THE PRICE OF ECONOMIC GROWTH
A study on economic growth and obesity
1975-2013
Osvaldo Quiroga
Department of Economic History
Course: Bachelor's Thesis (level C), 15 credits
Semester: Spring 2018
Public discussion of paper: 28/05/2018
Supervisor: Daniel Normark
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ABSTRACT
The world of 1975 was different the one of 2013 because of profound and big political,
economic and technological changes. The food availability increased due to changes in
production and distribution which made them more accessible and, because of that, the
consumption of food in Kcal per capita increased in all countries.
Urbanization and technological changes also contributed to the development of a sedentary
lifestyle and to the development of a food industry ready to satisfy the necessities of a
constantly growing urban population thru palatable and ready to eat products.
This thesis analyses the relationship between economic growth and the increase in the
prevalence of overweight and obesity during the period of 1975-2013 at a global level and
studies the driving economic factors behind that development.
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Index Introduction ................................................................................................................................ 4
Background ............................................................................................................................ 4
Historic perspective ................................................................................................................ 4
Technological changes ....................................................................................................... 6
Urbanization ....................................................................................................................... 6
Ultra-processed foods ......................................................................................................... 7
Purpose of the study ................................................................................................................... 9
Limitations ............................................................................................................................... 11
Research mode on possible economic causes of the prevalence of overweight and obesity. .. 11
Economic growth (GDP) ...................................................................................................... 12
Economic growth and calorie intake .................................................................................... 12
Economic growth and BMI .................................................................................................. 14
Calorie intake and BMI ........................................................................................................ 16
Energy Balance ................................................................................................................ 16
Body Mass Index (BMI) .................................................................................................. 17
Biometric techniques in economic history ....................................................................... 17
Definition of variables and Data sources ................................................................................. 18
GDP in USD current prices .................................................................................................. 18
Food Supply (kcal/capita/day).............................................................................................. 19
Body Mass Index (BMI) ...................................................................................................... 20
Method ..................................................................................................................................... 22
Grouping of countries ........................................................................................................... 22
Regression models ................................................................................................................ 23
Comparison criteria .............................................................................................................. 23
Results ...................................................................................................................................... 24
Hypothesis 1: Relation between GDP and energy intake (Kcal/capita/day) ........................ 24
Hypothesis 2: Relation between energy intake (Kcal/capita/day) and BMI ........................ 26
Hypothesis 3 ......................................................................................................................... 28
Relation between GDP and BMI ...................................................................................... 28
Relation between GDP and prevalence of Overweigh ..................................................... 29
Relation between GDP and prevalence of Obesity (BMI >30) ........................................ 31
Country analysis ................................................................................................................... 33
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GDP vs overweight .......................................................................................................... 33
GDP vs Obesity ................................................................................................................ 34
Analysis .................................................................................................................................... 36
Conclusions .............................................................................................................................. 42
Referenser ................................................................................................................................. 44
ANNEX 1 ................................................................................................................................. 48
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Introduction
Background
Overweigh and obesity has become a global problem and according to the World Health
Organization (WHO) affects 1.9 billion adults and of these over 650 million are obese. Most
of the world's population live in countries where overweight and obesity kills more people
than underweight, over 381 million children and adolescents aged under 19 were overweight
or obese in 2016. (WHO 2018) Looking to those figures is not difficult to understand the
magnitude of the problem, overweight and obesity once a problem related to rich countries
has become a global problem affecting even low and middle-income countries. Over the past
four decades, we have transitioned from a world in which underweight prevalence was more
than double that of obesity, to one in which more people are obese than underweight, both
globally and in all regions except parts of sub-Saharan Africa and Asia. (NCD Risk Factor
Collaboration (NCD-RisC) 2016, 1389) Without ignoring the problems associated with
obesity at the individual level, its prevalence in now a global economic problem affecting
economies around the world since the detrimental effects on health generates huge expenses
due to direct costs related to excess use of health and medical care, as well as due to indirect
costs related to increased sickness absence among others. (Ljungvall 2012, 5-6) Since the
macroeconomic effects of overweight and obesity are substantial it is, in my opinion,
important to analyse the potential economic drivers behind.
Historic perspective
From an economic historic perspective, the increase of the prevalence of overweigh and
obesity is closely related to the human ability to access food supply and made it easier
accessible. In the evolutionary history of humankind, bodily fat seems to have served nature’s
purpose by outfitting the species with a built-in mechanism for storing its own food reserves.
During prehistoric times, when the burden of disease was that of pestilence and famine,
natural selection rewarded the “thrifty” genotypes of those who could store the greatest
amount of fat from the least amount of the then erratically available foods and to release it as
frugally as possible over the long run. This ability to store surplus fat from the least possible
amount of food intake may have made the difference between life and death, not only for the
individual but also—more importantly—for the species. Those who could store fat easily had
an evolutionary advantage in the harsh environment of early hunters and gatherers. The
discovery of agriculture and domestication of animals some 10,000 years ago gradually
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known as the first agricultural revolution reduced the precarious food supply imposed
theretofore by hunting and gathering. (Eknoyan 2006, 422)
The second agricultural revolution which came almost parallel with the industrial revolution
from about 1760 to sometime between 1820 and 1840 enhanced even more the availability of
food with improved speed of production, large scale-growth of new crops, the development of
transport (railroads), development of new fertilizers and new planting methods and in this
way contributed to the industrial revolution. This contribution was mainly due to an increase
in the amount of energy available for work which had two effects. It raised the labour force
participation rate and for those already in the labour force, it helped to increase the intensity
of work per hour because the number of calories available for work each day increased.
(Fogel och Costa 1997, 49)
At the beginning 60s the world was facing the risk of mass starvation, depletion of non-
renewable resources, and increased poverty in low-income countries such as India, China,
Mexico, Brazil, etc. The discussions regarding the population growth and food production
where on place reviving Malthus theories who argued that while populations tend to increase
exponentially, food production increases only geometrically suggesting that development will
lead to starvation and some argued that the facts where there and that the battle to feed
humanity was already lost. (Lam 2011, 2). Because of that concern the third agricultural
revolution started with the introduction of new technology which increased the agricultural
productivity prioritizing the development and diffusion of high-yielding varieties of the major
staple crops, in combination with more intensive utilization of modern inputs such as
inorganic fertilizer and irrigation (Miguel I. Gómez 2013, 2). Agricultural production more
than tripled between 1960 and 2015, owing in part to productivity-enhancing Green
Revolution technologies and a significant expansion in the use of land, water and other natural
resources for agricultural purposes. The same period witnessed a remarkable process of
industrialization and globalization of food and agriculture. Food supply chains have
lengthened dramatically as the physical distance from farm to plate has increased; the
consumption of processed, packaged and prepared foods has increased in all but the most
isolated rural communities. (Food and Agriculture Organization of the United Nations 2017)
To better understand some of the drivers of this development we are going to see how some
factors that primarily contributed to increase the availability of food historically may have
contributed to the increase in the prevalence of overweight and obesity, those factors are
technological changes, urbanization and the development of process food industry.
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Technological changes
The technological changes followed by the agricultural revolutions has both lowered the cost
of intake of calories and raised the costs of expending calories. The price of calories has fallen
because food prices have declined, and income has grown and, consequently, a rise in weight
would be a natural consequence, but it would be due solely to a rise in calorie consumption.
An equally important change has been the amount of physical exertion required when
supplying labour (R. A. Philipson 1999, 4) In an agricultural or industrial society, work is
strenuous, and food is expensive; in effect, the worker is paid to exercise. He often must also
forego a larger share of his income to replace the calories spent on the job. (D. L. Philipson
2002, 2) Philipson suggested that in earlier agricultural and industrial revolution times, the
opportunity costs of physical activities were virtually zero and in today’s post-industrial
society, physical labour is rarer, and people must pay to—and budget time for—exercise. As
with jobs, technological changes have reduced the amount of physical activity required for a
host of other daily activities, from routine household work to transportation. The supply and
variety of passive entertainment options—from cable TV to video games, DVDs, and the
Internet—has exploded. Since time is finite, this creates an incentive to forgo physical activity
for more plentiful passive entertainment. The net effect of technological advances in the work
place, at home, in transportation, and in leisure-time choices is a reduction in daily energy
expenditure, leaving individuals with a stark choice: whether, or not to fill the gap through
voluntary physical activity. (Variyam 2005) In other words the technological development has
increased the accessibility to food on both sides the, supply and demand, and at the same time
reduced the energy expenditure to access the food and we can conclude that technological
progress is a main driver of economic growth and improvements in living standards. It
increases productivity, thereby boosting per capita income and consumption. Technology also
influences the nature and quality of work, as well as the structure of societies.
Urbanization
The agricultural revolutions had major effects on urbanization, it permitted the settlement in
communities and further the growth of cities, but the development of industries followed by
the industrial revolution led to the rise of new great cities, first in Europe and then in other
regions of the world, as new opportunities brought huge numbers of migrants from rural
communities into urban areas.
The planet has gone through a process of rapid urbanization over the past six decades. In
1950, more than two thirds of people worldwide lived in rural settlements and less than one-
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third in urban settlements. In 2014, more than half of the world’s population was urban.
(Department of Economic and Social Affairs 2014)
Analysing the impact of urbanization on overweigh and obesity Y. Goryakin and, M. Suhrcke
found that living in urban areas is related to significantly higher likelihood of being
overweight for countries at all income levels suggesting that the continuous urbanization
process taking place poses a serious public health challenge. Both car and TV ownership are
robustly related to overweight outcomes, shifting patterns of employment from agriculture
into services, traditionally associated with urbanization and technological change are also
significantly related to a greater probability of being overweight in both low and middle-
income countries. (Yevgeniy Goryakin,Marc Suhrcke 2014, 124)
The technological development and the increase of the urbanization were also the main
drivers to change the type of food we consume.
Ultra-processed foods
The processing of food has been done since time immemorial in which people cooked or
perhaps dried meats, vegetables and fruits for later consumption and its history is closely
related to war, longer expeditions and urbanization. Salt-cured fish and fowl were among the
many provisions entombed with Egyptian royalty to nourish them on their long journey.
Egyptians have been using salt to extend the lifetime of food for at least 4,000 years. Indeed,
for most of recorded human history, salt curing has been a preferred way to preserve food, a
necessity for cold winters, distant wars, or long expeditions. In 1810 Napoleon Bonaparte
awarded French innovator Nicolas Appert 12,000 francs for his invention of canning.
Napoleon had an army to feed and needed a ready supply of long-lasting food. Later that same
century industrialists used Appert’s discovery and the inventions of many others to begin
mass-producing canned produce, cereal, and crackers. For the first time in history food was
made at factories far from consumers. And by the 1910s the food we eat was well on its way
to industrialization. (Everts 2014) But it is not until the end of World War II that many new
processed foods, developed thru military research to feed the soldiers in the battle front, were
introduced to the consumer market. The economic growth which followed the second world
war induced to technological development and increased urbanization in the western world
and the food industry increased the production and development of new technologies to
create, new processed foods manufactured by adding fats, oils, sugars, salt, and other culinary
ingredients to minimally processed foods to make them more durable and usually more
palatable and since the 1980s new industrially produced foods referred to as ultra-processed
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food and drink products entered into the market arena, the new ultra-processed foods were
manufactured from substances derived from foods or synthesized from other organic sources
and the most of these products contain little or no whole food. Ultra-processed foods are
typically not consumed with or as part of minimally processed foods, dishes and meals. On
the contrary, they are designed to be ready-to-eat (sometimes with addition of liquid such as
milk) or ready-to-heat and are often consumed alone or in combination (such as savoury
snacks with soft drinks, bread with burgers). (C. A. Monteiro 2009). (J.-C. M. Monteiro 2014)
The consumption of industrially processed foods has increased constantly since 1910 and now
it accounts for almost 75% of the food sales but the consumption of Ultra-processed foods is
gaining new market shares and taking the major part of the food market globally and, they are
rapidly displacing traditional dietary patterns based on minimally processed foods and freshly
prepared dishes and meals specially in developing countries. The increase in the consumption
of Ultra-processed food has been related to the prevalence of overweight and obesity and
Non-Communicable Diseases (NCD).
Filippa Juul and Erik Hemmingsson in a study of how consumption of ultra-processed foods
has changed in Sweden in relation to obesity in the period of 1960-2010 using NOVA
classification found that from 1960 to 2010 the consumption of ultra-processed foods
increased dramatically in Sweden, which closely tracked the increased prevalence of obesity.
Of special note is the considerably increased intake of energy-dense and nutritionally empty
snack foods such as candies and crisps, and of sodas and other sweet beverages. (Filippa Juul
2015, 3096) The Pan American Health Organization in its report on “ultra-processed food and
drink products in Latin America: Trends, impact on obesity, policy implications” published in
2015 shows the increasing consumption of ultra-processed foods and found a positive
correlation with the obesity development in the region and countries where sales of ultra-
processed products are lower and traditional diets still prevail, such as Bolivia and Peru had
lower mean body mass. Countries where sales of these products are higher, such as Mexico
and Chile, had higher mean body mass. (Pan American Health Organization 2015, 45-46)
Professor Carlos Augusto Monteiro lead a research group that study the relation of ultra-
processed foods and obesity in 19 European cities using NOVA classification and found a
positive relation and the quantity of ultra-processed foods consumed could explain the
difference of obesity prevalence between countries. (C. A. Monteiro, J-C Moubarac, R B
Levy,D S Canella, Ma L da Costa Louzada and G. Cannon 2017, 24)
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To summarize. The prevalence on overweight and obesity is increasing globally, and it seems
that the economic growth, technological changes, urbanization and globalization plays a
major role in this development. The increasing urbanization and the technological changes has
induced to the development of a multinational food industry that can serve the necessities of a
constantly growing urban population with new ultra-processed products ready to be consumed
and the increase in the consumptions of those products has been linked to the development of
the prevalence of overweight and obesity. It seems that all the previous mentioned factors
contributed to the expansion and proliferation of a sedentary lifestyle, to the increase of the
availability of food at any time, to the reduction of the individual energy expenditure and to
increase of the daily calorie intake over the years.
