1

Economic Causes of Obesity:

Cross-country analysis

Kate Bray

A project presented in part requirement of the degree of Bachelor of Arts with

honours in Economics of the University of the West of England, Bristol

Academic year of presentation: 2010/2011

Bristol Business School

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Abstract

This dissertation provides a broad summary of the previous economic literature on obesity.

The literature, both theoretical and empirical, aims to explain the rise in obesity in recent

years and to identify the factors contributing towards the epidemic. The dissertation then aims

to analyse some of the factors thought to contribute towards obesity rates and to then use

econometric models to perform cross-sectional analysis of the significance of these factors,

differentiating between males and females. An understanding of the causes of obesity allows

economic policymakers to develop future policies which could help reverse the trend in rising

obesity rates around the world.

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CONTENTS

4 Introduction

5-9 Literature Review

10-16 Method

Rationale for choosing variables

Data collection

17-23 Regression Analysis (1)

24 Correlation Analysis

25 Correlation Matrix

26-31 Regression Analysis (2)

32-33 Physical Activity Model

34-36 Case Studies

US and Canada Comparison

Samoan Women

Japan

37-38 Evaluation

39 Conclusion

40-44 Acknowledgements & References

45–60 Appendix

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INTRODUCTION

“To say that obesity is caused by merely consuming too many calories is like saying that the

only cause of the American Revolution was the Boston Tea Party.” – Adelle Davis.

This dissertation aims to undertake econometric analysis into the varying degrees of

significance of factors affecting obesity rates in adult males and females, across 55 countries

worldwide: 26 European, 5 African, 2 South-East Asian, 7 Western Pacific, 5 Eastern

Mediterranean, 3 South American, 1 central American and 6 North American countries.

Several economic factors affect the amount of food we eat and the amount of physical

activity we take part in and ultimately our weight. The worldwide accepted definition of

obesity is people who have a body mass index (BMI) ≥30. The BMI is calculated by dividing

body weight (kg) by height (m) and squaring that number. The worldwide obesity problem is

a great cause of concern and, over the past few decades, obesity rates have been climbing at

an alarming rate. The cost to governments has been billions of pounds a year in health costs.

The World Health Organization (WHO) reported in 2003 there were at least 300 million

obese people worldwide. Obesity leads to detrimental changes in people‟s metabolism and an

increase in blood pressure and cholesterol. It can also increase the risk of heart disease,

strokes and cancer (WHO,2003). Obese people suffer serious chronic diseases, reduced

quality of life, shortened life expectancies and lowered levels of labour productivity, each of

which impedes a country's growth and development. (Gardner & Halweil, 2000). The obesity

problem used to be generally limited to the high-income countries of the world but in recent

years the problem has spread to developing countries. Therefore, if we can find out more

about the causes of obesity, then we should be closer to finding a solution, or at least

minimizing the problem.

Owing to the high costs of obesity, and the fact that the majority of these costs are financed

by the taxpayer, it is hardly surprising that the relationship between economic variables and

obesity has attracted so much academic attention in recent years.

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LITERATURE REVIEW

There have been plenty of theoretical and empirical studies on economic factors affecting

obesity rates but there have not been many studies based on econometric models. They have

been based more on case studies and quite often focusing on the US alone. This is probably

because the US currently has the highest obesity rates in the world. Since the late 1970s, the

number of obese adults in the US has grown by over 50%. A study by Chou et al. (2004) uses

micro-level data from the 1984- 99 Behavioural Risk Factor Surveillance System (BRFSS) to

discover which variables are most significant in explaining the rising trend in obesity. The

variables used are: per capita number of fast-food restaurants; per capita number of full-

service restaurants; price of a meal in each type of restaurant; price of food consumed at

home; price of cigarettes; clean indoor air laws; hours of work per week and hourly wage

rates by gender, race, years of formal schooling completed and marital status. They claim that

the growth in all types of restaurant is a response to increasing scarcity and value of

household or nonmarket time. This indicates there are positive effects of increased labour

market attachment on obesity levels. Rashad et al. (2006) also examined the effects of

relative prices on obesity.

The strong negative correlation over time between smoking rates and obesity has led some to

suggest that the reduction in smoking is increasing obesity levels. Chou et al. support this

conclusion by finding that higher cigarette prices have led to increased body weight.

However, Gruber & Frakes (2006) found no evidence to support this conclusion. They used

the cigarette tax instead of the cigarette price and found a negative effect of cigarette taxes on

body weight. Chou et al. used more variables in their analysis and therefore their conclusions

are more likely to be correct.

Schroder et al. (2007), conduct regression analysis to assess whether alcohol consumption

affects abdominal obesity. They find that high alcohol consumption (consuming more than 3

drinks per sitting) was significantly associated with the risk of abdominal obesity. However,

Kleiner et al. (2004) conducted a study of weight-management patients and found that as

BMI increased, rates of alcohol consumption decreased. This is owing to the theory that

overeating may be a substitute for alcohol for „brain reward sites‟, meaning food addiction

can be replaced by alcohol addiction and vice versa, which results in a negative correlation

between alcohol consumption and obesity.

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Much of the economic literature has focused on what has upset the balance between energy

intake and energy expenditure in recent decades. Finkelstein et al. (2005) reported that

technological advancement is the factor primarily responsible for the obesity epidemic. They

also report that lower food prices are also a contributing factor. From the law of demand, a

decrease in the price of food will cause consumption to increase. Ceteris paribus, if the price

of calorie-dense foods falls faster than the price of less calorie-dense foods, e.g. vegetables,

then individuals will shift their consumption to the cheaper alternatives. Finkelstein et al.

used primary historical material to demonstrate the changing patterns of obesity over the last

few decades. Because the majority of the population were more likely to suffer from weight

deficits in the past, increased BMI was typically associated with better health. This was the

case until obesity rates began to soar and so identifying economic factors that changed around

this time may help identify underlying causes of obesity.

Philipson & Lakdawalla (2002) argued that technological advancement is responsible for the

obesity epidemic largely because of its minimizing effect on energy expenditure in the

workplace. They found that 40% of the rise in obesity was due to expansion in the supply of

food through agricultural innovation, which lowered food prices, whereas 60% was due to

demand factors through more sedentary lifestyles. Exercise from household work has also

been reduced due to labour-saving machines, e.g. vacuum cleaners. Overall physical activity

has declined because the rise in recreational exercising does not compensate for the loss in

work-related exercise.

There is an abundance of literature relating to the impact of television viewing on obesity in

children and adolescents. However, there is less literature on the subject regarding adults. The

reason for television viewing affecting obesity is partially due to its potential as a stimulus for

eating. Gore et al. (2003) found that snacking, but not necessarily eating whole meals,

increased while watching television and the resulting overall caloric intake rose. However,

Lincoln (1972) performed a questionnaire study of food and beverage intake among 867 men

in the US and failed to find a direct relationship between overeating and obesity.

Tucker & Friedman (1989) studied 6138 adult males and found that those who viewed

television for more than three hours a day were twice as likely to be obese as those who

viewed less than one hour of television a day. They adjusted for age, smoking status, length

of working week, measured physical fitness and reported weekly hours of exercise. Physical

fitness confused the relationship between TV viewing and obesity but all the other variables

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behaved as expected. There have been no studies on the impacts of television viewing on

female adults. Physical activity has been shown to be a relevant factor contributing towards

obesity rates. Frank et al (2004) collected data from a travel survey in Georgia and found that

each additional kilometre walked per day was associated with a 4.8% reduction in the

likelihood of obesity.

Hinde & Dixon (2005) examine the impacts of car reliance in Australia on obesity levels.

They put emphasis on the social trends and processes behind the increasing reliance on

driving and the obesity epidemic.

Young & Nestle (2002) found evidence that portion sizes of nearly all food eaten outside the

home in the US have increased over time. Neilsen & Popkin (2003) found that the largest

increases were in French fries and sweetened beverages. However, there has been little

evidence relating to European countries of increased portion sizes. Neilsen and Popkin also

found that the increase in energy intake has been accompanied by changes in eating patterns;

snacking has become more popular over time.

There have been quite a few studies that try to link income and education to obesity rates on

an individual country level, but there has been little study of obesity rates across countries.

Wardle et al. (2002) investigated socioeconomic predictors of obesity in men and women.

Data from the 1996 Health Survey for England were used to compare ratios for obesity by

education, occupation while controlling for age, marital status and ethnicity. It was found that

obesity risk was greater among men and women with fewer years of education and poorer

economic circumstances, and of women, but not men, of lower occupational status. More

educated people are more knowledgeable about dieting. They are also more likely to be able

to afford the foods and programmes that supplement dieting. Nayga (2000) also found the

effects of increased schooling on obesity are due to increased health knowledge.

Eleuteri (2004) uses panel data to analyse obesity and income within the OECD. The paper

analyses the relationship between calorie intake, health spending, the percentage of the

population over 65 and obesity. The analysis shows that caloric intake is not the cause of

increased obesity in OECD countries. The results show that income has a positive impact on

obesity rates. In contrast, Cutler et al. (2003) run regressions using OECD countries data and

find that real income is unrelated to obesity.

