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Happiness and development in the BRICS

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Happiness and development in the BRICS a mixed methods analysis
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Breed, David D. ( author )
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Denver, CO
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Happiness -- Developing countries ( lcsh )
Happiness -- Testing ( lcsh )
Well-being -- Developing countries ( lcsh )
Quality of life -- Developing countries ( lcsh )
Economic development -- Developing countries ( lcsh )
Globalization ( lcsh )
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theses ( marcgt )
non-fiction ( marcgt )

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Abstract:
This thesis used ethnographic analysis and quantitative methods to explore the interplay between happiness, neoliberal economic development, and globalization in Brazil, Russia, India, China, and South Africa - also known as BRICS. Though the neoliberal path of development does not explicitly promise happiness, the hypotheses operates as theory testing. Correlation is used to evaluate happiness relating to four different variables: exports, electricity production, internet use, and income inequality. The quantitative results yield significant relationships that are sporadically placed across the BRICS. This is also observed when controlled for economic protectionism. Due to the ambiguities that this creates cultural notions of happiness in each individual BRICS county are examined using an ethnographic approach. This is done in search of potential reasons behind the inconclusive results. It is ultimately argued that culture is the mechanism through which the influence of neoliberal globalization on happiness is determined. In order to shed light on this, post colonialism is used in the final chapter to argue that the ways in which cultures attach meaning to development processes play a key intermediary role in how neoliberal globalization affects happiness.
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Political science
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Department of Political Science
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by David D. Breed.

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Full Text
HAPPINESS AND DEVELOPMENT IN THE BRIGS:
A MIXED METHODS ANALYSIS
by
DAVID D. BREED
BSc (hons), University of Plymouth, 2011
A thesis submitted to the
Faculty of the Graduate School of the
University of Colorado in partial fulfillment
of the requirements for the degree of
Master of Arts
Political Science
2013


This thesis for the Master of Arts degree by
David D. Breed
has been approved for the
Department of Political Science
by
Lucy McGuffey, Chair
Michael Berry
Betcy Jose
November


Breed, David D. (MA, Political Science)
Happiness and Development in the BRICS: A Mixed Methods Analysis
Thesis directed by Assistant Professor CT Lucy McGuffey
ABSTRACT
This thesis uses ethnographic analysis and quantitative methods to explore the
interplay between happiness, neoliberal economic development, and globalization in
Brazil, Russia, India, China, and South Africa also known as the BRICS. Though the
neoliberal path of development does not explicitly promise happiness, the hypotheses
operate as theory testing. Correlation is used to evaluate happiness relating to four
different variables: exports, electricity production, internet use, and income inequality.
The quantitative results yield significant relationships that are sporadically placed across
the BRICS. This is also observed when controlling for economic protectionism. Due to
the ambiguities that this creates, cultural notions of happiness in each individual BRICS
country are examined using an ethnographic approach. This is done in search of potential
reasons behind the inconclusive results. It is ultimately argued that culture is the
mechanism through which the influence of neoliberal globalization on happiness is
determined. In order to help shed light on this, postcolonialism is used in the final
chapter to argue that the ways in which cultures attach meaning to development processes
play a key intermediary role in how neoliberal globalization affects happiness.
The form and content of this abstract are approved. I recommend its publication.
Approved: Lucy McGuffey
m


ACKNOWLEDGEMENTS
First and foremost, I would like to thank parents and grandparents, both gone and still
with us, for their generous emotional and financial support through my education thus far. This
has instilled a lifelong love of learning, opened many doors, and aided in my own search of living
a happy, meaningful, and fulfilling life.
Since the mere three weeks between undergraduate and (post)graduate study have
peculiarly blurred both degrees into one, I would like to thank the faculty, staff, and students at
both of the following universities. At the University of Plymouth, School of Government, I
would like to thank Dr. Rebecca Davies, Dr. Brieg Powell, and Mr. James Goulboum for
demonstrating the rewarding opportunities for enriched analysis that can result from exploring the
nexus between critical theory, international development, and international political economy.
At the University of Colorado Denver, Department of Political Science, I would also
like to thank the wonderful team who graciously agreed to be part of the committee here. I would
like to thank Dr. Lucy McGuffey for asking the tough questions. To clarify, Lucy, thank you
for encouraging me to walk the fine line between finding answers and appreciating ambiguity,
while at the same time teaching me the knowledge necessary to strive towards unraveling these
unclarities in search of meaning. Moreover, I would also like to thank Dr. Michael Berry for his
helpfulness and guidance in better equipping me with the skills necessary to examine complex
questions such as the one in this thesis. These skills have proven to be invaluable and have given
structure to both this research and many papers to come. Additionally, I would also like to thank
Dr. Betcy Jose for her suggestions which greatly helped add meaning to the concepts used here
and her exceptional supervision while working as her teaching assistant. What a rewarding and
beneficial experience it was to experience academia from the other side of the classroom. I have
a wealth of gratitude to those above, in sum, and sincerely apologize to those inevitably left out.
IV


TABLE OF CONTENTS
CHAPTER
I. INTRODUCTION.................................................. 1
II. LITERATURE REVIEW............................................. 6
Happiness Across the Humanities and Social Sciences............6
Philosophy and happiness.............................7
Sociology and happiness..............................9
Economics and happiness............................ 10
Connecting the Disciplines................................... 12
Chapter Summary............................................... 13
III. CONCEPTS, VARIABLES, AND MEASUREMENT......................... 14
Happiness.................................................... 14
Defining happiness................................. 15
Measuring happiness................................ 18
Neoliberalism................................................ 19
Neoliberal Economic Development...............................21
Globalization.................................................23
Income Inequality.............................................25
Terminology Hereafter.........................................26
v


IV. HYPOTHESES AS PART OF THEORY TESTING..............................27
Why Neoliberal Globalization is Hypothesized to Further Happiness.28
Hypotheses....................................................... 31
V. METHODOLOGY...................................................... 35
Mixed Methods.....................................................35
Data..............................................................36
Sample............................................................37
Validity of Quantifying Happiness.................................41
Quantitative data: Happiness survey validity............41
Quantifying happiness...................................42
Controlling for Economic Protectionism............................47
Operationalizing the Methods......................................49
VI. QUANTITATIVE ANALYSIS............................................ 51
Happiness: Descriptive Statistics................................ 51
Independent Variables: Descriptive Statistics.................... 54
State-level Analysis Addressing Hypothesis One....................58
Exports................................................ 60
Industrialization...................................... 61
Globalization.......................................... 62
Income inequality...................................... 63
vi


Systemic-level Analysis Addressing Hypothesis Two:
Controlling for Protectionism.........................................64
Exports................................................... 66
Industrialization......................................... 68
Globalization..............................................70
Income inequality..........................................72
Summary of Quantitative Findings..................................... 74
VII. CULTURAL ANALYSIS: HAPPINESS IN THE BRICS.............................75
Defining Culture..................................................... 78
Brazil: Happiness as Envisioning the Ideal Joyous Life............... 80
China: Happiness as the Mentality of Look at
How Far Weve Come!................................................. 84
India: Happiness as Pleasantness through Social Capital...............88
Russia: Happiness as the Absence of Market Volatility.................91
South Africa: Happiness as Hope and Pride.............................94
Chapter Summary.......................................................97
VIII. DISCUSSION............................................................99
Income Inequality................................................... 100
Introducing Postcolonialism......................................... 103
Knowledge/Power, Othering, and Meaning in Cultural Contexts......... 106
Exports.................................................. 106
vii


Industrialization
107
Globalization........................................ 109
Chapter Summary................................................ Ill
IX. CONCLUSION..................................................... 112
REFERENCES............................................................... 115
APPENDIX
A. State-level Quantitative Results for Non-BRICS Countries.......... 124
B. WVS/EVS Happiness Survey Questions................................ 126
C. Methods Used to Calculate Gini Index Scores....................... 127
D. Skedacity and Awkward Data........................................ 128
E. Linear vs Non-linear Data Interpolation........................... 129
F. Descriptions of World Bank Independent Variables.................. 130
G. Veenhovens Affect Theory of Happiness Self-assessment............ 131
viii


LIST OF TABLES
Table
3.1 Overview of Happiness in the Non-philosophical Literature..................... 17
5.1 Happiness Ranked by Z-score................................................... 38
5.2 Exports per capita (constant 2000 US$) Ranked by Z-score...................... 39
5.3 Electricity Production per capita (kw/h) Ranked by Z-score.................... 39
5.4 Internet Users (% of population) Ranked by Z-score.............................40
5.5 Income Inequality (gini coefficient) Ranked by Z-score.........................40
5.6 Happiness and Economic Protectionism...........................................47
6.1 Happiness Descriptive Statistics...............................................52
6.2 Exports per capita (constant 2000 US$) Descriptive Statistics................. 54
6.3 Electricity production per capita (kw/h) Descriptive Statistics............... 55
6.4 Internet Users (% of population) Descriptive Statistics....................... 56
6.5 Income Inequality (gini coefficient) Descriptive Statistics................... 57
6.6 State-level Results: Pearson r Correlation Coefficient........................ 58
6.7 State-level Results: Regression Coefficient and Standard Error.................59
6.8 Cumulative Sample Countries: Pearson r Correlation Coefficient.................65
6.9 Cumulative Sample Countries: Regression Coefficient and Standard Error........65
6.10 Happiness and Exports per capita (constant 2000 US$)
Controlled for Economic Protectionism: Pearson r Correlation Coefficient.....66
6.11 Happiness and Exports per capita (constant 2000 US$) Controlled for
Economic Protectionism: Regression Coefficient and Standard Error............67
6.12 Happiness and Electricity Production per capita (kw/h) Controlled for
Economic Protectionism: Pearson r Correlation Coefficient....................68
IX


6.13 Happiness and Electricity Production per capita (kw/h) Controlled for
Economic Protectionism: Regression Coefficient and Standard Error............69
6.14 Happiness and Internet Use (% of population) Controlled for
Economic Protectionism: Pearson r Correlation Coefficient....................70
6.15 Happiness and Internet Use (% of population) Controlled for
Economic Protectionism: Regression Coefficient and Standard Error............71
6.16 Happiness and Income Inequality (gini coefficient) Controlled for
Economic Protectionism: Pearson r Correlation Coefficient....................72
6.17 Happiness and Income Inequality (gini coefficient) Controlled for
Economic Protectionism: Regression Coefficient and Standard Error............73
7.1 Cultural Notions of Happiness in the BRICS....................................98
x


LIST OF FIGURES
Figure
3.1 Terminology, Proxy Measures, and their Associated Variables....................26
6.1 Distribution of Responses in WVS Surveys...................................... 53
7.1 Brazil: Time Series Graphs.....................................................81
7.2 China: Time Series Graphs......................................................85
7.3 India: Time Series Graphs..................................................... 89
7.4 Russia: Time Series Graphs.....................................................92
7.5 South Africa: Time Series Graphs...............................................95
xi


LIST OF EQUATIONS
Equation
5.1 Quantifying Happiness with Mid-interval Values and a Weighted Mean.......43
5.2 Presuming Approximate Normal Distribution in a Random Sample.............44
xii


LIST OF ABBREVIATIONS
BIT bilateral investment treaty
BRICS Brazil, Russia, India, China, and South Africa
DSB Dispute Settlement Body
EOI export oriented industrialization
EU European Union
EVS European Values Study
FDI foreign direct investment
GDP gross domestic product
HDI Human Development Index
IFIs international financial institutions
IMF International Monetary Fund
IR International Relations
ISI import substitution industrialization
IQR interquartile range
LEDC less economically developed country
MAR missing at random
MCAR missing completely at random
MDGs Millennium Development Goals
MEDC more economically developed country
NAAH not at all happy
NTB non-tariff barrier
NVH not very happy
OECD Organization for Economic Cooperation and Development
SAP structural adjustment policy
SWB subjective well-being
Qi first quartile; 25th percentile of data
Q3 third quartile; 75th percentile of data
QH quite happy
QOL quality of life
UN United Nations
US United States of America
VH very happy
WB World Bank
WTO World Trade Organization
WVS World Values Survey
xm


CHATERI
INTRODUCTION
In the post-Second World War global political economy, forces of globalization
have fueled the exponentially growing rate of interconnectedness between states. The
liberalization of markets has played a key role in furthering this as states have become
increasingly interdependent in economic areas (Bhagwati 2007). The World Trade
Organization (WTO) and international financial institutions (IFIs) such as the
International Monetary Fund (IMF) and the World Bank (WB), have avidly encouraged
interdependence through market liberalization among their members and their authority
has become pronounced.
In the past few decades, the study of happiness has had a renaissance in the social
sciences and the scholarship has increasingly found connections between physical and
emotional well-being (Greve 2012; Thin 2013). Greater ease of global communication
and improvements in survey methodology have led modern scholars to explore happiness
in a different light than the Greek and Roman stoics did (Irving 2008; Thin 2013).
Despite the greater ability now to explain how happy people are and compare this with
other variables,1 the definition of happiness continues to be ambiguous in the literature as
many scholars eventually revert to the writings of the stoic philosophers (Irving 2008;
Thin 2013).
1 Thin ties the emergence of happiness studies in recent times with the birth of statistics
and utilitarianism in 1700s England and Scotland (2013). This is seen in John Sinclair
coining the new term statistics as a method of ascertaining the quantum of happiness
by its inhabitants (Sinclair 1798, xiii cited in Thin 2013, 5). Moreover, this is also seen
in Jeremy Benthams utilitarianism which seeks the greatest amount of happiness for the
greatest number of individuals (Thin 2013).
1


The United Nations (UN), IMF, and WB in recent years have adopted policies
which acknowledge that gross domestic product (GDP) alone is an insufficient measure
of well-being (Costanza et al. 2009). This is reflected in the UNs adoption of the Human
Development Index (HDI) and its enactment of the Millenium Development Goals
(MDGs) which offer a more comprehensive view of what development is and how
development affects individuals well-being (Costanza et al. 2009). Less discussed is
how development influences happiness, however. Due to information bias and the
difficulty found in accurately forecasting future conditions, individuals are decent judges
of their own happiness in the present, but experience difficulty in determining what
exactly will make them happier in the future (Veenhoven 2009). Examining happiness is
much more complex than examining well-being as the varying notions of happiness differ
considerably between cultures, but there is great potential for an enriched debate by
dissecting happiness rather than well-being (Selin and Davey 2012). At the same time,
the cloud of ambiguity surrounding the concept of happiness leads to obstacles and
difficulties.
None of the international institutions presented above explicitly make any
promises of happiness, and in many ways, to be very clear, this hypothetical aim is
neither built into their institutional framework nor is it what they were specifically
created to do. Inevitably, however, the UN, WTO, IFIs, and various other institutions
have had an effect on the happiness of individuals worldwide in the development process
through what has become known as the neoliberal path of development. Due to this
influence and that fact that the nexus between happiness and development has multiple
2


opportunities for expansion in the literature, this thesis seeks to contribute to the
scholarship in the following ways.
Instead of examining the effects of neoliberal globalization on poverty reduction
and well-being, this thesis examines the relationship between neoliberal globalization and
happiness in emerging economies. Rather than focusing on physical well-being, in other
words, this thesis examines the impact, if any, that neoliberal economic development and
globalization have on happiness. As emerging economies that have achieved profound
rates of economic growth post-1990, the focal points of this thesis are Brazil, Russia,
India, China, and South Africa also known and hereafter referred to as the BRICS
(ONeil 2011). Using survey data, happiness in the BRICS is assessed in the 1990-2007
time-period and analyzed alongside variables which measure various aspects of
neoliberal economic development and globalization.
The hypotheses that follow are a form of theory testing that ask two main
questions. The first of these is whether neoliberal globalization can raise happiness for
all countries equally or whether it reaches a point where happiness eventually stagnates.
For example, scholars such as Graham have found that increases in income lead to
increases in happiness up to the point of basic subsistence for individuals, but the
relationship soon dwindles past this point (2009). This is tested at the state level using
the variables of exports, electricity production, and internet use which operate as proxy
measures for neoliberal economic development and globalization. The second question
asked as part of theory testing is whether the feverishness of market liberalization plays a
role in the relationship between happiness and neoliberal globalization. In order to depict
which countries are more economically neoliberal, using the same variables, this is also
3


tested at the systemic level by controlling for economic protectionism. Although
neoliberalism makes claims to foster development but not happiness, the question of
whether development and happiness are related is interesting nevertheless, sparsely
examined in the literature, and therefore worthy of further investigation.
Due to inconsistent and inconclusive results found in the quantitative analysis,
this thesis ultimately argues that culture is the key mechanism through which happiness is
determined. Economic factors are still relevant in many ways, but the thrust of these
factors and the impact they have on happiness is determined in cultural contexts that are
not uniform across societies. Culture acts as a gatekeeper between economic factors and
happiness by determining the extent, if any, to which neoliberal globalization affects
happiness. When happiness is considered to be either pleasure, achievement, or
flourishing, none of these notions of happiness are universally applicable in the cultural
contexts of each BRICS country. In the pages to follow, quantitative methods are used to
examine the extent to which happiness is correlated with development, ethnographic
analyses of happiness in the BRICS are presented, and postcolonialism is used to further
analyze the role of culture and potential reasons why happiness and development are not
entirely connected in some contexts.
Due to the complexity of this research question and the absence of comprehensive
research covering this question specifically for the BRICS, the analysis that follows
leaves many open question, yet addresses the complexities and ambiguities found when
exploring a concept as subjective as happiness and its relationship with neoliberal
globalization. Though the concept of happiness has historically been a key curiosity of
humanity and is likely to remain so, this thesis seeks to contribute to this literature by
4


examining how development influences the pursuit of living a meaningful, fulfilling, and
happy life.
5


CHAPTER II
LITERATURE REVIEW
The link between happiness and development is an ambiguous and highly grey
area in the literature. Despite this contention, and critical to the theory testing to follow,
many scholars argue that the determinants of happiness have economic aspects. Though
many scholars state that social and political factors matter as well, others argue that these
aspects exist within an economic context that cannot be ignored. The extent to which
economic factors influence happiness forms the center of this debate. In many cases the
general extent of this influence varies among fields of the social sciences (Greve 2012;
Thin 2013). Despite the fields of Political Science and International Relations lacking a
substantive amount of literature explicitly utilizing the happiness lens, the vast majority
of happiness literature is interdisciplinary and holistic.
Happiness Across the Humanities and Social Sciences
Terminology in the happiness literature varies considerably. Terms such as
happiness, subjective well-being (SWB), quality of life (QOL), and life satisfaction are
often used interchangeably by scholars (Greve 2012). These terms are all different in
their own ways, but they are often used synonymously where the differences between
these terms revolve around time-frame (Graham 2009; Greve 2012). Happiness is a long-
term emotion that results from experiencing positive feelings when viewing life
retrospectively, for example, but other terms such as SWB operate similarly but use a
relatively smaller time-frame (Graham 2009; Greve 2012; Thin 2013). These terms are
6


not treated synonymously here because, although similar in some ways, they are overall
quite different concepts that measure very different phenomena.
Philosophy and happiness. In philosophy, happiness is divided into three broad
categories: 1) pleasure, 2) achievement, and 3) eudaimonia or a state of flourishing
(Annas 2008; Aristotle 2008; Davis 2008). Throughout the literature in all fields, many
scholars make arguments that either explicitly or implicitly attach happiness to these
three philosophical concepts. All three notions of happiness agree that happiness is a
long-term emotion that an individual feels when positively viewing life retrospectively
(Annas 2008; Aristotle 2008; Davis 2008). In other words, an individual may feel
unhappy for a short period of time, but still have a happy life and maintain an overall
feeling of happiness. This is largely because happiness and being happy are two different
and unrelated concepts (Thin 2013). This thesis examines happiness rather than being
happy. The following chapter will highlight how happiness is defined for the purposes of
this thesis.
Davis argues that happiness is the result of accumulating pleasure2 (2008). The
concept of pleasure is divided into higher and lower pleasures where the former
contribute more toward happiness and the later contribute less. While higher pleasures
result from indulging in intellectually stimulating activities such as the arts and exploring
philosophy, lower pleasures result from activities necessary for subsistence such as
obtaining proper nutrition and maintaining shelter. However, Davis clarifies that painful
2 Daviss argument draws heavily on utilitarianism (2008). The argument that Davis
presents is more robust, however, as he goes to great lengths to distinguish between what
creates the most and least pleasure (2008).
7


and unpleasurable events must sometimes take place in order to experience pleasure later
in life because a pleasurable future has potential to outweigh an unpleasurable past
(2008). Stated differently, happiness is determined by a complex equation that balances
higher pleasures, lower pleasures, and pains (2008).
Annas links happiness with achievement (2008). Within this, happiness is a
feeling of accomplishment that results from achieving ones aspirations that constitute a
good life. Annas is critical of the notion that pleasure is a determinant of happiness
because instances of desire satisfaction may induce being happy, but faulty
information and social pressures can actually stifle the achievement of long-term
happiness (Annas 2008, 240). Rather than impulsively satisfying desires, Annas argues,
happiness results when retrospectively viewing life and finding a positive ongoing series
of achievements that lead to ones desired outcome (2008).
In his discussion of instrumental goods, Aristotle does not neglect the need for
subsistence, but develops a concept of happiness that is different than those above (2008).
This notion is operationalized through the capability approach to development that is
presented by Nussbaum and Sen (Nussbaum 2011; Sen 1999; Sen 2008). The
Aristotelian conception equates happiness with a state of eudaimonia (Aristotle 2008).
This is a state of flourishing where excellence is strived for in order to reach the ultimate
good that encompasses life in its totality happiness (Aristotle 2008; Thin 2013). To
dissect this definition piece by piece, Aristotle argues that happiness is the ultimate goal
of humans because it is the only goal that is sought for itself and never for the sake of
something else (2008, 20). The excellence that Aristotle refers to is twofold and tied to
his notions of the good and the virtuous. Aristotles notion of the good is equated with
8


acting through reason and his notion of virtue is defined as the mean between defect and
excess (Aristotle 2008). Combining these two concepts, for example, excellence not only
makes us excellent, but also our work excellent. We become excellent by emanating
excellence. To piece all these parts together, finally, eudaimonia is a state of flourishing
where excellence and virtue further one another and lead to happiness the ultimate goal.
Sociology and happiness. Within the field of sociology, debates in the literature focus on
the interplay between socioeconomic and sociopolitical determinants of happiness and
the balance between these two. Despite these differences, all scholars argue that politics,
the economy, and social conditions are interconnected and affect happiness to varying
extents (Deiner and Seligman 2004; Greve 2012; Haller and Hadler 2006; Radfcliff
2001).
Radcliff argues that both socioeconomic and the sociopolitical factors are critical
determinants of well-being (2001). This argument is based on the interconnected
relationship between governments and their ability to influence markets. Radcliff
supports this by stating that happiness is often highest in social democratic countries
(2001). Since social welfare nets help protect residents from changes in the market and
democratic processes provide a sense of inclusion in the political process, well-being is
influenced by both sociopolitical and socioeconomic phenomena (Radcliff 2001).
Other scholars highlight the role of socioeconomic standing because of the way
that status is related to happiness through marriage, family, employment, health, and
having meaningful work. (Deiner and Seligman 2004; Haller and Hadler 2006). They
support this argument by arguing that although political aspects do influence the
9


perception of status, happiness is determined by both social and economic aspects of
status. Therefore, how happy a society is cannot be predicted or measured by economic
and political aspects alone, but rather the ways in which these influence perceptions of
status (Diener and Seligman 2004; Haller and Hadler 2006).
Although their study applies more to SWB and health than it does happiness,
Wilkinson and Pickett also argue that social inclusion and a sense of belonging are
critical determinants of SWB (2011). They support this argument by highlighting the
social nature of humans as they use income and social status to measure their own self-
worth relative to their peers. Societies with the highest levels of income inequality
typically experience the lowest levels of SWB as a whole because those that feel inferior
and ostracized by society are more likely to engage in risky behaviors in efforts to
alleviate their sense of inferiority and exclusion (Wilkinson and Pickett 2011). This is
counterproductive, however, and leads emotional well-being to decline (Haller and
Hadler 2006; Wilkinson and Pickett 2011).
Economics and happiness. Within the literature on happiness that is written by
economists, many scholars adopt a more socioeconomic standpoint by arguing that
socioeconomic standing is the largest determinant of happiness. Although sociologists
make the same argument while arguing that social standing is the best predictor,
economists divert from this societal-based perspective and argue that economic standing
is the greatest predictor while social standing and culture are less important (Graham
2009; Frey and Stutzer 2002).
10


Graham argues that income is not a valid or accurate predictor of happiness in all
cases (2009). However, an increase in financial standing that leads individuals from
extreme poverty into basic subsistence can have a significant positive effect on
happiness. After a basic level of subsistence has been achieved and income continues to
rise, however, the effect this has on happiness begins to dwindle and eventually plateau
or decrease. Graham argues that this is primarily due to the expectations that individuals
place on their spending (2009). Important to the arguments presented below, Graham
argues that the effect of income on happiness declines dramatically after subsistence has
been achieved (2009). In highly developed countries, for example, happiness can
actually decline and disappointment can result because the expectation of happiness in
exchange for income is not met. This is the Easterlin paradox that Graham highlights -
happy peasants and frustrated achievers (Easterlin 1974; Graham 2009, 19).
Although they are both economists, Frey and Stutzer argue that macroeconomic
factors have a relatively small effect on happiness (2002). Frey and Stutzer argue that
employment is a critical determinant of happiness, but their main argument is that
resources gained from employment have little effect on happiness (2002). This is
because a sense of belonging and a feeling that one contributes to society are the greatest
aspects of formal and informal employment that contribute to happiness (Frey and Stutzer
2002). Thus, income and absolute economic standing are not critical determinants of
happiness for those who are not lacking a basic level of subsistence (Frey and Stutzer
2002; Graham 2009; Greve 2012). Economic factors are important in maintaining both
formal and informal employment, but the influence of economic factors on happiness is
not paramount from this perspective (Frey and Stutzer 2002).
11


Connecting the Disciplines
Ahuvia argues that economic development and happiness are indeed connected,
but the mechanism that causes development to contribute to SWB is the shift from
identities of collectivism to individualism (2002). Within this, individuals shift from
being extrinsically motivated to intrinsically motivated (Ahuvia 2002). Stated
differently, economic development increases SWB by creating a cultural environment
where individuals make choices to maximize their SWB rather than meet social
obligations (Ahuvia 2002, 25). Shifts to individualism erode the cohesiveness of civil
society, but they also change individuals motivations from extrinsic motivations of I
ought/have to to intrinsic motivations ofI want/get to (Ahuvia 2002).
Drawing heavily on Aristotles eudaimonia, Nussbaum and Sen use the capability
approach to stress the importance of connecting social, economic, and political factors
holistically in order to better enable individuals to flourish. Within the capability
approach, capabilities are defined as a middle-ground between concepts such as 1)
positive and negative duties, 2) individual and group rights, and 3) security and
subsistence (Nussbaum 2011; Sen 1999; Shue 2008). In order for individuals to flourish,
Sen and Nussbaum stress the need for a cohesive civil society that expands social capital
so that individuals capabilities can be actualized and the society can better develop
(Nussbaum 2011; Sen 1999). Neither author argues that economic factors are irrelevant,
but rather that capabilities require freedoms or opportunities created by a combination of
personal abilities and the political, social, and economic environment (Nussbaum 2011,
20; Sen 1999).
12


Chapter Summary
What has been identified in this chapter is that happiness is a broad phenomenon
that conceived of differently by scholars in various fields. Happiness to individuals can
be thought of as either pleasure, achievement, or flourishing. Moreover, the determinants
of happiness vary and include political, social, and economic aspects. As noted above
and critical to the arguments to follow, all of the scholars above argue that there are
economic aspects which act as determinants of happiness. This is especially seen when
happiness is examined relative to economic development. Although economic matters
matter very much in happiness and development overall, a triad exists between the
economic, social, and political where each influences and is influenced by the others.
Arguably, what this demonstrates is that intermediary factors most likely exist between
happiness and development which determine the exact balance between economic, social,
and political factors (Selin and Davey 2012). Discussed in greater detail below, this
thesis argues that culture is this intermediary factor.
13


CHAPTER III
CONCEPTS, VARIABLES, AND MEASUREMENT
Due to the complex nature of many concepts used in this thesis, it is necessary to
clarify and present the ways in which these concepts are addressed. Considering the
broad nature of economic development and the loaded language used in the happiness
literature, it is necessary to clarify these complexities as much as possible. This is
imperative in order to foster valid measurement of these concepts and more
comprehensive discussion and analysis. Seen in the literature review, for example, the
concepts of happiness and development are conceived in vastly different ways between
scholars and fields of academia (Rist 2007). This chapter therefore seeks to bridge these
gaps, find commonalities between the differing notions, and create a general framework
through which meaning is attached to these concepts hereafter.
Happiness
Happiness in the literature is conceived of in many different ways which can be
broadly categorized into three different notions: 1) pleasure, 2) achievement, and 3)
eudaimonia. Happiness as pleasure is generally defined as the maximization of pleasant
emotions and the minimization of painful emotions (Davis 2008). Differently, happiness
as achievement is broadly defined as the result of retrospectively viewing ones life and
seeing a positive trend of accomplishments that lead one increasingly closer to his/her
desired outcome whatever that may be (Annas 2008). Happiness as eudaimonia,
defined by Aristotle, is a state of flourishing where one reaches the highest good and
14


flourishes by exercising rationality, good character, and virtue (2008). Notably, all of
these conceptions conceive of happiness as long-term phenomenon that individuals strive
to experience and possess (Annas 2008; Aristotle 2008; Davis 2008).
Defining happiness. Although the notion of happiness as eudaimonia is not ignored in
this thesis and is addressed in later chapters, the notions of happiness as achievement and
pleasure form the central foundation of how happiness is defined hereafter as part of
theory testing. As part of the theory testing executed here, the notions are used because
neoliberal globalization stresses the importance of pleasure and achievement both
explicitly and implicitly. Making reference to well-being is a very new phenomenon
among the IMF and the WB, for example, but the development policies they promote
stress economic achievement and the importance of subsistence (Costanza et al. 2007;
Potter et al. 2008; Thin 2013). The key aims of neoliberal economic development also
stress the importance of development for the sake of pleasure and achievement.
Development is not only an achievement in itself, for example, but also increases the
number of those gaining subsistence and therefore maintaining a higher level of pleasure.
Rustin argues that neoliberalism has as one of its basic presuppositions the idea that the
human world is composed essentially of individuals, who should as far as possible be free
to make their own choices and to advance their own interests, in pursuit of whatever they
may deem their happiness to be (2013, 23). However, Rustin neither clarifies what
happiness is nor what he considers it to be for the purposes of his argument.
Using previous anthropological and sociological studies applied to all humans,
Veenhoven argues that it is human nature that individuals assess their own happiness
15


based on three criteria: 1) inferred sense of overall mood, 2) gratification of needs,
and 3) the motivation to act in order to move beyond mere needs gratification
(Veenhoven 2009, 12). For the purposes of this thesis, in other words, the gratification
of needs can be tied to lower pleasures and the motivation to act can be considered a
key part of achievement3 (Veenhoven 2009). Ambiguities are reached, however,
because Veenhoven argues that this way of assessing happiness is part of human nature
and is not directly influenced by culture (2009). Others such as Abdel-Khalek disagree
and argue that culture influences how happiness is assessed (2006). This ambiguity is
examined in later paragraphs which discuss how to measure happiness.
The empirical literature cited in table 3.1 on the following page does not tie
happiness directly with notions such as Daviss pleasure, Annass achievement, or
Aristotles eudaimonia (Annas 2008; Aristotle 2008; Davis 2008). However, all of these
studies implicitly attach happiness with various notions. Seen in table 3.1, the most
common of these are pleasure and achievement. Of the studies in this table, those that
examine economic factors are more likely to connect happiness with pleasure while those
that examine socioeconomic factors tie happiness with achievement. Considering the
empirical studies findings which stress the pivotal role that subsistence plays as a
determinant of happiness, it is problematic that Annass notion of happiness as
achievement does not stress the need for subsistence in the same way that Daviss
pleasure does (Annas 2008; Graham 2009). However, the notion of happiness as
achievement fills in the gaps of the pleasure notion (Annas 2008; Davis 2008). What this
3 See Appendix G for further details about Veenhovens argument and a flow-chart
presented by Veenhoven that depicts how individuals assess their level of happiness
(2009).
16


Table 3.1 Overview of Happiness in the Empirical Literature
Author (year) Title Research Question Methods & Measurement Determinants / Notions of Happiness
Abdel- Khalek (2006) Measuring Happiness With a Single-Item Scale seeks to determine if happiness is best measured using single- question surveys or multi-question surveys with various weights attached to each question primary research in various countries; surveys; self-reported happiness; 0-10 scale of happiness; correlation Single-Item Scale intentionally unspecified Multi-Item Scale optimism, hope, self- esteem, positive affect, extraversion, physical health, mental health
Cummins (2012) The Determinants of Happiness seeks to assess the relationship between psychological factors and whether or not individuals happiness has a set-point primary research in Australia; surveys; self-reported SWB positive mood determined by genetics; otherwise unspecified
Di Telia, MacCulloch, and Oswald (2003) The Macroeconomics of Happiness seeks to determine the effects that GDP, unemployment, and inflation have on happiness Eurobarometer and USGSS surveys; self- reported happiness; least squares regression pleasure; life satisfaction; obtained utility
Graham (2009) Happiness Around the World: The Paradox of Happy Peasants and Miserable Millionaires seeks to assess the relationship between income and self- reported happiness secondary research in various countries; surveys; self-reported happiness; regression; econometrics various / unspecified
Haller and Hadler (2006) How Social Relations and Structures Can Produce Happiness and Unhappiness seeks to assess the relationship between macrosocial conditions, quality of life, and happiness self-reported surveys; WVS data from 1995 wave; multi-level regression; correlation achievement; social inclusion
Radcliff (2001) Politics, Markets, and Life Satisfaction: The Political Economy of Human Happiness seeks to assess the relationship between democracy, unemployment rates, GDP, and happiness surveys; self-reported happiness; WVS data from 1990 wave; least squares regression achievement; culturally determined
Rothstein (2010) Happiness and the Welfare State seeks to assess why Scandinavian welfare states are as happy as they are and determine which aspects contribute most to happiness qualitative; variables are social tmst and corruption virtue; social inclusion; belonging; social contract
Veenhoven (2009) How Do We Assess How Happy We Are? Tenets, Implications, and Tenability of Three Theories seeks to determine the thought processes through which individual assess their own happiness uses qualitative sociology and anthropology to evaluate different theories of how happiness is assessed pleasure; achievement
17


demonstrates is that each notion can compensate for the limitations of the other. Both
Annas and Davis argue that unpleasurable periods of pain are sometimes necessary to
promote happiness in the future, for example, but Annas offers a more nuanced notion of
happiness that considers how poor information quality and peer pressure can lead
individuals to seek pleasure yet stifle happiness (Annas 2008; Davis 2008).
Measuring happiness. Table 3.1 shows that self-reported survey data are the most
common way to measure happiness. As stated above, whether or not survey data
measuring self-reported happiness includes the elements of pleasure and achievement is
debatable. For example, Veenhoven argues that individuals are decent judges of their
own happiness and human nature causes happiness to be assessed using perceived levels
of pleasure and achievement, but determining what will increase happiness in the future
is much more difficult (2009). If this is the case, it is initially expected that the survey
data used here and the survey data used in the studies of table 3.1 measure individuals
feelings of happiness as pleasure and achievement. Whether or not this is what affects
happiness over time is questioned in later chapters, however.
Others such as Abdel-Khalek argue differently and highlight ambiguity (2006).
Abdel-Khalek conducts two studies where one study measures various determinants of
happiness and the other simply asks respondents how happy they are (2006). The
findings indicate that assessing happiness using a single-question survey by asking
respondents to assess their own happiness is the best form of measurement because this
allows the respondent to interpret the term in a way that best fits what they perceive
happiness to be (Abdel-Khalek 2006). This is also more beneficial when seeking to
18


assess how happiness in one place compares to happiness in another (Abdel-Khalek
2006). In other words, Abdel-Khalek argues that culture determines what happiness is
while Veenhoven argues that culture is less relevant (Abdel-Khalek 2006; Veenhoven
2009). If the arguments of both Abdel-Khalek and Veenhoven are considered together, it
is assumed that individuals measure happiness in self-reported surveys by assessing their
levels of pleasure and achievement, but the precise weights that are attached to the
determinants of pleasure and achievement are culturally determined (Abdel-Khalek 2006;
Veenhoven 2009). The validity of measuring happiness using survey data is discussed in
the methodology chapter. Nevertheless, happiness is considered to be pleasure and
achievement because these two factors are stressed in the neoliberal path of development
and are presumably reflected in survey data which measure self-reported happiness. This
notion of happiness is questioned in later chapters, however, as the cultural notions of
happiness in the BRICS are discussed.
Neoliberalism
Before continuing on to introduce neoliberal economic development and
globalization, it is necessary to clarify what is meant by neoliberalism and identify how it
fits within the lens of neoliberalism as a theory of international relations. This is also true
for the independent variables which measure neoliberal economic development.
Classical liberalism forms the justificatory foundation on which the post-WWII
neoliberal global political economy was built (OBrien and Williams 2007; Sterling -
Folker 2010). In the years following the Second World War, international financial
institutions (IFIs) were created at the Bretton Woods Conference in hopes to encourage
19


interdependence between states. What are now the IMF and the WB were created at
Bretton Woods and what has become the World Trade Organization (WTO) was created
several years later. The underlying assumptions which formed the IFIs institutional
framework were based on classical liberalism where it was argued that cooperation and
open communication would create interdependence and discourage states from being
distrustful of one another and pursuing relative gains (OBrien and Williams 2007;
Powell 1991).
The IFIs have evolved post-1945 and IR theory has followed suit giving way to
neoliberalism and neoliberal institutionalism. Scope is the main difference between the
two theories (Stein 2008). For example, neoliberal institutionalism is essentially the
theory of neoliberalism applied exclusively to international institutions such as the IFIs
(Stein 2008). The assumptions of both neoliberalism and neoliberal institutionalism are
that: 1) the structure of global order is anarchic, 2) states are rational and self-interested,
and 3) bargaining through cooperation can lead to the achievement of mutual gains. In
order to achieve these mutual aims: 1) iteration between states enables the creation of
formal agreements which allow states to pursue mutual gains, 2) institutional mechanisms
can discourage state defection from agreements, 3) processes encouraging
interdependence further absolute gains and discourage relative gains, and 4) the pursuit of
absolute gains will allow states as a whole to achieve more than otherwise and be more
peaceful (Powell 1991; Stein 2008; Sterling-Folker 2010). In other words, cooperation
discourages states from being distrustful of one another and better allows them to pursue
common interests. State membership in multilateral institutions is one key way to make
this process more feasible (Stein 2008).
20


