World Studies
Health Development Title:
An Investigation of the Global Hunger Index Through a Comparison of Related Factors.
Research Question:
What factors have the greatest association to the Global Hunger Index of the African
countries that experience the highest prevalence of hunger?
Session
May 2019
Word count:
3,989
0
Table of Contents
1. INTRODUCTION: ..................................................................................................................... 2
2. AIM: .......................................................................................................................................... 2
3. BACKGROUND INFORMATION:............................................................................................ 3
A. DEPENDENT VARIABLE: ............................................................................................................. 3
B. METHOD OF COUNTRY SAMPLE SELECTION:................................................................................ 4
C. INDEPENDENT VARIABLES: ......................................................................................................... 4
I. FOOD SECURITY INDEX ................................................................................................................ 4
II. HUMAN DEVELOPMENT INDEX ..................................................................................................... 5
III. GDP (PPP) PER CAPITA ............................................................................................................ 5
IV. ARABLE LAND ........................................................................................................................... 6
V. AVERAGE PRECIPITATION ........................................................................................................... 6
VI. TOTAL NATURAL RESOURCE RENTS (% GDP) ........................................................................... 6
VII. POLITICAL STABILITY INDEX ...................................................................................................... 7
D. STATISTICAL TOOLS USED IN THE BIVARIATE ANALYSIS:.............................................................. 7
4. DATA COLLECTION ............................................................................................................... 8
5. ANALYSIS: .............................................................................................................................. 9
A. THE NATURE AND CALCULATIONS OF THE STATISTICAL MEASUREMENTS INVOLVED: .................... 9
I. THE LINE OF REGRESSION: ........................................................................................................... 9
II. THE CORRELATION COEFFICIENT: .............................................................................................. 11
III. THE COEFFICIENT OF DETERMINATION: ..................................................................................... 12
B. DATA ANALYSIS AND PROCESSING OF THE CORRELATION BETWEEN THE FACTORS AND GHI: .... 13
I. THE ASSOCIATION BETWEEN FSI AND GHI: ................................................................................ 13
II. THE ASSOCIATION BETWEEN HDI INDEX AND GHI: ..................................................................... 14
III. THE ASSOCIATION BETWEEN GDP (PPP) PER CAPITA AND GHI:................................................ 16
IV. THE ASSOCIATION BETWEEN ARABLE LAND AND GHI:............................................................... 17
V. THE ASSOCIATION BETWEEN AVERAGE PRECIPITATION AND GHI: .............................................. 18
VI. THE ASSOCIATION BETWEEN TOTAL NATURAL RESOURCE RENTS AND GHI: ............................. 19
VII. THE ASSOCIATION BETWEEN POLITICAL STABILITY INDEX AND GHI: ......................................... 20
C. EVALUATION OF THE CORRELATION BETWEEN THE FACTORS AND GHI:..................................... 21
6. CONCLUSION AND EVALUATION: ..................................................................................... 25
1
1. Introduction:
Food insecurity and undernourishment is a major problem amongst the majority of
under developed countries. The global hunger index, GHI, was established in 2006 by
the International Food Policy Research Institute, IFPRI, and gauges the extent to which
a country’s population is suffering from widespread hunger (Ifpri.org). The Sustainable
Development Goals of the United Nations aims to mitigate poverty and decrease the
prevalence of hunger in developing countries and ultimately reach the goal of zero
hunger by 2030 (Goal 2. United Nations). Since the Sustainable Development Goals
were first implemented in 2015, both developed and underdeveloped countries have
made significant progress in reducing extreme poverty and hunger amongst the
underdeveloped parts of the world (2018 Global Hunger Index Results). Nevertheless,
the prevalency of hunger is still a major problem in many less economically developed
countries, LEDCs. African countries are struck the worst by starvation and
undernourishment; hence, I have decided to explore the GHI of the African countries
that experience the worst conditions of hunger.
