Economics and Human Biology 10 (2012) 147–153
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Economics and Human Biology
journal homepage: http://www.elsevier.com/locate/ehb
Economic growth and obesity: An interesting relationship with
world-wide implications
Garry Egger a,*, Boyd Swinburn b, F.M. Amirul Islam c
a
Health and Human Sciences, Southern Cross University, Lismore, NSW, Australia
WHO Collaborating Centre for Obesity Prevention, Deakin University, Melbourne, Victoria, Australia
c
School of Health and Social Development, Deakin University, Melbourne, Victoria, Australia
b
A R T I C L E I N F O
A B S T R A C T
Article history:
Received 14 October 2011
Received in revised form 10 January 2012
Accepted 11 January 2012
Available online 20 January 2012
The prosperity of a country, commonly measured in terms of its annual per capita Gross
Domestic Product (GDP), has different relationships with population levels of body weight
and happiness, as well as environmental impacts such as carbon emissions. The aim of this
study was to examine these relationships and to try to find a level of GDP, which provides for
sustainable economic activity, optimal happiness and healthy levels of mean body mass
index (BMI). Spline regression analyses were conducted using national indices from 175
countries: GDP, adult BMI, mean happiness scores, and carbon footprint per capita for the
year 2007. Results showed that GDP was positively related to BMI and happiness up to
$US3000 and $5000 per capita respectively, with no significant relationships beyond
these levels. GDP was also positively related to CO2 emissions with a recognised sustainable
carbon footprint of less than 5 tonnes per capita occurring at a GDP of <$US15,000. These
findings show that a GDP between $US5 and $15,000 is associated with greater population
happiness and environmental stability. A mean BMI of 21–23 kg/m2, which minimises the
prevalence of underweight and overweight in the population then helps to define an ideal
position in relation to growth, which few countries appear to have obtained. Within a group
of wealthy countries (GDP > $US30,000), those with lower income inequalities and more
regulated (less liberal) market systems had lower mean BMIs.
ß 2012 Elsevier B.V. All rights reserved.
Keywords:
Obesity
Economic growth
Happiness
Sustainability
1. Introduction
We have previously proposed an ecological model for
understanding obesity, which suggests that changes
towards a more ‘obesogenic’ environment explain the
rise of the obesity epidemic over the past three decades
(Egger and Swinburn, 1997). While this concept is now
widely accepted (Katan et al., 2009; Sassi, 2009), there
are clearly layers of environmental influence, which Rose
* Corresponding author. Tel.: +61 2 99777753.
E-mail addresses: [email protected] (G. Egger),
[email protected] (B. Swinburn), [email protected]
(F.M. Amirul Islam).
1570-677X/$ – see front matter ß 2012 Elsevier B.V. All rights reserved.
doi:10.1016/j.ehb.2012.01.002
(1992) referred to as the ‘causes of the cause’. The
immediate or proximal environments,1 which influence
changes in energy intake and physical activity levels,
1
‘Drivers’ are the key linear forces behind disease causality. They range
from proximal (i.e. more immediate to the disease), to distal. Obesity, for
example, has proximal drivers of energy over-consumption, medial
drivers of obesogenic food environments and distal drivers of economic
policies. ‘Mediators’ are influences on the causal pathway (e.g.
agricultural subsidies for fat/sugar, lower cost for manufacturing, lower
retail prices, increased consumption of high fat/sugar foods); ‘moderators’ accentuate or attenuate factors on the causal pathway (e.g. the built
environment or cultural impacts on health behaviours), and ‘enablers’
are conditions allowing causal factors to be exhibited (e.g. sufficient
disposable income to permit over-consumption of food).
148
G. Egger et al. / Economics and Human Biology 10 (2012) 147–153
include the food environment, the built environment,
and the entertainment environment – especially small
screen technology. These food and activity environments
can operate close to people in settings (such as homes,
schools, supermarkets, neighbourhoods) or at more of a
distance in sectors (such as food production, food
marketing, transportation systems) (Swinburn et al.,
1999). The next layers of environments, the medial
drivers, are generally societal in nature.
1.1. Obesity and inequality
Several researchers have studied the effects of socioeconomic position as a key determinant of chronic
disease, including obesity. Marmot and colleagues, for
example (Marmot et al., 1991; Elovainio et al., 2009;
Stringhini et al., 2010, 2011), have shown the impact of
income inequality on a range of health and social
problems. More specifically, Pickett et al. (2005) and
Wilkinson and Pickett (2010) have shown a relationship
between income inequality, as measured by the ratio of
the difference between the richest and poorest 20% of
income earners (RP20) and obesity in OECD countries,
but no relationship between obesity and average
income.