Purpose of the study
Economic growth measured by Gross Domestic Product (GDP) increases is closely related to
the previously mentioned factors and therefore is suitable to study its effects on the
development of the prevalence of overweight and obesity. The aim of this paper is to analyse
the relationship between GDP with the prevalence overweight (BMI 25-30) and GDP with the
prevalence of obesity (BMI>30) separately, the analysis covers the period of 1975 to 2013.
Why the difference on the prevalence of overweight and obesity is important? The
relationship between obesity (BMI >30) and the prevalence of Non-Communicable Diseases
(NCD) such as cardiovascular diseases, diabetes type 2; musculoskeletal disorders and some
cancers is well stablished in the medical literature, it is also well known its negative effects in
a country economy thru increased health costs and the costs related to increased sickness
absence. Without ignoring the problems at the individual level that obesity entails through
stigmatization and in many cases discrimination the obesity is cause of serious imbalances in
the economy and, in some extend can slow down the economic development in less developed
countries.
Whereas the relation between GDP and BMI has been addressed before in different studies
the specific relation of GDP with the prevalence of overweigh and/or the prevalence of
obesity separately has not been addressed at the same extend because of the lack of
information.
Since the possible mechanism linking the economic growth with overweight and obesity is an
impaired energy balance, the connection between calorie intake (consumption) with Body
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Mass Index (BMI) will also be analysed. In the medical literature there are plenty of evidence
that the difference between energy intake and expenditure, frequently referred to as energy
balance, has a direct relation to long-term gain or loss of adipose tissue and alterations in
metabolic pathways. (Romieu I 2017) But in economic literature this relation seems to be
considered obvious therefore its analysis is scarce or almost non-existent, however is, in my
opinion, crucial to understand at an aggregate level the link between economic growth and the
prevalence of overweight and obesity. However, there are some studies on living conditions
during the time of industrial revolutions that uses weigh, length and calorie consumption as
parameters. (Sara Horrell 2007) (Fogel.R & Costa 1977)
The hypothesis to be proven in this study are the following:
Hypothesis 1: During the studied period changes in economic growth leaded to changes in
daily calorie intake per capita. It means that increases in GDP induced to increases in calorie
intake per capita and day. Why the calorie intake should be correlated with economic
growth? To answer this question, you must see food as a source for energy and its effects on
the economic development. Robert Fogel in his theory of techno physio evolution, argued that
about 50 percent of the British economic growth since 1790 can be attributable to the
combined effect of the increase in dietary energy available for work and the increased human
efficiency in transforming dietary energy into work output (Fogel och Costa 1997) (Fogel.R
& Costa 1977).
Hypothesis 2: During the studied period changes in calorie intake, ceteris paribus, leaded to
changes in BMI. This hypothesis is based on the first law of thermodynamics in the human
body or energy balance stating that thru metabolism humans convert food into energy, which
is then used by the body to perform activities ranging from sleep to heavy exercise. It is well
known and stablished in the medical literature that individuals gain weight when calories
consumed exceed those expended, in other words thru an energy imbalance. the use of height
and weight to determine the living standards in the different era has been used in several
studies. (Fogel och Costa 1997) According to WHO the fundamental cause of obesity and
overweight is an energy imbalance between calories consumed and calories expended.
Globally. This hypothesis is based in the medical research and has never been proved using
macroeconomic data.
Hypothesis 3: During the studied period changes in economic growth, consequently, leaded
to changes in BMI and in the prevalence of overweight and obesity. This hypothesis is a
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consequence of hypothesis 1 and 2 saying that if those hypotheses are true increases in
economic growth, consequently, would tend to lead to a change in BMI and implicitly a
change in the prevalence of overweight and obesity in the observed period. I’m interested,
here, to analyses the outcomes of the relationship between; GDP and the prevalence of
overweight and GDP and the prevalence of obesity separately and to see if there are
differences in the outcome. This relation has not been analysed before.
Limitations This study covers the period of 1975 to 2013 and uses aggregate country data of GDP per
capita in current USD prices, food supply in Kilocalories per capita and day, mean BMI in
Kg/m2 and prevalence of overweight and obesity in percent of the total population by country.
The figures of BMI and prevalence of overweight and obesity are the mean of adult men and
women in the population per country and don’t consider gender, age or socio-economic
differences. Regarding the calorie intake in Kcal/capita/day it is assumed that food supply is
equal to food intake which tends to overestimate food consumption, the study, not either,
consider the composition of the food intake. I use the GDP at current USD prices as measure
of economic growth at it gives us a picture of the changes, but it doesn’t show the total
national income per country as the measure of Gross National Income (GNI) does and in that
case tends to underestimate the national per capita income specially in developing countries.
The study doesn’t take socioeconomic variables into account and it that case it can’t help us to
analyse and explain differences within countries/country groups.
Research mode on possible economic causes of the prevalence of
overweight and obesity.
The research regarding the overweigh and obesity in economics focus on possible economic
causes and its effects on the economy. The effects of overweight and obesity in the economy
are important topics and are well documented in the literature. (Ljungvall 2012) (Maximilian
Tremmel 2017) (Ulf Persson och Knut Ödegaard 2011) Respect to the possible
macroeconomic links to overweigh and obesity , there are studies linking macroeconomic
variables such as economic growth, technological changes, urbanization, globalization,
reduction of food prices and the increase of the production and consumption of ultra-
processed foods with the prevalence of overweight and obesity. (D. L. Philipson 2002)
(Ljungvall 2012) (R. A. Philipson 1999) (Yevgeniy Goryakin,Marc Suhrcke 2014) (C. A.
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Monteiro, J-C Moubarac, R B Levy,D S Canella, Ma L da Costa Louzada and G. Cannon
2017) (Pan American Health Organization 2015)
I’m going to focus on the quantitative research regarding the relationship between economic
growth and calorie intake, BMI and overweight and obesity since, in my opinion, the
economic growth calculated as Gross Domestic Product (GDP) per capita covers implicitly
variables like technological changes, urbanization, production/consumption of goods, prices,
etc. and therefore constitutes a good indicator of their development.
Economic growth (GDP)
The Gross Domestic Product (GDP) of a country is the total value of all final goods and
services produced within a country over a period of time period and is stablished as indicator
of economic growth since its measurable and comparable however it doesn’t consider income
socio economic elements as for example income distribution, educational level, mortality
rates, environment, institutional capacity, etc. and therefore it has limitations in its use as an
indicator of economic development. One way to see economic growth is also as the annual
increases in the ability of the average resident to consume, measured by GDP per capita.
Economic growth and calorie intake
The economic growth during the first industrial revolution was first a consequence of the
second agricultural revolution that increased the availability of food with improved speed of
production, large scale-growth of new crops and higher labour agricultural productivity,
which enabled the move of part of the agricultural labour force to the new nascent urban
industrial labour sector in the cities. Furthermore, the increased agricultural productivity and
production was large enough to improve the nutritional status of the growing urban
population.
Robert W. Fogel and Dora L. Acosta presented a theory of the techno physio evolution where
they suggest that during the last 300 years, particularly during the last century, humans have
gained an unprecedented degree of control over their environment-a degree of control that
enabled Homo sapiens to increase its average body size by over 50%, to increase its average
longevity by more than l00%, and to improve greatly the robustness and capacity of vital
organ systems. (Fogel.R & Costa 1977, 49) Robert Fogel argued that about 50 percent of the
British economic growth since 1790 can be attributable to the combined effect of the increase
in dietary energy available for work and the increased human efficiency in transforming
dietary energy into work output which had two effects. It raised the labor-force participation
rate by bringing into the labor force the bottom 20 percent of the population in 1790 who had,
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on average, only enough energy for a few hours of strolling (1900 Kcal/ capita/ day in
England 1600 Kcal/capita/day in France) and Moreover, for those in the labor force, increased
the intensity of work per hour because the number of calories available for work increased.
(R. Fogel 1994, 373) P.W. Gerbens-Leenes, S.Nonhebel, M.S.Krol in their study on food
consumption patterns and economic growth wanted to analyses the following questions: (i)
what are the trends in national per capita food supply, measured in terms of nutritional energy
and macronutrients that follow economic changes? (ii) what are the trends in individual per
capita food consumption, i.e. the food eaten, measured in terms of nutritional energy and
macronutrients, that accompany economic changes? (iii) in which regions will large changes
in food supply and consumption occur in the next 10 years? I’m going to lead my attention to
question (i) since it is related to this study, when analyzing it the authors tested the
relationship between GDP and national per capita food supply in Kcal/capita/day in three
different type of populations; first a longitudinal a study covering data from year 1700 to 2000
for England and France using the time series on calorie consumption developed by Robert
Fogel, second a cross sectional analysis of 72 countries 2001 and finally the study evaluates a
four-decade timeseries relationship in southern Europe (Italy, Spain, Portugal and Greece)
that assesses the relationship between the increase of per capita food supply, changes in the
composition of food consumption. The result of this part of the study concludes that there is a
positive relation between GDP and calorie intake in kcal/capita/day. (Gerbens-Leenes 2010,
5-6) I think that the approach used in this study is well designed combining both longitudinal
and cross-sectional regressions which gives a solid answer to the research question.
The authors also analyses the difference between food supply and the real consumption of it
using national food Supply (kcal/capita/day) data and consumption data from 11 surveys from
developing countries and they found a gap that increases with GDP meaning that in countries
with low GDP the consumption and food supply tends to be almost the same but in countries
with high GDP the gap can be as large as 50% suggesting that a great part of the purchased
foods are not consumed. (Gerbens-Leenes 2010, 7) According to European Commission 88
million tonnes of food waste generated annually in the EU and the estimates is that up to 10%
of the are linked to date marking. The main food categories contributing to food waste were
fruit and vegetables, bakery products, meat including fish and poultry, and dairy products.
(Directorate-General for Health and Food Safety (European Commission) 2018, iii). Although
the problem of food waste is well addressed I think that that the study doesn’t consider self-
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produced food which can be a substantial part of food security in developing countries and it’s
not registered in the statistics of food supply.
Economic growth and BMI
Considering the economic growth as a possible cause for overweigh and obesity Garry Egger,
Boyd Swinburn and F.M. Amirul Islam analyzed the relation of Gross Domestic Product
(GDP) and BMI in a cross-sectional analysis for 175 countries year 2007. The aim of the
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). I’m going to focus in results of the part of the study regarding the relationship between
GDP and BMI and the data presented here, although cross-sectional show a relationship
between BMI and GDP at low levels of GDP, followed by a levelling off at higher levels,
best-fit intersection of two positive linear relationships occurring 72 countries with
GPD/capita < 3000 USD and above that income countries showed no significant relationship
between GDP and BMI. (Garry Egger 2012, 149). The cross-sectional analysis of 175
countries can, give us an instant picture of the specific situation the year of the observation
but it will not tell us anything about changes in time. The use of spline regression techniques
is, in my opinion, appropriate to see the trend across the countries in the specific year, but it is
not suitable for taking strong conclusions over time, unless you can perform the same
calculation for different years and compare outcomes. I have some doubts about the concrete
use of the study since it intends to find an optimum level of GDP, which provides for
sustainable economic activity, optimal happiness and healthy levels of mean body mass index
(BMI). My concerns are the eventual political consequences that might suggest actions to
slowdown economic development in less developing countries in order to stablish the optimal
happiness and healthy levels of BMI.
Y. Goryakin and M. Suhrcke in their study on Economic development, urbanization,
technological change and overweight: What do we learn from 244 Demographic and Health
Surveys? studied the period of 1991-2009 using individual data on woman from 56 countries.
Some of the testing hypothesis where:
• As countries grow out of extreme poverty, overweight among women will increase.
However, as countries continue to grow richer, the increase should slow down at some
level of per capita income.
• An adverse economic shock (recession) will be associated with lower likelihood of
being overweight among women
15
• In low income countries, women of higher socioeconomic status (SES) will be more
likely to be overweight than those with lower SES, whereas in middle income
countries, the burden of overweight will shift towards women of lower SES, resulting
in an insignificant or mildly negative relationship between SES and the probability of
being overweight.
And the analysis found that (1) The relationship between national per capita income and
obesity was positive and concave and as part of the test they showed a very simple
relationship between the level of economic development and overweight prevalence, without
controlling for any socioeconomic or political factors the result shows a positive and concave
relationship; (2) In an economic recession, people in poor countries lose weight and, hence,
are less likely to be overweight (although this was not the case in the middle income
countries); (3) The relationship between education (as a proxy for socioeconomic status) and
the probability of being overweight is positive in the low income countries and negative in
medium-income countries. (Yevgeniy Goryakin,Marc Suhrcke 2014, 116-117) Although the
analysis was on woman it gives us a good picture of the relation between economic
development and BMI, however the study doesn’t differentiate overweight and obesity since
the focus is on overweight, defined as BMI greater or equal to 25 kg/m2.
C A Monteiro, W L Conde, B Lu & B M Popkin presented the study about obesity and
inequities in health in the developing world aimed to update the knowledge regarding the
social distribution of women's obesity in the developing world and, in particular, to identify
the specific level of economic development at which, if any, women's obesity in the
developing world starts to fuel inequities in health. They used a multilevel logistic regression
analyses applied to anthropometric and socioeconomic data collected by nationally
representative cross-sectional surveys conducted from 1992 to 2000 in 37 developing
countries within a wide range of world regions and stages of economic development (gross
national product (GNP) from US$190 to 4440 per capita). They considered obesity status
(body mass index (BMI) ≥30.0 kg/m2), age groups (5-year intervals) and SES and they used
the gross national product (GNP) per capita at the year of the survey to express the level of
economic development of each country included in the analyses. For certain analyses they
grouped the studied countries into (a) low-income economies (GNP <US$745 per capita), (b)
lower-middle-income economies (GNP <US$745–2994 per capita), and (c) upper-middle-
income economies (GNP ≥ US$2995 per capita). (C A Monteiro 2004, 1183) Although, the
study considers socio economic parameters like education and its performed-on woman the
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results of the relation between obesity (BMI) ≥30.0 kg/m2) and GNP showed to be positive.
The spread of the prevalence of obesity in the populations varied from 0.3% in Vietnam to
34.2% in Jordan. They conclude that the risk of been obese in countries can varies dependent
on socio economic status. I think that this study gives a good explanation on the possible risks
of becoming obese in developing countries however it doesn’t consider that the prevalence of
overweight may be also a factor that have influence on the risk of been obese.