In my exploration I am researching the factors which have the strongest
correlation to the predominance of hunger present in the African classified as a serious
or even alarming concern by the IFPRI. Investigating this will show what aspects of
countries best indicate the prevalence of hunger.
2. Aim:
In my investigation I am researching the factors which have the strongest relationship to
prevalence of hunger in the African countries with the highest GHI values. These factors
include the food security index, human development index, GDP, arable land, average
2
precipitation, resource rents, and political stability. Through a bivariate statistical
analysis, I will evaluate the correlation between the factors and GHI in the context of the
Global Theme - Health and Development.
3. Background Information:
A. Dependent Variable:
Global Hunger Index:
The GHI shows the prevalence of hunger and starvation in newly industrialized
countries, NICs, and less economically developed countries, LEDCs. The GHI is
catered towards indicating and measuring severe cases of hunger in the most
underdeveloped parts of the world. The GHI scores from 2018 were collected (2018
Global Hunger Index Results).
Figure. 1 represents a scale which categorises countries of given GHI values into five
groups showing the severity of hunger (2018 Global Hunger Index Results).
GHI has three dimensions which are based on four indicators:
• Inadequate food supply: Undernourishment (% of population). Indicator’s weight =
1/3
• Child undernutrition: Child wasting (% of children under five who have low weight for
their height). Indicator’s weight = 1/6
3
•
Child undernutrition: Child stunting (% of children under-five who have low height for
their age). Indicator’s weight = 1/6
• Child mortality: Under-five mortality rate (%). Indicator’s weight = 1/3
B. Method of country sample selection:
In my investigation I focus on countries which experience severe food insecurity in
Africa, hence, I have selected all of the African countries which have a GHI score of 20
or greater. On the GHI severity scale any country which obtained a score of 20 or
greater is classified as a country combating “serious, alarming or extremely alarming
hunger”.
C. Independent variables:
I. Food Security Index
The FSI is based on 28 indicators that measure the three dimensions of affordability,
availability, and quality of food. These three dimensions of the FSI are combined to
produce a score on a scale from 0 to 100. The 28 indicators include political stability risk
and gross domestic product per capita (PPP) which are two other independent variables
in my research. The FSI gives a more wholistic representation of how well a country is
performing in terms of food and nutrition by measuring the underlying factors affecting
food security. Correlating the GHI to FSI will show the extent to which food security is
related to the predominance of hunger (Nagle, Garrett. 266). The FSI values of 2018
were collected (The Global Food Security Index). The values for C.A. Republic, Liberia,
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Timor-Leste, Zimbabwe, Djibouti, Comoros, Guinea-Bissau, Republic of Congo,
Mauritania, Namibia, Lesotho, and Gambia were not available.
II. Human Development Index
HDI is composed of three indicators: Life expectancy at birth, average years of
schooling, and average income adjusted to the local currency. HDI is measured on a
scale of 1.00 to 0.00, 1 indicating the highest standard of living and 0.00 indicating the
worst. Finding the correlation between GHI and HDI will show whether a country’s
average standard of living is a good indicator of the amount of hunger in a LEDC. The
HDI values of 2018 were collected (Human Development Reports).
III. GDP (PPP) per capita
I will use GDP (PPP) per capita determine the strength of the association between the
average income of a country and its prevalence of hunger. Gross domestic product,
GNI, also includes the income earned by the residents from abroad, as well as, net
taxes and subsidies received from abroad (Amadeo, Kimberly. The Balance). I am
solely trying to find the average income of a country, so I used the GDP instead of the
GNI. The GDP (PPP) per capita is adjusted to the local cost of living, purchasing power
parity, PPP and is therefore measured in the current international dollar, intl. $. The
GDP (PPP) per capita values of 2017 were collected (GDP per Capita, PPP.
worldbank). These are the most recent values available.
5
IV. Arable Land
The amount arable land shows the amount to agricultural and fertile land within a
country. Statistically measuring the correlation between GHI and arable land shows if
agricultural land is a good determinant of hunger within a LEDC. Arable land is
measured in hectares per person. The values for the amount of arable land per capita
of 2015 were collected (Arable Land. worldbank). These are the most recent values
available.