1.2. Obesity and economic insecurity
More recently, Offer et al. (2010) used 96 data sets from
11 high-income countries over the ten years from 1994 to
2004 to test the effects of inequality and other factors
relating to the connection between affluence, welfare
regimes and obesity. They showed that while income
inequality, and the relative price of ‘junk food’ (food
‘shock’), were related to population obesity prevalence, the
relationship was much stronger using a measure of
economic insecurity based on a weighted composite of
four sub-indices: insecurity from unemployment, illness,
single parent poverty, and poverty in old age. Offer et al.
also showed that economic insecurity and obesity
prevalence were greater in a group of wealthy (mainly
English speaking) countries they classify as ‘marketliberal’ (US, Australia, Canada, NZ, UK, Ireland) compared
to a group they rate as ‘non-market liberal’ (Norway,
Sweden, Finland, Denmark, France, Germany). Market
liberalisation here refers to the level of regulatory
constraint on commerce and level of social spending and
market governance, which predominate within a country.
Offer et al. (2010) suggest that market-liberal reforms
stimulate greater competition in both labour and consumption markets, and that this undermines personal
stability and security, affecting body weight, particularly
amongst those lower down the social scale. A mediator
hypothesised for this is stress, stemming from class
inequalities and lack of trust. Offer et al. propose that
inequality, which is a social attribute, and economic
insecurity, which is a personal one, could be different, but
inter-relating mediators predisposing to obesity within a
country, driven by the form of market governance (‘liberal’,
or minimally regulated vs ‘non-liberal’ or more regulated)
of that country.
1.3. Obesity and economic growth
This type of evidence points to an even deeper layer of
distal environmental driver of obesity. Modern, marketbased economies have at their core a drive for economic
growth, so much so that increasing the annual per capita
Gross Domestic Product (GDP), the most commonly used
indicator of national improvements in prosperity, has
become a dominant political objective. A common way of
achieving this is through increasing consumption (including eating more, and buying more entertainment and
energy saving devices). But the more effective companies
become at selling products and services (and thus
contributing to GDP), the higher the likelihood that
beneficial consumption could tip over into over-consumption. The links from here to obesity, through an overconsumption of food energy (Hall et al., 2009; Swinburn
et al., 2009), and to climate change, through overconsumption of fossil fuel energy (Delpeuch et al., 2009;
Egger, 2008; Egger and Swinburn, 2010), appear obvious,
but have barely been explored. Socio-economic inequalities and economic insecurity for substantial proportions of
the population also so appear to be an outcome of less
regulated or more ‘liberal’ market-based economies
(James, 2008).
Clearly, economic growth is currently a primary means
by which low-income countries can lift themselves out of
poverty. It has also undoubtedly been one of the single
biggest influences on health improvements throughout
human history (Riley, 2001). However, by the law of
diminishing returns, beyond a point, the benefits from
continued economic growth start diminishing and ‘costs’
start rising (Egger, 2009). We have thus postulated that
there may be a theoretical GDP which is high enough to
produce good health, sufficient prosperity and happiness,
but not so high that it produces the overconsumption
problems of obesity and an unsustainable carbon footprint
(Egger and Swinburn, 2010). The primary aim of this paper
is thus to examine the possible relationship between GDP
and obesity, using cross-sectional data from 175 countries.
A secondary aim is to identify an ideal level of per capita
GDP within a country for optimal levels of body weight,
human happiness and sustainability. A third aim is to
assess the effects of different forms of market governance
on obesity. Because of the available data, this latter
analysis is confined to upper income countries.
2. Materials and methods
2.1. Data sources
GDP and social inequality data were obtained from the
Human Development Report (UNDP, 2010) and mean
national body mass index (BMI) data from the Global
Burden of Metabolic Risk Factors of Chronic Diseases
Collaborating Group (Imperial College, 2011). Social
inequality values were calculated as the ratio between
the richest and poorest quintiles (RP20) of average income
as reported in the Human Development Index (UNDP,
2010). The analysis year was 2007 unless otherwise
indicated. Countries used in the analysis (n = 175) were
G. Egger et al. / Economics and Human Biology 10 (2012) 147–153
Fig. 1. Per capita Gross Domestic Product (GDP) by mean BMI (males and
females combined) for 175 countries. Note: The extreme outlier in the top
left hand corner is Samoa and was not included in the analyses. The
vertical line at GDP of $3000 represents the GDP at which spline analysis
best separates a positive relationship at lower GDPs and no relationship at
higher GDPs.
those where both mean BMI and mean per capita GPD
measures were available for 2007 (or the closest possible
year). Happiness measures by country were obtained from
the Happy Planet Index (HPI) of the New Economics
Foundation (2010). Carbon footprint figures were obtained
from the International Energy Agency (2010), Meinshausen et al. (2009) and Nationmaster.com.