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Poverty is associated with lower food expenditures, lower fruit and vegetable consumption

and lower quality diets. One aspect of poverty is limited access to physical activity. Lower

income areas have fewer supermarkets selling fresh fruit and vegetables and have fewer parks

and playing fields for exercise. Rashad (2003) finds that across countries, more developed

nations have higher obesity rates, in other words GDP per capita and obesity rates are

positively correlated. However, within a country, those with higher incomes tend to have

lower rates of obesity. Incomes on a global level have risen in the past two decades which is

contradictory to the inverse relationship between income and obesity found by many

researchers.

Engel‟s Law states that, as incomes rise, the share of income spent on food decreases. (Engel,

1857). People in higher income nations consume more added sugars and fats than people in

lower income nations. Conversely, Drewnowski & Spectar (2004) and Drewnowski (2003)

find that the highest rates of obesity occur amongst populations with the highest poverty rates

and the least education. They find that there is an inverse relationship between energy density

(MJ/kg) and energy cost ($/MJ). This means that energy-dense foods, composed of refined

grains, added sugars and fats, are the cheapest option for the consumer. The lowering of

energy costs through technological advancement has been most rife for foods containing

added sugars and fat.

There has been research on whether obesity is a cause of unemployment but little work has

been done on whether unemployment contributes towards the likelihood of obesity. Ruhm

(2000) showed that obesity becomes more prevalent during macroeconomic upturns when

there is low unemployment because higher time costs lead to reductions in healthy activities

such as exercise.

Ewing et al. (2003) attributes part of the rise in obesity to the degree of urban sprawl. Urban

sprawl is defined as the process through which the spread of development across the

landscape far outpaces population growth. The urban sprawl variable can be used to measure

how conducive a city is to exercise. Vandegrift & Yoked (2004) use US state-level data from

the 1990s on obesity to show that states that increased the amount of developed land (holding

population constant) had higher obesity rates. Conversely, Eid et al. (2008) find no evidence

that urban sprawl causes obesity. They find that individuals, who are more likely to be obese,

choose to live in more sprawling neighbourhoods and argue that other studies have failed to

control for this factor.

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There is an abundance of literature on the causes of childhood obesity, which can sometimes

be linked to causes of adult obesity. Higher wages for women have led to a dramatic increase

in female labour force participation which could be one of the reasons for rising obesity rates

in children. Anderson et al. (2003) found evidence that increases in maternal employment

influenced the rise in childhood obesity; they suggest that children of working mothers eat

home-cooked meals less frequently. However, this increase in the female labour force

occurred long before the rise in obesity began, so it might not be an influential factor. Cutler

et al. (2003) find that female labour force participation rates are unrelated to adult obesity.

More research must be done in order to determine what percentage of males and females do

the majority of the household cooking.

In the non-economic literature, rises in obesity levels have been blamed on biological factors

like genetics and behavioural factors like addiction and time preference. The theory that there

is a large genetic component to obesity is refuted by Philipson (2001), who argues that,

although genetics may explain cross-sectional differences, it cannot explain a change over

time in the amount of obesity. Such a change would be much slower than observed if it were

a genetic phenomenon. An increase in the rate of time preference can cause a rise in obesity.

Time preference is defined as the rate at which people are willing to trade current utility for

future benefit. A high rate of time preference means people will be more likely to increase

their calorie intake now and forego exercise for future health benefits.

Bray & Popkin (1998) followed adolescents and adults in China and found that the increase

in dietary fat intake was significantly related to an increase in BMI. However, Willet (1998)

found that dietary fat intake does not seem to be the primary cause of high obesity rates.

Comparisons of obesity between rich and poor countries have been used to support a causal

association between dietary fat intake and body fat, but these are negated by stark differences

in physical activity and food availability.

Explanations for the rising obesity epidemic are still not yet fully understood. The literature

examines many factors which contribute to rising obesity but it is unclear which factors are

the most dominant in contributing to weight gain. In order to determine the best policies for

governments to combat the obesity epidemic, more research needs to be undertaken.

Econometric analysis would be the optimal method for determining the significance of the

variables.

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METHOD

The Model

OBg,c = a + bGDPc + cEDc + dUNEMPc + eFATc + fALCc + gCARc +….+µg,c

OB = obesity rate (% of total population)

g = m (male), f (female)

c = 1,2,…,N (country)

a = intercept

GDP = real GDP per head (measured in thousands of constant 2000 US$)

ED = secondary school enrolment (% net)

UNEMP = unemployment (% of total labour force)

FAT = daily fat supply, grams per head

ALC = alcohol consumption per head (litres of pure alcohol)

CAR = passenger cars per 1000 people

TV = television viewing time (hours per person)

SMO = smoking prevalence (% of population ages 15+)

BM = Big Mac Index ($)

BM2 = Big Mac Index relative to income ($)

µ = error term

Rationale for choosing variables

The dependent variables are male obesity and female obesity (% of the population). The

reason for distinguishing obesity by sex is to test whether there is a difference in the causes of

obesity in males and females. This could be useful in analysing the significance of economic

variables affecting obesity rates and to what extent biological factors must be considered.

The first independent variable is GDP per head measured in thousands of constant 2000 US$

(EarthTrends, 2011). Figures for GDP are converted from domestic currencies into dollars,

using 2000 official exchange rates. Constant figures are adjusted for inflation, so that the

countries can be compared with each other, despite the fact that the figures are for different

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years. The reason for including GDP is because the amount of wealth and disposable income

people have will affect their consumption and lifestyle choices, which influence a person‟s

weight.

The next independent variable is secondary school enrolment (% net)1 (World Bank, 2011).

The reason for including this is because education influences health and dietary knowledge.

The general consensus is that the more educated people are, the less likely they are to eat

unhealthy foods and become obese.

Unemployment as a percentage of the total labour force2 (World Bank, 2011) has been

included for a number of reasons. Unemployment can have psychological effects, including

anxiety and depression. Often, one of the side-effects of anxiety or depression is an increase

in calorie consumption. Unemployment can also suggest very low income, especially if one is

living on the „dole‟. Some argue that low income leads to eating more fattening foods, as they

cost less to purchase. However, others argue that low income leads to less overall calorie

consumption and hence a smaller risk of obesity.

Daily fat supply per head measured in grams (FAOSTAT, 2011) has been included owing to

the well-known fact that consumption of fatty foods has a direct positive effect on peoples

weight.

The next independent variable is recorded adult (15+ years) alcohol consumption per head

(WHO, 2011), measured in litres of pure alcohol. Alcohol is a highly calorific substance,

which can contribute to weight gain.

The number of passenger cars3 per 1000 people (World Bank, 2011) has been included

because studies have shown that increased exercise decreases the risk of obesity and the more

that people use motor vehicles to transport themselves instead of walking, the less exercise

they will get.

1 The net enrolment ratio is the ratio of children of official school age, based on the International Standard

Classification of Education 1997, who are enrolled in school to the population of corresponding official school age. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject or skill-orientated instruction using more specialized teachers (World Bank, 2011). 2 This measures the percentage of the total labour force that is without work but is available for and seeking

employment (World Bank, 2011). 3 Passenger cars refers to road motor vehicles, other than two-wheelers, intended for the carriage of

passengers and designed to seat no more than nine people (including the driver) (World Bank, 2011).

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Television viewing time (IP Network, 2011) has been included because the more hours of

television watched per person per day, the more sedentary their lifestyle and hence the greater

the likelihood of being obese.

Smoking prevalence in adults (WHO, 2011) has been included because people often

substitute food for smoking and vice versa. Obesity rates and cigarette consumption have

been known to have a negative correlation.

The Big Mac Index provides a variable for fast-food prices. It is an index of PPP exchange

rates based solely on the prices of the Big Mac Sandwich in McDonald‟s restaurants around

the world. It is published by The Economist once a year. The Big Mac Index has been

included as it is an approximate measure of relative food costs in different countries.

Countries with lower indexes have a higher obesity rate than countries with high indexes.

Graph 1a. Scatter plot of male obesity versus real GDP per head

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Graph 1b. Scatter plot of female obesity versus real GDP per head

Data Collection

When collecting worldwide data from various internet sources, various problems may be

encountered. Data were collected mainly from the international agency, the World Bank. The

main problem during data collection for this model was that nearly all of the data sets were

incomplete or not available for the specific years needed for this model. The success of any

econometric analysis relies on the availability of appropriate data. Owing to the

nonexperimental nature of the data used in this analysis, there was no choice but to depend on

the available data. Therefore, the results from this research can only be as good as the quality

of the data.

Television viewing time per person and smoking prevalence data were not available for

countries worldwide, only for European countries. Therefore, a regression is run for 53

countries to investigate the relationship between obesity and income. Another regression

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follows, which is restricted to 20 countries; this includes all of the variables except for

smoking and TV viewing. The next regression includes all of the variables but is restricted to

even fewer countries (20), as a result of only European data being available.

Regression

Regression measures the strength of the relationship between one dependent variable and a

series of other changing variables. Regressions are assumed to measure causal relationships

whereas correlations don‟t presume any causality. They merely measure whether two

variables tend to vary in a systematic linear fashion with each other.

