INTERDISCIPLINARY RESEARCH PROJECT

TO EXPLORE THE FUTURE OF GLOBAL

FOOD AND NUTRITION SECURITY

Country typology on the basis of FNS A typology of countries based on FNS outcomes and their

agricultural, economic, political, innovation and

infrastructure national profiles .

Hannah Pieters

Nicolas Gerber

Daniel Mekonnen

FOODSECURE Working paper no. 24

June 2014

D2.2 – Country typology on the basis of FNS

A typology of countries based on FNS outcomes and their

agricultural, economic, political, innovation and infrastructure

national profiles

Hannah Pieters1 (KULeuven), Nicolas Gerber and Daniel Mekonnen (ZEF-UBO)2

The research leading to these results has received funding from the European Union's Seventh Framework

Programme FP7/2007-2011 under Grant Agreement n° 290693 FOODSECURE.

This paper is work in progress; comments are welcome. The authors only are responsible for any omissions or

deficiencies. Neither the FOODSECURE project and any of its partner organizations, nor any organization of the

European Union or European Commission are accountable for the content of papers in this series.

1 Corresponding author: Hannah.Pieters@kuleuven.be 2 The authors thank especially Elena Alsonso Briones, Nathalie Francken, Andrea Guariso, and Jo Swinnen for their

valuable comments and suggestions. Also they would like to thank the many FoodSecure partners for their

comments and feedback received during the general consortium in Addis Ababa.

Table of Contents

1. Introduction ........................................................................................................................1

2. Other typologies .................................................................................................................2

3. Food and nutrition security framework ..........................................................................7

Summary of conceptual framework ..................................................................................... 7

Food security, nutrition security and obesity profiles at national level ............................... 7

Agricultural, economic, political, innovation and infrastructural national profiles ............. 8

4. Principal component analysis for food and nutrition security and its determinants 13

5. Classification of countries using quintile scores ............................................................16

Results ................................................................................................................................ 16

Evolution of the food and nutrition security analysis ........................................................ 20

6. Classification of countries using median scores ............................................................22

7. Conclusion ........................................................................................................................31

8. References .........................................................................................................................32

9. Appendix ...........................................................................................................................36

Principal Component Analysis .......................................................................................... 36

Quintile Scores ................................................................................................................... 39

Quintile Score Maps .......................................................................................................... 41

Summary Statistics for Nutrition Security Profile ............................................................. 42

Median Scores .................................................................................................................... 43

Data .................................................................................................................................... 45

List of Figures

Figure 1: Quintile score map of the food security profile ............................................................. 17

Figure 2: Quintile score map of the nutrition security profile ...................................................... 17

Figure 3: Quintile score map of the obesity profile ...................................................................... 17

Figure 4: Quintile score maps by thematic profile........................................................................ 41

List of Tables

Table 1: Average value of profiles and underpinning variables by food security quintile ........... 18

Table 2: Annual growth rates between 1999-2009 by food security quintile ............................... 21

Table 3: Clustering of countries based on median scores-food security and its determinants ..... 24

Table 4: Clustering of countries based on median scores-nutrition security and its determinants 26

Table 5: Clustering of countries based on median scores-obesity and its potential determinants 28

Table 6: Clusters of countries - food, nutrition and obesity profiles ............................................ 29

Table 7: Results PCA for the food security profile....................................................................... 36

Table 8: Results PCA for the nutrition security profile ................................................................ 36

Table 9: Results PCA for the agricultural potential profile .......................................................... 37

Table 10: Results PCA for the agricultural performance profile .................................................. 37

Table 11: Results PCA for the economic profile .......................................................................... 37

Table 12: Results PCA for the health infrastructure profile ......................................................... 38

Table 13: Results PCA for the political profile ............................................................................ 38

Table 14: Results PCA for the innovation profile ........................................................................ 38

Table 15: Quintile scores by thematic profile ............................................................................... 39

Table 16: Average value of profiles and underpinning variables by nutrition security quintile .. 42

Table 17: Annual growth rates between 1999-2009 by nutrition security quintile ...................... 42

Table 18: Median scores by thematic profile ................................................................................ 43

1

1. Introduction

Food and nutrition security regained a top position on the policy agenda after the dramatic

increase in food prices in 2007-2008 and in 2010-2011. The current and future challenges of

population growth, changing life styles, climate change and scarcity of agricultural inputs call for

new and innovative strategies to achieve food and nutrition security. It must be emphasized that

there exists no ‘one size fits all’ solution and that policies need to be adapted to the specific

social, economic and political context in a country.

A categorization of countries based on their characteristics will, however, help policy makers in

developing the right strategy as it facilitates the interpretation and drawing of suitable

conclusions from case studies and successful policies in other countries. For the purpose of the

FoodSecure project, the typology will help calibrating models and interpreting results at national

levels, as well as guide the selection of case studies by project partners. We develop a typology

based on the countries’ food or nutrition security profiles, as well as on their agricultural,

economic, (agricultural) innovation systems, social and political profiles.

We provide two sets of country characterization. In the first approach, each country is grouped

according to its quintile score on food or nutrition security and its quintile score on one of the

key thematic indicators mentioned above. This typology has the advantage to provide the reader

with a fine categorization of countries under each profile. The second approach is coarser in

describing the thematic profiles, relying on the median scores under each profile to classify

countries as strong or weak performers. With this simplified categorization, we can cluster

countries using all the thematic profiles simultaneously.

The next section presents a review of different classifications of countries and methodologies

used by international agencies. The third section describes the general framework behind the

thematic profiles and the variables used to derive the thematic scores for each country. Section 4

briefly explains how Principal Component Analysis (PCA) is applied to derive the thematic

scores. The fifth section presents the results under approach 1; Section 6 presents the results

under approach 2. Finally, section 7 concludes.