2.2. Defining the ideal GDP
A proposed ideal for GDP was defined as meeting the
three following criteria:
A. Having minimal prevalence of both underweight and
overweight. (The range of mean population BMI that
minimises underweight and overweight is 21–23 kg/m2
(James, 2004). A BMI above 23 kg/m2 is used by WHO
(2011) in its analyses of the burden of high BMI.)
B. Being above a GDP where further gains in human
happiness do not appear to be related to increases in
GDP.
C. Being below a GDP where the average carbon footprint
begins to rise above the estimated sustainable level of
5 tonnes per person per year for a 2007 world
population of 7 billion (Meinshausen et al., 2009).
2.3. Data analysis
The relationships between GDP and BMI, and GDP and
happiness (HPI) were analysed using spline regression
techniques. These were performed to identify the highest
GDP level beyond which there was no significant relationship between per capita GDP and average BMI (males and
females combined) and happiness. The GDP level for a per
capita carbon footprint of 5 tonnes per person was
obtained from Nationmaster.com. The findings were
plotted onto the GDP–BMI scatter plot to identify countries
in or near a proposed ideal GDP that would satisfy these
optimal conditions.
To examine the relationship between obesity, market
governance type, and social and environmental factors at
149
Fig. 2. Per capita Gross Domestic Product (GDP) by Happy Planet Index
(HPI) scores, for 168 countries. Note: The vertical line at $5000
represents the GDP at which spline analysis best separates a positive
relationship at lower GDPs and no relationship at higher GDPs.
high levels of per capita GDP (e.g. >$30,000) extra data
were collected from a number of sources. The proportion of
GDP allocated to social spending came from the World
Social Security Report of the ILO (2010). A measure of
individualism vs collectivism in a country was obtained
from Hofstede’s Index (Hofstede, 2001).
There were 26 countries with GDPs > $30,000 pa. Five
(Cyprus, UAR, Iceland, Kuwait and Brunei) were not
included in further analyses either because of lack of
available data in at least one variable, or a population of
<3 million (Brunei, Cyprus). This left 21 countries for
analysis. In line with findings by Offer et al. (2010), the six
English-speaking countries (US, UK, Australia, NZ, Ireland
and Canada), which also had the highest mean BMIs were
compared on various other indices with the remaining 13
non-English speaking European countries, and two Asian
counties (Singapore and Japan) with GDP > $30,000. Mean
values were analysed using one-way ANOVA with the
option of least significant difference (LSD) to show the
difference between pair groups of countries.
3. Results
The scatter plots of GDP vs mean BMI for males and
females in 174 countries are shown in Fig. 1. (Samoa, as an
extreme outlier was omitted from further analysis.2) These
suggested an initial close relationship between BMI and
GDP at low levels of GDP, followed by a levelling off at
higher levels. A spline analysis confirmed this with the
best-fit intersection of two linear relationships occurring
at a GDP of $3000. Below a GDP of $3000, 72 countries
had a significantly positive linear relationship (b:
0.0014431; 95% CI: 0.0010158, 0.0018704, r = 0.567;
p < 0.001) whereas above this level (i.e. >$3000), 102
countries showed no significant relationship between GDP
2
Pacific Islanders have a very high food energy intake associated with
cultural factors, religious and daily social activities, and a negative
attitude to unnecessary physical activity, the need for which can
otherwise be negated by energy-saving technology such as motor
vehicles. Cultural factors are beyond the scope of the current analysis, but
clearly need to be considered (Ulijaszek, 2003).
150
G. Egger et al. / Economics and Human Biology 10 (2012) 147–153
Fig. 3. Per capita Gross Domestic Product (GDP) by per capita carbon
emissions for 174 countries. Note: The points to the upper right of the
intersection line indicate the countries with GDP above US$15,000 (the
point above which no countries have a sustainable carbon footprint based
on current recommendations) and carbon emissions above the
sustainability limit of 5 tonnes per person per year (Meinshausen
et al., 2009).
and BMI (b: 9.10e 06 (95% CI: 6.08e 06, 0.0000243,
r = 0.130; p = 0.18)).