Calorie intake and BMI
Energy Balance
The FAO, WHO and United Nations University (UNU) consulted an expert group to make
recommendations for energy requirements of populations throughout the life cycle. This
consultation took place from 17 to 24 October 2001 at FAO headquarters in Rome. The main
issue of the research was to determine the energy balance which is achieved when input (i.e.
dietary energy intake) is equal to output (i.e. total energy expenditure), plus the energy cost of
growth in childhood and pregnancy, or the energy cost to produce milk during lactation.
When energy balance is maintained over a prolonged period, an individual is in a steady state.
The report indicates that total energy expenditure varies from 1700 Kcal/day and 1550
kcal/day for sedentary adult men and woman to 4200 Kcal/day and 3600 Kcal/day for very
active men and women. (FAO/WHO/UNU Expert Consultation 2001). After doing a survey
of studies realized between 1980 and 2000 researchers found that in most cases the current
FAO/WHO/UNU predictive equations overestimate basal energy expenditure or base
metabolic rate in many communities. Using a new dataset including a larger number of people
from the tropics they develop new equations that tend to produce lower values than the
current FAO/WHO/UNU. (Henry 2015, 1148) The Henry equations may prove to be the
most accurate and generalizable, considering the number of or base metabolic rate
measurements used to develop them and the wide spectrum of populations and geographical
origins they represent. (Camps, C. Jeyakumar Henry Stefan G.J.A. 2018, 118).
In the medical literature there are plenty of evidence that that overweight and obesity are
major risk factors for cancer, cardiovascular disease, diabetes, and many other health
conditions, the difference between energy intake and expenditure, frequently referred to as
energy balance, has become of great interest because of its direct relation to long-term gain or
loss of adipose tissue and alterations in metabolic pathways. (Romieu I 2017, 249) One way
to measure the energy balance is with the Body Mass Index (BMI)
17
Body Mass Index (BMI)
The Quetelet Index (weight/height2; kg/m2) was first proposed in 1869 by Adolphe Quetelet
and over time the Quetelet index has come to be more commonly known and referred to as the
Body Mass Index or BMI. (Kuczmarski 2007, 26) and is the most commonly used body
composition marker in marker in epidemiologic studies due to its simplicity of assessment and
high precision of accuracy. (Romieu I 2017, 248) The BMI ranges are based on the effect
excessive body fat has on disease and death and are reasonably well related to adiposity.
(WHO europe u.d.) (Maximilian Tremmel 2017) (WHO 2018) Even if BMI is widely used
there are several difficulties using this measurement some of them related to its accuracy
across different ethnic groups. P Duerenberg found that BMI and percent of Body fat differs
among different ethnic groups. (P Deurenberg 1998) and WHO in its rapport on obesity
prevention (1997) pointed out that BMI does not account for wide variation in body fat
distribution and may not correspond to the same degree of fatness or associated heath risk
across different individual and populations. (WHO 1999, 2000, 2004) Having this into
account, for most people in the general population, higher BMI values will be indicative of
higher levels of body fatness. However, there may be exceptions in certain subgroups of the
population where higher body mass values are attributable to excess lean mass (muscle)
instead of fat, such as in body builders, professional athletes, or military personnel, resulting
in an erroneous overestimate of body fatness. Higher BMI values associated with higher lean
mass may also apply to certain ethnic groups. (Kuczmarski 2007, 26) The Global Burden
Disease (GBD) collaborators in its rapport on Health Effects of Overweight and Obesity in
195 Countries over 25 years concluded that the study provides a comprehensive assessment of
the trends in high BMI and the associated disease burden and that the results showed that both
the prevalence and disease burden of high BMI are increasing globally. (The GBD 2015
Obesity Collaborators 2017, 13-14)
Biometric techniques in economic history
The use of biomedical techniques like weight and height, when integrated with economic
techniques, make it possible to probe deeply into the extent of chronic malnutrition from the
beginning of the 18th century in Europe and North America, to chart and explain the escape
from such malnutrition, and to consider the impact of improved nutrition on the secular trend
in health and life expectation, on labor productivity, and on economic growth. (R. Fogel 1994,
371) the measures of height, weight or Body Mass Index (BMI) has been used in economic
historic research to analyse the living conditions during the period of the industrial revolution.
Sara Horrell in her paper on Measuring misery: Body mass, ageing and gender inequality in
18
Victorian London used height, weight and body mass for 32,584 individual prisoners
incarcerated in the Surrey House of Correction in Wandsworth between 1858 and 1878, and a
second data set on 1018 English female prisoners up to 1887, to examine what it was like for
the working (and sometime criminal) poor to grow up and grow old in mid-Victorian London
(Sara Horrell 2007) . R Fogel emphasized that there are numerous levels at which a
population and a food supply can be in equilibrium meaning that energy intake is equal to
energy output but the constant increase in the prevalence of overweight and obesity probes
that the balance is broken.
Definition of variables and Data sources To perform the analysis, I used data from the following sources: Data on Gross Domestic
Product per capita in current USD from World Bank Data set. (World Bank national accounts
data u.d.) Data on food supply is taken from the statistics division of Food and Agriculture
Organization of the United Nations FOASTAD. (Food and Agricultural Organiszation of the
United Nations u.d.). Finally, the data of BMI and prevalence on overweigh and obesity is
taken from The NCD Risk Factor Collaboration which is a network of health scientists around
the world that provides rigorous and timely data on major risk factors for non-communicable
diseases for all the world’s countries. (NCD Risk Factor Collaboration (NCD-RisC) 2016)
GDP in USD current prices
The data set is from World Bank national accounts data, and OECD National Accounts data
file and covers the years from 1960 to 2016.
GDP at purchaser's prices is the sum of gross value added by all resident producers in the
economy plus any product taxes and minus any subsidies not included in the value of the
products. It is calculated without making deductions for depreciation of fabricated assets or
for depletion and degradation of natural resources. Data are in current U.S. dollars. Dollar
figures for GDP are converted from domestic currencies using single year official exchange
rates. For a few countries where the official exchange rate does not reflect the rate effectively
applied to actual foreign exchange transactions, an alternative conversion factor is used.
(World Bank national accounts data u.d.).
The GDP per capita at current USD prices give us the information at an aggregate country
level and it doesn’t take any consideration of socio-economic or environmental variables like
income distribution, education level, occupation, CO2 emissions, etc at a country level. The
GDP per capita at current USD prices has also limitations when comparing the real value of
19
the money between countries compared when using GDP at Purchasing Parity Prices (PPP)
and, it doesn’t either take into consideration the total national income as when using Gross
National Income (GNI) as measure. GNI considers also the income from citizens and
businesses earned abroad less the income remitted by foreigners living in the country back to
their home countries. The GNI for developing countries tends to be substantially higher since
it takes into consideration international help and remittance from emigrants to their countries,
and it have a substantial effect on the national income and in that case, GDP tends to
underestimate the real national income. The main reason that I didn’t used GNI per capita at
PPP was that the series from the world bank data starts at 1990 but since I’m interested in
changes and general trend I consider that GDP at current USD prices give us a good picture of
the economic growth.
Food Supply (kcal/capita/day)
The data is from FAOSTAT showing series of food supply from 1960 to 2013 and shows the
calorie supply per capita which is the amount of food available for consumption measured in
kilocalories per capita per day (Kcal/capita/day). This figure is reached by dividing the total
available food supply for human consumption by the population. At country level, it is
calculated as the food remaining for human use after deduction of all non-food utilizations
(i.e. food = production + imports + stock withdrawals − exports − industrial use − animal feed
– seed – wastage − additions to stock). Wastage includes losses of usable products occurring
along distribution chains from farm gate (or port of import) up to the retail level. However,
such values do not include consumption-level waste (i.e. retail, restaurant and household
waste) and therefore overestimates the average amount of food consumed. However, the food
supply statistics doesn’t either consider self-produced food which can be a substantial part in
less developing countries.
Food consumption expressed in Kcal/capita/day is a key variable used for measuring and
evaluating the evolution of the global and regional food situation. A more appropriate term for
this variable would be “national average apparent food consumption” since the data come
from national Food Balance Sheets rather than from food consumption surveys. (World
Health Organisation u.d.) In this paper we are going to use food supply in kcal/capita/day as
calorie intake assuming that all food supply is equal to food consumption or intake and it
gives an overestimation of the consumption, however it doesn’t consider either the
The data used has limitations since we use the total calories and doesn’t take consideration of
the content of those calories and therefore we can’t see the differences in consumption
20
between the different groups of countries and neither the differences in consumption within
the country group or in a country. Another limitation is the assumption that the calorie intake
is equal in the country group or within each country since we don’t use information from
surveys at a country bases that can give us a good picture of the differences in calorie intake
within countries.
The series are taken from FAOSTAT and the name is “Food Supply - Livestock and Fish
Primary Equivalent” aggregate figures of Food Supply (kcal/capita/day) and the Grand Total
which give us the total amount of kilocalories per capita per day. (Food and Agricultural
Organiszation of the United Nations u.d.).
Body Mass Index (BMI)
The data is from The NCD Risk Factor Collaboration website and it shows worldwide trends
in body-mass index, underweight, overweight, and obesity from 1975 to 2016 and is the
results of a pooled analysis of 2416 population-based measurement studies in 128.9 million
children, adolescents, and adults. NCD Risk Factor Collaboration (NCD-RisC) is a network
of health scientists around the world that provides rigorous and timely data on risk factors for
non-communicable diseases (NCDs) for 200 countries and territories. The group works
closely with the World Health Organisation (WHO), through the WHO Collaborating Centre
on NCD Surveillance and Epidemiology at Imperial College London. NCD-RisC pools high-
quality population-based data using advanced statistical methods, designed specifically for
analysing NCD risk factors. (NCD Risk Factor Collaboration (NCD-RisC) 2016)
The data set contains information of 200 countries which includes Mean BMI by gender,
prevalence of overweigh, obesity and undernourishment. (The Lancet 2017) This information
gives us the unique possibility to study connections with different variables over time at
country level. To perform the analysis, I used the data in the populations and took a mean
between male and female figures to get the mean of BMI, overweight (BMI 25-30) and
obesity (BMI >30) of each country. The figures used correspond the adult population.
One of the main problems using BMI is when the measures are self-reported since there can
be a tendency of misreport. To avoid that NCD risk excluded all data sources that were solely
based on self-reported weight and height without a measurement component as waist
circumference, or hip circumference. We also excluded data sources on population subgroups
whose anthropometric status may differ systematically from the general population. (The
Lancet 2017) The information used has also limitations when showing gender or socio-
21
economic differences within the countries but, in my opinion, it gives us a good and
comparable picture at aggregate country level that fulfil the needs of this study.
22
Method The analysed period is between 1975 and 2013 and the reason of that is mainly due to the
BMI dataset which have the information from 200 countries from 1975, this gives us the
unique possibility to study connections with different variables over time at a country as well
as at an aggregate level. The information of GDP per capita from World Bank data and
Energy Intake from FOASTAT extends from 1960 to 2016. All the information was
introduced into a database to be matched and at the end I retained in the final dataset the
countries that had complete series from 1975.
Grouping of countries
After the synchronization of the data from the different sources I got a list of 105 countries
which I divided it in 4 groups regarding the GDP in current USD price level in 1975 as
follows:
Group of countries by GDP per capita 1975
Code Country
Group
GDP per capita in USD
current prices
Number of
countries
VL Very Low <400 32
L Low >400 & < 800 20
M Medium >800 & 3000 24
H High > 3000 29
To perform the country analysis, I selected one country per group and the selected countries
are China (VL), El Salvador (L), Mexico (M) and USA (H). The list of countries per group
the average data of all variables per group and country are in Annex 1.
The method of grouping countries by GDP per capita at current USD prices can be considered
as simplistic one since it shows the information at an aggregate level and it doesn’t take any
consideration of the socio-economic and environmental variables neither the real value of the
money or the total national income as explained before and the implicit underlying
supposition will be of an equal resource allocation within the group of countries and in each
country. However, the aim of this study is to analyse the relationships at aggregate level and
compare the outcomes between the groups of countries as well as between the selected
countries and I consider that GDP gives us a good picture of the changes occurred during the
analysed period.
23
Regression models
The relations to be study at aggregate level are between GDP and Calorie intake
(Kcal/capita/day), Calorie intake (Kcal/capita/day) and Body Mass Index (BMI), GDP and
BMI as well as GDP and prevalence of overweight (BMI 25-30) and GDP and prevalence of
Obesity (BMI >30) and a country level are GDP and prevalence of overweight (BMI 25-30)
and GDP and prevalence of Obesity (BMI >30).
To perform the study, I used a lineal regression model: 𝑦 = 𝛼 + 𝛽𝑥;
where y = The dependent variable, 𝛼 = constant, β = regression coefficient which indicates
the magnitude of the effect on dependent variable or the slope of the line and x = the
independent explanatory variable.
Specifically, the regressions are the following:
𝐾𝑐𝑎𝑙 = 𝛼 + 𝛽 𝐺𝐷𝑃
𝐵𝑀𝐼 = 𝛼 + 𝛽 𝐾𝑐𝑎𝑙
To analyse the relation between GDP with BMI, the prevalence of overweight (BMI 25-30)
and Obesity (BMI >30) at an aggregate and country level, I converted GDP data into natural
logarithm (ln). The conversion is mainly because a lineal regression doesn’t fit the model
adequately to the pattern of the data and I found that a semi logarithmic regression could fit
the model better as well as it is easier to explain. The results will tell us that 1% change in x
tends, ceteris paribus, to lead to a β/100 change in y.
The regressions to study are the following:
𝐵𝑀𝐼 = 𝛼 + 𝛽 𝑙𝑛 𝐺𝐷𝑃
𝑂𝑣𝑒𝑟𝑤𝑒𝑖𝑔ℎ𝑡 = 𝛼 + 𝛽 𝑙𝑛 𝐵𝑁𝑃
𝑂𝑏𝑒𝑠𝑖𝑡𝑦 = 𝛼 + 𝛽 𝑙𝑛 𝐵𝑁𝑃
Comparison criteria
The regression results of each group will be specially compared in relation to: R2 which tells
how much variation is explained by the model, β the regression coefficient which tells us
about the impact of the independent variable or the size of the slope of the line and p which
tell if the model is significant at a 95% confidence interval.
