these four equations. (See T a b le: 4.2 in the Appendix).

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214 Hypothesis 4.: The variation in the $ Ic 3 from 1990 to 2008 is not explained by the va ri a tion in the HDI 1990 the SE 2008 and the change in EE 3 from1990 to 2008. Equation 4.3 Test: Linear Regression 95% Confidence Level Re gressed dependent variable $ Ic 3 using independent variables HDI 1990 SE 2008 and $ EE 3 Source | SS df MS Number of obs = 36 ------------+ -----------------------------F( 3, 32) = 22.69 Mode l | 226036995 3 75345665.1 Prob > F = 0.0000 Residual | 106276152 32 3321129.75 R squared = 0.6802 ------------+ -----------------------------Adj R squared = 0.6502 Total | 332313147 35 9494661.35 Root MSE = 1822.4 -----------------------------------------------------------------------------$ 3Ic | Coef. Std. Err. t P>|t| [95% Conf. Interval] ------------+ --------------------------------------------------------------HDI1990 | 25958.14 5993.159 4.33 0.000 13750.47 38165.8 SE08 | 81.43262 33.0398 2.46 0.019 148.7325 14.13275 $ 3EE | .017643 .0194572 0.91 0.371 .02199 .0572759 _cons | 14916.48 5521.05 2.70 0.011 26162.5 3670.474 Post Estimation Statistics for Regression White's test for Ho: homoscedasticity against Ha: unrestricted heteroscedasticity chi2(9) = 25.39 Prob > chi2 = 0.0026 Cameron & Trivedi's decomposition of IM test Source | chi2 df p --------------------+ ----------------------------Heteroskedasticity | 25.39 9 0.0026 Skewness | 10.37 3 0.0157 Kurtosis | 0.96 1 0.3283 --------------------+ ----------------------------Total | 36.72 13 0.0005 Ramsey RESET test using powers of the fitted values of 3Ic Ho: model has no omit ted variables F(3, 15) = 3.79 Prob > F = 0.0207 Model | Obs ll(null) ll(model) df AIC BIC --------------------------------------------------------------------. | 36 339.767 319.2464 4 646.4927 652.8268

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215 The regression output shows a high F score at 22.69 with 35 degrees of freedom, at most, 65.02% of the variation in the $ Ic 3 can be explained by the variation in the independent vari ables, and the t values are all significant. This test does no 2 t change in the total education expenditures dollars per country, $ EE 3 t score is less than 2 at .91 (Gujarati & Porter, 2009) The R Heteroscedasticity reports a critical X 2 value of 25.39 which greater than the X 2 score of 9, and which means heteroscedasticity exists (pp. 386 397) The IM test confirms left skewed data at 10.37 and a platykurtic at .96. The AIC is 646.4927. The analysis of the equation suggests r ejecting the null hypothesis and confirmin g for now that a statistically significant relationship exists. Testing this equation using the change in the dollars spent, however, will tend to provide a skew in the results that captures bigness in the available budget due to the larger economy, and not necessarily a better picture of the goodness of fit. This can be seen in Table 4, on the graphic comparison of these four equations. (See Table: 4.3 in the Appendix). Hypothesis 4.4: The variation in the $ Ic 3 from 1990 to 2008 is not explained by t he va riation in the HDI 1990 the SE 2008 and the dollar change in EEc 3 per capita from1990 to 2008. Equation 4.4 Null Hypothesis : H O : $ Ic 3 1990 + SE 2008 + $ EEc 3 Maintained Hypothesis : H 1 : $ Ic 3 = HDI 1990 + SE 2008 +$ EEc 3

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216 Test: Linear Re gression 95% Confidence Level Regressed dependent variable Ic 3 using independent variables HDI 1990 SE 2008 and $ EEc3 Source | SS df MS Number of obs = 36 ------------+ -----------------------------F( 3, 32) = 81.38 Model | 293803978 3 97934659.2 Prob > F = 0.0000 Residual | 38509169.7 32 1203411.55 R squared = 0.8841 ------------+ -----------------------------Adj R squared = 0.87 33 Total | 332313147 35 9494661.35 Root MSE = 1097 -----------------------------------------------------------------------------Dch3Ic | Coef. Std. Err. t P>|t| [95% Conf. Interval] ------------+ --------------------------------------------------------------HDI1990 | 16384.67 3823.468 4.29 0.000 8596.518 24172.82 SE08 | 44.14497 20.11301 2.19 0.036 85.11383 3.176101 DCH3EEc | 3.618593 .47278 04 7.65 0.000 2.655571 4.581615 _cons | 9615.573 3396.708 2.83 0.008 16534.44 2696.706 Post Estimation Statistics for Regression White's test for Ho: homoscedasticity against Ha: unrestricted heteroscedasticity chi2(9) = 8.73 Prob > chi2 = 0.4624 Cameron & Trivedi's decomposition of IM test Source | chi2 df p --------------------+ ----------------------------Heteroskedasticity | 8.73 9 0.4624 Skewness | 6.33 3 0.0964 Kurtosis | 0.06 1 0.8014 --------------------+ ----------------------------Total | 15.13 13 0.2993 Ramsey RESET test using powers of the fitted values of 3Ic Ho: model has no o mitted variables F(3, 16) = 1.38 Prob > F = 0.2695 Model | Obs ll(null) ll(model) df AIC BIC ------------+ -------------------------------------------------------------. | 36 339.767 300.9738 4 609.9475 616.2816 The regression output shows the highest measure of goodness of fit of the four equations with an F score of 81.38 and 35 degre es of freedom, at most, 87.33% of the variation in the $ Ic3 can be explained by the variation in the independent variables, and the t values are all significant. t Gujarati & Porter 2009). The RMSE is 1097, the lo w-

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217 X 2 value of 8.73 which is smaller than the X 2 score of 9, and which means heteroscedasticity does not exist (pp. 386 397). The IM test confirms left skewed dat a at 6.33 and a platykurtic at .06. The AIC is 609.9475. The analysis of the equation suggests rejecting the null hypothesis and confirming for now that a statistically significant relationship exists. (See Table: 4.4 in the A ppendix). Testing this equa tion using the change in the dollars spent per person, will tend to provide a skew in the results that underestimates spending in larger economies, and overestimates smaller economies possibly missing a variable such as efficiency or effectiveness in the e ducation system. Equation 4.4 is the highest scoring equation in each of the categories. This can be seen on Table: 4.5 in the Appendix, which is the graphic comparison of these four equations. Hypothesis 4.5: The variation in the $ Ic3 from 1990 to 2 008 is not explained by the variation in the HDI1990, the SE2008, the dollar change in EEc3 per capita from1990 to 2008 and the Cou n try Group. Equation 4.5 Null Hypothesis : H O : $ Ic 3 1990 + SE 2008 + $ EEc 3 + Group Maintained Hypothesis : H 1 : $ Ic 3 = HDI 1990 + SE 2008 +$ EEc 3 + Group Test: Linear Regression 95% Confidence Level Regressed variable Ic 3 using independent variables HDI 1990 SE 2008 $ EEc3, and Group Source | SS df MS Number of obs = 36 -----------+ -----------------------------F( 4, 31) = 59.30 Model | 293904763 4 73476190.8 Prob > F = 0.0000 Residual | 38408384 31 1238980.13 R squared = 0.8844 ------------+ ----------------------------Adj R squared = 0.8695 Total | 332313147 35 9494661.35 Root MSE = 1113.1 -----------------------------------------------------------------------------$ Ic3 | Coef. Std. Err. t P>|t| [95% Conf. I n terval] -----------+ ---------------------------------------------------------------HDI 1990 | 16208.84 3928.236 4.13 0.000 8197.152 24220.53 SE 2008 | 40.91068 23.34705 1.75 0.090 8 8.5273 6.705943 $ EEc3 | 3.581477 .4970541 7.21 0.000 2.567729 4.595226 Group | 109.3131 383.2702 0.29 0.777 672.3716 890.9979 _cons | 9775.285 3491.734 2.80 0.009 16896.72 2653.846

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218 P ost Estimation Statistics for Regression White's test for Ho: homoscedasticity against Ha: unrestricted heteroscedasticity chi2(14) = 13.01 Prob > chi2 = 0.5256 Cameron & Trivedi's decomposition of IM test S ource | chi2 df p --------------------+ ----------------------------Heteroskedasticity | 13.01 14 0.5256 Skewness | 5.35 4 0.2535 Kurtosis | 0.06 1 0.7995 Tot al | 18.42 19 0.4944 Ramsey RESET test using powers of the fitted values of $ Ic3 Ho: model has no omit ted variables F(3, 28) = 1.42 Prob > F = 0.2564 Model | Obs ll(null) ll(model) df AIC BIC -----+ --------------------------------------------------------------. | 36 339.767 300.9266 5 611.8532 619.7708 The regression output shows the second highest mea sure of goodness of fit of the four equations with an F score of 59.3 and 35 degrees of freedom, at most, 86.99% of the variation in the $ Ic3 can be explained by the variation in the independent variables, and the t values are all significant. This test t nomy variables ( Gujarati & Porter Heteroscedasticity reports a critical X 2 value of 13.01 which is smaller than the X 2 score of 1 4, and which means heteroscedasticity does not exist (pp. 386 397). The IM test confirms left skewed data at 5.35 and a platykurtic at .06. The AIC is 611.85. The analysis of the equation suggests rejecting the null hypothesis and confirming for now tha t a statistically significant rel ationship exists. Adding the Country Group to the equation tended to absorb the skewness in the results and the significance of the Shadow Economy, possibly suggesting that the Country Group may

