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A happiness Kuznets curve? Using model-based cluster analysis to group countries based on happiness, development, income, and carbon emissions

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This exploratory study uses model-based cluster analysis to group sixty-one countries based on statistical similarities in terms of happiness, development, income, and carbon emissions. Model-based cluster analysis is appropriate for an initial identification of a pattern that is worthy of further investigation. A key finding is that there may be a Kuznets curve for happiness. The Kuznets curve graphs the proposition that, as an economy develops, economic inequality first increases and then decreases. Similarly, the authors find that clusters of countries at the extremes of the lowest and highest average levels of development and income have the highest self-reported levels of happiness. Clusters of countries in the middle of the development and income spectrum have the comparatively lowest average levels of happiness. Further, carbon emissions are not perfectly associated with happiness. For example, between two clusters with the highest average levels of development, income, and happiness there is a 43 % difference in carbon emissions. A highly developed cluster has roughly the same mean carbon emissions as a cluster with 83 % less income, and the least developed cluster has 93 % of the happiness as the most developed cluster yet 86 % less carbon emissions. Despite limitations of both data and methodology, the overall pattern—that there may be a happiness Kuznets curve and that development, income, and carbon emissions are not associated lockstep with happiness—contributes to the literature on decoupling development from growth in emissions.
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A happiness Kuznets curve? Using model-based cluster
analysis to group countries based on happiness,
development, income, and carbon emissions
Adam Sulkowski
1
D. Steven White
2
Received: 30 December 2014 / Accepted: 24 June 2015
Springer Science+Business Media Dordrecht 2015
Abstract This exploratory study uses model-based cluster analysis to group sixty-one
countries based on statistical similarities in terms of happiness, development, income, and
carbon emissions. Model-based cluster analysis is appropriate for an initial identification of
a pattern that is worthy of further investigation. A key finding is that there may be a
Kuznets curve for happiness. The Kuznets curve graphs the proposition that, as an econ-
omy develops, economic inequality first increases and then decreases. Similarly, the
authors find that clusters of countries at the extremes of the lowest and highest average
levels of development and income have the highest self-reported levels of happiness.
Clusters of countries in the middle of the development and income spectrum have the
comparatively lowest average levels of happiness. Further, carbon emissions are not per-
fectly associated with happiness. For example, between two clusters with the highest
average levels of development, income, and happiness there is a 43 % difference in carbon
emissions. A highly developed cluster has roughly the same mean carbon emissions as a
cluster with 83 % less income, and the least developed cluster has 93 % of the happiness as
the most developed cluster yet 86 % less carbon emissions. Despite limitations of both data
and methodology, the overall pattern—that there may be a happiness Kuznets curve and
that development, income, and carbon emissions are not associated lockstep with happi-
ness—contributes to the literature on decoupling development from growth in emissions.
Keywords Sustainability Happiness Indicators HDI Kuznets Cluster analysis
&Adam Sulkowski
asulkowski@babson.edu
D. Steven White
swhite@umassd.edu
1
Babson College, 231 Forest Street, Babson Park, MA 02457, USA
2
Charlton College of Business, University of Massachusetts Dartmouth, 285 Old Westport Road,
North Dartmouth, MA 02747, USA
123
Environ Dev Sustain
DOI 10.1007/s10668-015-9689-z
1 Introduction
The realms of science, business studies, humanities, and spiritual traditions all contain
important observations about what makes people happy. Increasingly, the study of hap-
piness as it relates to business and development appears to be gaining popularity and
urgency. Yet, in both the arenas of academic studies and management of organizations,
several key challenges exist. Using indicators of happiness—whether in the management
of firms or public policy or other contexts—is still in its nascent stages. Most critically,
given that individuals, firms, and societies want to both improve their circumstances and
reduce their carbon emissions, a better understanding of the relationship between happi-
ness, development, income, and carbon emissions is needed. This article reviews literature
and then uses model-based cluster analysis to reveal groups of countries with shared
characteristics in terms of self-reported happiness, development, income, and carbon
emissions. This exploratory work should provoke further thought and progress on several
fronts, including the desire to understand key performance indicators (KPIs) and their
integration into the management of human affairs in several contexts, including companies
and public policy.
2 Literature review
2.1 Happiness and its connection to business and economics
There is a rich literature dissecting, defining, and discussing methodologies for measuring
happiness (Herva
´s and Va
´zquez, 2013). Psychologists have established that neither money
nor consumption guarantee greater happiness (Csikszentmihalyi 1999). Indeed, Maslow’s
hierarchy of needs—emphasizing that, for example, self-respect and serving greater pur-
poses gain in importance once basic needs are met—is even a staple of business education
(Maslow 1943). Consistent with this theory are observations that, for example, happiness
of employees is not correlated with financial performance, but is directly and positively
impacted by the extent to which a firm has a green reputation (Walsh and Sulkowski 2010).
A review of interdisciplinary research identified common proximal mediators of life sat-
isfaction such as quality of work life, quality of non-work life, and feelings of self-worth,
career satisfaction, job performance, turnover intentions, and organizational commitment
(Erdogan et al. 2012).