Although the IMF and the WB were originally created in efforts to discourage
war through economic regulation, the two institutions have shifted over the years to
encourage development policies based on economic liberalism (Evans and Newnham
1998; OBrien and Williams 2007). This is one aspect of what has become known as the
Washington Consensus, or more simply the Western path of development (OBrien and
Williams 2007). The IMF and the WB are key players within the Washington Consensus
and encourage neoliberal policies in hopes to spur development through interdependence
between states by stressing the importance of market deregulation, low barriers to
international trade, small government, democratization, and good governance (OBrien
and Williams 2007). Concerning neoliberal economic development specifically, which is
defined in the following pages, the policies of the Washington Consensus are adopted in
efforts to spur development by increasing growth through the interplay between
industrialization and exporting. By reducing barriers to trade and investment, as
neoliberalism argues, global interconnectedness is more likely to result and where states
are more likely to pursue absolute gains by working together to achieve mutual
objectives.
Neoliberal Economic Development
Development is often a loaded term that is used in quite different ways throughout
fields of academia (Rist 2007). This is true for Political Science and International
Relations where the term includes political, economic, and social aspects. Development
in this thesis refers primarily to neoliberal economic development. This is especially true
for the quantitative section of the thesis as the variables of exports and electricity
21


production are proxy measures of neoliberal economic development as part of the theory
testing which this thesis seeks to accomplish. Highlighted in the previous section, the
theoretical assumptions of neoliberalism play a critical role in neoliberal economic
development.
Neoliberal economic development is based heavily on economic liberalism4 as
outlined by theorists such as Adam Smith, David Ricardo, and Fredrich Hayek (Evans
and Newnham 1998). Assuming that actors are rational and seek absolute gains,
neoliberal economic development is characterized as a hand-off approach on behalf of the
state which emphasizes low trade barriers, privatization, a small public sector, and a
general deregulation of economic matters in efforts to industrialize by increasing exports
(Bhagwati 2007; Evans and Newnham 1998; Goldin and Reinert 2010; Nafziger 2012).
This definition is also used because the neoliberal path of development has become an
emerging norm among the BRICS during the time-period studied here (Bhagwati 2007;
Hicks and Streeten 1979; Ingam 1993; ONeil 2011).
Within neoliberal economic development, the push for low state involvement in
economic affairs is encouraged in order to foster industrialization and increase the
feasibility of exporting5 (Bhagwati 2007; Evans and Newnham 1998; Goldin and Reinert
2010). Because of this, the two independent variables which measure neoliberal
economic development in this thesis are exports and electricity production due to the fact
4 In many ways, market liberalization as part of neoliberal economic development is
based on the arguments of both the Chicago School of economics and the Austrian
School of economics.
5 The neoliberal path of development stresses export oriented industrialization (EOI)
while other development paths exercising high protection stress import substitution
industrialization (ISI).
22


that the vast majority of electricity produced is used in industrial processes rather than
household consumption (Hughes 1983). In other words, electricity production operates as
a proxy measure of industrialization.
Throughout the literature, economic development in general is often measured
using a myriad of variables which measure GDP, consumption, population growth,
industrialization, living standards,6 and the structure of the workforce7 (Bhagwati 2007;
Goldin and Reinert 2010). Although these do offer a more comprehensive measure of
economic development, they do not isolate the neoliberal aspect of economic
development. Given the research question here, these measures would not be entirely
relevant. Moreover, to analyze this many variables non-numerically would not be
entirely possible in this thesis. Exports and electricity production are, nevertheless, basic
yet comprehensive measures of neoliberal economic development that are feasible to use
in a mixed methods research design such as the one used here.
Globalization
Globalization is also a loaded term that is used to mean many things throughout
academia (Strange 1996). Globalization in this thesis is broadly defined as the figurative
shrinking of time and space (Evans and Newnham 1998). Globalization is not a new
process in world history and has been happening for a very long time, but recent
technological innovation has sped this process considerably and its presence and impacts
6 Living standards include phenomena such as literacy rates, infant mortality, nutrition,
and life expectancy (Potter et al. 2008).
7 The structure of the workforce takes into account differences between formal and
informal employment and what sectors of the economy have the highest levels of
employment (Potter et al. 2008).
23


have become especially pronounced in the post-1945 world (Bhagwati 2007; McGrew
2008). This is not directly due to neoliberalism as all paths of development carry the
potential to increase the thrust of globalization, but globalization and neoliberalism
further and complement one another. Though globalization and neoliberal economic
development in this study are not used synonymously, they are both referred to under the
umbrella term of neoliberal globalization and their relationship with happiness is tested
using the same methods in the quantitative chapter.
Internet use is included as a proxy measure of globalization. Due to the internets
key role in speeding up the transfer of information across borders, it in many ways has
become a symbol of the technological aspects of globalization (Creshnaw and Robinson
2006). Creshnaw and Robinson argue that both conduciveness to internet technology as
well as globalization...are important in the diffusion of the internet (2006, 190). In
other words, globalization and the growing prominence of internet use are closely tied in
many ways. Moreover, conduciveness to internet technology can be tied to neoliberal
economic development as the infrastructure for increasing use of the net is gained by
developing economically (Creshnaw and Robinson 2006, 190). Thus, neoliberal
economic development and globalization are tightly related concepts, but are still
different in several ways.
Although it does not produce one single measure of globalization, the
Organization for Economic Cooperation and Development (OECD) measures
globalization by using numerous variables which fall into four general categories: 1)
trade and investment, 2) technology and knowledge, 3) multinational enterprises,
and 4) the cohesiveness and interconnectedness of global supply chains (OECD 2010, 5).
24


Especially considering the categories other than technology and knowledge, these
variables overlap with neoliberal economic development. In the technology and
investment category, however, there is less overlap as the vast majority of the variables
that are measured rely heavily on internet use (OECD 2010). In order to limit the number
of variables so that mixed methods analysis is more feasible, therefore, internet use in
included as a proxy measure for globalization.
Income Inequality
Although not necessarily a measure of economic development proper, income
inequality measured using gini index scores is the final independent variable that is
included. It is included to foster discussion in later chapters about why the variables may
not be as closely related as originally thought. De Maio highlights other measures of
income inequality, but ultimately argues that each method has both merits and limitations
(2007). The primary weakness of gini index scores is that they are highly sensitive to
inequalities in the middle of the income spectrum, but they are the most common
measure of income inequality due to their ability to condense income inequality into one
summary statistic8 (De Maio 2007, 850). Lower gini index scores indicate lower levels
of income inequality while higher scores indicate higher levels. This variable is not
included as a part of the theory testing, but rather because the effect of income inequality
on happiness is largely determined by culture (Selin and Davey 2012). Nevertheless, its
correlation with happiness is tested in the same way as the other variables according to
the hypothesis that concerns income inequality. Due to income inequalitys strong
8 See Appendix C for further details about how the World Bank measures gini index
scores.
25


impacts on measures of physical and emotional well-being, it is a critical aspect to be
analyzed in relation to happiness (Diener and Seligman 2004; Wilkinson and Pickett
2011). In other words, it is important to examine how the benefits of neoliberal economic
development and globalization are distributed among populations. This can then be
examined ethnographically in greater depth by dissecting the role of culture in
determining how income inequality affects happiness.
Terminology Hereafter
Figure 3.1 below summarizes how terminology will be used from this point
forward and the variables associated with these concepts. Neoliberal globalization refers
to neoliberal economic development and globalization. The variables to measure
neoliberal economic development are exports and electricity production. Electricity
production is a proxy measure for industrialization. Similarly, internet use is a proxy
measure for globalization. Finally, income inequality is measured using gini index
scores, also known as gini coefficients.
Neoliberal Globalization Income Inequality
Neoliberal Economic Development Globalization i
. Electricity Production Exp0rtS (Industrial,zat,on) Internet Use Gini Index Score
Figure 3.1 Terminology, Proxy Measures, and their Associated Variables
26


CHAPTER IV
HYPOTHESES AS PART OF THEORY TESTING
In the theory testing to follow, there is good reason to assume that exports,
industrialization, and globalization will witness positive relationship with happiness.
Although not included as part of theory testing proper, it is hypothesized that income
inequality will witness a negative relationship with happiness. Before presenting the
hypotheses, it is first necessary to highlight the ways in which neoliberal globalization
can contribute to both development and happiness as pleasure and achievement. The
neoliberal path of development, as with other paths, is well-founded and has a rich
theoretical foundation on which the policies that it seeks are built. For example, the
policies which emerged out of the Bretton Woods conference post-WWII were enacted to
foster interconnectedness in efforts to avoid the tensions in the international political
economy that contributed to the outbreak of the Second World War. Although the
effectiveness of these policies in contributing to happiness is questioned in later chapters,
both the hypotheses to follow and the theory which they seek to test are both informed
and well theoretically founded. Since the neoliberal path of development seeks
achievement and pleasure, as discussed above, it is necessary to highlight ways in which
exporting, industrialization, and globalization can contribute to happiness in the
development process.
27


Why Neoliberal Globalization is Hypothesized to Further Happiness
Considering how happiness is defined here, it is hypothesized that neoliberal
globalization can further happiness by making the pleasures of subsistence more feasible
and economic achievement more obtainable. Exporting is one achievement that can
increase pleasure. Several scholars argue that the post-1945 world has witnessed a swift
rate of economic development that is largely due to the growing prominence of
international institutions with high levels of membership (Goldin and Reinert 2010;
Bhagwati 2007). Goldin and Reinert attribute this notable increase in economic
development specifically with the growth of heavy industry and the increased rates of
exports that shortly followed (2010). An explanation behind this argument is that the
achievement of export growth per head increases incomes and therefore the feasibility of
subsistence and further achievements.
Both Bhagwati and Goldin and Reinert argue that the growth of the Bretton
Woods institutions and the WTO have been particularly useful in doing this as they have
provided a nearly universal platform for trade diplomacy where states can pursue mutual
goals of development with greater ease (Bhagwati 2007; Goldin and Reinert 2010). At
the same time, legally binding dispute resolution mechanisms built into the frameworks
of institutions such as the WTOs dispute settlement body (DSB) discourage actors from
feeling the need to be distrustful of their peers while providing them with the ability to
counter allegations of misconduct and defect (Van den Bossche 2008). Within this,
international trade law and international economic law have become key areas where
international actors have been able to achieve cooperation due to its oftentimes binding
qualities although sometimes with contention (Van den Bossche 2008).
28


Considering that 1) increases in exports lead to increases in incomes and 2)
Grahams argument that subsistence is the most critical determinant of happiness, it is
reasonable to argue that increasing exports contributes to increasing happiness in LEDCs
(2009). As the worst-off in society achieve a basic level of subsistence and therefore a
greater sense of happiness as achievement/pleasure, the average happiness for the entire
society rises (Graham 2009). Of course this neither indicates that exporting alone is the
only cause of happiness nor does it indicate that increasing exports is the only way to
increase the achievement of subsistence. Instead, this indicates that increasing exports is
merely one way to do so where membership in international institutions aids this process
and holds potential to establish subsistence and encourage post-subsistence happiness as
achievement.
By increasing electricity production, achievement and pleasure can also be
increased through the process of industrialization. Electricity production is a key aspect
of neoliberal economic development due to its crucial role in heavy industry and
manufacturing (Potter et al. 2008). Since the vast majority of states total electricity
production is used in heavy industry and manufacturing as opposed to household
consumption, the role of electricity is paramount in economic development led by
industrialization (Hughes 1993; Rud 2012). If achievements in increasing manufacturing
are made assuming that protectionism is low, the ability to export and increase inflowing
funds is fueled. As highlighted in the previous paragraphs, the increase of exports and
the additional revenues from this can have positive effects on a states overall attainment
of subsistence and feasibility of achievement.
29


A key push of the neoliberal strand of development is that the deregulation and
privatization of public services leads to efficiency through competition (Williams and
Ghanadan 2006). This is also a key aspect of development that the IFIs promote. As far
as neoliberalism is concerned, the underlying assumption behind this is that deregulation
fosters interconnectedness between states so that phenomena such as foreign direct
investment (FDI) and bilateral investment treaties (BITs) can spur economic
development. Within this interconnectedness, one state seeking relative gains over
another state would ultimately be harming itself. One way to increase this
interconnectedness is through the opening of markets and privatization of previously
nationalized industries so that foreign entities can invest locally and both parties can
achieve absolute gains. By achieving increases in electricity production, LEDCs
especially would be predicted to experience increases in happiness by easing the
feasibility of achieving subsistence and better enabling achievement beyond the point of
subsistence.
The effectiveness of privatization as a strategy of development is a contested and
complex debate between scholars (Bhagwati 2007; Stiglitz 2003). The debate is
ultimately centered on which types of well-being are aimed for in development policies,
who should reap the benefits of development, and what national industries really mean to
a society. Despite the varying levels of effectiveness, privatization is argued to be a key
force driving the electrification and industrialization of societies in development
(Williams and Ghanadan 2006).
Since globalization and neoliberal economic development simultaneously further
one another, they too have potential to increase pleasure and achievement. Like other
30


inventions that have revolutionized the global economy, the internet in many ways has
had a positive impact on development in LEDCs (Lucas and Sylla 2003). Lucas and
Sylla highlight the various benefits of internet use in LEDCs, all of which center on the
ability to participate in commerce with greater thrust (2003). Moreover, the internet has
in several ways given economic development a boost for both LEDCs and MEDCs that
use it the most through the process of technology transfer9 (Litan and Rivlin 2001).
Within this, economic development led by greater productivity and efficiency is able to
encourage a higher quality of life (Litan and Rivlin 2001). In other words,
globalizations impact on the transfer of information has aided neoliberal economic
development by encouraging interdependence (Keohane and Nye 1998).
Hypotheses
Considering the arguments seen in the literature and the potential for neoliberal
globalization to have a positive relationship with happiness, the hypotheses as part of
theory testing are as follows. By testing theory, these hypotheses seek answers to two
main questions. The first of these is whether neoliberal globalization will only further
happiness up to a certain point. This question is addressed at the state level by hypothesis
one. The second of these questions seeks to determine if more feverish policies of market
liberalization play a role in the relationship between neoliberal globalization and
happiness. This is addressed at the systemic level by hypothesis two, which takes
9 Technology transfer refers to the relationship between producers and consumers of
technology as it can be identified to operate across state boundaries and to involve
various international actors (Evans and Newnham 1998, 529). Technology transfer has
become especially important in economic development since the original industrial
revolution demonstrated the importance of technological inputs (Evans and Newnham
1998, 529).
31


economic protectionism into account so that a picture is presented of which sample
countries have economic policies that are the most neoliberal. In order for the data to
support the argument that neoliberal globalization correlates positively with happiness, it
is expected that the least protected sample countries will have strong positive
relationships. Finally, hypothesis three addresses income inequality at the state and
systemic levels and sets up conditions that can foster later discussion about the role of
culture.
Hypothesis #1: All sample countries will witness positive relationships between
happiness and neoliberal globalization, but change in happiness relative to neoliberal
globalization will be conditional on a states level of economic development relative
to other sample countries.
o Hypothesis la: Less economically developed countries (LEDCs) will
experience the largest increase in happiness relative to neoliberal economic
development and globalization.
o Hypothesis lb: More economically developed countries (MEDCs) will
experience the smallest increase in happiness relative to neoliberal economic
development and globalization.
Hypothesis one concerns the relationship between happiness and neoliberal
globalization at the state level, but also takes into account the arguments of Graham
(2009). Grahams analysis centers on individuals and argues that increases in income
raise happiness substantially for those moving from absolute poverty to a basic level of
subsistence. After subsistence has been established, the effects of income on happiness
begin to dwindle. Since Grahams argument is applicable in this hypothesis and should
therefore not be ignored, this hypothesis is conditional on a states level of exports,
electricity production, and internet use relative to the other sample countries. For LEDC
32


sample countries, therefore, hypothesis la assumes that as the worst-off acquire a basic
level of subsistence the average happiness for the entire country will therefore increase.
Differently for MEDCs, hypothesis lb assumes that happiness will not rise considerably
because a basic level of subsistence has already been established. Descriptive statistics
are provided in chapter six which rank countries for all variables so that it is better
evident where sample countries stand relative to other sample countries concerning their
levels of exports, electricity production, and internet use. This will provide a general
picture of which sample countries operate as LEDCs and MEDCs for the purposes here.
Hypothesis #2: Change in happiness relative to economic development and
globalization will be conditional on a states level of economic protectionism relative
to other sample countries.
o Hypothesis 2a: More economically protected countries will experience the
smallest increase in happiness relative to economic development and
globalization.
o Hypothesis 2b: Less economically protected countries will experience the
greatest increase in happiness relative to economic development and
globalization.
Hypothesis two operates as a way to assess the effects of economic protectionism
on happiness by identifying a countrys relative level of economic liberalization.
Although the more precise methods to do this are presented later, sample countries are
divided into different levels of economic protectionism where those with lower levels of
protectionism have quite neoliberal economic policies and those with higher
protectionism have economic policies that are less liberalized. Due to neoliberalisms
argument that more protected economies often face obstacles in economic development,
hypotheses 2a assumes that happiness will increase less relative to economic
33


development since obstacles to development will make establishing subsistence less
feasible and achievement more difficult (Bhagwati 2007; Goldin and Reinert 2010).
Since liberalized economies trade more feverishly in the global economy and increase
incomes by industrializing and exporting abroad, hypothesis 2b assumes that less
protected economies will experience the greatest increase in happiness due to the greater
feasibility of subsistence (Bhagwati 2007; Goldin and Reinert 2010). These countries
have economic policies that are the most neoliberal. What hypothesis two does not
concern is how the benefits of neoliberal globalization are distributed among individuals
in the sample countries.
Hypothesis #3: Income inequality in all countries and all levels of economic
protectionism will witness negative relationships with happiness.
Hypothesis three does not operate as part of theory testing formally, but rather
operates as a way to set up further discussion about the role of culture in determining how
economic development and globalization affect happiness. This hypothesis concerns
both individual countries and the various levels of economic protectionism. Since more
unequal countries typically perform less well economically, have high levels of
psychosocial stress, and have poorer levels of health, hypothesis three assumes that
happiness will decrease if income inequality increases (Wilkinson and Pickett 2011).
34


CHAPTER V
METHODOLOGY
This thesis conducts a time-series in which happiness data and are compared with
neoliberal globalization and income inequality data over time between the years of 1990
and 2007. In doing this, what is primarily observed is how changes in happiness
correlate with changes in the other variables.
Mixed Methods
Seen in the literature review, happiness research often reaches different
conclusions due to the different methodologies used (Greve 2012; Thin 2012). In order
to bridge these gaps, mixed methods are used here. The large number of countries and
years that are being examined would be quite difficult to examine non-numerically. At
the same time, however, obstacles to validity are found when attempting to quantitatively
evaluate a concept as subjective as happiness. Therefore, the overarching methodology
used in this thesis is mixed methods due to its ability to explore both breadth and depth
while setting up conditions to ground normative arguments (Creswell and Plano Clark
2011). In this thesis, quantitative methods are used to depict the general relationship
between happiness, neoliberal globalization, and income inequality while other methods
are used to expand the analysis. Ethnography is used to examine cultural notions of
happiness while the theory of postcolonialism is used to foster discussion.
35


Data
Functioning as the dependent variable, happiness data are gathered from the
World Values Study (WVS) and the European Values Study (EVS). The vast majority of
data are gathered from the WVS, but EVS data are used for European countries where
WVS data are not available for years at the end of this studys time-frame.10 Despite
these different data sources, the happiness questions used in the WVS and EVS are
worded identically.11 Because of this, it is presumed that WVS data and EVS data are
very comparable to one another. Both of these surveys are conducted in waves that do
not take place annually. Throughout the time-period that this thesis examines, surveys
were conducted in the BRICS between three and four times.
Functioning as the independent variables, data for exports, electricity production,
internet use, and gini index scores are gathered from the WB12 (World Bank 2013a). Due
to different population sizes among the BRICS, exports and electricity production data
are manipulated so that they are measured per capita. Internet use data are left as is
because they are measured as a percentage of the population. Data for gini index scores
are also left as is because these are a summary statistic that already takes population into
account.
10 See Appendix B for more information on happiness data availability.
11 See Appendix B for more information about the exact wording used in the WVS and
EVS surveys.
12 See Appendix C and F for more detailed explanations about how the World Bank
measures these variables.
36


Sample
Stated above, the sample which is the primary focus consists of Brazil, Russia,
India, China, and South Africa. The BRICS are often regarded by development
economists as the star pupils of neoliberal economic development (ONeil 2011).
Having received the treatment of neoliberalism, so to speak, these countries provide great
opportunity to conduct theory testing. Without the addition of countries utilizing
different development paths, however, there is nothing to compare the BRICS to. In
order to compensate for this difficulty, the sample is expanded beyond the BRICS to
include countries that are following various paths of development. The countries seen in
tables 5.1-5.5 are purposively included because there are data available for all variables.
Due to data availability or lack thereof, unfortunately, the chosen sample is skewed in
favor of MEDCs such as those in Europe and the ex-USSR.13 Due to the skewed nature
of the sample, therefore, the quantitative results to follow in chapter six are not validly
able to be applied to the world at large.
Despite this skewed sample, however, there are countries at various stages of
economic development ensuring that there is variation across the independent variables.
Seen in tables 5.2-5.5 on the following pages, descriptive statistics show that there is
variation across all independent variables except internet use which has slightly less
variation. In other words, the BRICS countries are at various levels of the spectrum for
all independent variables except internet use.
13 To limit this skew, five of the twenty-eight EU member states were selected through a
random sample.
37


Table 5.1 Happiness
Ranked by Z-Score
Country Z-Score
Belarus -1.834
Moldova -1.804
Russia -1.609
Albania -1.524
Ukraine -1.384
Latvia -1.154
Slovakia -0.962
Serbia -0.742
Macedonia -0.302
Peru -0.217
China -0.161
South Korea -0.146
India -0.018
Bosnia and Herzegovina 0.040
South Africa 0.161
Brazil 0.298
Chile 0.343
Argentina 0.487
Turkey 0.511
Japan 0.621
Mexico 0.708
Malta 0.754
France 0.788
Norway 1.005
Nigeria 1.050
Canada 1.056
Sweden 1.177
United States 1.212
Switzerland 1.249
Iceland 1.609
t*x = 66.925 S* = 7.079
* z-score is based on mean of all years with available data for each individual country * years represented vary in 1989-2008 period
38


Table 5.2 Exports per capita
(constant 2000 US$)
Ranked by Z-Score
Country Z-Score
India -0.748
China -0.701
Moldova -0.700
Serbia -0.699
Albania -0.688
Peru -0.681
Brazil -0.680
Ukraine -0.663
Bosnia and Herzegovina -0.640
Argentina -0.592
South Africa -0.589
Turkey -0.589
Russia -0.588
Macedonia -0.587
Belarus -0.576
Mexico -0.481
Chile -0.468
Latvia -0.435
United States -0.055
South Korea -0.043
Japan 0.024
Slovakia 0.146
France 0.408
Canada 1.077
Malta 1.265
Iceland 1.485
Sweden 1.738
Switzerland 2.408
Norway 2.653
Mx = 3529.060 S* = 4645.619
* insufficient data for Nigeria * z-score is based on mean of all years with available data for each individual country * years represented vary in 1989-2008 period
Table 5.3 Electricity Production
per capita (kw/h)
Ranked by Z-Score
Country Z-Score
Nigeria -0.859
India -0.810
Peru -0.764
Moldova -0.722
China -0.714
Albania -0.671
Mexico -0.622
Turkey -0.620
Brazil -0.618
Latvia -0.614
Argentina -0.577
Chile -0.549
Belarus -0.463
Bosnia and Herzegovina -0.444
Macedonia -0.418
Ukraine -0.354
Serbia -0.205
South Africa -0.197
Malta -0.196
South Korea -0.176
Slovakia -0.153
Russia 0.000
Japan 0.209
France 0.308
Switzerland 0.341
United States 1.025
Sweden 1.460
Canada 1.751
Iceland 2.739
Norway 2.913
Mx =6279.862 S* = = 7151.450
* z-score is based on mean of all years with available data for each individual country * years represented vary in 1989-2008 period
39


Table 5.4 Internet Users
(% of population)
Ranked by Z-Score
Country Z-Score
Nigeria -1.146
Belarus -1.131
India -1.095
Ukraine -0.938
China -0.910
South Africa -0.873
Russia -0.844
Mexico -0.816
Albania -0.755
Argentina -0.694
Brazil -0.676
Turkey -0.578
Peru -0.569
Moldova -0.564
Chile -0.350
Bosnia and Herzegovina -0.257
Latvia 0.139
Malta 0.171
Macedonia 0.228
France 0.388
Slovakia 0.425
Japan 0.427
South Korea 0.726
United States 1.171
Canada 1.177
Switzerland 1.375
Sweden 1.922
Norway 1.956
Iceland 2.092
Hx = 15.675 S* = 13.661
* insufficient data for Serbia * z-score is based on mean of all years with available data for each individual country * years represented vary in 1989-2008 period
Table 5.5 Income Inequality
(gini index score)
Ranked by Z-Score
Country Z-Score
Japan -1.261
Sweden -1.247
Norway -1.170
Slovakia -1.081
Belarus -0.979
Ukraine -0.830
Serbia -0.633
South Korea -0.605
Albania -0.594
India -0.555
Canada -0.510
France -0.492
Bosnia and Herzegovina -0.434
Switzerland -0.401
Latvia -0.399
Moldova -0.089
China 0.019
Macedonia 0.035
Russia 0.229
United States 0.294
Turkey 0.354
Nigeria 0.773
Argentina 1.136
Mexico 1.163
Peru 1.341
Chile 1.643
Brazil 2.103
South Africa 2.190
A * insufficient data for Iceland and Malta
* z-score is based on mean of all years with
available data for each individual country
* years represented vary in 1989-2008 period
40


When ranked by z-score as the sample countries are in tables 5.1-5.6 it is seen that
those with negative z-scores are below average and those with positive z-scores are above
the average of sample countries. The general skew of the data can also be seen. For all
independent variables, it is also seen in these tables that the data are skewed right as there
are more countries with negative z-scores than positive ones. What this indicates is that
there are several countries at the upper end of the spectrum for the independent variables
that are much higher than the majority of other sample countries, therefore skewing the
selected sample. For the dependent variable, happiness, it is seen that this is more
normally distributed. This can be seen in Russias position as one of the least happy
sample countries.
Validity of Quantifying Happiness
Happiness functions as the dependent variable. As mentioned, quantitative
happiness data are available from the WVS and the EVS. Ethnographic analyses of
happiness in the BRICS are available from various scholars who dissect what happiness
is and what it mean in each BRICS country (Biswas-Deiner, Tay, and Deiner 2012;
Bookwalter 2012; Davey 2012; Islam 2012; Zavisca and Hout 2005).
Quantitative data: Happiness survey validity. While some scholars mentioned in the
preceding paragraph explore happiness non-numerically in the BRICS, others quantify
happiness and meticulously outline the steps needed in order to quantify validly (Abdel-
Khalek 2006; Graham 2009; Greve 2012; Kalmijn, Arrends, and Veenhoven 2011). In
order to quantify happiness validly, surveys that measure happiness must: 1) assess long-
41


term happiness, 2) ask respondents to assess happiness towards the beginning of the
survey, and 3) leave the connotation of happiness open in the question so that
respondents can interpret as they please (Abdel Khalek 2006; Graham 2009; Greve
2012). Both the WVS and the EVS do exactly this. First, happiness survey questions in
the WVS and EVS ask respondents to assess happiness long term by including the phrase
taking all things together into the question wording (EVS 2013; WVS 2013). Second,
both survey questions that assess happiness in the WVS and the EVS are located towards
the beginning of both surveys. In the WVS and the EVS surveys, the question that
measures happiness is placed as the third and eighth question, respectively, out of
hundreds of questions (WVS 2013; EVS 2013). Lastly, WVS and EVS happiness
questions leave the connotation of the term happiness open for interpretation. This is a
critical aspect of assessing happiness cross-culturally because this allows the respondent
to interpret the term happiness in a way that best fits what they perceive happiness to be
(Abdel-Khalek 2006).
Quantifying happiness. Happiness is quantified as an interval variable. Although some
scholars caution quantifying happiness, others do not and outline the steps needed to do
so (Greve 2012; Thin 2012). There are two obstacles found when quantifying happiness
using survey data: 1) the limitations of ordinal data and 2) the fact that the WVS/EVS
surveys are not conducted yearly.
The first obstacle found in quantifying happiness data is changing WVS/EVS data
gathered ordinally into interval data that can be used in statistical analyses with greater
ease. Based on a method outlined by Kalmijn, Arrends, and Veenhoven, a weighted
42


mean is calculated using the equation below for each country and year when the WVS
and EVS were conducted (2011). Kalmijn, Arrends, and Veenhoven conduct a study
among Dutch participants where they ask participants to place four marks on a 10-point
scale for which they feel best represent four categories of happiness (2011). Although it
is problematic that this study only included Dutch participants, especially considering the
role of culture, it is the best option that is currently available in the literature. Seen in the
weights used in the below equation, they then find the mid-interval value for each
category based on the participant responses (Kalmijn, Arrends, and Veenhoven 2011).
This is the formula used in this thesis to calculate levels of happiness for WVS/EVS
survey years.14
Equation 5.1 Quantifying Happiness with Mid-interval Values
and a Weighted Mean
_ (NAAH X 1.7) + (NVH X 4.1) + (QH X 6.7) + (VH X 9.3)
n
The weights used in the above formula do not reflect the exact results of Kalmijn,
Arrends, and Veenhovens study, however, as the value of not at all happy (NAAH) had
to be increased in order for approximate normal distribution to be assumed in a random
sample (2011). For example, the equation below is used by Kalmijn, Arrends, and
Veenhoven in order to demonstrate that the midpoint of all categories on a 10-point scale
is 5. In other words, the weights attached to the levels of happiness are not equidistant,
but a normal distribution across all four categories of happiness would yield a mean and
14 H is multiplied by 10 so that happiness can be graphically depicted on a 100-point
scale.
43


median of 5. Although the exact value of H is therefore inflated by using the altered
weight for NAAH, its consistent use in all countries and years will still reflect how
happiness changes over time in the time-series executed here.
Equation 5.2 Presuming Approximate Normal Distribution
in a Random Sample
(VH QH) + (NVH NAAH) = (9.3 6.7) + (4.1 1.7) = 5
The second obstacle in quantifying happiness is that WVS/EVS surveys are
conducted in waves that do not take place yearly. Throughout 1990-2007, WVS surveys
were conducted three times in Brazil and four times in the other BRICS countries. In
order to compensate for this obstacle, data are interpolated for years when WVS/EVS
surveys were not conducted. That is, years with missing data are estimated assuming that
happiness moves linearly between years. In overcoming the obstacles to validity that
missing happiness data provide, however, other obstacles to validity are created by using
linear interpolation. These potential threats to validity are: 1) estimating values which
were never officially measured and 2) assuming that levels of happiness move linearly
over time. The reasons why these issues are problematic center on their lack of
falsifiability. Interpolated values cannot be proven to be either correct or incorrect.
Allison distinguishes between data that are missing completely at random
(MCAR) and data that are missing at random (MAR) (2002). Data that are MCAR do
not enable valid data imputation or interpolation (Allison 2002). In this thesis, the data
are not MCAR because the happiness literature indicates that happiness would not
deviate significantly beyond data that is linearly interpolated between two data points
44


from officially measured WVS/EVS surveys. Data imputation is most appropriate when
data are MAR (Allison 2002; Honaker and King 2010). Within this type of missingness,
the probability of missing data on Y is unrelated to the value of Y after controlling for
other variables in the analysis (Allison 2002, 4). The same can be said if X is the
variable with missing data, as seen here (Allison 2002). In order for MAR data to be
interpolated validly, in other words, the likelihood for the data to be missing does not
depend on the actual values for all variables (Allison 2002). In this thesis, for example,
happiness is MAR because the particular levels of happiness or economic development
are not the reasons why data are missing. Happiness data are not missing because there
was a spike in electricity production, for example. Since the data in this thesis are MAR
instead of MCAR, validity is added to data interpolation. When data are MAR as they
are here, Allison argues that data can be validly interpolated using methods that
determine the maximum likelihood by using linear or non-linear methods whichever
fits best with what specifically is being measured (Allison 2002, 18).
Within the literature there are opposing viewpoints about the validity of imputing
and interpolating data. This controversy centers on the difficulty found in accounting for
error with interpolated data. One issue would be to exercise pairwise deletion and ignore
data for all variables when WVS/EVS surveys were not conducted. However, King et al.
strongly criticize pairwise deletion due to the implications found when ignoring large
portions of data that are already available (2001). This is because data that are present
are therefore lost due to the absence of data for one variable. In this thesis, pairwise
deletion of years with missing data could result in misleading findings because the
independent variables vary more over time than happiness does. Even more problematic,
45


pairwise deletion would effectively remove the possibility to include income inequality
as an independent variable because years where there are data for inequality rarely
coincide with years where there are WVS/EVS happiness data.
For time-series analyses specifically, Honaker and King present and suggest an
algorithm that interpolates data non-linearly so that there are smooth transitions (2010).
This method generally produces results that are similar to interpolating data non-linearly
by using polynomials (King et al. 2010). The use of any method that uses non-linear
methods of data interpolation would also be neither valid nor invalid, however. This
includes algorithmic models that create smooth transitions between data points such as
that presented by Honaker and King (2010). This would be neither valid nor invalid for
happiness because this method does not consider data that lay before and after the period
that the study is examining. The inclusion of survey years before and after the years of
the time-series would affect the curvature of these transitions which interpolated data are
based on especially if the years outside of the studys scope are significantly higher or
lower. Without knowledge of past happiness survey data, this method is neither valid nor
invalid.15
Similar to non-linear method of data interpolation, linear data interpolation is
neither valid nor invalid. However, it does find the values with the maximum
likelihood as linear interpolation estimates values between two known data points by
assuming linearity (Allison 2002). As argued in the preceding paragraphs, the
interpolation of data provides a trade-off of validity. Validity is lost by estimating data
that have never formally existed and assuming that they move linearly. But, validity is
15 See Appendix E for further discussion and a pictoral demonstration of linear vs. non-
linear data interpolation.
46


also gained by not ignoring the greater variation observed in the independent variables.
Due these reasons, happiness data are interpolated linearly.
Controlling for Economic Protectionism
To enrich the systemic analysis of the variables while taking economic
protectionism into account, the sample is divided into terciles for both happiness and
economic protectionism. Given the overarching selection of independent variables that
measure different aspects of neoliberal globalization, this fosters a better comparison of
how counties happiness correlates with the independent variables at three relative levels
of protectionism. Countries with lower levels of protectionism generally have more
neoliberal economic policies while those with higher protectionism have policies that are
less liberalized. In the following chapter, economic protectionism is controlled for when
testing all variables. The sectors created by dividing the data into terciles can be seen in
table 5.6.
Table 5.6 Happiness and Economic Protectionism
Belarus Argentina China Brazil
5 HIGH Peru South Korea Mexico
Cfl y z Russia India Nigeria

% o Moldova Bosnia and Herzegovina Switzerland Canada
O H 5 u o m r) e MID Serbia Macedonia Norway Iceland
Ukraine South Africa
w o Albania Slovakia Chile France
PS Clh LOW Latvia Turkey Japan Malta
Sweden USA
LOW MID HIGH
HAPPINESS
* The tercile that each country is placed in is based on
the most recent year with paired tariff and happiness data.
47


The tercile that each country is placed in is determined by the most recent year
where paired happiness and import tariff data are available. For most countries, this is in
the mid-2000s or late-2000s. Another option would be to use the mean happiness and
tariff levels for all years with available data for each country, but this would not be
entirely valid considering that all of the BRICS were significantly more economically
protected in the early 1990s than they are today especially when compared to MEDCs
(ONeil 2011). In order to maintain relevance to the present and reduce misleading
findings, the most recent year with paired data is used.
Notably, the BRICS excluding South Africa are placed in the category of high
economic protectionism while South Africa is placed in the moderate category of
economic protectionism. This does not indicate that the BRICS economies are highly
protected on a global scale, but rather that they are more protected than the majority of
the other countries in the sample. In other words, although the BRICS have shifted to the
neoliberal path of development in recent years, they function as more economically
protected in the sample of this study (ONeil 2011). Again, this is due to the lack of
available happiness data and the purposively selected sample.
Measuring economic protectionism by using tariff data is an efficient way to
measure economic protectionism due to its breadth across all sectors of the economy, but
this does raise challenges. In the WTO, for example, the large-scale reduction of tariffs
among member states has led many states to adopt non-tariff barriers (NTBs) to trade in
efforts to protect the domestic economy from outside market fluxuations.16 NTBs
provide obstacles to trade that are not formally included in tariff rates (Deardorff and
16 Examples of NTBs include phenomena such as quotas, health and safety regulations,
countervailing duties, and anti-dumping duties (Deardorff and Stern 1999).
48