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219 approximate the degree of the Shadow Economy of a country in this sample set. Table: 4.5 in the Appendix, is the graphic comparison of these equations. Table 5.1 in the A p pendix provides the Correlation Coefficient for the variables. The Country Group degree of association with the Total Change in Income per Capita is 0.6549, with the HDI in 1990 is 0.5661, with the Education E xpenditure is 0.5648, and with the Shadow Econ o my in 1990 is 0.6958. The widely accepted method for determining the factors of economic growth is the OLS linear regression, ( e.g., Sachs and Werner (1995) Economic Convergence and Economic Pol i cies Gupta et al., (1998) Does Corruption Affect Income Inequality and Poverty? ). However, the structural equation method appears in important articles since 1984, wh ere Frey and Weck Hannemann (1984) apply the MIMIC method used in ps y chometrics starting in the 1970s in The Hidden Economy as an "Unobserved" Variable and Sachs and Werner (1997) apply the Two Stage Least Squared method in Fundame n tal Sources of Long Run Growth The relatively new body of literature delineating corruption from the underground or shadow, parallel, off the books, non observed economy makes use of the complex and relativ ely new structural equation approach or MIMIC model, which stands for Multiple Indicator a latent variable or index, which has causes and effects that are observable but which cannot itself p. 1). over time are inferred from data on causes and indicators by estima ting the statistical model and predicting the index. The fitted index is then interpreted as a time series estimate of the magn itude of the underground economy. Usually the measure is hidden output or income as a percentage of recorded GDP, although some etween actual revenue and the potential revenue when all taxable income is reported (p. 1).

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220 Critics of the MIMIC method cite instability in the findings with minor changes in the p eriod or the countries studie d, the absence of important economic, political, or social influences in the embodied in the variables, and the reliance on multiple and different variables for each equ ation (p. 2). While employing a simultaneous equation method would allow us to solve for the effects of the shadow economy on both income and education in isolation and together and pote nusing the complex MIMIC method is be beyond the scope o f this thesis and possibly of the avai lable data. The Two Stage Least Squared method (2SLS), however, is within the possibilities of methods useful to this thesis and potentially helpful given that the variables are endogenous, and likely correlated with the error term, violating the rules of OLS regression assumptions with bias in the test results (Nagler, 1999). From Leigh and Schimbri (2004, p. 286 7) comes the logic o ffered in the following discussion regarding the 2SLS method. To e s timate the causa l effect of the Shadow Economy on Income per Capita, we can use an instrument, which affects Income only through its effect on the Shadow Economy. Correlation between Income and the Shadow Eco nomy does not imply that the Shadow Economy causes lower Income because other variables, such as regime changes or armed conflicts, may affect both Income and the Shadow Economy. In addition, Income may affect the Shadow Economy in addition to the Shadow Economy causing changes in Income, in a cyclical relationship. To attempt to estimate the causal effect of the Shadow Economy on Income from the sample data, we use the Education Expenditures as an i nstrument for the Shadow Economy in an Income regression. If Education Expenditures only affect Income per Capita beca use it affects the Shadow Economy (ceteris paribus), correlation b etween Education Expenditures and Income is evidence that Shadow Eco n omy causes changes in Income. An estimate of the effect of the Shadow Economy on Income can be made by also ma king use o f the correlation between Education Expenditures and the Shadow Economy pa t terns.

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221 Research Question 4.6 The null hypothesis asserts that there is no significant relationship between the level of Individual Income 3 in 2008 (Ic 3 08) and the explanatory vari ables, HDI1990, SE08, and the level of Education Expenditures per Capita in 2008 (EE 3 08Dc) Hypothesis 4.6 : The variation in the Ic 3 08 is not explained by the variation in the HDI1990, the SE08, and the level of EEc 3 08Dc. Equation 4.6 Null Hypothesis : H O : Ic 3 08 3 08Dc Maintained Hypothesis : H 1 : Ic308 = HDI1990 + SE08 + EE308Dc Following is the STATA output for the single equation instrumental variables 2SLS r egression equation reporting small sample results that are adjusted for the degrees of freedom. In the first stage, Education Expenditures per Capita in 2008 are the dependent variable. The Sha dow Economy in 2008 and the Human Development Index in 1990 are the ind e pendent variables. The second stage sets Individual Income i n 2008 as the dependent variable, and the employs the independent variables in the first stage as instrumental var i ables and tests their joint effect along with Education Expenditures on Individual Income. Test: ivregress 2sls Ic32008 SE08 (EE308Dc = HDI1 990), first small First stage regressions Number of obs = 36 F( 2, 33) = 26.77 Prob > F = 0.0000 R squared = 0.6187 Adj R squared = 0.5956 Root MSE = 667.8208 ----------------------------------------------------------------------------EE308Dc | Coef. Std. Err. t P>|t| [95% Conf. Interval] ------------+ ---------------------------------------------------------------SE08 | 26.1214 11.70901 2.23 0 .033 49.94357 2.299236 HDI1990 | 9073.607 2155.828 4.21 0.000 4687.542 13459.67 _cons | 5219.72 1999.873 2.61 0.014 9288.493 1150.948

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222 Instrumental variables (2SLS) regression (with adjustments to the degrees of freedom on a small sample) Source | SS df MS Number of obs = 36 ------------+ -----------------------------F( 2, 33) = 162.38 Model | 2.8105e+09 2 1.4052e+09 Prob > F = 0.0000 Residual | 215380231 33 6526673.65 R squared = 0.9288 ------------+ -----------------------------Adj R squared = 0.9245 Total | 3.0258e+09 35 86452635.7 Root MSE = 25 54.7 -----------------------------------------------------------------------------Ic32008 | Coef. Std. Err. t P>|t| [95% Conf. Interval] ------------+ ---------------------------------------------------------------EE308Dc | 9.329588 .9089084 10.26 0.000 7.4804 11.17878 SE08 | 7.666987 62.0813 0.12 0.902 133.9723 118.6384 _cons | 743.211 3103.729 0.24 0.812 7057.795 5571.373 Instrumented: EE308Dc Instru ments: SE08 HDI1990 Instrumental variables (2SLS) regression (without adjustments) Number of obs = 36 Wald chi2(2) = 354.29 Prob > chi2 = 0.0000 R squared = 0.9288 Root MSE = 2446 ----------------------------------------------------------------------------Ic32008 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------------+ ---------------------------------------------------------------EE308Dc | 9.329588 .8702136 10.72 0.000 7.624001 11.03518 SE08 | 7.666987 59.43832 0.13 0.897 124.1639 108.83 _cons | 743.211 2971.594 0.25 0.803 6567.428 5081.006 The regression output shows a high F score at 26.77 with 35 degrees of freedom, at most, 59.56% of th e variation in the Education Expenditures per capita in 2008 is explainable by the variation in the Shadow Economy on average from 2000 2007 and the Human Development I ndex 1990, at the 95% confidence level. The signs are correct. This test does not pass t score of the Shadow Economy 08 and the HDI 1990 on Ed ucation Expenditures per Capita in 2008 is less than 2, at 0.12 ( Gujarati & Porter 2009). The test results suggest that Individual Income in 2008 is a functio n of Education Expenditures, both of which are affected by the Shadow Economy, and both the Income and Education variables are a ffected by the initial level of Human Develo p ment. This methodological option suggests a causal relationship from the HDI 1990 level and Shadow Economy percentage toward the Education Expenditures and Income variables. The analysis of the equation suggests rejecting the null h y-

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223 pothesis and confirming for now that a statistically significant and causal relationship exists b etween the independent variables, HDI1990, the Shadow Economy in 2008, and Education E xE x penditures in 2008; on Individual Income in 2008. However, the high Wald Wolfowitz score of 354.29, along with the Hausman test of Endogeneity that shows a significant coeffi cient of the EE308Dc in the second stage with a high t score of 10.72 suggests that the new variables are not independent (p. 705). In Notes on Simultaneous Equations and Two Stage Least Squares Estimates Nagler (1999, p. 7) cautions the researcher ab out shortcomings of the 2SLS estimates; consistent results require large samples, the B 1 will be consistent but asymptotic to infinity toward zero, and ther efore, will not be unbiased. If one considers the sample set to be relatively small, n=36, and cons iders that the 2SLS method may report inconsistent B 1 results which may be (diminishing marginally) biased, it may be less attractive a method option than is OLS as the simplest method is desirable. However, we decide against the simultaneous equ a tion opt ion at the expense of the preliminary information on causality ( Gujarati & Porter 2007, p. 96). Further analysis may yield a more telling decision rule. The most common method found in the literature on the effects of corruption and the d eterminants of economic growth is, by far, the Ordinary Least Squared (OLS) linear regression. An important example of its use is found in Gupta, Davoodi, and Alonso Term (1998). Does Corruption Affect Income Inequality and Poverty? International Monetary Fund Working Paper, 98(76), 1 41. The benefits of a linear regression include that we learn more about the relatio nships among and between several independent variables and a dependent variable in a simple o balance the effect of that variable across variables so that we can minimize differences statistically and just study the relatio nship between the independent and the dependent variables. The li mit ations of regression techniques include three is sues found here. (1) We can only deduce relationships and cannot be