The topic of happiness and satisfaction in the workplace is a vital area in business
scholarship, given that these feelings among employees boost all measures of firm per-
formance, including financial results (Edmands 2011, 2012). In terms of practical appli-
cation, Google has tested and deployed a free meditation course for its employees based on
the science of happiness and mindfulness (Tan 2012). As discussed below, aspects of
happiness have been identified and specifically defined, but for the present moment, suffice
it to summarize that emotional states of individuals clearly have an impact on organiza-
tional performance.
Clearly, just as individual and firm-level attitudes and activity cumulatively result in
country-level economic conditions, sentiments of individuals and within firms collectively
are reflected in national-level surveys of self-reported happiness—the connection between
emotional states of people and health of businesses and entire economies has long been
observed (Akerlof and Shiller 2009; Shiller 2006).
A. Sulkowski, D. S. White
123
Strangely, for purposes of both managing people and organizations and in the public
policy arena, much more attention has been given to the development, implementation, and
maximization of a completely different set of measures than those related to happiness.
Indeed, public economics ‘‘fails to explain the recent history of human welfare and it
ignores some of the key findings of modern psychology’’ (Layard 2006). An overemphasis
on a narrow range of metrics (typically GDP, company revenues and profits, stock returns
and indices, income, and other measures of material wealth and consumption) at the
individual, firm, and national level, among other widely acknowledged-as-erroneous
assumptions at the foundation of economics (Sen 1977) has resulted in real problems. For
several decades, other indicators reflect very real, growing, and global crises, particularly
with respect to climate change, ecosystem collapse, and related problems (Brown 2009;
IPCC 2007; Lovelock 2006,2010; McKibben 2010). Incredibly, even the creator of GDP
warned against using his creation as a gauge of the success of an economy (Kuznets 1934).
2.2 An emerging trend: measuring happiness
Over the past few decades, a body of research and literature has flourished around the topic
of combining knowledge about happiness and economics to better inform policy-making
(Graham 2012). Widely cited literature in the field of positive psychology on subjective
well-being (SWB) argues that the components of SWB and their underpinnings in terms of
culture and temperament as well as sampling methodologies are advanced enough to
produce national indicators of happiness (Diener 2000). Indeed, some believe that the
pursuit of happiness rather than constant growth of consumption may be the organizing
principle that replaces our current predominant fixation in business and economics. Among
these are Peter Senge (Senge 2006; Senge et al. 2008), who argues that a substantial change
of mindset, or metanoia, is needed. The Brundtland Commission’s definition of sustain-
ability, i.e., ‘meeting the needs of the present without compromising the ability of future
generations to meet their needs’ (Brundtland 1987) somewhat presaged current awareness
that humanity may be better served by moving away from pursuit of growth of con-
sumption as an organizing principle.
Several solutions have been proposed and to some extent implemented based on the
twin truisms that ‘‘we manage what we measure,’’ and that ‘‘we are statistically blind to the
ecological and societal dimensions of our activities.’’ New types of KPIs, such as the
Genuine Progress Indicator, the Gross National Happiness Indicator, the UN’s Human
Development Indicator, and the Calvert-Henderson Quality of Life Indicators have been
developed as ways of focusing attention away from material and financial growth.
Famously, in 1972, the King of Bhutan, Jigme Singye Wangchuck, suggested the
development and growth of a Gross National Happiness Index of his country, which is now
being applied globally (Bates 2009). In 1990 Mahbub ul Haq and Amartya Sen initiated the
U.N. Human Development Index, which reflects average life expectancy, years of edu-
cation, and income—in ul Haq’s words: ‘‘just one number which is of the same level of
vulgarity as the GNP—but a measure that is not as blind to social aspects of human lives as
the GNP is’’ (Jahan 2004). In 2010, the Inequality-adjusted HDI (IHDI) was introduced,
which adjusts down a countries’ overall score as inequality increases in each of the three
dimensions of the HDI (health, education and income). It similarly has been suggested that
the HDI be adjusted for sustainability (Ray 2014).
Since 2000, interest among country governments in full-spectrum evaluations of
national well-being has greatly increased. In 2006 China created a green GDP index that
adjusts for costs of environmental harm; by this standard, 3 % points of annual GDP
A happiness Kuznets curve? Using model-based cluster analysis
123
growth should have been subtracted from official statistics (Li and Lang 2010). In 2008 the
USA began funding of the State of the USA project to create a ‘‘key national indicator
system’’ with new data points to supplement standard GDP measures based on a review of
best practices (Government Accountability Office 2011). In 2009 the French government
released a report co-authored by Nobel Prize-winning economist Joseph Stiglitz, sug-
gesting an end to ‘‘GDP fetishism’’ (Commission on the Measurement of Economic Per-
formance and Social Progress 2009). By 2010, the UK government announced that surveys
of happiness will be taken and considered together with other economic measures.
To summarize, the governments of the UK, France, and the USA have started to catch
up with Bhutan in terms of giving serious consideration to tracking happiness as an
indicator along with other measures of success. The Bhutanese experiment in defining and
implementing a Gross National Happiness (GNH) Index is based in what has been char-
acterized as a Buddhist perspective (that material and spiritual development can comple-
ment each other rather than compete), but the Index could be readily applied elsewhere in
other cultural contexts. The four essential aspects of the GNH are: (1) conservation of the
natural environment; (2) preservation of cultural values; (3) good governance; and (4)
ecologically sustainable development (Tideman, 2011). The Center for Bhutan Studies
collaborated with empirical researchers to arrive at specific measurable contributors to
happiness: physical, mental and spiritual health; time balance; social and community
vitality; cultural vitality; education; living standards; good governance; and ecological
vitality (Zurick 2006).