24
Results
Hypothesis 1: Relation between GDP and energy intake (Kcal/capita/day)
Table1: results of the regression (𝐾𝑐𝑎𝑙 = 𝛼 + 𝛽 𝐺𝐷𝑃)
Country
Group R2 Constant 𝜷 P
VL 0,89 2 072 0,310 0,00
L 0,85 2 331 0,071 0,00
M 0,87 2 631 0,031 0,00
H 0,71 3 006 0,006 0,00
The result of the regression shows a positive significant relation between GDP and energy
intake suggesting that an increase in GDP tends to lead to an increase of calorie intake
however the effect calculated by the coefficient β is decreasing with increasing GDP in the
different groups as you can see in table 1 above. In countries with very low economic growth
(VL) the increase of the value of coefficient β is 0,31 indicating that for every additional
increase in GDP you can expect energy intake to increase by an average of 0,31
Kcal/Capita/day and in the group of countries with high economic growth (H) the coefficient
β is 0,006 which indicates that for every additional increase in GDP you can expect energy
intake to increase by an average of 0,006 Kcal per Capita and day. The marginal effect of an
increase in GDP on calorie intake in VL countries is almost 52 times the ones of H countries
suggesting a parabolic pattern as you can appreciate in the following diagrams 1:1, 1:2, 1;3
and 1:4.
Diagram1:1 GDP vs Kcal in VL countries Diagram1:2 GDP vs Kcal in L countries
Diagram1:3 GDP vs Kcal in M countries Diagram1: GDP vs Kcal in H countries
y = 0,3123x + 2072,1
R² = 0,8883
2 000
2 200
2 400
2 600
2 800
3 000
3 200
3 400
- 1 000 2 000
Kca
l/ca
pit
a/d
ay
GDP/capita in current USD prices
y = 0,0705x + 2321,5
R² = 0,8532
2 000
2 200
2 400
2 600
2 800
3 000
3 200
3 400
- 2 000 4 000 6 000
Kca
l/ca
pit
a/d
ay
GDP/capita in current USD prices
25
Worth to note is that at the starting point it was big differences in the calorie intake between
the country groups, VL countries showed a calorie intake of 2094 Kcal/capita/day compared
with 2920 Kcal/capita/day in H countries suggesting a difference of 827 Kcal/capita/day and
at the end of the period the difference reduce to 748 Kcal/capita/day see table 1:1 and 4:1 in
Annex 1.
y = 0,0318x + 2631,2
R² = 0,869
2 000
2 200
2 400
2 600
2 800
3 000
3 200
3 400
- 5 000 10 000 15 000
Kca
l/ca
pit
a/d
ay
GDP/capita in current USD prices
y = 0,0061x + 3005,6
R² = 0,7153
2 000
2 200
2 400
2 600
2 800
3 000
3 200
3 400
- 20 000 40 000 60 000
Kca
l/ca
pit
a/d
ay
GDP/capita in current USD
26
Hypothesis 2: Relation between energy intake (Kcal/capita/day) and BMI
Table2: results of the regression
(𝐵𝑀𝐼 = 𝛼 + 𝛽 𝐾𝑐𝑎𝑙)
Country
Group R2 Constant 𝜷 p
VL 0,92 2,15 0,0086 0,00
L 0,93 -5,08 0,0117 0,00
M 0,89 -2,85 0,0102 0,00
H 0,91 0,22 0,0081 0,00
Testing the relation of calorie intake (Kcal/capita/ day) with the mean BMI the results show a
positive significant relation suggesting that an increase of calorie intake, ceteris paribus, tends
to lead to an increase in BMI. The relation is strong with R2 between 0,89 to 0,93 and the
values of the coefficient β are almost similar between VL and H counties showing the same
effect on BMI on an increase in calorie intake. The same similitude shows the coefficient β in
L and M countries as you can see in table 2 and the diagrams 2:1, 2:2, 2:3 and 2:4 below. The
results of the regressions suggest a positive lineal relation independent of the level of GDP.
In other words, there is a strong possibility that changes in energy intake (Kcal/capita/day)
cetris paribus induced to changes in BMI at almost the same extend in all groups of countries
however the relation is not one to one and varies slightly between the country groups, but the
tendency is clear.
Diagram2:1 Kcal/ vs BMI in VL countries Diagram2:2 Kcal/ vs BMI in L countries
y = 0,0086x + 2,1458
R² = 0,9199
18,0 19,0 20,0 21,0 22,0 23,0 24,0 25,0 26,0 27,0 28,0
2 000 2 200 2 400 2 600
Mea
n B
MI
Kcal/capita/day
y = 0,0117x - 5,0827
R² = 0,9273
18,0
19,0
20,0
21,0
22,0
23,0
24,0
25,0
26,0
27,0
28,0
2 200 2 400 2 600 2 800
Mea
n B
MI
Kcal/capita/day
27
Diagram2:3 Kcal/ vs BMI in M countries Diagram2:4 Kcal/ vs BMI in H countries
As indicated in before the calorie intake per country group showed big differences between
country groups at starting point 1975 and the Mean BMI in kg/m2 in the same year shows the
same pattern. The mean BMI in VL countries was 19,8 kg/m2 compared with 24,3 8 kg/m2 in
in H countries. The results of the regression suggest that an increase of the calorie intake with
100 Kcal/capita/day tends to increase BMI with 0,96 kg/m2 everything else constant.
Now when the link between energy intake (Kcal/capita/day) and BMI (Kg/m2) has been
established and corroborated by the regression analysis we are going to see the relation
between GDP and BMI, overweigh and obesity.
y = 0,0102x - 2,8544
R² = 0,8892
18,0
19,0
20,0
21,0
22,0
23,0
24,0
25,0
26,0
27,0
28,0
2 5
50
2 6
50
2 7
50
2 8
50
2 9
50
3 0
50
Mea
n B
MI
in k
g/m
2
Kcal/capita/day
y = 0,0081x + 0,223
R² = 0,9059
18,0
19,0
20,0
21,0
22,0
23,0
24,0
25,0
26,0
27,0
28,0
2 9
00
3 0
00
3 1
00
3 2
00
3 3
00
Mea
n B
MI
in k
g/m
2
Kcal/capita/day
28
Hypothesis 3
Relation between GDP and BMI
Table3: results of the regression
(𝐵𝑀𝐼 = 𝛼 + 𝛽 𝑙𝑛𝐺𝐷𝑃) Country
Group R2 Constant 𝜷 p
VL 0,87 8,45 2,08 0,00
L 0,90 10,15 1,84 0,00
M 0,95 10.51 1,79 0,00
H 0,96 12,14 1,36 0,00
The correlation between GDP and mean BMI and is positive and significant in all groups with
high R2 and p values tending to 0 as you can see in table 3 above. The coefficient β shows a
decreasing tendency between the different group of countries, the less size of the coefficient β
the highest GDP. This relation follows the same pattern as the one showed between GDP and
Energy intake. The relationship suggests a pattern that follows the law of diminishing returns
showing a parabolic form. The marginal increase of BMI due to an increase in GDP is 50%
higher in the group of VL countries compared with the group of H countries as you can see in
the following diagrams 3:1, 3:2, 3:3 and 3:4. The trend in all groups except in country group
H seems to be divided in a period of fast increase of BMI between 1975 and 2001, where the
increases in GDP leaded to remarkable high increases in BMI and, after that in a period where
the increments of BMI shows a slower rate of growth, however in the group of H countries
the increase of BMI has been steady and at almost the same rate during the studied period.
Diagram 3:1 GDP vs BMI in VL countries Diagram 3:2 GDP vs BMI in L countries
y = 2,0845ln(x) + 8,4509
R² = 0,8738
19,0
20,0
21,0
22,0
23,0
24,0
25,0
26,0
27,0
28,0
- 500 1 000 1 500 2 000
Mea
n B
MI
in k
g/m
2
GDP/capita in current USD prices
y = 1,8431ln(x) + 10,154
R² = 0,9023
19,0
20,0
21,0
22,0
23,0
24,0
25,0
26,0
27,0
28,0
- 2 000 4 000 6 000
Mea
n B
MI
in k
g/m
2
GDP/capita in current USD prices
29
Diagram 3:3 GDP vs BMI in M countries Diagram 3:4 GDP vs BMI in H countries
Relation between GDP and prevalence of Overweigh
Table 4: results of the regression
(𝑂𝑣𝑒𝑟𝑤𝑒𝑖𝑔ℎ𝑡 = 𝛼 + 𝛽 ln 𝐺𝐷𝑃) Country
Group R2 Constant β p
LV 0,89 -0,251 0,064 0,00
L 0,87 -0,158 0,055 0,00
M 0,89 -0,040 0,043 0,00
H 0,90 0,043 0,029 0,00
The correlation between GDP and the prevalence of overweight (BMI >25 & BMI<30) is
significant showing that a percental change in GDP tends to lead to an increase in the
prevalence of overweight and that effect is decreasing as GDP increases. coefficient β is
decreasing with increasing GDP in the different samples. In the group of countries with very
low economic growth (VL) the increase of the value of coefficient β is 0,064 indicating that
for every additional percentage change in GDP you can expect the prevalence of overweight
to increase by an average of 0,064/100 units and in the group of countries with high
economic growth (H) the coefficient β is 0,029 which indicates that for every additional
percentage increase in GDP you can expect the prevalence of overweight to increase by an
average of 0,029/100 units. This result seems to be obvious and follows the same pattern of
diminishing rate as the relations between GDP with calorie intake & mean BMI.
As you can see in the following diagrams 4:1, 4:2, 4:3 and 4:4. The trend in all groups except
in country group H is divided in a period of fast increase of the prevalence of overweight
between 1975 and 2001, showing a steep curve almost exponential in VL and L countries
suggesting a fast increase in the prevalence of overweight and, after that a period the
increments in the prevalence of overweight shows a slower rate of growth, however the rate
seems to be increasing the last years. In the group of H’s countries, the prevalence of
y = 1,7982ln(x) + 10,519
R² = 0,9518
19,0 20,0 21,0 22,0 23,0 24,0 25,0 26,0 27,0 28,0
- 5 000 10 000 15 000
Mea
n B
MI
in k
g/m
2
GDP/capita in current USD prices
y = 1,3674ln(x) + 12,142
R² = 0,9657
19,0 20,0 21,0 22,0 23,0 24,0 25,0 26,0 27,0 28,0
- 20 000 40 000 60 000
Mea
n B
MI
in k
g/m
2
GDP/capita in current USD prices
30
overweight has been growing at an even pace but in 2005 the development turns to at almost
decreasing rate when the prevalence of overweight reached at an average of 35% at the adult
population and GDP at 39 482 USD in those countries.
Diagram 4:1 GDP vs BMI (≥25, ≤30) Diagram 4:2 GDP vs BMI ((≥25, ≤30)
VL Countries L countries
Diagram 4:1 GDP vs BMI (≥25, ≤30) Diagram 4:2 GDP vs BMI (≥25, ≤30)
M Countries H Countries
The prevalence of overweight in the group M and H countries was 26,3% and 29,3%
respectively.
y = 0,0638ln(x) - 0,2509
R² = 0,8932
10,0%
12,0%
14,0%
16,0%
18,0%
20,0%
22,0%
24,0%
- 500 1 000 1 500 2 000
Pre
val
ence
of
over
wei
gth
GDP/capita in current USD prices
y = 0,0548ln(x) - 0,1583
R² = 0,8708
10,0%
12,0%
14,0%
16,0%
18,0%
20,0%
22,0%
24,0%
26,0%
28,0%
30,0%
32,0%
- 2 000 4 000 6 000
Pre
val
ence
of
over
wei
gth
GDP/capita in current USD prices
y = 0,0431ln(x) - 0,0397
R² = 0,8872
10,0%
12,0%14,0%16,0%18,0%20,0%
22,0%24,0%26,0%
28,0%30,0%32,0%34,0%36,0%
38,0%
- 5 000 10 000 15 000
Pre
val
ence
of
over
wei
gth
GDP/capita in current USD prices
y = 0,0292ln(x) + 0,0431
R² = 0,8951
10,0%12,0%14,0%16,0%18,0%20,0%22,0%24,0%26,0%28,0%30,0%32,0%34,0%36,0%38,0%
- 20 000 40 000 60 000
Pre
val
ence
of
over
wei
gth
GDP/capita in current USD prices
31
Relation between GDP and prevalence of Obesity (BMI >30)
Table5: results of the regression (𝑂𝑏𝑒𝑠𝑖𝑡𝑦 = 𝛼 + 𝛽 ln 𝐺𝐷𝑃)
Country
Group R2 Constant 𝜷 p
LV 0,94 -0,204 0,039 0,00
L 0,94 -0,396 0,068 0,00
M 0,97 -0,535 0,084 0,00
H 0,96 -0,578 0,076 0,00
The correlation between GDP and the prevalence of obesity (BMI >30) as percentage of the
total adult population is also significant with a high-level of explanation, R2 above 90% as
you can see in table 5 above, and it have a different pattern compared with the relation
between GDP and overweight. Here the effect is that the coefficient β is increasing as the
GDP increases. Although the effect is diminishing between the Medium and high GDP groups
the trend is still upwards. In the group of countries with very low economic growth (VL) the
increase of the value of coefficient β is 0,039 indicating that for every additional percentage
change in GDP you can expect obesity to increase by an average of 0,039/100 units and in the
group of countries with high economic growth (H) the coefficient β is 0,076 indicating that
for every additional percental increase in GDP you can expect obesity to increase by an
average of 0,076/100 units. Here, the marginal effect of a GDP increases on obesity in VL
countries is less than in the group of H countries meaning that as greater GDP increases
higher prevalence of obesity.