V. Average Precipitation
Calculating the correlation between GHI and this independent variable will indicate the
extent to which the country relies on rainfall for agricultural use especially for LEDCs
located in the sub-Saharan and Sahel region. The average precipitation in depth is
measured in millimetres per year. The values for the amount of average precipitation of
2014 were collected (Average Precipitation. worldbank). These are the most recent
values available.
VI. Total Natural Resource Rents (% GDP)
The natural resource rent is the percentage of revenue made by a country through
exporting raw materials, this includes forests, minerals, and fossil fuels. This variable
indicates the abundance of natural resources of a country and the extent to which the
country relies on those resources for economic development. I will use this variable to
find whether the reliance of a country on its primary sector is a good indicator of the
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prevalency of hunger. The total natural resource rents of 2016 were collected (Total
Natural Resources Rents. worldbank). These are the most recent values available.
VII. Political Stability Index
This variable directly conveys the quality of governance. Poor governance may lead to
civil conflicts, corruption and less aid from HICs which causes a risk of an increase on
food scarcity as protection of the population is reduced. Correlating the political stability
to GHI will show the extent to which the quality of governance is related to the
predominance of hunger. The political stability index measures each country on a scale
from 2.5 to -2.5, the greater the number the better the political stability. The political
stability index values of 2017 were collected (Political Stability. TheGlobalEconomy).
These are the most recent values available. The value for Timor-Leste was not
available.
D. Statistical tools used in the bivariate analysis:
A bivariate statistical analysis is rendered and implemented between each independent
variable and GHI. This is done in order to identify the extent to which the different
variables are related to the prevalency of hunger in the LEDCs. The statistical tools
used are the correlation coefficient, coefficient of determination, and the equation of the
regression line or line of best fit.
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4. Data Collection
Table. 1 shows the data for each of the 33 countries.
In table 1 the sample of 33 countries are organized in order of the country’s GHI
score, starting with the most severe cases of civil hunger at the top.
8
5. Analysis:
A. The nature and calculations of the statistical measurements involved:
I. The line of regression:
Using a scatter plot graph of the equation of the regression line shows the
general linear trend between the two variables the, as well as, the extent to which the
line of independent variables changes when the dependent variable changes. The
regression line describes the direction of the correlation between the two variables. The
equation of the regression line also enables predictions to be made.
The equation of regression line is always represented in one of the two forms
below:
y – y1 = m (x – x1) or y = mx +c
The gradient of the regression line, m, is calculated using the least squares
regression formula:
Sxy
m = (S
x )²
The line that allows for the smallest sum of each residual squared. A residual is equal to
the vertical distance from any point to the line of regression.
Figure. 2 a diagram representing a residual (Buchanan, Laurie. 345).
9
In the formula Sxy = ∑ xy -
( ∑ x)( ∑ y)
n
Sxy is equal to the two sums of the data sets multiplied by each other, divided by the
number of values, and then subtracted from the sum of the 33 values of the data sets
which are all multiplied by each other. The following is a sample calculation of Sxy for
the data sets of arable land per capita and GHI.
Sxy = 244 -
976 - 7.64
33.0
= 215
The denominator of the formula (sx)2 = ∑ x2 -
( ∑ x)²
n
(Sx)2 is equal to the sum of the values of the dependent variable squared, divided by the
number of values, and then subtracted from the sum of each value squared of the
dependent variable. The following is a sample calculation of (Sx)2 for the data sets of
arable land per capita and GHI.
(Sx)2 = 3.35 x 104 –
976²
33
= 26.5
Thus, the gradient of the line of best fit of a scatter plot graph representing arable land
against GHI would be 8.11.