It is important to note that the methodology and data used in this typology are both based on the

work done under FoodSecure Milestone 4 (MS4) – Guide to the case study selection. MS4 was

presented and discussed during the FoodSecure Workshop in Addis Ababa (October 6-8, 2013).

Based on these discussions, we have refined our methodology and the typology produced here.

Deliverable D2.2 is to be used as the final guideline for case study selection, following the

process outlined during the workshop: mark countries where work has already been planned in

the typology, identify gaps in coverage and encourage partners to come forth with further case

studies in relation with these gaps.

2

2. Other typologies

This section provides a review of methods used in major publications and by international

agencies in grouping or classifying nations.

Gross National Income (GNI) per capita is the main criterion used by the World Bank for

classifying countries into different income groups. According to the information available from

the World Bank3, these classifications are set each year on July 1 based on the previous year’s

estimates of GNI for each economy. The most recent classification of July 1, 2012, for example,

shows that countries are grouped into four categories including: low income, lower middle

income, upper middle income and high income. While low- and middle-income groups are

sometimes referred to as developing economies, it is noted that it does not necessarily reflect

their development status since each country in a given group may be at different stages of

development. Geographical region is another criterion applied by the World Bank for low- and

middle- income economies. In connection with income level, the bank also classifies countries

by the type of loans they are eligible for.

FAO, WFP and IFAD (2012a) - The State of Food Insecurity in the World - employs various

country groupings to report the status of the world food situation. For example, the prevalence of

undernourishment and progress towards the World Food Summit (WFS) and the MDG targets

are reported by various categories including world, regions, sub regions, and countries. Estimates

are also provided by developed and developing country classifications. Comparisons are

furthered by the special composition of country groupings including: least-developed countries,

landlocked developing countries, small island developing states, low-income economies, lower-

middle-income economies, and low-income food-deficit countries. In addition, to show the role

of agriculture for economic growth and poverty reduction, FAO, WFP and IFAD (2012a) adopts

the World Bank’s (World Development Report 2008) groupings of countries by type of

economies including: agriculture based economies, transforming economies and urbanized

economies.

IFPRI’s global hunger index (GHI) is a multidimensional measure of hunger calculated from

three equally weighted indicators, namely under-five mortality rate, prevalence of underweight

in children, and proportion of undernourished in the population. Based on the GHI scores,

countries are then classified as having a low-, moderate-, serious-, alarming-, or extremely

alarming- level of hunger. Further cross-country comparisons are also based on the level of

progress in reducing hunger, by region and over time (von Grebmer, et al. 2012).

In a similar fashion to IFPRI’s GHI, a multidimensional measure of poverty index (MPI) is

calculated from three equally weighted dimensions of poverty: education, health, and living

3http://data.worldbank.org/news/newest-country-classifications-released

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standards (Alkire and Santos 2013). In total, there are 10 indicators which fall under one or the

other of these three dimensions. Weights for each indicator are equal within the same dimension

but vary across dimensions since each dimension comprises of a different number of indicators.

For each indicator, there are cutoff points under which the individual or household is considered

deprived. The novelty of the MPI, according to Alkire and Santos (2013), is that identification of

who is poor uses data from all household members, and its use of 33.3% poverty cutoff of the

weighted indicators to capture the acutely poor. The MPI along with its two components

(headcount ratio and poverty intensity) are then used to assess the distribution of global poverty

and to compare the distribution of poverty intensity within and among countries. Average

simultaneous deprivations experienced by one are measured by poverty intensity (Alkire and

Santos, 2013).

The Human Development Index (HDI) of the UNDP is also a composite measure comprising

three dimensions and related to the ones included in MPI (life expectancy, educational

attainment and income). The indicators are relatively narrow (only four indicators in comparison

to Alkire and Santos’ MPI). According to the UNDP (2011), the calculation of the HDI involves

two steps- creating dimension indices and aggregating them using geometric mean to produce

the HDI. In other words, the HDI assigns equal weight to all three dimension indices. Only the

education dimension has two indicators each of which is also equally weighted. An Inequality-

adjusted HDI (IHDI) is also calculated in a similar fashion to show how the achievements are

distributed among residents by discounting the value of each dimension according to its level of

inequality. Countries are then ranked according to their HDI and IHDI scores, and divided into

four groups: very high human development, high human development, medium human

development, and low human development. Further classifications are also made by region (Arab

States, East Asia and the Pacific, Europe and Central Asia, Latin America and the Caribbean,

South Asia, and Sub-Saharan Africa), least developed countries, and small island developing

states. According to the HDR 2013 report, the IHDI can be interpreted as the actual level of

human development (accounting for inequality), while the HDI is the potential human

development that could be obtained if achievements were distributed equally among residents

(UNDP 2013).

Gatzweiler et al. (2011) construct a framework for understanding the root causes of extreme

poverty. The framework, based on the concept of marginality as a network of causal factors

leading to extreme poverty, defines how single causal factors alone are not enough to lead to

marginality and extreme poverty. Rather, it is specific combinations and inter-relationships

between sets of causal factors (i.e. the network of causal factors) which marginalize (groups of)

individuals. Gatzweiler et al. (2011, p.8) define the “spheres of life” in which the different

patterns of causality operate as:

1. Economy (Production, consumption, different types of income, income inequality, assets,

ownership of land or other property, social‐ and network capital, access to social transfer

systems, prices, labor supply/demand, resource flows, investments, trade);

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2. Demography (Population size, ‐density, birth/death rates, migration, ethnicity);

3. Landscape design, land use and location (Urban/rural space, agricultural/forest use,

proportion of land used for recreation, road traffic, settlement, protected areas, areas for

water retention, distance from urban centers, remoteness);

4. Behavior and quality of life (Health, security, human rights, education, social

connectedness, exclusion, social segregation/integration, crime, ethnic tensions, civil war;

Aspirations, happiness, mutual support, alienation, gender equality);

5. Ecosystems, natural resources and climate (Precipitation, soil fertility, soil erosion,

biodiversity, ecosystem intactness, goods and services);

6. Infrastructure (Communication, transport, market places, hospitals, schools, universities,

power supply system, water supply system, sanitation);

7. Public domain and institutions (Regulations, laws, contract, contract enforcement,

conflict resolution mechanisms, formal and informal institutions).