Diagram 5:1 GDP vs BMI> 30 Diagram 5:2 GDP vs BMI>30
VL Countries L Countries
y = 0,039ln(x) - 0,204
R² = 0,9422
0,0%
5,0%
10,0%
15,0%
20,0%
25,0%
30,0%
- 500 1 000 1 500 2 000
Pre
val
ence
of
ob
esit
y
GDP/capita in current USD prices
y = 0,0681ln(x) - 0,3961
R² = 0,9426
0,0%
5,0%
10,0%
15,0%
20,0%
25,0%
30,0%
- 1 000 2 000 3 000 4 000 5 000
Pre
val
ence
of
ob
esit
y
GDP/capita in current USD prices
32
Diagram 5:3 GDP vs BMI> 30 Diagram 5:4 GDP vs BMI >30
M Countries H Countries
Here we can see that the prevalence of obesity in 1975 in the group of M and H countries was
8,6% and 10,2% of the total population. The prevalence of overweight and obesity in
countries in M and H group was already 34,9% and 39,5% respectively.
y = 0,0841ln(x) - 0,5353
R² = 0,9654
0,0%
5,0%
10,0%
15,0%
20,0%
25,0%
30,0%
- 2 0
00
4 0
00
6 0
00
8 0
00
10
00
0
12
00
0
Pre
val
ence
of
ob
esit
y
GDP/capita in current USD prices
y = 0,0758ln(x) - 0,5784
R² = 0,95830,0%
5,0%
10,0%
15,0%
20,0%
25,0%
30,0%
- 5 0
00
10
00
0
15
00
0
20
00
0
25
00
0
30
00
0
35
00
0
40
00
0
45
00
0
50
00
0
Pre
val
ence
of
ob
esit
y
GDP/capita in current USD prices
33
Country analysis
GDP vs overweight
Table 4: results of the regression
(𝑂𝑣𝑒𝑟𝑤𝑒𝑖𝑔ℎ𝑡 = 𝛼 + 𝛽 ln 𝐺𝐷𝑃)
There are big differences among countries within a group and, of course, between the groups
of countries but the tendency observed at an aggregate level is still valid at a country level and
the slope of the coefficient β follows the pattern of diminishing rates of return. When looking
to the relation of GDP and overweight we can see a significant relation with a high level of
explanation (R2) in China, El Salvador and Mexico but the figure for USA varies significantly
with a low R2 of 0,55 as you can see in diagram 6:1,6:2,6:3 and 6:4 below, the
semilogarithmic model fits very well the pattern except for USA. The possible reason of that
is that since year 2005 the prevalence of overweight in USA decreased as GDP is increased
which can be seen in diagram 4 below. We can also see that the prevalence of overweight
increased at a diminishing rate in China and Mexico (diagram 1 and 3) while increased at
higher incremental rate in El Salvador, see diagram 2. Regarding the development in Mexico,
it seems that prevalence of overweight increased even in periods of diminishing economic
growth (GDP) but in the last years the increments are at a decreasing rate.
Diagram 6:1 China: Overweight and GDP Diagram 6:2 El Salvador: Overweight and GDP
y = 0,0462ln(x) - 0,1333R² = 0,9815
10%
12%
14%
16%
18%
20%
22%
24%
26%
28%
30%
0 5000 10000
Pre
vale
nce
of
ove
rwei
gth
GDP in USD
y = 0,0746ln(x) - 0,2298R² = 0,9572
10%
15%
20%
25%
30%
35%
40%
45%
0 2000 4000 6000
Pre
vale
nce
of
ove
rwei
gth
GDP in USD
R2 constant Β p
China 0,98 -0,13 0,046 0,00
El Salvador 0,95 -0,22 0,075 0,00
Mexico 0,85 0,046 0,036 0,00
USA 0,55 0,22 0,016 0,00
34
Diagram: 6:3 Mexico: Overweight and GDP Diagram 6:4 USA: Overweight and GDP
GDP vs Obesity
Table5: results of the regression
(𝑂𝑏𝑒𝑠𝑖𝑡𝑦 = 𝛼 + 𝛽 ln 𝐺𝐷𝑃)
R2 constant 𝜷 p
China 0,98 - 0,06 0,011 0,00
El Salvador 0,96 - 0,39 0,066 0,00
Mexico 0,90 - 0,47 0,078 0,00
USA 0,92 - 1,07 0,120 0,00
The results show a strong positive correlation between GDP and the prevalence of Obesity
and this correlation follows a different pattern namely that obesity increased with GDP at an
increasing rate. The values of β indicates that percent changes in GDP tends to lead to
increases in the prevalence of Obesity contrary to the effect on Overweight that tends to curve
at some level of GDP.
The semilogarithmic model used underestimates the last observations showing a decrease
while, as we can see in the following diagrams (7:1, 7:2, 7:3 and 7:4) the incremental
tendency is marked suggesting an almost exponential trend in the last years. Here the trend is
almost the same on all countries and Mexico shows again increments of obesity even in
periods of diminishing economic growth.
y = 0,0355ln(x) + 0,046R² = 0,8546
25,0%
27,0%
29,0%
31,0%
33,0%
35,0%
37,0%
39,0%
41,0%
43,0%
45,0%
- 5 000 10 000 15 000
Pre
vale
nce
of
ove
rwei
gth
GDP in USD
y = 0,0165ln(x) + 0,2271
R² = 0,5542
25%
27%
29%
31%
33%
35%
37%
39%
41%
43%
0 20000 40000 60000
Pre
val
ence
of
over
wei
gth
GDP in USD
35
Diagram 7:1 China: Obesity and GDP Diagram 7:2 El Salvador: Obesity and GDP
Diagram 7: 3 Mexico: Obesity and GDP Diagram 7:4: USA: Obesity and GDP
y = 0,0112ln(x) - 0,0565
R² = 0,979
0%
1%
2%
3%
4%
5%
6%
- 2 000 4 000 6 000 8 000 10 000
Pre
val
ence
of
ob
esit
y
GDP in USD
y = 0,0661ln(x) - 0,3909
R² = 0,9578
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
20%
0 2000 4000 6000
Pre
val
ence
of
Ob
esit
y
GDP in USD
y = 0,0781ln(x) - 0,4655R² = 0,9052
0%
5%
10%
15%
20%
25%
30%
- 5 000 10 000 15 000
Ptr
eval
ence
of
ob
esit
y
GDP in USD
y = 0,1262ln(x) - 1,0704
R² = 0,9225
0%
5%
10%
15%
20%
25%
30%
35%
40%
0 20000 40000 60000
Pre
val
ence
of
ob
esit
y
GDP in USD
36
Analysis
Testing, the hypothesis 1, if changes in economic growth tends to lead to changes in daily
calorie intake per capita we found a positive relationship suggesting that increases in GDP,
ceteris paribus, tends to lead to increases in calorie intake at diminishing rate. This follows the
law of diminishing returns suggesting a parabolic pattern where at high GDP the further
increments in economic growth tends to affect the calorie intake marginally and can turn to be
negative which it seems to be quiet logic since the increments of calorie intake can’t raise
infinitely. The results are in line with those presented by P.W. Gerbens-Leenes, S.Nonhebel,
M.S.Krol showing a positive relationship between GDP and national per capita food supply
in Kcal/capita/day in three different types of populations (Gerbens-Leenes 2010).
Furthermore, this relationship shows the effect of the great economic growth occurred during
the studied period.
What were the major drivers that may had contributed to increase the availability of food
during this period? The world of 2013, at the end of the studied period, was not the same as
the one of 1975 and besides the big and dramatic political changes the most predominant
change was in fact the technological one with the development of the digitalization of
electronics, which enabled information to play a transformative role in the social, economic
and political spheres and the world became global and increasingly interconnected. One of the
consequences of this main technological change and other innovations is that those
contributed also to enhance the international trade lowering the cost of production of food
thru economies of scale, increased the availability of food and in that way increased also the
accessibility of calories globally and at the same time raised the cost of expending energy, the
price of calories has fallen because food prices have declined, and income has grown due to
the economic growth.. (R. A. Philipson 1999) In fact, according to FAO, prices of food
commodities on world markets, adjusted for inflation, declined substantially from the early
1960s, apart from a peak in 1975, to the early 2000s, when they reached a historic low. They
increased slowly from 2003 to 2006 and then surged upwards from 2006 to the middle of
2008 before declining in the second half of that year. (FAO 2011)
As we can see in diagram 8:1 and 8:2 below, during the studied period GDP growth shows
almost the same pattern in all country groups and you can observe a markedly change of trend
at the beginning of 2000 where the growth rate is substantially incremented in all country
groups, but we can also see a slowdown in the group of H countries the last years. This trend
37
combined with the food price development can help us to explain the increase of calorie
consumption during the studied period. The increase in calorie intake during 1975 to 2000 can
be attributable to the reductions of worldwide food prices. The impact of the food price
reduction seems to be bigger in the group of H countries which increased the calorie intake
with 320 kcal/capita/day compared with the group of VL countries that increased with 216
Kcal/capita/day se diagram 9:1 and 9:2 below. After 2000 the increase on calorie intake seems
to be induced by the economic growth that increased the disposable income specially in the
group of VL countries that augmented the average calorie intake with 213 Kcal/capita/day
compared with a raise of 29 Kcal/capita/day in the group of H countries.
8:1 GDP development at current USD prices 8:2 GDP development Index 1975=100
9:1 Calorie intake in Kcal/capita/day 9:2 Calorie consumption Index 1975=100
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
50000
19
75
19
78
19
81
19
84
19
87
19
90
19
93
19
96
19
99
20
02
20
05
20
08
20
11
VL L M H
0%
100%
200%
300%
400%
500%
600%
700%
800%
900%
19
75
19
78
19
81
19
84
19
87
19
90
19
93
19
96
19
99
20
02
20
05
20
08
20
11
VL L M H
2000
2200
2400
2600
2800
3000
3200
3400
19
75
19
78
19
81
19
84
19
87
19
90
19
93
19
96
19
99
20
02
20
05
20
08
20
11
VL L M H
100%
105%
110%
115%
120%
125%
19
75
19
78
19
81
19
84
19
87
19
90
19
93
19
96
19
99
20
02
20
05
20
08
20
11
VL L M H
38
Take note of the differences on calorie intake at the beginning of the period where the group
of VL countries showed an average calorie intake of 2093 Kcal/capita/day compared with
2920 kcal/capita/day in the group of H countries and that the group of H countries reached a
plateau with a tendency to decrease at a calorie intake of an average of 3253 Kcal/capita/day.
The test of hypothesis 2 whether changes in calorie intake, ceteris paribus, tends to lead to
changes in BMI has given also a positive result indicating that changes in calorie intake tends
to lead to changes in BMI independent of the level of economic growth. This hypothesis is
based in the first law thermodynamics on the human body or energy balance theory that states
that body weight cannot change if, over a specified time, energy intake and energy
expenditure are equal. When energy intake exceeds energy expenditure, a state of positive
energy balance occurs, and the consequence is an increase in body mass. Conversely, when
energy expenditure exceeds energy intake, a state of negative energy balance ensues, and the
consequence is a loss of body mass. (Hill 2012) We can assume that the technological
changes and innovations implemented during the studied period tended to reduce the energy
expenditure per capita and to increase the availability of food which can tended to create an
energy imbalance.
The enabling of an increasing variety of entertainment at home thru the television, DVD,
computer, mobile phone had a great effect on the development of a sedentary lifestyle and the
growth of urbanization reinforced this trend through the increase in the use of the car for
transportation. The technological change affected also the nature of work performed reducing
the physical activity. The net effect of technological advances in the work place, at home, in
transportation, and in leisure-time choices is a reduction in daily energy expenditure, leaving
individuals with a stark choice: whether, or to fill the gap through voluntary physical activity.
(Variyam 2005) On the other side the historical reduction of worldwide food prices and the
increase of urbanization leaded to the proliferation of supermarkets with a variety of ready-to-
eat food offers and fast-food outlets on almost every corner which increased the availability of
food at any time.
In table 6 and 7 below we can see the increase of calorie intake per country group.
39
Table 6 Table 7:
Calorie intake by country group BMI by country group
in Kcal/capita/day in (kg/m2)
The biggest increase in calorie intake was, as you can expect, in the group of VL which also
shows the major increase in BMI however, the increase of calorie intake in the group of H
countries is almost similar of those registered in the group of L countries but a smaller
increase of BMI and one possible explanation of this is the difference of food supply and
consumption in the group of H countries tends to be important and according with Gerbens-
Leens it can be as large as 50% suggesting that a great part of the purchased foods are not
consumed. (Gerbens-Leenes 2010).
The results of the test of these two basic hypotheses can serve us to link economic growth
with BMI and the prevalence of overweight and obesity.
The test of hypothesis 3 is divided in three parts, first the test of the relationship between
GDP and BMI, second the test of the relation between GDP with the prevalence of overweigh
and finally the test relationship between GDP and the prevalence of obesity. The correlation
between GDP and mean BMI is positive and significant in all groups and the coefficient β
shows a decreasing tendency between the different group of countries, the less size of the
coefficient β the highest GDP. This relation follows the same pattern as the one showed
between GDP and Energy intake and suggests a pattern that follows the law of diminishing
returns showing a parabolic pattern. The correlation between GDP and the prevalence of
overweight (BMI ≥25 & BMI≤30) is also significant showing that a percental change in GDP
tends to lead to an increase in the prevalence of overweight and that effect is decreasing as
GDP increases. coefficient β is decreasing with increasing GDP in the different samples. This
result corroborates in part the ones obtained by Gerry Egger indicating that a spline analysis
with the best-fit intersection of two linear relationships occurring at a GDP of $3000. Below
that level, 72 countries had a significantly positive linear relationship whereas above this level
,102 countries showed no significant relationship between GDP and mean BMI. (Garry Egger
2012) The results of this study using longitudinal data from 104 countries divided by GDP per
capita in 4 groups shows a positive significant relation in all country groups however the
1 975 2 013 Differece 1 975 2 013 Differece
VL 2 094 2 522 429 VL 19,6 23,4 3,9
L 2 284 2 631 348 L 21,8 25,5 3,8
M 2 594 2 989 395 M 23,5 27,2 3,6
H 2 921 3 270 349 H 24,3 26,9 2,6
40
effect of a change in GDP on mean BMI and on the prevalence of overweight tends to
decrease as GDP increases. The difference here is that we can see a significant correlation at
all levels at a diminishing rate and that the we couldn’t find a specific turning point which can
be used as a rule.
The result support also in part the ones obtained by Y. Goryakin and M. Suhrcke indicating a
positive and concave relationship between economic development and obesity. (Yevgeniy
Goryakin,Marc Suhrcke 2014) The actual study found the tendency of a concave relationship
between GDP and mean BMI as well as GDP and the prevalence of overweight, but this
pattern cannot be applied to the relationship between GDP and the prevalence of obesity.