Sxy
m = (S
x )²
215
= 26.5 = 8.11
The y-intercept, c, is calculated by substituting the x and y values of the point that
lies on the regression line, and then isolating c. For these two variables, the data point
from Nigeria lies on the regression line. Nigeria has 0.19 hectares of arable land per
person and obtain a GHI score of 31.1. The sample calculation for the y-intercept is
shown below:
31.1 = 8.11 (0.19) + c
10
c = 3.11 – (8.11 (0.19)) = 25.6
II. The correlation coefficient:
The correlation coefficient for a sample, r, portrays the strength of the
relationship between two variables on a scale from 1 to - 1 and it measures the average
vertical deviation of all the points from the regression line or the average length of the
residuals. Subsequently, r numerically shows the extent to which the points on a scatter
plot graph are scattered away from the line of regression. If r is equal to 1 or -1 then all
points on the graph lie perfectly on the regression line. A r value of 0 indicates there is
no relationship between the variables.
The correlation coefficient does not determine whether a variable causes the
other variable to change. The three statistical measures of correlation do not prove that
there is a causal relationship between GHI and any of the seven factors. The correlation
coefficient only shows the degree to which the variables associate with each other.
The equation for r is shown below:
r=
∑(
xi - x$ yi - ȳ
)(
)
Sx
Sy
n-1
In the equation x$ and ȳ are the averages of the data sets of the two variables,
and Sx and Sy are the standard deviations from the data sets. The average for the data
set of arable land is 0.232 hectares per person.
Sx calculates the average spread of the values of a data set. It is equal to the
sum of all deviations between each value divided by number of values of a data set. A
sample calculation of Sx for the data set of arable land per capita, as well as, its
equation is shown below:
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∑ ni = n ( x - x$ )2
Sx = %
n
(0.400 - 0.232)2 + (0.350 - 0.232)2 + (0.220 - 0.232)2…+ (0.270 - 232)2
Sx = %
33.0
= 0.148
The average and standard deviation for the data set of GHI have a value of: ȳ =
31.2 and Sy = 6.56. Now that the values of means and standard deviations are
calculated, the r value for the correlation between arable land and GHI can be found.
r=
'
53.7 - 31.2 0.40-0.232
(
)x(
6.56
0.148
+'
43.5 - 31.2 0.350-0.232
21.1 - 31.2 0.270-0.232
(…+ '
(
)x(
)x(
6.56
0.148
6.56
0.148
33.0 - 1
= 0.183
III. The coefficient of determination:
The coefficient of correlation, r2, shows the extent to which a change in the
dependent variable can be explained by a change in the independent variable.
Correlation of determination is equal to the correlation coefficient square. A sample
calculation of r2 between the data set of arable land and GHI, as well as, its equation is
shown below:
2
r =(
∑(
xi - x$ yi - ȳ
)(
)
Sx
Sy
n-1
)2
r2 = 0.1832 = 0.0334
Unlike the r value, the r2 value can be expressed as a percentage to show the extent to
which the variables correlate. Using the r2 it can be predicted that 3.34% of the changes
in GHI value can be explained by changes in arable land.
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B. Data analysis and processing of the correlation between the factors and GHI:
I. The association between FSI and GHI:
Graph. 1 shows the scatter plot graph of FSI against GHI and the corresponding line of
regression.
A negative correlation exists between the FSI data set and GHI data set. The r
value of between the data sets of the two indexes is -0.632 indicating a moderate
correlation strength. This shows LEDCs that have worse levels of food security are
more likely to encounter a higher prevalence of hunger.
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Table. 2 represents the correlation between FSI and GHI through the equation for the
regression, the correlation coefficient, and the coefficient of determination.
The FSI values of eleven countries are not available. This was taken into consideration
when calculating the statistical measures of correlation.
II. The association between HDI index and GHI:
Graph. 2 shows the scatter plot graph of HDI against GHI and the corresponding line of
regression.
14
A negative correlation exists between the FSI data set and GHI data set. The r
value between the data sets of the two indexes is -0.394 indicating a relatively weak
correlation strength. This may show LEDCs that have lower standards of living do not
necessarily have a high risk of experiencing widespread hunger.