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224 sure about underlying causal mech a nism. (2) Inherent in these techniques is the tendency toward closely to fully redundant variables, collinearity. (3) Violating the assumptions of norm al distr ibutions in the variance (and then in the standard errors), heteroscedasticity, may lead to bias in the inferences made from the resulting tests ( Gujarati & Porter 2009). Several tests that offer decision rules exist. Using the sample size decis ion rule, we F value exceeds the logarithm of the sa mple size (n=36 = ln 1.55), which it does in both the 2SLS and OLS equations, so neither method is favored based on the sample size decision rule. If o ne assumes that n=36 is relatively large sa mple size and employs the 2SLS equation, the decision rule falls to the tests for endogeneity. If the null hypothesis states that the variables are not endogenous, we would reject the null hypothesis in favor of the maintained hypothesis, that the variables are likely not independent, and opt for the OLS equation. According to Gujarati & Porter ion is used when the best unbiased estimators are incapable of producing estimators with smaller over the 2SLS test results of a 2446 MSE, and accept the tradeoff of a smaller MSE, at the e xpense of some bias. Rather disregard the learn ing from 2SLS method, a researcher may employ other tests for causality; however, these may be best reserved for future study. For the purposes of this th e sis, the OLS method is maintained as the best linear unbiased estimator (p. 422). Test: R egress I c32008 HDI1990 SE08 EE308Dc Source | SS df MS Number of obs = 36 ------------+ -----------------------------F( 3, 32) = 208.31 Model | 2.8784e+09 3 959482690 Prob > F = 0.0000 Residual | 147394179 32 4606068.1 R squared = 0.9513 ------------+ -----------------------------Adj R squared = 0.9467 Total | 3.0258e+09 35 86452635.7 Root MSE = 2146.2 ----------------------------------------------------------------------------Ic32008 | Coef. Std. Err. t P>|t| [95% Conf. Interval] ------------+ ---------------------------------------------------------------HDI1990 | 19501.72 8588.73 2.27 0.030 2007.052 36996.39 SE08 | 63.8092 40.36713 1.58 0.124 146.0344 18.41596 EE308Dc | 7.180308 .5594332 12.83 0.000 6.04078 8.319836 _cons | 11961.85 7059.257 1.69 0.100 26341.09 2417.384

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225 From Equation 4.4 t he 2SLS method yields results consistent with those in Equation 4.6, and the same decision is made as was made in Equation 4.6; the OLS method remains the best linear unbiased estimator of Equation 4.4 using the minimum MSE decision rule. The STATA command reads: Instrumental Variable 2SLS, where the first the Change in Total Education Expenditures per Capita from 1990 to 2008 stated in dollars is regressed against the average Shadow Economy from 200 0 to 2008 and the Human Development Index in 1990. Test: Inverse Regress 2sls Ic32008 HDI1990 (EE3ChDc = SE08), first First stage regressions Number of obs = 36 F( 2 33) = 9.80 Prob > F = 0.0005 R squared = 0.3727 Adj R squared = 0.3 347 Root MSE = 402.0028 -----------------------------------------------------------------------------EE3ChDc | Coef. Std. Err. t P>|t| [95% Conf. Interval] ------------+ ---------------------------------------------------------------HDI1990 | 2877.536 1297.727 2.22 0.034 237.2912 5517.78 SE08 | 12.10331 7.048382 1.72 0.095 26.44335 2.236729 _cons | 1544.868 1203. 848 1.28 0.208 3994.114 904.3786 Instrumental variables (2SLS) regression Number of obs = 36 Wald chi2(2) = 68.08 Prob > chi2 = 0.0000 R squared = 0.6296 Root MSE = 5580 ----------------------------------------------------------------------------Ic32008 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------------+ ---------------------------------------------------------------EE3ChDc | 20.76861 8.083341 2.57 0.010 4.92555 36.61167 H DI1990 | 24890.61 37032.29 0.67 0.501 47691.35 97472.57 _cons | 17356.3 27202.64 0.64 0.523 70672.48 35959.89 Instrumented: EE3ChDc Instruments: HDI1990 SE08 The STATA command that adjust s for the small sample size does not change the decision rule.

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226 Test: ivregress 2sls Ic32008 HDI1990 (EE3ChDc = SE08), first small First stage regressions Number of obs = 36 F( 2, 33) = 9.80 Prob > F = 0.0005 R squared = 0.3727 Adj R squared = 0.3347 Root MSE = 402.0028 -----------------------------------------------------------------------------EE3ChDc | Coef. Std. Err. t P>|t| [95% Conf. Interval] -----------+ ---------------------------------------------------------------HDI1990 | 2877.536 1297.727 2.22 0.034 237.2912 5517.78 SE08 | 12.10331 7.048382 1.72 0.095 26.44335 2.236729 _cons | 1544.868 1 203.848 1.28 0.208 3994.114 904.3786 Instrumental variables (2SLS) regression Source | SS df MS Number of obs = 36 ------------+ -----------------------------F( 2, 33) = 31.20 Model | 1.9049e+09 2 952464510 Prob > F = 0.0000 Residual | 1.1209e+09 33 33967067.5 R squared = 0.6296 ------------+ -----------------------------Adj R squared = 0.6071 Total | 3.0258e+0 9 35 86452635.7 Root MSE = 5828.1 -----------------------------------------------------------------------------Ic32008 | Coef. Std. Err. t P>|t| [95% Conf. Interval] ------------+ --------------------------------------------------------------EE3ChDc | 20.76861 8.442775 2.46 0.019 3.591654 37.94556 HDI1990 | 24890.61 38678.97 0.64 0.524 53802.35 103583.6 _cons | 17356.3 28412.23 0.61 0.545 751 61.4 40448.81 -----------------------------------------------------------------------------Instrumented: EE3ChDc Instruments: HDI1990 SE08 R egress Ic32008 HDI1990 SE08 EE3ChDc Source | SS df MS Number of obs = 36 ------------+ -----------------------------F( 3, 32) = 63.79 Model | 2.5924e+09 3 864128087 Prob > F = 0.0000 Residual | 433457989 32 13545562.2 R squared = 0.8567 -----------+ -----------------------------Adj R squared = 0.8433 Total | 3.0258e+09 35 86452635.7 Root MSE = 3680.4 -----------------------------------------------------------------------------Ic32008 | Coe f. Std. Err. t P>|t| [95% Conf. Interval] ------------+ ---------------------------------------------------------------HDI1990 | 57561.19 12735.36 4.52 0.000 31620.12 83502.26 SE08 | 137.417 67.35091 2 .04 0.050 274.6063 .2276653 EE3ChDc | 9.41494 1.593722 5.91 0.000 6.168635 12.66125 _cons | 34896.21 11293.16 3.09 0.004 57899.63 11892.79

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227 TABLES AND FIGURES

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228 T able 1 Hypothesis 1 Data

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229 Table 2 Hypothesis 2 Data

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230 Table 3 Hypothesis 3 Data

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231 Table 4 H ypothesis 4 Data

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232 Figure 5 Shadow Economy MIMIC Diagram Figure 5 Shadow Economy MIMIC Model (Schneider, et al., 2010; Buehn, et al., 2009 Fi g ure 3. Path Diagram; Breusch, Trevor, 2 005) + + + + + ), and + + + + ) Where causal variables are Where Indicator variables are = Business Regulation = GDP Growth Uunemployment Rate Labor Force Participation = Transfers and Subs i dies = Ratio of M0 to M1 = Government Consum p tion + + + + + ), and + + + + ) Where causal variables are Where indicator variables are = Government Effectiv e ness = Real GDP per Capita Fiscal Freedom Bribes = Bureaucracy Costs = Judicial Independence = Rule of Law Figure 6 Shadow Economy S imultaneous E quations

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233 Table 5 GDP per Capita Cycle [ T y p e

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234 T able 7 .1 Data Validation

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235 Table 7. 1 Data Valida tion (Continued)

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236 Table 7.1 Data Validation (Continued)

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237 Table 7.1 Data Validation (Continued)

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238 Table 7.1 Data Validation (Continued)

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239 Figure 7 Data Validation Comparison Equation 4.1 4.4 Analyses and Comparison

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240 Table 7 2 Data Validati on Equation Analysis