2.3 What cross-national comparisons reveal
Two efforts to arrive at national rankings that are related to human happiness are partic-
ularly noteworthy: the World Happiness Report (Helliwell et al. 2012,2013,2015) and
Happy Planet Index, or HPI (New Economics Foundation 2015). The HPI takes a holistic
view of well-being, taking into account objective measures such as longevity and envi-
ronmental footprint as well as happiness and economic activity. The World Happiness
Report starts with self-reported emotional state as measured by the Worldwide Independent
Network of Market Research/Gallup International Association’s End of Year annual global
survey—hereinafter Gallup global survey (WIN/Gallup International Association 2015)—
and adds layers of interpretation to the raw data.
The World Happiness Report and Happy Planet Index are therefore both useful and
valuable, with the caveat that they are not raw, unadulterated reflections of subjective
emotional state. One key observation of these reports is that countries can still have happy
populations while, on an average per capita basis, exacting much less harm on the natural
environment as others, as in the case of the HPI score of Costa Rica.
Several factors contribute to happiness levels; for example, in developed countries, it
has been found to depend on whether respondents live stable relationships, life satisfaction
is related to respondents’ feelings of control, and social capital of a country is an important
predictor of happiness (Gundelach and Kreiner 2004). However, greater levels of wealth
and development carry their own set of stresses and miseries. The World Happiness Report
dedicates a chapter to mental health problems of depression, anxiety and stress, which
persist—or could even be exacerbated by features of economic development such as
consumerism and the phenomena of unnatural diets, dislocation and destruction of social
connections and connections to nature and traditions and lifestyles and sleeping patterns
(Helliwell et al. 2012).
A. Sulkowski, D. S. White
123
As will be expanded upon in the section below, implications for future research, a larger
question is whether human well-being can be decoupled from constant growth in con-
sumption and associated ecological devastation, including carbon emissions (Dietz et al.
2012). Based on Kuznets’ hypothesis that economic inequality worsens and then dimin-
ishes as countries develop (Kuznets 1955), an environmental Kuznets curve (EKC) has
been suggested (Grossman and Krueger 1995; Selden and Song 1994; Shafik 1994; Stern
et al. 1996). The EKC holds that environmental conditions worsen as countries begin to
develop but eventually improve as countries become more fully developed, a theory that
has been tested and critiqued (Stern 2004; White and Sulkowski 2010). An EKC can be
substantiated or discredited depending on the variables used and context considered
(Apergis and Ozturk 2015; Dietz et al. 2012; Stern 2004; Yang et al. 2015; Yin et al. 2015).
Based on the foregoing research, the authors seek to establish—using an objective
statistical test—whether there exist clusters of countries defined by similar levels of
average happiness, development, income, and carbon emissions and what kind of com-
parisons or contrasts can be drawn between them. The novel contribution of this paper to
the foregoing literature is the use of model-based cluster analysis.
3 Test
Model-based cluster analysis is a data reduction technique appropriate for identifying
relationships that are not readily apparent in a given a data set. It is critical to point out that
model-based cluster analysis is used purely as a tool of exploratory research in this con-
text—there is no model proposed nor hypothesis tested in this study. Therefore, while the
authors are not testing correlations between any of the variables below, the results do serve
as valuable observations about reality that can inform and serve as a foundation for further
research.
4 Variable definition
The authors selected variables that reflect happiness, human development, per capita
income, and per capita carbon dioxide emissions.
5 Methodology
To determine the extent of similarities and differences between countries, the technique of
model-based cluster analysis is employed. The goal of cluster analysis is to identify
homogeneous groups in a given population based upon the data being analyzed (Hair et al.
2006). One of the limitations of cluster analysis is, however, that determination of the
optimal number of clusters is more art than science (e.g., it depends on researcher inter-
pretation). The technique of model-based cluster analysis addresses this limitation by
defining the optimal solution using a multivariate Gaussian mixture (Fraley and Raftery
2002,2006):
A happiness Kuznets curve? Using model-based cluster analysis
123
fxijK;hðÞ¼
X
K
k¼1
pk/ðxijmk;RkÞ
where the pk’s are the mixing proportions and /(. | mk, Rk) denotes a Gaussian density
with mean mkand variance matrix Rk. This analysis is used in conjunction with a Bayesian
criterion (BIC) to determine the optimal model based upon a given dataset. The Bayesian
criterion approximates the integrated likelihood of the data:
pxjmðÞ¼pxjm;hmðÞphmðÞdhm;phmðÞbeing a prior distribution for parameter hm:
BIC is calculated as:
BIC mðÞ¼log p xjm;
^
hm

vm
2log nðÞ:
A model-based cluster analysis is a useful method for establishing cohorts of entities
that are statistically similar to each other (homogeneous groupings). In this case, the
method is used to establish cohorts of countries, based on measures of happiness, devel-
opment, emissions, and income, which are similar to each other. Most importantly, one
may examine the countries within a cohort to speculate on what underlying factors explain
each cohort’s similarities.