Stern 1999). Although scholars such as Deardorff and Stern measure NTBs
quantitatively, this is quite tedious and is limited to one sector of the economy at a time
rather than the economy as a whole (1999). Despite this challenge, an emerging norm of
bilateral trade reciprocity is emerging that influences both tariffs and NTBs (Limao
2006). As of 2005, towards the end of this studys scope, all but one WTO member was
engaged in some sort of bilateral or multilateral preferential trade agreement
characterized by tariff reciprocity (Limao 2006). This includes all in the sample except
Belarus (WTO 2013). For these reasons, tariffs are used as a measure of economic
protectionism.
Operationalizing the Methods
In order to assess the relationship between happiness and economic development,
hypotheses will be tested quantitatively using correlation analysis. This is true for both
state-level analysis and systemic-level analysis when controlling for economic
protectionism. Correlation rather than regression is chosen for hypothesis testing
because: 1) the relationship between variables is sometimes bidirectional and 2) the
sample is not randomly selected and skewed.
Pearsons r is used to calculate correlation coefficients in this study. Given that
all of the variables other than income inequality are highly time dependent and would
increase over time regardless of many factors, data in the time-series are made stationary
by converting data to reflect year-to-year change in order to avoid spurious results17
(Hamilton 1994). Since income inequality is not time dependent, these data are left as is.
17 In the tables of the following chapter, delta (A) is used to note year-to-year change.
49


In testing the relationship between happiness and income inequality, moreover, pairwise
deletion is used since income inequality would be less appropriate to interpolate than
happiness. In some cases when conducting the hypothesis testing, the data violate some
of the assumptions of Pearsons r. For example, non-stationary data for some countries
are heteroskedastic and cone-shaped. Moreover, the data interpolation methods used here
yield awkward placement of the data when plotted on a scatterplot.18
In order to select BRICS countries to test hypothesis one, Seawright and
Gerrings diverse method of case study selection is used (2008). For reasons discussed in
the following chapter, Russia and India are the focus of further discussion due to their
standings at the opposite ends of the spectrum for exports, electricity production, and
internet use. This can be seen in the descriptive statistics in the following chapter. Other
BRICS are also discussed, but Russia and India particularly are examined in greater depth
throughout the quantitative analysis chapter.
18 See Appendix D for scatterplots of heteroskedastic non-stationary data and stationary
data that are awkwardly placed when graphed on a scatterplot.
50


CHAPTER VI
QUANTITATIVE ANALYSIS
Using the methodology presented in the previous chapter, quantitative analysis is
performed in this chapter to examine the relationships between happiness and the
independent variables. In line with the hypotheses presented in chapter three, tests are
conducted at both the state level and the systemic level when controlling for economic
protectionism. The state-level analysis to follow assesses the relationship of neoliberal
globalization and income inequality with happiness at the state level while taking into
consideration the arguments of Graham (2009). Moreover, the systemic-level analysis to
follow tests the impact of neoliberal globalization and income inequality on happiness by
determining which countries have economic policies that are the most and least
liberalized of the sample countries.
Happiness: Descriptive Statistics
Concerning happiness observed alone, table 6.1 on the following page shows that
average happiness in the BRICS is roughly similar for all countries except Russia. On
average, Russia is not only considerably less happy than the other BRICS counties, but
also less happy than the vast majority of other sample countries. This was also observed
in the ranked z-scores seen in table 5.1 in the previous chapter where only Belarus and
Moldova are on average less happy than Russia. When examining the dispersion of
happiness, table 6.1 shows that South Africa has the largest dispersion while happiness in
China and India has the lowest level of dispersion. In other words, this demonstrates that
51


the level happiness in South Africa varied much more across the time-period than it did
in China and India.
Table 6.1 Happiness Descriptive Statistics
NAAH = 17-40.9; NYH = 41-66.9; QH = 67-92.9; VH = 93
Country Years Mean Qi Median Q3 IQR
Brazil 1991 -2006 69.0 67.0 68.7 71.0 4.0
China 1990 2007 65.8 64.6 65.6 66.8 2.1
India 1990 2006 66.8 66.3 66.9 67.4 1.2
Russia 1990 2006 55.5 54.1 54.7 56.4 2.3
South Africa 1990 2007 68.4 66.2 68.9 70.4 4.2
BRICS various years 65.0 64.2 66.5 68.1 3.9

Albania 1998 2008 56.1 53.4 57.3 59.3 5.9
Argentina 1991 -2006 70.4 69.4 70.3 71.3 1.9
Belarus 1990 2000 53.9 52.5 52.9 54.6 2.2
Bosnia and Herzegovina 1998 2008 67.2 66.9 67.8 68.2 1.2
Canada 1990 2006 74.4 71.8 75.4 77.4 5.6
Chile 1990 2006 69.4 71.8 75.4 77.4 5.6
France 1990 2008 72.5 71.9 72.6 73.2 1.2
Iceland 1990 2005 78.3 77.6 78.4 79.0 1.4
Japan 1990 2005 71.3 71.3 71.6 72.3 0.9
Latvia 1990 2008 58.8 57.4 58.9 60.1 2.7
Macedonia 1998 2008 64.8 63.6 65.3 66.5 2.8
Malta 1991 -2008 72.3 71.7 72.2 72.7 1.0
Mexico 1990 2005 71.9 65.4 70.4 79.5 14.1
Moldova 1996 2008 54.2 53.1 54.1 54.6 1.4
Nigeria 1990 2000 74.4 70.5 74.4 78.2 7.7
Norway 1990 2008 74.0 73.1 73.8 74.8 1.7
Peru 1996 2006 65.4 65.2 65.5 65.6 0.4
Serbia 1996 2008 61.7 61.3 62.0 62.4 1.2
Slovakia 1990 2008 60.1 58.9 60.5 61.7 2.8
South Korea 1990 2005 65.9 65.7 66.2 66.6 0.9
Sweden 1990 2008 75.3 74.4 75.9 76.2 1.8
Switzerland 1989 2008 75.8 75.5 75.9 76.1 0.6
Turkey 1990 2008 70.5 68.0 70.4 72.5 4.5
Ukraine 1996 2008 57.1 52.8 57.0 60.9 8.1
United States 1990 2006 75.5 74.7 75.3 76.3 1.6
* data include all years in time-period for each country
52


Seen in figure 6.1 below is a visual representation of happiness dispersion in each
WVS survey conducted in the BRICS. Happiness in each year is roughly centered on
either the quite happy or not very happy categories; which is also seen in the means and
medians in table 6.1 on the previous page. Also shown below is how happiness survey
responses change across survey years.
Brazil
60%
40%
20%
0%
I
I I
1991 1997 2006
India
60%
40%
20%
0%
1 U
1990 1995 2001 2006
South Africa
60%
40%
20%
0%

I
|]=i_fc]=L
1990 1996 2001 2007
China
J.J ill!
1990 1995 2001 2007
Russia
60% -
40% i
20%
h j n
1990 1995 1999 2006
= Very Happy
= Quite Happy
= Not Very Happy
= Not At All Happy
Figure 6.1 Distribution of Happiness Responses in WVS Surveys
53


Independent Variables: Descriptive Statistics
In order to be better prepared to address hypothesis one which concerns state-
level relationships between variables, tables 6.2-6.5 on the following pages show the
descriptive statistics for all of the independent variables. These are periodically referred
to in the following pages which conduct hypothesis testing at the state level.
Table 6.2 Exports per capita (constant 2000 US$)
Descriptive Statistics
Country Years Mean Qi Median Q3 IQR
Brazil 1992 2006 372 290 325 434 144
China 1991 -2007 274 98 159 370 271
India 1991 -2006 56 30 43 71 41
Russia 1991 -2006 796 626 714 941 315
South Africa 1991 -2007 791 697 802 844 147
BRICS various years 461 117 443 746 630

Albania 1998 2008 332 256 335 428 172
Argentina 1992 2006 780 627 831 881 254
Belarus 1991 -2000 851 696 779 893 196
Bosnia and Herzegovina 1999 2008 554 436 499 652 216
Canada 1991 -2006 8,532 6,952 9,061 10,346 3,394
Chile 1991 -2006 1,356 1,001 1,370 1,604 603
France 1991 -2008 5,423 4,161 5,622 6,506 2,345
Iceland 1991 -2005 10,427 8,594 10,105 11,765 3,172
Japan 1991 -2005 3,642 2,990 3,582 4,055 1,065
Latvia 1991 -2008 1,507 1,041 1,334 1,798 756
Macedonia 1999 2008 802 707 735 897 190
Malta 1992 2008 9,408 9,059 9,413 9,797 737
Mexico 1991 -2005 1,296 767 1,353 1,720 953
Moldova 1997 2008 276 203 245 377 173
Norway 1991 -2008 15,853 13,882 17,012 17,735 3,853
Peru 1997 2006 365 301 349 413 112
Serbia 1999 2008 280 195 275 338 142
Slovakia 1992 2008 4,208 2,446 3,755 5,266 2,821
South Korea 1990 2005 3,330 1,714 3,082 4,451 2,737
Sweden 1991 -2008 11,602 8,004 11,627 14,350 6,345
Switzerland 1990 2008 14,716 11,329 14,030 16,400 5,070
Turkey 1991 -2008 795 524 838 1,005 481
Ukraine 1997 2008 447 336 455 531 195
United States 1991 -2006 3,273 2,685 3,497 3,621 936
* insufficient data for Nigeria data include all years in time-period for each country
54


Table 6.3 Electricity Production per capita (kw/h)
Descriptive Statistics
Country Years Mean Qi Median Q3 IQR
Brazil 1991 -2006 1,863 1,685 1,878 2,002 317
China 1990 2007 1,176 793 965 1,436 643
India 1990 2006 485 407 488 549 141
Russia 1990 2006 6,282 5,799 6,121 6,644 846
South Africa 1990 2007 4,869 4,677 4,769 5,061 384
BRICS various years 2,949 793 1,937 5,061 4,268

Albania 1998 2008 1,483 1,197 1,607 1,741 545
Argentina 1991 -2006 2,151 1,898 2,125 2,417 519
Belarus 1990 2000 2,966 2,510 2,642 3,470 960
Bosnia and Herzegovina 1998 2008 3,108 2,831 2,978 3,349 519
Canada 1990 2006 18,802 18,568 18,975 19,128 560
Chile 1990 2006 2,351 1,783 2,361 2,767 984
France 1990 2008 8,484 8,124 8,618 8,922 797
Iceland 1990 2005 25,868 18,296 25,914 29,315 11,020
Japan 1990 2005 7,777 7,382 7,978 8,156 774
Latvia 1990 2008 1,891 1,705 1,817 2,131 426
Macedonia 1998 2008 3,292 3,164 3,323 3,419 254
Malta 1991 -2008 4,878 4,416 4,915 5,541 1,124
Mexico 1990 2005 1,831 1,597 1,830 2,066 468
Moldova 1996 2008 1,117 997 1,063 1,150 153
Nigeria 1990 2000 138 136 140 144 9
Norway 1990 2008 27,114 25,922 27,322 28,685 2,763
Peru 1996 2006 815 744 792 871 127
Serbia 1996 2008 4,797 4,675 4,896 4,952 277
Slovakia 1990 2008 5,184 4,749 5,168 5,738 988
South Korea 1990 2005 5,018 3,541 4,751 6,627 3,068
Sweden 1990 2008 16,718 16,263 16,800 17,056 793
Switzerland 1989 2008 8,719 8,350 8,764 8,930 580
Turkey 1990 2008 1,846 1,409 1,857 2,180 771
Ukraine 1996 2008 3,746 3,515 3,601 3,947 432
United States 1990 2006 13,607 13,119 13,554 13,999 880
* data include all years in time-period for each country
55


Table 6.4 Internet Users (% of population)
Descriptive Statistics
Country Years Mean Qi Median Q3 IQR
Brazil 1991 -2006 6.4 0.1 1.8 10.2 10.1
China 1990 2007 3.2 0.0 0.4 5.8 5.8
India 1990 2006 0.7 0.0 0.1 1.5 1.5
Russia 1990 2006 4.1 0.1 0.9 5.2 5.0
South Africa 1990 2007 3.8 0.4 3.5 6.9 6.6
BRICS various years 3.6 0.0 0.8 5.3 5.3

Albania 1998 2008 5.4 0.2 1.0 7.8 7.6
Argentina 1991 -2006 6.2 0.1 2.1 11.1 11.1
Belarus 1990 2000 0.2 0.0 0.0 0.1 0.1
Bosnia and Herzegovina 1998 2008 12.2 1.1 4.0 23.2 22.1
Canada 1990 2006 31.8 2.4 24.9 61.6 59.2
Chile 1990 2006 10.9 0.1 1.7 22.1 22.0
France 1990 2008 21.0 1.3 9.1 37.6 36.4
Iceland 1990 2005 44.2 9.0 41.3 83.5 74.5
Japan 1990 2005 21.5 0.7 11.3 40.5 39.8
Latvia 1990 2008 17.6 0.0 4.4 32.8 32.8
Macedonia 1998 2008 18.8 3.0 19.1 27.5 24.6
Malta 1991 -2008 18.0 0.4 10.4 33.9 33.4
Mexico 1990 2005 4.5 0.0 0.9 8.3 8.2
Moldova 1996 2008 8.0 0.6 3.8 14.6 14.0
Nigeria 1990 2000 0.0 0.0 0.0 0.0 0.0
Norway 1990 2008 42.4 5.3 40.0 77.9 72.6
Peru 1996 2006 7.9 1.6 7.6 12.9 11.3
Slovakia 1990 2008 21.5 0.4 5.4 48.0 47.5
South Korea 1990 2005 25.6 0.3 5.2 57.3 57.0
Sweden 1990 2008 25.6 0.3 5.2 57.3 57.0
Switzerland 1989 2008 34.5 2.6 29.4 65.8 63.2
Turkey 1990 2008 7.8 0.1 2.3 13.5 13.4
Ukraine 1996 2008 2.9 0.4 1.9 3.7 3.3
United States 1990 2006 31.7 4.9 30.1 58.8 53.9
* insufficient data for Serbia data include all years in time-period for each country
56


Table 6.5 Income Inequality (gini coefficient)
Descriptive Statistics
Country Years Mean Qi Median Q3 IQR
Brazil 1993 2006 59.4 58.5 60.0 60.4 1.9
China 1990 2005 38.0 35.6 37.5 41.7 6.1
India 1994 2005 32.1 31.5 32.1 32.7 1.3
Russia 1993 2006 40.1 37.3 37.5 42.1 4.9
South Africa 1993 2006 60.3 57.5 58.6 61.3 3.9
BRICS various years 48.9 37.5 51.2 59.4 21.9

Albania 2000 2008 31.7 30.4 32.1 33.4 3.0
Argentina 1991 -2006 49.4 47.4 49.4 50.8 3.4
Belarus 1993 2000 27.7 27.0 29.5 30.3 3.3
Bosnia and Herzegovina 2001 -2007 33.3 31.9 35.8 36.0 4.1
Canada 2000 32.6
Chile 1990 2006 54.7 54.7 55.0 55.3 0.5
France 1995 32.7
Japan 1993 24.9
Latvia 1993 2008 33.7 31.7 34.6 36.2 4.5
Macedonia 1998 2008 38.2 37.7 38.9 40.0 2.4
Mexico 1990 2005 49.7 48.8 49.7 51.5 2.7
Moldova 1997 2008 36.9 35.8 36.3 37.7 2.0
Nigeria 1992 1996 45.7 45.3 45.7 46.1 0.8
Norway 2000 25.8
Peru 1997 2006 51.6 50.8 52.6 55.5 4.8
Serbia 2002 2008 31.3 29.5 32.7 32.9 3.4
Slovakia 1992 2008 26.7 26.3 27.7 28.6 2.3
South Korea 1998 31.6
Sweden 2000 25.0
Switzerland 2000 33.7
Turkey 1994 2008 41.4 40.1 42.0 42.7 2.6
Ukraine 1996 2008 29.3 28.1 28.3 29.6 1.5
United States 2000 40.8
* insufficient data for Iceland and Malta *data do not include all years in time-period for each country dash indicates insufficient data
57


State-level Analysis Addressing Hypothesis One
In the following tables which present the results of the quantitative analysis at the
state level, two tables are provided which include each independent variable. To measure
the strength of relationships, table 6.6 includes Pearson r correlation coefficients. As
discussed in the methodology chapter, the statistical significance of relationships is
accomplished using Pearson r. In order to be better equipped to address the hypotheses,
table 6.7 includes regression coefficients using least squares regression and standard error
of the mean. The regression coefficients presented measure how much happiness
changes on average relative to a one unit increase in the independent variable. Due to
their much larger scales, the regression coefficients for exports and electricity production
show how much happiness is expected to change in relation to 100 units of change.
Table 6.6 State-level Results: Pearson r Correlation Coefficient
Legend A Happiness & A Exports per capita (hundreds of constant 2000 US$) A Happiness & A Electricity Production per capita (hundreds of kw/li) A Happiness & A Internet Users (% of population) Happiness & Gini Index Score
Pearson r [dfj
Brazil d£ 1992-2006 13 0.490 -0.073 0.623 -0.246 [11]
China d£ 1991-2007 15 0.260 0.374 0.163 -0.691 [4]
India d£ 1991-2006 14 0.220 0.166 0.043 insufficient data
Russia d£ 1991-2006 14 0.527 0.717 0.779 -0.327 [7]
South Africa d£ 1991-2007 15 -0.046 -0.233 -0.127 0.603 [2]
1 1
BRICS d£ 1991-2007 79 0.277 0.279 0.326 0.563 [32]
Non-BRICS various years -0.058 [348] -0.029 [364] -0.141 [356] 0.526 [120]
1 1
P>1 p < .05 p < .01
* data are based on each country/year with paired data between variables
58


Table 6.7 State-level Results: Regression Coefficient and Standard Error
Legend A Happiness & A Exports per capita (hundreds of constant 2000 US$) A Happiness & A Electricity Production per capita (hundreds of kw/h) A Happiness & A Internet Users (% of population) Happiness & Gini Index Score
slope (standard error)
Brazil 1992-2006 0.319 (0.113) -0.016 (0.129) 0.033 (0.101) -0.308 (2.532)
China 1991-2007 0.319 (0.604) 0.252 (0.581) 0.072 (0.618) -0.238 (1.149)
India 1991-2006 1.260 (0.448) 0.909 (0.453) 0.081 (0.459) insufficient data
Russia 1991-2006 0.326 (0.597) 0.201 (0.489) 0.347 (0.440) -0.196 (2.706)
South Africa 1991-2007 -0.054 (0.330) -0.055 (0.321) -0.066 (0.327) 0.325 (2.571)

BRICS 1991-2007 0.252 (0.495) 0.104 (0.495) 0.112 (0.487) 0.290 (4.852)
Non-BRICS various years -0.014 (0.874) -0.002 (0.892) -0.029 (0.887) 0.355 (5.625)
* data are based on each country/year with paired data between variables
Seen in table 6.6 on the previous page, statistically significant relationships
among the individual BRICS are found in only Brazil and Russia.19 In Brazil, the
relationship between happiness and exports is seen to be a significant positive correlation
as is the relationship between happiness and internet use. Similar to Brazil, significant
positive relationships in Russia are also found between happiness and both exports and
internet use. Different than Brazil, however, happiness and electricity production in
Russia are also positively correlated at a significant level. Other than income inequality,
South Africa displays virtually no relationships between variables. In none of the BRICS
are there significant relationships between happiness and income inequality. What these
observations indicate is that the significant relationships found are sporadically placed
across the BRICS and the values of r vary as well.
19 See Appendix A for state-level quantitative results from non-BRICS sample countries.
59


Exports. When comparing Brazil and Russia, it is seen that they have comparable
regression coefficients and statically significant relationships between happiness and
exports. Seen in the descriptive statistics, however, Russia is a much larger exporter per
capita than Brazil. This provides a useful comparison to test hypothesis one which
assumes that LEDCs will experience the greatest increase in happiness relative to
economic development. Given that Brazil is a ranked third out of the five BRICS
concerning exports per capita wile Russia is ranked as the top exporter, hypotheses one is
not supported by the data when analyzing exports alone. Seen in table 6.7, this is because
their regression coefficients are comparable despite their different levels of economic
development.
Different conclusions can be reached when comparing Russia as the highest per
capita exporter among the BRICS and India as the lowest. A comparison of these two
countries is needed to test hypothesis one for this variable, but the relationship in India is
not statistically significant. As table 6.7 shows, the regression coefficient of happiness
relative to exports per capita is nearly three times higher in India than it is in any of the
other BRICS. Additionally, it is not only much higher than the other BRICS, but also
much higher than the cumulative non-BRICS and all other sample countries except
Serbia. The value of r between exports per capita and happiness in India is neither
statistically significant at any level nor is it close to being significant, however. This
creates ambiguities. If only the statistically significant relationships are analyzed, such as
those seen in Brazil and Russia, hypotheses one is not supported by the data. If India and
Russia are analyzed despite the lack of statistical significance, however, hypotheses one
is at best mildly supported by the data. Overall, these results indicate that the correlation
60


between happiness and exports is not uniform across the BRICS and no clear trends in
the data are observed. Thus, the relationship between variables is blurry and unclear.
Industrialization. Seen above in table 6.6, Russia is the only BRICS country that has a
significant relationship between happiness and electricity production. With a lack of
other significant relationships, the feasibility of conducting a valid test of hypotheses one
is limited in the same way as seen for exports. Similar to the above situation concerning
exports, nevertheless, it can be seen in the descriptive statistics that Russia is the top
producer of electricity per capita while India is the lowest among the BRICS. Although
the validity of using these two countries to test hypotheses one and two is limited due to
the lack of statistical significance, they will nevertheless be examined keeping this in
mind.
With a regression coefficient in India that is more than four times greater than all
other BRICS, the cumulative non-BRICS, and all other individual sample countries, this
is quite notable. Although there could be something happening between happiness and
electricity production that supports hypothesis one, the relationship in India is neither
significant nor close to being significant at any level used here. Similar to the state-level
analysis of exports, an obstacle is reached. If statistical significance is set aside, there
very well could be a relationship present that supports hypotheses one due to the
regression coefficient that is much higher than other sample countries. If non-significant
relationships are ignored, however, hypotheses one is not supported by the data. Due to
the considerable differences in regression coefficients between Russia and India,
nevertheless, the data indicate that there could be something happening that provides
61


support for hypotheses one but this is not certain. To summarize these results, the
relationship between happiness and industrialization in the BRICS is mostly unclear.
Globalization. Both Brazil and Russia have strong positive correlations between
happiness and internet use. When observing the regression coefficients of this
relationship, that of Brazil is 0.033 and that of Russia is 0.347 which is much stronger
than all the other BRICS, the cumulative non-BRICS, and many other sample countries.
Not to be misled, however, internet use is the variable which has the weakest relationship
with happiness among all of the independent variables when examining their
corresponding regression coefficients. Consistently among the BRICS, these values are
less than one tenth of one point of happiness when viewing internet use. What this
indicates is that at all levels of statistical significance and non-significance, the actual
effect that internet use has on happiness is actually quite minimal for most of the sample
countries.
To test hypothesis one for internet use in Brazil and Russia, the descriptive
statistics show that Brazil and Russia have the top and second to top levels of internet use
respectively. When looking at the regression coefficients found for these countries,
however, internet use in Brazil experienced the least effect on happiness other than South
Africa, which actually has weakly negative correlation and regression coefficients.
Russia is an anomaly in this case, therefore, as it is the only BRICS country with a
regression coefficient that is numerous times higher than the cumulative non-BRICS and
all of the other BRICS. Given that those with the lowest means and medians in the
descriptive statistics actually have the smallest regression coefficients, it can be
62


concluded that hypotheses one is not at all supported by the data when examining internet
use.
Income inequality. Measured using gini index scores, income inequality is addressed by
hypothesis three which states that in all countries, increases in income inequality will be
correlated with decreases in happiness. No significant relationships for any individual
BRICS countries are found for this independent variable. When looking at the
descriptive statistics in table 6.5, it is seen that Brazil and South Africa have the highest
levels of income inequality among all of the sample countries. Also, China has the
highest level of dispersion while India had the lowest level of dispersion. This indicates
that inequality in China witnessed the largest amount of change in the study time-period
while inequality in India experienced the smallest amount of change.
In all BRICS except South Africa, the values of r and the regression coefficients
for each country are negative. For both the cumulative BRICS and the cumulative non-
BRICS, however, the regression coefficients are positive and the relationships are
statically significant at the 99% level. Although the results when controlling for
economic protectionism will be discussed later, the data at the state level do not support
hypothesis three because there are no significant relationships between happiness and
income inequality in the BRICS. Even among the significant relationships seen in non-
BRICS countries, there are no general relationships that can be identified because both
positive and negative relationships exist in both LEDCs and MEDCs.
63


Systemic-level Analysis Addressing Hypothesis Two: Controlling for Protectionism
When comparing the cumulative BRICS with the cumulative non-BRICS
countries seen above in table 6.6, the results yield interesting findings. Crucially, in the
cumulative BRICS all independent variables are positively correlated with happiness at a
significance level of 95% or higher. In the cumulative non-BRICS, internet use is
negatively correlated with happiness while income inequality is positively correlated with
happiness. Both of these relationships are significant at the 99% level. Since comparing
BRICS with non-BRICS does not allow us to determine which sample countries have
economic policies that are the most neoliberal, this section will control for economic
protectionism by using the methods presented in chapter four. Countries with the lower
levels of economic protectionism have more neoliberal economic policies while those
with higher levels of protectionism have economic policies that are less neoliberal. In
each variables respective section, tables show the quantitative results when controlling
for economic protectionism. Similar to the state-level analysis above, two tables are
shown for each independent variable where one shows Pearson r correlation coefficient
and the other shows regression coefficients and standard error of the mean. In order for
hypothesis two to be supported in the analysis for exports, electricity production, and
internet use, we would expect to see a trend in the tables below where those in the upper
left-hand sector have weaker regression coefficients and those in the lower right-hand
sector stronger regression coefficients and statistically significant levels of r. Before
proceeding, tables 6.8 and 6.9 on the following page show the analysis of all sample
countries cumulatively to assist in comparing the terciles to the average of all sample
countries.
64


Table 6.8 Cumulative Sample Countries:
Pearson r Correlation Coefficient
Variables Pearson r df
A Happiness & A Exports per capita (hundreds of constant 2000 US$) -0.051 429
A Happiness & A Electricity Production per capita (hundreds of kw/li) -0.023 444
A Happiness & A Internet Users (% of population) -0.178 437
Happiness & Gini Index Score 0.516 150
1 1
p < .01
* data are based on each country/year with data between variables
Table 6.9 Cumulative Sample Countries:
Regression Coefficient and Standard Error
Variables Slope Standard Error
A Happiness & A Exports per capita (hundreds of constant 2000 US$) -0.012 0.818
A Happiness & A Electricity Production per capita (hundreds of kw/h) -0.002 0.836
A Happiness & A Internet Users (% of population) -0.142 2.730
Happiness & GiniIndex Score 0.305 5.440
* data are based on each country/year with data between variables
65


Exports. When divided into terciles, table 6.10 below shows that a significant
relationship exists among sample countries with low happiness and high economic
protectionism. Compared to the average of all countries, the relationship between
happiness and exports is much stronger in countries with high protectionism and low
happiness. Although statistically significant relationships are not found in the other
sectors, table 6.10 shows that there are virtually no relationships between variables
among the happiest countries in the sample and countries with lower levels of economic
protectionism. When viewing the regression coefficients seen in table 6.11 on the
following page, the largest average influence of exports on happiness is seen among
Russia and other formerly Soviet states. In other words, happiness increases the most on
average in countries with low happiness and high protectionism. As indicated by these
data, therefore, hypothesis two is not supported regarding happiness and exports.
Table 6.10 Happiness and Exports per capita (constant 2000 US$)
Controlled for Economic Protectionism: Pearson r Correlation Coefficient
Belarus Peru South Korea Argentina Mexico Nigeria
s HIGH Russia China India Brazil
u e 0.440 [34] 0.052 [611 0.020 [49]
a 5 £ 2 9 H o w U H Moldova Serbia Bosnia and Herzegovina Canada Iceland
MID Ukraine Macedonia South Africa Norway Switzerland
0.077 [31] -0.245 [35] -0.006 [69]
w o PS - Albania Slovakia Civile Japan France Malta
LOW Latvia Turkey Sweden USA
0.015 [59] -0.154 [47] 0.020 [49]
LOW MID HIGH
HAPPINESS
* happiness and exports per capita are measured as year-to-year change
Legend
* the tercile that each country is placed in is determined by the most recent
year where paired happiness and tariff data are available
Pearson x [dfj
* data are based on each country/year with paired data between variables
P<1
p < .05
p<.l
66


Table 6.11 Happiness and Exports per capita (constant 2000 US$) Controlled
for Economic Protectionism: Regression Coefficient and Standard Error
£ HIGH Belarus Peru Russia South Korea Argentina China India Mexico Nigeria Brazil
C/3 u a 0.249 (0.643) 0.011 (0.428) 0.002 (0.300)
a 5 £ 2 2 H o W U H MID Moldova Serbia Ukraine 0.230 (1.074) Bosnia and Herzegovina Macedonia South Africa -0.193 (0.421) Canada Iceland Norway Switzerland 0.000 (0.293)
w o Pi Ph LOW Albania Slovakia Latvia Turkey Chile Japan Sweden France Malta USA
0.009 (1.412) -0.029 (0.774) 0.002 (0.300)
LOW MID HIGH
HAPPINESS
: exports per capita are measured in hundreds of US dollars (constant 2000)
: slope values represent average change in happiness
relative to an increase of 100 US dollars (constant 2000)
: both happiness and exports are measured as year-to-year change
: the tercile that each country is placed in is determined by the most recent
year where paired happiness and tariff data are available
: data are based on each country/year with paired data between variables
Legend
Slope (Standard Error)
To identity general trends for happiness and exports, though possibly due to
chance, it is seen that as a countrys level of economic protectionism decreases the
correlation between exports and happiness begins to weaken. Likewise, table 6.11 shows
that this is also true for the average change in levels of happiness relative to exports. In
other words, the relationship between exports and happiness is strongest among relatively
unhappy countries with relatively high levels of economic protectionism. Although this
trend is not perfectly clear in the data, in a way this actually runs counter to hypothesis
two. In addition to hypothesis two not being supported by the data, there is also no clear
trend to demonstrate something else happening. What this indicates is that ambiguity is
found when determining whether a countrys level of economic protectionism has an
influence on the relationship between happiness and exports.
67


Industrialization. Included as a proxy measure of industrialization, the analysis for
electricity production does not support hypothesis two. Seen in tables 6.12 and 6.13
below, the sectors for electricity production per capita are somewhat similar to those seen
for exports per capita. There is one significant relationship in the low happiness and high
protectionism sector which includes Belarus, Peru, and Russia. This relationship is
significant and positive. Compared to the cumulative average of all sample countries,
this sector is far different as the variables are much more closely related. Not only do the
other sectors have r values that are not close to being significant, but they also have
regression coefficients that are much smaller than seen in the sector of Belarus, Peru, and
Russia. Although hypothesis two is not clearly supported by the data, the data somewhat
indicate the opposite of what hypothesis two predicts. The countries with economies that
are the most liberalized display the weakest relationships between variables.
Table 6.12 Happiness and Electricity Production per capita (kw/h)
Controlled for Economic Protectionism: Pearson r Correlation Coefficient
ECONOMIC PROTECTIONISM HIGH Belarus Peru Russia South Korea Argentina China India 0.076 [61] Mexico Nigeria Brazil -0.021 [38]
0.529 [34]
MID Moldova Serbia Ukraine 0.056 [33] Bosnia and Herzegovina Macedonia South Africa -0.249 [35] Canada Iceland Norway Switzerland -0.009 [69]
LOW Albania Slovakia Latvia Turkey -0.123 [62] Chile Japan Sweden -0.100 [47] France Malta USA 0.005 [49]
LOW MID HIGH
HAPPINESS
* happiness and electricity production per capita are measured as year-to-year change
* the tercile that each country is placed in is determined by the most recent
year where paired happiness and tariff data are available
* data are based on each country/year with paired data between variables
Legend
Pearson r [dfj
El
P
P
<.l
<.05
<.l
68


Table 6.13 Happiness and Electricity Production per capita (kw/h) Controlled
for Economic Protectionism: Regression Coefficient and Standard Error
g HIGH Belarus Peru Russia South Korea Argentina China India Mexico Nigeria Brazil
cn u a 0.169 (0.608) 0.017 (0.427) -0.044 (1.097)
a 5 % 2 2 H o W U H MID Moldova Serbia Ukraine 0.038 (1.046) Bosnia and Herzegovina Macedonia South Africa -0.051 (0.421) Canada Iceland Norway Switzerland 0.000 (0.293)
w o Pi Ph LOW Albania Slovakia Latvia Turkey Chile Japan Sweden France Malta USA
-0.063 (1.405) -0.012 (0.779) 0.001 (0.300)
LOW MID HIGH
HAPPINESS
: electricity production per capita is measured in hundreds of kilowatts per hour
: slope values represent average change in happiness
relative to an increase of 100 kilowatts per hour
: happiness and electricity production are measured as year-to-year change
: the tercile that each country is placed in is determined by the most recent
year where paired happiness and tariff data are available
: data are based on each country/year with paired data between variables
Legend
Slope (Standard Error)
Seen in table 6.13 above, even if statistical significance is set aside and only the
regression coefficients are examined, the sector with Belarus, Peru, and Russia is much
higher than the others. It is also much higher than the cumulative average of all
countries. Although this certain sector standing out is noteworthy, the general trend
across the data is not clear. In some ways the trend across the data indicates the opposite
of hypothesis two, but this is neither certain nor clear. Since the hypotheses are not
supported and no general trend is found, the explanation for this correlation most likely
lies outside the bounds of economic protectionism. In other words, the data do not
indicate that the most economically liberal countries have the strongest relationship
between happiness and industrialization. Due the identified vagueness, the reason for the
instances of significant correlations lies outside the bounds of economic protectionism.
69


Globalization. Included as a proxy measure for globalization, the analysis of happiness
and internet use also does not support hypothesis two. Table 6.14 below shows that
significant relationships between happiness and internet use are found in one sector.
Within this sector, there is a significant negative relationship in the middle happiness and
middle protectionism sector which includes Bosnia and Herzegovina, Macedonia, and
South Africa. The relationship in this sector is not very different than the cumulative
average of all countries when examining r, but the regression coefficient is much less.
Setting statistical significance aside, table 6.15 on the following page shows that the
regression coefficient for Belarus, Peru, and Russia is considerably higher than those in
the other sectors. It is also higher than that of the cumulative average for all sample
countries.
Table 6.14 Happiness and Internet Users (% of population)
Controlled for Economic Protectionism: Pearson r Correlation Coefficient
Belarus Peru South Korea Argentina Mexico Nigeria
g HIGH Russia China India Brazil
u a 0.241 [34] -0.150 [61] -0.087 [38]
a 5 £ 2 9 H o w U H Moldova Serbia Bosnia and Herzegovina Canada Iceland
MID Ukraine Macedonia South Africa Norway Switzerland
-0.129 [26] -0.303 [35] 0.041 [69]
w o Albania Slovakia Chile lapan France Malta
P5 Ph LOW Latvia Turkey Sweden USA
-0.061 [62] -0.113 [47] -0.147 [49]
LOW MID HIGH
HAPPINESS
* happiness and internet users (% of pop.) are measured as year-to-year change Legend
* the tercile that each country is placed in is determined by the most recent I
year where paired happiness and tariff data are available Pearson r [df] I
* data are based on each country/year with paired data between variables H
p
p
<.l
< .05
<.l
70