Table. 3 represents the correlation between HDI and GHI through the equation for the
regression, the correlation coefficient, and the coefficient of determination.
15
III. The association between GDP (PPP) per capita and GHI:
Graph. 3 shows the scatter plot graph of GDP (PPP) per capita against GHI and the
corresponding line of regression.
A negative correlation exists between the GDP data set and GHI data set. The r
value of between the data sets of the two variables is -0.265 indicating a low correlation
strength. This may show LEDCs that have a low a GDP (PPP) per capita are not
necessarily at risk of experiencing widespread hunger.
Table. 4 represents the correlation between GDP and GHI through the equation for the
regression, the correlation coefficient, and the coefficient of determination.
16
IV. The association between Arable Land and GHI:
Graph. 5 shows the scatter plot graph of arable land capita against GHI and the
corresponding line of regression.
A positive correlation exists between the arable land data set and GHI data set.
The r value of between the data sets of the two variables is 0.183 indicating a weak
correlation strength. This may show LEDCs that have more arable land per person are
not necessarily at risk of experiencing widespread hunger.
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Table. 6 represents the correlation between arable land and GHI through the equation
for the regression, the correlation coefficient, and the coefficient of determination.
V. The association between Average Precipitation and GHI:
Graph. 5 shows the scatter plot graph of average precipitation against GHI and the
corresponding line of regression.
A positive correlation exists between the precipitation data set and GHI data set.
The r value of between the data sets of the two variables is 0.171 indicating a weak
correlation strength. This shows LEDCs that experience more rainfall per year are not
necessarily at risk of encountering widespread hunger.
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Table. 7 represents the correlation between avg. precipitation and GHI through the
equation for the regression, the correlation coefficient, and the coefficient of
determination.
VI. The association between Total Natural Resource Rents and GHI:
Graph. 6 shows the scatter plot graph of total natural resource rents against GHI and
the corresponding line of regression.
A positive correlation exists between the FSI data set and GHI data set. The r
value of between the data sets of the two variables is 0.246 indicating a low correlation
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strength. This shows LEDCs that make a high percentage of revenue through exporting
raw materials are not necessarily at risk of experiencing widespread hunger.
Table. 8 represents the correlation between total natural resource rents and GHI
through the equation for the regression, the correlation coefficient, and the coefficient of
determination.
VII. The association between Political Stability Index and GHI:
Graph. 7 shows the scatter plot graph of political stability index against GHI and the
corresponding line of regression.
20
A negative correlation exists between the political stability index data set and GHI
data set. The r value of between the data sets of the two indexes is -0.350 indicating a
relatively weak correlation strength. This shows LEDCs with worse political stability
index scores are not necessarily at a higher risk of experiencing widespread hunger.
Table. 9 represents the correlation between political stability and GHI through the
equation for the regression, the correlation coefficient, and the coefficient of
determination.
The political stability index of Timor-Leste is not available. This was taken into
consideration when calculating the statistical measures of correlation.
C. Evaluation of the correlation between the factors and GHI:
Table. 10 shows the correlation coefficients of the relationships between each
independent variable and GHI in descending order.
21
The first three r values are multiplied by a minus one, so that all correlations are
positive and can be compared.
Graph. 8 represents a bar chart of the relationships between each independent variable
and GHI in descending order.
When referred back to the research question, it is evident that the three indexes
of FSI, HDI, and political stability index have the greatest association to the GHI. These
indexes are best at determining the severity of hunger in a LEDC. The FSI and HDI
enable a well-rounded portrayal of the standard of living within a country, hence the
relatively strong relationship in comparison to the correlations of the other factors. FSI
and GHI measure similar aspects of countries to certain extent. Both, FSI and GHI deal
22
with the average calorie intake of a country. Another reason for the relatively strong
association is that sufficiency of food supply is one of the 28 indicators of FSI and also
one of the three dimensions of the GHI.