6 Data
For a cross-national comparison of happiness—that is, subjective emotional state, unal-
tered by a formula that includes information about societal conditions or environmental
footprint—the basis for the World Happiness Report can be used: the annual Gallup global
survey. Among other questions, typically 1000 respondents in each country answer whe-
ther they are happy, and Net Happiness is calculated as the percent answering ‘‘yes’’ minus
those answering ‘‘no’’ or the equivalent of ‘‘don’t know’’ or a failure to respond. Appendix
1lists the raw data used in the present study, sorted by Net Happiness. One of the most
obvious features about the ten countries at the top of the list is that seven are countries that
are not high in GNI nor HDI. The Gallup global survey has a 37 years history and, while
one might suspect occasional problems in surveying, it is doubtful that this pattern is a
result of widespread errors or intentional deception. Country data are generally consistent
year-to-year. The average of end-of-year 2011 and 2012 was used (or otherwise the
statistic for the available year if only one year of data is public). This approach—using
most recently available data from Gallup and averaging the recent results of annual surveys
on happiness—was adopted by the authors of the World Happiness Report (Helliwell et al.
2012,2013,2015) and others (Ott 2011).
The HDI of each country is included. This serves several purposes. One is to explore
whether there is a connection between HDI and happiness. The HDI is determined by not
just GNI per capita adjusted for purchasing power, but also life expectancy at birth, mean
years of schooling, expected years of schooling, and, since 2010, it is adjusted for income
inequality. Therefore, to the extent that health, education, and absence of vast income
differences should affect levels of happiness, one might expect to see a closer connection
between HDI and happiness than GNI and happiness when characteristics of the clusters
are finally compared. Gross National Income per capita data from the World Bank from the
year 2010 is included in the analysis. It does not change drastically year-to-year. Carbon
A. Sulkowski, D. S. White
123
dioxide (CO2) emissions per capita from the year 2010 (also from the World Bank) is
included and likewise does not drastically change year-to-year.
7 Results
The data were analyzed using the R statistical package (R Development Core Team 2013)
module for model-based cluster analysis. Model-based cluster analysis identified five
clusters as the optimal solution for the dataset.
Given the data, model-based cluster analysis identified an EVI (diagonal, equal volume,
varying shape) model with five components:
Appendix 2presents individual results for each of the 61 countries included in the
analysis.
Based on the results above, the five clusters and their membership and characteristics
are summarized in the following Tables 1and 2.
The statistical clusters in the two tables above are noteworthy for at least three reasons, all
of which are clarified in the table and graph below. First, among wealthy countries, happiness,
HDI, and GNI levels are barely distinguishable, despite Cluster 4 (High Development and
Income and Happy, but Middle CO2) having a mean of 57 % of the emissions as that of
Cluster 3 (High Development and Income and CO2, and Happy). Second, the poorest and
Table 1 Countries, grouped by cluster membership
Cluster Cluster members
Least Development and Income and CO2, but
Happy
Fiji, Nigeria, Colombia, Ghana, Philippines, Uzbekistan,
Peru, Ecuador, Armenia, India, Mozambique, Cameroon,
Kenya, Vietnam, Tunisia, Moldova, Pakistan, Georgia,
Morocco, Iraq, Egypt
Middle Development and Income and CO2,
but Least Happy
Brazil, Malaysia, Azerbaijan, Bosnia and Herzegovina,
Macedonia, South Africa, Russian Federation, Bulgaria,
Ukraine, China, Turkey, Poland, Serbia, Lithuania,
Romania, Lebanon
High Development and Income and CO2, and
Happy
Netherlands, Finland, Germany, Singapore, Japan, Canada,
Belgium, Australia, USA, Ireland
High Development and Income and Happy,
but Middle CO2
Switzerland, Denmark, Iceland, Spain, Austria, Sweden,
France, Hong Kong, United Kingdom, Italy
Upper Middle Development and Income, but
High CO2, and 2nd Least Happy
Saudi Arabia, South Korea, Czech Republic, Portugal
Log likelihood ndf BIC ICL
-943.1975 61 40 -2050.83 -2056.657
Clustering summary
Cluster 1 2 3 4 5
Number of countries 21 16 10 10 4
A happiness Kuznets curve? Using model-based cluster analysis
123
least polluting cluster, Cluster 1 (Least Development and Income and CO2, but Happy), while
enjoying a mean of 93 % of the happiness of the happiest cluster, has a mean carbon footprint
of 14 %—and a mean income of 5 %—of the happiest cluster, Cluster 3 (High Development
and Income and CO2, and Happy). Third, the poorest and least polluting cluster has a mean
happiness higher than two of its counterparts (Clusters 5 and 2), even though one of these
wealthier counterparts contains countries that, on average, emit seven times more CO2 per
capita (Upper Middle Development and Income, but High CO2, and 2nd Least Happy). These
results would be even more dramatic if Iraq and Egypt had been excluded on the grounds of
their having experienced recent violent upheavals.
The key findings emerge most clearly, however, if the characteristics are indexed
(Table 3below) and then illustrated on a chart (Fig. 1below), with clusters on the hori-
zontal axis, sorted by relative income and development.