Table 6.15 Happiness and Internet Users (% of population) Controlled
for Economic Protectionism: Regression Coefficient and Standard Error
£ HIGH Belarus Peru Russia South Korea Argentina China India Mexico Nigeria Brazil
C/3 u a 0.119 (0.695) -0.017 (0.423) -0.052 (1.093)
a 5 £ 2 2 H o W U H MID Moldova Serbia Ukraine -0.080 (1.070) Bosnia and Herzegovina Macedonia South Africa -0.038 (0.414) Canada Iceland Norway Switzerland 0.002 (0.293)
w o Pi Ph LOW Albania Slovakia Latvia Turkey Chile Japan Sweden France Malta USA
-0.019 (1.413) -0.019 (0.778) -0.012 (0.296)
LOW MID HIGH
HAPPINESS
* happiness and internet users (% of pop.) are measured as year-to-year change Legend
* slope values represent average change in happiness relative to an increase in
internet use of one percentage point
* the tercile that each country is placed in is determined by the most recent Slope (Standard Error)
year where paired happiness and tariff data are available
* data are based on each country/year with paired data between variables
Similar to exports and electricity production, there is no obvious trend found
across the sectors. When looking at regression coefficients, the results are mostly
uniform across sectors where the relationship between internet use and happiness is
mildly negative and statistically insignificant. The only exception to this is in the upper-
left sector which contains Belarus, Peru, and Russia. Because of this, there is insufficient
evidence to support hypothesis two. Hypothesis two is not supported by the data and the
explanations for the significant relationships above are likely not to include element of
economic protectionism. In other words, the data do not indicate that a countrys level of
economic protectionism plays an influencing role in the relationship between happiness
and globalization.
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Income inequality. Table 6.16 shows that there are numerous significant relationships
found between happiness and income inequality. Considering that the cumulative
analysis of all sample countries yielded a positive and statistically significant correlation,
this is not surprising. What these positive relationships indicate is that increases in
happiness are correlated with increases in income inequality. This runs counter to
hypothesis three which predicted that income inequality would be negatively correlated
with happiness in all sample countries and all levels of economic protectionism. In three
of the four sectors in table 6.16 which contain BRICS countries, positive relationships are
observed. The only BRICS sector with a negative correlation is the high happiness and
high protectionism sector which contains Brazil. Out of the three hypotheses presented in
this thesis, hypothesis three is least supported by the data.
Table 6.16 Happiness and Income Inequality (gini coefficient)
Controlled for Economic Protectionism: Pearson r Correlation Coefficient
ECONOMIC PROTECTIONISM HIGH Belarus Peru Russia South Korea Argentina China India Mexico Nigeria Brazil
0.745 [21] 0.733 [23] -0.376 [20]
MID Moldova Serbia Ukraine Bosnia and Herzegovina Macedonia South Africa Canada Iceland Norway Switzerland 0.874 [1]
-0.706 [25] 0.546 [13]
LOW Albania Slovakia Latvia Turkey Chile lapan Sweden France Malta USA insufficient data
0.693 [25] -0.592 [8]
LOW MID HIGH
HAPPINESS
Legend
* the tercile that each country is placed in is determined by the most recent
year where paired happiness and tariff data are available
p<.l
Pearson x [dfj
p < .05
* data are based on each country/year with paired data between variables
p<.l
72


Table 6.17 Happiness and Internet Users (% of population) Controlled
for Economic Protectionism: Regression Coefficient and Standard Error
£ HIGH Belarus Peru Russia South Korea Argentina China India Mexico Nigeria Brazil
C/3 u a 0.378 (3.559) 0.230 (1.630) -0.314 (4.401)
a 5 £ 2 2 H o W U H MID Moldova Serbia Ukraine -0.673 (2.714) Bosnia and Herzegovina Macedonia South Africa 0.122 (2.284) Canada Iceland Norway Switzerland 0.323 (1.084)
w o Pi Ph LOW Albania Slovakia Latvia Turkey Chile Japan Sweden France Malta USA
0.602 (3.507) -0.058 (1.047) insufficient data
LOW MID HIGH
HAPPINESS
: the tercile that each country is placed in is determined by the most recent
year where paired happiness and tariff data are available
* data are based on each country/year with paired data between variables
Legend
Slope (Standard Error)
The relationship between happiness and income inequality is also considerably
ambiguous when searching for tends across the data when setting statistical significance
aside. Despite the large number of significant relationships, there is no clear trend that
occurs between economic protectionism and happiness when examining income
inequality. Both positive and negative correlations are scattered as the regression
coefficients and correlation coefficients vary considerably. Although the cumulative
average of all sample countries indicates that positive correlations are most prevalent,
there are no trends observed across the sectors of tables 6.16 and 6.17. What this
indicates is that the influence of economic protectionism on the relationship between
happiness and income inequality is unclear. Because of these inconsistencies, hypothesis
three is not supported by the data and the reasons for the large number of positive
correlations must be something else that this chapter has not considered.
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Summary of Quantitative Findings
The quantitative findings are largely inconclusive. At the state level, it was
argued that the data indicate a mild relationships in support of hypotheses one that is
difficult to ignore despite the overall lack of statistical significance. When controlling for
economic protectionism, hypotheses two was not supported by the data for any of the
independent variables. Moreover, hypothesis three was not supported by the data at both
the state and systemic levels.
In sum, economic aspects of neoliberalism are positively correlated with
happiness in some cases at the state level, but by no means all cases. This is particularly
evident at the systemic level when considering how liberalized a countrys economic
policies are. At the systemic level, the data indicate that countries with feverish
neoliberal economic policies consistently have a weaker relationship between neoliberal
globalization and happiness than countries with less emphasis on free markets.
What these findings do not indicate is how neoliberalism compares to other paths
of development in furthering happiness. In other words, cases above where variables
were unrelated or negatively correlated do not indicate that another path of development
would necessarily increase or decrease happiness more. To examine this would require a
completely different set of variables and methods. Despite the mostly inconclusive
findings, happiness and neoliberal globalization are discussed below in search of
explanations for these findings.
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CHAPTER VII
CULTURAL ANALYSIS
HAPPINESS IN THE ERICS
Seen in the previous chapter, the quantitative analysis yielded results that were
mostly inconclusive. As the hypotheses chapter argued prior to presenting the
hypotheses themselves, there was good reason to assume that neoliberal globalization
would be positively related with happiness as achievement and pleasure. This would
especially be assumed for the BRICS due to the fact that the path of neoliberal economic
development has fueled economic growth considerably post-1990 (ONeil 2011). Aside
from Russia, this was not supported by the data. This could be due to two key factors
which complicate and blur the relationships between happiness and neoliberal
globalization: 1) the chosen independent variables do not adequately measure neoliberal
globalization or 2) pleasure and achievement do not universally increase happiness
equally across societies. Since the validity of the independent variables has been
defended as adequate measures of neoliberal globalization in previous chapters, this
chapter uses ethnography to dissect what happiness means in the various cultural contexts
of each BRICS country. In doing this, Aristotles notion of happiness as eudaimonia is
also discussed in search for reasons why the variables are not always related. If
neoliberal globalization stresses pleasure and achievement but a countrys cultural notion
more closely reflects Aristotles eudaimonia, for example, perhaps this is a reason behind
the sporadic results of the previous chapter.
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To briefly return to the differing arguments between Veenhoven and Abdel-
Khalek which center on how individuals assess their happiness, the results from the
previous chapter neither support nor challenge the arguments of both scholars (Abdel-
Khalek 2006; Veenhoven 2009). This is because both scholars make arguments about
how individuals assess their happiness, but not what makes individuals happier two
very different concepts. For example, Veenhoven argues that it is human nature for
individuals to assess happiness based on their perceived levels of pleasure and
achievement, but what actually makes individuals happier is much less known and varies
between individuals (Veenhoven 2009).
This argument is challenged by the results found here, however. Since a time
series was performed, for example, it would be assumed that if the pleasures and
achievements induced by neoliberal globalization were experienced over the 17 years
examined, happiness would be significantly positively related with neoliberal
globalization. Even if individuals are dreadful judges of what will increase their
happiness in the future but experience the pleasures and achievements of neoliberal
globalization, hypothetically, an assessment of happiness 17 years later would yield
results that mirror the change in neoliberal globalization. This was not supported by the
data for most of the BRICS.
Different than Veenhoven, Abdel-Khalek argues that happiness is assessed in a
culturally-determined context (Abdel-Khalek 2006; Veenhoven 2009). Unfortunately,
Abdel-Khalek does not examine what actually makes individuals happier (2006). Due
this uncertainty, this chapter argues that the different cultural notions of happiness within
each BRICS country are potential reasons behind the results of the previous chapter. If
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happiness is something other than pleasure/achievement and disjoint from neoliberal
globalization, in other words, it would be expected that the variables would be most
closely associated in places where pleasure and achievement are part of the cultural
notion of happiness. Some of the BRICS have cultural notions of happiness that more
closely reflect achievement and pleasure while others have notions that more closely
align with Aristotles eudaimonia, but none of these Western notions of happiness fit
neatly with what happiness is on the ground in the BRICS.
Seen below in this chapter, Aristotles notion of eudaimonia, or happiness as
flourishing, is analyzed alongside the notions of happiness in various BRICS countries.
Since the results in the above section were mostly inconclusive, this is done to question
whether happiness as something other than pleasure/achievement can explain the lack of
significant relationships found in the previous chapter. Aristotles eudaimonia does not
neatly apply to any of the BRICS notions of happiness, but is nevertheless discussed in
the cultural analyses to follow in efforts to exhaust the three notions of happiness used
here as much as possible.
In order to examine the relationships between variables further, ethnography is
used in this chapter in efforts to bring clarity to the ambiguities found in the quantitative
analysis. In doing this, the variables above are examined in relation to each BRICS
countrys cultural notion of happiness. Seen below, the mixture of ethnography and
quantitative methods can be illuminating for some relationships between variables. In
other cases, however, the relationship between variables continues to be unclear and
ambiguous. Overall, this chapter argues that the inconclusive results of the previous
chapter are due to notions of happiness being determined in culturally-based contexts. In
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other words, culture operates as a lurking variable that determines the extent, if any, that
neoliberal globalization has on happiness. Before continuing, however, it is necessary to
define what is meant my culture and how this relates with happiness.
Defining Culture
Despite arguing that culture plays a large role in influencing the velocity of
economic growth, Forson, Janrattanagul and Carsamer argue that culture is a
definitionally problematic term because its relativity and ambiguity affected by
contextual factors is...difficult to objectify and assess (2013, 288). For example, what
is perceived as culture in one locality or region might not be applicable to another
(Forson, Janrattanagul and Carsamer 2013, 288). Since culture exists and is defined in
cultural contexts, definitions of culture are limited to that culture. Therefore, the validity
of an outside definition is limited.
In the introduction to their edited collection of happiness in non-Western
societies, a source of much information on cultural happiness used here, Selin and Davey
also argue that happiness exists within cultural contexts and go to great lengths to clarify
that the authors in their edited collection of articles have spent a considerable amount of
their lives living in these cultures (2012). In making the argument that happiness can be
validly summarized ethnographically, they also clarify that no notion of happiness is able
to be universalized and the act of defining culture experiences the same fate (Selin and
Davey 2012). Ultimately, Selin and Davey do not provide a single definition of culture
in the same way that they do not for happiness. By arguing that happiness exists in a
cultural context and varies across place, in other words, the act of defining culture would
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undermine the arguments of all the articles which provide cultural notions of what
happiness is.
Due to the dense cloud of ambiguity surrounding the term, definitions of culture
in the literature are typically quite vague if provided at all. Inevitably, however, culture
is a term that must be defined for the purposes of this thesis because the term can mean
numerous things depending on the context in which it is used. For example, the term can
refer to pop culture phenomena such as the visual arts, music, and fashion or it can refer
to aspects of organizational culture such as common practices, procedures, and forms of
communication (Scott and Marshall 2009).
Given these considerable obstacles to providing a definition of culture that is
applicable here, culture is defined and operationalized as the following. Culture is a
learned complex of knowledge, tradition, custom, and values through which meaning is
attached to a collection of [emotions,] ideas and symbols (Scott and Marshall 2009,
152). Culture affects how societies perceive the past and present, and also how the future
is idealized to be. Stated differently, culture in this thesis is defined as a lens through
which societies experience the world, react to the world, and plan for the future.
This notion of culture is operationalized with happiness in the following ways.
Although culture is not necessarily confined to one physical space and can often overlap
with types of culture other than the one used here, the cultural notions of happiness in the
BRICS are all at the state level. This is because 1) cultural notions of happiness in the
literature are usually focused at the state level and 2) the data for all variables used here
are also measured at the state level. Despite this limitation, cultural notions of happiness
in the BRICS vary and do not fit neatly with the three philosophical notions of happiness
79


used here: pleasure, achievement, and eudaimonia. For some of the BRICS countries,
notions of happiness closely reflect happiness as pleasure and achievement. This could
explain why happiness is positively correlated with neoliberal economic development and
globalization. In other BRICS countries, however, happiness is something other than
pleasure and achievement. This could explain why happiness was either negatively
correlated or unrelated with neoliberal economic development and globalization. What
this chapter ultimately identifies is that the reason why there are sporadic instances of
significant relationships in the previous chapter is because happiness is not a uniform
concept across cultures.
Brazil: Happiness as Envisioning the Ideal Joyous Life
Islam connects the Brazilian notion of happiness with its carnival culture (2012).
Happiness is determined temporally across time in relation to happiness levels of the past
(Islam 2012). It is also oriented towards the future, however, where happiness in Brazil
is a utopian vision that is upheld as a potential, rather than...realized event (Islam
2012, 235). Thus, happiness is both forward-looking and backward-looking. At the
surface this seems to reflect Annass notion of happiness as achievement, but this
connection is neither neat nor perfect because Annass achievement notion focuses on
realized rather than potential events (2008). Instead, everyday life and the ideal life are
contrasted where happiness is increased by envisioning the ideal life through processes]
of finding spaces outside of daily struggles.. .in which temporary experiences of the ideal
life are possible and joy is therefore felt (Islam 2012, 235). In other words, happiness is
more a product of the imagination than it is a product of ones physical surroundings.
80


Based on Islams arguments, therefore, it is quite unlikely that the significant
relationships in the previous chapter are part of a wider causal relationship (2012).
in r- os IT,
os os os os os o o
os os os os os o O o
rt rt rt
1,500 j-
'a
1,000 w
a
500 -2
M
o
a
x
0 H
Happiness
Exports
per capita
(constant
2000 US$)
in r- os IT/
os OS os os os o o
os os os os os o O o
rt rt rt
8,000 fi tO
6,000 s -o o .ti - a
4,000 0- fi
3 2 Production
2,000 .z a per capita
(kw/h)
0
Internet Use

30 c
2
as
20 ^
+r> Om
fi a
10 *S
Happiness
Internet
Users (% of
population)
Figure 7.1 Brazil: Time Series Graphs
81


80
% 70
a
'a 60
a
ce
W 50
Income Inequality
40

r75
- 65 fl
- 55 (J --
V
- 45 o u
Coefficient
- 35 - 25 c o
0\
o\
o\
o\
in
o\
o\
o\
o\
Os
o\
o\

o
rt
o
o
rt
in

o
rt
Figure 7.1 cont. Brazil: Time Series Graphs
Seen above in the graphs of figure 7.1, all variables of economic development and
globalization increased throughout the time-period being studied while income inequality
decreased. At the same time, happiness increased continuously without any decreases.
Since happiness is significantly positively related with exports and internet use, the
question remains as to whether or not these variables have made escaping the everyday
and envisioning the ideal more feasible. It could be argued that exports and internet use
have aided in economic development and made subsistence and achievement more
feasible, but Brazils high level of income inequality challenges this argument due to the
unequal distribution of neoliberal globalizations benefits.
As the data here and various scholars indicate, inequality in Brazil is very high by
global standards20 yet the country maintains and takes pride in its overall feeling of joy
that is not necessarily material-based (Islam 2012). Income inequality influences self-
perceived social class in Brazil and is also a large determinant of SWB, but not happiness
(Islam, Willis-Herrera, and Hamilton 2009; Wilkinson and Pickett 2011). While Islam
argues that Brazil is still a largely collectivist culture and Dunker argues that the
20
See table 6 on page 27 for z-score ranking of gini index scores for sample countries.
82


abolishment of Brazilian authoritarianism in favor of democracy is indicative of a shift in
the direction of individualism, Brazil currently occupies a middle-ground between
individualism and collectivism (Dunker 2008; Islam 2012). The shift towards
individualism could be explained to increase happiness as motivations shift from
extrinsic to intrinsic, therefore encouraging individual creativity in envisioning the ideal
life, but this does not explain the association of happiness with joy in the Brazilian
context by escaping the everyday (Ahuvia 2002; Zha et al. 2006).
Since subsistence as a precursor for happiness is explained by both Aristotles
notion of instrumental goods and the happiness notion of achievement/pleasure used here,
the reasons why happiness increased in Brazil can at best only be partially explained
because Davis and Annas neglect the concept of joy while the Aristotelian conception
does not (Armas 2008; Aristotle 2008; Davis 2008; Irvine 2008). The Aristotelian
conception of eudaimonia is associated with flourishing a concept more similar to joy
(Irvine 2008). If happiness in Brazil is the process of envisioning the ideal life, this
coincides with Aristotles argument that happiness is the only goal which is sought for
itself and never for the sake of something else (2008, 20). Arguably, the mere act of
envisioning the ideal life achieves little that is useful for something else, yet still
contributes to happiness. The connection of Brazilian happiness with Aristotles
excellence is less clear, however (Aristotle 2008). On one hand, Aristotle argues that
happiness results from action not simply envisioning action (2008). Interpreted less
literally, envisioning the ideal life is similar to what Aristotles refers to as activity of
the soul, but whether or not envisioning leads to virtue and the best and most
complete...in a complete life would vary between individuals (2008, 21-22). Despite
83


the ambiguities found when drawing similarities between Aristotles eudaimonia and
Brazilian happiness, it is still seen that happiness is something other than achievement
and pleasure. Therefore, the significant correlations for Brazil that were identified in the
previous chapter may be related more by chance than causality.
China: Happiness as the Mentality of Look at How Far Weve Come!
Davey argues that happiness in China is primarily based on the increases in
quality of life (QOL) that have resulted due to its recent success in economic
development (2012). Similar to Brazil, happiness in China is also temporal and based on
comparisons to the past, but in Brazil this is compared to happiness in the past while in
China it is compared to QOL in the past and its recent economic achievements (Davey
2012; Islam 2012). Supported by Deng Xiaoping orations of to get rich is glorious and
take the lead in getting rich, happiness in post-1970s urban China has become tied with
consumer culture where each furthers the other, but still retains elements of collectivism
that are stronger than seen in Brazil (Islam 2012; Davey 2012, 59). Even among the rural
poor who have gained less materially from economic development, the economic
achievements of China still provide happiness based on a sense of national pride (Davey
2012). Thus, happiness in both rural and urban China is based on pleasure and
achievement, but through quite different ways.
84


Figure 7.2 China: Time Series Graphs
85


As seen above in figure 7.2, Chinas industrialization has led to significant
increases in exports and electricity production while raising the feasibility of subsistence
for some as they gained income to support happiness based on material consumption
(Davey 2012). Despite the fact that income inequality in China has risen significantly
over the past two decades and the majority of its population continues to be quite poor by
global standards, post-1981 China has lifted...680m people out [of] poverty21 more
than the entire population of Latin America (The Economist 2013, 1). This has been
achieved by using neoliberal trade policies with foreign actors and joining the WTO in
2001, while at the same time maintaining an internal economy that is more state-
controlled than that of other global powers (Phillon 2007; Urio 2012). Thus, Chinas
economic model is quite unique by global standards.
Considering Daveys argument that happiness in China is tied to development-
induced materialism and national pride, this notion most closely reflects the happiness as
pleasure/achievement notion outlined in this thesis (2012). Problematically, however, if
this were entirely true it would be expected that happiness in China would experience
growth in line with its economic achievements and statistically significant correlations to
have been found (Knight and Gunatilaka 2011). As seen in the above graphs, this is not
supported by the data as happiness in China remained mostly stagnant throughout the
time-period being studied.
Along with the profound growth of the Chinese economy has also come a wealth
of social and environmental issues such as rising income inequality, work-related
migration splitting familial structures, decreasing levels of physical/mental health, and
21 Poverty is defined as living on less than 1.25 US dollars per day the cut-off point for
absolute poverty (The Economist 2013).
86


rapidly deteriorating environmental conditions (Davey 2012; Phillon 2007). In other
words, as economic development increases QOL by making subsistence more feasible,
social and environmental issues counterbalance this by causing happiness to remain
virtually stagnant (Davey 2012). This can be argued to not only stifle happiness as
achievement/pleasure, but also happiness as a eudaimonic state of flourishing.
Although centered on the pursuits of the group in rural areas and the pursuits of
both the group and the individual in urban areas, both rural and urban China associate
happiness with achievement and pleasure although in quite different ways (Davey
2012). Despite the slight urban-rural differences, for example, happiness in China is
mostly hedonic and related to achievement and pleasure (Davey, Zhenghui, and Lau
2009; Knight and Gunatilaka 2011). For rural Chinese where societies are highly
collectivist, pleasure is gained from subsistence and inclusion in familial and societal
groups while achievement is determined by the success of the collective group (Davey,
Zhenghui, and Lau 2009). In urban areas where collectivism is slowly giving way to
individualism, the exact determinants of happiness are centered on personal pleasures and
group achievements (Knight and Ganatilaka 2010). Therefore, both urban and rural
happiness is derived from achievement and pleasure despite maintaining varying balances
between individualism and collectivism.
Given that achievement and pleasure are used for the sake of something else,
happiness in China does not neatly fit into the Aristotelian notion of happiness (Aristotle
2008). Since achievements of the group are desired for the sake of subsistence while the
pleasures of materialism are desired for the sake of subsistence and status, arguably,
happiness in China is not entirely eudaimonic in the Aristotelian sense (2008). Although
87


Full Text

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HAPPINESS AND DEVELOPMENT IN THE BRICS: A MIXED METHODS ANALYSIS by DAVID D. BREED BSc (hons), University of Plymouth, 2011 A thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the re quirements for the degree of Master of Arts Political Science 2013

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ii This thesis for the Master of Arts degree by David D. Breed has been approved for the Department of Political Science by Lucy McGuffey, Chair Michael Berry Betcy Jose Novemb er 14, 2013

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iii Breed, David D. (MA, Political Science) Happiness and Development in the BRICS: A Mixed Methods Analysis Thesis directed by Assistant Professor CT Lucy McGuffey ABSTRACT This thesis uses e thnographic analysis and quantitative methods to expl ore the interplay between happiness, neoliberal economic development, and globalization in Brazil, Russia, India, China, and South Africa also known as the BRICS. Though the neoliberal path of development does not explicitly promise happiness the hypot heses operate as theory testing C orrelation is used to evaluate happiness relating to four different variables : exports, electricity production, internet use, and income inequality. The quantitative results yield significant relationships that are spora dically placed across the BRICS. This is also observed when controlling for economic protectionism. Due to the ambiguities that this creates, cultural notions of happiness in each individual BRICS country are examined using an ethnographic approach. Th is is done in search of potential reasons behind the inconclusive results. It is ultimately argued that culture is the mechanism through which the influence of neoliberal globalization on happiness is determined. In order to help shed light on this post colonialism is used in the final chapter to argue that the ways in which cultures attach meaning to development processes play a key intermediary role in how neoliberal globalization affects happiness The form and content of this abstract are approved. I recommend its publication. Approved: Lucy McGuffey

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iv ACKNOWLEDGEMENTS First and foremost I would like to thank parents and grandparents both gone and still with us for their generous emotional and financial support through my education thus far. Thi s has instilled a lifelong love of learning, opened many doors and aided in my own search of living a happy meaningful, and fulfilling life. Since the mere three weeks between undergraduate and (post) graduate study have peculiarly blurred both degrees into one, I would like to thank the faculty, staff, and students at both of the following universities. At the University o f Plymouth, School o f Government, I would like to thank Dr. Rebecca Davies, Dr. Brieg Powell, and Mr. James Goulbourn for demonstrat ing the rewarding opportunities for enriched analysis that can result from exploring the nexus between critical theory, international development, and international political economy. At the University of Colorado Denver Department of Political Science I would also like to thank the wonderful team who graciously agreed to be part of the committee here I would To clarify, Lucy, thank you for encouraging me to walk the fine line between while at the same time teaching me the knowledge necessary to strive towards unraveling the se unclarities in search of meaning. Moreover I would also like to thank Dr. Michael Berry for his helpfulness and gu idance in better equipping me with the skills necessary to examine complex questions such as the one in this thesis. These skills have proven to be invaluable and have given structure to both this research and many papers to come. Additionally, I would a lso like to thank Dr. Betcy Jose for her suggestions which greatly helped add meaning to the concepts used here and her exceptional supervision while working as her teaching assistant. What a rewarding and beneficial experience it was to experience academ ia from the other side of the classroom I have a wealth of gratitude to those above in sum, and sincerely apologize to those inevitably left out.

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v TABLE OF CONTENTS CHAPTER I. II. 6 Happiness A cross the Humanities Philosophy a Sociology and Economics a Chapter Sum III. CONCEPTS, VARIAB Happiness Defini ng h Measuri n Neoliberalism Neoliberal Econom ic D Globalizati Income Inequa Terminology Here

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vi IV. HYPOTHESES AS PART OF THEORY TESTING Why Neoliberal Globalization is Hypothes i Hypothe s V. METHO Mixed Meth Data Sample Validity of Quantifyi Quantitative data: Happin Quantifyin Controlling for Economic P 47 Operationalizing t VI. QUANTITATIVE Happiness: Descriptive S Independent Variables: Descript State level Analysis Add 58 Ex 60 Income i

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vii Systemic level Analysis Addressing Hypothesi s Two : Ex Industrialization Income i Summary of Quantitati ve VII. CULTURAL ANALYSIS : 75 Defining Cu Brazil: Happiness as Envisioning t China: Happiness as the Mentality of at 84 Russia: Happiness as the Absence ............................. 94 Cha pter Sum VIII. DISCUSSION 99 Income Inequality Knowledge/Power, Othering, and Mean Exports

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viii Indust ria Globaliz Chapter Su IX. CONCLU 115 APPENDIX A. State level Quantitative Results for Non B. WVS/EVS Happiness Survey C. Methods Used to Calculate Gini D. Skedacity and Awkw E. Linear vs Non linear Data Inte F. Description s of World G. appiness Self assessment ...

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ix LIST OF TABLES Table 3.1 Overview of Ha ppiness in the Non philosophical 5.1 Happiness Ranked by Z 5.2 Exports per capita (constant 2000 US$) Ranked by Z 5.3 Electricity Production per capita (kw/h) Ranked by Z score 5.4 Internet Users (% of population) R anked by Z 5.5 Income Inequality (gini coefficient) Ranked by Z score 5.6 Happiness and Economic Pr 6.1 Happiness Descriptive St 6.2 Exports per capita (constant 2000 US$) Descriptive Statistics 6.3 Electricity production per capita (kw/h) Des 6.4 Internet Users (% of population) Descri 56 6.5 Income Inequality (gini coefficient) Des 6.6 State level Results: Pearson r 6.7 State level Results: R 6.8 Cumulative Sample Countries: Pearson r 6.9 Cumulative Sample Countries: Regression Coefficient and Standard Error ... 6.10 Happiness and Exports per capita (constant 2000 US$) Controlled for Econom ic Protectionism: Pearson r Correlat ion Coefficient 6.11 Happiness and Exports per capita (constant 2000 US$) Controlled for Economic Protectionism: Regression Coefficient and Standard Error 6.12 Happiness and Electricity Producti on per capita (kw/h) Controlled for Economic Protectionism: Pearson r

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x 6.13 Happiness and Electricity Production per capita (kw/h) Controlled for Economic Protectionism: Regression Coefficient and Standard Error 6.14 Happiness and Internet Use (% of popu l ation) Controlled for Economic Protectionism: Pearson r 6.15 Happiness an d Internet Use (% of population) Controlled for Economic Protectionism: Regression Coefficient and Standard Error 6.16 Happiness and Income Inequality (gin i coefficient) Controlled for Economic Protectionism: Pearson r 6.17 Happiness and Income Inequality (gin i coefficient) Controlled for Economic Protectionism: Regression Coefficient and Sta ndard Error 7.1 Cultural Notions of Happiness

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xi LIST OF FIGURES Figure 3.1 Terminology, Proxy Measures, and th eir 6.1 Distribution of Responses in WVS 7.1 Brazil: Time Series 7.2 China: Time Series Gra 7.3 India: Time Series G 7.4 Russia: Time Series 7.5 South Africa: Time Serie s Graphs

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xii LIST OF EQUATIONS Equation 5.1 Quantifying Happiness with Mid interval Val 5.2 Presuming Approximate Normal Distribution in a .... 44

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xiii LIST OF ABBREVIATIONS BIT bilateral investment treaty BRICS Brazil, Russia, India, China, and South Africa DSB Dispute Settlement Body EOI export oriented industrialization EU European Union EVS European Values Stu dy FDI foreign direct investment GDP gross domestic product HDI Human Development Index IFIs international financial institutions IMF International Monetary Fund IR International Relations ISI import substitution industrialization IQR interquartile range LEDC less economically developed country MAR missing at random MCAR missing completely at random MDGs Millennium Development Goals MEDC more economically developed country NAAH not at all happy NTB non tariff barrier NVH not very happy OECD Organization for Econom ic Co operation and Development SAP structural adjustment policy SWB subjective well being Q1 first quartile; 25 th percentile of data Q3 third quartile; 75 th percentile of data QH quite happy QOL quality of life UN United Nat ions US United States of America VH very happy WB World Bank WTO World Trade Organization WVS World Values Survey

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1 CHATER I INTRODUCTION In the post Second World War global political economy, forces of globalization have fueled the exponentially g rowing rate of interconnectedness between states. The liberalization of markets has played a key role in furthering this as states have become increasingly interdependent in economic areas (Bhagwati 2007). The World Trade Organization (WTO) and i nternati onal financial institutions (IFIs) such as the In ternational Monetary Fund (IMF) and the World Bank (WB), have avidly encouraged interdependence through market liberalization among their members and their authority has b ecome pronounced. In the past few d ecades, the study of happiness has had a renaissance in the social sciences and the scholarship has increasingly found connections between physical and emotional well being (Greve 2012; Thin 2013). Greater ease of global communication and improvements in survey methodology have led modern scholars to explore happiness in a different light than the Greek and Roman stoics did (Irving 2008; Thin 2013). Despite the greater ability now to explain how happy people are and compare this with other variables, 1 the definition of happiness continues to be ambiguous in the literature as many scholars eventually revert to the writings of the stoic philosophers (Irving 2008; Thin 2013). 1 Thin ties the emergence o f happiness studies in recent times with the birth of statistics and utilitarianism in 1700s England and Scotland (2013). This is seen in John Sinclair by its inhabita greatest number of individuals (Thin 2013).

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2 The United Nations (UN), IMF, and WB in recent years have adopted policies which ack nowledge that gross domestic product (GDP) alone is an insufficient measure of well being (Costanza et al. 2009). T Development Index (HDI) and its enactment of the Millenium Development Goals (MDGs) which offer a more comprehensive view of what development is and how being (Costanza et al. 2009). Less discussed is how development influences happiness, however Due to information bias and the difficulty found in accur ately forecasting future conditions, individuals are decent judges of their own happiness in the present, b ut experience difficulty in determining what exactly will make them happier in the future (Veenhoven 2009). Examining happiness is much more complex than examining well being as the varying notions of happiness differ considerably between cultures but there is great potential for an enriched debate by dissecting happiness rather than well being (Selin and Davey 2012). At the same time, the c lou d of ambiguity surrounding the concept of happiness leads to obstacles and difficulties. None of the international institutions presented above explicitly make any promises of happiness, and in many ways, to be very clear, this hypothetical aim is neither built into their institutional framework nor is it what they were specifically created to do. Inevitably, however, the UN, WTO, IFIs, and various other institutions have had an effect on the happiness of individuals worldwide in the development process through what has become known as the neoliberal path of development. Due to this influence and that fact that the nexus between happiness and development has multiple

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3 opportunities for expansion in the literature, this thesis seeks to contribute to the scholarsh ip in the following ways. Instead of examining the effects of neoliberal globalization on poverty reduction and well being, this thesis examines the relationship between neoliberal globalization and happiness in emerging economies. Rather than focusing on physical well being, in other words, this thesis examines the impact, if any, that neoliberal economic development and globalization have on happiness. As emerging economies that have achieved profound rates of economic growth post 1990, the focal poin ts of this thesis are Brazil, Russia, India, China, and South Africa also known and hereafter referred to as the BRICS he 1990 2007 time period and analyzed alongside variables whi ch measure various aspects of neoliberal economic development and globalization. The hypotheses that foll ow are a form of theory testing that ask two main questions. The first of these is whether neoliberal globalization can raise happiness for a ll coun tries equally or whether it reaches a point where happiness eventually stagnates. For example, scholars such as Graham have found that increases in income lead to increases in happiness up to the point of basic subsistence for individuals, but the relatio nship soon dwindles past this point (2009). This is tested at the state level using the variables of exports, electricity production, and internet use which operate as proxy measures for neoliberal economic development and globalization. The second quest ion asked as part of theory testing is whether the feverishness of market liberalization plays a role in the relationship between happiness and neoliberal globalization. In order to depict which countries are more economically neoliberal, using the same v ariables, this is also

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4 tested at the systemic level by controlling for economic protectionism. Although neoliberalism make s claims to foster development bu t not happiness, the question of whether development and happiness are related is interesting nevert heless, sparsely examined in the literature, and therefore worthy of further investigation. Due to inconsistent and inconclusive results found in the quantitative analysis, t his thesis ultimately argues that culture is the key mechanism through which happ iness is determined Economic factors are still relevant in many ways, but the thrust of these factors and the impact they have on happiness is determined in cultural contexts that are not uniform across societies C ulture acts as a gatekeeper between ec onomic factors and happiness b y determining the extent if any, to which neoliberal globalization affect s happiness. When happiness is considered to be either pleasure achievement, or flourishing, none of these notions of happiness are universally applic able in the cultural contexts of each BRICS country. In the pages to follow, quantitative methods are used to examine the extent to which happiness is correlated with development ethnographic analyses of happiness in the BRICS are presented, and postcolo nialism is used to further analyze the role of culture and potential reasons why happiness and development are not entirely connected in some contexts Due to the complexity of this research question and the absence of comprehensive research covering this question specifically for the BRICS, the analysis that follows leaves many open question, yet addresses the complexities and ambiguities found when exploring a concept as subjective as happiness and its relationship with neoliberal globalization Though t he concept of happiness has historically been a key curiosity of humanity and is likely to remain so, this thesis seeks to contribute to this literature by

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5 examining how development influences the pursuit of living a meaningful, fulfilling, and happy life.

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6 CHAPTER II LITERATURE REVIEW The link between happiness and development is an ambiguous and highly grey area in the literature Despite this contention, and critical to the theory testing to follow many scholars argue that the de terminants of happiness have economic aspects. Though many scholars state that social and political factors matter as well, others argue that these aspects exist within an economic context that cannot be ignored. The extent to which economic factors infl uence happiness forms the center of this debate. In many cases the general extent of this influence varies among fields of the social sciences (Greve 2012; Thin 2013). Despite the fields of Political Science and International Relations lacking a substant ive amount of literature explicitly utilizing the happiness lens, the vast majority of happiness literature is interdisciplinary and holistic. Happiness A cross the Humanities and Social Sciences Terminology in the happiness literature varies considera bly. Terms such as happiness, subjective well being (SWB), quality of life (QOL) and life satisfaction are often used interchangeably by scholars (Greve 2012). T hese terms are all different in their own ways, but they are often used synonymously where t he differences between these terms revolve around time frame (Graham 2009; Greve 2012). Happiness is a long term emotion that results from experiencing positive feelings when viewing life retrospectively, for example, but other terms such as SWB operate s imilarly but use a relatively smaller time frame (Graham 2009; Greve 2012; Thin 2013). These terms are

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7 not treated synonymously here because, although similar in some ways, they are overall quite different concepts that measure very different phenomena. Philosophy and h appiness In philosophy, happiness is divided into three broad categories: 1) pleasure, 2) achievement, and 3) eudai monia or a state of flourishing (Annas 2008; Aristotle 2008; Davis 2008). Throughout the literature in all fields, many scholars make arguments that either explicitly or implicitly attach happiness to these three philosophical concepts. All three notions of happiness agree that happiness is a long term emotion that an individual feels when positively viewing life retrospec tively (Annas 2008; Aristotle 2008; Davis 2008). In other words, an individual may feel unhappy for a short period of time, but still have a happy life and maintain an overall feeling of happiness. This is largely because happiness and being happy are tw o different and unrelated concepts ( Thin 2013 ). This thesis examines happiness rather than being happy. The following chapter will highlight how happiness is defined for the purposes of this thesis. Davis argues that happiness is the result of accumulat ing pleasure 2 (2008) The concept of pleasure is divided into higher and lower pleasures where th e former contribute more toward happiness and the later contribute less. While higher pleasures result from indulging in intellectually stimulating activitie s such as the arts and exploring philosophy, lower pleasures result from activities necessary for subsistence such as obtaining proper nutrition and maintaining shelter. However, Davis clarifies that painful 2 ilitarianism (2008). The argument that Davis presents is more robust, however, as he goes to great lengths to distinguish between what creates the most and least pleasure (2008).