Each sphere of life can then be represented by an index based on the indicators listed in the

brackets. Graw and Ladenburger (2012) get around the issues associated with constructing

indices by representing each dimension with a single indicator. These are then used to map

hotspots of marginality along the seven dimensions represented by the spheres of life (actually,

as represented by a single indicator in this case).

A new index, The Hunger and Nutrition Commitment Index (HANCI 2012), measuring political

commitment to reduce hunger and undernutrition in developing countries has just been released

by the Institute of Development Economics (te Lintelo et al., 2013). The HANCI is based on two

equally weighted sub-indices (hunger reduction commitment and nutrition commitment) each of

them comprising three themes which are also equally weighted. The number of indicators under

each theme is uneven (vary from 1 to 10) and hence the weights assigned to indicators are equal

within themes but vary across themes. In general, the HANCI is constructed from 2 sub-indices,

6 themes, and 22 indicators. Countries are ranked according to outcomes of their HANCI and

also by the sub-indices separately. According to te Lintelo et al. (2013), the HANCI compares

the countries’ performance relative (not absolute) to one another, and aggregates relative

political commitment levels. They also note that HANCI commitment rankings should not be

confused with hunger and nutrition outcomes.

The OECD’s your better life index is another innovative multidimensional measure of what

drives the well-being of people and nations in OECD countries. This measure is based on 11

dimensions (each comprising of one to three indicators, 24 indicators in total) considered

essential to well-being. The topics cover measures from health and education to local

environment, personal security and overall satisfaction with life, as well as more traditional

measures such as income. The indicators are averaged with equal weights within each topic

producing 11 indices, and countries are then compared along each of these indices. Following

this approach as an example, OECD (2011) classifies countries as top performers, bottom

performers, or average performers for each headline indicator used. However, since indices

5

depend on the relative importance that each person or society attaches to them, OECD (2011)

results show that no country ranks consistently at the top or bottom of the distribution. The

novelty of your better life index is that it is designed to let the user investigate how each of the

11 topics can contribute to well-being and also he/she can decide how much weight to give for

each topic. Relatedly, OECD (2013) offers measures of subjective wellbeing or happiness as a

central element of quality of life. Broadly defined, subjective wellbeing includes “[…] good

mental states, including all of the various evaluations, positive and negative, that people make of

their lives and the affective reactions of people to their experiences[…]” (OECD 2013). The

report suggests various methods of reporting including share of people who are: thriving,

struggling, and suffering; share of people who are: very satisfied, satisfied, neither satisfied nor

dissatisfied, dissatisfied/very dissatisfied.

Similar to OECD (2011), Helliwell et al. (2012) published a world happiness report. Various

measures are discussed and reported by country including: average life satisfaction, average

happiness, average positive affect, average negative affect, and happy index. The Happiness

Index (HI) in the report is defined as the weighted (by sampling weights) rate of respondents

reporting “Very happy” or “Quite happy” less the weighted rate of respondents reporting “Not

very happy” or “Not at all happy.” Similarly, positive affect is defined as the average of

happiness, laughter, and enjoyment yesterday; and, negative affect is defined as the average of

worry, sadness, depression, and anger yesterday. The case of Bhutan which calculates a Gross

National Happiness index (GNH) from 9 domains and 33 clustered indicators (each composed of

several variables resulting in 124 variables in total) is also presented in the report as a variant of

happiness measures. According to Helliwell et al. (2012), the 9 domains are equally weighted but

weights attached to the variables differ, with lighter weights attached to highly subjective

variables.

In summary, most of the multidimensional typologies or classifications of countries presented in

this section and relying on indices (GHI, MPI, HDI, HANCI, HI) apply equal weights to

indicators in the computation of their “dimensional” or categorical indices. The dimensional

indices are then also usually equally weighted to compute the general indices. The OECD’s your

better life provides the data but leaves it up to the user to allocate weights to the variables and

indicators. Bhutan’s GNH applies equal weights to dimensional indices, but indicator weights

within the dimensions are subjective. The more general classifications of the World Bank and the

FAO presented here are based on single indicators.

Our methodology differs from the multidimensional typologies presented in this section. In our

case, the indicator weights within thematic profiles (i.e. the food and nutrition security profiles,

as well as the agricultural, economic, political, health infrastructure and innovation profiles

presented in Section 3) are empirically determined through the PCA and its component loadings

rather than being equal or fixed ex ante. This follows Yu et al. (2010) in their article aiming

explicitly at building a typology of food security in developing countries. They derive a food

security score using PCA on three variables of food consumption: daily per capita intake of

6

calories, protein and fat. The three variables are highly correlated and thus the weights are almost

similar (0.961, 0.959, and 0.929 respectively). Yu et al. (2010) then split countries in five groups

according to their food security (FS) score: lowest FS, low FS, lower middle FS, upper middle

FS, high FS. Within each group, countries are then sequentially clustered using one variable of

food trade security (ration of total exports to food imports), one variable of food production

(annual food production per capita in US$), one variable of soil fertility (percentage of land

surface without major soil constraint) and one climate variable (based on temperature and

rainfall conditions). The clustering relies on high-low descriptions for these four variables, based

on the sign of the variables z-scores for the production security and the two environmental /

agricultural potential variables (thus the ranking is conditional to the mean value in the sample)

and on an “arbitrary” benchmark for the trade security variables (imports more or less than 10%

of exports).