In Table 1 are the regression results from exploring the relationship between obesity rates and

GDP (income). The intercepts have been excluded from the tables because they are not of

direct concern. Model (1) checks for a linear monotonic relationship; monotonic meaning

always rising or falling, but never both (as with a parabola). Model (2) has a dummy variable

to control for the outlier, Samoa. The outliers can be seen clearly from the scatter plots in

graphs 1a and 1b. Model (3) includes the logarithm of GDP (LNGDP). This is to check for a

monotonic relationship exhibiting diminishing returns. The gradient of the fitted relationship

between income and obesity is measured by multiplying the coefficient of LNGDP by the

derivative of LNGDP (=1/GDP). Models (4) and (5) have dummy variables for the outliers

USA and Luxembourg. Luxembourg is an outlier because of its very high GDP per head; it

does not have an unusual obesity rate.

To make the coefficients of GDP more manageable to work with, GDP was divided by 1000,

so that it measures income in thousands of constant US $.

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Table 1a. Regression (dependent variable: male obesity)

(1) (2) (3) (4) (5)

GDP

0.135

(0.078)

1.733

0.159

(0.073)

2.183

-0.299

(0.136)

-2.197

-0.462

(0.130)

-3.567

-0.651

(0.144)

-4.529

DumSamoa

21.634

(7.051)

3.068

25.314

(6.326)

4.002

25.525

(5.654)

4.514

25.834

(5.362)

4.818

LNGDP

5.348

(1.400)

3.821

6.275

(1.276)

4.916

7.452

(1.296)

5.749

DumUS

22.235

(6.088)

3.652

26.599

(6.024)

4.416

DumLUX

16.142

(6.378)

2.531

R²

0.056

0.205

0.388

0.521

0.578

Adjusted

R²

0.037

0.173

0.350

0.481

0.533

Note: standard errors are in parentheses and t-ratios are in bold.

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Table 1b. Regression (dependent variable: female obesity)

(1) (2) (3) (4) (5)

GDP

-0.098

(0.108)

-0.909

-0.050

(0.089)

-0.565

-0.380

(0.181)

-2.098

-0.581

(0.176)

-3.296

-0.810

(0.198)

-4.087

DumSamoa

44.451

(8.595)

5.172

47.105

(8.424)

5.591

47.364

(7.687)

6.162

47.738

(7.390)

6.460

LNGDP

3.857

(1.864)

2.069

4.995

(1.735)

2.878

6.421

(1.787)

3.594

DumUS

27.279

(8.277)

3.296

32.572

(8.302)

3.923

DumLUX

19.579

(8.790)

2.227

R²

0.016

0.359

0.410

0.519

0.565

Adjusted R²

-0.003

0.333

0.374

0.479

0.519

Note: standard errors are in parentheses and t-ratios are in bold.

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REGRESSION ANALYSIS

R² is the proportion of variation in the dependent variable explained by the regression model.

The values of R² range from 0 to 1. Small values indicate that the model does not fit the data

well. In Tables 1a and 1b, the values of R² increase as each independent variable is added.

For both male and female obesity, model (5) has the best R² and adjusted R². The model for

males fits the obesity data slightly better than does the model for females. It is quite rare to

get a high R² value in cross-country studies in the social sciences. This may be due to the

individual characteristics of different countries, many omitted factors, poor data etc.

A variable for GDP² was added to try to see if the data would fit a parabola but this was

unsuccessful. This suggests that the relationship between income and obesity is linear. If the

data fitted a parabola, this would mean that income and obesity would be positively related

but at a certain level of income, there would be a turning point and they would become

negatively related.

The negative coefficients show an inverse relationship between income and obesity. This is

rather surprising because there have been various studies (Vandegrift & Yoked, 2003), which

have shown large increases in obesity at the same time as rising incomes and economic

growth.

If Obesity = a + bGDP + cLNGDP, the partial derivative of Obesity with regard to GDP is

b+c/GDP, which varies with GDP. By interpreting the combined derivative, we can see the

relationship between Obesity and GDP.

The gradient for low-income countries is positive and that for high-income countries is

negative. This means in low-income countries, the richer the person, the more likely he or she

is to be obese, and in high-income countries, the richer the person, the less likely he or she is

to be obese. This is consistent with the findings of Rashad (2003), as mentioned in the

literature review. From the original data, for example, the gradients for Zimbabwe are +16.76

for males and +14.19 for females and those for Switzerland are -0.4333 for males and -0.622

for females. This is consistent with the theory because Zimbabwe has very low GDP, whereas

Switzerland has very high GDP.

The turning point at which the gradient changes from positive to negative is GDP = 11.447

for males and GDP = 7.927 for females. In countries with a GDP lower than 7.927, the higher

the GDP, the higher the obesity rate for both males and females. In countries with a GDP

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between 7.927 and 11.447, female obesity will fall as GDP increases but male obesity will

continue to rise. In countries with a GDP greater than 11.447, both male and female obesity

rates will fall as GDP rises.

One important assumption of the linear regression model is that the variance of the error term

is a constant σ². This is the assumption of homoscedasticity, or equal variance. Increasing

heteroscedasticity could occur because of greater variation in obesity in richer countries than

in poorer countries. Alternatively, there may be better data on obesity in richer countries,

which would cause decreasing heteroscedasticity. To test for heteroscedasticity, the squared

residuals are regressed on the squared fitted values. If the null hypothesis of homoscedasticity

is true, the coefficient will be close to zero (Flegg, 2004). The coefficients for males and

females, respectively, are -0.016 and -0.012. Neither of these values is significant, which

means that there is no problem of heteroscedasticity in the model.

The presence of outliers causes a lack of normality, shown by large residuals. The error term,

µ, represents the influence on the dependent variable of a large number of independent

variables that are not individually included in the model. It is hoped that the influence of

these omitted variables is small and random (Gujarati, 2004, p.109). To account for the

outliers, dummy variables have been included in the model. If outliers are present, the results

can be skewed.

To test for normality, we can look at histograms of the residuals in Graphs 2a and 2b. A

histogram of the residuals allows us to visually assess the assumption that the errors in the

dependent variable are normally distributed.

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Graph 2a. Histogram of Residuals (male obesity)

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Graph 2b. Histogram of residuals (female obesity)

The histogram of residuals for male obesity in Graph 2a follows the normal curve more or

less but it shows an outlier. A symmetric bell-shaped histogram which is evenly distributed

around zero indicates that the normality assumption is likely to be true (Gujarati, 2004,

p.147). In both histograms, the residuals are positively skewed. This suggests that the

model‟s underlying assumptions might have been violated.

To further check that the data are normally distributed, we can look at the Shapiro-Wilk

statistic. This is 0.958 for male obesity and 0.897 for female obesity. Both these figures are

significant at the 5% level; hence the residuals are normally distributed, so the t-ratios can be

trusted.

The “residuals” are defined as the differences between the observed (actual) values of obesity

and the predicted values. Because the Shapiro-Wilk test indicated a normal distribution, the

histograms might be skewed due to the outliers present.

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Graph 3a below shows actual versus predicted values for the initial model (1). This shows

that the initial model is extremely bad at predicting the actual male obesity rate.

Graph 3a. Scatterplot of actual vs. predicted values for model (1).

Graph 3b below shows actual versus predicted values for the refined model (5). This shows

the predictors used in this refined regression yield fairly good estimates of the actual obesity

levels.

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Graph 3b. Scatterplot of actual versus predicted values for model (5).

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LINEAR REGRESSION MODEL

This uses data for 20 countries: Bulgaria, Croatia, Estonia, Finland, France, Greece, Hungary,

Italy, Lithuania, Luxembourg, Malta, Netherlands, Norway, Sweden, Switzerland, UK,

Japan, Brazil, Mexico and USA.

There are 8 independent variables (regressors): GDP, Secondary school enrolment,

unemployment, daily fat supply, alcohol consumption, passenger cars, Big Mac Index and

relative Big Mac Index (2).

Descriptive Statistics

Table 2a shows the correlation between the 2 dependent variables (male obesity and female

obesity) and the 8 independent variables.

Table 2a.Correlation Coefficients

Male obesity Female obesity

GDP -0.110 -0.175

LNGDP -0.104 -0.241

Secondary school enrolment -0.303 -0.472

Unemployment 0.045 0.001

Daily Fat supply 0.147 -0.008

Alcohol 0.097 0.081

Passenger cars -0.074 -0.325

Big Mac Index -0.124 -0.256

Big Mac Index (2) 0.074 0.246

Correlation is a statistical technique that measures the degree of linear association between

two variables. The correlation coefficient measures this strength of association. Correlation is

different from regression analysis, in which we try to estimate the average value of one

variable on the basis of the fixed values of other variables. N.B. Correlation suffers from

ceteris non paribus.

In regression analysis, there is an asymmetry between the dependent and independent

variables. The dependent variable is assumed to be statistical and random and to have a

probability distribution. The independent variables are assumed to have fixed values. In

correlation analysis, any two variables are symmetrical; there is no distinction between the

two variables and they are both assumed to be random. A random variable is a variable that

can take on any set of values, positive or negative, with a given probability (Gujarati, 2004,

p.23).

24

CORRELATION ANALYSIS

The correlation between obesity and GDP for males is -0.11 and, for females, is -0.175. This

is as expected in rich countries, which have an inverse relationship between obesity and GDP,

but in poor countries there would be a positive correlation.

The correlation between obesity and secondary school enrolment for males is -0.303 and, for

females, is -0.472. This is negative as expected because, as education enrolment increases,

obesity should decrease owing to improved dietary knowledge.