8 Key findings explained: a happiness Kuznets curve and differences
in carbon emissions
Figure 1illustrates the pattern that emerged from the model-based cluster analysis. The
vertical bars indicate relative development, income, and carbon emissions. The line tracing
mean happiness from cluster-to-cluster shows that the happiest clusters are at the extremes
Table 2 Cluster means
Happiness CO2 per
capita
HDI GNI per
capita
Cluster
High Development and Income and CO2, and
Happy
48.0 11.31 0.914 45,086 3
High Development and Income and Happy, but
Middle CO2
46.9 6.43 0.897 44,663 4
Upper Middle Development and Income, but
high CO2, and 2nd Least Happy
35.5 11.02 0.845 19,830 5
Middle Development and Income and CO2, but
Least Happy
29.2 6.22 0.750 7364 2
Least Development and Income and CO2, but
Happy
44.8 1.54 0.616 2467 1
Table 3 Indexed average of indicators in each country cluster, displayed as a percent of the highest value
for each indicator
Happiness
(%)
CO2 pc
(%)
HDI
(%)
GNI pc
(%)
Cluster
High Development and Income and CO2, and Happy 100 100 100 100 3
High Development and Income and Happy, but
Middle CO2
98 57 98 99 4
Upper Middle Development and Income, but High
CO2, and 2nd Least Happy
74 97 92 44 5
Middle Development and Income and CO2, but Least
Happy
61 55 82 16 2
Least Development and Income and CO2, but Happy 93 14 67 5 1
A. Sulkowski, D. S. White
123
of most and least developed country clusters. Happiness is lowest in the two clusters of
medium-developed, medium-income countries. It is important to clarify that no causality is
implied or tested by the analysis or this representation. Instead, the pattern is simply
highlighted that middle-developed country clusters have the lowest mean happiness.
The finding that mean happiness was markedly lower in the middle-developed two
clusters of countries and highest in the least and most developed clusters is evocative of the
Kuznets curve (albeit inverted in shape)—the theory positing that economic inequality is
exacerbated as countries start developing, but then is reduced as countries become more
developed (Kuznets 1955).
The results indicate that a variation of the Kuznets curve—a happiness Kuznets curve
(HKC)—may exist, whereby average happiness may first decline as companies move from
being undeveloped to medium developed and then rises as countries move from being
medium developed to developed. While, as elaborated upon below, a Kuznets curve for
economic inequality and environmental degradation have been hypothesized and tested in
various ways, it appears that so far the notion of a Kuznets curve for happiness has not
been posited.
The second key takeaway is that carbon emissions do not track perfectly with either
development or happiness. Of particular interest is the second most developed cluster
with the second highest mean income, yet with roughly 40 % less carbon emissions
than both the most developed and one of the middle-developed clusters. Its mean
emissions per capita approximate the second least developed cluster, whose mean
income is 86 % lower. Visualized this way, the results of the cluster analysis beg
further investigation as to whether and how other countries could emulate the outcome
of having development and high incomes with low carbon emissions. Again, such
speculation is outside the bounds of this study, but these results do provide direction
for new research questions.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
High D evelopme nt
& Inc ome & CO2,
and Happy
High Development
& Inc ome &
Happy, but Middle
CO2
Upper Middl e
Development &
Income, but High
CO2, and 2nd L east
Happy
Mid dle
De ve lopm en t &
Income & CO2, but
Least Happy
Least Development
& Inc ome & CO2,
but Happy
CO 2 per capit a
HDI
GNI per capita
Happiness
Fig. 1 Indexed average of indicators in each country cluster, displayed as a percent of the highest value for
each indicator
A happiness Kuznets curve? Using model-based cluster analysis
123
9 Implications for future research
An environmental Kuznets curve (EKC) has been posited (Grossman and Krueger 1995;
Selden and Song 1994; Shafik 1994; Stern et al. 1996) and tested and critiqued (Babcicky
2013; Stern 2004; White and Sulkowski 2010). This hypothesis holds that environmental
conditions worsen as countries begin to develop but eventually improve as countries
become more fully developed. One study tested environmental intensity of human well-
being (EIWB), represented as the ratio of a nation’s per capita ecological footprint to its
average life expectancy at birth (Dietz et al. 2009), finding an inverse of the Kuznets curve.
However, to date, it seems that no one has explicitly articulated the existence of a
happiness Kuznets curve (HKC). Ott (2011) came closest: in a cross-country comparison of
governance quality and happiness, the study found a curve showing that happiness
inequality increased as countries moved from lowest governance quality to medium
governance quality and then decreased as among countries with highest governance
quality. Ott noted the similarity of this bell curve to the Kuznets curve. However, this study
has revealed that an HKC may exist, with happiness decreasing and then increasing as
countries move through stages of development. This should provoke further studies that
test for causality. For example, it may be that indicators of human development, income, or
some other factor cause average happiness to improve as a country moves through phases
of development.
To a greater degree extant literature has explored how carbon emissions do not track
perfectly with greater development, prosperity, and well-being (Steinberger and Roberts
2010). The second key finding builds upon those findings. The key next questions remain
how countries—and by extension, the economic and societal units that comprise coun-
tries—can further the goals of happiness and prosperity while further decoupling these
aims from environmental degradation.