In table 8 and 9 below can we see the increase of the prevalence of overweight between 1975
and 2013 in the different country groups as well as in the selected countries, What we can see
is that the increase in the prevalence of overweight is smaller in the group oh H countries and
USA compared with the group of VL and china respectively, The result suggests that each
country in the group of H countries had a turning point regarding the increase on BMI and the
prevalence of overweight at different levels of GDP indicating that there can be other factors
involved in a such change. In the specific USA case, the turning point came 1998 when the
level of GDP reached 32949 USD/capita, the calorie intake extended to 3658 Kcal/capita/day
and the prevalence of overweight was 40,7% but for the whole group it came 2010 when the
average GDP reached 46898 USD/capita, the calorie intake 3238 and the prevalence of
overweight was 35,07 % see table 4.1 and 4.1.1 in annex 1.
Table 8 Table 9
Prevalence of overweight by country group Prevalence of overweight by country
Testing the correlation between GDP and the prevalence of obesity the result shows a
different pattern namely that as GDP increased the prevalence of obesity also increased
showing a positive and increasing rate suggesting that at higher level of GDP the prevalence
of obesity is greater. The trend is more remarkable when looking at country level where we
can see that the effect almost shows an exponential relationship. This result doesn’t support
the earlier mentioned studies of Gerry Egger and Y. Goryakin and M. Suhrcke since further
increases of GDP showed continuous increases in the prevalence of obesity. In table 10 and
1 975 2 013 Difference 1975 2013 Difference
VL 9,6% 21,1% 121% China 9,0% 27,7% 206,7%
L 18,4% 29,4% 60% El Salvador 22,4% 38,7% 72,8%
M 26,3% 35,0% 33% Mexco 28,6% 37,0% 29,2%
H 29,3% 35,0% 19% USA 36,3% 38,8% 7,0%
41
11 below you can see the increase on the prevalence of obesity per country group and in the
selected countries between 1975 and 2013.
Table 10 Table 11
Prevalence of obesity by country group Prevalence of obesity by country
Noteworthy here is that in all cases the increase of the prevalence of obesity is bigger than the
observed on overweight and as in the case of China, for example, the prevalence of obesity in
2013 was 19,5 times the observed in 1975 while the prevalence of overweight was
incremented 2 times in the same period.
Observing this unprecedent development we can’t ignore the process of rapid urbanization,
technological changes and economic growth that take place during the studied period and its
effects on the transformation of societies in a relatively short period of time. This
transformation leaded among others to a dramatic change in lifestyle, alimentation and
increased the availability of food. Along with this development we can also see the
development of a food industry that produces ready-to-eat food to satisfy the needs of a
modern urban life. The increase of calorie consumption is directly related to the increased
availably of food due to the technological improvements in agriculture and the economic
development and at the same time the technological changes, innovations as well as the
increasing urbanization influenced in the development of a sedentary lifestyle causing an
energy imbalance that lead to increase in weight, this process is well described in the
literature. (Fogel.R & Costa 1977) (D. L. Philipson 2002) (R. A. Philipson 1999) However,
one should expect that the increase in the prevalence of obesity follows the same pattern as
the prevalence of overweight but our study shows that it doesn’t and that the prevalence of
obesity is still growing in all country groups independent of the level of GDP. This trend is
bigger when we see at country level were the increase in the prevalence of obesity is more
than fivefold the increase in the prevalence of overweight, see table 8,9,10 and 11 above.
How can we explain this outcome? One possible answer and obvious one is that overweight
persons tends to be obese and that obese persons tends to stay obese. It seems also that, in
developed countries, the different programs against overweight and obesity and the constant
information on health regarding diet and exercise gave positive results in part of the
1 975 2 013 Difference 1975 2013 Difference
VL 1,5% 8,6% 470% China 0,24% 4,98% 1948,32%
L 4,8% 18,3% 281% El Salvador 3,08% 17,84% 479,20%
M 8,6% 25,5% 196% Mexco 9,74% 27,94% 186,71%
H 10,4% 25,4% 145% USA 10,71% 34,30% 220,18%
42
population, the lean and slightly overweight ones, but not affected the already obese. Given
that we didn't consider socioeconomic variables or age in this study it is difficult to have
strong conclusions about this and it is an issue to be addressed in future studies. The WHO
indicates that the prevalence of overweigh and obesity increased in children and adolescents
aged under 19 and it seems that the increases in obesity can also be correlated with that since
children and adolescents become adults.
Those possible explanations can only explain a part of the problem, but we can assume that it
must be other element that affect all countries independent of the level GDP and, I think that
the increase in the consumption of ultra-processed foods may be that factor. According to the
Pan American Health Organisation Sales (and therefore production and consumption) of ultra-
processed products increased worldwide. The main change from 2000 to 2013 was
accelerated sales in middle-income countries in the Global South (Asia, Africa, Eastern
Europe, and Latin America) in tandem with a slowdown in sales in fully industrialized, high-
income countries in the Global North, where overall consumption nonetheless remains
highest. More than half of all current sales of ultra-processed products are in the expanding
markets of the Global South. (Pan American Health Organization 2015) The increase in the
consumption of ultra-processed foods that has been linked to the increase of obesity and
metabolic diseases. (C. A. Monteiro, J-C Moubarac, R B Levy,D S Canella, Ma L da Costa
Louzada and G. Cannon 2017) (Filippa Juul 2015) (Pan American Health Organization 2015)
(C. A. Monteiro 2009)
Conclusions The period between 1975 and 2013 is remarkable in many ways with a great economic growth
in most of the countries, big political changes, increased urbanization and the introduction of
new revolutionary technologies and innovations that changed substantially the way we live.
Unlike other historical periods the changes were implemented very fast across the globe and
in some cases helped to reduce the enormous economic breach between countries. China, now
a super power started in the group of VL countries with a GDP of 178 USD per Capita and at
the end of the studied period the GDP reached 7684 USD per capita. This unprecedent
economic growth has also direct relation with changes in our lifestyle, the type of food we eat
and our environment. You can, without exaggeration, say that the economic growth increased
also the dish and the composition of the food on it incorporating more processed and ultra-
processed products to the diet which combined with a sedentary lifestyle tended to increase
the prevalence of overweight and obesity. Since obesity is linked to several diseases, it affects
43
negatively the economy thru increased health costs and the indirect costs related to increased
sickness absence and this effect can slow down the economic development in less developed
countries since greater resources must be allocated to health care.
The results of this study show, in general, the difficulties of already overweight and obese
people to reduce weight and particularly, at an aggregate level, shows that the economic
growth which was a product of technological development, urbanisation and globalization
increased the calorie intake and to reduce the calorie expenditure raising the prevalence
overweight and obesity around the world.
We can conclude that it seems that, in some sense we are victims of our own success and that
the price of economic growth, technological changes and rapid urbanization was an increased
prevalence of overweight and obesity.
44
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48
ANNEX 1
Table 1: Very Low Economic growth (VL)
Country
Group
Country Name Country
Code
GDP in
USD
Calorie intake
Kcal/cap/day
Mean
BMI
Prevalence of
Obesity in %
(BMI >30))
Prevalence of
overweight in
% (BMI >25 &
<30
VL Afghanistan AFG 188 2752 18,9 0,51% 5,80%
VL Bangladesh BGD 273 1946 17,4 0,24% 4,58%
VL Benin BEN 207 1596 18,8 1,08% 8,47%
VL Burkina Faso BFA 153 1507 18,6 0,45% 6,41%
VL Cameroon CMR 369 2334 19,9 1,35% 9,72%
VL Central African Republic CAF 188 2376 19,0 1,21% 9,05%
VL Chad TCD 212 1722 18,5 0,73% 7,44%
VL China CHN 178 1929 21,1 0,51% 9,76%
VL Egypt EGY 292 2430 24,7 12,14% 26,93%
VL Gambia GMB 221 1809 18,5 1,02% 8,51%
VL Ghana GHA 286 2019 19,8 1,60% 9,94%
VL Guinea-Bissau GNB 140 1680 19,1 0,91% 8,04%
VL Honduras HND 356 2077 21,1 4,47% 21,25%
VL India IND 156 2060 18,5 0,37% 5,24%
VL Indonesia IDN 248 2080 19,3 0,44% 6,22%
VL Kenya KEN 242 2312 19,2 0,86% 8,00%
VL Lesotho LSO 130 2082 21,8 3,24% 11,15%
VL Liberia LBR 355 2271 20,0 1,38% 9,84%
VL Madagascar MDG 302 2543 18,6 0,57% 7,71%
VL Malawi MWI 116 2378 19,1 0,67% 7,62%
VL Mali MLI 128 1864 18,7 0,92% 7,98%
VL Mauritania MRT 358 1961 20,2 1,20% 8,38%
VL Nepal NPL 118 1687 17,8 0,31% 5,18%
VL Pakistan PAK 170 2245 19,7 1,07% 7,70%
VL Philippines PHL 361 2009 19,8 0,72% 8,39%
VL Rwanda RWA 131 2298 18,4 0,53% 7,28%
VL Sierra Leone SLE 227 2158 19,8 1,28% 9,16%
VL Solomon Islands SLB 386 2184 21,9 5,20% 21,47%
VL Sri Lanka LKA 276 2169 19,5 0,60% 7,07%
VL Thailand THA 352 2275 20,4 0,86% 8,01%
VL Togo TGO 256 1884 19,1 1,10% 8,88%
VL Uganda UGA 218 2363 18,6 0,63% 7,42%
Sources: GDP in current USD prices world bank data, Kcal/capita/day: Food supply:
FOASTAD, BMI, Prevalence of overweight and obesity: NCD Risk Factor Collaboration
(NCD-RisC)
49
Tabel 2: List of countries with GDP >400 a < 800 USD in 1975 current prices