The correlation between obesity and unemployment for males is 0.045 and for females is

0.001. This very low positive correlation is unexpected. As mentioned in the literature

review, according to Ruhm (2000), a negative correlation would be expected, as unemployed

people have lower time costs, so are able to take part in more physical activities. When a

correlation is unexpected, this is a case of ceteris non paribus. This problem is addressed in

regression analysis.

The correlation between obesity and daily fat supply for males is 0.147 and for females is

-0.008. The male correlation is positive, as expected. A study mentioned in the literature

review (Willet, 1998), argues that there is very weak positive relationship between dietary fat

and body fat. The female correlation is very slightly negative, which is unexpected.

The correlation between obesity and alcohol consumption for males is 0.097 and for females

is 0.081. This is slightly positive, as expected, owing to the high calorie content of alcohol

(Schroder et al. 2007). However, according to Kleiner et al. (2004), this would be as expected

owing to alcohol addiction being a substitute to obesity (food addiction) in individuals.

The correlation between obesity and passenger cars for males is -0.074 and for females is

-0.325. These negative correlations are surprising because cars are a labour-saving good and

the less walking people do, the higher the obesity rate should be. This negative correlation

could be explained by collinearity between GDP and passenger cars, which have a correlation

of 0.649.

The correlation between obesity and the relative Big Mac Index (2) is 0.074 for males and for

females it is 0.246. This is not as expected because the higher the relative price of a Big Mac

is, the lower the consumption of „fast-food‟ expected and hence the lower rate of obesity

expected.

25

Table 2b. Correlation Matrix

GDP1000

Secondary school

enrolment (% net)

Unemployment (% of total

labour force) Daily fat supply (g) per capita

Alcohol Consumption

Passenger cars per 1000

people Big Mac Index

GDP Pearson Correlation

Sig. (2-tailed)

N 20

Secondary school enrolment (% net)

Pearson Correlation .320

Sig. (2-tailed) .170

N 20 20

Unemployment (% of total labour force)

Pearson Correlation -.586** .003

Sig. (2-tailed) .007 .989

N 20 20 20

Daily fat supply (g) per capita

Pearson Correlation .594** .187 -.367

Sig. (2-tailed) .006 .429 .111

N 20 20 20 20

Alcohol Consumption Pearson Correlation -.122 .254 .287 .136

Sig. (2-tailed) .608 .280 .220 .566

N 20 20 20 20 20

Passenger cars per 1000 people

Pearson Correlation .649** .530

* -.303 .575

** .165

Sig. (2-tailed) .002 .016 .194 .008 .488

N 20 20 20 20 20 20

Big Mac Index Pearson Correlation .694** .420 -.482

* .673

** -.160 .619

**

Sig. (2-tailed) .001 .065 .031 .001 .502 .004

N 20 20 20 20 20 20 20

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

26

Table 2b above shows how strongly correlated the variables are with each other. This helps

us to determine whether there are any problems of multicollinearity.

REGRESSION ANALYSIS

Multicollinearity is a problem with estimating the regression coefficients caused by

predictors that are linearly related with each other. The problem of multicollinearity can be

tested using the Variance Inflation Factor (VIF). VIF shows how the variance of a regressor

is inflated by the presence of multicollinearity. If VIF = 1, there is no collinearity. As a

general rule of thumb, if the VIF > 10, which will happen if R² exceeds 0.90, there is likely

to be a high degree of multicollinearity and there should be a cause for concern. Studenmund

(5th

ed, p.259) mentions a rule of thumb of VIF > 5, which is considered to be a very

conservative rule and is much less commonly used. Tolerance (TOL) can also be used as a

measure of multicollinearity as it is the reciprocal of VIF. If TOL<1, there could be

multicollinearity present. For var1, we would regress it on var2,3,4 etc. and get R². TOL(1) =

1 - R² and VIF(1) = 1/TOL(1). However, VIF (or TOL) is not a perfect measure of

multicollinearity. A high VIF is neither necessary nor sufficient to get high variances and

high standard errors. Therefore, a high degree of multicollinearity may not necessarily cause

high standard errors (Gujarati, 2004, p.326).

27

Table 3. Collinearity Statistics

Model

Collinearity Statistics

Tolerance VIF

1 (Constant)

GDP .158 6.348

Secondary school enrolment

(% net)

.303 3.304

Unemployment (% of total

labour force)

.541 1.849

Daily fat supply (g) per capita .338 2.963

Alcohol Consumption .569 1.757

Passenger cars per 1000

people

.307 3.259

Big Mac Index .224 4.471

DUMUS .483 2.069

DUMLUX .281 3.555

The VIF and TOL figures in Table 3 give no cause for concern as the VIF‟s are all less than

10. However, according to Studenmund, the VIF of GDP (6.348) could be a cause for

concern as it is > 5. The figures suggests that a certain degree of multicollinearity is present

(VIF>1), but not a serious problem. Some variables are more affected by others. GDP has the

highest VIF (6.348). GDP could be affected by unemployment because the higher the

unemployment rate, the lower the GDP will be. Secondary school enrolment could also affect

GDP because the more educated the work force, the higher the GDP is likely to be. The Big

Mac Index has the second highest VIF (4.471). The Big Mac Index also has a positive

correlation of 0.694 with GDP, significant at the 0.01 level; this means the higher the prices

of Big Macs are in a certain country, the higher GDP is likely to be.

28

Table 4a. Regression (dependent variable: male obesity)

(1) (2)

GDP -1.164

(0.408)

-2.85

-1.718

(0.554)

-3.100

LNGDP

11.853

(6.062)

1.955

31.402

(13.204)

2.378

EDUC

-0.084

(0.314)

-0.267

-0.062

(0.297)

-0.208

UNEMP

-0.038

(0.616)

-0.062

-0.023

(0.583)

-0.039

FAT

-0.071

(0.092)

-0.775

-0.042

(0.075)

-0.563

ALCO

0.391

(0.619)

0.632

0.203

(0.567)

0.359

CAR

-0.007

(0.019)

-0.353

-0.017

(0.020)

-0.853

BM

3.910

(3.226)

1.212

BM2 42.395

(25.854)

1.640

DUMUS

29.449

(9.659)

3.049

36.261

(8.371)

4.332

DUMLUX

15.546

(12.660)

1.228

31.715

(12.721)

2.493

R² 0.705 0.736

Adjusted R² 0.377 0.442

Note: standard errors are in parentheses and t-ratios are in bold.

29

Table 4b. Regression (dependent variable: female obesity)

(1) (2)

GDP -0.885

(0.217)

-2.113

-1.661

(0.496)

-3.351

LNGDP

7.458

(5.929)

1.258

34.422

(11.811)

2.915

EDUC

-0.180

(0.307)

-0.585

-0.152

(0.265)

-0.571

UNEMP

-0.432

(0.602)

-0.717

-0.407

(0.521)

-0.781

FAT

-0.103

(0.090)

-1.147

-0.069

(0.067)

-1.027

ALCO

0.731

(0.606)

1.208

0.491

(0.507)

0.968

CAR

-0.015

(0.018)

-0.819

-0.036

(0.018)

-2.059

BM

5.023

(3.155)

-1.180

BM2 58.486

(23.125)

2.529

DUMUS

38.105

(9.052)

4.210

38.278

(7.487)

5.112

DUMLUX

25.025

(12.348)

2.027

31.921

(11.378)

2.806

R² 0.769 0.827

Adjusted R² 0.513 0.635 Note: standard errors are in parentheses and t-ratios are in bold.

30

The t-ratio for unemployment in model (2) is very low which means the variable is not

statistically significant. By excluding the unemployment variable, the model does not

improve and so it is kept in the model on the basis that unemployment is a relevant variable.

Model (2) in Table 4a. is considered to be the preferred model, as it has a better fit (higher

R²). The following analysis will be based on model (2).

GDP and LNGDP must be interpreted together to determine the relationship between Obesity

and GDP. The gradient of the curve of obesity versus GDP in Bulgaria (the lowest income

country in the model) is 14.222 for males and 15.812 for females. In Luxembourg, (the

highest income country in the model), the gradient for males is -1.109 and for females it is

1.328, cet. par. In Bulgaria, there is a positive relationship between income and obesity for

both males and females, and in Luxembourg there is an inverse relationship between income

and obesity, for males but a positive relationship for females, cet. par. This supports the

theory mentioned previously, that the gradient of the curve is positive to a certain level of

GDP, and then it will become negative. The turning point at which the overall gradient

changes from positive to negative is GDP = 18.278 for males and GDP = 20.724 for females.

Luxembourg‟s GDP is 51.59 and the female gradient is still slightly positive at this level.

This shows that Luxembourg women do not follow the trend.

For every percentage point increase in secondary school enrolment, there is a decline in male

obesity of 0.062 percentage points and for female obesity there is a decline of 0.152

percentage points, cet. par. The higher effect in females might suggest that males choose to

pay less attention to dietary advice provided by schools and are more likely to ignore the

health risks associated with obesity.

For every percentage point increase in unemployment, there is a decline in male obesity of

0.023 percentage points and for female obesity there is a decline of 0.407 percentage points,

cet. par. The higher effect in females may be explained if females spend more of their free

time exercising than males do.

For every 1g increase in fat supply, there is a decline in male obesity of 0.013 percentage

points and for female obesity there is a decline of 0.069 percentage points, cet. par. For every

1 litre increase in alcohol consumption, there is an increase in male obesity of 0.181

percentage points and an increase in female obesity of 0.491 percentage points, cet. par. This

may be explained if females drink alcoholic drinks that are more fattening than those drunk

by males.