In the present paper, we differ from Yu et al. (2010) mostly by incorporating more variables of

food and nutrition security, and especially more potential determinants of food and nutrition

security. As this renders the sequential process too difficult on a per variable basis, and probably

less relevant, we resort to computing PCA scores for the different groups of variables – the

thematic profiles mentioned earlier. Finally, we use two approaches to provide a clear picture of

the countries food and nutrition security situation and of their potential determinants: the quintile

estimations as well as the high-low classification relative to the median scores (in order to avoid

bias of extreme values).

Our broad coverage of indicators of food and nutrition security and of their determinants comes

at the cost of PCA results which are not as “strong” as in Yu et al. (2010) in terms of the

proportion of the variation found in the data explained by the PCA indices. However, this

broader coverage serves the purpose of the typology, as it follows more closely the indicator

definitions put forth in Pangaribowo et al. (2013) and the analytical needs of the different

modeling teams in FoodSecure.

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3. Food and nutrition security framework

Summary of conceptual framework

At the World Food Summit of 1996 in Rome, food and nutrition security was defined as the

situation ‘when all people, at all times, have physical and economic access to sufficient, safe and

nutritious food that meets their dietary needs and food preferences for an active and healthy life’

(World Food Summit 1996). This definition of food and nutrition security reflects the four key

dimensions of food and nutrition security: food availability, food access, food utilization and

food variability (FAO 2006a).

Food availability reflects the supply side of food and nutrition security and is determined by the

domestic food production and a country’s food imports. Despite the fact that there is enough

food in the world, one in eight still suffer from hunger (FAO, WFP and IFAD 2013). Food

availability is thus a necessary, but not a sufficient condition to guarantee food and nutrition

security. In other words, access to food for households is not assured even if there is adequate

food at national and international level, since it is also determined by factors such as income,

markets, and prices. In turn, having access to food is not a sufficient condition for the realization

of food utilization. Food utilization refers to the nutritional status of an individual, which is

determined by an individual’s dietary intake and absorption of energy and nutrients. A good

nutritional status is the result of a combination of several factors such as good nutritional

knowledge, feeding practices, intra-household distribution of food, a good health status, and

diversity of the diet. The final dimension of food and nutrition security emphasizes the stability

of the other three dimensions. A person might not be food and nutrition insecure today, but can

be considered food and nutrition insecure if s/he is not able to remain this status when s/he is hit

by a temporary negative shock.

Food security, nutrition security and obesity profiles at national level

The food security, nutrition security and obesity indicators are used to proxy the real food and

nutrition security situation in a country. The food security profile describes the food availability

dimension of the food and nutrition security problem, while the nutrition security profile defines

food access and food utilization. As more and more countries are struggling with overweight, we

also construct an obesity profile. For better cross-country comparison the three profiles are

described by seven variables.

Food Security Profile

Average daily calorie intake, the share of animal proteins in average daily calorie intake and the

average daily calorie deficit of undernourished people per capita4 describe the food security

index. The first two variables are chosen to represent food availability. The average daily calorie

4 A full description of the variables included in the typology can be found in section F in the appendix.

8

intake and the share of animal proteins in the diet are indicators for the average level of

consumption in a country. The last variable accounts for the dimension of accessibility.

Nutrition Security Profile

The nutrition security index should proxy both the utilization and access dimension of food and

is described by the percentage of people who are undernourished, the percentage of women who

suffer from anemia and the under-five mortality rate (as a general indicator of children

nutritional status, as included in the GHI). The percentage of people who are undernourished

accounts for the general lack of food consumption. Micronutrient deficiencies and more

specifically iron deficiency are accounted for by the inclusion of the percentage of women who

suffer from anemia (the most basic and important micronutrient for physical health and

development, including brain development, are vitamin A, iron, zinc and iodine – UNICEF

report on “Improving Children Nutrition”, 2013). Although it would be desirable to add more

variables to each of the indicators, data availability is limited, especially for the dimensions of

food accessibility and utilization.

Obesity profile

Today many developing countries are confronted with a double burden of malnutrition, facing

both the prevalence of undernutrition, while at the same time overweight and obesity is on the

rise (FAO 2006b). In China 8 percent of the children have underweight, while the percentage of

adults with overweight and obesity has reached a rate of 23 percent (FAO 2006b). Investments to

improve sanitation and water facilities and increase access to proper health care have been

slowing down, and led to a slackening of the progress in reducing undernutrition. In the past

twenty years, diets have shifted as a result of changing diets towards food high in saturated fats,

sugar and refined foods. To account for this double burden, we include besides a nutrition

security profile with a strong focus towards undernutrition, also an obesity index which is based

on the prevalence of obesity among adult women.

Agricultural, economic, political, innovation and infrastructural national

profiles

Agricultural Profile

Agriculture is an important determinant to solve hunger and poverty in developing countries.

Headey (2011) shows that with exception of India agricultural growth has a much bigger impact

on food insecurity than growth in non-agricultural sectors. The size of the reduction in food

insecurity is conditional on the importance of the agricultural sector in a country’s economy. We

include two categories to account for the link between agriculture and food security: a country’s

agricultural performance and potential.