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241 Table 7. 3 Data Sources

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242 Table 4.1 Correlation Coefficient Matrix Variable IcChDc HDICh LEI EAI IcChDc 1.0000 HDICh 0.5005 1.0000 LEI_EAI 0.6793 0.2639 1.000 Table 4.2 Corre lation Coefficient Matrix Variable HDI1990 IcChDc SE2008 HDI1990 1.0000 IcChDc 0.6532 1.0000 SE2008 0.5148 0.5981 1.0000 Table 4.3 Correlation Coefficient Matrix Variable SE1990 SE2008 EEDc2008 SE1990 1.0000 SE2008 0.8359 1.0000 EEDc2008 0.4450 0.5807 1.0000 Table 4.4 Correlation Coefficient Matrix Variable Ic O Ic U Ic T HDI1990 EE $c Ic O 1.0000 Ic U 0.8882 1.0000 Ic T 0.9971 0.9070 1.0000 HDI 1990 0.7260 0.7994 0.7359 1.0000 EE $c 0.7763 0. 7129 0.7875 0.5704 1.0000 Table 7. 4 Correlation Coefficient Matrix Test: Correlate the change in Total Income per Capita, change in Total Education Expend i tures, pre test Human Development Index, pre test Shadow Economy change in Life Expec tancy I ndex, change in Education Attainment Index, post test Shadow Economy change in Shadow Economy Country Group (obs=36) Variable $ Ic3 $ EEc3 HDI 1990 SE 200 8 LEI EAI SE 199 0 SE G roup $ Ic3 1.0000 $ EEc3 0.8619 1.0000 HDI 1990 0.7710 0.5 549 1.0000 SE 2008 0.687 0.5285 0.6040 1.0000 LEI 0.0807 0.1046 0.1254 0.1996 1.0000 EAI 0.2757 0.2394 0.4848 0.2350 0.0567 1.0000 SE 19 9 0 0.586 0.510 0.4409 0.8660 0.0844 0.2308 1.0000 SE 0.126 0.0618 0.307 1 0.1641 0.1256 0.0313 0.3163 Group 0.6549 0.5661 0.5648 0.6958 0.3222 0.0434 0.6077 0.089

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243 GLOSSARY Organizations and Acronyms Organizations: Affiliated Programs, Reports and Data Intergovernmental Organizations (IGO) Internati onal Governmental Organizations (IGO) Non governmental Organizations (NGO) Nonprofit or Not for profit Organizations (NO) IGOs United Nations (UN): United Nations Education, Science, and Cultural Organization (UNESCO) Statistical Information System on Expenditure in Education (SISEE) United Nations Development Program (UNDP) Human Development Report (HDR) Human Development Index (HDI) Gross Domestic Product Index (GDPI) Life Expectancy Index (LEI) Educational Attainment Index (EAI) Income per capita ( Ic) Millennium Development Goals (MDGs) Millennial Development Goals Report (MDGR) World Bank (Bank): World Bank Group (WBG) Global Monitoring Report (GMR) International Comparisons Progra m (ICP) World Bank Development Economics Research Group (DERG) World Development Report (WDR) World Development Indicators (WDI) World Governance and Anticorruption Indicators (WGI) Business Environment and Enterprise Performance Survey (BEEPS) World Bank Human Development Network (HDN) Education Statistics (EdStats) The International Monetary Fund (IMF) Global Monitoring Report (GMR) World Economic Outlook (WEO) International Financial Statistics (IFS) International Accounting Standards (IAS) Internationa l Financial Reporting Standards (IFRS) Organisation for Economic Co operation and Development (OECD)

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244 NGOs: International and National Institutes, Policy Centers, and Foundations Council on International and Public Affairs (CIPA) World Trade Organizati on ( WTO) Brooking Institution (BI) Transparency International (TI) Global Corruption Report (GCR) Corruptions Perceptions Index (CPI) Global Corruption Barometer (GCB) Bribe Payers Index (BPI) National Integrity System (NIS) The Heritage Foundation (HF) I ndex of Economic Freedom (EFI) Governmental Bodies and Data United States (US) CIA World Fact Book (CIA) US Library of Congress (LOC) United States State Department (DOS) Countries and Regions: Background Notes United States Agency for International De velopment (USAID) System of National Accounts or National Income accounting (NI) European Commission (EU) Europa World Fact Book (Europa) European Statistics (Eurostat) European Statistics, Data, and Metadata Exchange (SDMD) European Bank for Reconstruc tion and Development (EBRD)

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245 Organizations and Acronyms Glossary (Continued) Several agencies are sub divisions of the United Nations (UN): the United Nations Ed ucation, Science, and Cultural Organization (UNESCO), the United Nations Development uman Development Program (HDP), and the Millennium Development Goals (MDGs). Other agencies exist within the broader UN system. This, according to the IMF (IMF, 2010g, p 1). The IMF and the World Bank are institutions in the United Nations system. They share the same goal of raising living standards in their member countries. Their approaches to this goal are complementary, with the IMF focusing on macroeconomic issues and the World Bank co ncentrating on long term economic development and poverty reduction. Several agencies are sub divisions of the World Bank (Bank), specifically, the World Bank Group (WBG), along with the World Bank Human Development Network and its af fil i ates, the World Bank Development Economics Research Group, and the World Bank Education and Development Research Groups, which produce the World Governance and Anticorruption Indic ators (WGI), and Education Statistics (EdStats). The International Mone tary Fund (IMF) co produces the Global Monitoring Report (GMR), sharing its banking and financial statistics for the Millennial Development Goals (MDGs) and the network of agencies. Regional and national agencies such as Organisation for Economic Co opera tion and Development (OECD), Europa World Fact Book (Europa), European Commission (Eurostat), CIA World Fact Book (CIA), The US Library of Congress (LOC), and the United States State Department (DOS) all research, co mpile, share, and report research, infor mation, and data. An agency or an affiliated institute, think tank, or foundation may employ researchers adding to this network of information, research, and ire c tor at the World Bank Institute, where he pioneered new approaches to measure and analyze governance and corruption, helping countries form u

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246 F i g u r e 8 T h e P o l i c y P r o b l e m

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247 REFERENCES Anonymous. (1985). Charter of the United Nations and Statute of th e International Court of Ju stice. New York. NY: United Nations. _____. (1990). Defining and Measuring Human Development New York, NY: United Nations Develo p ment Programme. ______. (1998). Statistical information system on expenditure in education. Paris, France: United Nations Education, Science, and Cultural Organization _____. (2000a). Anticorruption in Transition: A Contribution to the Policy Debate. Washington, D.C.: The World Bank. _____. (2000b). Confronting Corruption: The Elements of a National I ntegrity System. Berlin, Germany: Transparency International. _____. (2005). Global Monitoring Report : Understanding Education Quality. Washin g ton, D.C.: The World Bank _____. (2006). Anticorruption Report Washington, D.C.: United States Agency for I n tern ational Development _____. (2007a). Human Development Report New York, NY: United Nations Development Pr ogramme. _____. (2007b). Towards Better Measurement of Government OECD Working Paper Series on Public Governance, 2007(1). Paris, Fr ance: Organisati on for Economic Co operation and Development Publishing. _____. (2008a). Business Environment and Enterprise Performance Survey. Washington, D.C.: World Bank Group. _____. (2008b). Global Education Digest Montreal, Quebec: United Nations Education, Scienc e, and Cultural Organization Institute for Statistics. _____. (2008c). Human Development Report New York, NY: United Nations Development Pr ogramme. _____. (2008d). Millennium Development Goals Report New York, NY: United Nations Deve lopment Programme. _____. (2009a). CIA World Fact Book Washington, D.C.: Central Intelligence Agency of the United States of America. _____. (2009b). Corruption in the Education Sector. Transparency International Working Paper Series, 2009 (4). _____. (2009c). Global Corrupt ion Report Berlin, Germany: Transparency International.

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248 _____. (2009d). Global Education Digest : Comparing education statistics across the world. Mo ntreal, Quebec: United Nations Education, Science, and Cultural Organization Institute for Statistics. ____ _. (2009e). Human Development Report New York, NY: United Nations Development Pr ogramme. _____. (2009f). What is Good Governance?. Bangkok, China: United Nations Economic and S ocial Commission for Asia and The Pacific. _____. (2009g). World Development In dicators Retrieved 6/12/2011 from The World Bank: http://go.worldbank.org. _____. (2010a). Conflict Barometer 2010 Heidelberg, Germany: Heidelberg Institute for Intern ational Conflict Research. _____. (2010b). The Corruptions Perceptions Index Berlin, Germany: Transparency Internatio nal. _____. (2010c). Countries and Regions. U.S. State Department Retrieved 6/12/2011 from http://www.state.gov/r/pa/ei/bgn/index.htm. _____. (2010d). Education for All Global Monitoring Report : Reaching the marginalized. M o ntreal, Quebec: United Nations Education, Science, and Cultural Organization _____. (2010e). Fostering Social Accountability: From Principle to Practice. Conference paper. Washington, D.C.: United Nations Development Program _____. (2010f). Internatio nal Human Development Indicators. Human Development Report 2010. _____. (2010g). Recovery and Reform London, UK: European Bank for Reconstruction and D evelo p ment. _____. (2010h). Regional Trade Agreement Database Retrieved 8/26/2011 from World Trade Or ganiz a tion: www.wto.org _____. (2010i). Transition Report 2010: Recovery and Reform London, UK: European Bank for Reconstruction and Development. _____. (2010j). World Bank Education Projects Database Retrieved 8/26/2010 from http://go.worldbank.org. ___ __. (2011a ). Country Studies. Washington, D.C.: Library of Congress _____. (2011b). EU Candidate and Pre Belgium: European Commission. _____. (2011c). Eurostat Retrieved 6/28/2011 from http://ec.europa. eu/statistics.