31

For each additional car per 1000 people, there is an increase in male obesity of 0.004

percentage points, and for females there is a decrease of 0.036 percentage points, cet. par.

For every dollar increase in the Big Mac Index, there is a rise in male obesity of 3.910

percentage points, and for females there is a rise of 5.049 percentage points, cet. par. This is

not the expected outcome, as it is logical that more expensive „fast-food‟ would encourage

people to eat more healthy food and hence the obesity rate would fall, cet. par. The model

does not include any variable for physical activity, which has been shown to be a significant

determinant of obesity rates (Frank et al. 2004; Lincoln 1972). The reason for not including

physical activity as a regressor is because the relevant data for all countries in the model were

not available.

In model (2), the Big Mac Index has been divided by GDP, to create a relative price variable,

BM2. This improved the explanatory power of the model (R² is higher) but the coefficient of

BM2 is unrealistic. BM2 has a slightly higher t-ratio than the original Big Mac Index, but it is

still not significant at conventional levels. A one-unit increase in BM2 would mean an

increase in male obesity of 42.395 percentage points, cet. par. This is unrealistic; a rise of

say, 0.1 units, would be more meaningful. The Big Mac Index in Japan (2004) was 2.33 and

the BM2 was 0.06. This shows that the Big Mac Index was less than average (the average Big

Mac Index in this model is 3.05), and combining this with a very high GDP of 38.2, means

that the BM2 figure is the smallest of all countries in the model. This suggests the Japanese

could consume large quantities of fast-food with very little cost. However, the Japanese

obesity rates are the lowest in the developed world. This contradiction cannot be due to a lack

of availability of fast-food restaurants. According to Nationmaster.com, Japan has the second

highest number of McDonald‟s restaurants in the world, after the USA. 99% of Japanese are

enrolled in secondary education. This is the highest rate of all countries in the model, and

could help explain the lack of obesity is due to a good dietary education. This supports the

idea of Steven E. Landsburg who argues that McDonald‟s cannot be blamed for the obesity

epidemic (Landsburg, 2007, p.138). The Big Mac Index in Bulgaria (2004) was 1.85 and the

BM2 was 0.94. This BM2 figure is the highest of all the countries in the model. This shows

that Big Macs in Bulgaria are relatively more expensive than anywhere else because Bulgaria

has a very low GDP of 1.97 (000‟s US$), but McDonald‟s have not adjusted their prices

according to GDP.

32

MODEL 3 - PHYSICAL ACTIVITY

This uses 10 countries‟ data and 7 independent variables.

Physical activity has been found to be a very significant factor affecting obesity rates. For the

countries in this study, physical activity data were only available for 10 countries, so a

smaller model has been created to see how significant it is for explaining obesity rates. The

physical activity figures are defined as the percentage of the population that partake in either

„moderate‟ or „high‟ activity, three or more days a week (Bauman et al. 2009).

The model does not include secondary school enrolment, television viewing or smoking

prevalence as there was not enough data available for the 10 specific countries.

33

Table 5. Two Linear Regression Models (male and female)

Male Female

GDP

2.951

(2.050)

1.440

3.140

(1.918)

1.637

LNGDP

-54.940

(31.801)

-1.728

-58.286

(29.761)

-1.958

UNEMP

3.878

(8.908)

0.435

3.101

(8.336)

0.372

FAT

0.074

(0.341)

0.217

-0.008

(0.319)

-0.024

ALCO

0.110

(1.295)

0.085

0.375

(1.212)

0.310

CAR

0.125

(0.098)

1.277

0.103

(0.091)

1.132

BM2

-35.212

(88.211)

-0.399

-57.690

(82.551)

-0.699

PA

0.241

(0.906)

0.266

0.538

(0.848)

0.635

R² 0.943 0.952

Adjusted R² 0.489 0.565 Note: standard errors are in parentheses and t-ratios are in bold.

The R² for both male and female obesity is very high, 0.943 and 0.952, respectively. This

suggests that physical activity is a crucial variable to include when trying to explain variance

in obesity rates. For every percentage point increase in the male population taking part in

physical activity, there is an increase in male obesity of 0.241 percentage points, cet. par. By

contrast, for every percentage point increase in the female population taking part in physical

activity, there is an increase in female obesity of 0.538 percentage points, cet. par.

34

CASE STUDIES

Case Study: Canada and US comparison

From the original data, it can be seen that for men, the prevalence of obesity was 9.3

percentage points lower in Canada than in the United States (22.9% compared with 32.3%)

and for women, 12.3 percentage points lower (23.2% compared with 35.5%).

Given the similarities between Canada and the US, it is difficult to see what major cultural or

lifestyle differences could account for such a difference. One major difference is the per

capita GDP. The differences in standard of living as captured by GDP may affect the

resources available for food consumption. However, the relationship between income, food

consumption and obesity may not necessarily be positive, as it would also depend on whether

food is a normal or inferior good. Furthermore, income and education are also correlated

(0.320). This positive correlation shows that countries with higher incomes tend to have

better educated populations and therefore their populations are more knowledgeable about

health and food consumption, meaning the relationship between obesity and income could be

negative rather than positive or it could be non-linear, i.e. a parabola.

Different ethnic groups seem to be an important consideration when measuring obesity rates.

Flegal et al. (2002) found that differences in ethnic groups did not vary significantly in the

prevalence of obesity for men, but for women, it was found that the prevalence of obesity was

highest for non-Hispanic black women.

A recent study of data collected between 2007-2009 by the Centers for Disease Control

(CDC), has shown that more than a third of the US population are obese, compared with

about a quarter of the Canadian population. The CDC attributed part of the difference in

obesity to the larger percentage of Black and Hispanic people in the US. These demographics

are shown to be more prone to obesity. This is backed up by Flegal et al. (2002), who cite

ethnicity as a variable affecting obesity rates. When comparing the white populations in the

US and Canada, 25.6% of Canadians and 33% of Americans were obese, whereas over the

whole of the populations, the figures have a wider spread of 24.1% and 34.4% respectively.

Case Study: Japan

The Japanese have the lowest obesity rates in the Developed World (3.4% of males and 3.8%

of females are obese). From the data, it can be seen that the percentage of the population

35

enrolled in secondary school is the highest in all countries (99%). This suggests that obesity

is very low among the well-educated.

According to the study by Bauman et al. (2009), Japan reported less than a third of their

population taking part in „high‟ physical activity. This could suggest that physical activity is

an irrelevant variable for a model trying to explain obesity. 43.3% of Japanese people are

classified as „inactive‟. This is the highest rate in the physical activity model of 10 countries,

as discussed previously. This statistic is very surprising as a low obesity rate would be

expected to be associated with high activity levels. However, this physical activity measure

does not account for everyday exercise. The Japanese have very good public transport

services and tend to walk around more in their day-to-day lives rather than use cars.

The Japanese have a very unique diet, consisting predominantly of fish. Sakata (1995) found

that a conventional Japanese diet consisting mainly from chicken fillet, egg white, fish,

mushroom, seaweed and low-calorie vegetables, has been shown to be useful for keeping a

healthy weight.

However, Asians are particularly susceptible to the health risks associated with excess body

fat and so the Japanese have redefined obesity as BMI > 25 (Anuurad et al. 2003). This has

not been accounted for in the obesity data used in this study.

Case Study: Samoan Women

From the original data, it can be seen that 63% of Samoan women and 32.9% of Samoan men

were obese in 1995. The reasons for such high obesity rates, especially in females, are mainly

owing to modernisation and biological factors. The significantly higher rate in females could

be associated with significant reductions in their specific subsistence activities in modern

settings.

According to Pawson & Janes (1981), peoples of the Pacific Islands tend to become

overweight when they migrate or are exposed to modernization. They conducted a survey of

height, weight, blood pressure and fasting plasma glucose (FPG) among an urbanized

Samoan community in the San Francisco Bay Area, to try to determine the reason behind the

high obesity rate in migrant Samoans. Although the participants‟ average height fell between

the 25th

and 50th

percentile of the US population, about one half of the sample exceeded the

95th

percentile for weight. They concluded that it was difficult to comment on the results they

produced because they were unsure whether the sample population used was in some way a

36

“selected” group of Samoans, becoming fat before they migrate. They also comment that the

cultural beliefs are currently unknown but may be important in determining the Samoan‟s

attitudes toward food and obesity. Baker (1982) supports Pawson & Janes‟ study as he argues

that Samoans suffer large weight gain when they migrate to Hawaii and San Francisco, or

live a relatively affluent life in American Samoa.

Another reason why Samoans might be unusually fat is because of their lifestyle and

surroundings. Their staple diet consists of coconut, taro root, breadfruit, pig and fish, which is

native on the island. The Samoans need to do very little work to maximise the production of

these foods. Due to low work requirements in traditional Samoan society, levels of physical

fitness are proportionately low.

McGarvey (1991) takes an evolutionary perspective on Polynesian obesity, based on

scenarios of the fates of sailors on the voyages of discovery and of settlers in the pioneer

island villages. Their biological characteristics allowed them to rapidly grow adipose-tissue

which might have increased their chances of survival. According to Baker (1982), who

supports this view, Samoans, along with many other Pacific populations, seem to have

undergone a natural selection which favoured those who could rapidly gain weight.