9

Agricultural performance links a household’s current food and nutrition security status with

actual food production. A country’s total production depends on the performance of the

agricultural sector, which is described in our typology by the following variables: food

production per capita, added value of agriculture per worker and the import share of agricultural

products. The food production per capita captures a country’s ability to produce food given its

current production technologies. A country’s agricultural value added per worker is an indicator

for productivity of the agricultural sector. The import share of agricultural products measures the

dependence of a country on the international market for the acquisition of these products.

A country’s agricultural potential captures a country’s future potential to cope with food

insecurity through agriculture and is determined by exogenous factors like the length of the

growing period, the average precipitation and the percentage of land without major constraints

(see also Yu et al. 2010). Given the fact that smallholder agriculture is rainfed and since water

availability is one of the most binding constraints, one of the main determinants of the

agricultural potential is rainfall. Total rainfall in a country is proxied by two indicators – average

precipitation and the length of the growing period. Average precipitation is defined as the long

term average of precipitation expressed in mm per year and is a measure for total rainfall in one

year. The length of the growing period refers to the number of days in a year in which the

moisture availability and temperature are favorable for growth of crops and pasture. Soil fertility

is besides water another key factor in determining the agricultural potential of a country, since all

plants take up nutrients from the soil that are essential for their growth. Soil fertility is defined as

the percentage of land that has no major soil constraints based on 8 criteria.

Economic profile

The economic profile describes the ability of households to access food. Both the level and the

distribution of income among individuals matters for an individual’s food and nutrition status.

The level of income, proxied by the GDP per capita, provides households with the necessary

means to buy food on the market. A countries income is, however, not equally distributed among

all citizens within a country and since poverty is still one of the main determinants of restricted

access to food, we also included the GINI coefficient. The final indicator included in the

economic profile index is the women’s economic opportunity index coming from the Economist

Inteligence Unit (EIU). The participation of women in the economy is a key factor for economic

growth. In many developing countries, women form a critical part of the active labor in

agriculture, but they often do not have the same access to inputs and credits as their manly

counterparts. The FAO (2011) estimated that if women have equal access to seeds, fertilizers,

credits, pesticides, etc. – it would increase their yields up to 30%.

Health Infrastructure Profile

Good health is one of the cornerstones of securing an individual’s food and nutrition status, as it

allows to have appetite and to be able to absorb the vital nutrients. The health status of an

10

individual depends crucially on his health environment and his access to health services (World

Food Program 2007). The quality of the health environment is defined by the percentage of the

population who have access to improved water sources and who have access to proper sanitation

facilities. Having access to improved water and sanitation reduces the risk of infections and

prolonged disease and is especially important for young children (World Food Program 2007).

Access to health services is another essential tool to prevent and to combat diseases and will be

represented in this typology by the health expenditures per capita and the number of hospital

beds per 1,000 citizens. The health expenditures per capita describe the total amount of money

that is spent by the government on health care in a country. To account for accessibility and

quality of the health services we included the number of hospital beds per 1,000 citizens.

Political profile

The policy environment determines which policies the government will implement and who will

be benefitting. The political index will include variables on the level of democratization, the

efficiency and quality of the government and the political stability in a country. The EIU

democracy index will represent the level of democratization in a country and will proxy the

accountability of the government towards its voters. Efficiency and quality of the government is

described by the control of corruption. Corruption can lead to serious inefficiencies in markets,

which in turn might reduce the average supply of food in a country (Economist Intelligence Unit

2013). Political stability, as measured by the political stability and absence of violence, accounts

for the negative effects conflicts might have on infrastructure – access to health services,

markets, water supplies, etc. - and economic growth (Messer et al. 2001).

Innovation profile

Investments that lead to innovation are a very important driver to enhance future food and

nutrition security in developing countries. Current and future challenges of feeding a growing

population in a way that its inputs as land and water are used in a sustainable manner, will

require changes in production methods and innovations at each step of the supply chain. Our

innovation profile is based on the Knowledge Economy Index developed by the World Bank,

which consists of 4 pillars: education, the economic incentive regime, information infrastructure

and the innovation system (World Bank 2012a). Education and skills describe the first pillar of

the knowledge economy of a country. Human capital is still low in many developing countries

and poses a significant barrier to their own economic development and restricts the smooth

diffusion of innovation in these countries. To proxy human capital we make use of the education

index of the World Bank, which in turn is based on adult literacy rate, gross secondary enrolment

rate and gross tertiary enrollment rate. A country’s business environment must stimulate the

efficient use of existing and new technologies, but it must also create a platform for companies to

develop new technologies. The World Bank’s economic incentive regime index will represent

the second pillar and will exist of the following three indicators: tariff and non-tariff barriers,

regulatory quality and the rule of law. The third pillar – a country’s information infrastructure -

11

facilitates the communication, diffusion and processing of information within a country. The

indicator of information infrastructure is constructed from the number of telephones, computers

and internet users per 1,000 people. The innovation system is the fourth pillar and is described by

a measure of the World Bank that is based on royalty payments and receipts, technical journal

articles and the patents granted to nationals. The indicator measures the productivity of the

innovation system consisting of firms, universities, research centers, think thanks, etc.

Linkages between the thematic indices and the long term drivers of food and nutrition security

Laborde et al. (2013, FoodSecure WP6) have highlighted the long terms drivers of FNS, at the

individual/household levels as well as at the market/global level. As mentioned in this paper and

in Pangarobowo et al. (2013), FNS is a complex multidimensional issue for which there is no

established theory formally linking FNS with economic variables and frameworks. Laborde et al.

(2013) provide a reduced form representation of a person’s long term FNS, as a function of food

quantity and quality consumed. Further, the authors link the individual level FNS with global

markets and policies via the connection between the prices paid by the consumer and the world

prices and their drivers.