The relationship between per capita happiness as
measured by the Happy Planet Index (HPI) and GDP is
shown in Fig. 2 (n = 168). The vertical line at $5000
represents the intersection of the two linear relationships
from the best-fit spline analysis. With GDP per capita
<$5000, 92 countries showed a positive relationship
between GDP and HPI scores (b = 0.0041906; 95% CI:
0.0026263, 0.005755; r = 0.479, p < 0.000), whereas the 76
countries with GDP > $5000, showed no significant relationship between HPI and GDP (b = 0.0000879; 95% CI:
0.0002119, 0.0000361, r = 0.171; p = 0.164). At very low
levels of GDP, happiness is more strongly associated with
GDP than appears in the figure.
The relationship between GDP and the per capita
carbon footprint is shown in Fig. 3 for 174 countries.
The horizontal line is the carbon footprint of 5 tonnes
per person per annum, which is considered the upper limit
for sustainability for a global population of 7 billion people
(Allen and Frame, 2007; Meinshausen et al., 2009). No
countries above a GDP of $15,000 have a sustainable per
capita carbon footprint.
Several countries had a mean BMI within the optimal
range specified by WHO (21–23 kg/m2). Japan and
Singapore were the only high-income countries within
that bracket. A number of lower income countries, such as
the Philippines, India, Indonesia and Uganda were in the
optimal BMI range, but below the mean GDP for optimal
happiness.
A theoretical ideal for happiness, sustainability and
health was therefore defined as having a per capita GDP
between $5000 and $15,000 and a mean population BMI
between 21 and 23 kg/m2.
Indices for the wealthiest countries (GDP > $30,000) are
shown in Table 1. These form three distinct groups
(English-speaking, European and Asian – although there
were only 2 countries in the Asian grouping). One-way
ANOVA showed significant differences on all measures
except GDP amongst the country groupings.
A diagrammatic representation of results from Figs. 1 to
3 and Table 1 is shown in Fig. 4. The theoretical ideal is
shown as the intersection of a mean BMI of 21–23 kg/m2
and GDP of $5000–15,000. This is the hypothetical zone for
an optimally happy, healthy-weight population living in a
sustainable environment. No countries strongly met these
criteria. Forty countries (not all shown here) were within
the optimal BMI zone and their range of GDPs was
$US264 (Guinea-Bissau)–$US39,000 (Japan/Singapore).
Thirty-seven countries were within the optimal GDP zone
and their range of mean BMIs were 22.45 (Angola)–
27.7 kg/m2 (Barbados). Within the wealthy countries,
Japan and Singapore were within the optimal BMI range
with South Korea not far from the limit.
4. Discussion
4.1. The relationship between GDP and BMI
The data presented here, although cross-sectional show a
relationship between body weight and economic development as measured by the per capita GDP of a country.
4.2. Identifying a GDP ideal
We analysed the relationship between GDP and three
other indices to identify the boundaries of a theoretical ideal
for population happiness, body weight and economic
sustainability. Our main dependent variable here was
BMI, but other measures such as life expectancy, infant
mortality etc. could have been used. Only two countries
(Malaysia and Angola) sat on the edges of this zone.3 Four
countries (India, Indonesia, The Philippines, Uganda) were
on the verge of moving into the zone with increased
prosperity (but only if mean BMI increases minimally).
Several others (China, Thailand, Cameroon, Mongolia etc.)
had already passed the optimal mean national BMI at a level
of prosperity below that defined for optimal ‘happiness’.
Two Asian countries (Japan and Singapore) on the other
hand were within the BMI/happiness zone, but beyond the
level of GDP associated with sustainable CO2 emissions
(Table 1). All developed countries were outside this zone,
Sweden (5.1 t/pp/py), Switzerland (5.6 t/pp/py) and France
(5.8 t/pp/py) come close to sustainable emissions (see Table
1), and are within the happiness and income boundaries,
and are therefore closest to the ideal amongst wealthy
countries, but well outside the limit for optimal healthy
body weight as defined for populations (WHO, 2011). In
popular parlance this suggests the populace of these
wealthy countries is unhealthily fat and (marginally)
unsustainably polluting – but happy enough!