One vital implication for scholars and policy-makers in wealthy, developed countries is
that role models, best practices, and good ideas should not chauvinistically be assumed to
be found exclusively in their own countries. Less wealthy countries where there are high
levels of happiness and well-being could be a source of ideas worthy of emulation or
adaptation.
The corollary for developing or undeveloped countries is not to imitate blindly the
practices—nor unquestioningly to follow the advice—of authorities in more developed
countries. To some extent this has occurred, and the clusters described here may hint at
this: An example of this is the ‘‘leap-frogging’’ of stages of development in telecommu-
nications infrastructure, with developing countries adopting cellular phone and data net-
works rather than building the physical infrastructure of transmission lines. The result is
advancement in connectivity with a comparably lower amount of negative environmental
externalities (relative to imitating the stages of development of historically wealthier
countries).
As suggested elsewhere (Dietz et al. 2012), it is appropriate to consider whether
national-level trends and comparisons hold any implications or raise questions for sub-
national units of analysis such as firms. Just as the environmental footprint of a country
cannot be divorced from the environmental footprint of commerce, neither can we separate
the happiness of a society from the happiness of employees of businesses. Therefore, there
are several implications for managers, policy-makers, and management scholars in the
results of this study.
A. Sulkowski, D. S. White
123
A critical question—though obviously a provocative one in the realms of both public
policy and management—is what is either an optimal level of compensation and con-
sumption, and development, or else what are the tipping points of the factors that con-
tribute to happiness? If governments decide to cease treating constantly increasing
consumption of material goods and GDP growth as policy goals, then understanding
alternative KPIs will continue to gain importance. If the trend of dematerialization con-
tinues to take hold, beyond emphasis on renewability and supply loops and servicing
(Reiskin et al. 2008; Rothenberg 2007; White et al. 1999) to a fundamental downsizing of
possessions and materially consumptive lifestyle, what will be the KPIs of successful
organizations and economies?
Besides joining others who have called for development and adoption and use of an
expanded range of KPIs for both firms and economies, the authors believe that there is a
need to specifically focus on measures of happiness and using them in the management.
Potentially, the annual publication of statistics on the happiness of employees may become
as commonplace as reporting on environmental and societal impacts and governance (ESG
or sustainability reporting). Ninety-five percent of the Global Fortune 250 now engage in
this practice, along with thousands of other organizations (KPMG 2011). Inasmuch as it
has been established that financial performance of firms is positively impacted by having
happier employees, it is logical that not only employees and clients, but also investors
would gain from including company happiness indicators in annual reporting practices.
10 Limitations
The first possible critique is the choices of data used. Arguably the methodology by which
happiness data from sixty-one countries was gathered may have had imperfections, with no
guarantee that samples were demographically representative. This concern is ameliorated,
however, by the fact that the dataset resulting from the Gallup surveys roughly corrobo-
rates with the findings of scholarly studies. On a mood happiness scale ranging from 0 to
100 with an overall mean of 75 (Cummins et al. 2014), developed countries tended to have
means in the range of 70–80 points (Cummins 1995), while less developed countries had a
mean mood happiness of between 60 and 80 points (Cummins 1998). In the present study
using Gallup data, the least developed cluster had a mean self-reported happiness of 89 %
or roughly what one would expect based on these earlier studies. Studies that have explored
differences in methodologies for gauging happiness have found that they ultimately do not
yield vastly varying results (Ferrer-i-Carbonell and Frijters 2004).
A related weakness in the data is the potential that the question ‘‘are you happy’’ may be
interpreted differently across cultures and in different languages. If so, should (at the firm
level and country level) we further develop baseline definitions, methods, metrics, and
databases of this vital measure? This question has been investigated to some extent and
cultural differences tend not to be a significant obstacle to international comparisons of
happiness (Diener and Oishi 2000).
Conceivably more countries could be included, as well as more variables, such as
measures of average daylight-hours-per-day, mean temperature, and average leisure time,
but these are more fairly viewed as ideas for further studies rather than critical weaknesses
in this study. It bears repeating that the goal of this study is just to test for interesting
aspects of reality; it is not to propose and test specific hypotheses. An inherent limitation,
A happiness Kuznets curve? Using model-based cluster analysis
123
therefore, is that correlation or causality between variables is not being tested at this stage.
There could be a variety of causal relationships proposed and tested moving forward.
11 Conclusions
This exploratory study used a novel approach in its field—model-based cluster analysis—
to evaluate data on happiness, development, income, and carbon emissions. This data
reduction technique identifies clusters of statistically similar entities. The intent is to
identify patterns that may previously have been underappreciated or not noticed. The
model-based cluster analysis indicated that there are five distinct groups of similar
countries.
Two key patterns are identified. When country clusters are distributed on a horizontal
axis by level of income and development and happiness is charted on the vertical axis, a
happiness Kuznets curve (HKC) emerges, whereby the average happiness of countries
appears to decline as they transition from less-developed to medium-developed, and then
rises as they become highly developed. Second, carbon emissions do not track perfectly
with development and income. A particularly interesting cluster is one where happiness,
income, and development are all second highest and near parity with the most developed
cluster, but carbon emissions are roughly 40 % below that of the most developed and a
middle-developed cluster of companies, and roughly the same as the second least devel-
oped cluster.