For the purpose of this country typology, we have taken some liberties with the list of drivers

mentioned in Laborde et al. (2013). Their reduced form representations of the different drivers of

food quantity and quality provide a natural starting point for the thematic profiles described

above. However, given that the typology was hardly feasible if it had to account for the evolution

of all the drivers and FNS outcomes (data and methodological restrictions), we have opted for a

selection of key (and available) variables describing the FNS status of the countries’ population

over the past 5 years. We have also selected the thematic profiles describing generally some of

(types of) the long terms drivers of FNS presented in Laborde et al. (2013), and in line with

thematic research work under the FoodSecure project. The match with the long term drivers is

far from perfect: combining some of the drivers listed in Laborde et al. (2013) is simply not

appropriate in the context of this typology: given the methodology we apply here, if drivers have

different signs in their impact on the thematic scores, the thematic scores (described in next

section) will be impossible to interpret.

Our agricultural potential profile matches with long term drivers of aggregate food supply (at the

national level) such a land available for food production and normalized average yields, but only

from a natural endowment perspective (as opposed to economic decisions based on trade-offs).

The agricultural performance profile also matches with the long term drivers of aggregate food

supply (at national level), as food production per capita reflects both the share of per capita land

used for food production and yields. We added the agricultural import share (which contains

both food and non-good products) as a general indicator of the importance of the agricultural

sector to the national needs for agricultural products and the growth of agricultural value added

per worker to characterize the agricultural system (performance with respect to labor intensity).

12

Our economic profile matches well with the individual (and household) long term drivers of

FNS, with GPD per capita and its distribution represented, as well as intra-household distribution

being proxied by the economic opportunity index for women. Other individual level drivers are

accounted for under our infrastructure profile, which covers different types of infrastructures

(health, water and sanitation) described as part of the sanitary conditions in Laborde et al. (2013).

The authors also mention education and knowledge dissemination as individual drivers, which

we capture under our innovation profile, together with knowledge creation.

Finally, our political profile describes the general nature of the national political system. As such

it is only loosely related to governance, trade policies, and market structure and distortions as

drivers of the global vs domestic prices and of the aggregate food demand.

What we did not cover in our FNS typology of countries are prices, non-price costs associated

with food, and preferences. The latter, and the global change in diets, is partly reflected by the

share of animal protein in daily intake. Global prices did not make sense to include in our

national typologies, but the degree of price transmission between international and national food

prices was originally included. It had to be dropped from the analysis as it did not correlate to

any of the other variables in a consistent way and made results un-interpretable. Demographics

are not relevant in our typology which is based entirely on relative variables (per capita, per

1000, percentages, etc.). We did try variables showing population trends and sizes, but these

variables were rejected from the thematic scores based on their lack of correlations with other

variables.

13

4. Principal component analysis for food and nutrition security and its

determinants

A short introduction to principal component analysis

The purpose of Principal Component Analysis (PCA) is data reduction. PCA will transform the

original variables into a new set of variables – also known as principal components – and by

definition there will be as many components as there are variables (Jolliffe 2002). The first

component extracted from the data is the linear combination of the variables that accounts for the

largest share of variance exhibited in the data. The second component is the best linear

combination that accounts for the highest variance possible under the constraint that it must be

independent to the preceding principal component, and so on until all the variance is explained.

Each principal component is related with a set of component loadings, which is the weight by

which each of the original variables is multiplied to transform the original variables into the

principal component (Jolliffe 2002).

Principal component analysis is sensitive to the unit of measurement of the original variables.

For ease of computation and comparison, and following Yu et al. (2010), we transformed all our

variables into standardized scores (Z-scores). Hence our PCA is not calculated from the

covariance matrix, but from the correlation matrix5.

As we have a wide range of variables to describe countries based on the 6 thematic themes we

have chosen, it proved an impossible task to use them all to determine the latent factors behind

the data. Thus we instead decided to run a PCA on thematic groups of data with the sole purpose

of data reduction: the many variables within each thematic group are reduced to only one

component. This first principal component provides directly the thematic index. When and why

to retain one or several principal components is discussed at the end of this section.

As an example, we refer briefly to our first table of PCA results (Appendix – Section A). The list

of variables used in the PCA is given at the top of the table, followed by the number of

observations entering the PCA (i.e. the number of countries for which each variable displays an

observation), the number of variables (and hence of components) and the trace of the matrix

(which should be equal to the number of observations, otherwise there are two identical variables

in the matrix). The middle part of the table relates the eigenvalues and share of total variance

accounted for by each component. The bottom part of the table shows the loading of each

variable in each linear combination – the principle components (columns named Comp1, Comp2,

etc.).

5 It must be noted that the weight attributed to each variable in each specific linear combination/component is

different if we work from the raw data or from the z-scores. Z-scores are justified if the variance of few raw

variables would disproportionally dominate the overall variance.

14

Composition of the 6 thematic indices

Our methodology differs from the multidimensional typologies presented in the introduction. In

our case, the indicator weights within thematic profiles (i.e. the 9 themes presented in Section 3

with an exception for obesity) are empirically determined through the PCA and its components’

loadings rather than arbitrarily fixed (thus following Yu et al. 2010). In our typology, we retain

only the first principal component of the analysis (also following Yu et al. 2010), which accounts

for the highest share of the total variance, to derive a unique thematic score for each country. In

other words, the thematic index is directly compiled from the first principal component. Based

on the component loadings X, we compute the component scores for each theme and each

country: the loading of each variable in component 1 times the value the variable assumes for a

given country. As example, a country’s food security score is thus expressed as:

𝐹𝑆 = 𝑋 ∙ 𝑐𝑎𝑙𝑜𝑟𝑖𝑒𝑠 + 𝑋 ∙ 𝑠ℎ𝑎𝑟𝑒 𝑜𝑓 𝑝𝑟𝑜𝑡𝑒𝑖𝑛𝑠 + 𝑋 ∙ 𝑓𝑜𝑜𝑑 𝑑𝑒𝑓𝑖𝑐𝑖𝑡

A reason to retain only the first principal component is that, by definition, it provides the best

summary of the data (Jolliffe 2002). However, our typology needs to reflect a good share of the

variation in the data, so we have set that thematic indices based solely on the first principal

component should account for at least 50% of the variation in the data. In our results, effectively

almost all thematic indices are based on principal components accounting for between 60% and

80% of total variation in the data (i.e. the variables used to compute the thematic indices). This

“fit” is not as high as in Yu et al. (2010), where the food security score accounts for 90% of the

variation in the three variables they use. Yet we must stress that this is the cost of expending our

representation of food and nutrition security and their determinants.

Another reason to follow this course lies in the interpretation and traceability of the results: if the

loadings of the variables in the principal components are not of identical sign, it becomes very

complex to identify reasons why a given country has a high or low thematic score. In such cases,

we would increase the chances of the typology leading to grouping together countries which

have seemingly little in common. In some cases, one or two variables show loadings of opposite

sign in the first principal component for an obvious reason which we will discuss in our result

section. In such cases, we made exceptions and kept the variables in the PCA (e.g. daily calorie

intake versus food deficit).

Classification of the countries

Working from the countries’ thematic scores we will provide two different analyses:

1. We will divide each thematic index in quintiles based on a country’s thematic score

which will allow us to analyse within each index how countries perform compared to

the other countries included.

15

2. We will compute the median thematic scores and classify countries as below or above

the median scores, then we will group them according to their relative composition

and provide a pairwise matching of the thematic scores. This will allow us to analyse

which countries perform better than the median both in terms of food (or nutrition)

security and each of the six determinants profiles (agricultural performance,

agricultural potential, economic performance and distribution, health infrastructure,

political stability and freedom, innovation performance and potential).

16

5. Classification of countries using quintile scores

Results

Each thematic index will be divided into 5 categories based on the country’s scores. The results

of the classification and the quintile scores can be found in the Appendix – Section B. The

figures below summarize the results for the food, nutrition security and obesity profiles6. A score

of 5 (or a country color in orange) can be interpreted as the best score a country can get in terms

of food or nutrition security. A country like Argentina belongs to the 20% most food and

nutrition secure developing countries included in each sample, but Argentina ranks high in terms

of obesity, as 31% of the female adults are obese. In general, we can conclude from the figure

that both in terms of food and nutrition security most countries that perform badly are located in

Middle and Eastern Africa and South Asia. It must be noted that not all countries achieve the

same relative quintile score for both categories. For example, India has a better food security

score than nutrition security score. This implies that other factors as food availability in a country

might play a role in securing a nutritious diet for its citizens.

6 The graphs for the other thematic scores can be found in section C of the appendix.

17

Figure 1: Quintile score map of the food security profile

Figure 2: Quintile score map of the nutrition security profile

Figure 3: Quintile score map of the obesity profile

18

The means of all indicators and their composed thematic indices, as well as food imports, are

summarized in Table 1.

Table 1: Average value of profiles and underpinning variables by food security quintile

The table above shows that the average values of the food security indicators – share of animal

proteins in the diet and daily calorie intake – increase with the food security quintile. The share

of animal proteins in the diet varies, on average, from 10.91% to 38.63% from the lowest to the

highest food secure group. The difference in daily calories between the highest and lowest

categories is 1074 per day. Food deficit reduces from 306 to 19 kcal per person per day from the

lowest to the highest quintile.

Variable Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5

Share of animal protein in diet 10.91304 17.3913 21.86364 34.04348 38.63636

Average daily calorie intake 2057.339 2340.383 2547.791 2829.574 3131.355

Food deficit 305.913 167.1739 84.68182 45.56522 18.77273

Nutrition Security Profile 1.909091 3.047619 2.954545 3.434783 3.65

Undernourishment 39.57391 23.00909 13.18182 7.256522 5.27

Anaemia women 45.71364 38.17727 40.91364 31.43913 25.52273

Child mortality 103.2557 77.19826 68.87364 35.21391 20.72273

Obesity 1.434783 2.227273 2.681818 3.478261 4.272727

Female obesity 7.665217 13.80455 18.67727 26.56957 34.31364

Agricultural Potential Profile 2.75 3.090909 3.55 3.055556 2.368421

Length of growing period 186.3979 182.5435 231.6197 208.4511 169.2662

Percentage without major soil constraints 24.65 26.90909 28.8 27.38889 27.15789

Percipitation 1068.739 1227.435 1585.273 1275.619 938.5238

Agricultural Performance Profile 1.55 2.714286 2.714286 3.521739 4.318182

Value added per worker in agriculture 437.2887 1108.964 1681.366 3262.709 6323.256

Import share of agriculture 19.29998 17.39461 18.30734 15.35314 11.50931

Food production per capita 125.2826 198.1385 228.4945 330.0233 381.8908

Economic Performance Profile 1.833333 2.266667 2.928571 3.818182 4.142857

Gini 42.015 40.23083 44.58133 42.31088 42.25843

GDP per capita 1716.768 2830.23 4171.295 8064.346 9422.144

Women economic opportunity index 37.94118 41.91667 43.93333 50.29333 54.56875

Political Profile 2.434783 2.952381 3.157895 3.315789 3.578947

Political stability and violence -0.7574593 -0.6739797 -0.4582975 -0.133793 -0.1056486