Low Economic growth (L)
Country
Group
Country Name Country
Code
GDP in
USD
Calorie intake
Kcal/cap/day
Mean
BMI
Prevalence of
Obesity in %
(BMI >30)
Prevalence of
overweight in
% (BMI >25 &
<30
L Bolivia BOL 480 2131 21,8 4,92% 23,32%
L Botswana BWA 430 1948 20,4 2,59% 10,25%
L Chile CHL 730 2490 24,9 11,45% 29,34%
L Colombia COL 529 2292 22,1 6,74% 26,10%
L Congo COG 482 2003 19,5 1,55% 10,21%
L Cote d'Ivoire CIV 589 2698 20,1 1,56% 10,24%
L Dominican Republic DOM 699 2083 22,1 6,18% 21,58%
L El Salvador SLV 454 2033 22,7 5,91% 23,99%
L Guatemala GTM 567 2123 21,9 5,32% 23,36%
L Guyana GUY 663 2415 22,4 5,30% 19,31%
L Jordan JOR 661 2138 25,4 12,88% 28,95%
L Malaysia MYS 765 2596 20,8 1,52% 10,50%
L Morocco MAR 503 2617 22,1 6,55% 23,90%
L Nigeria NGA 438 1836 20,0 0,91% 8,30%
L Paraguay PRY 484 2378 22,3 4,30% 21,06%
L Senegal SEN 453 2204 19,8 1,45% 9,69%
L Swaziland SWZ 558 2428 22,6 3,52% 11,68%
L Tunisia TUN 766 2674 22,8 7,87% 25,43%
L Zambia ZMB 527 2324 19,5 1,56% 10,60%
L Zimbabwe ZWE 715 2264 22,0 3,74% 12,43%
Sources: GDP in current USD prices world bank data, Kcal/capita/day: Food supply:
FOASTAD, BMI, Prevalence of overweight and obesity: NCD Risk Factor Collaboration
(NCD-RisC)
50
Tabel 3: List of countries with GDP >800 a < 3000 USD in 1975 current prices
Medium Economic growth (M)
Country
Group
Country Name Country
Code
GDP in
USD
Calorie intake
Kcal/cap/day
Mean
BMI
Prevalence of
Obesity in %
(BMI >30)
Prevalence of
overweight in
% (BMI >25 &
<30
)
M Algeria DZA 931 2058 21,9 7,08% 23,87%
M Argentina ARG 2 012 3259 24,2 11,57% 29,46%
M Belize BLZ 886 2413 25,1 9,04% 23,83%
M Brazil BRA 1 150 2489 22,7 5,41% 22,99%
M Costa Rica CRI 935 2425 22,6 5,48% 23,58%
M Cuba CUB 1 380 2675 22,7 8,13% 24,44%
M Cyprus CYP 976 2376 23,8 5,74% 27,51%
M Ecuador ECU 1 107 2226 22,4 5,16% 24,07%
M Fiji FJI 1 187 2335 23,3 9,98% 26,34%
M Iran IRN 1 582 2431 22,5 6,88% 24,71%
M Iraq IRQ 1 152 2200 24,5 11,91% 28,58%
M Jamaica JAM 1 411 2647 22,5 6,96% 20,92%
M Kiribati KIR 998 2914 24,2 16,76% 34,11%
M Malta MLT 1 560 3221 26,3 15,97% 34,83%
M Mexico MEX 1 446 2715 23,4 9,74% 28,63%
M Panama PAN 1 395 2368 22,7 6,30% 25,42%
M Peru PER 1 108 2188 23,0 6,52% 27,20%
M Portugal PRT 2 128 3027 24,0 5,50% 26,71%
M South Africa ZAF 1 454 2897 24,2 10,11% 18,14%
M Suriname SUR 1 282 2220 23,1 7,83% 23,22%
M Trinidad and Tobago TTO 2 415 2589 23,7 3,95% 16,93%
M Turkey TUR 1 136 3300 23,7 8,83% 26,72%
M Uruguay URY 1 250 2890 24,3 11,76% 29,43%
M Venezuela VEN 2 343 2391 24,0 9,97% 30,43%
Sources: GDP in current USD prices world bank data, Kcal/capita/day: Food supply:
FOASTAD, BMI, Prevalence of overweight and obesity: NCD Risk Factor Collaboration
(NCD-RisC)
51
Tabel 4: List of countries with GDP >3000 USD in 1975 current prices
High Economic growth (H)
Country
Group
Country Name Country
Code
GDP in
USD
Calorie
intake
Kcal/cap/day
Mean
BMI
Prevalence
of Obesity in
% (BMI >30)
Prevalence of
overweight in
% (BMI >25 &
<30
H Australia AUS 6 998 3093 24,0 10,88% 32,16%
H Austria AUT 5 286 3142 24,2 7,33% 28,55%
H Bahamas BHS 3 156 2341 24,5 12,18% 26,59%
H Bermuda BMU 6 509 2718 26,1 20,87% 30,54%
H Brunei Darussalam BRN 7 226 2095 23,1 2,76% 14,53%
H Canada CAN 7 490 2880 24,4 10,19% 31,13%
H Denmark DNK 7 999 2989 23,8 7,43% 29,86%
H Finland FIN 6 260 3205 24,5 7,06% 29,07%
H France FRA 6 673 3242 24,4 9,09% 32,08%
H French Polynesia PYF 5 285 2841 27,1 29,34% 35,59%
H Gabon GAB 3 321 2200 19,9 1,77% 10,60%
H Germany DEU 6 213 3118 24,5 9,12% 30,37%
H Greece GRC 3 153 3342 25,4 10,09% 31,84%
H Iceland ISL 6 507 2975 23,9 8,77% 32,47%
H Ireland IRL 2 977 3466 23,8 6,64% 28,35%
H Israel ISR 4 444 3194 24,5 13,39% 34,28%
H Italy ITA 4 093 3393 24,5 8,63% 32,26%
H Japan JPN 4 635 2716 22,1 1,06% 14,26%
H Kuwait KWT 11 732 2538 26,5 20,66% 33,37%
H Netherlands NLD 7 242 2974 23,5 5,64% 28,52%
H New Zealand NZL 4 172 3134 24,1 11,29% 32,06%
H Norway NOR 8 204 2969 24,4 7,68% 29,26%
H Saudi Arabia SAU 6 296 1792 25,0 11,48% 28,11%
H Spain ESP 3 201 3084 24,9 9,95% 32,31%
H Sweden SWE 9 975 2932 23,6 8,25% 30,45%
H United Arab
Emirates
ARE 26 556 3141 25,9 17,35% 31,85%
H United Kingdom GBR 4 300 3126 23,8 9,71% 31,71%
H United States USA 7 820 3033 25,0 12,27% 30,12%
Sources: GDP in current USD prices world bank data, Kcal/capita/day: Food supply:
FOASTAD, BMI, Prevalence of overweight and obesity: NCD Risk Factor Collaboration
(NCD-RisC)
52
Tabel 1.1.: Very Low Economic growth (VL): Developing of the variables
Year GDP in
USD
Calorie
intake
Kcal/cap/day
Mean
BMI
Prevalence of
overweight
(BMI 25-30)
Prevalence
of Obesity
(BMI >30)
ln
GDP
1975 237 2 094 19,6 9,6% 1,5% 5,4692
1976 242 2 118 19,7 9,8% 1,6% 5,4908
1977 270 2 104 19,8 10,1% 1,7% 5,6001
1978 300 2 141 19,9 10,3% 1,7% 5,7024
1979 341 2 142 20,0 10,6% 1,8% 5,8306
1980 380 2 125 20,1 10,9% 1,9% 5,9401
1981 381 2 136 20,3 11,1% 2,0% 5,9432
1982 379 2 142 20,4 11,4% 2,1% 5,9368
1983 364 2 151 20,5 11,7% 2,2% 5,8967
1984 371 2 141 20,6 12,0% 2,4% 5,9149
1985 369 2 182 20,7 12,3% 2,5% 5,9101
1986 387 2 191 20,8 12,6% 2,6% 5,9586
1987 419 2 193 21,0 12,9% 2,7% 6,0375
1988 444 2 207 21,1 13,2% 2,9% 6,0962
1989 440 2 229 21,2 13,5% 3,0% 6,0871
1990 453 2 199 21,3 13,8% 3,2% 6,1161
1991 476 2 216 21,4 14,1% 3,3% 6,1657
1992 486 2 212 21,5 14,7% 3,5% 6,1867
1993 504 2 204 21,6 15,0% 3,7% 6,2234
1994 494 2 242 21,7 15,3% 3,8% 6,2025
1995 556 2 249 21,8 15,6% 4,0% 6,3205
1996 595 2 259 21,9 15,9% 4,2% 6,3883
1997 581 2 280 22,0 16,2% 4,4% 6,3646
1998 528 2 291 22,1 16,5% 4,6% 6,2694
1999 545 2 303 22,2 16,8% 4,8% 6,3016
2000 543 2 310 22,3 17,1% 5,0% 6,2973
2001 521 2 318 22,4 17,4% 5,2% 6,2562
2002 539 2 336 22,5 17,7% 5,4% 6,2893
2003 654 2 347 22,6 18,0% 5,7% 6,4831
2004 725 2 358 22,7 18,4% 5,9% 6,5861
2005 835 2 384 22,8 18,7% 6,2% 6,7276
2006 970 2 406 22,9 19,0% 6,4% 6,8776
2007 1 119 2 436 22,9 19,3% 6,7% 7,0205
2008 1 131 2 463 23,0 19,6% 7,0% 7,0304
2009 1 297 2 483 23,1 19,9% 7,3% 7,1675
2010 1 467 2 503 23,2 20,2% 7,6% 7,2912
2011 1 540 2 517 23,3 20,5% 7,9% 7,3399
2012 1 606 2 528 23,4 20,8% 8,2% 7,3816
2013 1 642 2 522 23,4 21,1% 8,6% 7,4034
GNP < 400 USD in 1975 current prices
Sources: GDP in current USD prices world bank data, Kcal/capita/day: Food supply:
FOASTAD, BMI, Prevalence of overweight and obesity: NCD Risk Factor Collaboration
(NCD-RisC)
53
Tabel 2.1.: Low Economic growth (L): Developing of the variables
Year GDP in
USD
Calorie intake
Kcal/cap/day
Mean BMI Prevalence
of
overweight
(BMI 25-30)
Prevalence
of Obesity
(BMI >30)
Ln of GDP
1975 575 2 284 21,8 18,4% 4,8% 6,3538
1976 626 2 303 21,9 18,7% 5,0% 6,4388
1977 709 2 319 22,0 19,1% 5,2% 6,5643
1978 799 2 336 22,1 19,5% 5,4% 6,6829
1979 957 2 361 22,2 19,8% 5,7% 6,8638
1980 1 136 2 389 22,3 20,2% 5,9% 7,0354
1981 1 176 2 410 22,4 20,5% 6,2% 7,0700
1982 1 108 2 395 22,6 20,9% 6,4% 7,0100
1983 1 061 2 370 22,7 21,3% 6,7% 6,9672
1984 1 026 2 392 22,8 21,6% 7,0% 6,9339
1985 902 2 403 22,9 22,0% 7,3% 6,8041
1986 925 2 408 23,0 22,3% 7,6% 6,8299
1987 1 002 2 390 23,1 22,7% 7,9% 6,9101
1988 1 067 2 393 23,2 23,0% 8,2% 6,9724
1989 1 078 2 391 23,3 23,3% 8,5% 6,9826
1990 1 186 2 394 23,4 23,7% 8,9% 7,0784
1991 1 250 2 405 23,5 24,0% 9,2% 7,1312
1992 1 359 2 408 23,6 24,6% 9,6% 7,2146
1993 1 371 2 418 23,7 24,9% 9,9% 7,2232
1994 1 474 2 434 23,8 25,2% 10,3% 7,2957
1995 1 688 2 452 23,9 25,5% 10,6% 7,4312
1996 1 784 2 455 24,0 25,8% 11,0% 7,4864
1997 1 828 2 464 24,1 26,0% 11,4% 7,5112
1998 1 716 2 458 24,2 26,3% 11,8% 7,4476
1999 1 696 2 475 24,3 26,6% 12,2% 7,4362
2000 1 769 2 483 24,4 26,8% 12,6% 7,4784
2001 1 706 2 515 24,5 27,1% 12,9% 7,4420
2002 1 727 2 529 24,6 27,3% 13,4% 7,4540
2003 2 170 2 543 24,7 27,5% 13,8% 7,6823
2004 2 488 2 557 24,8 27,8% 14,2% 7,8193
2005 2 811 2 544 24,9 28,0% 14,6% 7,9412
2006 3 175 2 571 24,9 28,2% 15,0% 8,0631
2007 3 538 2 568 25,0 28,4% 15,5% 8,1714
2008 3 344 2 556 25,1 28,6% 15,9% 8,1148
2009 3 917 2 567 25,2 28,7% 16,4% 8,2731
2010 4 401 2 583 25,3 28,9% 16,8% 8,3896
2011 4 501 2 611 25,3 29,1% 17,3% 8,4120
2012 4 641 2 621 25,4 29,2% 17,8% 8,4426
2013 4 681 2 631 25,5 29,4% 18,3% 8,4513
GDP >400 a < 800 USD in 1975 current prices
Sources: GDP in current USD prices world bank data, Kcal/capita/day: Food supply:
FOASTAD, BMI, Prevalence of overweight and obesity: NCD Risk Factor Collaboration
(NCD-RisC)
54
Tabel 3.1.: Medium Economic growth (M): Developing of the variables
Year GDP in
USD
Calorie intake
Kcal/cap/day
Mean
BMI
Prevalence
of
overweight
(BMI 25-30)
Prevalence
of Obesity
(BMI >30)
Ln of GDP
1 975 1 384 2 594 23,5 26,3% 8,6% 7,2330
1 976 1 462 2 591 23,6 26,7% 8,9% 7,2876
1 977 1 626 2 607 23,8 27,1% 9,3% 7,3936
1 978 1 799 2 626 23,9 27,5% 9,6% 7,4950
1 979 2 154 2 662 24,0 27,8% 9,9% 7,6750
1 980 2 605 2 718 24,1 28,2% 10,3% 7,8651
1 981 2 654 2 710 24,2 28,6% 10,7% 7,8840
1 982 2 622 2 721 24,3 28,9% 11,1% 7,8717
1 983 2 515 2 712 24,4 29,2% 11,4% 7,8300
1 984 2 422 2 738 24,5 29,6% 11,8% 7,7924
1 985 2 411 2 772 24,6 29,9% 12,2% 7,7879
1 986 2 536 2 758 24,7 30,2% 12,6% 7,8385
1 987 2 668 2 779 24,8 30,5% 13,0% 7,8893
1 988 2 848 2 773 24,9 30,8% 13,4% 7,9543
1 989 2 810 2 747 25,0 31,0% 13,9% 7,9411
1 990 3 472 2 740 25,1 31,3% 14,3% 8,1526
1 991 3 443 2 733 25,2 31,6% 14,7% 8,1441
1 992 3 726 2 748 25,3 32,1% 15,2% 8,2231
1 993 3 623 2 760 25,4 32,3% 15,6% 8,1950
1 994 3 871 2 757 25,5 32,5% 16,0% 8,2613
1 995 4 310 2 753 25,6 32,8% 16,5% 8,3686
1 996 4 434 2 765 25,7 33,0% 17,0% 8,3970
1 997 4 570 2 752 25,8 33,2% 17,4% 8,4274
1 998 4 739 2 784 25,9 33,4% 17,9% 8,4636
1 999 4 702 2 800 26,0 33,6% 18,4% 8,4558
2 000 4 769 2 817 26,1 33,7% 18,9% 8,4699
2 001 4 722 2 839 26,2 33,9% 19,4% 8,4600
2 002 4 612 2 826 26,3 34,1% 19,9% 8,4365
2 003 5 872 2 843 26,4 34,2% 20,4% 8,6780
2 004 6 552 2 857 26,5 34,3% 20,9% 8,7875
2 005 7 265 2 870 26,5 34,4% 21,4% 8,8909
2 006 8 399 2 879 26,6 34,6% 21,9% 9,0359
2 007 9 673 2 898 26,7 34,7% 22,4% 9,1771
2 008 8 861 2 907 26,8 34,7% 22,9% 9,0894
2 009 9 770 2 927 26,9 34,8% 23,4% 9,1871
2 010 10 658 2 951 26,9 34,9% 23,9% 9,2741
2 011 10 725 2 966 27,0 34,9% 24,4% 9,2803
2 012 10 953 2 974 27,1 35,0% 24,9% 9,3014