PAGE 261

249 _____. (2011d). The IMF and Good Governance., The Role of the IMF in Governance Issues Washin g ton, D.C.: International Monetary Fund. _____. (2011e). International Comparisons Program Washington, D.C.: The World Bank. _____. (2011f). IMF's Lagarde Calls for Unity Wall Street Journal, p. 1. U nited Press Intern ational. September 23. Abed, George T., & Gupta, Sanjeev (2002a). The Economics of Corruption: An Overview. Was hington, D.C.: International Monetary Fund. Abed, George T., & Gupta, Sa njeev (Eds.). (2002b). Governance, Corruption, & Economic Pe rformance. Washington, D.C.: International Monetary Fund. Alesina, Alberto F., Devleeschauwer, Arnaud, Easterly, William, Kurlat, Sergio, & Wacziarg, Romain T. (2002). Fractionalization. Harvard I nstitute of Economic Research Discu s sion Paper, 1959 64. Alkire, Sabina (2005). Why the Capability Approach?. Journal of Human Development, 6 (1), 115 133. Anand, Sudhir, & Sen, Amartya (2000). Human Development and Economic Sustainability. World Developm ent, 28 (12), 2029 2049. Ar mit age, Richard L., & Nye, Joseph S. Jr. (2007). A Smarter, More Secure America Washin gton, D.C.: Center for Strategic and International Studies. Armstrong, Elia (2005). Integrity, Transparency and Accountability in Public Admini stration: Recent Trends, Regional and International Developments and Emerging Issues. Washin gton, D.C.: Uni t ed Nations Public Administration Programme. Arrow, Kenneth J. (1962). The Economic Implications of Learning by Doing. Review of Econo mic Statistics, 29 (3), June, 155 173. Arrow, Kenneth J. (1963a). Social Choice and Individual Values (2 nd ed.). New York, NY: Wiley. Arrow, Kenneth J. (1963b). Uncertainty and the Welfare Economics of Medical Care. American Economic Review of Economic Statistics, 53 941 973. Baddeley, Michelle (2006). Convergence or Divergence? The Impacts of Globalisation on Growth and Inequality in Less Developed Countries. International Review of Applied Economics, 20 (3), 391. Barro, Robert J. (1991). Economic Growth in a Cross Sectio n of Countries. The Quarterly Jou rnal of Economics, 106 (2), 407 443. Barro, Robert J. (2000). Inequality and Growth in a Panel of Countries Journal of Economic Growth, 5 (1), 5 32. Barro, Robert J. (2001a). Determinants of Economic Growth: A Cross country Empirical Study (5 th ed.). Cambridge, MA: Massachusetts Institute of Technology Press.

PAGE 262

250 Barro, Robert J. (2001b). Human Capital and Growth. The American Economic Review, 91 (2), 12. Barro, Robert J., & Lee, JongWha (1993). International Comparisons of Educat ional Attainment. Journal of Monetary Economics, 32 (3), 363 394. Barro, Robert J., & Lee, JongWha (1996). International measures of schooling years and schoo ling quality. The American Economic Review, 86 (2), 218. Barro, Robert J., & Lee, JongWha (2001). In ternational data on educational attainment: Updates and implications. Oxford Economic Papers, 53 (3), 541. Barro, Robert J., Lee, JongWha, & Stokey, Nancy L. (1994). Sources of economic growth : Comments on Barro and Lee. Carnegie Rochester Conference Seri es on Public Policy, 40 (1). Barro, Robert J., & McCleary, Rachel M. (2003). Religion and Economic Growth Across Cou nties. American Sociological Review, 68 (5), 760. Barro, Robert J., & Sala i Martin, Xavier (1991). Convergence across states and regions. Bro o kings Papers on Economic Activity (1), 107 182. Barro, Robert J., & Sala i Martin, Xavier (2004). Economic Growth (2 nd ed.). Boston, MA: Ma ssachusetts Institute of Technology Press. Baumgartner, Frank R., & Jones, Bryan D. (1993). Agendas and Instability i n American Politics. Chicago, Il: University of Chicago Press. Beissinger, Mark (2006). Soviet Empire as "Family Resemblance''. Slavic Review, 65 (2), 294. Besancon, Marie (2003). Good Governance Rankings: The Art of Measurement. World Peace Found a tion, 36 (40). Boswell, Nancy Zucker, & Rose Ackerman, Susan (1996). Are International Institutions Doing Their Job?. Corruption and Democracy, 90, 83 90. Boughton, James, & Bradford Jr., Colin (2007). Global Governance: New Players, New Rules: Why the 20th centur y model needs a makeover. Finance and Development, 44 (4). Bovaird, Tony, & Loffler, Elke (2003). Evaluating the quality of public governance: Indicators, models and methodologies. International Review of Administrative Sciences, 69 (3), 313 328. Breusch, Tr evor (2005 ). Estimating the Underground Economy using MIMIC Models. Canberra, Australia: Australian National University Brewer, Dominic J., Krop, Cathy, Gill, Brian P., & Reichardt, Robert (1999). Estimating the Cost of National Class Size Reductions Unde r Different Policy Alternatives. Educational Evaluation and Policy Analysis, 21 (2), 179 192.

PAGE 263

251 Brewer, Garry D., & deLeon, Peter (1983). The Foundations of Policy Analysis Homewood, Il: The Dorsey Press. Brinkley, Douglas (2003). Wheels for the World: Henry Ford, His Company, and a Century of Progress,1903 2003. New York: Viking. Buehn, Andreas, & Schneider, Freidrich G. (2009). Corruption and the Shadow Economy: A Structural Equation Model Approach. Bonn, Germany: The Institute for the Study of L abor Disc ussion P a per No. 4182. Burleigh, Michael (Ed.). (1996). Confronting the Nazi Past: New Debates on Modern German History London, UK: Collins and Brown, Li mited. Burns, Arthur F., & Mi tchell Wesley C. (1946). Measuring Business Cycles New York, NY: N ation al Bureau of Economic Research. Carilli, Anthony M., Coyne, Christopher J., & Leeson, Peter T. (2008). Government I n tervention and the Structure of Social Capital. Austrian Economics Journal 21, 209 218. Chua, Yvonne T. (2006). An Investigation of Corrupt ion in Philippine Education. Manila, Phili ppines: Philippine Center for Investigative Journalism Cobb, C. W., & Douglas, P. H. (1928). A Theory of Production The American Economic Review, Supplement (18), 139 165. Cortright, Joseph (2001). New Growth The ory, Technology and Learning: A Practitio n ers Guide. Reviews of Economic Development Literature and Practice, No 4 U nited States Ec onomic Development Administration. Deininger, Klaus W., & Squire, Lyn (1996). A new data set measuring income inequality. T he World Bank Economic Review, 10 (3), 565 591. deLeon, Peter (1993). Thinking About Political Corruption Armonk, New York: M.E. Sharpe. deLeon, Peter (1998). Introduction: The Evidentiary Base For Policy Analysis: Empiricist Versus Postpositivist Position s. Policy Studies Journal, 26 (1), 109 113. deLeon, Peter, & Green, Mark T. (2004). Political Corruption: Establishing the Parameters Inte rnational Public Management Review, 5 (1), 70 98. Dell'Anno, Roberto, & Schneider, Freidrich G e org (2006). Estimating t he Underground Economy by Using MIMIC Models: A Response to T. Breuschs Critique., Johannes Kepler Un iversity Linz, Austria.: Economics Working P aper S eries (2006) 07. Diamond, Jarod Mason (1997). Guns, Germs, and Steel: The Fates of Human Societies New Y ork, NY: W.W. Norton & Co. Dreher, Axel, Kotsogiannis, Christos, & McCorriston, Steve (2005). How do Institutions Affect Corruption and the Shadow Economy?. Public Economics Working Paper No. 502012. Thurgau Institute of Economics Kreuzlingen, Switzerland