The authors underscore that this analysis does not suggest causality between variables.
Rather, it contributes to extant literature on the topics of happiness and sustainable
development by deploying a novel tool and finding that a happiness Kuznets curve may
exist and that carbon emissions do not perfectly track with indicators of income, devel-
opment, and happiness.
Appendix 1
Country data, listed by level of happiness
Country Net
Happiness
Happiness (%
of max)
CO2 CO2 (%
of max)
HDI HDI (%
of max)
GNI GNI (%
of max)
Fiji 85 100 1.5 9 0.70 75 3670 5
Nigeria 84 99 0.5 3 0.47 50 1240 2
Netherlands 77 91 11.0 62 0.92 98 48,530 66
Colombia 73 86 1.6 9 0.72 77 5460 7
Ghana 72 85 0.4 2 0.56 59 1260 2
Switzerland 69.5 82 5.0 28 0.91 97 73,680 100
Finland 69 81 11.5 66 0.89 95 47,140 64
Philippines 69 81 0.9 5 0.65 70 2060 3
Brazil 68.5 81 2.2 12 0.73 78 9520 13
Malaysia 68 80 7.7 44 0.77 82 8150 11
Saudi Arabia 66 78 17.0 97 0.78 83 19,360 26
Denmark 64 75 8.3 48 0.90 96 59,590 81
A. Sulkowski, D. S. White
123
Country Net
Happiness
Happiness (%
of max)
CO2 CO2 (%
of max)
HDI HDI (%
of max)
GNI GNI (%
of max)
Iceland 63.5 75 6.2 35 0.91 97 33,900 46
Uzbekistan 62 73 3.7 21 0.65 70 1300 2
Azerbaijan 60 71 5.1 29 0.73 78 5370 7
Peru 59.5 70 2.0 11 0.74 79 4720 6
Ecuador 58.5 69 2.2 12 0.72 77 4330 6
Spain 55 65 5.9 33 0.89 94 31,420 43
Armenia 53 62 1.4 8 0.73 78 3330 5
Germany 52.5 62 9.1 52 0.92 98 43,300 59
Austria 51 60 8.0 45 0.90 95 47,060 64
Singapore 50 59 2.7 15 0.90 95 42,530 58
Sweden 50 59 5.6 32 0.92 98 50,860 69
Japan 49 58 9.2 52 0.91 97 42,190 57
Canada 47.5 56 16.2 92 0.91 97 43,250 59
Belgium 44 52 10.0 57 0.90 96 45,840 62
Korea, Rep
(South)
43.5 51 11.5 65 0.91 97 19,720 27
India 40.5 48 1.7 9 0.55 59 1290 2
Bosnia and
Herzegovina
39.5 46 8.1 46 0.74 78 4640 6
Australia 39 46 16.9 96 0.94 100 46,310 63
Mozambique 39 46 0.1 1 0.33 35 430 1
France 38 45 5.6 32 0.89 95 42,280 57
Cameroon 36 42 0.4 2 0.50 53 1130 2
Macedonia 35.5 42 5.2 29 0.74 79 4580 6
South Africa 35 41 9.2 52 0.63 67 6100 8
USA 33.5 39 17.6 100 0.94 100 48,960 66
Kenya 32.5 38 0.3 2 0.52 55 800 1
Russian
Federation
31.5 37 12.2 70 0.79 84 10,000 14
Vietnam 30.5 36 1.7 10 0.62 66 1270 2
Bulgaria 29.5 35 5.9 34 0.78 83 6320 9
Tunisia 29.5 35 2.5 14 0.71 76 4150 6
Ukraine 29 34 6.6 38 0.74 79 2990 4
Moldava 28 33 1.4 8 0.66 70 1820 2
Pakistan 28 33 0.9 5 0.52 55 1060 1
Hong Kong 27.5 32 5.2 29 0.91 97 33,630 46
China 27 32 6.2 35 0.70 75 4240 6
UK 27 32 7.9 45 0.88 93 38,690 53
Georgia 25 29 1.4 8 0.75 79 2680 4
Czech Republic 24.5 29 10.6 60 0.87 93 18,370 25
Turkey 24.5 29 4.1 24 0.72 77 9980 14
Morocco 24 28 1.6 9 0.59 63 2880 4
Italy 23 27 6.7 38 0.88 94 35,520 48
Ireland 18 21 8.9 51 0.92 98 42,810 58
A happiness Kuznets curve? Using model-based cluster analysis
123
Appendix 2
Country Net
Happiness
Happiness (%
of max)
CO2 CO2 (%
of max)
HDI HDI (%
of max)
GNI GNI (%
of max)
Poland 18 21 8.3 47 0.82 88 12,400 17
Serbia 14.5 17 6.3 36 0.77 82 5550 8
Iraq 12 14 3.7 21 0.59 63 4380 6
Lithuania 9 11 4.1 23 0.82 87 11,620 16
Portugal 8 9 4.9 28 0.82 87 21,870 30
Egypt 0 0 2.6 15 0.66 71 2550 3
Romania -10 -12 3.7 21 0.79 84 8010 11
Lebanon -12.5 -15 4.7 27 0.75 79 8360 11
Countries and associated data listed by cluster
Country Happiness CO2 per capita HDI GNI per capita Cluster
Fiji 85 1.499937 0.702 3670 1
Nigeria 84 0.494091 0.471 1240 1
Colombia 73 1.629452 0.719 5460 1
Ghana 72 0.370888 0.558 1260 1
Philippines 69 0.873148 0.654 2060 1
Uzbekistan 62 3.656678 0.654 1300 1
Peru 59.5 1.967658 0.741 4720 1
Ecuador 58.5 2.175598 0.724 4330 1
Armenia 53 1.424236 0.729 3330 1
India 40.5 1.666209 0.554 1290 1
Mozambique 39 0.120258 0.327 430 1
Cameroon 36 0.350799 0.495 1130 1
Kenya 32.5 0.303782 0.519 800 1
Vietnam 30.5 1.728118 0.617 1270 1
Tunisia 29.5 2.453102 0.712 4150 1
Moldova 28 1.363005 0.66 1820 1
Pakistan 28 0.932118 0.515 1060 1
Georgia 25 1.401643 0.745 2680 1
Morocco 24 1.599383 0.591 2880 1
Iraq 12 3.703433 0.59 4380 1
Egypt 0 2.622791 0.662 2550 1
Brazil 68.5 2.150268 0.73 9520 2
Malaysia 68 7.667467 0.769 8150 2
Azerbaijan 60 5.050749 0.734 5370 2
Bosnia and Herzegovina 39.5 8.093102 0.735 4640 2
Macedonia 35.5 5.171997 0.74 4580 2
South Africa 35 9.204085 0.629 6100 2
A. Sulkowski, D. S. White
123