Even though, the association between HDI and GHI is not as strong, it is still
moderate especially when compared to the correlations of the other factors. The
correlation is not as strong because the HDI focuses more on a on a country’s health
and development instead of its prevalence of food insecurity. Regardless, the strength
of the correlation is due to the accurate indication of country’s living standards through
the HDI. A LEDC that is not able to provide a large share of its population with adequate
health care or education indicates that this LEDC is likely struggle with a high
prevalency of hunger. Therefore, standards of living and the prevalence of hunger within
a LEDC are strongly related. For these reasons, FSI and HDI are have the strongest
correlation to GHI.
The strength of the association between the political stability index and GHI is
surprisingly high as the index only shows the quality of a country’s governance. The
quality of governance is directly related to civil unrest, corruption, the portrayal of the
country through the media, and emergency aids and support from received from
MEDCs. For instance, the political instability of Sudan appears to have a strong
correlation to its predominance of hunger. The second Sundanese civil war from 1983
to 2005 had long lasting negative impacts which still affect the LEDC’s economy to this
day (Sudan, Economist Intelligence. Britannica). Subsequently, Sudan has the eighth
highest 2018 GHI value. This shows that the quality of governance in a LEDC is good
determinant of the amount of hunger.
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GDP (PPP) per capita shows the average income of a country. The average
income does not indicate the proportion of the population that is undernourished or the
degree to which the country struggles with starvation. This lack of indication is due to
the fact that GDP (PPP) per capita is not directly related to the proportion of the
population that lives below the global poverty line. In addition, GDP does not take into
consideration the income inequality which is essential in determining the prevalence of
poverty in a LEDC. For example, Namibia has a significantly higher GDP than the rest
of the data set, however it still has relatively high GHI value. The high GHI value is
better explained by the distribution of income as Namibia had the fourth highest income
inequality in 2015 (GINI Index, World Bank Estimate). Therefore, the correlation
between GDP and GHI is relatively weak as GDP is a significantly worse determinant of
widespread hunger than the three indexes.
Initially, negative correlation may be expected between the total natural resource
rents and GHI. One can suppose that a LEDC, which generates a higher percentage of
its GDP through exporting raw materials, is likely to be more sustainable in terms of
food security. However, the opposite seems to be true. A country with a higher total
natural resource rents is an indication that it is economically underdeveloped because it
is more reliant on its primary sector and not its secondary or tertiary sector. For this
reason, there is a positive correlation between the two variables. The extent to which a
LEDC is reliant on exporting raw materials does not directly relate to the prevalency of
hunger, therefore, the association strength between resource rents and GHI is low.
Similarly, to the total natural resource rent, a positive correlation exists between
the physical factors and GHI. The amount of arable land or rainfall may be an indication
24
of the abundance of raw materials within a country and its reliance on the primary
sector. As mentioned before, there may be a relationship between the a LEDCs reliance
on its primary sector and the prevalence of hunger. Nevertheless, the associations
between the physical factors and GHI are the weakest as they are the worst at
determining the prevalency hunger in a country.
6. Conclusion and evaluation:
Based on the factors explored, I was able to find the aspects of the LEDCs that best
indicate the extent to which the LEDC struggles with widespread hunger. I found that
the amount of food insecurity has the highest association hunger, followed by the
standards living and then the political stability of a country. The wealth of a LEDC, as
well as, the percentage of revenue made through exporting raw materials only
determined the predominance of hunger to a minimal extent. I also discovered that the
physical factors of arable land and rainfall do not really indicate hunger. However, I
could have explored the GHI through more human factors which are directly related to
the prevalence of hunger such as the percentage of the population living below the
global poverty line. Furthermore, the quality of the Investigation could be improved if
prevalency of hunger in particular countries were explored further individual.
When the correlations of all the factors are compared it can be concluded that political
stability index is a strongest determinant which does not directly link to the health or
food security of a country. Through this index the investigation can be furthered.
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Moreover, the exploration can be extended by evaluating the positive relationship
between the total natural resource rents and GHI.
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