PAGE 264

252 Easterly, William, & Levine, Ross (2001). What have we learned from a decade of empirical r esearch on growth? It's Not Factor Accumulation: Stylized Facts and Growth Models. World Bank Economic Review, 15 (2), 177 219. Eilat, Yair, & Zinnes, Clifford (200 0). The Evolution of the Shadow Economy in Transition Countries: Consequences for Economic Growth and Donor Assistance. Consulting A ssistance on Economic Reform II Discussion Paper No. 65 : Harvard Institute for International Development Elisseeff, Vadime (Ed.). (1998). The Silk Roads: Highways of Culture and Commerce Paris, France: United Nations Education, Science, and Cultural Organization Publis h ing. Feige, Edgar L., & Urban, Ivica (2008). Measuring underground (unobserved, non observed, u nrecorded) e conomies in transition countries: Can we trust GDP?. Journal of Co m parative Economics, 36 (2), 287 306. Freire, Paulo (1970). Pedagogy of the Oppressed (30 th Anniversary Edition). London, UK: Co ntinuum International Publishing Group Inc. Friedman, Milton (1 997). Public schools: Make them private. Education Economics 5 (3), 341. Friedman, Thomas L. (2005). The World is Flat: A Brief History of the Twenty First Century. Public Affairs Program (Lecture) New York, NY: Carnegie Council for Ethics in Inte rnatio nal Affairs Gadotti, Moacir, & Torres, Carlos Alberto (2005). Paulo Freire: A Homage (Lecture). Chicago, I L : National Louis University. Galbraith, James K., & Kum, Hyunsub (2005). Estimating the Inequality of Household Incomes: A Statistical Approach to the Creation of a Dense and Consistent Data Set. The Review of Income and Wealth, 51 (1), 115 143. Gini, Corrado (1921). Measurement of Inequality and Incomes. The Economic Journal, 31, 124 126. Glasner, David, & Cooley, Thomas F. (Eds.). (1997). Business C ycles and Depressions: An E ncyclop e dia. Taylor & Francis. Goodwin, Richard M. (1951). The Non Linear Accelerator and the Persistence of Business C ycles Econometrica 19, 1 17. Grindle, Merilee S. (2011). Governance Reform: The New Analytics of Next Steps. Go v ernance: An International Journal of Policy, Administration, and Institutions, 24 (3), 415 418. Gruber, Jonathan (2010). Public Finance and Public Policy New York, NY: Worth Pu b lishers Gujarati, Damodar N., & Porter, Dawn C. (2009). Basic Econometrics (5 th ed.). New York, NY: McGraw Hill. Gupta, Dipak K. (2001). Analyzing Public Policy: Concepts, Tools, and Techniques. Washington, D.C.: CQ Press.

PAGE 265

253 Gupta, Sanjeev, Davoodi, Hamid, & Alonso Term, Rosa (1998). Does Corruption Affect Income Inequality and Po verty?. International Monetary Fund Working Paper, 98 (76), 1 41. Gupta, Sanjeev, Davoodi, Hamid, & Tiongson, Erwin R (2000). Corruption and the Pr o vision of Health Care and Education Services International Monetary Fund Working Paper, 00 ( 116 ), 1 33. Hahn Frank, & Solow, Robert (1997). A Critical Essay on Modern Macroeconomic Theory. Cambridge, MA: Massachusetts Institute of Technology Press. Hallak, Jacques, & Poisson, Muriel (2007). Corrupt schools, corrupt universities: What can be done?. Internationa l Institute for Educational Planning. Paris, France: United Nations Education, Science, and Cultural Organization Hayek, Friedrich A. von (1933). Monetary Theory and the Trade Cycle (1975 ed.). New York, NY: Augustus M. Kelley, 1975. Heidenheimer, Arnold J., & Johnston, Michael (2002). Political Corruption: Concepts & Co ntexts (3 rd ed.). Piscataway, NJ: Transaction Publishers. Hicks, John R. (1950). A Contribution to the Theory of the Trade Cycle (2 nd ed.). Oxford, UK: Clarendon Press. Hirsch, Donald E., K ett, Joseph F., & Trefil, James S. (Eds.). (2002). The New Dictionary of Cu ltural Literacy. New York, NY: Houghton Mifflin Harcourt. Howitt, Peter W. (1986). The Keynesian Recovery. The Canadian Journal of Economics, 19 (4), 626. Howitt, Peter W. (1997). A critical essay on modern macroeconomic theory. Journal of Economic Literature, 35 (1), 132. Howitt, Peter W. (1999). Steady endogenous growth with population and R & D inputs growing. The Journal of Political Economy, 107 (4), 715 730, August. Howitt, Peter W., Aghion, Philippe, & Zilibotti, Fabrizio (1999). Endogenous growth theory. The Canadian Journal of Economics, 32 (5), 1338 1341. Howitt, Peter W., & Mayer Foulkes, David (2005). R&D, Implementation, and Stagnation: A Schumpeterian Theory of Convergence C lubs. Journal of Money, Credit, and Banking, 37 147 177. Johnston, Michael (1986). Right & Wrong in American Politics: Popular Conceptions of Corru ption. Polity, 18 (3), 367 391. Johnston, Michael (2007). Benchmarks for Integrity: Tracking Trends in Govern ance. Organis ation for Economic Co ordination and Development Papers, 7 (7), 1. Juglar, Clement (1893). A Brief History of Panics and Their Periodical Occurrence in the United States (D. W. Thom, Trans. 1 st ed.). New York, NY: G. P. Putnam's Sons.

PAGE 266

254 Kaldor, N icholas (1959). Economic Growth and the Problem of Inflation. Economica, 26 287 298. Kalecki, Michal (1954). Theory of Economic Dynamics: An essay on cyclical and long run changes in capitalist economy Monthly Review Press. New York, NY. Kaminski, Matthe w (2005). Membership Has Its Privileges -Or Does It? Wall Street Journal, p. A15. New York, NY. Kargbo, Abubakar H. (2006). Corruption: Definition and Concept Manifestations and Typology in the Africa Context Paper presented at The Training for Members of Parliament and Members of Ci v il Society from English speaking West Africa: Gambia, Ghana, Nigeria, Liberia, and Sierra Leone. Kaufmann, Daniel (2003). Rethinking Governance: Empirical Lessons Challenge Orthodoxy Washin g ton, D.C.: World Bank Institute Kaufmann, Daniel (2006). Myths and Realities of Governance and Corruption. In World Ec onomic Forum, Global Competitiveness Report 2005 2006 (pp. 81 98). Washington, D.C.: Palgrave Macmillan. Kaufmann, Daniel Kraay, Aart, & Mastruzzi, Massimo (2008). Gov ernance Matters VII: Aggr egate and Individual Governance Indicators, 1996 2007 The World Bank Group Policy Research Working Paper No. 4654 : Washington, D.C. Kaufmann, Daniel, Kraay, Aart, & Zoido Lobatn, Pablo (2000). Governance Matters. World Bank Wor king Paper Series, No. 2196 1 61. Intangible Infrastructure: The key to growth: Credit Suisse Research Institute. Zurich, Switzerland. Kettl, Donald F. (2000). The Transformation of Governa nce: Globalization, Devolution, and the Role of Government. Public Administration Review, 60 (6), 488 497. Keynes, John Maynard (1936). The General Theory of Interest, Employment, and Money Lo ndon, UK: Macmillan. King, Robert G., & Levine, Ross (1993). Fin ance and Growth Schumpeter Might Be Right. The Quarterly Journal of Economics, 108 (3), 717 737. Kitchin, Joseph (1923). Cycles and Trends in Economic Factors. The Review of Economics and Statistics, 5 (1), 10 16. Klenow, Peter J., & Rodrguez Clare, Andr s (1997). Economic growth: A review essay. Journal of Monetary Economics, 40 (3), 597 617. Klenow, Peter J., & Rodrguez Clare, Andrs (2005). Externalities and Growth In P. Aghion & S. N. Durnlauf (Eds.), Handbook of Economic Growth (Vol. 1, Part A, pp. 8 17 861). Amsterdam, The Netherlands: Elsevier B.V.

PAGE 267

2 55 Klingner, Donald E., & Sabet, Gamal M. (2005). Knowledge Management, Organizational Lear ning, Innovation, and Technology Transfer: What They Mean and Why They Matter. Comparative Technology Transfer and So ciety, 3 (3), 199 210. Klitgaard, Robert E. (1988). Controlling Corruption Berkeley, CA: University of California Press. Kondratiev, Nikolai D. (1926). The Long Waves in Economic Life Sozialwissenschaft und Sozialpolitik. 56(3). 573 609. Kooiman, Jan, & J entoft, Svein (2009). Meta Governance: Values, Norms, and Principles, and the Making of Hard Choices. Public Administration 87 (4), 818 836. Kornai, Janos (1992). The Socialist System: The Political Economy of Communism. Princeton, NJ: Princeton University Press. Kornai, Janos (2005). The Great Transformation of Central and Eastern Europe: Success and Disappointment. Conference Paper. I nternational E conomics A ssociaton 14 th World Congress: Marrakech, Morocco. Krueger, Anne O. (1974). The Political Economy o f the Rent Seeking Society. American Ec onomic Review, 64 (3), 291 303. Krugman, Paul R. (1998). What's New About the New Economic Geography?. Oxford Review of Economic Policy, 14 (2), 7. Krugman, Paul R. (2000). Technology, Trade and Factor Prices. Journal o f International Ec onomics, 50 (1), 51. Kurtzman, Joel, Yago, Glenn, & Phumiwasana, Tripho (2004). The Global Costs of Opacity Massach u setts Institute of Technology, Sloan Management Review, 46 (1), 38. Kuznets, Simon S. (1934). National Income 1929 1932 Li brary of Congress. Kuznets, Simon S. (1940). Schumpeter's Business Cycles. The American Economic Review, 30 (2), 257 271. Kuznets, Simon S. (1966). Modern Economic Growth: Rate, Structure and Spread New Haven, CT: Yale University Press. Kuznets, Simon S. ( 1971). Economic Growth of Nations: Total Output and Production Structure. Boston, MA: Harvard University Press. Kuznets, Simon S. (1973). Modern Economic Growth: Findings and Reflections. The American Econo m ic Review, 63 (3), 247 258. La Porta, Rafael, Lope z De Silanes, Florencio, Shleifer, Andrei, & Vishny, Robert W. (1999). The Quality of Government. Journal of Law, Economics, and Organization, 15 (1), 222 279.