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This study aims to address the economic, social and environmental wellbeing issues simultaneously by measuring the carbon intensity of wellbeing (CIWB) of Asian economies employing Prais-Winsten and pooled OLS estimator. The measure of carbon intensity of wellbeing (CIWB) is made taking into account a ratio of the two indicators-CO2 emissions per capita and life expectancy at birth. There is paucity of studies that concentrate on human and social wellbeing indicators (i.e., water, sanitation, life expectancy) together applying Environmental Kuznets Curve (EKC) hypothesis. Therefore, we have also investigated the EKC hypothesis as this theory hypothesizes the link involving human and environmental wellbeing and development. The findings utilizing the two econometric techniques indicate that in both the estimation models urban population access to an improved water source and total population access to improved water source has consistently negative and significant effects on CIWB. The fertility rate and prevalence of HIV poses no threat to CIWB. These findings demonstrate that social and human wellbeing indicators of the Asian economies are sustainable to this moment as they are lowering CIWB which is desirable. Contrary, GDP per capita, exports as a percent of GDP and urban population have a significant and positive impact on CIWB which pose a challenge for the sustainability issue. We also have found the existence of EKC hypothesis indicating environmental quality will increase past a turning point. The findings of the paper are well matched with the view of ‘Economic and ecological modernization’ theory and ‘human ecology’ theory. JEL Classification: Q54; Q56; R11
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The global financial crisis has made it painfully clear that powerful psychological forces are imperiling the wealth of nations today. From blind faith in ever-rising housing prices to plummeting confidence in capital markets, "animal spirits" are driving financial events worldwide. In this book, acclaimed economists George Akerlof and Robert Shiller challenge the economic wisdom that got us into this mess, and put forward a bold new vision that will transform economics and restore prosperity. Akerlof and Shiller reassert the necessity of an active government role in economic policymaking by recovering the idea of animal spirits, a term John Maynard Keynes used to describe the gloom and despondence that led to the Great Depression and the changing psychology that accompanied recovery. Like Keynes, Akerlof and Shiller know that managing these animal spirits requires the steady hand of government--simply allowing markets to work won't do it. In rebuilding the case for a more robust, behaviorally informed Keynesianism, they detail the most pervasive effects of animal spirits in contemporary economic life--such as confidence, fear, bad faith, corruption, a concern for fairness, and the stories we tell ourselves about our economic fortunes--and show how Reaganomics, Thatcherism, and the rational expectations revolution failed to account for them.
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Based on environmental Kuznets curve theory, a panel data model which takes environmental regulation and technical progress as its moderating factors was developed to analyse the institutional and technical factors that affect the path of low-carbon economic development. The results indicated that there was a CO2 emission Kuznets curve seen in China. Environmental regulation had a significant moderating effect on the curve, and the inflection of CO2 emissions could come substantially earlier under stricter environmental regulation. Meanwhile, the impact of technical progress on the low-carbon economic development path had a longer hysteresis effect but restrained CO2 emission during its increasing stage and accelerated its downward trend during the decreasing stage which was conducive to emission reduction. Strict environmental regulation could force the high-carbon emitting industries to transfer from the eastern regions to the central or the western regions of China, which would make the CO2 Kuznets curve higher in its increasing stage and lower in its decreasing stage than that under looser regulation. Furthermore, energy efficiency, energy structure, and industrial structure exerted a significant direct impact on CO2 emissions; we should consider the above factors as essential in the quest for low-carbon economic development.