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8 and unpleasurable events must sometimes take pl ace in order to experience pleasure later in life because a pleasurable future has potential to outweigh an unpleasurable past (2008) Stated differently, happiness is determined by a complex equation that balances higher pleasures, lower pleasures, and p ains (2008). Annas links happiness with achievement (2008). Within this, happiness is a feeling of accomplishment good life. Annas is critical of the notion that pleasure is a determinant of happiness term happiness (Annas 2008, 240). Rather than impulsively satisfying desires Annas argues, happiness results when retrospectiv ely viewing life and finding a positive ongoing series of achievements that le In his discussion of instrumental goods, Aristotle does not neglect the need for subsistence, b ut develops a concept of happiness that is different than those above (2008). T his notion is operationalized through the capability approach to development that is presented by Nussbaum and Sen (Nussbaum 2011; Sen 1999 ; Sen 2008 ). The Aristotelian concep tion equates happiness with a state of eudaimonia (Aristotle 2008). This is a state of flourishing where excellence is strived for in order to reach the ultimate good that encompasses life in its totality happiness (Aristotle 2008; Thin 2013). To disse ct this definition piece by piece, Aristotle argues that happiness is the ultimate goal of humans because it is the only goal that is sought ld and tied to the good is equated with

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9 acting through reason and his notion of virtue is defined as the mean between def ect and excess (Aristotle 2008). Combining these two concepts for ex ample, excellence not only makes us excellent but also our work excellent. W e become excellent by emanating excellence. To piece all these parts together, finally, eudaimonia is a state of flourishing where excellence and virtue further one another and lead to happiness the ultimate goal. Sociology and h appiness Within the field of sociology, debates in the literature focus on the interplay between socioeconomic and sociopolitical determinants of happiness and the balance between these two. Despi te these differences, all scholars argue that politics, the economy, and social conditions are interconnected and affect happiness to varying extents (Deiner and Seligman 2004; Greve 2012; Haller and Hadler 2006; Radfcliff 2001) Radcliff argues that bot h socioeconomic and the sociopolitical factors are critical determinants of well being (2001). This argument is based on the interconnected relationship between governments and their ability to influence markets. Radcliff supports this by stating that ha ppiness is often highest in social democratic countries (2001). Since social welfare nets help protect residents from changes in the market and democratic processes provide a sense of inclusion i n the political process well being is influenced by both soc iopolitical and socioeconomic phenomena (Radcliff 2001). Other scholars highlight the role of socioeconomic standing because of the way that status is related to happiness through marriage, family, employment, health, and having meaningful work. (Deiner and Seligman 2004; Haller and Hadler 2006). They support this argument by arguing that although political aspects do influence the

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10 perception of status, happiness is determined by both social and economic aspects of status. Therefore, how happy a society is cannot be predicted or measured by economic and political aspects alone, but rather the way s in which these influence perceptions of status (Diener and Seligman 2004; Haller and Hadler 2006). Although their study applies more to SWB and health than it does happiness, Wilkinson and Pickett also argue that social inclusion and a sense of belonging are critical determinants of SWB (2011). They support this argument by highlighting the social nature of humans as they use income and social status to measure their own self worth relative to their peers. Societies with the highest levels of income inequality typically experience the lowest levels of SWB as a whole because those that feel inferior and ostracized by society are more likely to engage in risky be haviors in efforts to alleviate their sense of inferiority and exclusion (Wilkinson and Pickett 2011). This is counterproductive, however, and leads emotional well being to decline (Haller and Hadler 2006; Wilkinson and Pickett 2011). Economics and happi ness. Within the literature on happiness that is written by economists, many scholars adopt a more socioeconomic standpoint by arguing that socioeconomic standing is the largest determinant of happiness. Although sociologists make the same argument while arguing that social standing is the best predictor, economists divert from this societal based perspective and argue that economic standing is the greatest predictor while social standing and culture are less important (Graham 2009; Frey and Stutzer 2002)

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11 Graham argues that income is not a valid or accurate predictor of happiness in all cases (2009) However, an increase in financial standing that leads individuals from extreme poverty into basic subsistence can have a significant positive effect on ha ppiness. After a basic level of subsistence has been achieved and income continues to rise, however, the effect this has on happiness begins to dwindle and eventually plateau or decrease Graham argues that this is primarily due to the expectations that individuals place on their spending (2009). Important to the arguments presented below, Graham argues that the effect of income on happiness declines dramatically after subsistence has been achieved (2009). In highly developed countries, for example, hap piness can actually decline and disappointment can result because the expectation of happiness in exchange for income is not met. This is the Easterlin paradox that Graham highlights 19). Although they are both economists, Frey and Stutzer argue that macroeconomic factors have a relatively small effect on happiness (2002). Frey and Stutzer argue that employment is a critical determinant of happiness, but their main argument is that resources gained from employment have little effect on happiness (2002). This is because a sense of belonging and a feeling that one contributes to society are the greatest aspects of formal and informal employment that contribute to happiness (Frey and S tutzer 2002). Thus, income and absolute economic standing are not critical determinants of happiness for those who are not lacking a basic level of subsistence (Frey and Stutzer 2002; Graham 2009; Greve 2012). E conomic facto rs are important in maintainin g bot h formal and informal employment, but the influence of economic factors on happiness is not paramount from this perspective (Frey and Stutzer 2002).

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12 Connecting the Disciplines Ahuvia argues that economic development and happiness are indeed connec ted, but the mechanism that causes development to contribute to SWB is the shift from identities of collectivism to individualism (2002). Within this, indi viduals shift from being extrinsically motivated to intrinsically motivated (Ahuvia 2002). Stated d where individuals make choices to maximize their SWB rather than meet social society but they also change extrinsic motivations of ought/ motivations want /get eudaimonia Nussbaum and Sen use the capability approach to stress the importance of connecting social, economic, and political factors holistically in order to better enable individuals to flourish Within the capability approach, capabilities are defined as a middle ground between concepts such as 1) positive and negative duties, 2) individual and group rights, and 3) security and subsistence (Nussbaum 2011; Sen 1999; Shue 2008). In order for individuals to flourish, Sen and Nussbaum stress the need for a cohesive civil society that expands social capital so that individua (Nussbaum 2011; Sen 1999). Neither author argues that economic factors are irrelevant, but rather that capabilities require person 20; Sen 1999).

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13 Chapter Summary What has been identified in this chapter is that happiness is a broad phenomenon that conceived of differently by scholars in various fiel ds. Happiness to individuals can be thought of as either pleasure, achievement, or flourishing. Moreover, the determinants of happiness vary and include political, social, and economic aspects. As noted above and critical to the arguments to follow, all of the scholars above argue that there are economic aspects which act as determinants of happiness. This is especially seen when happiness is examined relative to economic development. Although economic matters matter very much in happiness and developm ent overall, a triad exists between the economic, social, and political where each influences and is influenced by the others Arguably, w hat this demonstrates is that intermediary factors most likely exist between happiness and development which determin e the exact balance between economic, social, and political factors (Selin and Davey 2012). Discussed in greater detail below, this thesis argues that culture is this intermediary factor.

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14 CHAPTER III CONCEPTS, VARIABLES AND MEASUREMENT Due to the complex nature of many concepts used in this thesis, it is necessary to clarify and present the ways in which these concepts are addressed. Considering the broad nature of economic development and the loaded language used in the happiness lit erature, it is necessary to clarify these complexities as much as possible. This is imperative in order to foster valid measurement of these concepts and more comprehensive discussion and analysis Seen in the literature review, for exa mple, the concepts of happiness and development are conceived in vastly different ways between scholars and fields of academia (Rist 2007) This chapter therefore seeks to bridge these gaps, find commonalities between the differing notions, and create a general framework t hrough which meaning is attached to these conce pts hereafter. Happiness H appiness in the literature is conceived of in many different ways which can be broadly categorized into thre e different notions : 1) pleasure, 2) achievement, and 3) eudaimonia Hap piness as pleasure is generally defined as the maximization of pleasant emotions and the minimization of painful emotions (Davis 2008). Differently, happiness seeing a positive trend of accomplishments that lead one increasingly closer to his/her desired outcome whatever that may be (Annas 2008). Happiness as eudaimonia defined by Aristotle, is a state of flourishing where one reaches the highest good and

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15 flourish es by exercising rationality good character and virtue (2008). Notably, all of these conceptions conceive of happiness as long term phenomenon that individuals strive to experience and possess (Annas 2008; Aristotle 2008; Davis 2008). Defining happines s. Although the notion of happiness as eudaimonia is not ignored in this thesis and is addressed in later chapters the notions of happiness as achievement and pleasure form the central foundat ion of how happiness is defined hereafter as part of theory te sting As part of the theory testing executed here, the notions are used because neoliberal globalization stresses the importance of pleasure and achievement both explicitly and implicitly. M aking reference to well being is a very new phenomenon among th e IMF and the WB, for example, but the development policies they promote stress economic achievement and the importance of subsistence (Costanza et al 2007; Potter et al. 2008; Thin 2013). The key aims of neoliberal economic development also stress the i mportance of development for the sake of pleasure and achievement. Development is not only an achievement in itself, for example, but also increases the number of those gaining subsistence and therefore maintaining a higher level of pleasure. Rustin argu eoliberalism has as one of its basic presuppositions the idea that the human world is composed essentially of individuals, who should as far as possible be free to make their own choices and to advance their own interests, in pursuit of whatever they may happiness is nor what he considers it to be for the purposes of his argument. Using previous anthropological and sociological studies applied to all humans, Veenhoven argues that it is human nature that individuals assess their own happiness

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16 and move beyond mere needs gratification (Veenhove key part of achievement 3 (Veenhoven 2009). Ambiguities are reached, however, becaus e Veenhoven argues that this way of assessing happiness is part of human nature and is not directly influenced by culture (2009). Others such as Abdel Khalek disagree and argue that culture influences how happiness is assessed (2006). This ambiguity is e xamined in later paragraphs which discuss how to measure happiness. The empirical literature cited in table 3.1 on the following page does not tie eudaimonia (A nnas 2008; Aristotle 2008; Davis 2008). However, all of these studies implicitly attach happiness with various notions. Seen in table 3.1, the most common of these are pleasure and achievement. Of the studies in this table, those that examine economic f actors are more likely to connect happiness with pleasure while those that examine socioeconomic factors tie happiness with achievement. Considering the determinant of h achievement does not stress the need for subsistence pleasure does (Annas 2008; Graham 2009). However, the notion of happiness as achievement fills in the gaps of the pleasure notion (Annas 2008; Davis 2008). What this 3 c hart presented by Veenhoven that depicts how individuals assess their level of happiness (2009).

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17 Table 3.1 Overview of Happiness in the Empirical Literature Author (year) Title Research Question Methods & Measurement Determinants / Notions of Happiness Abdel Khalek (2006) Measuring Happines s With a Single Item Scale seeks to determine if happiness is best measured using single question surveys or multi question surveys with various weights attached to each question primary research in various countries; surveys; self reported happiness; 0 10 scale of happiness; correlation Single Item Scale intentionally unspecified Multi Item Scale optimism, hope, self esteem, positive affect, extraversion, physical health, mental health Cummins (2012) The Determinants of Happiness seeks to assess the r elationship between psychological factors and whether or not happiness has a set point primary research in Australia; surveys; self reported SWB positive mood determined by genetics; otherwise unspecified Di Tella, MacCulloch, and Oswald (20 03) The Macroeconomics of Happiness seeks to determine the effects that GDP, unemployment, and inflation have on happiness Eurobarometer and USGSS surveys; self reported happiness; least squares regression pleasure; life satisfaction; obtained utility Gra ham (2009) Happiness Around the World: The Paradox of Happy Peasants and Miserable Millionaires seeks to assess the relationship between income and self reported happiness secondary research in various countries; surveys; self reported happiness; regressio n; econometrics various / unspecified Haller and Hadler (2006) How Social Relations and Structures Can Produce Happiness and Unhappiness seeks to assess the relationship between macrosocial conditions, quality of life, and happiness self reported surveys; WVS data from 1995 wave; multi level regression; correlation achievement; social inclusion Radcliff (2001) Politics, Markets, and Life Satisfaction: The Political Economy of Human Happiness seeks to assess the relationship between democracy, unemployment rates, GDP, and happiness surveys; self reported happiness; WVS data from 1990 wave; least squares regression achievement; culturally determined Rothstein (2010) Happiness and the Welfare State seeks to assess why Scandinavian welfare states are as happy as they are and determine which aspects contribute most to happiness qualitative; variables are social trust and corruption virtue; social inclusion; belonging; social contract Veenhoven (2009) How Do We Assess How Happy We Are? Tenets, Implications, and Tenability of Three Theories seeks to determine the thought processes through which individual assess their own happiness uses qualitative sociology and anthropology to evaluate different theories of how happiness is assessed pleasure; achievement

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18 demons trates is th at each notion can compensate for the limitations of the other. Both Annas and Davis argue that unpleasurable periods of pain are sometimes necessary to promote happiness in the future, for example, but Annas offers a more nuanced notion of h appiness that considers how poor information quality and peer pressure can lead individuals to seek pleasure yet stifle happiness (Annas 2008; Davis 2008). Measuring happiness. Table 3.1 shows that self reported survey data are the most common way to mea sure happiness. As stated above, whether or not survey data measuring self reported happiness includes the elements of pleasure and achievement is debatable. For example, Veenhoven argues that individuals are decent judges of their own happiness and hum an nature causes happiness to be assessed using perceived levels of pleasure and achievement but determining what will increase happiness in the future is much more difficult (2009). If this is the case, it is initially expected that the survey data used here and the survey data used in the studies of table 3.1 measure feelings of happiness as pleasure and achievement. Whether or not this is what affects happiness over time is questioned in later chapters, however. Others such as Abdel Khal ek argue differently and highlight ambiguity (2006). Abdel Khalek conducts two studies where one study measures various determinants of happiness and the other simply asks respondents how happy they are (2006). The findings indicate that assessing happin ess using a single question survey by asking respondents to assess their own happiness is the best form of measurement because this allows the respondent to interpret the term in a way that best fits what they perceive happiness to be (Abdel Khalek 2006). This is also more beneficial when seeking to

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19 assess how happiness in one place compares to happiness in another (Abdel Khalek 2006). In other words, Abdel Khalek argues that culture determines what happiness is while Veenhoven argues that culture is less relevant (Abdel Khalek 2006; Veenhoven 2009) If the arguments of both Abdel Khalek and Veenhoven are considered together it is assumed that individuals measure happiness in self reported surveys by assessing their levels of pleasure and achievement, bu t the precise weight s that are attached to the determinants of pleasure and achievement are culturally determined (Abdel Khalek 2006; Veenhoven 2009). The validity of measuring happiness using survey data is discussed in the methodology chapter. Neverthe less, happiness is considered to be pleasure and achievement because these two factors are stressed in the neoliberal path of development and are presumably reflected in survey data which measure self reported happiness. This notion of happiness is questi oned in later chapters, however, as the cultural notions of happiness in the BRICS are discussed. Neoliberalism Before continuing on to introduce neoliberal economic development and globalization it is necessary to clarify what is meant by neo liberalism and identify how it fits within the lens of neoliberalism as a theory of international relations. This is also true for the independent variables which measure neoliberal economic development. Classical liberalism forms the justificatory found ation on which the post WWII Folker 2010). In the years following the Second World War, international financial institutions (IFIs) were created at the Bretton Woods Confer ence in hopes to encourage

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20 interdependence between states. What are now the IMF and the WB were created at Bretton Woods and what has become the World Trade Organization (WTO) was created several years later. The underlying assumptions which formed the I framework were based on classical liberalism where it was argued that cooperation and open communication would create interdependence and discourage states from being lliams 2007; Powell 1991). The IFIs have evolved post 1945 and IR theory has followed suit giving way to neoliberalism and neoliberal institutionalism. Scope is the main difference between the two theories (Stein 2008). For example, neoliberal institut ionalism is essentially the theory of neoliberalism applied exclusively to international institutions such as the IFIs (Stein 2008). The assumptions of both neoliberalism and neoliberal institutionalism are that: 1) the structure of global order is anarch ic, 2) states are rational and self interested, and 3) bargaining through cooperation can lead to the achievement of mutual gains. In order to achieve these mutual aims: 1) iteration between states enables the creation of formal agreements which allow sta tes to pursue mutual gains, 2) institutional mechanisms can discourage state defection from agreements, 3) processes encouraging interdependence further absolute gains and discourage relative gains, and 4) the pursuit of absolute gains will allow states as a whole to achieve more than otherwise and be more peaceful (P owell 1991; Stein 2008; Sterling Folker 2010). In other words, cooperation discourages states from being distrustful of one another and better allows them to pursue common interests. State me mbership in multilateral institutions is one key way to make this process more feasible (Stein 2008).

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21 Although the IMF and the WB were originally created in efforts to discourage war through economic regulation, the two institutions have shifted over the years to encourage development policies based on economic liberalism (Evans and Newnham Williams 2007). The IMF and the WB are key players within the Washington Consensus and encourage neoliberal policies in hopes to spur development through interdependence between states by stressing the importance of market deregulation, low barriers to i nternational trade, small government, democ ratization, and good governance and Williams 2007). Concerning neoliberal economic development specifically, which is defined in the following pages, the policies of the Washington Consensus are adopte d in efforts to spur development by increasing growth through the interplay between industrialization and exporting. By reducing barriers to trade and investment, as neoliberalism argues, global interconnectedness is more likely to result and where states are more likely to pursue absolute gains by working together to achieve mutual objectives. Neoliberal Economic Development Development is often a loaded term that is used in quite different ways throughout fields of academia (Rist 2007). This is true for Political Science and International Relations where the term includes political, economic, and social aspects Development in this thesis refers primarily to neoliberal economic development. This is especially true for the quantitative section o f the thesis as the variables of exports and electricity

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22 production are proxy measures of neoliberal economic development as part of the theory testing which this thesis seeks to accomplish. Highlighted in the previous section, the theoretical assumptions of neoliberalism play a critical role in neoliberal economic development. Neoliberal economic development is b ased heavily on economic liberalism 4 as outlined by theorists such as Adam Smith, David Ricardo, and Fredrich Hayek (Evans and Newnham 1998) Assuming that actors are ration al and seek absolute gains, neoliberal economic development is characterized as a hand off approach on behalf of the state which emphasize s low trade barriers privatization, a small public sector, and a general deregulation of economic matters in efforts to industrialize by increasing exports (Bhagwati 2007; Evans and Newnham 1998; Goldin and Reinert 2010; Nafziger 2012). This definition is also used because the neoliberal path of development has become a n emerging norm amo ng the BRICS during the time period studied here ( Bhagwati 2007; Within neoliberal economic development, the push for low state involvement in economic affairs is encouraged in order to foster industriali zation and increase the feasibility of exporting 5 ( Bhagwati 2007; Evans and Newnham 1998; Goldin and Reinert 2010) Because of this, the two independent variables which measure neoliberal economic development in this thesis are exports and electricity pro duction due to the fact 4 In many ways, market liberalization as part of neoliberal economic development is based on the arguments of both the Chicago School of economics and the Aust rian School of economics. 5 The neoliberal path of development stresses export oriented industrialization (EOI) while other development paths exercising high protection stress import substitution industrialization (ISI).

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23 that the vast majority of electricity produced is used in industrial processes rather than household consumption (Hughes 1983). In other words, electricity production operates as a proxy measure of industrialization. Throughout the literature economic development in general is often measur ed using a myriad of variables which measure GDP, consumption, population growth, industrialization, living standards, 6 and the structure of the workforce 7 (Bhagwati 2007; Goldin and Reinert 2010) Although these do offer a more comprehensive measure of economic development, they do not isolate the neoliberal aspect of economic development. Given the research question here, these measures would not be entirely relevant. Moreover, to analyze this many variables non numerically would not be entirely possible in this thesis. E xports and electricity production are nevertheless, basic yet comprehensive measures of neoliberal economic development that are feasible to use in a mixed methods research d esign such as the one used here. Globalization Globalization is also a loaded term that is used to mean many things throughout academia (Strange 1996). Globalization in this thesis is broadly defined as the figurative shrinking of time and space (Evans a nd Newnham 1998) Globalization is not a ne w process in world history and has been happening for a very long time, but recent t echnological innovation has sped this process considerably and its presence and impacts 6 Living standards include phen omena such as literacy rates, infant mortality, nutrition, and life expectancy (Potter et al. 2008). 7 The structure of the workforce takes into account differences between formal and informal employment and what sectors of the economy have the highest le vels of employment (Potter et al. 2008).

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24 have become especially pronounced in the post 1945 world (Bhagwati 2007; McGrew 2008). This is not directly due to neoliberalism as all paths of development carry the potential to increase the thrust of globalization but globalization and neoliberalism further and complement one another Thou gh globalization and neoliberal economic development in this study are not used synonymously, they are both referred to under the umbrella term of neoliberal globalization and their relationship with happiness is tested using the same methods in the quanti tative chapter. Internet use is included as a proxy measure of globalization. key role in speeding up the transfer of inform ation across borders, it in many ways has become a symbol of the technological aspects of globalization (Cres hnaw and Robinson nternet technology as well as globalization nternet In other words, globalization and the growing prominence of interne t use are closely tied in neoliberal economic development as the infrastructure for increasing use of the net is gained by developing economically (Creshnaw and Robinson 2006, 190). Thus, neoliberal economic development and globalization are tightly related concepts, but are still different in several ways. Although it does not produce one single measure of globalization, the Organization for Economic Cooperation and Development (OE CD) measure s globalization by using numerous variables which fall into four general categories: 1) trade and investment, 2) technology and knowledge, 3) multinational enterprises, and 4) the cohesiveness and interconnectedness of global supply chains (OECD 2010, 5).

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25 Especially considering the categories other than technology and knowledge, the se variable s overlap with neoliberal economic development. In the technology and investment category, however, there is less overlap as the vast majority of th e variables that are measured rely heavily on internet use (OECD 2010) In order to limit the number of variables so that mixed methods analysis is more feasible, therefore, internet use i n included as a proxy measure for globalization. Income Inequality Although not necessarily a measure of economic development proper, income inequality measured using gini index scores is the final independent variable that is included. It is included to foster discussion in later chapters about why the variables may no t be as closely related as originally thought. De Maio highlights other measures of income inequality, but ultimately argues that each method has both merits and limitations (2007). The primary weakness of gini index scores highly sensi tive to inequalit ies in the middle of the income spectrum measure of income inequality due to their ability to condense income inequality into one summary statistic 8 (De Maio 2007, 850). Lower gini index scores indicate lowe r levels of income inequality while higher scores indicate higher levels This variable is not included as a part of the theory testing, but rather because the effect of income inequality on happiness is largely determined by culture (Selin and Davey 2012 ). Nevertheless, its correlation with happiness is tested in the same way as the other variables according to the hypothesis that concerns income inequality. Due to income 8 See Appendix C for further details about how the World Bank measures gini index scores.

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26 impacts on measures of physical and emotional well being, it i s a critical aspect to be analyzed in relation to happiness (Diener and Seligman 2004; Wilkinson and Pickett 2011). In other words, it is important to examine how the benefits of neoliberal economic development and globalization are distributed among popul ations. This can then be examined ethnographically in greater depth by dissecting the role of culture in determining how income inequality affects happiness. Terminology Hereafter Figure 3.1 below summarizes how terminology will be used from this point forward and the variables associated with the se concepts. Neoliberal globalization refers to neoliberal economic development and globalization The variables to measure neoliberal economic development are exports and electricity production Electricity production is a proxy measure for industrialization. Similarly, internet use is a proxy measure for globalization. Finally, i ncome inequality is measured using gini index scores also known as gini coefficients. Figure 3.1 Terminology Proxy Measures, and their Associated Variables Neoliberal Globalization Neoliberal Economic Development Exports Electricity Production (Industrialization) Globalization Internet Use Income Inequality Gini Index Score

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27 CHAPTER IV HYPOTHESES AS PART OF TH EORY TESTING In the theory testing to follow, there is good reason to assume that exports, industrialization, and globalization will witness positive relationship with happiness. Although not included as part of theory testing proper it is hypothesized that income inequality will witness a negative relationship with happiness. Before presenting the hypotheses, it is first necessary to highlight the ways in which neoliberal globalization can contribute to both development and happiness as pleasure and ac hievement The neoliberal path of development, as with other paths, is well founded and has a rich theoretical foundation on which the policies that it seeks are built. For example, the policies which emerged out of the Bretton Woods conference post WWII were enacted to foster interconnectedness in efforts to avoid the tensions in the international political economy that contributed to the outbreak of the Second World War. Although the effectiveness of these policies in contributing to happiness is quest ioned in later chapters, both the hypotheses to follow and the theory which they seek to test are both informed and well theoretically founded. Since the neoliberal path of development seeks achievement and pleasure, as discussed above it is necessary to highlight ways in which exporting, industrialization and globalization can contribute to happiness in the development process.

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28 Why Neoliberal Globalization is Hypothesized to Further Happiness Considering how happiness is defined here, it is hypo thesized that neoliberal globalization can further happiness by making the pleasures of subsistence more feasible and economic achievement more obtainable. Exporting is one achievement that can increase pleasure. Several scholars argue that the post 194 5 world has witnessed a swift rate of economic development that is largely due to the growing prominence of international institutions with high levels of membership (Goldin and Reinert 2010; Bhagwati 2007). Goldin and Reinert attribute this notable incre ase in economic development specifically with the growth of heavy industry and the increased rates of exports that shortly followed (2010). An explanation behind this argument is that the achievement of export growth per head increases incomes and therefo re the feasibility of subsistence and further achievements. Both Bhagwati and Goldin and Reinert argue that the growth of the Bretton Woods institutions and the WTO have been particularly useful in doing this as they have provided a nearly universal platf orm for trade diplomacy where states can pursue mutual goals of development with greater ease (Bhagwati 2007; Goldin and Reinert 2010). At the same time, legally binding dispute resolution mechanisms built into the frameworks of institutions such as the W feeling the need to be distrustful of their peers while providing them with the ability to counter allegations of misconduct and defect (Van den Bossche 2008). W ithin this, international trade law and international economic law have become key areas where international actors have been able to achieve cooperation due to its oftentimes binding qualities although sometimes with contention (Van den Bossche 2008).

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29 Considering that 1) increases in exports lead to increases in incomes and 2) reasonable to argue that increasing exports contributes to increasing happiness in LEDCs (2009). As the worst off in societ y achieve a basic level of subsistence and therefore a greater sense of happiness as achievement/pleasure, the average happiness for the entire society rises (Graham 2009). Of course thi s neither indicates that exporting alone is the only cause of happine ss nor does it indicate that increasing exports is the only way to increase the achievement of subsistence. Instead, this indicates that increasing exports is merely one way to do so where membership in international institutions aids this process and hol ds potential to establish subsistence and encourage post subsistence happiness as achievement. By increasing electricity production, achievement and pleasure can also be increased through the process of industrialization. Electricity production is a k ey aspect of neoliberal economic development due to its crucial role in heavy industry and production is used in heavy industry and manufacturing as opposed to househ old consumption, the role of electricity is paramount in economic development led by industrialization (Hughes 1993; Rud 2012). If achievements in increasing manufacturing are made assuming that protectionism is low, the ability to export and increase inf lowing funds is fueled. As highlighted in the previous paragraphs the increase of exports and of subsistence and feasibility of achievement.

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30 A key push of the n eoliberal strand of development is that the deregulation and privatization of public services leads to efficiency through competition (Williams and Ghanadan 2006). This is also a key aspect of development that the IFIs promote. As far as neoliberalism is concerned, the underlying assumption behind this is that deregulation fosters interconnectedness between states so that phenomena such as foreign direct investment (FDI) and bilateral investment treaties (BITs) can spur economic development. Within this interconnectedness, one state seeking relative gains over another state would ultimately be harming itself. One way to increase this interconnectedness is through the opening of markets and privatization of previously nationalized industries so that forei gn entities can invest locally and both parties can achieve absolute gains By achieving increases in electricity production, LEDCs especially would be predicted to experience increases in happiness by easing the feasibility of achieving subsistence and b etter enabling achievement beyond the point of subsistence. The effectiveness of privatization as a strategy of development is a contested and complex debate between scholars (Bhagwati 2007; Stiglitz 2003). The debate is ultimately centered on which types of well being are aimed for in development policies, who should reap the benefits of development, and what national industries really mean to a society. Despite the varying levels of effectiveness, privatization is argued to be a key force driving the el ectrification and industrialization of societies in development (Williams and Ghanadan 2006). Since globalization and neoliberal economic development simultaneously further one another, they too have potential to increase pleasure and achievement. Like oth er

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31 inventions that have revolutionized the global economy, the internet in many ways has had a positive impact on development in LEDCs (Lucas and Sylla 2003). Lucas and Sylla highlight the various benefits of internet use in LEDCs, all of which center on the ability to participate in commerce with greater thrust (2003). Moreover, the internet has in several ways given economic development a boost for both LEDCs and MEDCs that use it the most through the process of technology transfer 9 (Litan and Rivlin 20 01). Within this economic development led by greater productivity and efficiency is able to encourage a higher quality of life (Litan and Rivlin 2001). In other words, d evelopment by encouraging interdependence (Keohane and Nye 1998). Hypotheses Considering the arguments seen in the literature and the potential for neoliberal globalization to have a positive relationship with happiness the hypot heses as part of theory t esting are as follows. By testing theory, these hypotheses seek answers to two main questions. The first of these is whether neoliberal globalization will only further happiness up to a certain point. This question is addressed at the state level by hyp othesis one. The second of these questions seeks to determine if more feverish policies of market liberalization play a role in the relationship between neoliberal globalization and happiness. This is addressed at the systemic level by h ypothesis two whi ch take s 9 technology as it can be identified to o perate across state boundaries and to involve revolution demonstrated the importance of 1998, 529).

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32 economic protectionism into account so that a picture is presented of which sample countries have economic policies that are the most neoliberal I n order for the data to support the argument that neoliberal globalization correlates positively wi th happiness it is expected that the least protected sample countries will have strong positive relationships Finally, hypothesis three addresses income inequality at the state and systemic levels and sets up conditions that can foster later discussion about the role of culture. Hypothesis #1: All sample countries will witness positive relationships between happiness and neoliberal globalization, but c hange in happiness relative to neoliberal globalization will be conditional on a econo mic development relative to other sample countries. o Hypothesis 1a : L ess economically developed countries (LEDCs) will experience the largest increase in happiness relative to neoliberal economic development and globalization o Hypothesis 1b : M ore economi cally developed countries (MEDCs) will experience the smallest increase in happiness relative to neoliberal economic development and globalization Hypothesis one concerns the relationship between happiness and neoliberal globalization at the state level but also takes into account the arguments of Graham raise happiness substantially for those moving from absolute poverty to a basic level of subsistence. After subsist ence has been established, the effects of income on happiness argument is applicable in this hypothesis and should therefore not be ignored, t his hypothesis is conditional on electricity product ion and internet use relative to the other sample countries. For LEDC

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33 sample countries, therefor e, hypothesis 1a assumes that as the worst off acquire a basic level of subsistence the average happiness for the entire country will therefore increase. Dif ferently for MEDC s hypothesis 1b assumes that happiness will not rise considerably because a basic level of subsistence has already been established. Descriptive statistics are provided in chapter six which rank countries for all variables so that it is better evident where sample countries stand relative to other sample countries concerning their level s of exports, electricity production, and internet use This will provide a general pictu re of which sample countries operate as LEDCs and MEDCs for the p urposes here Hypothesis #2: Change in happiness relative to economic development and glob alization will be conditional on to other sample countries. o Hypothesis 2a : More economically protected countries will experience the smallest increase in happiness relative to economic development and globalization. o Hypothesis 2b : Less economically protected countries will experience the greatest increase in happiness relative to economic development and globalizati on. Hypothesi s two operate s as a way to assess the effects of economic protectionism on happiness by identifying Although the more precise methods to do this are presented later, sample countries ar e divided into different levels of economic protectionism where those with lower levels of protectionism have quite neoliberal economic policies and those with higher protectionism have economic policies that are less liberalized. Due to a rgument that more protected economies often face obstacles in economic development, hypotheses 2a assumes that happiness will increase less relative to economic

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34 development since obstacles to development will make establishing subsistence less feasible and achievement more difficult (Bhagwati 2007; Goldin and Reinert 2010). Since liberalized economies trade more feverishly in the global e conomy and increase incomes by industrializing and e xporting abroad, hypothesis 2b assumes that less protected economies will experience the greatest increase in happiness due to the greater feasibility of subsistence (Bhagwati 2007; Goldin and Reinert 2010). These countries have economic policies that are the most neoliberal. What hypothesis two does not concern is ho w t he benefits of neoliberal globalization are distributed among individuals in the sample countries. Hypothesis #3 : Income inequality in all countries and all levels of economic protectionism will witness negative relationships with happiness. Hypothesi s three does not operate as part of theory testing formally, but rather operates as a way to set up further discussion about the role of culture in determining how economic development and globalization affect happiness. This hypothesis concerns both indi vidual countries and the various levels of economic protectionism. Since more unequal countries typically per form less well economically, have high levels of psychosocial stress, and have poor er l evels of health, hypothesis three assumes that happiness wi ll decrease if income inequality increases (Wilkinson and Pickett 2011).

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35 CHAPTER V METHODOLOGY This thesis conducts a time series in which happiness data and are compared with neoliberal globalization and income inequality data ove r time between the years of 1990 and 200 7 In doing this, what is primarily observed is how changes in happiness correlate with changes in the other variables. Mixed Methods Seen in the literature review, happiness research often reaches different conclusions due to the different methodologies used (Greve 2012; Thin 2012). In order to bridge these gaps mixed methods are used here T he large number of countries and years that are being examined would be quite difficult to examine non numerically At the same time, howe ver, obstacles to validity are found when attempting to quantitatively evaluate a concept as subjective as happiness. Therefore, the overarching methodology used in this thesis is mixed methods due to its ability to explore both breadth and depth while se tting up conditions to ground normative arguments (Creswell and Plano Clark 2011). In this thesis, quantitative methods are used to depict the general relationship between happiness neoliberal globalization, and income inequality while other methods are used to expand the analysis. Ethnography is used to examine cultural notions of happiness while the theory of postcolonialism is used to foster discussion.

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36 Data Functioning as the dependent variable, happiness data are gathered from the World Values St udy (WVS) and the European Values Study (EVS). The vast majority of data are gathered from the WVS, but EVS data are used for European countries where frame. 10 Despite these different da ta sources, the happiness questions used in the WVS and EVS are worded identically. 11 Because of this, it is presumed that WVS data and EVS data are very comparable to one another. Both of these surveys are conducted in waves that do not take place annual ly. Throughout the time period that this thesis examines, surveys were conducted in the BRICS between three and four times. Functioning as the independent variables, data for exports, electricity production, internet use, and gini index scores are gathe red from the WB 12 (World Bank 2013a). Due to different population sizes among the BRICS, exports and electricity production data are manipulated so that they are measured per capita. Internet use data are left as is because they are measured as a percenta ge of the population. Data for gini index scores are also left as is because these are a summary statistic that already takes population into account. 10 See Appendix B for more information on happiness data availability. 11 See Appendix B for more information about the exact wording used in the WVS and EVS surveys. 12 See Appendix C and F for more d etailed explanations about how the World Bank measures these variables.

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37 Sample Stated above, the sample which is the primary focus consists of Brazil, Russia, India, Chin a, and South Africa The BRICS are often regarded by development Having received the treatment of neoliberalism, so to speak, these countries provide great opportunity to c onduct theory testing. Without the addition of countries utilizing different development paths, however, there is noth ing to compare the BRICS to. In order to compensate for this difficulty, the sample is expanded beyond the BRICS to include countries t hat are following various paths of development. The countries seen in tables 5.1 5.5 are purposively included because there are data available for all variables Du e to data availability or lack thereof unfortunately, the chosen sample is skewed in favo r of MEDCs such as those in Europe and the ex USSR. 13 Due to the skewed nature of the sample, therefore, the quantitative results to follow in chapter six are not validly able to be applied to the world at large. Despite this skewed sample, however, ther e are countries at various stages of economic development ensuring that there is variation across the independent variables Seen in tables 5.2 5.5 on the following page s descriptive statistics show that there is variation across all independent variable s except internet use which has slightly less variation. In other words, the BRICS countries are at various levels of the spectrum for all independent variables except internet use. 13 To limit this skew, five of the twenty eight EU member states were selected through a random sample.