It is worth considering more closely the measure of
happiness used here. Coyle (2011) points out the inconsistencies in the vast research on happiness and wealth and
the fact that the relationship may be more linear with
increasing wealth than shown in our Fig. 3, depending on
the measure used.
3
It should be stressed that this ‘zone’ has been created based on three
measures only and is not meant to not imply any level of political or social
perfection.
G. Egger et al. / Economics and Human Biology 10 (2012) 147–153
151
Table 1
Characteristics of wealthy countries (>$30,000 pc GDP), measures on ideal GDP boundaries and social statistics.
Country
BMI 1
GDP 2
RP20 3
HPI 4
CO2 5
Soc Sp 6
Indiv 7
A. US
A. UK
A. Australia
A. NZ
A. Ireland
A. Canada
Mean
B. Spain
B. Germany
B. Norway
B. Finland
B. Belgium
B. Netherlands
B. Austria
B. Sweden
B. Denmark
B. Greece
B. Italy
B. France
B. Switzerland
Mean
C. Japan
C. Singapore
Mean
28.0
26.3
26.7
26.8
24.7
26.3
26.5a
25.4
26.2
24.8
26.1
24.7
24.6
26.1
24.9
24.6
27.1
24.8
24.1
25.5
25.3b
22.5
22.4
22.4c
47.4
43.7
46.8
30.0
60.5
45.1
45.6
35.1
44.7
94.4
51.6
47.3
52.5
50.0
52.2
62.1
32.1
39.0
46.0
68.4
52.0
38.5
39.0
38.7
8.4
7.2
7.0
6.8
5.6
5.5
6.8a
6.0
4.3
3.9
3.8
4.9
5.1
4.4
4.0
4.3
6.2
6.5
5.6
5.5
5.0b
3.4
9.7
6.6ab
28.8
40.3
34.1
41.9
39.4
39.8
37.4a
43.0
43.8
39.2
37.4
44.0
46.0
48.8
38.2
41.4
35.7
48.3
36.4
48.3
42.3b
41.7
36.1
38.9ab
19.1
9.4
18.8
8.5
10.4
16.7
13.8a
7.7
9.7
7.9
12.7
10.0
10.3
8.4
5.1
9.2
8.7
7.4
5.8
5.6
8.3b
9.7
12.8
11.3ab
15.9
21.3
17.1
18.5
16.7
16.5
17.7a
21.2
26.7
21.6
26.1
26.4
20.9
27.2
29.4
27.1
20.5
25.0
29.2
20.3
24.7b
18.6
1.5
10.1c
91
89
90
79
70
80
83.2a
51
67
69
63
75
80
55
71
74
35
76
71
68
65.8b
46
20
33c
Data and sources: 1. Mean BMI in kg/m2. Imperial College (2011); 2. GDP per capita 2007 ($US,000) UNDP (2010); 3. RP20 = ratio of top 20% of income to
bottom 20% (UNDP, 2010); 4. New Economics Foundation (2010); 5. CO2 emissions per capita/per annum (International Energy Agency, 2010); 6. Social
spending (% GDP) (ILO, 2010); 7. Hofstede’s Individual vs collectivism index (Hofstede, 2001).
a,b,c
indicates the means difference between English and non-English speaking countries. The same letter indicates no difference between means and
different letters indicate significant difference (e.g. mean BMI with letters a, b, and c indicate differences of p < 0.05 minimum between all scores. Happiness
index is different between English and non-English speaking countries but Asian countries are not different from either of the groups.).
4.3. BMI, social factors and forms of market governance
We found that at high levels of national wealth, body
weight may be influenced by factors other than just GDP.
Our three groups of countries; English-speaking, European
and Asian, and the market governance and social factors
associated with these, suggests that those with a more
market-liberal form of governance, with greater emphasis
Fig. 4. Diagrammatic representation of an ideal level in the relationship between per capita Gross Domestic Product (GDP) and mean body mass index (BMI).
Note: Some representative countries with a GDP below $3000 are shown on the regression line where the relationship between BMI and GDP is positive.
Three groups of wealthy countries (>$30,000 pc GDP) are also shown above the break in the x axis. The ideal level as defined here, is the theoretical zone
where populations would have optimal happiness and healthy weight within an economy, measured by per capita GDP, which would be ecologically
sustainable.