PAGE 268

256 La Porta, Rafael, & Shleifer, Andrei (2008). The Unofficial Economy and Economic Develo pment. Wor king Paper No. 2009 57 Tuck School of Business, Levitt, Theodore (1983). The Globalization of Markets. Harvard Business Review, 61 (3), 92 102. Levy, Daniel (2007). Price Adjustments Under the Table: Evidence on Efficiency enhancing Co rruption. European Jo urnal of Political Economy, 17 749 777. Lozada, Carlos (2002). Economic Growth is Reducing Global Poverty. National Bureau of Ec onomic Research Digest, October Lucas, Robert E. Jr. (2009). Ideas and Growth. Economica, 76 (301), 1 19. Lynn, Laurence E. Jr. (1998). The new public management: How to transform a theme into a le gacy. Public Administration Review, 58 (3), 231. Maddison, Angus (2009). Historical Statistics of the World Economy: 1 2006 AD Un i versity of Groningen, Groningen, the Netherlands. Maher, Joanne (Ed.). (2004). The Europa World Year Book (45 ed. Vol. 1& 2). London, UK: E uropa Publications, Taylor and Francis Group. Maher, Joanne (Ed.). (2008). The Europa World Year Book ( 49 ed Vol. 1) London, UK: Europa Publications, Taylor and Francis Gr oup. Martin, Ron, & Sunley, Peter (1998). Slow Convergence? The new endogenous growth theory and regional development. Economic Geography, 74 (3), 201 227. Matheson, Troy D. (2008). Decomposing Productivity Growth and Divergence: An Index Nu mber Approach. E mpirical Economics, 34 (2), 273 284. Mauro, Paolo (1995). Corruption and Growth. The Quarterly Journal of Economics, 110 (3), 681 712. Mauro, Paolo (1997). The Effects of Corruption on Growth, Investment, and Government E xpenditure: A Cross Country Analysis Working Paper No. 96 /98. Washington, D.C.: International Monetary Fund. Mauro, Paolo (1998a). Corruption and the Composition of Government Expenditure. Journal of Public Economics, 69 263 279. Mauro, Paolo (1998b). Corruption: Causes, Consequences, and A genda for Further Research. F inance and Development, 35 (1), 11 14. Mauro, Paolo (2000). Why Worry About Corruption?. Washington, D.C.: International Monetary Fund. Mauro, Paolo (2004). The Persistence of Corruption and Slow Economic Growth. Inte r national M onetary Fund Staff Papers, 51 (1), 1 18. Washington, D.C.: International Monetary Fund.

PAGE 269

257 Mauro, Paolo, Abed, George T., & Gupta, Sanjeev (1998). Governance, Corruption, and Ec ono m ic Performance (pp. 225 244). Washington, D.C.: International Monetary Fund M auro, Paolo, Abed, George T., & Gupta, Sanjeev (2002). Corruption and the Composition of Gover n ment Expenditure. Washington, D.C.: International Monetary Fund Mazumdar, Krishna (2003). Do standards of living converge? A cross country study. Social Ind ica tors Research, 64 (1), 29 50. Merton, Robert K. (1968). Social Theory and Social Structure (1968 Enlarged ed.). New York, NY: Simon and Schuster. Metzler, Lloyd A. (1941). The Nature and Stability of Inventory Cycles. Review of Economic Studies, Vol. 23 11 3 129. Mitc hell, Wesley C. (1928). Business Cycles; The Problem and Its Setting New York, NY: N ational Bureau of Economic Research, Inc. Monas, Sidney (1984). Censorship, Film, and Soviet Society: Some reflections of a Ru s sia watcher. Studies in Comparati ve Communism, 17 (3/4), 163 172. Moody Stuart, George (1996). The Costs of Grand Corruption. Economic Reform Today, 4 19 24. Movsovic, M. I. (1959). Technical and Vocational Education in the Union of Soviet Socialist R epublics Paris, France: United Natio ns Education, Science, and Cultural Organization Nelson, Richard R., & Phelps, Edmund S. (1966). Investment in Humans, Technological Diff usion, and Economic Growth. American Economic Review, 56 (2), 69 75. Nissan, Edward (1991). The Dynamics of Agricultura l Contribution to Economic Growth. Review of Black Political Economy, 20 (1), 5. North, Douglass C. (1990 ). Institutions, Institutional Change and Economic Performance New York, NY: Cambridge University Press. North, Douglass C. (1991a) Institutions. The Journal of Economic Perspectives, 5 (1), 97. North, Douglass C. (1991b). Towards a Theory of Institutional Change. The Quarterly Review of Economics and Business, 31 (4), 3. North, Douglass C. (1992). Institutions and Economic Theory. The American Economist, 36 (1), 3 6. North, Douglass C. (1994). Economic Performance Through Time. The American Economic R eview, 84 (3), 359. Nye, Joseph S. (1967). Corruption and Political Development: A Cost Benefit Analysis. Amer ican Political Science Review, 61 (2), 417 427.

PAGE 270

258 N ye, Joseph S. (2006). The Fragility of a Flat World. Project Syndicate (June). Retrieved on 6/13/2011 from http://www.project syndicate.org/nye34. Nye, Joseph S. (2008). Smart Power. Harvard Business Review (November). pp. 55 59. Ofer, Gur (1987). Soviet Economic Growth: 1928 1985. Journal of Economic Literature, 25 (4), 1767. Olson, Mancur Jr., Sarna, Naveen, & Swamy, Anand V. (2000). Governance and Growth: A si mple hypothesis explaining cross country differences in productivity growth. Public Choice, 102 (3 4), 341 364. Patrinos, Harry Anthony (2007). Demand side Financing in Education Paris, France: The Inte rnational Institute for Educational Planning, The International Academy of Education. Pesic, Vesna (2007). State Capture and Widespread Corruption in Serbia. Brussels, Belgium: Center for European Policy Studies Phelps, Edmund S. (2008). Macroeconomics for a Modern Economy American Economist, 52 (1), 20. Phelps, Edmund S., & Nelson, Richard R (1966). Investment in Humans, Technological Diff usion, and Economic Growth American Economic Review, 56 (2), 69 75. Pritchett, Lant H. (1997). Divergence, Big Time. Journal of Economic Perspectives, 11 (3), 14. Pritchett, Lant H. (2001). Where has all the education gone?. The World Bank Economic Review, 15 (3), 367 391. Putnam, Robert D. (2000). Bowling Alone: The Collapse and Revival of American Community. New York, NY: Simon and Schuster. llenge Account. Washington, D.C.: C enter for Global Development. Relly, Jeannine (2011). Testing Vertical Accountability: A Cross National Study of the Influence of Information Access on the Control of Corruption Conference paper. the 1 st Global Conference on Transparency Research. Rutger s University, Newark, NJ. Robinson, James Harvey (1902). An Introduction to the History of Western Europe Bo s ton, MA: Ginn & Company. Romer, Paul M. (1986). Increasing Returns and Long Run Growth. The Journal of Political Economy, 94 (5), 1002 1037. Romer Paul M. (1990). Endogenous Technological Change. The Journal of Political Economy, 98 (5), Part 2: The Problem of Development: A Conference of the Institute for the Study of Free Ente r prise Systems, S71 S102.

PAGE 271

259 Romer, Paul M. (1993). Implementing a National Technology Strategy with Self Organizing I ndustry Investment Boards. Brookings Papers on Economic Activity, Microeconomics 2, 345 399. Romer, Paul M. (1994a). Beyond Classical and Keynesian Macroeconomic Policy. Policy O ptions, 15 (6), 15 21. Romer, Paul M (1994b). The Origins of Endogenous Growth. The Journal of Economic Perspe ctives, 8 (1), 3 22. Romer, Paul M. (1996). Why, indeed, in America? Theory, history, and the origins of modern economic growth. The American Economic Review, 86 (2), 202. Romer, Paul M. (1998a). Economic Growth: It's All in Your Head. Outlook, No. 1. Romer, Paul M. (1998b). Innovation: The New Pump of Growth. Blueprint Magazine: Ideas for a New Century, Winter (1), 27 31. Romer, Paul M. (2001). What have we learned from a decade of e mpirical research on growth? l liam Easterly and Ross Levine. The World Bank Economic Review, 15 (2), 225 227. Romer, Paul M. (Ed.). (2007) The Concise Encyclopedia of Economic s. Indianapolis, IN: Liberty Fund. Romer, Paul M., & Barro, Robert J. (1990). Human Capital and Growth: Theory and E v idence; A Comment. Carnegie Rochester Conference Series on Public Policy, 32 251 286. Els evier. Rose Ackerman, Susan (1978). Corruption : A Study in Political Economy New York, NY: Ac ademic Press. Rose Ackerman, Susan (1999). Corruption and Government: Causes, Consequences, and R eform. New York, NY: Cambridge University Press. Rose Ackerman, Susan (2008). Corruption and Government Intern ational Peace Kee p ing, 15 (3), 328 343. Rose Ackerman, Susan (Ed.). (2006 ). International Handbook on the Economics of Co r ruption. Cheltenham, Gloucestershire: Edward Elgar Publishing. Rostow, Walt Whitman (1971). The Stages of Economic Growth: A Non Commun ist Manifesto (2 nd ed.). New York, NY: Cambridge University Press. Rostow, Walt Whitman (1975). Kondratieff, Schumpeter, and Kuznets: Trend Periods Revisited. The Journal of Economic History, 35 (4), 719 753. Rostow, Walt Whitman (1991). The Stages of Econo mic Growth: A Non Communist Manifesto (3 rd ed.). New York, NY: Cambridge University Press. (Original work published 1960)