2 013 11 150 2 989 27,2 35,0% 25,5% 9,3192
GDP >800 a < 3000 USD in 1975 current prices
Sources: GDP in current USD prices world bank data, Kcal/capita/day: Food supply:
FOASTAD, BMI, Prevalence of overweight and obesity: NCD Risk Factor Collaboration
(NCD-RisC)
55
Tabel 4.1.: High Economic growth (H): Developing of the variables
Year GDP
in USD
Calorie intake
Kcal/cap/day
Mean
BMI
Prevalence
of
overweight
(BMI 25-30)
Prevalence
of Obesity
(BMI >30)
Ln of GDP
1 975 6 686 2 921 24,3 29,3% 10,4% 8,8077
1 976 7 281 2 969 24,4 29,7% 10,7% 8,8930
1 977 8 078 2 956 24,5 30,0% 11,0% 8,9969
1 978 8 792 2 992 24,5 30,3% 11,3% 9,0816
1 979 10 574 3 017 24,6 30,5% 11,6% 9,2661
1 980 12 476 3 063 24,7 30,8% 12,0% 9,4316
1 981 12 037 3 049 24,8 31,1% 12,3% 9,3957
1 982 11 437 3 083 24,8 31,4% 12,6% 9,3446
1 983 10 940 3 075 24,9 31,6% 13,0% 9,3002
1 984 10 852 3 099 25,0 31,9% 13,3% 9,2921
1 985 10 914 3 112 25,0 32,1% 13,7% 9,2978
1 986 12 585 3 114 25,1 32,3% 14,0% 9,4402
1 987 14 724 3 131 25,2 32,5% 14,4% 9,5972
1 988 16 142 3 136 25,3 32,8% 14,7% 9,6892
1 989 16 652 3 148 25,3 33,0% 15,1% 9,7203
1 990 19 024 3 104 25,4 33,1% 15,5% 9,8534
1 991 19 266 3 108 25,5 33,3% 15,8% 9,8661
1 992 20 677 3 141 25,6 33,7% 16,2% 9,9368
1 993 19 539 3 121 25,7 33,8% 16,6% 9,8801
1 994 20 458 3 136 25,7 34,0% 17,0% 9,9261
1 995 22 981 3 158 25,8 34,1% 17,4% 10,0424
1 996 23 963 3 180 25,9 34,2% 17,8% 10,0843
1 997 23 430 3 199 26,0 34,4% 18,3% 10,0618
1 998 23 227 3 220 26,0 34,5% 18,7% 10,0531
1 999 23 935 3 233 26,1 34,6% 19,1% 10,0831
2 000 23 956 3 241 26,2 34,7% 19,5% 10,0840
2 001 24 319 3 250 26,3 34,7% 20,0% 10,0990
2 002 25 883 3 253 26,3 34,8% 20,4% 10,1613
2 003 34 189 3 235 26,4 34,9% 20,9% 10,4396
2 004 36 963 3 244 26,5 34,9% 21,3% 10,5177
2 005 39 482 3 244 26,5 35,0% 21,8% 10,5836
2 006 44 194 3 246 26,6 35,0% 22,2% 10,6964
2 007 47 168 3 252 26,7 35,0% 22,7% 10,7615
2 008 40 915 3 241 26,7 35,1% 23,1% 10,6193
2 009 42 593 3 233 26,8 35,1% 23,6% 10,6594
2 010 46 898 3 238 26,8 35,1% 24,0% 10,7557
2 011 46 358 3 244 26,8 35,1% 24,5% 10,7441
2 012 47 117 3 259 26,9 35,0% 25,0% 10,7604
2 013 45 561 3 270 26,9 35,0% 25,4% 10,7268
GDP >3000 USD in 1975 current prices
Sources: GDP in current USD prices world bank data, Kcal/capita/day: Food supply:
FOASTAD, BMI, Prevalence of overweight and obesity: NCD Risk Factor Collaboration
(NCD-RisC)
56
Table 1.1.1.: China developing of the variables
Year GDP in
USD
Calorie
intake
Kcal/cap/
day
Mean
BMI
Prevalence
of
overweight
(BMI 25-
30)
Prevalence
of Obesity
(BMI >30)
Ln of GDP
1975 178 1 929 20,9 9,0% 0,2% 5,183702 1976 165 1 896 21,0 9,2% 0,3% 5,1084003 1977 185 1 934 21,0 9,5% 0,3% 5,2226388 1978 156 2 080 21,1 9,7% 0,3% 5,0523937 1979 184 2 095 21,1 10,0% 0,3% 5,2148442 1980 195 2 161 21,2 10,3% 0,3% 5,2719976 1981 197 2 178 21,3 10,6% 0,4% 5,2835665 1982 203 2 339 21,3 11,0% 0,4% 5,3148545 1983 225 2 407 21,4 11,3% 0,4% 5,4180182 1984 251 2 440 21,5 11,7% 0,5% 5,5243127 1985 294 2 437 21,5 12,1% 0,5% 5,6851393 1986 282 2 433 21,6 12,4% 0,6% 5,6416521 1987 252 2 448 21,7 12,9% 0,6% 5,5286826 1988 284 2 427 21,7 13,3% 0,7% 5,6473451 1989 311 2 417 21,8 13,7% 0,7% 5,7394131 1990 318 2 515 21,9 14,1% 0,8% 5,7616887 1991 333 2 444 21,9 14,6% 0,9% 5,8085693 1992 366 2 468 22,0 15,1% 1,0% 5,9038913 1993 377 2 550 22,0 15,5% 1,0% 5,9332787 1994 473 2 614 22,1 16,0% 1,1% 6,1601356 1995 610 2 701 22,2 16,6% 1,2% 6,412896 1996 709 2 704 22,2 17,1% 1,3% 6,5644389 1997 782 2 734 22,3 17,6% 1,5% 6,6615275 1998 829 2 776 22,4 18,2% 1,6% 6,719714 1999 873 2 770 22,4 18,7% 1,7% 6,7722643 2000 959 2 814 22,5 19,3% 1,9% 6,8662794 2001 1 053 2 821 22,6 19,9% 2,0% 6,9595013 2002 1 149 2 836 22,6 20,5% 2,2% 7,0462192 2003 1 509 2 838 22,7 21,1% 2,4% 7,3189825 2004 1 753 2 861 22,8 21,8% 2,5% 7,4693222 2005 2 099 2 883 22,9 22,4% 2,8% 7,6493256 2006 2 695 2 886 23,0 23,1% 3,0% 7,8992893 2007 3 471 2 921 23,1 23,7% 3,2% 8,1522695 2008 3 838 2 977 23,2 24,4% 3,5% 8,2528197 2009 4 561 2 995 23,3 25,1% 3,7% 8,4251903 2010 5 634 3 044 23,4 25,7% 4,0% 8,6365387 2011 6 338 3 080 23,6 26,4% 4,3% 8,7543001 2012 7 078 3 098 23,7 27,0% 4,6% 8,8647143 2013 7 684 3 108 23,8 27,7% 5,0% 8,9468308
Sources: GDP in current USD prices world bank data, Kcal/capita/day: Food supply:
FOASTAD, BMI, Prevalence of overweight and obesity: NCD Risk Factor Collaboration
(NCD-RisC)
57
Table 2.1.1. El Salvador developing of the variables
Year GDP in
USD
Calorie
intake
Kcal/cap/
day
Mean
BMI
Prevalence
of
overweight
(BMI 25-
30)
Prevalence
of Obesity
(BMI >30)
Log of
GDP
1975 454 2033 22,9 22,4% 3,1% 6,11870
1976 549 2064 23,0 22,9% 3,2% 6,30840
1977 679 2109 23,1 23,3% 3,4% 6,52116
1978 708 2147 23,2 23,8% 3,6% 6,56256
1979 769 2211 23,3 24,3% 3,8% 6,64571
1980 780 2246 23,4 24,8% 4,0% 6,65958
1981 738 2282 23,5 25,3% 4,2% 6,60439
1982 719 2242 23,6 25,8% 4,4% 6,57827
1983 732 2229 23,7 26,3% 4,7% 6,59519
1984 754 2259 23,8 26,8% 4,9% 6,62494
1985 772 2240 23,9 27,3% 5,2% 6,64876
1986 756 2180 24,0 27,8% 5,4% 6,62805
1987 783 2169 24,1 28,3% 5,7% 6,66338
1988 818 2226 24,3 28,8% 6,0% 6,70746
1989 843 2256 24,4 29,2% 6,3% 6,73707
1990 914 2308 24,5 29,7% 6,6% 6,81738
1991 997 2406 24,6 30,2% 7,0% 6,90481
1992 1 103 2410 24,7 30,7% 7,3% 7,00548
1993 1 267 2384 24,8 31,2% 7,7% 7,14476
1994 1 458 2379 24,9 31,7% 8,1% 7,28495
1995 1 693 2392 25,0 32,2% 8,6% 7,43435
1996 1 819 2404 25,1 32,8% 9,0% 7,50587
1997 1 944 2428 25,2 33,3% 9,5% 7,57250
1998 2 078 2432 25,4 33,8% 10,0% 7,63918
1999 2 140 2411 25,5 34,2% 10,5% 7,66846
2000 2 238 2541 25,6 34,7% 11,0% 7,71352
2001 2 339 2628 25,7 35,2% 11,5% 7,75738
2002 2 408 2643 25,8 35,6% 12,0% 7,78672
2003 2 633 2597 25,9 36,0% 12,5% 7,87577
2004 2 835 2567 26,0 36,3% 13,1% 7,94990
2005 3 063 2571 26,1 36,7% 13,6% 8,02713
2006 3 305 2577 26,2 37,0% 14,1% 8,10314
2007 3 507 2566 26,3 37,3% 14,6% 8,16262
2008 3 366 2574 26,4 37,6% 15,1% 8,12162
2009 3 474 2574 26,4 37,8% 15,6% 8,15317
2010 3 737 2515 26,5 38,1% 16,2% 8,22593
2011 3 828 2537 26,6 38,3% 16,7% 8,25004
2012 3 896 2559 26,7 38,5% 17,3% 8,26762
2013 3 989 2577 26,8 38,7% 17,8% 8,29124
Sources: GDP in current USD prices world bank data, Kcal/capita/day: Food supply:
FOASTAD, BMI, Prevalence of overweight and obesity: NCD Risk Factor Collaboration
(NCD-RisC)
58
Table 3,1.1. Mexico developing of the variables
Year GDP in
USD
Calorie intake
Kcal/cap/day
Mean
BMI
Prevalence of
overweight
(BMI 25-30)
Prevalence of
Obesity (BMI
>30)
Log of GDP
1 975 1 384 2 594 23,5 26,3% 8,6% 7,2330
1 976 1 462 2 591 23,6 26,7% 8,9% 7,2876
1 977 1 626 2 607 23,8 27,1% 9,3% 7,3936
1 978 1 799 2 626 23,9 27,5% 9,6% 7,4950
1 979 2 154 2 662 24,0 27,8% 9,9% 7,6750
1 980 2 605 2 718 24,1 28,2% 10,3% 7,8651
1 981 2 654 2 710 24,2 28,6% 10,7% 7,8840
1 982 2 622 2 721 24,3 28,9% 11,1% 7,8717
1 983 2 515 2 712 24,4 29,2% 11,4% 7,8300
1 984 2 422 2 738 24,5 29,6% 11,8% 7,7924
1 985 2 411 2 772 24,6 29,9% 12,2% 7,7879
1 986 2 536 2 758 24,7 30,2% 12,6% 7,8385
1 987 2 668 2 779 24,8 30,5% 13,0% 7,8893
1 988 2 848 2 773 24,9 30,8% 13,4% 7,9543
1 989 2 810 2 747 25,0 31,0% 13,9% 7,9411
1 990 3 472 2 740 25,1 31,3% 14,3% 8,1526
1 991 3 443 2 733 25,2 31,6% 14,7% 8,1441
1 992 3 726 2 748 25,3 32,1% 15,2% 8,2231
1 993 3 623 2 760 25,4 32,3% 15,6% 8,1950
1 994 3 871 2 757 25,5 32,5% 16,0% 8,2613
1 995 4 310 2 753 25,6 32,8% 16,5% 8,3686
1 996 4 434 2 765 25,7 33,0% 17,0% 8,3970
1 997 4 570 2 752 25,8 33,2% 17,4% 8,4274
1 998 4 739 2 784 25,9 33,4% 17,9% 8,4636
1 999 4 702 2 800 26,0 33,6% 18,4% 8,4558
2 000 4 769 2 817 26,1 33,7% 18,9% 8,4699
2 001 4 722 2 839 26,2 33,9% 19,4% 8,4600
2 002 4 612 2 826 26,3 34,1% 19,9% 8,4365
2 003 5 872 2 843 26,4 34,2% 20,4% 8,6780
2 004 6 552 2 857 26,5 34,3% 20,9% 8,7875
2 005 7 265 2 870 26,5 34,4% 21,4% 8,8909
2 006 8 399 2 879 26,6 34,6% 21,9% 9,0359
2 007 9 673 2 898 26,7 34,7% 22,4% 9,1771
2 008 8 861 2 907 26,8 34,7% 22,9% 9,0894
2 009 9 770 2 927 26,9 34,8% 23,4% 9,1871
2 010 10 658 2 951 26,9 34,9% 23,9% 9,2741
2 011 10 725 2 966 27,0 34,9% 24,4% 9,2803
2 012 10 953 2 974 27,1 35,0% 24,9% 9,3014
2 013 11 150 2 989 27,2 35,0% 25,5% 9,3192
Sources: GDP in current USD prices world bank data, Kcal/capita/day: Food supply:
FOASTAD, BMI, Prevalence of overweight and obesity: NCD Risk Factor Collaboration
(NCD-RisC)
59
Table 4.1.1.: USA developing of the variables
Year GDP in
USD
Calorie
intake
Kcal/cap/day
Mean BMI Prevalence
of
overweight
(BMI 25-30)
Prevalence
of Obesity
(BMI >30)
Log of
GDP
1975 7 820 3033 25,3 36% 11% 8,96445
1976 8 611 3163 25,3 37% 11% 9,06084
1977 9 471 3135 25,4 37% 11% 9,15602
1978 10 587 3155 25,5 37% 12% 9,26741
1979 11 696 3214 25,5 38% 12% 9,36696
1980 12 598 3178 25,6 38% 13% 9,44127
1981 13 993 3218 25,7 38% 13% 9,54632
1982 14 439 3191 25,7 38% 13% 9,57769
1983 15 561 3230 25,8 39% 14% 9,65255
1984 17 134 3275 25,9 39% 14% 9,74884
1985 18 269 3380 26,0 39% 15% 9,81298
1986 19 115 3352 26,1 39% 15% 9,85823
1987 20 101 3450 26,2 40% 16% 9,90852
1988 21 483 3458 26,3 40% 16% 9,97503
1989 22 922 3433 26,4 40% 17% 10,03987
1990 23 954 3493 26,5 40% 18% 10,08391
1991 24 405 3522 26,6 40% 18% 10,10255
1992 25 493 3559 26,7 40% 19% 10,14616
1993 26 465 3605 26,9 40% 20% 10,18357
1994 27 777 3665 27,0 41% 20% 10,23195
1995 28 782 3580 27,1 41% 21% 10,26751
1996 30 068 3587 27,3 41% 22% 10,31122
1997 31 573 3648 27,4 41% 22% 10,36005
1998 32 949 3658 27,5 41% 23% 10,40272
1999 34 621 3673 27,6 41% 24% 10,45221
2000 36 450 3755 27,8 41% 25% 10,50369
2001 37 274 3707 27,9 41% 26% 10,52604
2002 38 166 3783 28,0 41% 26% 10,54970
2003 41 922 3777 28,1 40% 27% 10,64356
2004 44 308 3809 28,2 40% 28% 10,69892
2005 46 437 3828 28,3 40% 29% 10,74585
2006 48 062 3783 28,4 40% 29% 10,78024
2007 48 401 3757 28,5 40% 30% 10,78728
2008 47 002 3700 28,6 40% 31% 10,75794
2009 48 374 3645 28,7 40% 31% 10,78672
2010 49 791 3650 28,7 39% 32% 10,81558
2011 51 450 3649 28,8 39% 33% 10,84837
2012 52 787 3687 28,8 39% 34% 10,87402
2013 54 599 3682 28,9 39% 34% 10,90776
Sources: GDP in current USD prices world bank data, Kcal/capita/day: Food supply:
FOASTAD, BMI, Prevalence of overweight and obesity: NCD Risk Factor Collaboration
(NCD-RisC)