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38 Table 5.1 Happiness Ranked by Z Score Country Z Score Belarus 1.83 4 Moldova 1.804 Russia 1.609 Albania 1.524 Ukraine 1.384 Latvia 1.154 Slovakia 0.962 Serbia 0.742 Macedonia 0.302 Peru 0.217 China 0.161 South Korea 0.146 India 0.018 Bosnia and Herzegovina 0.040 South Africa 0.161 Brazil 0.298 Chile 0.343 Argentina 0.487 Turkey 0.511 Japan 0.621 Mexico 0.708 Malta 0.754 France 0.788 Norway 1.005 Nigeria 1.050 Canada 1.056 Sweden 1.177 United States 1.212 Switzerland 1.249 Iceland 1.609 z score is based on mean of all years wi th available data for each individual country years represented vary in 1989 2008 period

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39 Table 5.2 Exports per capita (constant 2000 US$) Ranked by Z Score Country Z Score India 0.748 China 0.701 Moldova 0.700 Serbi a 0.699 Albania 0.688 Peru 0.681 Brazil 0.680 Ukraine 0.663 Bosnia and Herzegovina 0.640 Argentina 0.592 South Africa 0.589 Turkey 0.589 Russia 0.588 Macedonia 0.587 Belarus 0.576 Mexico 0.481 Chile 0.468 Latvia 0.435 United S tates 0.055 South Korea 0.043 Japan 0.024 Slovakia 0.146 France 0.408 Canada 1.077 Malta 1.265 Iceland 1.485 Sweden 1.738 Switzerland 2.408 Norway 2.653 insufficient data for Nigeria z score is based on mean of all years with availab le data for each individual country years represented vary in 1989 2008 period Table 5.3 Electricity Production per capita (kw/h) Ranked by Z Score Country Z Score Niger ia 0.859 India 0.810 Peru 0.764 Moldova 0.722 China 0.714 Albania 0.6 71 Mexico 0.622 Turkey 0.620 Brazil 0.618 Latvia 0.614 Argentina 0.577 Chile 0.549 Belarus 0.463 Bosnia and Herzegovina 0.444 Macedonia 0.418 Ukraine 0.354 Serbia 0.205 South Africa 0.197 Malta 0.196 South Korea 0.176 Slovakia 0.153 Russia 0.000 Japan 0.209 France 0.308 Switzerland 0.341 United States 1.025 Sweden 1.460 Canada 1.751 Iceland 2.739 Norway 2.913 z score is based on mean of all years with available data for each individual country years represen ted vary in 1989 2008 period

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40 Table 5.4 Internet Users (% of population) Ranked by Z Score Country Z Score Nigeria 1.146 Belarus 1.131 India 1.095 Ukraine 0.938 China 0.910 South Africa 0.873 Russia 0.844 Mexico 0.81 6 Albania 0.755 Argentina 0.694 Brazil 0.676 Turkey 0.578 Peru 0.569 Moldova 0.564 Chile 0.350 Bosnia and Herzegovina 0.257 Latvia 0.139 Malta 0.171 Macedonia 0.228 France 0.388 Slovakia 0.425 Japan 0.427 South Korea 0.726 United St ates 1.171 Canada 1.177 Switzerland 1.375 Sweden 1.922 Norway 1.956 Iceland 2.092 insufficient data for Serbia z score is based on mean of all years with available data for each individual country years represented vary in 1989 2008 perio d Table 5.5 Income Inequality (gini index score) Ranked by Z Score Country Z Score Japan 1.261 Sweden 1.247 Norway 1.170 Slovakia 1.081 Belarus 0.979 Ukraine 0.830 Serbia 0.633 South Korea 0.605 Albania 0.594 India 0.555 Canada 0.5 10 France 0.492 Bosnia and Herzegovina 0.434 Switzerland 0.401 Latvia 0.399 Moldova 0.089 China 0.019 Macedonia 0.035 Russia 0.229 United States 0.294 Turkey 0.354 Nigeria 0.773 Argentina 1.136 Mexico 1.163 Peru 1.341 Chile 1.643 Brazi l 2.103 South Africa 2.190 insufficient data for Iceland and Malta z score is based on mean of all years with available data for each individual country years represented vary in 1989 2008 period

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41 When ranked by z score a s the sample countries are in tables 5.1 5.6 it is seen that those with negative z scores are below average and those with positive z scores are above the average of sample countries. The general skew of the data can also be seen. For all independent var iables, it is also seen in these tables that the data are skewed right as there are more countries with negative z scores than positive ones What this indicates is that there are several countries at the upper end of the spectrum for the independent varia bles that are much higher than the majority of other sample countries therefore skewing the selected sample. For the dependent variable, happiness, it is seen that this is more normally distributed. ast happy sample countries Validity of Quantifying Happiness Happiness functio ns as the dependent variable. As mentioned, q uantitative happiness data are available from the WVS and the EVS Ethnographic analyses of happiness in the BRICS are available from various scholars who dissect what happiness is and what it mean in each BRICS country ( Biswas Deiner, Tay, and Deiner 2012; Bookwalter 2012; Davey 2012; Islam 2012; Zavisca and Hout 2005 ). Quantitative d ata: Happiness survey validity. While some sch olars mentioned in the preceding paragraph explore happiness non numerically in the BRICS, others quantify happiness and meticulously outline the steps needed in order to quantify validly (Abdel Khalek 2006; Graham 2009; Greve 2012; Kalmijn, Arrends, and V eenhoven 2011). In order to quantify happiness validly, surveys that measure happiness must: 1) assess long

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42 term happiness, 2) ask respondent s to assess happiness towards the beginning of the survey, and 3) leave the connotation of happiness open in the q uestion so that respondents can interpret as they please (Abdel Khalek 2006; Graham 2009; Greve 2012). Both the WVS and the EVS do exactly this. First, happiness survey questions in the WVS and EVS ask respondents to assess happiness long term by includi ng the phrase both survey questions that assess happiness in the WVS and the EVS are located towards the beginning of both surveys In the WVS and the EVS surveys the q uestion that measures happiness is placed as the third and eighth question, respectively, out of hundreds of questions ( WVS 2013; EVS 2013). Lastly WVS and EVS happiness questions leave the connotation of the term happiness open for interpretation. This is a critical aspect of assessin g happiness cross culturally because this allows the respondent to interpret the term happiness in a way that best fits what they perceive happiness to be (Abdel Khalek 2006) Quantifying happiness. Happiness is quantifie d as an interval variable. Although some scholars caution quantifying happiness others do not and outline the steps needed to do so (Greve 2012; Thin 2012). There are two obstacles found when quantifying happiness using survey data: 1) the limitations o f ordinal data and 2) the fact that the WVS/EVS surveys are not conducted yearly. The first obstacle found in quantifying happiness data is changing WVS/EVS data gathered ordinally into interval data that can be used in statistical analyses with greater ease. Based on a method outlined by Kalmijn, Arrends, and Veenhove n a weighte d

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43 mean is calculated using the equation below for eac h country and year when the WVS and EVS were conducted (2011). Kalmijn, Arrends, and Veenhoven conduct a study among Dutch participants where they ask participants to place four marks on a 10 point scale for which they feel best represent four categories of happiness (2011). Although it is problematic that this study only included Dutch participants, especially considering th e role of culture, it is the best option that is currently available in the literature. Seen in the weights used in the belo w equation, they then find the mid interval value for each category based on the participant responses (Kalmijn, Arrends, and Veenh oven 2011). This is the formula used in this thesis to calculate levels of happiness for WVS/EVS survey years 14 The weights used in the above formula do not reflect the exact results of Kalmijn, as the value of not at all happy (NAAH) had to be increased in order for approximate normal distribution to be assumed in a random sample (2011). For example, the equation below is used by Kalmijn, Arrends, and Veenhoven in order to demonstrate that the midpoint of all c ategories on a 10 point scale is 5. In other words, the weights attached to the levels of happiness are not equidistant, but a normal distribution across all four categories of happiness would yield a mean and 14 H is multiplied by 10 so that happiness can be graphically depicted on a 100 po int scale. Equation 5.1 Quantifying Happiness with Mid interval Values and a Weighted Mean

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44 median of 5 Although the exact value of H i s therefore inflated by using the altered weight for NAAH its consistent use in all countries and years will still reflect how happiness changes over time in the time series executed here. The second obstacle in quantifying happiness is that WVS/EVS sur veys are conducted in waves that do not tak e place yearly. Throughout 1990 2007 WVS surveys were conducted three times in Brazil and four times in the other BRICS countries. In order to compe nsate for this obstacle, data are interpolated for years when WVS/EVS surveys were not conducted. That is, years with missing data are estimated assuming that happiness moves linearly between years. In overcoming the obstacle s to validity that missing happiness data provide however, other obstacles to validity are created by using linear interpolation. These potential threats to validity are: 1) estimating values which were never officially measured and 2) assuming that levels of happiness move linearly over time The reasons why these issues are problematic cent er on their lack of falsifiability. Interpolated values cannot be proven to be either correct or incorrect. Allison distinguishes between data that are missing completely at random (MCAR) and data that are missing at random (MAR) (2002). Data that are MC AR do not enable valid data imputation or interpolation (Allison 2002). In this thesis, the data are not MCAR because the happiness literature indicates that happiness would not deviate significantly beyond data that is linearly interpolated between two d ata points Equation 5.2 Presuming Approximate Normal Distribution in a Random Sample

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45 from officially measured WVS/EVS surveys. Data imputation is most appropriate whe n data are MAR (Allison 2002; Honaker and King 2010) Within this type of missingness, after c ontrolling for other variables in the analysis 4 ). The same can be said if X is th e variable with missing data, as seen here (Allison 2002). In order for MAR data to be interpolated validly in other words, the likelihood for the data to be missing does not depend on the actual values for all variables (Allison 2002) In this thesis, for example, happiness is MAR because the particular levels of happiness or economic development are not the reasons why data are missing. Happiness data ar e not missing because there was a spike in electricity production, for example. Since the data in this thesis are MAR instead of MCAR validity is added to data interpolation When data are MAR as they are here, Allison argues that data can be validly in terpolated using methods that linear methods whichever fits best with what specifically is being measured (Allison 2002, 18). Within the literature there are opposing viewpoints about the valid ity of imputing and interpolating data. This controversy centers on the difficulty found in accounting for error with interpolated data. One issue would be to exercise pairwise deletion and ignore data for all variables when WVS/EVS surveys were not cond ucted However, King et al. strongly criticize pairwise deletion due to the implications found when ignoring large portions of data that are already available (2001). This is because data that are present are therefore lost due to the absence of d ata for one variable. In this thesis, pairwise deletion of years with missing data could result in misleading findings because the independent variable s vary more over time than happiness does Even more problematic,

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46 pairwise deletion would effectively remove th e possibility to include income inequality as an independent variable because years where there are data for inequality rarely coincide with years where there are WVS/EVS happiness data. For time series analyse s specifically, Honaker and King present and suggest an algorithm that interpolates data non linea rly so that there are smooth transitions (2010). This method generally produces results that are similar to interpolating data non linearly by using polynomials (King et al. 2010). The use of any meth od that uses non linear methods of data interpolation would al so be neither valid nor invalid, however. This includes algorithmic models that create smooth transitions between data points such as that presented by Honaker and King (2010). This would be n either valid nor invalid for happiness because this method does not consider data that lay before and after the period that the study is examining. The inclusion of survey years before and after the years of the time series would affect the curvature of t hese transitions which interpolated data are based on higher or lower Without knowledge of past happiness survey data, this method is neither valid nor invalid. 15 Similar to non li near method of data interpolation, linear data interpolation is neither valid nor invalid. estimates values between two known data points by assuming linearity (Allison 2002). As argued in the preceding paragraphs, the interpolation of data provides a trade off o f validity. Validity is lost by estimating data that have never formally existed and a ssuming that they move linearly B ut validity is 15 See Appendix E for further discussion and a pictor al demonstration of linear vs. non linear data interpolation.

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47 also gained by not ign oring the greater variation observed in the independent variables Due these reasons, happiness data are interpolated linearly Controlling for Economic Protectionism To enrich the systemic analysis of the variables while taking economic protectionism in to account, the sample i s divided into terciles for both happiness and economic protectionism. Given the overarching selection of independent variables that measure different aspects of neoliberal globalization this fosters a better comparison of how cou the independent variables at three relative levels of protectionism. Countries with lower levels of protectionism generally have more neoliberal economic policies while those with higher protectionism have policies that ar e less liberalized In the following chapter, economic protectionism is controlled for when testing all variables. The sectors created by dividing the data into terciles can be seen in table 5.6 Table 5.6 Happiness and Economic Protectionism ECONOMIC PROTECTIONISM HIGH Belarus Argentina China Brazil Peru South Korea Mexico Russia India Nigeria MID Moldova Bosnia and Herzegovina Switzerland Canada Serbia Macedonia Norway Iceland Ukraine South Africa LOW Albania Slovakia Chile France Latvia Turkey Japan Malta Sweden USA LOW MID HIGH HAPPINESS The tercile that each country is placed in is based on the most recent year with paired tariff and happiness data.

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48 The tercile that each country is placed in is determined by the most recent year where paired happiness and import tariff data are available. For most countries, t his is in the mid 2000s or late 2000s. Another option would b e to use the mean happiness and tariff levels for all years with available data for each country, but this would not be entirely valid considering that all of the BRICS were significantly more economically protected in the early 1990s than they are today especially when compared to MEDCs the present and reduce misleading findings the most recent year with paired data is used Notably, the BRICS excluding South Africa are placed in the category of high e conomic protectionism while South Africa is placed in the moderate category of protected on a global scale, but rather that they are more protected than the majority of th e other countries in the sample. In other words, although the BRICS have shifted to the neoliberal path of development in recent years, they function as more economically Again, this is due to the lack of available happiness data and the purposively selected sample. Measuring economic protectionism by using tariff data is an efficient way to measure economic protectionism due to its breadth across all sectors of the economy, but this does raise cha llenges. In the WTO, for example, the large scale reduction of tariffs among member states has led many states to adopt non tariff barriers (NTBs) to trade in efforts to protect the domestic economy from outside market fluxuations. 16 NTBs provide obstacle s to trade that are not formally included in tariff rates (Deardorff and 16 Examples of NTBs include phenomena such as quotas, health and safety regulations, countervailing duties, and anti dumping duties (Deardorff and Stern 1999).

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49 Stern 1999). Although scholars such as Deardorff and Stern measure NTBs quantitatively, this is quite tedious and is limited to one sector of the economy at a time rather than the ec onomy as a whole (1999). Despite this challenge, an emerging norm of bilateral trade reciprocity is emerging that influences both tariffs and NTBs (Lim o engaged in some sort of bilateral or multilateral preferential trade agreement characterized by tariff reciprocity (Lim o 2006). This includes all in the sample except Belarus (WTO 2013). For these reasons, tariffs are used as a measure of economic protectionism. Oper ationalizing the M ethods In order to assess the relationship between happ iness and economic development, hypotheses will be tested quantitatively using correlation analysis. This is true for both state level analysis and systemic level analysis when con trolling for economic protectionism. Correlation rather than regression is chosen for hypothesis testing because: 1) the relationship between variables is sometimes bidirectional and 2) the sample is not randomly selected and skewed. r is use d to calculate correlation coefficients in this study. Given that all of the variables other than income inequality are highly time dependent and would increase over time regardless of many factors, data in the time series are made stationary by convertin g data to reflect year to year change in order to avoid spurious results 17 ( Hamilton 1994 ). Since income inequality is no t time dependent, these data are left as is. 17 In the tables of the following chapter, delta ( to year change.

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50 In testing the relationship between happiness and income inequality, more over, pairwise d eletion is used since income inequality would be less appropriate to interpolate than happiness. In some cases when conducting the hypothesis testing the data violate some r For example, non stationary data for some coun tries are heteroskedastic and cone shaped. Moreover, the data interpolation methods used here yield awkward placement of the data when plotted on a scatterplot. 18 In order to select BRICS countries to test hypothesis one Seawright and thod of case study selection is used (2008) For reasons discussed in the following chapter, Russia and India are the focus of further discussion due to their standings at the opposite ends of the spectrum for exports, electricity production, and internet use. This can be seen in the descriptive statistics in the following chapter. Other BRICS are also discussed, but Russia and India particularly are examined in greater depth throughout the quantitative analysis chapter. 18 See Ap pendix D for scatterplots of heteroskedastic non stationary data and stationary data that are awkwardly placed when graphed on a scatterplot.

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51 CHAPTER VI QUANTIT ATIVE ANALYSIS Using the methodology presented in the previous chapter, quantitative analysis is performed in this chapter to examine the relationships between happiness and the independent variables In line with the hypotheses presented in chapter thre e, tests are conducted at both the state level and the systemic level when controlling for economic protectionism. The state level analysis to follow assesses the relationship of neoliberal globalization and income inequality with happiness at the state l evel while taking into consideration the arguments of Graham (2009). Moreover, the systemic level analysis to follow tests the impact of neolib eral globalization and income inequality on happiness by determining which countries have economic policies that are the most and least liberalized of the sample countries Happiness: Descriptive Statistics Concerning happiness observed alone table 6.1 on the following page shows that average happiness in the BRICS is roughly similar for all cou n tries except Russ ia. On average, Russia is not only considerably less happy than the other BRICS counties but also less happy than the vast majority of other sample countries This was also observed in the ranked z scores seen in table 5.1 in the previous chapter where only Belarus and Moldova are on average less happy than Russia. When examining the dispersion of happiness, table 6.1 shows that South Africa has the largest dispersion while happiness in China and India has the lowest level of dispersion. In other words, this demonstrates that

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52 the level happiness in South Africa varied much more across the time period than it did in China and India. Table 6.1 Happiness Descriptive Statistics NAAH = 17 40.9; NVH = 41 66.9; QH = 67 92.9; VH = 93 Country Years Mean Q1 M edian Q3 IQR Brazil 1991 2006 69.0 67.0 68.7 71.0 4.0 China 1990 2007 65.8 64.6 65.6 66.8 2.1 India 1990 2006 66.8 66.3 66.9 67.4 1.2 Russia 1990 2006 55.5 54.1 54.7 56.4 2.3 South Africa 1990 2007 68.4 66.2 68.9 70.4 4.2 BRICS various year s 65.0 64.2 66.5 68.1 3.9 Albania 1998 2008 56.1 53.4 57.3 59.3 5.9 Argentina 1991 2006 70.4 69.4 70.3 71.3 1.9 Belarus 1990 2000 53.9 52.5 52.9 54.6 2.2 Bosnia and Herzegovina 1998 2008 67.2 66.9 67.8 68.2 1.2 Canada 1990 2006 74.4 71.8 7 5.4 77.4 5.6 Chile 1990 2006 69.4 71.8 75.4 77.4 5.6 France 1990 2008 72.5 71.9 72.6 73.2 1.2 Iceland 1990 2005 78.3 77.6 78.4 79.0 1.4 Japan 1990 2005 71.3 71.3 71.6 72.3 0.9 Latvia 1990 2008 58.8 57.4 58.9 60.1 2.7 Macedonia 1998 2008 6 4.8 63.6 65.3 66.5 2.8 Malta 1991 2008 72.3 71.7 72.2 72.7 1.0 Mexico 1990 2005 71.9 65.4 70.4 79.5 14.1 Moldova 1996 2008 54.2 53.1 54.1 54.6 1.4 Nigeria 1990 2000 74.4 70.5 74.4 78.2 7.7 Norway 1990 2008 74.0 73.1 73.8 74.8 1.7 Peru 1996 2006 65.4 65.2 65.5 65.6 0.4 Serbia 1996 2008 61.7 61.3 62.0 62.4 1.2 Slovakia 1990 2008 60.1 58.9 60.5 61.7 2.8 South Korea 1990 2005 65.9 65.7 66.2 66.6 0.9 Sweden 1990 2008 75.3 74.4 75.9 76.2 1.8 Switzerland 1989 2008 75.8 75.5 75.9 76 .1 0.6 Turkey 1990 2008 70.5 68.0 70.4 72.5 4.5 Ukraine 1996 2008 57.1 52.8 57.0 60.9 8.1 United States 1990 2006 75.5 74.7 75.3 76.3 1.6 data include all years in time period for each country

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53 Seen in figure 6.1 below is a visual representati on of happiness dispersion in each WVS survey conducted in the BRICS. Happiness in each year is roughly centered on either the quite happy or not very happy categories; which is also seen in the means and medians in table 6.1 on the previous page. Also s hown below is how happiness survey responses change across survey years.

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54 Independent Variables : D escriptive Statistics In order to be better prepared to address hypothesis one which concerns sta te level relationships between variables, tables 6.2 6.5 on the following pages show the descriptive statistics for all of the independent variables. These are periodical ly referred to in the following pages w hich conduct hypothesis testing at the state l evel. Table 6.2 Exports per capita (constant 2000 US$) Descriptive Statistics Country Years Mean Q1 Median Q3 IQR Brazil 1992 200 6 372 290 325 434 144 China 1991 2007 274 98 159 370 271 India 1991 2006 56 30 43 71 41 Russia 1991 2006 796 626 714 941 315 South Africa 1991 2007 791 697 802 844 147 BRICS various years 461 117 443 746 630 Albania 1998 2008 332 256 335 428 172 Argentina 1992 2006 780 627 831 881 254 Belarus 1991 2000 851 696 779 893 196 Bosnia and Herzegovina 1999 2008 554 436 499 652 216 Canada 1991 2006 8,532 6,952 9,061 10,346 3,394 Chile 1991 2006 1,356 1,001 1,370 1,604 603 France 19 91 2008 5,423 4,161 5,622 6,506 2,345 Iceland 1991 2005 10,427 8,594 10,105 11,765 3,172 Japan 1991 2005 3,642 2,990 3,582 4,055 1,065 Latvia 1991 2008 1,507 1,041 1,334 1,798 756 Macedonia 1999 2008 802 707 735 897 190 Malta 1992 2008 9,4 08 9,059 9,413 9,797 737 Mexico 1991 2005 1,296 767 1,353 1,720 953 Moldova 1997 2008 276 203 245 377 173 Norway 1991 2008 15,853 13,882 17,012 17,735 3,853 Peru 1997 2006 365 301 349 413 112 Serbia 1999 2008 280 195 275 338 142 Slovakia 19 92 2008 4,208 2,446 3,755 5,266 2,821 South Korea 1990 2005 3 330 1 714 3 082 4 451 2 737 Sweden 1991 2008 11,602 8,004 11,627 14,350 6,345 Switzerland 1990 2008 14,716 11,329 14,030 16,400 5,070 Turkey 1991 2008 795 524 838 1,005 481 Ukrain e 1997 2008 447 336 455 531 195 United States 1991 2006 3,273 2,685 3,497 3,621 936 insufficient data for Nigeria data include all years in time period for each country

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55 Table 6.3 Electricity Production per capita (kw/h) Descriptive Statistics Country Years Mean Q1 Median Q3 IQR Brazil 1991 2006 1,863 1,685 1,878 2,002 317 China 1990 2007 1,176 793 965 1,436 643 India 1990 2006 485 407 488 549 141 Russia 1990 2006 6,282 5,799 6,121 6,644 846 South Africa 1990 2007 4,869 4,677 4,769 5,061 384 BRICS various years 2,949 793 1,937 5,061 4,268 Albania 1998 2008 1,483 1,197 1,607 1,741 545 Argentina 1991 2006 2,151 1,898 2,125 2,417 519 Belarus 1 990 2000 2,966 2,510 2,642 3,470 960 Bosnia and Herzegovina 1998 2008 3,108 2,831 2,978 3,349 519 Canada 1990 2006 18,802 18,568 18,975 19,128 560 Chile 1990 2006 2,351 1,783 2,361 2,767 984 France 1990 2008 8,484 8,124 8,618 8,922 797 Icela nd 1990 2005 25,868 18,296 25,914 29,315 11,020 Japan 1990 2005 7,777 7,382 7,978 8,156 774 Latvia 1990 2008 1,891 1,705 1,817 2,131 426 Macedonia 1998 2008 3,292 3,164 3,323 3,419 254 Malta 1991 2008 4,878 4,416 4,915 5,541 1,124 Mexico 199 0 2005 1,831 1,597 1,830 2,066 468 Moldova 1996 2008 1,117 997 1,063 1,150 153 Nigeria 1990 2000 138 136 140 144 9 Norway 1990 2008 27,114 25,922 27,322 28,685 2,763 Peru 1996 2006 815 744 792 871 127 Serbia 1996 2008 4,797 4,675 4,896 4,9 52 277 Slovakia 1990 2008 5,184 4,749 5,168 5,738 988 South Korea 1990 2005 5,018 3,541 4,751 6,627 3,068 Sweden 1990 2008 16,718 16,263 16,800 17,056 793 Switzerland 1989 2008 8,719 8,350 8,764 8,930 580 Turkey 1990 2008 1,846 1,409 1,857 2 ,180 771 Ukraine 1996 2008 3,746 3,515 3,601 3,947 432 United States 1990 2006 13,607 13,119 13,554 13,999 880 data include all years in time period for each country

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56 Table 6.4 Internet Users (% of population) Descriptive Statistics Country Years Mean Q1 Median Q3 IQR Brazil 1991 2006 6.4 0.1 1.8 10.2 10.1 China 1990 2007 3.2 0.0 0.4 5.8 5.8 India 1990 2006 0.7 0.0 0.1 1.5 1.5 Russia 1990 2006 4.1 0.1 0.9 5.2 5.0 South Africa 1990 2007 3.8 0.4 3.5 6.9 6.6 BRICS various years 3.6 0.0 0.8 5.3 5.3 Albania 1998 2008 5.4 0.2 1.0 7.8 7.6 Argentina 1991 2006 6.2 0.1 2.1 11.1 11.1 Belarus 1990 2000 0.2 0.0 0.0 0.1 0.1 Bosnia and Herzegovina 1998 2008 12.2 1.1 4.0 23.2 22.1 Canada 1990 2006 31.8 2.4 24.9 61.6 59.2 Chi le 1990 2006 10.9 0.1 1.7 22.1 22.0 France 1990 2008 21.0 1.3 9.1 37.6 36.4 Iceland 1990 2005 44.2 9.0 41.3 83.5 74.5 Japan 1990 2005 21.5 0.7 11.3 40.5 39.8 Latvia 1990 2008 17.6 0.0 4.4 32.8 32.8 Macedonia 1998 2008 18.8 3.0 19.1 27.5 24 .6 Malta 1991 2008 18.0 0.4 10.4 33.9 33.4 Mexico 1990 2005 4.5 0.0 0.9 8.3 8.2 Moldova 1996 2008 8.0 0.6 3.8 14.6 14.0 Nigeria 1990 2000 0.0 0.0 0.0 0.0 0.0 Norway 1990 2008 42.4 5.3 40.0 77.9 72.6 Peru 1996 2006 7.9 1.6 7.6 12.9 11.3 S lovakia 1990 2008 21.5 0.4 5.4 48.0 47.5 South Korea 1990 2005 25.6 0.3 5.2 57.3 57.0 Sweden 1990 2008 25.6 0.3 5.2 57.3 57.0 Switzerland 1989 2008 34.5 2.6 29.4 65.8 63.2 Turkey 1990 2008 7.8 0.1 2.3 13.5 13.4 Ukraine 1996 2008 2.9 0.4 1. 9 3.7 3.3 United States 1990 2006 31.7 4.9 30.1 58.8 53.9 insufficient data for Serbia data include all years in time period for each country

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57 Table 6.5 Income Inequality (gini coefficient) Descriptive Statistics Country Years Mean Q1 Median Q3 IQR Brazil 1993 2006 59.4 58.5 60.0 60.4 1.9 China 1990 2005 38.0 35.6 37.5 41.7 6.1 India 1994 2005 32.1 31.5 32.1 32.7 1.3 Russia 1993 2006 40.1 37.3 37.5 42.1 4.9 South Africa 1993 2006 60.3 57.5 58.6 61.3 3.9 BRICS various years 48. 9 37.5 51.2 59.4 21.9 Albania 2000 2008 31.7 30.4 32.1 33.4 3.0 Argentina 1991 2006 49.4 47.4 49.4 50.8 3.4 Belarus 1993 2000 27.7 27.0 29.5 30.3 3.3 Bosnia and Herzegovina 2001 2007 33.3 31.9 35.8 36.0 4.1 Canada 2000 32.6 Chile 1 990 2006 54.7 54.7 55.0 55.3 0.5 France 1995 32.7 Japan 1993 24.9 Latvia 1993 2008 33.7 31.7 34.6 36.2 4.5 Macedonia 1998 2008 38.2 37.7 38.9 40.0 2.4 Mexico 1990 2005 49.7 48.8 49.7 51.5 2.7 Moldova 1997 2008 36.9 35.8 36.3 37.7 2.0 Nigeria 1992 1996 45.7 45.3 45.7 46.1 0.8 Norway 2000 25.8 Peru 1997 2006 51.6 50.8 52.6 55.5 4.8 Serbia 2002 2008 31.3 29.5 32.7 32.9 3.4 Slovakia 1992 2008 26.7 26.3 27.7 28.6 2.3 South Korea 1998 31.6 Sweden 200 0 25.0 Switzerland 2000 33.7 Turkey 1994 2008 41.4 40.1 42.0 42.7 2.6 Ukraine 1996 2008 29.3 28.1 28.3 29.6 1.5 United States 2000 40.8 i nsufficient data for Iceland and Malta *data do not include all years in time perio d for each country dash indicates insufficient data

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58 State level Analysis Addressing Hypothesis One In the following tables which present the results of the quantitative analysis at the state level, two tables are provided which include each independent variable To measure the strength of relationships, t able 6.6 includes Pears on r correlation coefficients As discussed in the methodology chapter the statistical significance of relationships is accomplished using Pearson r In order to be better equipped to address the hypotheses, table 6.7 includes regression coefficients us ing least squares regression and standard err or of the mean The regression coefficients presented measure how much happiness changes on average relative to a one unit increase in the independent variable. Due to their much larger scales, t he regression coefficients for exports and electricity production show how much happiness is expected to change in relation to 100 units of chan ge. Table 6.6 State level Results: Pearson r Correlation Coefficient Legend Happiness & Pearson r [df] capita (hundreds of constant 2000 US$) Production per capita (hundreds of kw/h) (% of population) Gini Index Score Brazil df 0.490 0.073 0. 623 0.246 [11] 1992 2006 13 China df 0.260 0.374 0.163 0.691 [4] 1991 2007 15 India df 0.220 0.166 0.043 insufficient data 1991 2006 14 Russia df 0.527 0.717 0.779 0.327 [7] 1991 2006 14 South Africa df 0.046 0.233 0.127 0.603 [2] 1991 2007 15 BRICS df 0.277 0.279 0.326 0.563 [32] 1991 2007 79 Non BRICS 0.058 [348] 0.029 [364] 0.141 [356] 0.526 [120] various years p > .1 p < .05 p < .01 data are based on each country/year with paired data between variables

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59 Seen in table 6.6 on the previous page statistically significant relationships among the individual BRICS are fo und in only Brazil and Russia. 19 In Brazil, the relationship between happiness and exports is seen to be a signif icant positive correlation as is the relationship between happiness and internet use Similar to Brazil, significant positive rela tionships in Russia a re also found between happiness and both e xports and internet use Different than Brazil, however, happiness and elec tricity production in Russia a re also positively correlated at a significant level. Other than income inequality, So uth Africa displays virtually no relationships between variables. In none of the BRICS are there significant relationships between happiness and income inequality. What these observations indicate is that the significant relationships found are sporadica lly placed across the BRICS and the values of r vary as well. 19 See Appendix A for state level quantitative results from non BRICS sample countries. Table 6.7 State level Results: Regression Coefficient and Standard Error Legend Happiness & slope (standard error) capita (hundreds of constant 2000 US$) Production per capita (hundreds of kw/h) (% of population) Gini Index Score Brazil 0.319 (0.1 13) 0.016 (0.129) 0.033 (0.101) 0.308 (2.532) 1992 2006 China 0.319 (0.604) 0.252 (0.581) 0.072 (0.618) 0.238 (1.149) 1991 2007 India 1.260 (0.448) 0.909 (0.453) 0.081 (0.459) insufficient data 1991 2006 Russia 0.326 (0.5 97) 0.201 (0.489) 0.347 (0.440) 0.196 (2.706) 1991 2006 South Africa 0.054 (0.330) 0.055 (0.321) 0.066 (0.327) 0.325 (2.571) 1991 2007 BRICS 0.252 (0.495) 0.104 (0.495) 0.112 (0.487) 0.290 (4.852) 1991 2007 Non BRICS 0. 014 (0.874) 0.002 (0.892) 0.029 (0.887) 0.355 (5.625) various years data are based on each country/year with paired data between variables

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60 Exports When co mparing Brazil and Russia, it is seen that they have comparable regression coefficients and statically significant relationships between happiness and exports. Seen in the d esc riptive statistics however, Russia is a much larger exporter per capita than Brazil. This provides a useful comparison to test hypothesi s one which assume s that LEDCs will experience the greatest increase in happiness relative to economic development. G iven that Brazil is a ranked third out of the five BRICS concerning exports per capita wile Russia is ranked as the top exporter, hypotheses one is not supported by the data when analyzing exports alone. Seen in table 6.7 t his is because their regression coefficients are comparable despite their different levels of economic development. D ifferent conclusions can be reached when comparing Russia as the highest per capita exporter among the BRICS and India as the lowest A comparison of these two count ri es is needed to test hypothesi s one for this variable but the relationship in India is not statistically significant As table 6.7 shows, t he regression coefficient of happiness relative to exports per capita is nearly three times higher in India than it is in any of the other BRICS. Additionally it is not only much higher than the other BRICS, but also much higher than the cumulative non BRICS and all other sample countries except Serbia T he value of r between exports per capita and happiness in Indi a is neither statistically significant at any level nor is it close to being significant however This creates ambiguities. If only the statistically significant relationships are analyzed, such as those seen in Brazil and Russia, hypotheses one is not supported by the data. If India and Russia are analyzed despite the lack of statistical significance, however, hypotheses one is at best mildly supported by the data Overall, these results indicate that the correlation

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61 between happiness and exports is n ot uniform across the BRICS and no clear trends in the data are observed. Thus, the relationship between variables is blurry and unclear. Industrialization Seen above in table 6.6 Ru ssia i s the only BRICS country that has a significant relationship between happiness a nd electricity production. With a lack of other significant relationships, the feasibility of conducting a valid test of hypotheses one is limited in the same way as seen for exports Similar to the above situation concerning exports, nevertheless, it can be seen in the descr iptive statistics that Russia is the top producer of elect ricity per capita while India is the lowest among the BRICS Although the validity of using these two countries to test hypotheses one and two is limited du e to the lack of statistical significance, the y will nevertheless be examined keeping this in mind With a regression coefficient in India that is more than four times greater than all other BRICS the cumulative non BRICS, and all other individual sample countries, this is quite notable. Although there could be something happening between happiness and electricity pr oduction that supports hypothesi s one the relationship in India is neither significant nor close to being significant at any level used here Similar to the state level analysis of exports, an obstacle is reac hed. If statistical significance is set aside, there very well could be a relationship present that supports hypotheses one due to the regression coefficient that is much higher than ot her sample countries If non signi ficant relationships are ignored however, hypotheses one is not supported by the data. Due to the considerable differences in regression coefficients between Russia and India, nevertheless, the data indicate that there could be something happening that provides

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62 sup port for hypotheses one but this is not certain. To summarize these results, the relationship between happiness and industrialization in the BRICS is mostly unclear. Globalization Both Brazil and Russia h ave strong positive correlations between happiness and internet use When observing the regression coefficients of this relationship, that of Brazil is 0.033 and that of Russia i s 0.347 which is much stronger than all the ot her BRICS the cumulative non B RICS, and many other sample countries. Not to be mi sled, however, internet use is the variable which has the weakest relationship with happiness among all of the independent variables when examining their corresponding regression coefficients Consistent ly among the BRICS, these values are less than one tenth of one point of happiness when viewing internet use What this indicates is that at all levels of statistical significance and non significance, the actual effect that internet use has on happiness is actually quite minimal for most of the sample countries. To test hypothesi s one for internet use in Brazil and Russia, the de scriptive statistics show that Brazil and Russia ha ve the top and second to top levels of internet use respectively. When looki ng at the regression coefficients found for these countries however, in ternet use in Brazil experienced the least effect on happiness other than South Africa, which actually ha s weakly negative correlation and regression coefficients Russia is an anomal y in this case therefore, as it is the only B RICS country with a regression coefficient that is numerous times higher than the cumulative non BRICS and all of the other BRICS. Given that those with the lowest means and medians in the descriptive statisti cs actual ly ha ve the smallest regression coefficients it can be

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63 concluded that hypotheses one is not at all supported by the data when examining internet use Income i nequality Measured using gini index scores, income i nequality is addressed by hypothe sis three which states that in all countries i ncreases in income inequality will be correlated with decreases in happiness. No significant relationships for any individual BRICS countries are found for this independent variable. When looking at the de sc riptive statistics in table 6.5 it is seen that Brazil and South Africa have the highest levels of income inequality among all of the sample countries. Also, China has the highest lev el of dispersion while India had the lowest level of dispersion. This indicates that inequality in China witnessed the largest amount of change in the study time period while inequality in India experienced the smallest amount of change. In all BRICS except South Africa, the values of r and the regression coefficients for each country are negative. For both the cumulative BRICS and the cumulative non BRICS however, the regression coefficients are positive and the relationships are statically significant at the 99% level. Although the results when controlling for eco nomic protectionism will be discussed later, the data at the state level do not support hypothesis three because there are no significant relationships between happiness and income inequality in the BRICS. Even among the significant relationships seen in non BRICS countries, there are no general relati onships that can be identified because both positive and negative rel ationships exist in both LEDCs and MEDCs.

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64 Systemic level Analysis Addressing Hypothesis Two: Controlling for Protectionism Wh en comparing the cumulative BRICS with the cumulative non BRICS countries seen above in table 6.6 the results yield interesting findings. Crucially, i n the cumulative BRICS all independent variables are positively correlated with happiness at a significa nce level of 95% or higher. In the cumulative non BRICS, internet use is negatively correlated with ha ppiness while income inequality is positively correlated with happiness. Both of these relationships are significant at the 99% level. Since comparing BRICS with non BRICS does not allow us to determine which sample countries have economic policies that are the most neoliberal, this section will control for economic protectionism by using the methods presented in chapter four Countries with the lower l evel s of economic protectionism have more neoliberal economic policies while those with higher levels of protectionism have economic policies that are less neoliberal. In show the quantitative results when contro lling for economic protectionism. Similar to the state level analysis above, two tables are shown for each independent variable where one shows Pearson r correlation coefficient and the other shows regression coefficients and standard error of the mean. In order for hypothesis two to be supported in the analysis for exports, electricity production, and internet use, we would expect to see a trend in the tables below where those in the upper left hand sector have weak er regression coefficients and those in the lower right ha nd sector stronger regression coefficients and statistically significant levels of r Bef ore proceeding, tables 6.8 and 6.9 on the following page show the analysis of all sample countries cumulatively to assist in comparing the terciles to the average of all sample countries.