PAGE 272

260 Rotberg, Robert I. (2005). Strengthening Governance: Ranking Countries Would Help. Washin gton Quarterly, 28 (1), 71 81. Rotberg, Robert I. (2009). Governance and Leadership in Africa: Measures, Methods, and R esults. Journal of International Affairs, 62 (2), 113. Rothschild, Emma (1986). A Divergence Hypothesis. Journal of Development Econo m ics, 23 (2), 205 226. Russell, Barrie (2010). Reven ue Administration: Managing the Shadow Economy Washington, D.C.: International Monetary Fund. Sabet, Mohamed Gamal, & Klingner, Donald (1993). Exploring The Impact of Professionalism on Administrative Innovation. Journal of Public Administration Research and Theory, 3 (2), 252 266. Sachs, Jeffrey D. (2005). The End of Poverty: Economic Possibilities of Our Time New York, NY: Penguin Group. Sachs, Jeffrey D., & Warner, Andrew M. (1995). Economic Convergence and Economic Policies. National Bureau of Economic Research Working Paper, No. 5039 Sadik, Jacques (2008). Technology Adoption, Convergence, and Divergence. European Econo mic Review, 52 (2), 338. Sala i Martin, Xavier (1997). I Just Ran Two Million Regressions. The American Economic R eview Papers and Proc eedings 87 (2), 7. Sala i Martin, Xavier (2002). The World Distribution of Income: Estimated from Individual Country Distributions. National Bureau of Economic Research, Working Paper No. 8933 Salamon, Lester M., & Odus, Elliot, V. (2002). The Tools of Go vernment: A Guide to The New Gover n ance London, UK: Oxford University Press. Sandholtz, Wayne, & Koetzle, William (2000). Accounting for Corruption: Economic Structure, Democracy, and Trade International Studies Quarterly, 2000 (44), 31 50. Schneider, Fri edrich G eorge (2009). The Institutional Economics of Corruption and Reform: Theory, Evidence and Policy. Public Choice, 138 (1 2), 257 258. Schneider, Friedrich Georg Buehn, Andreas, & Montenegro, Claudio E. (2010). Shadow Econ omies All Over the World: New Estimates for 162 Countries from 1999 to 2007. World Bank Working P a per Series, 535 6, 53. Schneider, Friedrich G eorge & Enste, Dominik H. (2002). Hiding in the Shadows: The Growth of the Underground Economy Economic Issues Number 30. Washington, D. C.: In ternatio nal Monetary Fund. Schneider, Friedrich Georg & Enste, Dominik H. (2000). Shadow Economies Around the World: Size, Causes, and Consequences International Monetary Fund Working Paper 00/26, 56.

PAGE 273

261 Schneider, Mark, Teske, Paul, Marschall, Melissa, Min trom, Michael, & Roch, Christine (1997). Institutional arrangements and the creation of social capital: The effects of public school choice. The American Political Science Review, 91 (1), 82. Schumpeter, Joseph A. (1939). Business Cycles: A Theoretical, His torical, and Statistical Anal ysis of the Capitalist Process (Vol. I & II). New York, NY: McGraw Hill. Schumpeter, Joseph A. (1942). Capitalism, Socialism and Democracy (3 rd 1950 ed.). New York, NY: Harper & Borthers. Sen, Amartya (1984). The Living Standar d Oxford Economics Papers, 36 (supp), 74 90. Sen, Amartya (1988). Mortality as an indicator of economic success and failure. The Economic Journal, 108 (446), 1 25. Sen, Amartya (1997). Human Capital and Human Capability. World Development, 25 (12), 1959 1961 Sen, Amartya (1999). Development as Freedom Oxford, UK: Oxford University Press. Sen, Amartya (2000). Cost Benefit Analysis: Legal, Economic, and Philosophical Perspectives. The Journal of Legal Studies, 29 (2), 931 952. Sen, Amartya (2004). Freedom as P rogress. Finance & Development, 41 (3), 4 7. Serra, Danila (2006). Empirical Determinants of Corruption: A sensitivity analysis. Public Choice, 126 (1 2), 225 256. Shleifer, Andrei, & Vishny, Robert W. (1993). Corruption. The Quarterly Journal of Economics, 108 (3), 599 617. Simon, Herbert (1972). Theories of Bounded Rationality In C. B. McGuire & R. Radner (Eds.). Decision and Organization (pp. 161 176): Amsterdam, the Netherlands: North Holland Publishing. Simon, Herbert A. (1986). Rationality in Psychology and Economics. The Journal of Business, 59 (4), Part 2, S209 224. Simon, Herbert A. (1997). Models of Bounded Rationality: Empirically Grounded Economic Reason (Vol. 3) Cambridge, MA: Massechuttets Institute of Technology Press. Slutsky, Eugen (1929). Ran dom Events and Cyclical Oscillations. Journal of the American Stati stical Association 258 276. Solow, Robert M. (1956). A Contribution to the Theory of Economic Growth The Quarterly Journal of Economics, 70 (1), 65 94. Solow, Robert M. (1957). Technical C hange and the Aggregate Production Function. The Review of Economics and Statistics, 39 (3), 312 320.

PAGE 274

262 Solsten, Eric (Ed.). (1991). Cyprus: A Country Study. Washington, D.C.: Government Post Office for the Library of Congress Stefes, Christoph H. (2006). Un derstanding Post Soviet Transitions: Corruption, Collusion and Clientelism. New York, NY: Palgrave MacMillan. Stern, David I. (2003). The Environmental Kuznets Curve. International Society for Ecological Econo m ics, June. Stoker, Gerry (1998). Governance a s Theory: Five propositions International Social Science Journal, 50 (155), 17 28. Suetonius (110). The Lives of the Caesars, The Deified Julius (J. C. Rolfe, Trans. Vol. 1). Ca mbridge, MA: Harvard University Press. Tanzi, Vito (1998). Corruption Around th e World: Causes, Consequences, Scope, and Cures. I nternatio n al Monetary Fund Staff Paper 9863, 45 (4), 559 594. Taylor, Fredrick Winslow (1911). The Principles of Scientific Management New York, NY: Harper & Row, Publishers Incorporated. Teske, Paul, & Sch neider, Mark (2001). What research can tell policymakers about school choice Journal of Policy Analysis and Management, 20 (4), 609. Thomann, Andreas (2008). Global Trends: Building Blocks of Healthy Economic Growth. Inte rview with Richard Kersley, Stephan Intangible Infrastructure, the Key to Growth. Credit Suisse Online Publications, 5/12 Retrieved 6/12/2012 from https://infocus.credit suisse.com/.../RI_Intangible_Infrastructure_081 Thompson, Dennis F. (2007). Two Conce pts of Corruption. [ Conference paper ] Corru p tion and Democracy University of British Columbia Treib, Oliver, Bahr, Holger, & Falkner, Gerda (2007). Modes of Governance: Towards a Conce ptual Clarification. Journal of European Public Policy, 14 (1), 1 20. Treisman, D. (2000). The Causes of Corruption: A Cross national Study. Journal of Public Ec onomics, 76 (3), 399 45 8 Weber, Max (1930). The Protestant Ethic and the Spirit of Capitalism. (T. Parsons, Trans. 1950 ed.). London, UK: George Allen & Unwin Ltd. W eimer, David L., & Vining, Aidan R. (2004). Policy Analysis: Concepts and Practice (4 th ed.).Upper Saddle River, NJ: Prentice Hall. Werlin, Herbert H. (1994). Revisiting Corruption With a New Definition. International Review of Administrative Sciences, 6 0 (4), 547 558. Werlin, Herbert H. (2000). The Concept of Secondary Corruption. International Review of A dministrative Sciences, 66 (1), 181 185.

PAGE 275

263 Wong, W. K. (2007). Economic Growth: A channel decomposition exercise. B. E Journal of Macroec o nomics, 7 (1), 38 http://www.bepress.com/bejm Retrieved 6/11/201 1. cThe Review of Economics and Statistics 5 (1): 10 16. Cambridge, MA: Harvard University. Xu, Z., & Li, H (2008). Political Freedom, Economic Freedom, and Income Convergence: Do stages of economic development matter?. Public Choice, 135 (3 4). Economy and Public Debt Sustainability in Turkey. SAOPSTENJA/Communications. Anakara, Turkey: Hecettepe Univeristy, 85 104. Zhou, Fujin (2007). The Shadow Economy in Mon golia: Size, Causes and Consequences. Amsterdam, the Netherlands: Tinbergen Institute Working Paper No. 42. Ed u