152
G. Egger et al. / Economics and Human Biology 10 (2012) 147–153
on individualism and less on social spending have a greater
body weight (and presumably accompanying health
problem) than those with a more collectivist approach
and greater emphasis on equality, as first suggested by
Offer et al. (2010). The variance and limited number of
Asian countries makes it difficult to draw conclusions
about these in relation to this thesis. However our findings
concur with those of Marmot et al. (1991) and Wilkinson
and Pickett (2010), and extend the findings of Offer et al.
(2010), with a larger number of countries.
4.4. Reducing obesity: the big picture
There are a number of possible implications from these
findings. It seems unlikely that any country has, or will
ever pass through the theoretical ideal that we have
proposed during its development. If this is the case, then it
is not just a matter of halting poorer countries as they pass
through this ideal, or returning wealthy countries to it. The
paradigms and structures that appear to have created the
observed patterns in the first place cannot be expected to
get countries to another healthier, sustainable pattern
without major interventions that foster sustainable
technological change.
New paradigms and approaches will be needed and
some of these would apply to achieving sustainable
economies, some would apply to reducing the prevalence
of obesity, some to increasing wealth and happiness and
some to all of these objectives. The prospects of an
increasing global population and runaway climate change
due to the over-consumption of fossil fuels, has markedly
increased the focus in recent years on achieving sustainable economies. However, the power of the current
business model, which is based on unsustainably high
consumption and perpetual growth, does not allow other
paradigms such as ‘prosperity without growth’ (Jackson,
2009), or ‘ecological economics’ (Daly and Farley, 2004) to
gain public or political traction. Events such as global
financial crises may only serve to force politicians to
maintain the status quo, which creates over-consumption,
in a desperate bid to maintain GDP growth. Reducing
national carbon footprints, which might reduce consumption-based GDP, is still anathema to most politicians,
businesses and the public. Several economists are now
considering other models for sustainable prosperity
(Coyle, 2011; Heinberg, 2011), and it is likely that a
transition to these models will be needed as part of the
response to reducing greenhouse gas emissions.
Are there approaches to reducing population levels of
obesity? The findings from our analysis support previous
studies (Pickett et al., 2005; Wilkinson and Pickett, 2010;
Offer et al., 2010) that have shown the influence of social
inequality and economic insecurity on health outcomes,
including obesity, at least at higher levels of income. It
seems that a constellation of factors distinguish countries
with ‘market-liberal’ and ‘market non-liberal’ versions of
capitalism (Hall and Soskice, 2001). The former is
characterised by a more individualistic orientation, philosophy associated with a strong growth ethos, less
government influence in the market economy, less social
spending and regulation, and tax structures which favour
wide gaps in incomes. In contrast, a more social (market
non-liberal) humanistic capitalism (Komlos, in press) is
characterised by a greater collective ethos, more government constraints on the market, higher social spending
and more even distribution of wealth. The latter form of
capitalistic governance is not only associated with a lower
prevalence of obesity, but also with better health and social
outcomes for such disparate indicators as teenage pregnancies, incarceration rates, mental illness, life expectancy,
infant mortality etc. (Wilkinson and Pickett, 2010). Thus,
for those countries with ‘market-capitalism’ models, a shift
towards the governance, economic and social policies of
the ‘social-capitalism’ form might produce health and
social and environmental dividends – at least at this level
of economic development – for already wealthy countries.
4.5. Strengths and weaknesses
The strengths of this study include incorporating high,
middle and low-income countries in the analysis to show
the full range of the relationships, rather than restricting
the analyses to wealthy countries. We have incorporated
the big issues of economic development, carbon footprints,
human happiness and over- and under-nutrition on one
canvas to identify the relationships between key indicators
and characterise the directions in which countries should
be heading in order to optimise health, happiness,
sustainability and economic development. The weaknesses are those of using cross-sectional data, using only
a limited number of indices and making assumptions
about changes over time and the usual risks related to
misattribution of causality with ecological data.
5. Conclusion
Economic growth, under-and over-nutrition, and environmental sustainability are interlinked. Consumptiondriven increases in GDP may be beneficial in the
developing economies, but the detrimental impacts of
the over-consumption they have created in wealthy
countries are now becoming apparent. While specific
policies can counter the obesity epidemic and high
greenhouse gas emissions, new paradigms will be needed
to influence the underlying economic and political
structures, which are the ‘causes of the cause’ of overconsumption. A more controlled form of capitalism may
need to be part of newer approaches to maximising
sustainability, health and happiness in an increasingly
developed world.
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