Combining sudden diet and physical activity changes from modernization with such genetic

predispositions might be a cause of the high levels of obesity we see today.

Hodge et al. (1994) conducted a follow-up survey to a survey conducted in 1978, which

showed large differences in the prevalence of obesity between rural and urban populations in

Western Samoa. Cross-sectional differences in the prevalence of obesity, mean BMI and

waist-hip circumference ratio (WHR) were examined after adjusting for age, in urban Apia

and rural Poutasi and Tuasivi. Increased physical activity in men was associated with lower

obesity rates but this was not the case with women. Increased education and job status in

males were associated with increased obesity levels. This might be explained by the culture

of being overweight equating to status and success, which was not so important for females at

the time. However, increasing equality between sexes continues to grow and this could help

explain the very high prevalence of obesity in females.

37

EVALUATION

One fundamental flaw of the study is that BMI is based on weight and so very muscular

people, e.g. sportsmen, might have BMI values which suggest they are overweight or obese

but of course they are not. BMI can be a good measure of total body fat, but it cannot

distinguish between different types of fat distribution. For example, someone with excess fat

in the region of the stomach is more at risk of serious medical conditions such as heart

disease, raised blood pressure and diabetes; whereas someone with fat under the skin, and in

the hip and thigh regions are at risk of medical conditions which are less serious. To sidestep

this problem with BMI measurement, some sort of measurement involving a waist-height

ratio could be used instead (Ashwell, 2011).

The models could be improved by having data for all countries in the same year. The

investigation could be expanded by using more independent variables to try to explain the

causes of obesity. Urbanization could be included as an independent variable as various

studies suggest the prevalence of obesity may be higher in rural than in urban settings.

Technological advancement could also be included as an independent variable; after reading

numerous journals, it is evident that technology has played a crucial role in the change in

obesity rates.

Eating processed or fast food that is high in fat has been shown to be one of the main causes

of obesity. Primary research could be taken to measure the availability of fast food

restaurants compared with „sit down‟ restaurants in different countries. Data for the number

of McDonald‟s restaurants per capita would be helpful, but unfortunately after emailing their

headquarters, this information could not be obtained.

Snacking and comfort eating can also contribute towards obesity rates. This could be

measured by collecting primary data on people‟s mental health. However, this would be a

very time-consuming process and not everybody „comfort eats‟ when they find themselves in

a bad mental state. Dallman et al. (2003) propose people eat comfort food in an attempt to

reduce the activity in the chronic stress-response network of the brain. Mental health remains

a grey area when it comes to data collection and so more work must be done to discover the

association between obesity and mental health problems.

From surveying the literature on obesity it is evident that childhood obesity is an increasingly

prevalent problem and therefore the model might be improved by controlling for age. This

38

would give a clearer view of the relationship between childhood and adult obesity rates. It has

been shown that obesity in children predicts obesity later in life (Deckelbaum & Williams,

2001).

The prevalence of obesity is supposed to increase with age so „population over 65‟ could be

included as an independent variable, as discussed in the literature review (Eleuteri, 2004).

From trying to research the cultural differences between the US and Canada for the first case

study, it was clear that there is a large knowledge gap on the cultural determinants of obesity.

To assess beliefs, expectations and perceptions in relation to body size and food will take

many further years, but it is a subject of great importance for future policy implementation.

It is impossible to include every relevant variable that may contribute towards weight gain as

there are infinite possibilities. These models have tried to narrow down the possibilities and

include what appears to be the most significant variables relating to obesity, having read the

surrounding literature.

The statistical software used in this study was IBM‟s SPSS. This software is adequate for

small-scale research but it has its limitations for more in-depth research. SPSS users have

limited control over the statistical output and would be better off choosing another package.

39

CONCLUSION

From the first model, it was found that income and obesity do not follow a linear relationship.

It can be assumed that increasing incomes in countries will carry on increasing levels of

obesity worldwide, especially in lower income countries.

The second model then gives some hints as to what is contributing towards obesity and from

that, policymakers can try and form policies specific to the circumstances in individual

countries. The most significant factor affecting obesity for both males and females was found

to be GDP and the least significant factor for both males and females was the number of

passenger cars per 1000 people. „CAR‟ was found to be an insignificant variable owing to the

fact it was highly correlated with GDP. The final model included physical activity as an

independent variable which was found to be highly relevant, shown by the large R² value.

From the US and Canada case study, it is shown that ethnicity is an important factor in

explaining obesity rates for women (Flegal, 2002). The Samoan case study demonstrates that

modernization and biological factors are to blame for sky-high obesity rates. It can be

deduced that increased „westernization‟ in Samoa will most likely lead to increased levels of

obesity in the future. From the Japan case study it is shown that education and diet are the

most influential factors affecting obesity rates and these should be specifically targeted by

policymakers. These 3 case studies each have different reasons behind the obesity rates in the

different countries. This shows how complex the model could potentially become. Modelling

causes of obesity cross-country is near impossible using linear regression analysis as there are

too many inter-related determinants of obesity and each country‟s obesity rates will be

contextual.

Much more econometric work must be done on the causes of the rising obesity epidemic. It is

still not yet fully understood why general rises in income have failed to lower obesity rates on

a country level, despite a strong inverse relationship between obesity and income on an

individual level.

40

ACKNOWLEDGEMENTS

The author wishes to acknowledge the contributions of Tony Flegg of the University of the

West of England, who supervised the statistical analysis of this dissertation.

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45

APPENDIX

Country Year Male Obesity (% of total population))Female Obesity (% of total population)Real GDP per capita (constant 2000 US$)Secondary school enrollment (% net)Unemployment (% of total labour force)Daily fat supply (g) per capitaAlcohol Consumption Passenger cars per 1000 peopleSmoking prevalence (%) ages 15+Television viewing time per individualBig Mac Index ($) Physical Activity

EUROPE

Azerbaijan 2006 4.3 17.9 1571 82 6.8 51.61 .. 57 (2005) ..

Bulgaria 2004 13.4 19.2 1973 86 12.0 96.67 11.34 314 .. 225 (2007) 1.85

Croatia 2003 21.6 22.7 4818 86 13.9 94.30 12.62 291 27.4 167 (2000) 2.17

Czech Republic 2004 13.4 16.1 6285 .. 8.3 116.00 14.76 363 (2003) 25.4 194 2.13 90.1

Denmark 2001 11.8 12.5 30104 .. 4.2 137.40 11.60 360 (2002) 30.5 159 (2000) 2.93

Estonia 2004 13.7 14.9 5610 90 9.6 96.17 14.65 349 28.0 243 (2007) 2.27

Finland 2005 14.9 13.5 26329 95 8.4 127.41 9.95 460 23.0 166 (2007) 3.58

France 2006 16.1 17.6 23970 98 8.8 163.15 13.24 (2005) 496 .. 219 (2007) 3.77

Greece 2003 26.0 18.2 14886 84 9.3 145.24 8.73 348 .. 220 (2000) 2.89

Hungary 2004 17.1 18.2 5626 90 5.8 146.47 11.97 274 (2003) 33.8 273 (2007) 2.52

Latvia 2006 12.3 18.1 5681 .. 6.8 122.66 11.20 357 30.1 (2005) 213 (2007) 2.47

Italy 2005 10.5 9.1 19380 92 7.7 157.16 8.02 595 24.0 239 3.58

Lithuania 2006 20.6 19.2 5277 93 5.6 109.21 12.90 468 27 (2005) 212 (2007) 2.41 85.0

Luxembourg 2005 18.8 18.5 51590 82 4.5 163.89 11.75 647 (2004) 33.0 155 (2007) 3.58

Malta 2003 22.9 16.9 9828 87 7.5 108.51 5.24 520 23.4 .. 2.89

Netherlands 2004 9.0 11.0 24678 89 5.1 132.10 9.56 429 28.0 194 (2007) 3.28

Norway 2004 8.4 8.2 39805 95 4.4 144.03 6.22 431 26.0 154 (2007) 5.18 73.9

Poland 2000 15.7 19.9 4455 90 16.1 112.63 8.27 .. 32.5 235 1.28

Romania 2000 7.7 9.5 1651 76 7.1 92.52 9.89 .. 20.8 223

Serbia 2000 14.3 20.0 1193 .. .. .. 9.89 .. 38.0 239

Slovakia 2004 18.8 25.4 4467 .. 18.1 103.40 10.10 222 .. 195 (2007) 1.98

Slovenia 2001 16.5 13.8 9952 92 (2002) 5.7 115.39 7.84 .. 23.7 173 (2000) 2.27

Sweden 2004 11.0 9.8 29378 99 6.5 120.21 6.50 458 16.0 167 (2007) 3.94 76.1

Switzerland 2002 8.3 7.8 34232 83 2.9 152.83 10.70 508 22.0 .. 3.81

United Kingdom 2004 22.1 22.8 26695 95 4.6 137.95 11.78 451 26.0 233 3.37

Uzbekistan 2002 5.4 7.1 590 .. .. 63.56 1.47 .. 12.5 ..

AFRICA

Algeria 2003 8.8 21.4 1973 65 23.7 62.88 0.60 56

Mauritius 1998 8.0 20.0 3499 65 6.9 84.20 3.29 ..