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65 Table 6.8 Cumulative Sample Countries: Pearson r Correlation Coefficient Variables Pearson r df 0.051 429 0.023 444 ricity Production per capita (hundreds of kw/h) 0.178 437 Happiness & 0.516 150 Gini Index Score p < .1 p < .05 p < .01 data are based on each country/year with data between variables Ta ble 6.9 Cumulative Sample Countries: Regression Coefficient and Standard Error Variables Slope Standard Error 0.012 0.818 0.002 0.836 pita (hundreds of kw/h) 0.142 2.730 Happiness & 0.305 5.440 Gini Index Score data are based on each country/year with data between variables

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66 Exports. When divided into terciles, table 6.10 below shows that a significant relationship exists among sample countries with low happiness and high economic protectionism. Compared to the average of all countries, the relationship between happiness and exports is much stronger in countries with high protectionism and low happiness. Although statistically significant relationships are not foun d in the other sectors, table 6.10 shows that there are virtual ly no relationships between variables among the happiest countries in the sample and countries wi th lower levels of economic protectionism. When viewing the regression coefficients seen in table 6.11 on the following page, the largest average influence of exports on happiness is seen among Russia and other formerly Soviet states. In other words, hap piness increases the most on average in countries with low happiness and high protectionism. As indicated by these data therefore, hypothesis two is not supported regarding happiness and exports. Table 6.10 Happiness and Exports per capita (constant 200 0 US$) Controlled for Economic Protectionism: Pearson r Correlation Coefficient ECONOMIC PROTECTIONISM HIGH Belarus Peru South Korea Argentina Mexico Nigeria Russia China India Brazil 0.440 [34] 0.052 [61] 0.020 [49] MID Moldova Serbia Bosni a and Herzegovina Canada Iceland Ukraine Macedonia Norway Switzerland South Africa 0.077 [31] 0.245 [35] 0.006 [69] LOW Albania Slovakia Chile Japan France Malta Latvia Turkey Sweden USA 0.015 [59] 0.154 [47] 0.020 [49] LOW MI D HIGH HAPPINESS happiness and exports per capita are measured as year to year change Legend the tercile that each country is placed in is determined by the most recent year where paired happiness and tariff data are available Pearson r [df] p < .1 p < .05 data are based on each country/year with paired data between variables p < .1

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67 Table 6.11 Happiness and Exports per capita (constant 2000 US$) Controlled for Economic Protectionism: Regression Coefficient and Standard Error ECONOMIC PROTECTIONISM HIGH Belarus Peru South Korea Argentina Mexico Nigeria Russia China India Brazil 0.249 ( 0.643 ) 0.011 ( 0.428 ) 0.002 ( 0.300 ) MID Moldova Serbia Bosnia and Herzegovina Canada Iceland Ukraine Macedonia Norway Switzerland Sou th Africa 0.230 ( 1.074 ) 0.193 ( 0.421 ) 0.000 ( 0.293 ) LOW Albania Slovakia Chile Japan France Malta Latvia Turkey Sweden USA 0.009 ( 1.412 ) 0.029 ( 0.774 ) 0.002 ( 0.300 ) LOW MID HIGH HAPPINESS exports per capita are measured in hundre ds of US dollars (constant 2000) Legend slope values represent average change in happiness relative to an increase of 100 US dollars (constant 2000) Slope (Standard Error) both happiness and exports are measured as year to year change the terc ile that each country is placed in is determined by the most recent year where paired happiness and tariff data are available data are based on each country/year with paired data between variables To identity general trends for happiness and ex ports though possibly due to chance, it is seen correlation between exports and happiness begins to weaken. Likewise, table 6.11 shows that this is also true for the average change in leve ls of happiness relative to exports. In other words, the relationship between exports and happiness is strongest among relatively unhappy countries with relatively high levels of economic protectionism. Although this trend is not perfectly clear in the d ata, in a way this actually runs counter to hypothesis two. In addition to hypothes is two not being supported by the data, there is also no clear trend to demonstrate something else happening. What this indicates is that a mbiguity is found when determini ng whether a influence on the relationship between happiness and exports.

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68 Industrialization Included as a proxy measure of industrialization, the analysis for electricity production does not support hypothesis two. Seen in tables 6.12 and 6.13 below the sectors for electricity pro duction per capita are somewhat similar to those seen for ex ports per capita. There is one significant relationship in the low happiness and high pro tectionism sector which includes Belarus, Peru, and Russia. This relationship is significant and positive Compared to the cumulative average of all sample countries this sector is far different as the variables are much more closely related. Not only do the other sectors have r values that are not close to being significant, but they also have regression coefficients that are much smaller than seen in the sector of Belarus, Peru, and Russia. Although hypothesis two is not clearly supported by the data, the data somewhat indicate the opposite of what hypothesis two predicts. The countries with economies t hat are the most liberalized display the weakest relationships between variables. Table 6.12 Happiness and Electricity Production per capita (kw/h) Controlled for Economic Protectionism: Pearson r Correlation Coef ficient ECONOMIC PROTECTIONISM HIGH Belarus Peru South Korea Argentina Mexico Nigeria Russia China India Brazil 0.529 [34] 0.076 [61] 0.021 [38] MID Moldova Serbia Bosnia and Herzegovina Canada Iceland Ukraine Macedonia Norway Switzerlan d South Africa 0.056 [33] 0.249 [35] 0.009 [69] LOW Albania Slovakia Chile Japan France Malta Latvia Turkey Sweden USA 0.123 [62] 0.100 [47] 0.005 [49] LOW MID HIGH HAPPINESS happiness and electricity production per capita are measured as year to year change Legend the tercile that each country is placed in is determined by the most recent year where paired happiness and tariff data are available Pearson r [df] p < .1 p < .05 data are based on each country/year with paired data between variables p < .1

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69 Table 6.13 Happiness and Electricity Production per capita (kw/h) Controlled for Economic Protectionism: Regression Coefficient and Standard Error ECONOMIC PROTECTIONI SM HIGH Belarus Peru South Korea Argentina Mexico Nigeria Russia China India Brazil 0.169 (0.608) 0.017 (0.427) 0.044 (1.097) MID Moldova Serbia Bosnia and Herzegovina Canada Iceland Ukraine Macedonia Norway Switzerland South Africa 0.038 (1.046) 0.051 (0.421) 0.000 (0.293) LOW Albania Slovakia Chile Japan France Malta Latvia Turkey Sweden USA 0.063 (1.405) 0.012 (0.779) 0.001 (0.300) LOW MID HIGH HAPPINESS electricity production per capita is measured in hu ndreds of kilowatts per hour Legend slope values represent average change in happiness relative to an increase of 100 kilowatts per hour Slope (Standard Error) happiness and electricity production are measured as year to year change the tercil e that each country is placed in is determined by the most recent year where paired happiness and tariff data are available data are based on each country/year with paired data between variables Seen in table 6.13 above e ven if statistical sig nificance is set aside and only the regression coefficients are examined, the sector with Belarus, Peru, and Russia is much higher than the others. It is also much higher than the cumulative average of al l countries Although this certain sector standing out is noteworthy, the general trend across the data is not clear. In some ways the trend across the data indicates the opposite of hypothe sis two, but this is neither certain nor clear. Since the hypotheses are not supported and no general trend is fou nd, the explanation for this correlation most likely lies outside the bounds of economic protectionism. In other words, the data do not indicate that the most economically liberal countries have the strongest relationship between happiness and industriali zation. Due the identified vagueness the reason for the instances of significant correlations lies outside the bounds of economic protectionism.

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70 Globalization Included as a proxy measure for g lobalization, the analysis of happiness and internet use als o does not support hypothesis two. Table 6.14 below shows that significant relationships between happiness and internet us e are found in one sector Within this sector, there is a s ignificant negative relationship in the middle happiness and middle prote ctionism sector which includes Bosnia and Herzegovina, Macedonia, and South Africa. The relationship in this sector is not very different than the cumulative average of all countries when examining r but the regression coefficient is much less. Setting statistical significance aside, table 6.15 on the following page shows that t he regression coefficient for Belarus, Peru, and Russia is considerably higher than those in the other sectors It is also higher than that of the cumulative average for all samp le countries. Table 6.14 Happiness and Internet Users (% of population) Controlled for Economic Protectionism: Pearson r Correlation Coefficient ECONOMIC PROTECTIONISM HIGH Belarus Peru South Korea Argentina Mexico Nigeria Russia China India Braz il 0.241 [34] 0.150 [61] 0.087 [38] MID Moldova Serbia Bosnia and Herzegovina Canada Iceland Ukraine Macedonia Norway Switzerland South Africa 0.129 [26] 0.303 [35] 0.041 [69] LOW Albania Slovakia Chile Japan France Malta Lat via Turkey Sweden USA 0.061 [62] 0.113 [47] 0.147 [49] LOW MID HIGH HAPPINESS happiness and internet users (% of pop.) are measured as year to year change Legend the tercile that each country is placed in is determined by the most re cent year where paired happiness and tariff data are available Pearson r [df] p < .1 p < .05 data are based on each country/year with paired data between variables p < .1

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71 Similar to exp orts and elec tricity production, there is no obvious trend found across the sectors When look ing at regression coefficients, the results are mostly uniform across sectors where the relationship between internet use and happiness is mild ly negative and st atistically insignificant The only exception to this is in the upper left sector which contains Belarus, Peru, and Russia. Because of this, there is insufficient evidence to support hypothesis two Hypothesis two is not supported by the data and the ex planations for the significant relationships above are likely not to include elem ent of economic protectionism plays an influencing role in the relationship between happiness and globalization. Table 6.15 Happiness and Internet Users (% of population) Controlled for Economic Protectionism: Regression Coefficient and Standard Error ECONOMIC PROTECTIONISM HIGH Belarus Peru South Korea Argentina Mexico Nigeria Russia China India Brazil 0.119 (0.695) 0.017 (0.423) 0.052 (1.093) MID Moldova Serbia Bos nia and Herzegovina Canada Iceland Ukraine Macedonia Norway Switzerland South Africa 0.080 (1.070) 0.038 (0.414) 0.002 (0.293) LOW Albania Slovakia Chile Japan France Malta Latvia Turkey Sweden USA 0.019 (1.413) 0.019 (0.778) 0.012 (0.296) LOW MID HIGH HAPPINESS happiness and internet users (% of pop.) are measured as year to year change Legend slope values repres ent average change in happiness relative to an increase in internet use of one percentage point Slope (Standard Error) the tercile that each country is placed in is determined by the most recent year where paired happiness and tariff data are available data are based on each country/year with paired data between variables

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72 Income i nequality. Table 6.16 shows that there are numerous significant relationships found between happiness and income inequality. Considering that the cumulative analysis of all sample countries yielded a positive and st atistically significant correlation this is not surprising. What these positive relationships indicate is that increases in happiness are correlated with increases in income inequality. This runs counter to hypothesis three which predicted that income i nequality would be negatively correlated with happiness in all sample countries and all levels of economic protectionism. In three of the four sectors in table 6.16 which contain BRICS countries, positive relationships are observed. The only BRICS sector with a negative correlation is the high happiness and high protectionism sector which contains Brazil. Out of the three hypotheses presented in this thesis hypothesis three is least supported by the data. Tab le 6.16 Happiness and Income Inequality (gini coefficient) Controlled for Economic Protectionism: Pearson r Correlation Coefficient ECONOMIC PROTECTIONISM HIGH Belarus Peru South Korea Argentina Mexico Nigeria Russia China India Brazil 0.745 [21] 0.733 [23] 0.376 [20] MID Moldov a Serbia Bosnia and Herzegovina Canada Iceland Ukraine Macedonia Norway Switzerland South Africa 0.706 [25] 0.546 [13] 0.874 [1] LOW Albania Slovakia Chile Japan France Malta Latvia Turkey Sweden USA 0.693 [25] 0.592 [8] insuffic ient data LOW MID HIGH HAPPINESS the tercile that each country is placed in is determined by the most recent year where paired happiness and tariff data are available Legend Pearson r [df] p < .1 p < .05 data are based on each country/y ear with paired data between variables p < .1

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73 The relationship between happiness and income inequality is also considerably ambiguous when searchi ng for tends across the data when setting statistical significance aside Despite the large number of significant relationships, there is no clear trend that occurs between economic protectionism and happiness when examining income inequality. Both posit ive and negative correlations are scattered as the regression coefficients and correlation coefficients vary considerably Although the cumulative average of all sample countries indicates that positive correlation s are most prevalent there are no trends observed across the sectors of tables 6.16 and 6.17 What this indicates is that the influence of economic protectionism on the relationship between happiness and income inequality is unclear. Because of these inconsistencie s, hypothesis three is not su pported by the data and the reasons for the large number of positive correlations must be something else that this chapter has not considered Table 6.17 Happiness and Internet Users (% of population) Controlled for Economic Protectionism: Regression Coefficient and Standard Error ECONOMIC PROTECTIONISM HIGH Belarus Peru South Korea Argentina M exico Nigeria Russia China India Brazil 0.378 (3.559) 0.230 (1.630) 0.314 (4.401) MID Moldova Serbia Bosnia and Herzegovina Canada Iceland Ukraine Macedonia Norway Switzerland South Africa 0.673 (2.714) 0.122 (2.284) 0.323 ( 1.084 ) LOW Albania Slovakia Chile Japan France Malta Latvia Turkey Sweden USA 0.602 (3.507) 0.058 (1.047) insufficient data LOW MID HIGH HAPPINESS the tercile that each country is placed in is determined by the most recent year where pa ired happiness and tariff data are available Legend Slope (Standard Error) data are based on each country/year with paired data between variables

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74 Summary of Quantitative Findings The quantitative findings are largely inconclusive. At the state level, it was argued that the data indicate a mild relationships in support of hypotheses one that is difficult to ignore despite the overall lack of statistical significance. When controlling for economic protectionism, hypotheses two was not supported by the data fo r any of the independent variables Moreover, hypothesis three was not supported by the data at both the state and systemic levels. In sum, economic aspects of neoliberalism are positively correlated with happiness in some cases at the state level, but by no means all cases. This is particularly policies are. At the systemic level, the data indicate that countries with feverish neoliberal economic policies consistently have a weaker relationship between neoliberal globalization and happiness than countries with less emphasis on free markets. What these findings do not indicate is how neoliberalism compares to other paths of development in furthering happiness. In other words, cases above where variables were unrelated or negatively correlated do not indicate that another path of development would necessarily increase or decrease happiness more To examine this would require a completely different set of variables and me thods Despite the mostly inconclusive findings, happiness and neoliberal globalization are discussed below in search of explanations for these findings.

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75 CHAPTER VII CULTURAL ANALYSIS HAPPINESS IN THE BRICS Seen in the previous chapter, the quantit ative analysis yielded results that were mostly inconclusive. As the hypotheses chapter argued prior to presenting the hypotheses themselves there was good reason to assume that neoliberal globalization would be positively related with happiness as achie vement and pleasure. This would especially be assumed for the BRICS due to the fact that the path of neoliberal economic development has fueled econ omic growth considerably post 1990 11). Aside from Russia, this was not supported by the data. T his could be due to two key factors which complicate and blur the relationships between happiness and neoliberal globalization: 1) the chosen independent variables do not adequately measure neoliberal globalization or 2) pleasure and achievement do not uni versally increase happiness equally across societies Since the validity of the independent variables has been defended as adequate measures of neoliberal globalization in previous chapters, this chapter uses ethnography to dissect what happiness means in the various cultural contexts of each BRICS country. eudaimonia is also discussed in search for reasons why the variables are not always related. If neoliberal globalization stresses pleasure and achieve more closely reflects eudaimonia for example, perhaps this is a reason behind the sporadic results of the previous chapter.

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76 To briefly return to the differing arguments between Veenhoven and Abdel Khalek which center on how individuals assess their happiness the results from the previous chapter neither support nor challenge the arguments of both scholars (Abdel Khalek 2006; Veenhoven 2009) This is because both scholars make arguments about how individu als assess their happiness, but not what makes individuals happier two very different concepts. For example, Veenhoven argues that it is human nature for individuals to assess happiness based on their perceived levels of pleasure and achievement, but wh at actually makes individuals happier is much less known and varies between individuals (Veenhoven 2009). This argument is challenged by the results found here, howev er. Since a time series was per formed, for example, it would be assumed that if the ple asures and achievements induced by neoliberal globalization were experienced over the 17 years examined, happiness would be significantly positively related with neoliberal globalization Even if individuals are dreadful judges of what will increase their happiness in the future but experience the pleasures and achievements of neoliberal globalization, hypothetically, an assessment of happiness 17 years later would yield results that mirror the change in neoliberal globalization. This was not supported by t he data for most of the BRICS Different than Veenhoven, Abdel Khalek argues that happiness is assessed in a culturally determined context (Abdel Khalek 2006; Veenhoven 2009). Unfortunately, Abdel Khalek does not examine what actually makes individual s happier (2006). Due this uncertainty, this chapter argues that the different cultural notions of happiness within each BRICS country are potential reasons behind the results of the previous chapter. If

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77 happiness is something other than pleasure/achieve ment and disjoint from neoliberal globalization, in other words, it would be expected that the variables would be most closely associated in places where pleasure and achievement are part of the cultural notion of happiness. Some of the BRICS have cultura l notions of happiness that more closely reflect achievement and pleasure while other s have notions that more closely eudaimonia but none of these Western notions of happiness fit neatly with what happiness is on the ground in the B RICS. eudaimonia or happiness as flourishing, is analyzed alongside the notions of happiness in various BRICS countries Since the results in the above section were mostly inconclusive, this is done to q uestion whether happiness as something other than pleasure/achievement can explain the lack of significant relationships found in the previous chapter eudaimonia does not neverthe less discussed in the cultural analyses to follow in efforts to exhaust the three notions of happiness used here as much as possible. In order to examine the relationships between variables further, ethnography is used in this chapter in efforts to bring clarity to the ambiguities found in the quantitative analysis. In doing this, the variables above are examined in relation to each BRICS Seen below, the mixture of ethnography and quantitative methods can be illum inating for some relationships between variables. In other cases, however, the relationship between variables continues to be unclear and ambiguous. Overall, t his chapter argues that the inconclusive results of the previous chapter are due to notions of happiness being determined in culturally based contexts In

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78 other words culture operates as a lurking variable that determines the extent, if any, that neoliberal globalization has on happiness. Before continuing, however, it is necessary to define what is meant my culture and how this relates with happiness. Defining Culture Despite arguing that culture plays a large role in influencing the velocity of economic growth, Forson Janrattanagul and Carsamer argue that culture is a definitionally proble matic ts relativity and ambiguity af fected by contextual factors is difficult to objectify and assess (2013, 288). hat is perceived as culture in one locality or region might not be applicable to another ( Forson Janrattan agul and Carsamer 2013, 288). Since culture exists and is defined in cultural contexts, definitions of culture are limited to that culture. T herefore, the validity of an outside definition is limited. In the introduction to their edited collection of h appiness in non W estern societies, a source of much information on cultural happiness used here, Selin and Davey also argue that happiness exists within cultural contexts and go to great lengths to clarify that the authors in their edited collection of art ic les have spent a considerable am ou n t of their lives living in these cultures (2012). In making the argument that happiness can be validly summarized ethnographically, they also clarify that no notion of happiness is able to be universalized and the act of defining culture experiences the same fate (Selin and Davey 2012). Ultimately, Selin and Davey do not provide a single definition of culture in the same way that they do not for happiness. By arguing that happiness exists in a cultural context and var ies across place, in other words, the act of defining culture would

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79 undermine the arguments of all the articles which provide cultural notions of what happiness is. Due to the dense cloud of ambiguity surrounding the term, definitions of culture in the lit erature are typically quite vague if provided at all. Inevitably, however, culture is a term that must be defined for the purposes of this thesis because the term can mean numerous things depending on the context in which it is used For example the t erm can refer to pop culture phenomena such as the visual arts, music, and fashion or it can refer to aspects of organization al culture such as common practices, procedures, and forms of communication (Scott and Marshall 2009). Given these considerable o bstacles to providing a definition of culture that is applicable here, culture is defined and operationalized as the following. Culture is through which meaning is a collection o f [emotions,] ideas and symbols (Scott and Marshall 2009, 152). Culture affects how societies perceive the past and present, and also how the future is idealize d to be Stated differently, culture in this thesis is defined as a lens through which societ ies experience the world, react to the world, and plan for the future. This notion of culture is operationalized with happiness in the following ways. Although culture is not necessarily confined to one physical space and can often overlap with types o f culture other than the one used here, the cultural notions of happiness in the BRICS are all at the state level. This is because 1) cultural notions of happiness in the literature are usually focused at the state level and 2) the data for all variables used here are also measured at the state level. Despite this limitation, cultural notions of happiness in the BRICS vary and do not fit neatly with the three philosophical notions of happiness

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80 used here: pleasure, achievement, and eudaimonia For some of the BRICS countries, notions of happiness closely reflect happiness as pleasure and achievement. This could explain why happiness is positively correlated with neoliberal economic development and globalization. In other BRICS countries, however, happines s is something other than pleasure and achievement. This could explain why happiness was either negatively correlated or unrelated with neoliberal economic development and globalization. What this chapter ultimately identifies is that the reason why ther e are sporadic instances of significant relationships in the previous chapter is because happiness is not a uniform concept across cultures. Brazil: Happiness as Envisioning the Ideal Joyous Life Islam connects the Brazilian notion of happiness with its carnival c ulture (2012). Happiness is determined temporally across time in relation to happiness levels of the past (Islam 2012). It is also oriented towards the future, however, where h appiness in Brazil 2012, 235). Thus, happiness is both forward looking and backward looking. At the connection is neither neat nor perfect s achievement notion focuses on realized rather than potential events (2008). Instead, everyday life and the ideal life are of finding spaces outside of daily strug and joy is therefore felt (Islam 2012, 235). In other words, happiness is

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81 s therefore, it is quite unlikely that the significant relationships in the previous chapter are part of a wider causal relationship (2012). Figure 7.1 Brazil: Time Series Graphs 0 500 1,000 1,500 40 50 60 70 80 1991 1993 1995 1997 1999 2001 2003 2005 Exports per capita Happiness Exports Happiness Exports per capita (constant 2000 US$) 0 2,000 4,000 6,000 8,000 40 50 60 70 80 1991 1993 1995 1997 1999 2001 2003 2005 Electricity Production per capita Happiness Electricity Production Happiness Electricity Production per capita (kw/h) 0 10 20 30 40 50 60 70 80 1991 1993 1995 1997 1999 2001 2003 2005 Internet Users (% of population) Happiness Internet Use Happiness Internet Users (% of population)

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82 Figure 7.1 cont. Brazil: Time Series Graphs S een above in the graphs of figure 7.1 all variables of economic development and globalization increased throughout the time period being studied while income inequality decreased. At the same time, happiness increased continuously without any decreases Since happin ess is significantly positively related with exports and internet use the question remains as to whether or not these variables have made escaping the everyday and envisioning the ideal more feasible. It could be argued that exports and internet use have aided in economic development and made s ubsistence and achievement more feasible but high level of income inequality challenges this argument due to the unequal distribution of benefits. As the data here and various s cholars indicate, inequality in Brazil is very high by global standards 20 yet the country maintains and takes pride in its overall feeling of joy that is not necessa rily material based (Islam 2012 ). I ncome inequality influences self perceived social class in Brazil and is also a large determinant of SWB but not happiness (Islam, Willis Herrera, and Hamilton 2009; Wilkinson and Pickett 2011). While Islam argues that Brazil is still a largely collectivist culture and Dunker argues that the 20 See table 6 on page 27 fo r z score ranking of gini index scores for sample countries. 25 35 45 55 65 75 40 50 60 70 80 1991 1993 1995 1997 1999 2001 2003 2005 Gini Coefficient Happiness Income Inequality Happiness Gini Coefficient

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83 abolishment of Br azilian authoritarianism in favor of democracy is indicative of a shift in the direction of individualism, Brazil currently occupies a middle ground between individualism and collectivism (Dunker 2008; Islam 2012). The shift towards individualism could be explained to increase happiness as motivations shift from extrinsic to intrinsic, therefore encouraging individual creativity in envisioning the ideal life, but this does not explain the association of happiness with joy in the Brazilian context by escapi ng the everyday (Ahuvia 2002 ; Zha et al. 2006 ). notion of instrumental goods and the happiness notion of achievement/pleasure used here the reasons why happiness increased in Brazil can at best only be partially explained because Davis and Annas neglect the concept of joy while the Aristotelian conception does not (Annas 2008; Aristotle 2008; Davis 2008 ; Irvine 2008 ) The Aristotelian conception of eudaimonia is associated wi th flourishing a concept more similar to joy (Irvine 2008) If happiness in Brazil is the process of envisioning the ideal life, this coincides with itself and never for the sake envisioning the ideal life achieves little that is useful for something else, yet still excellence is less clear, ho wever (Aristotle 2008). On one hand, Aristotle argues that happiness results from action not simply envisioning action (2008). Interpreted less literally, envisioning the ideal life is similar to 22). Despite

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84 the ambiguities found when drawing similarities between eudaimonia and Brazilian happiness it is still seen that happiness is something other than achievement and pleasure Therefore, the significant correlations for Brazil that were identified in the previous chapter may be related more by chance than causality. China: Happiness as the Mental at Davey argues that happiness in China is primarily based on the increases in quality of life (QOL) that have resulted due to its recent success in economic development (2012). Similar to Brazil happiness in China is a lso temporal and based on comparisons to the past, but in Brazil this is compared to happiness in the past while in China it is com pared to QOL in the past and its recent economic achievements (Davey 2012; Islam 2012). Supported by Deng Xiaoping orations 1970s urban China has become tied with consumer culture where each furthers the other, but still retains elements of collectivism that are stronger than seen in Brazil (Isl am 2012; Davey 2012 59 ). Even among the rural poor who have gained less materially from economic development, the economic achievements of China still provide happiness based on a sense of national pride (Davey 2012). Thus, happiness in both rural and u rb an China is based on pleasure and achievement, but through quite different ways.

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85 Figure 7.2 China: Time Series Graphs 0 500 1,000 1,500 40 50 60 70 80 1990 1992 1994 1996 1998 2000 2002 2004 2006 Exports per capita Happiness Exports Happiness Exports per capita (constant 2000 US$) 0 2,000 4,000 6,000 8,000 40 50 60 70 80 1990 1992 1994 1996 1998 2000 2002 2004 2006 Electricity Production per capita Happiness Electricity Production Happiness Electricity Production per capita (kw/h) 0 10 20 30 40 50 60 70 80 1990 1992 1994 1996 1998 2000 2002 2004 2006 Internet Users (% of population) Happiness Internet Use Happiness Internet Users (% of population) 25 35 45 55 65 75 40 50 60 70 80 1990 1992 1994 1996 1998 2000 2002 2004 2006 Gini Coefficient Happiness Income Inequality Happiness Gini Coefficient

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86 As s een above in figure 7.2 to significant increases in exp orts and electricity prod uction while raising the feasibility of subsistence for some as they gained income to support happiness based on material consumption (Davey 2012) Despite the fact that income inequality in China has risen significantly over the past two decades and the majority of its population continues to be quite poor by global standards, post 680m people out [of] poverty 21 more 1 ). This has been achieved by using neoliberal tr ade policies with foreign actors and joining the WTO in 2001, while at the same time maintaining an internal economy that is more state controlled than that of economic model is quite unique by global standards induced materialism and national pride, this notion most closely reflects the happiness as pleasure/achievement notion outlined in this thesis (2012). Problemat ically, however, if this were entire ly true it would be expected that happiness in China would experience growth in line with its economic achievements and statistically significant correlations to have been found (Knight and Gunatilaka 2011). As seen in the above graphs, this is not supported by the data as happiness in China remained mostly stagnant throughout the time period being studied. Along with the profound growth of the Chinese economy has also come a wealth of social and environmental issues su ch as rising income inequality, work related migration splitting familial structures, decreasing levels of physical/mental health, and 21 Poverty is defi ned as living on less than 1.25 US dollars per day the cut off point for absolute poverty (The Economist 2013).

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87 rapidly deteriorating environmental conditions (Davey 2012; Phillon 2007). In other words, as economic development incre ases QOL by making subsistence more feasible, social and environmental issues counterbalance this by causing happiness to remain virtually stagnant (Davey 2012). This can be argued to not only stifle happiness as achievement/pleasure, but also happiness a s a eudaimonic state of flourishing. Although centered on the pursuits of the group in rural areas and the pursuits of both the group and the individual in urban areas, both rural and urban China associate happiness with achievement and pleasure althoug h in quite different ways (Davey 2012) Despite the slight urban rural differences, for example, happiness in China is mostly hedonic and related to achievement and pleasure (Davey, Zhenghui and Lau 2009; Knight and Gunatilaka 2011). For rural Chinese w here societies are highly collectivist, pleasure is gained from subsistence and inclusion in familial and societal groups while achievement is determined by the success of the collective group (Davey, Zhenghui and Lau 2009). In urban areas where collecti vism is slowly giving way to individualism, the exact determinants of happiness are centered on personal pleasures and group achievements (Knight and Ganatilaka 2010). Therefore, both urban and rural happiness is derived from achievement and pleasure desp ite maintaining varying balances between individualism and collectivism. Given that achievement and pleasure are used for the sake of something else, happiness in China does not neatly fit into the Aristotelian notion of happiness (Aristotle 2008). Sinc e achievements of the group are desired for the sake of subsistence while the pleasures of materialism are desired for the sake of subsistence and status, arguably, happiness in China is not entirely eudaimonic in the Aristotelian sense (2008) Although

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88 h appiness is largely based on pleasure and achie ve ment this would indicate that happiness would rise in relation to economic development as pleasure and achievement increase but the social problems furthered by development and globalization counterbalance this therefore leading to stagnant happiness (Davey 2012; Phillon 2007). India: Happiness as Pleasantness through Social Capital happiness and SWB are roughly average (Bisw as Deiner, Tay, and Deiner 2012). Although developing economically at an alarming rate, happiness in India is largely unconnected with economic factors (Biswas Deiner, Tay, and Deiner 2012). Instead happiness is determined by a triad relationship betwe en 1) social capital 22 2) Deiner, Tay, and Deiner 2012, 21). With similarities between urban and rural, elements of social capital are relatively high in India and are argued to s upport its col lectivist culture (Biswas Deiner, Tay, and Deiner 2012). C emotions which are used to determine happiness alongside peers (Biswas Deiner, Tay, and Deiner 2012, 20). Biswas Deiner, Tay, and Dein er present the notion of negotiable fate in Indian culture which is rooted in the concept of karma (2012). Within this, one Deiner, Tay and Deiner 2012, 21). 22 Social capital is heavily based on high levels of trust wher e the capital needed to acquire goods and services is not always monetary. Fueled by collectivism, social networks are beneficial for acquiring necessities through non monetary means. In some ways, social capital in India has benefitted reducing absolute poverty (Morris 1998).

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89 Figure 7.3 India: Time Series Graphs 0 500 1,000 1,500 40 50 60 70 80 1990 1992 1994 1996 1998 2000 2002 2004 2006 Exports per capita Happiness Exports Happiness Exports per capita (constant 2000 US$) 0 2,000 4,000 6,000 8,000 40 50 60 70 80 1990 1992 1994 1996 1998 2000 2002 2004 2006 Electricity Production per capita Happiness Electricity Production Happiness Electricity Production per capita (kw/h) 0 10 20 30 40 50 60 70 80 1990 1992 1994 1996 1998 2000 2002 2004 2006 Internet Users (% of population) Happiness Internet Use Happiness Internet Users (% of population) 25 35 45 55 65 75 40 50 60 70 80 1990 1992 1994 1996 1998 2000 2002 2004 2006 Gini Coefficient Happiness Income Inequality Happiness Gini Coefficient

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90 As seen in figure 7.3 dwarfed by the other BRICS, yet India maintains a fairly stable level of happiness very similar to Chi na throughout the time period being studied. Moreover, income inequality in India is the lowest of all the BRICS and rises only slightly throughout the study. Although happiness in India and China follows the same trend, what it is based on is quite diff erent between the two. Biswas Deiner, Tay, and Deiner distinguish between individualistic outside in happiness and collectivist inside out happiness which is prevalent in India (2012). Inside physical envir onment than it is Deiner, Tay, and Deiner 2012, 21). In other words, success in economic development and more feasible subsistence may indeed pro mote being happy as achievement and p leasure, but happiness is a more robust concept that is inside out and based on collective social capital rather than outside in and based on financial capital. This does not indicate that achievement and pleasure contribute nothing towards happiness, but these notions are insufficient to explain why happiness is not connected with neoliberal globalization. The notion of negotiable fate in relation to social capital is particularly complex eudaimonia (Aristotle 2008; Au et al. 2012). Rather than settling for existence, life of excellence (2008, 21 22). In many ways iculties yet strives for the best possible outcome within a certain set of limitations (Au et al. 2012). However, whether

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91 (2008). Accepting limitations and operati ng through negotiable fate could be considered reasonable for example but could also be the refusal to strive for excellence. importance of social capital as a tool for th riving is much less considered (2008). Not only is social capital a great contributor to happiness in India specifically, but is also as a mean lying between the vices of excess and defect (Aristotle 2008; Biswas Deiner, Tay, and Deiner 2012). This clarifies the ambiguities of the previous paragraph to some extent because negotiable fate is neither of environment (Au et al. 2012). Although Aristotle discusses virtue in various areas of life, ambiguity remains because the mean between defect and excess depends largely on which action the believer in negotiable fate is doing. Generally speaking, however, negotiable fate does discourage the path of defect, but without more information it is unknown if negotiable fate avoids excess. Since h appiness in India is therefore something other than economic development alone can provide through pleasure and achievement the possibility for hypothesis one to be supported by the data is diminished. Russia: Happiness as the Absence of Market Volatility low level of happiness on a global scale and argues that happiness is tied to e conomic factors 23 (Graham and Pettiano 2002; Veenhoven 2001; Zavisca and Hout 2005) Seen in chapter six this is also supported by the data as Russia and its more protectionist peers displayed the majority of 23 See table 2 on page 25 for z score ranking of happiness in the sample countries.

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92 significant relationships Happiness is not t ied to income alone, but rather the sense of financial s ecurity that income can provide (Zavisca and Hout 2005). Happiness in Russia is not necessary tied to consumer ism in the same ways that it is in urban China, however. Instead, income operates as a b uffer against unhappiness caused by market volatility (Zavisca and Hout 2005). This mirrors North America and Western Europe (2003). Nevertheless, Zavisca and Hout argue that money can buy happiness in Rus sia up to a point that is higher than many other countries (2005). Considering these arguments, any type of development could act as a buffer against market volatility and neoliberal globalization may be one way to do this. Figure 7.4 Russia: Time S eries Graphs 0 500 1,000 40 50 60 70 80 1990 1992 1994 1996 1998 2000 2002 2004 2006 Exports per capita Happiness Exports Happiness Exports per capita (constant 2000 US$) 0 2,000 4,000 6,000 8,000 40 50 60 70 80 1990 1992 1994 1996 1998 2000 2002 2004 2006 Electricity Production per capita Happiness Electricity Production Happiness Electricity Production per capita (kw/h)

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93 Figure 7.4 cont. Russia: Time Series Graphs Given that statistically significant relationships were found for exports, electricity production, and internet use, it i s seen in the graphs of figure 7.4 that movement in happiness mirr ors movement in the independent variables across time income inequality excluded P articularly for exports and electricity production the transition trauma period following the collapse of the Soviet Union up until the 1998 crisis of the rouble currenc y is correlated with a decrease in happiness (Zavisca and Hout 2005). Within this period, the restructuring of glasnost and perestroika led to periods of unpredictability and volatility in the Russian market. Considering that happiness in Russia is parti cularly tied the stability of the market, this could be why happiness witnessed a similar trough (Zavisca and Hout 2005) 0 5 10 15 20 25 30 35 40 50 60 70 80 1990 1992 1994 1996 1998 2000 2002 2004 2006 Internet Users (% of population) Happiness Internet Use Happiness Internet Users (% of population) 25 35 45 55 65 75 40 50 60 70 80 1990 1992 1994 1996 1998 2000 2002 2004 2006 Gini Coefficient Happiness Income Inequality Happiness Gini Coefficient

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94 Happiness in Russia is more easily connected with the notions of happiness as achievement and pleasure. This does not mean that ha ppiness is not something else, but rather that it does not coincide with eudaimonia Since happiness is tied to the market, the absence of market volatility furthers pleasure as market stability maximizes pleasure by reducing pain. Happiness in Russia is more difficult to attach to achievement, but can be done so in one way. Although the absence of market volatility cannot create achievement in and of itself, it can enable individuals to get ahead and form a financial buffer against future in stances of shakiness in the market. Due to fact that the vast majority of significant relationships in the quantitative analysis were seen with Russia, for example it makes sense to connect happiness with the market conditions associated with neoliberal economic development. South Africa: Happiness as Hope and Pride Despite income inequality being even higher than in Brazil, South Africa is overall one of the happiest BRICS countries Several scholars note that SWB/QOL and happiness are not entirely co nnected in South Africa (Bookwalter 2012 ; M ller 2007). For example, lower class South Africans rate their SWB and QO L quite low due to ostracization yet are still quite happy as survey data indicate (Bookwalter 2012; M ller Dickow, and Harris 1999). Differences in h appiness, SWB, and QOL during apartheid were reflected in racial inequalities where black South Africans typically reported low levels an d white South Africans reported high levels for all three categories (Bookwalter 2012). Post apartheid South Africa is divided more by class than race, but critically, black South Africans continue to be predominantly lower class with lower SWB/QOL

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95 while white South Africans are predominantly upper class and have higher SWB/QOL (Bookwalter 2012). However, this division is not reflected in post apartheid happiness as all South Africans are overall quite happy (Fitch Fleischmann, Bookwalter, and Dalenberg 2011). Figure 7.5 South Africa: Time Series Graphs 0 500 1,000 40 50 60 70 80 1990 1992 1994 1996 1998 2000 2002 2004 2006 Exports per capita Happiness Exports Happiness Exports per capita (constant 2000 US$) 0 2,000 4,000 6,000 8,000 40 50 60 70 80 1990 1992 1994 1996 1998 2000 2002 2004 2006 Electricity Production per capita Happiness Electricity Production Happiness Electricity Production per capita (kw/h) 0 5 10 15 20 25 30 35 40 50 60 70 80 1990 1992 1994 1996 1998 2000 2002 2004 2006 Internet Users (% of population) Happiness Internet Use Happiness Internet Users (% of population)

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96 Figure 7.5 cont. South Africa: Time Series Graphs Seen in the graphs of figure 7 .5 and argued by several scholars, p ost apartheid South Africa witnessed rises in happiness due to increases in hope and national pride for all classes and race s, but these hopes soon dwindled and happiness began to p lateau around 2002 (Bookwalter 2012; M ller Dickow, and Harris 1999). Bookwalter highlights the blurriness between happiness and QOL/ SWB by suggesting three possible explanations: 1) psychological factors, 2) changes in the ways people live, or 3) a combination of both (2012, 341). Regarding hope, Thin makes this argument more generally and states that if hope and expectations for a better future are not met, happiness eventually begins to plateau or decline (2013). Whether or not this is the re ason, a plateau can be seen in the graphs above As seen in chapter six the data for South Africa indicate virtually no relationship between happiness and neoliberal globalization One explanation for this is the way that SWB/QOL is not connected to happ iness L evels of SWB and QOL are unequally divided by class while happi ness remains quite high for all (Bookwalter 2012) Applying this to the notions of happiness used here, happiness in South Africa cannot be applied to 25 35 45 55 65 75 40 50 60 70 80 1990 1992 1994 1996 1998 2000 2002 2004 2006 Gini Coefficient Happiness Income Inequality Happiness Gini Coefficient

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97 p leasure (2008). S ince QOL is especially associated with hedonic aspects of well being on one hand, if happiness in South Africa was primarily associated with pleasure it would be expected that changes in happiness would closely reflect changes in SWB and QOL. On the other hand, happiness as achievement may be an aspect since the achievement of removing the apartheid regime witnessed a notable increase in happiness ( M ller Dickow, and Harris 1999). Happiness as achievement and happiness as eudaimonia bo th require action, but the element of hope in South Africa is not an action in itself ye t still provides a basis for happiness Since happiness is not related to pleasu re or achievement, this could explain why the variables a re virtually unrelated. Chap ter Summary As this chapt er has demonstrated and table 7.1 on the following page highlights, each BRICS country has a notion of happiness that is unique to its culture. Table 7.1 summarizes the various notions of happiness in the BRICS. In Brazil and Sou th Africa, happiness is optimistic and both backward looking and forward looking In China and Russia, happiness is backward looking and based more on how current conditions relate to conditions of the past. In Russia especially, happiness is correlated with neoliberal globalization due to its happiness including pleasure and achievement In India, finally, happiness is collectively determined and based more on current cond itions than it is influenced by macroeconomic factors.