Seychelles 2004 14.7 34.2 6740 93 .. 63.43 9.94 74 (2003)

South Africa 2003 8.8 27.4 3186 .. 31.2 79.79 6.93 .. 1.74

Zimbabwe 2005 3.9 19.4 428 38 .. 57.94 3.82 ..

SOUTH EAST ASIA

Indonesia 2000 1.3 4.5 800 50 6.1 44.81 0.06 .. 1.83

Thailand 2004 4.7 9.1 2403 .. 1.5 54.57 6.36 .. 1.45

WESTERN PACIFIC

Australia 2008 25.6 24.0 40500 88 4.2 142.82 (2007) 10.08 545 (2007) 3.36 69.6

China 2002 2.4 3.4 1106 .. 4.0 82.94 4.42 8 1.27 93.1

Japan 2004 3.4 3.8 38236 99 4.7 89.83 7.70 441 2.33 56.7

Malaysia 1996 4.0 7.6 3722 .. 2.5 84.38 0.94 .. 1.51

Mongolia 2005 7.2 12.5 584 84 3.3 77.25 1.39 ..

Philippines 1998 2.1 4.4 948 48 9.4 44.92 3.82 .. 1.08

Samoa 1995 32.9 63.0 1136 .. .. 108.93 2.38 ..

EASTERN MED

Iran 2005 9.1 19.2 1919 .. 11.4 65.68 0.03 13 (2007)

Morocco 2000 8.2 21.7 1302 30 .. 60.08 0.44 ..

Oman 2000 16.7 23.8 8271 68 .. .. 0.35 ..

Tunisia 1997 6.7 22.7 1814 .. 15.9 90.92 1.03 ..

UAE 2000 17.1 31.4 21741 68 2.3 91.10 0.64 ..

AMERICAS

Brazil 2003 8.9 13.1 3733 76 9.7 103.63 6.16 131 1.44 86.3

Canada 2004 22.9 23.2 24975 .. 7.2 148.03 7.80 561 (2003) 2.33 93.1

Chile 2003 19.6 29.3 5215 .. 7.4 87.89 6.39 88 1.95

Cuba 1998 7.1 10.2 .. 72 6.2 45.90 2.96 ..

Guyana 2000 14.3 26.9 970 .. .. 58.91 6.36 ..

Jamaica 1999 7.6 23.9 3099 83 15.7 79.48 2.72 ..

Mexico 2006 24.2 34.5 6387 69 3.2 93.97 5.09 (2005) 147 2.57

Panama 2000 27.9 36.1 3939 61 13.8 66.61 5.67 ..

Trinidad & Tobago 1999 10.7 21.1 5931 70 13.1 72.85 3.81 ..

USA 2007 32.2 35.5 45674 88 4.6 160.16 8.78 451 3.22 84.1

46

MODEL 1 Regression

Model Summaryb

Model R R Square Adjusted R Square

Std. Error of the

Estimate

1 .126a .016 -.003 10.3858

a. Predictors: (Constant), GDP1000

b. Dependent Variable: Female Obesity (% of total population)

ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression 89.088 1 89.088 .826 .368a

Residual 5501.133 51 107.865

Total 5590.221 52

a. Predictors: (Constant), GDP1000

b. Dependent Variable: Female Obesity (% of total population)

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig. B Std. Error Beta

1 (Constant) 19.982 1.867 10.702 .000

GDP1000 -.098 .108 -.126 -.909 .368

a. Dependent Variable: Female Obesity (% of total population)

47

Residuals Statisticsa

Minimum Maximum Mean Std. Deviation N

Predicted Value 14.906 19.939 18.887 1.3089 53

Residual -16.4727 43.1302 .0000 10.2855 53

Std. Predicted Value -3.041 .804 .000 1.000 53

Std. Residual -1.586 4.153 .000 .990 53

a. Dependent Variable: Female Obesity (% of total population)

Regression

Variables Entered/Removed

Model Variables Entered Variables Removed Method

1 DumSamoa,

GDP1000a

. Enter

a. All requested variables entered.

Model Summaryb

Model R R Square Adjusted R Square

Std. Error of the

Estimate

1 .599a .359 .333 8.4663

a. Predictors: (Constant), DumSamoa, GDP1000

b. Dependent Variable: Female Obesity (% of total population)

ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression 2006.271 2 1003.136 13.995 .000a

Residual 3583.949 50 71.679

Total 5590.221 52

a. Predictors: (Constant), DumSamoa, GDP1000

b. Dependent Variable: Female Obesity (% of total population)

48

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig. B Std. Error Beta

1 (Constant) 18.606 1.545 12.042 .000

GDP1000 -.050 .089 -.064 -.565 .575

DumSamoa 44.451 8.595 .589 5.172 .000

a. Dependent Variable: Female Obesity (% of total population)

Residuals Statisticsa

Minimum Maximum Mean Std. Deviation N

Predicted Value 16.020 63.000 18.887 6.2115 53

Residual -15.1505 19.1837 .0000 8.3019 53

Std. Predicted Value -.462 7.102 .000 1.000 53

Std. Residual -1.789 2.266 .000 .981 53

a. Dependent Variable: Female Obesity (% of total population)

Regression

Variables Entered/Removed

Model Variables Entered Variables Removed Method

1 LNGDP1000,

DumSamoa,

GDP1000a

. Enter

a. All requested variables entered.

Model Summaryb

Model R R Square Adjusted R Square

Std. Error of the

Estimate

1 .641a .410 .374 8.2015

a. Predictors: (Constant), LNGDP1000, DumSamoa, GDP1000

b. Dependent Variable: Female Obesity (% of total population)

49

ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression 2294.239 3 764.746 11.369 .000a

Residual 3295.981 49 67.265

Total 5590.221 52

a. Predictors: (Constant), LNGDP1000, DumSamoa, GDP1000

b. Dependent Variable: Female Obesity (% of total population)

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig. B Std. Error Beta

1 (Constant) 15.836 2.008 7.885 .000

GDP1000 -.380 .181 -.488 -2.098 .041

DumSamoa 47.105 8.424 .624 5.591 .000

LNGDP1000 3.857 1.864 .485 2.069 .044

a. Dependent Variable: Female Obesity (% of total population)

Residuals Statisticsa

Minimum Maximum Mean Std. Deviation N

Predicted Value 11.421 63.000 18.887 6.6423 53

Residual -12.4035 22.2986 .0000 7.9614 53

Std. Predicted Value -1.124 6.641 .000 1.000 53

Std. Residual -1.512 2.719 .000 .971 53

a. Dependent Variable: Female Obesity (% of total population)

50

Regression

Variables Entered/Removed

Model Variables Entered Variables Removed Method

1 DumUS,

DumSamoa,

LNGDP1000,

GDP1000a

. Enter

a. All requested variables entered.

Model Summaryb

Model R R Square Adjusted R Square

Std. Error of the

Estimate

1 .721a .519 .479 7.4829

a. Predictors: (Constant), DumUS, DumSamoa, LNGDP1000, GDP1000

b. Dependent Variable: Female Obesity (% of total population)

ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression 2902.535 4 725.634 12.959 .000a

Residual 2687.686 48 55.993

Total 5590.221 52

a. Predictors: (Constant), DumUS, DumSamoa, LNGDP1000, GDP1000

b. Dependent Variable: Female Obesity (% of total population)

51

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig. B Std. Error Beta

1 (Constant) 15.659 1.833 8.542 .000

GDP1000 -.581 .176 -.745 -3.296 .002

DumSamoa 47.364 7.687 .627 6.162 .000

LNGDP1000 4.995 1.735 .628 2.878 .006

DumUS 27.279 8.277 .361 3.296 .002

a. Dependent Variable: Female Obesity (% of total population)

Residuals Statisticsa

Minimum Maximum Mean Std. Deviation N

Predicted Value 5.393 63.000 18.887 7.4711 53

Residual -12.4616 15.8814 .0000 7.1893 53

Std. Predicted Value -1.806 5.904 .000 1.000 53

Std. Residual -1.665 2.122 .000 .961 53

a. Dependent Variable: Female Obesity (% of total population)

Regression

Variables Entered/Removed

Model Variables Entered Variables Removed Method

1 DumLUX, DumUS,

DumSamoa,

LNGDP1000,

GDP1000a

. Enter

a. All requested variables entered.

Model Summaryb

Model R R Square Adjusted R Square

Std. Error of the

Estimate

1 .752a .565 .519 7.1920

a. Predictors: (Constant), DumLUX, DumUS, DumSamoa, LNGDP1000,

GDP1000

b. Dependent Variable: Female Obesity (% of total population)

52

ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression 3159.155 5 631.831 12.215 .000a

Residual 2431.066 47 51.725

Total 5590.221 52

a. Predictors: (Constant), DumLUX, DumUS, DumSamoa, LNGDP1000, GDP1000

b. Dependent Variable: Female Obesity (% of total population)

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig. B Std. Error Beta

1 (Constant) 15.362 1.767 8.695 .000

GDP1000 -.810 .198 -1.039 -4.087 .000

DumSamoa 47.738 7.390 .632 6.460 .000

LNGDP1000 6.421 1.787 .808 3.594 .001

DumUS 32.572 8.302 .432 3.923 .000

DumLUX 19.579 8.790 .259 2.227 .031

a. Dependent Variable: Female Obesity (% of total population)

Residuals Statisticsa

Minimum Maximum Mean Std. Deviation N

Predicted Value 6.796 63.000 18.887 7.7944 53

Residual -13.1886 15.1232 .0000 6.8375 53

Std. Predicted Value -1.551 5.660 .000 1.000 53

Std. Residual -1.834 2.103 .000 .951 53

a. Dependent Variable: Female Obesity (% of total population)

53

Model 2

Regression (Male, BigMacIndex)

Variables Entered/Removed

Model Variables Entered

Variables

Removed Method

1 Big Mac Index,

DUMUS,

DUMLUX, Alcohol

Consumption,

Unemployment

(% of total labour

force), Secondary

school enrolment

(% net), Daily fat

supply (g) per

capita, Passenger

cars per 1000

people, LNGDP,

GDP1000a

. Enter

a. All requested variables entered.