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98 Table 7.1 Cultural Not ions of Happiness in the BRICS Country Scope Determinants Notes Brazil (Islam 2012) past / future joy; imagination relative to happiness in the past China (Davey 2012) past development success; living standards; national pride relative to QOL in the past India ( Biswas Deiner, Tay, and Deiner 2012) present social capital; negotiable fate influenced by collectivism Russia (Zavisca and Hout 2005) past stability; predictability relative to QOL in the past; tied to market South Africa (Bookwalter 2012) past / future hope; national pride; optimism SWB/QOL not connected to happiness Since the cultural notions of happiness are not uniform across the BRICS, it makes sense to assume that this could be an explanation why happiness and neoliberal globa lization are not uniformly correlated across the BRICS. Culture may not be the only reason behind this, but is likely to be one key reason. On one hand, i f happiness is based on pleasure and achievement such as in Russia, this could explain w hy the variables are part of a long string of causal factors If happiness is something other than pleasure and achievement, on the other hand, this could explain why happiness and neoliberal globalization are negatively correlated or completely unrelated Stated differently the pleasure and achievement that neoliberal economic development seeks to create may further happiness more in cultures that value pleasure and achievement. In cultures with other values, however, this may not happen despite mainta ining an economy based on economic liberalism and participating in processes of globalization.

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99 CHAPTER VIII DISCUSSION As the previous chapter demonstrated, the reason behind the sporadic quantitative results is that culture operates as a lurking variable The Western notions of happiness such eudaimonia are not entirely applicable in the BRICS (Annas 2008; Aristotle 2008; Davis 2008). In search for explanations behind this, the international relat ions theory of postcolonialism is used in this chapter in efforts to being clarity to why this might be. Since postcolonial scholarship is often critical of the Washington Consensus and the implementation of the Western neoliberal path of development in t he non West, its explanatory ability is best centered on explaining why the variables in chapter six were either negatively related or virtually unrelated. Although the positive relationships are assumed to be explainable by neoliberalism since the the ch osen variables measure neoliberal economic development, the majority of relationships across all sample countries were statistically insignificant and followed no pattern across sample countries and levels of economic protectionism. Postcolonia lism is used in this chapter due to three main reasons: 1) its acknowledgement that there are cultural differences, 2) its ability to explain why culture shifts when interacting with other cultures, and 3) its ability to identify why paths of development experience di fficulty when attempting to be universalized. Although postcolonialism cannot be attached to any notions of happiness in the same way that neoliberal economic development was tied to pleasure and achievement, this is actually a benefit in this chapter due to cultural variation in happiness notions Unfortunately, the

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100 applicability of a postcolonial lens to income inequality is quite limited. In the analysis to follow, therefore, income inequality is first discussed on a more empirical level before the ot her variables are discussed exclusively through a postcolonial lens. Income Inequality Of all of the variables included in this thesis, arguably, culture plays the largest role in determining how income inequality affects happiness (Selin and Davey 2012) This is due to the balance between individualism and collectivism in a society. Within this, culture plays a large role in determining what individuals view as fair in the scale changes in income inequality over a short period of time can exacerbate tensions and negatively influence happiness, for example (Dowling and Yap 2013). This is not reflected in all of the data here as happiness and income inequality were positively correlated i n quite a few sample countries. This section argues that although the economic factors affected by income inequality affect happiness to a moderate extent, culture is the largest intervening factor influencing the relationship between happiness and income inequality. Wilkinson and Pickett argue that countries with greater income equality typically perform better economically and have higher levels of both physical and emotional well being (2011). They support this argument by forming and testing t heir psychosocial hypothesis which argues that income does not increase emotional well being in itself, but rather is a tool through which individuals assess their socioeconomic standing relative to their peers (Wilkinson and Pickett 2011). Within this ar gument: 1) inequality causes individuals to lack a sense of belonging, 2) a lack of belonging causes individuals to

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101 engage in activities that lead to poor physical and emotional health, and therefore, 3) the economy is ultimately harmed as individuals with poor psychosocial health are less able to contribute to economic growth 24 (Wilkinson and Pickett 2011). The relationship between inequality and happiness has also been influenced by neoliberal economic policies. A key part of neoliberal development polic ies encouraged by the IMF and the WB have been austerity programs and structural adjustment policies (SAPs). As a requirement of the IMF and WB on condition of receiving loans, SAPs require that planned economies move toward free market neoliberal economi es in efforts to expand development led by international trade and investment (Crisp and Kelly 1999; Easterly 2009). Whether poverty has been reduced more by SAPs than would have happened without their enactment is a contested subject among scholars, but it is much less disputed that SAPs have played a role in increasing inequality at both the state and systemic levels particularly for transition economies such as the BRICS (Easterly 2009, Rimashevskaia and Kislitsyna 2004). Given that all levels of e conomic protectionism used here contain states with varying levels of inequality, the data indicate that the influence of market liberalization on the relationship between happiness and inequality is difficult to determine. If happiness is considered to b e development led achievement and pleasure, increasing inequality would have a stifling effect on happiness as inequality stifles development due to the debilitating nature of psychosocial stress (Wilkinson and Pickett 2011). Depending on the absolute lev el of inequality in LEDCs particularly, a high rate of inequality would 24 Reasons for this include anxiety, depression, alcoholism, drug use, interpersonal violence, imprisonment, lower life expectancy, obesity, and lac k of social mobility all of which are typically higher in countries with high levels of income inequality (Wilkinson and Pickett 2011).

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102 also have a detrimental effect on the average happiness of the country as those without subsistence would have a lower level of happiness. If happiness is considered to be something o ther than achievement/pleasure, happiness is also negatively influenced by inequality due to the ways in which psychosocial stress can inhibit happiness (Wilkinson and Pickett 2011). Notably, however, this cannot explain why happiness and inequality would be positively correlated. Ahuvia addresses culture and ties increases in happiness with societal shifts from collectivism to individualism (2002). Since societies with an individualistic culture generally have higher rates of income inequality, this is an interesting finding that indicates that income inequality may not negatively influence happiness as much as previously thought (Loughnan et al. 2011). In more unequal and individualistic societies, the least well off individuals typically experience so they view themselves as average or better than average (Loughnan et al. 2011, 1254). correlated with inequality in the level of happ iness (2010). With income being representative of achievement and pleasure, therefore, happiness is much more complex than these concepts alone. Differently stated, shifts towards individualism may be an explanation behind the positive correlations betwe en happiness in income inequality among sample countries (Ahuvia 2002). From a postcolonial perspective, in other words, the socioeconomic standing of the self is based on a binary perception of the Other as inferior. Moving deeper, another explanation behind the positive correlations between happiness and inequality could be the shift from extrinsic to intrinsic motivation that

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103 typically occurs as countries develop economically (Ahuvia 2002). Ahuvia examines this conundrum and argues that materialism and individualism alone are not what leads countries to become happier as they develop economically (2002). If this were true, for example, we would expect all MEDCs to be extraordinarily happy and all LEDCs to be miserable a result that is not at all s upported by the data (Ahuvia 2002). Instead, Ahuvia argues that economic development causes identities to shift from the collective to typically results in a decline in social capital, Ahuvia clarifies, but the individualism and intrinsic motivation that result counterbalance this and lead happiness to increase (2002). Collective group s do exist in individualistic societies, for example, but the motivations of individuals in these groups are intrinsic rather than extrinsic (Ahuvia 2002). Since countries with higher levels of individualism typically have higher levels of inequality, thi s potentially explains why inequality is positively correlated with happiness. Introducing Postcolonialism Emerging during the period of post Second World War decolonization and taking root after the period came to an end formally, postcolonialism as a theory of international relations examines the relationship between power, knowledge, and the ways in which identities are constructed in relation to the Other (Grovogui 2010). Although many countries in the sample used here have been either subjects of colonization or colonizers themselves, the theory is applicable in a more general nature and will be used that way here. Since postcolonialism primarily concerns the individual and systemic levels of

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104 analysis, its ability to explain is limited to this and will be used for systemic analysis hereafter. Especially considering that all of the systemic analyses of the previous chapter were inconclusive, postcolonialism holds potential to explain why this is. By drawing influence from poststructuralism, Marxis t IR theory, and communitarian notions of justice, postcolonialism is often at odds with neoliberal globalization and Western development policies promoted in the non West (Grovogui 2010; McEwen 2009). Despite development currently being characterized by neoliberal assumptions and of thought yields an enriched debate (McEwen 2009). The first main argument of postcolonialism is that identities are constructed in rel ation to the Other and that these identities form the justification that actors use to explain their actions (Grovogui 2010; Jabri 2013). If the Other is perceived to be helpless, infantile, and pre modern, for example, the identity of the self is constru cted to be powerful, mature, and modern the binary opposite (Jabri 2013; Said 1979). Within this binary, the Other can be perceived to be a helpless victim in need of rescue and the self can use this to justify its actions of acting as a savior (McEwen 2009; Said 1979). Binary creations in development cause a paradox to emerge where the development policies created in hopes to ameliorate the difficulties experienced by the miserable are in essence dependent on viewing the Other as miserable (Wright 2012 ; Jabri 2013). Thus, processes of othering are used to create both identities themselves and the policies that these identities influence. The second main argument concerns the relationship between knowledge and power where the creation and enactment of non local knowledge is a form of power over

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105 the Other (Grovogui 2010). Postcolonial scholarship is therefore quite critical of the Washington Consensus and many aspects of international trade/economic law as these are argued to be disempowering of the Oth er (Anghie 2006; Jabri 2013). Through binary identity formation, power is exerted on the other through processes of othering. Different than variables are positively related, postcolonialism is potentially better at explaining why variables are either negatively correlated or unrelated. Considering that the neoliberal path of development is largely a Western construct, for example, postcolonialism would argue that the imposition of Western know ledge is disempowering of the Other and leads to lower levels of well being (Jabri 2013; McEwen 2009). classify postcolonialism as adhering more in favor of one notion of happi ness. Instead of accepting universal definitions for terminology, for example, postcolonialism allows for flexibility and variation. The mere act of defining happiness would involve othering because this would require the self to define the Other as unhap py (Ahmed 2010). Due to its standing as a critical theory and its emphasis on culture, therefore, it is able to provide an alternative explanation by arguing that perhaps happiness is something without a true universal definition. Instead, happiness can be defined in multiple ways that vary between geographic locations, societies, and individuals. Since the systemic analysis was virtually inconclusive, postcolonialism can potentially shed light on why this is In some cases below, postcolonial analysis can be illuminating and provide explanation, but in other cases the findings continue to be unexplained.

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106 Knowledge/ Power, Othering, and Meaning in Cultural Contexts Exports. For the relationship between happiness and exports at the systemic level, i t was seen in the quantitative analysis that all of the BRICS and all levels of economic protectionism displayed various correlation coefficients and regression coefficients that followed no general trend in the relationship between happiness and neoliberal globalization. From the viewpoint of postcolonialism, this could be explained as the adoption of non local knowl to form economic policies. cy tables of all countries world matters (Zein Elabdin and Charusheela 2004). In doing this, economic factors inevitably affect emotions, are affected by emotions, and reflect both a way of lif e and the values that a culture holds (Wright 2012; Zein Elabdin and Charusheela 2004). Since non local models of development are rapidly enacted as part of the Washington Consensus, in other words, local policies are affected in ways that do not always f it within the cultural context of policies originally in place. This could be an explanation behind the relationships that were virtually nonexistent. This does not explain why market structures influenced by outside influences would experience positive correlations between happiness and exports, however. By s hielding away outside influences and maintaining relatively higher levels of economic protectionism, for example, could this mean that Russia and the ex Soviet states were spared from having econom ic changes? Absolutely not. The opposite happened in the time period of this study where the collapse of the Soviet Union and the following period of transition trauma were characterized by the rapid introduction of neoliberal economic

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107 policies in Russia and many ex Soviet states, Belarus excluded (Inglehart et al. 2008; Sapsford and Abbott 2006). This demonstrates that postcolonialism is not completely able to explain this observation because postcolonialism would argue that large scale economic shocks a nd the introduction of non local knowledge would negatively affect well being as culture is imposed on by non local knowledge (Wright 2012). its happiness notion incorp orating pleasure and achievement. Regarding market deregulation during the period of transition trauma, in other words, the non local knowledge that was rapidly introduced in 1990s Russia happened to help further happiness as achievement/pleasure by reduc ing market volatility and spurring Russia to become a re emerging economic power. This could indicate that the statistically significant correlation identified in chapter six is indicative of a causal relationship in the data, but a slew of variables that are unexamined here are very likely to exist in between happiness and exports. Since happiness is based on pleasure/ achievement induced by the absence of market volatility, in other words, happiness and exporting are two of many variables in a very long chain of causality characterized by numerous influencing factors. Industrialization. In the systemic analysis of happiness and electricity production, a proxy measure for industrialization, the quantitative analysis yielded inconclusive results that did not follow any clear trend. Perhaps this is because the meaning behind electricity and industrialization is constructed in cultural contexts and valued to different extents in various ways. Electricity in Russia and the West is tied to the noti on of modernity, for example, but this is not necessarily true in all non Western societies (Banjaree 2003;

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108 Protschky 2012). Particularly in Russia, on one hand, electricity is tied with modernity, progress, and national pride (Banjaree 2003). In the Eas t Asian context, on the other hand, Lee and Cho argue that a hybrid notion of modernity exists where local and Western concepts of modernity have merged and work in conjunction with industrialization (2012). In other words, the industrialization of societ ies that is paramount in neoliberal development policies identifies the relationship between knowledge and power. With industrialization representing a Western knowledge that is used as a form of power over the Other in neoliberal development policies, t he meaning that is attached to industrialization varies by culture. For example, Portes argues that development is used to further the Western notion of modernity by creating a dichotomy between the traditional and the modern (1973). The terms developed and modern have become synonymous with terms such as industrialized (Portes 1973). Due to processes of othering where the agrarian and unindustrialized are associated with backwardness, Portes argues nd living standards, but also of national self sought to further development as much as it is to gain a sense of belonging in international society The meaning that cultures attach to indust rialization determines the extent if any, to which industrialization affects happiness. that developing countries seek both living standards and belonging in the national community, this can be applied to all three notions of happiness used here to varying extents. When industrialization raises living standards and the cultural notion of happiness reflects pleasure and achievement, this can

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109 explain why variables would be positively related. If a cultural notion of happine ss incorporates pleasure and achievement less and industrialization is not viewed as a step this explains why variables are not related. In other words, the meaning that a culture attaches to industrialization in some cases can explain more about the relationship between happiness and industrialization that the actual changes in electricity production can. This meaning is influenced by the relationship between knowledge and power. Globalization. By restructuring the concept of space, globalization has transformed the relationship between knowledge and power through technologies such as the internet. For example, King identifies how the internet has transcended cul ture 25 and given those in different cont inents a platform through which agency is transferred to cultures and subcultures both local ly and transnational ly (2008). Stated differently the knowledge and information shared through these technologically forge d pathways attach meaning to numerous phenomena in a way that is neither Western nor non Western in its entirety In other ways, moreover, the internet can be used as a tool for resistance and a mechanism to reinforce the status quo (Liu 2013 ). For examp le, Liu discusses the role of the internet restructuring the concept of space in postcolonial Maccau and its 1999 r eturn from Portugal to China policy (2013 ). Within this, the internet created virtual space that w as less controlled by the new government and also transformed agency in cyberspace int o agency in real space (Liu 2013 ). What this 25 slightly differs from the one used here (2008) King defines cultures and sub cult ures as a set of narratives, and shared knowledge (2008, 138 )

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110 demonstrates is that globalization has challenged the postcolonial argument that Western knowledge is always disempowering o f the non West. If this had positive effects on happiness, however, it would be expected that all of the correlations in chater six would be positive a result not supported by the data. Despite the success es of the internet playing a signi ficant role in globalization and furthering economic development, as discussed in chapter three, there are also consequences that potentially harm emotional well being. When used for personal purposes rather than business related or advocacy purposes, emp irically the exact sense of inclusion that one would expect from having a wealth of information at their fingertips can contribute towards feelings of unhappiness and a lack of social inclusion (Akin 2012; Mitchell 2011). More specifically, the unhappine contributes to are a lack of trust in others, a lower sense of social inclusion, and a general lack of belonging in civil (rather than cyber) society (Mitchell 2011, 1857). Even more problematic, common emotions amo neuroticism, loneliness, lower self (Akin 2012, 407). In other words, the restructuring of space through the growing prominence of the internet has indeed affected the relationship between knowledge and power between the West and the non West, but has also had negative consequences. agency through the use of the inter net and promote a sense of belonging in civil society and enable achievement such as in Maccau, but it also can lead to feelings of exclusion that that do not at all coincide with any of the three notions of happiness used here.

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111 Chapter Summary The meani ng s that culture s place on the various aspects of neoliberal globali zation determine notion of happiness is also a key determining factor in this. When Western neoliberal development policie s are enacted in the non adjustment policies, for example, the meaning a society places on the aspects of the economy that are changed can either have no effect on happiness or a negative effect (Wright 1012). Since cultures do not always value pleasure and achievement in the same way that they are encouraged in policies of neoliberal economic development, this is why happiness is not affected. In other words the knowledge and power relationship affects how non loca l policies implicitly stressing pleasure and achievement do not always fit neatly in all contexts due to the role of culture.

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112 CHAPTER I X CONCLUSION As this thesis has argued, c ulture acts as a gatekeeper between happiness and neoliberal g lobalization Operating as a sort of filter, culture determines the extent to which neoliberal economic development and globalization affect happiness. Although a wealth of literature suggests that economic factors affect happiness in processes of develo pment, this thesis has found that these factors are not significantly related with happiness in all cases (D. Bok 2011; S. Bok 2011; Graham 2009; Greve 2012; Thin 2013) In cases where they are significant, on one hand, this is due to a cultural context w hich accommodates happiness as achievement and pleasure. When neoliberal globalization is not significantly related with happiness, on the other hand, a cultural context that values achievement and pleasure to a lesser extent is the reason for this lack o f significance. Within each BRICS country, happiness does not neatly coincide with any of the three philosophical notions of happiness used here. This demonstrates that ambiguity emerges when trying to connect non Western cultures with Western notions of happiness. Considering that neoliberal economic develo pment and globalization stress achievement and pleasure, this is also supported by the data here as the significant relationships found were sporadically placed among sample countries. The relationsh ip between neoliberal economic development and globalization was most correlated in countries that valued pleasure and achievement while incorporating this into their cultural notions of happiness. Postcolonialism is able to explain the se inconclusive fin dings by challenging the notion that the neoliberal path of development is universally applicable in all countries. Instead, represent their culture and ha ve meaning s that expand beyond what numbers can va lidly measure (Charusheela and Zein Elabdin 2004). In the

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113 same way that the Washington Consensus enacted in the non West can be analyzed as the contention between knowledge and power, Western notions of happiness in the non West also experience this tensi on This does not indicate that Western development policies implemented in the non West make societies completely miserable, but it does indicate that happiness will not always be benefitted by using non local knowledge in development policies Although the vast majority of individuals know what happiness feels like and are able to identify when someone else is happy, the concept is quite elusive and difficult to define (S. Bok, 2011). This is especially true when considering how notions of happiness va ry across cultures (Selin and Davey 2012). Regardless of whether there is a truly universal definition, happiness is an emotion that is difficult to argue against. Seen in the analysis above, for example, an underlying presumption in this thesis is that happiness is something everyone seeks regardless of culture. V iewing the world through the happiness studies lens is portrayed in the literature as a holistic and progressive approach to policymaking, for example, and in many ways it is (D. Bok 2011; Thin 2013). Despite the considerable opportunities for holistic analysis that adopting a happiness lens can encourage, incorporating this approach into policymaking can have unintended negative consequences In her historical analysis of immigrants and homo sexuals in the US for example, Ahmed highlights various ways in which the portrayal of the majority as happy and carefree has been used to ostracize and portray the Other as miserable and inferior ( 2010). Moreover, even the mere act of being happy can ve to see happily is not to see violence, (Ahmed 2010, 132). Nevertheless, normatively happiness is something that should be considered alongside development policies and their impact on those that they affect. Even when adopting a broad perspective such as the one used here the relationship between happiness and neoliberal globalization is complex and expands into

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114 various fields throughout academia The arguments here are limited and cannot be validly appli ed to paths of development other than neoliberalism. Although there is not sufficient evidence to argue that neoliberal globalization increases happiness for all in sum, there is also not sufficient evidence to argue that it decreases happiness. As a fi eld that has recently witnessed a renaissance, nevertheless, the study of happiness holds great potential for further scholarship

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115 REFERENCS Abdel Measuring Happiness with a Single Item Scale Social behavior an d Personality 34(2): 139 150 Ahmed, Sara. 2010. The Promise of Happiness Durham: Duke University Press. iness: A Theoretical Conjecture on the Relationship between Consumption, Cu lture and Subjective Well Journal of Happiness Studies 3(1): 23 36. The Relationsh ips between Internet Addiction, Subjective Vitality, and Subjective Happiness Cyberpsychology, Behavior, and Social Networking 15(8): 404 410. Allison, Paul D. 2002. Missing Data Thousand Oaks: Sage. The Evolution of International Law: Colonial and Postcolonial R ealities Third World Quarterly 27(5): 739 753 Happ iness: Cl assic and Contemporary Readings in Philosophy eds. Steven M. Cahn and Ch ristine Vitrano. Oxford: Oxford University Press, 238 244. In Happiness: Cl assic and Contemporary Readings in Philosophy eds. Steven M. Cahn and Ch ristine Vitrano. Oxford: Oxford University Press, 19 34. Au, Evelyn W. M. Chi yue Chiu, Zhi Xue Zhang, LeeAn n Mallorie, Avin ish ate: Social Ecological Foundation and Psychological Functions Journal of Cross Cultural Psychology 43(6) : 931 942 Banjar ee, Anindita Electricity: Science Fiction and Modernity in Early Twentieth Century Russia Science Fiction Studies 3 0(1): 49 71. Berg Maarten and Ruut Veenhoven N ations : In Search for an Optimum that Does Not Appear to E xist Social Policy and Happiness in Europe ed. Bent Breve. Cheltenham: Edward Elgar, 174 1 94 Bhagwati, Jagdish. 2007. In Defense of Globalization: With a New Afterward Oxford: Oxford University Press.

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116 Biswas In Happines s Across Cultures: Views of Happiness and Quality of Life in Non Western Cultures eds. Helaine Salin and Gareth Davey. New York: Springer, 13 25. Bok, Derek. 2011. The Politics of Happiness: What Government Can Learn from the New Research on Well Being Princeton: Princeton University Press. Bok, Sissela. 2011. Exploring happiness: From Aristotle to Brain Science New Haven: Yale University Press. Happiness Across Cultures: Views of Happines s and Quality of Life in Non Western Cultures eds. Helaine Salin and Gareth Davey. New York: Springer, 329 344. Costanza Robert Maureen Hart, Stephen Posner, and John Talberth GDP: The Need for New Measures of Progress The Parde e Papers 4: 1 37. Crenshaw, Edward M and Kristopher K. Globalization and the Digital Divide: The Roles of Structural Conduciveness and Global Connection in Internet Diffusion. Social Science Quarterly 87(1): 190 207. Creswell, John W. and Vicki L. Plano Clark. 2011. Designing and Conducting Mixed Methods Researc h 2nd ed. Thousand Oaks: Sage. International Studies Quarterly 43(3): 5 33 552. International Journal of Happiness and Development 1(1): 86 101. In Happiness Across Cultures: V iews of Happiness and Quality of Life in Non Western Cultures eds. Helaine Salin and Gareth Davey. New York: Springer, 57 73. that is Journal of Happiness Studies 10(2): 235 252. Happiness: Classic and Contemporary Readings in Philosophy eds. Steven M. Cahn and Ch ristine Vitrano. Oxford: Oxford University Press, 163 172.

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123 Wilkinson, Richard and Kate Pickett. 2011. The Spirit Level: Why Greater Equality Makes Societies Stronger New York: Bloomsbury. icity Reform in Developing and T ransition Countries: A R eappraisal Energy 31(6): 815 844 World Bank. 2013 a July 23 2013). alysis http://web.worldbank.org/ WBSITE/EXTERNAL/TOPICS/EXTPOVERTY/EXTPA/0,,contentMDK:20238 991~menuPK:492138~pagePK:148956~piPK:216618~theSitePK:430367,00.html (September 29, 2013). World Trade Orga WTO | dispute settlement Map of disputes between WTO Members http://www.wto.org/english/tratop_e/dispu_e/dispu_ maps_e.htm (August 19, 2013). World Values Survey 2013. Survey http://www.wor ldvaluessurvey.org/index_surveys ( July 23 2013). Third World Quarterly 33(6): 1113 1127. Zavisca Jane and Michael Hout Does Money Buy Happiness in Unhappy Russia? Berkeley Progra m in Soviet and Post Soviet Studies Working Paper Series 1 59. Zein on: Economics and Postcolonialism Meets Economics eds. Eiman O. Zein Elabdin and S. Charusheela. L ondon: Routledge, 1 18. Zha Peijia, Jeffrey J. Walczyk Diana A. Griffith Ross J erome J. Tobacyk and Daniel F. Walczyk The Impact of Culture and Individualism Collectivism on the Creative Potential and Achievement of American and Chinese A dults Creativity Research Journal 18(3): 355 366

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124 APPENDIX A STATE LEVEL QUANTITATIVE RESULTS FOR NON BRICS COUNTRIES Legend Happiness & Pearson r [df] slope (standard error) capita (hun dreds of constant 2000 US$) Production per capita (hundreds of kw/h) (% of population) Gini Index Score Albania df 0.403 0.162 0.668 0.955 [2] 1998 2008 8 1.162 (1.245) 0.031 (0.711) 0.153 (0.536) 0.690 (0.717) Argenti na df 0.017 0.081 0.554 0.603 [14] 1992 2006 13 0.002 (0.053) 0.003 (0.053) 0.019 (0.044) 0.234 (0.930) Belarus df 0.570 0.608 0.539 0.605 [2] 1991 2000 8 0.302 (0.869 ) 0.219 (0.840) 1.237 (0.891) 0.460 (3.090) Bosnia & Herzegovina df 0.771 0.121 0.586 0.888 [1] 1999 2008 8 1.048 (0.375) 0.021 (0.585) 0.089 (0.447) 0.077 (0.258) Canada df 0.742 0.391 0.167 insufficient data 1991 2006 14 0.075 ( 0.276 ) 0.032 ( 0.379 ) 0.014 ( 0.406 ) Chile df 0.110 0.023 0.076 0.054 [6] 1991 2006 14 0.093 ( 0 .387 ) 0.016 ( 0.389 ) 0.009 ( 0.388 ) 0.052 ( 1.210 ) France df 0.345 0.371 0.672 insufficient data 1991 2008 16 0.015 ( 0.082 ) 0.019 ( 0.081 ) 0.012 ( 0.065 ) Iceland df 0.271 0.310 0.070 insufficient data 1991 2008 16 0.000 ( 0.008 ) 0.000 ( 0.008 ) 0.000 ( 0 .009 ) Japan df 0.234 0.252 0.659 insufficient data 1991 2005 13 0.077 ( 0.757 ) 0.133 ( 0.754 ) 0.118 ( 0.586 ) Latvia df 0.140 [15] 0.257 0.148 0.638 [8] 1992 2008 16 0.076 ( 0.726 ) 0.052 ( 0.694 ) 0.024 ( 0.711 ) 0.339 ( 1.448 ) Macedonia df 0.103 0.33 0 0.559 0.983 [ 6 ] 1999 2008 8 0.049 ( 0.461 ) 0.085 ( 0.438 ) 0.054 ( 0.385 ) 0.468 ( 0.474 ) Malta df 0.063 0.127 0.615 insufficient data 1992 2008 15 0.004 ( 0.281 ) 0.025 ( 0.279 ) 0.054 ( 0.222 ) Mexico df 0.409 0.209 0.064 0.308 [ 5 ] 1991 2005 13 0.76 4 ( 1.583 ) 0.674 ( 1.697 ) 0.071 ( 1.732 ) 1.050 ( 7.469 ) p > .1 p < .05 p < .01 data are based on each country/year with paired data between variables s lope value is the ordinary least squares regression coefficient all analyses are bivariate

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125 Legend Happiness & Pearson r [df] slope (standard error) capita (hundreds of constant 2000 US$) Production per capita (hundreds of kw/h) (% of population) Gini Index Score Moldova df 0.032 0.178 0.361 0.616 [9] 1997 2008 10 0.094 (1.000) 0.151 (0.985) 0.195 (0.933) 0.739 (1.554) Nigeria df insufficient data 0.463 0.839 insufficient data 1991 2000 8 0.137 (1.973) 2.189 (0.012) Norway df 0.52 5 0.144 0.105 insufficient data 1991 2008 16 0.016 (0.130) 0.001 (0.151) 0.003 (0.152) Peru df 0.305 0.791 0.432 0.606 [8] 1997 2006 8 0.290 (0.182) 0.973 (0.117) 0.055 (0.172) 0.030 (0.270) Serbia df 0.278 [7] 0.006 0.641 [2] 0.381 [5] 1997 2008 10 1.491 (1.342) 0.003 (1.169) 0.540 (1.679) 0.273 (1.560) Slovakia df 0.200 [15] 0.323 0.178 0.849 [5] 1991 2008 16 0.011 (0.186) 0.075 (0.611) 0.017 (0.635) 0.498 (1.168) South Korea df 0.189 0.144 0.712 insufficient data 1991 2005 1 3 0.023 (0.356) 0.000 (0.359) 0.037 (0.255) Sweden df 0.092 0.202 0.133 insufficient data 1991 2008 16 0.018 (1.003) 0.018 (0.987) 0.023 (0.998) Switzerland df 0.135 0.004 0.152 insufficient data 1990 2008 17 0.003 (0.141) 0.000 (0.142) 0.00 5 (0.141) Turkey df 0.182 0.390 0.142 0.150 [6] 1991 2008 16 0.848 (2.329) 0.000 (2.181) 0.116 (2.345) 0.265 (3.220) Ukraine df 0.136 0.594 0.551 0.523 [7] 1997 2008 10 0.293 (1.020) 0.690 (0.828) 0.432 (0.859) 0.854 (3.430) United States df 0.017 0.004 0.654 insufficient data 1991 2006 14 0.005 (0.450) 0.001 (0.450) 0.096 (0.340) p > .1 p < .05 p < .01 data are based on each country/year with paired data between variables s lope value is the ordinary least squares regression coefficient all analyses are bivariate

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126 APPENDIX B WVS/EVS HAPPINESS SURVEY QUESTIONS European Values Study o Data only used from 2008 survey wave for only the following countries: Albania, Bosnia and Herzegovina, France, Iceland, Latvia, Macedoni a, Malta, Moldova, Norway, Serbia, Slovakia, Sweden, Switzerland, Turkey, and Ukraine. o Question 3: Taking all things together, would you say you are: 1 very happy 2 quite happy 3 not very happy 4 not at all happy 8 9 no answer (spontaneous) World Values Survey o Consistent wording in all survey years. WVS data are used in all cases except those in 2008 where EVS data are used instead. These are the ones listed directly above. o Question 8: Taking all things toge ther, would you say you are: 5 Missing; Unknown 4 Not asked in survey 3 Not applicable 2 No answer 1 Dont know 1 Very happy 2 Quite happy 3 Not very happy 4 Not at all happy

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127 APPENDIX C METHODS USED TO CALCULATE GINI INDEX SCORES As stated by calculated as the area A divided by the sum of areas A and B b 1). Expressed differently: If the line in the above figure was hypothetically linear, this would indicate that income distribution is equal throughout the country being examined and the gini index score would be zero (World Bank 2013 b ). As the curvature of line become s sharper, hypothetically, income inequality becomes more pronounced in the country being examined and the gini index score therefore increases (World Bank 2013 b ). Source: World Bank

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128 -400 -300 -200 -100 0 100 200 300 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.2 Exports Per Capita (constant $US 2000) Happiness Russia 1991 2006: Happiness & Exports (stationary) r(14) = .527, p < .05 APPENDIX D SKEDACITY AND AWKWARD DATA The top graph shows an example of heterosk edacity when the data are non stationary. T he bottom graph shows an example of awkw ardly placed stationary data when graphed on a scatter plot. These are the data that are used in the hypothesis testing 0 200 400 600 800 1000 1200 1400 1600 52 53 54 55 56 57 58 59 60 61 62 Exports Per Capita (constant $US 2000) Happiness Russia 1990 2006: Happiness & Exports (non stationary) r(15) = .897, p < .01

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129 0 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 Data Interpolation: Hypothetical Scenarios Happiness Time (years) WVS/EVS Survey Years APPENDIX E LINER VS NONLINEAR DATA INTERPOLATION This is a hypothetical scenario that i s dramatized to show the vastly different results that could result depending on which method is used. In the figure below, the horizontal line shows linear interpolation between survey years. The curved lines show non linear data interpolation using pol ynomials. One dashed line assumes that happiness was significantly higher in the years before the WVS/EVS surveys were conducted and the other assumes that it was much lower. What this figure demonstrates is that if happiness is greatly different in one y ear than the other years, the results of polynomial non linear interpolation can show peaks and troughs while happiness continues to stay at exactly the same level in each survey year. Due to the greatly different results that polynomial interpolation can create, linear interpolation is used because it finds a middle ground so that significantly higher/lower data points do not lead to potentially misleading results.

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130 APPENDIX F DESCRIPTION S OF WORLD BANK INDEPENDENT VARIABLES The following describe pr ecisely how the World Bank describes the independent variables used in this thesis. All of these quoted explanations were taken directly from the World Bank, which is also the data source. Exports of goods and services (constant 2000 US$) o Exports of g oods and services represent the value of all goods and other market services provided to the rest of the world. They include the value of merchandise, freight, insurance, transport, travel, royalties, license fees, and other services, such as communication construction, financial, information, business, personal, and government services. They exclude compensation of employees and investment income (formerly called factor services) and transfer payments. Data ar e in constant 2000 2 013 a 1). Electricity Production (kw/h) o Electricity production is measured at the terminals of all alternator sets in a station. In addition to hydropower, coal, oil, gas, and nuclear power generation, it covers generation by geothermal, solar, wind, and tide and wave energy, as well as that from combustible renewables and waste. Production includes the output of electricity plants that are designed to produce electricity only as well as that of 1). Inte net Users per 100 people o Internet users are people with Bank 2013a, 1). GINI Index o Gini index measures the extent to which the distribution of income or consumption expenditure among individuals or households withi n an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality Data are based on primary household survey data obtained from government statistical agencies and World Bank country dep

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131 A PPENDIX G RY OF HAPPINESS SELF ASSESSMENT Veenhoven considers variou s models of how humans assess their own happiness (2009). He considers the role of culture, but ultimately argues that the model below best represents how humans assess their own happiness because the model below is best supported by sociologica l and anth ropological studies that apply to all human beings 26 (Veenhoven 2009). The figure below as depicted by Veenhoven, shows that pleasure and achievement are key aspects of how ind ividuals as sess their happiness ( 2009 26 ). 26 For further discussion about this model and the other models that Veenhoven considers for how indiv id uals assess their own happiness, see Veenhov en 2009. (Veenhoven 2009, 26)