Model Summaryb

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .840a .705 .377 5.7793

a. Predictors: (Constant), Big Mac Index, DUMUS, DUMLUX, Alcohol

Consumption, Unemployment (% of total labour force), Secondary school

enrolment (% net), Daily fat supply (g) per capita, Passenger cars per 1000

people, LNGDP, GDP1000

b. Dependent Variable: Male Obesity (% of total population))

54

ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression 718.727 10 71.873 2.152 .132a

Residual 300.602 9 33.400

Total 1019.330 19

a. Predictors: (Constant), Big Mac Index, DUMUS, DUMLUX, Alcohol Consumption, Unemployment (%

of total labour force), Secondary school enrolment (% net), Daily fat supply (g) per capita, Passenger

cars per 1000 people, LNGDP, GDP1000

b. Dependent Variable: Male Obesity (% of total population))

Regression (Male, BigMacIndex2)

Variables Entered/Removed

Model Variables Entered

Variables

Removed Method

1 BigMacIndex2,

DUMUS, Alcohol

Consumption,

DUMLUX,

Unemployment

(% of total labour

force), Secondary

school enrolment

(% net), Daily fat

supply (g) per

capita, Passenger

cars per 1000

people,

GDP1000,

LNGDPa

. Enter

a. All requested variables entered.

55

Model Summaryb

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .858a .736 .442 5.4695

a. Predictors: (Constant), BigMacIndex2, DUMUS, Alcohol Consumption,

DUMLUX, Unemployment (% of total labour force), Secondary school

enrolment (% net), Daily fat supply (g) per capita, Passenger cars per 1000

people, GDP1000, LNGDP

b. Dependent Variable: Male Obesity (% of total population))

ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression 750.088 10 75.009 2.507 .091a

Residual 269.242 9 29.916

Total 1019.330 19

a. Predictors: (Constant), BigMacIndex2, DUMUS, Alcohol Consumption, DUMLUX, Unemployment (%

of total labour force), Secondary school enrolment (% net), Daily fat supply (g) per capita, Passenger

cars per 1000 people, GDP1000, LNGDP

b. Dependent Variable: Male Obesity (% of total population))

56

Regression (Female, BigMacIndex)

Model Variables Entered

Variables

Removed Method

1 DUMLUX,

DUMUS, Big Mac

Index, Alcohol

Consumption,

Unemployment

(% of total labour

force), Secondary

school enrolment

(% net), Daily fat

supply (g) per

capita, Passenger

cars per 1000

people, LNGDP,

GDP1000a

. Enter

a. All requested variables entered.

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .877a .769 .513 5.6519

a. Predictors: (Constant), DUMLUX, DUMUS, Big Mac Index, Alcohol

Consumption, Unemployment (% of total labour force), Secondary school

enrolment (% net), Daily fat supply (g) per capita, Passenger cars per 1000

people, LNGDP, GDP1000

ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression 959.241 10 95.924 3.003 .057a

Residual 287.497 9 31.944

Total 1246.737 19

a. Predictors: (Constant), DUMLUX, DUMUS, Big Mac Index, Alcohol Consumption, Unemployment (%

of total labour force), Secondary school enrolment (% net), Daily fat supply (g) per capita, Passenger

cars per 1000 people, LNGDP, GDP1000

b. Dependent Variable: Female Obesity (% of total population)

57

Regression (Female, BigMacIndex2)

Model Variables Entered

Variables

Removed Method

1 BM2, DUMUS,

Alcohol

Consumption,

DUMLUX,

Unemployment

(% of total labour

force), Secondary

school enrolment

(% net), Daily fat

supply (g) per

capita, Passenger

cars per 1000

people,

GDP1000,

LNGDPa

. Enter

a. All requested variables entered.

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .910a .827 .635 4.8922

a. Predictors: (Constant), BM2, DUMUS, Alcohol Consumption, DUMLUX,

Unemployment (% of total labour force), Secondary school enrolment (%

net), Daily fat supply (g) per capita, Passenger cars per 1000 people,

GDP1000, LNGDP

ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression 1031.333 10 103.133 4.309 .019a

Residual 215.405 9 23.934

Total 1246.737 19

a. Predictors: (Constant), BM2, DUMUS, Alcohol Consumption, DUMLUX, Unemployment (% of total

labour force), Secondary school enrolment (% net), Daily fat supply (g) per capita, Passenger cars per

1000 people, GDP1000, LNGDP

b. Dependent Variable: Female Obesity (% of total population)

58

MODEL 3 – Physical Activity Regression (male)

Variables Entered/Removed

Model Variables Entered

Variables

Removed Method

1 LNGDP, Alcohol

Consumption,

Unemployment (%

of total labour

force), Physical

Activity, Passenger

cars per 1000

people, Daily fat

supply (g) per

capita, BM2,

GDP1000a

. Enter

a. All requested variables entered.

Model Summary

Model R R Square Adjusted R Square

Std. Error of the

Estimate

1 .971a .943 .489 7.1291

a. Predictors: (Constant), LNGDP, Alcohol Consumption, Unemployment (% of

total labour force), Physical Activity, Passenger cars per 1000 people, Daily fat

supply (g) per capita, BM2, GDP1000

ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression 843.652 8 105.456 2.075 .493a

Residual 50.824 1 50.824

Total 894.476 9

a. Predictors: (Constant), LNGDP, Alcohol Consumption, Unemployment (% of total labour force), Physical

Activity, Passenger cars per 1000 people, Daily fat supply (g) per capita, BM2, GDP1000

b. Dependent Variable: Male Obesity (% of total population))

59

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig. B Std. Error Beta

1 (Constant) .149 95.191 .002 .999

GDP1000 2.951 2.050 5.242 1.440 .386

Unemployment (% of total labour

force)

3.878 8.908 .759 .435 .739

Daily fat supply (g) per capita .074 .341 .194 .217 .864

Alcohol Consumption .110 1.295 .036 .085 .946

Passenger cars per 1000 people .125 .098 2.228 1.277 .423

BM2 -35.212 88.211 -1.182 -.399 .758

Physical Activity .241 .906 .282 .266 .834

LNGDP -54.940 31.801 -7.184 -1.728 .334

a. Dependent Variable: Male Obesity (% of total population))

Regression (female)

Variables Entered/Removed

Model Variables Entered

Variables

Removed Method

1 LNGDP, Alcohol

Consumption,

Unemployment (%

of total labour

force), Physical

Activity, Passenger

cars per 1000

people, Daily fat

supply (g) per

capita, BM2,

GDP1000a

. Enter

a. All requested variables entered.

60

Model Summary

Model R R Square Adjusted R Square

Std. Error of the

Estimate

1 .976a .952 .565 6.6717

a. Predictors: (Constant), LNGDP, Alcohol Consumption, Unemployment (% of

total labour force), Physical Activity, Passenger cars per 1000 people, Daily fat

supply (g) per capita, BM2, GDP1000

ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression 875.749 8 109.469 2.459 .458a

Residual 44.512 1 44.512

Total 920.261 9

a. Predictors: (Constant), LNGDP, Alcohol Consumption, Unemployment (% of total labour force), Physical

Activity, Passenger cars per 1000 people, Daily fat supply (g) per capita, BM2, GDP1000

b. Dependent Variable: Female Obesity (% of total population)

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig. B Std. Error Beta

1 (Constant) 8.241 89.084 .093 .941

GDP1000 3.140 1.918 5.498 1.637 .349

Unemployment (% of total labour

force)

3.101 8.336 .598 .372 .773

Daily fat supply (g) per capita -.008 .319 -.019 -.024 .985

Alcohol Consumption .375 1.212 .120 .310 .809

Passenger cars per 1000 people .103 .091 1.822 1.132 .461

BM2 -57.690 82.551 -1.909 -.699 .612

Physical Activity .538 .848 .621 .635 .640

LNGDP -58.286 29.761 -7.514 -1.958 .301

61

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig. B Std. Error Beta

1 (Constant) 8.241 89.084 .093 .941

GDP1000 3.140 1.918 5.498 1.637 .349

Unemployment (% of total labour

force)

3.101 8.336 .598 .372 .773

Daily fat supply (g) per capita -.008 .319 -.019 -.024 .985

Alcohol Consumption .375 1.212 .120 .310 .809

Passenger cars per 1000 people .103 .091 1.822 1.132 .461

BM2 -57.690 82.551 -1.909 -.699 .612

Physical Activity .538 .848 .621 .635 .640

LNGDP -58.286 29.761 -7.514 -1.958 .301

a. Dependent Variable: Female Obesity (% of total population)