Control of corruption -0.7509381 -0.6573134 -0.5884211 -0.2606177 -0.2486755

Democracy index 3.944348 4.810952 5.104211 5.21 4.890526

Innovation Profile 1.666667 2.2 2.6875 3.882353 4.388889

Innovation system 2.29875 2.6885 3.0275 4.342941 5.275

Economic incentive regime 2.70625 2.8105 3.194375 4.052353 4.458889

Education and skills 1.513333 2.35 3.090625 4.679412 5.603333

Information infrastructure 1.681875 2.4135 3.321875 5.003529 4.782778

Health Infrastructure Profile 1.65 2.105263 2.7 3.714286 4.52381

Health expenditures per capita 55.42976 76.24217 129.1779 215.7315 338.3809

Sanitation 35.79783 45.51304 54.86364 73.33913 89.67273

Water supply 64.02826 78.14783 80.04545 87.70725 95.31111

Hospital beds 1.355831 1.624787 1.621066 2.499455 3.468039

19

The nutrition security index increases with food security, due to a significant decrease in

undernourishment and child mortality along the food security categories. The average percentage

of undernourished people is almost 8 times higher if we compare the lowest and highest food

secure countries. The number of children who die before the age of five is 5 times higher in the

lowest category compared to the most food secure countries in our sample. For the final indicator

of the nutrition security indicators - percentage of women that suffer from anemia – it is shown

that there is a steady decline along the food security index.

An opposite trend is revealed for female obesity rates. In the 20% most food insecure countries,

on average, only 7,66% of the female population suffer from obesity, while the countries that

belong to the highest food security category reach obesity rates of 34.31%. The ‘double burden

of malnutrition’ is especially pronounced in the three middle food secure categories.

In terms of agricultural potential, it is hard to define clear trends in terms of length of growing

period, the level of precipitation and the percentage of major soil constraints. Despite the same

level of agricultural potential among all developing countries, there is, however, a clear

difference between the different categories in terms of agricultural performance. Agricultural

performance of the 20% most food insecure countries is significantly lower than the performance

of the highest food secure group, especially in terms of food production volumes and agricultural

productivity. The added value per worker in agriculture is 437$ in the lowest and 6323$ in the

highest category and total food production is more than three times higher when comparing the

average values in the low- and high food secure countries. In terms of agricultural imports, the

data show that there is a reduction in agricultural import values, but this trend is however not

stable across the different categories.

The increasing economic performance is mainly driven by the higher levels in income and the

better economic opportunities for women along the different levels of food security. The lowest

food security countries show the lowest per capita income (1716$) and the lowest economic

opportunity index for women (37.94 points). The highest category is much better off, with an

average per capita income of 9422$ and an opportunity index of 54.56 points. Within country

inequality is not significantly higher in the lower food secure categories.

The political index increases along the food security index. Political stability and the absence of

violence and the control of corruption can have values between -2,5 to 2,5, but the values are, on

average, negative among all categories of the food security index. The democracy index is

significantly lower for the least food secure countries, but for the following categories it reaches,

on average, a score close to 5, which is still half of the score a country can get on the democracy

index.

Innovation is an important determinant in the fight against hunger. From the agricultural

performance index, it was shown that food insecure countries have, on average, a very low

agricultural productivity. The increasing trend in innovation along the food security categories

20

coincides with the rising trend in productivity. For all the four indices from which the innovation

index is constructed, it is shown that the score increases as countries belong to a higher food

security category.

A lack of proper health infrastructure is often emphasized as one of the main determinants of

food and nutrition insecurity, for example in India. Per capita health expenditures are higher in

the countries that have a score of 5 for the food security index than those of their counterparts.

Governments of countries in the lowest food security category spend, on average, only 55 $ on

health, a sixth of what is spent by governments in the highest food security group (338 $).

Additionally, access to sanitation is lacking behind in the least food secure countries, barely 36%

of the population has access to proper sanitation facilities and access increases up to 89% for the

highest category of food secure countries. Access to proper water supply is less problematic,

however, 36% of the population still has no access to improved water sources compared to less

than 5% in the most food secure developing countries. The number of hospital beds per 1,000

people increases across the food security categories.

In general, results for the indicators are quite similar to classification of the food security index

and can be found in the Appendix Table 16. Nutrition security is linked with the utilization and

access dimension of the food and nutrition security problem. The most nutrition insecure

countries have, on average, an undernourishment rate of 29% and more than 49% of the female

population has an iron deficiency. Food production per capita, the daily calorie intake and the

share of animal proteins in the diet are higher in the 20% most nutrition insecure countries than

in the less food secure countries.

Evolution of the food and nutrition security analysis

In this section we will give an overview of how the food and nutrition security indices changed

over time. For each food and nutrition security classification, we will shed light on how the daily

calorie intake per capita, the food deficit, the share of animal proteins in the diet and

undernourishment rates changed over time. Furthermore, the productivity of the agricultural

sector, food production per capita, health expenditures per capita and sanitation will be included

in the analysis. The table below shows the growth rates for the variables between the period from

1999 to 2009.

21

Table 2: Annual growth rates between 1999-2009 by food security quintile

For the three food security indicators, Table 2 only shows a clear trend for the food deficit. Food

deficit for the most food insecure countries increased on average by 2.77% per year, but it

decreased for the countries in the three highest quintiles of the food security index. In terms of

nutrition security, there has been an annual increase in the percentage of undernourished people

in the first and third quintile, while there was a decrease in the other quintiles. In terms of

agricultural indicators, agriculture productivity, measured as the value added per worker, the

20% countries with the lowest food security status have experienced an annual growth rate of

0.79%, while the most food secure countries have grown by 5.22% per year. All countries report

a positive growth for the food production level per capita, but there is no clear trend across the

different food security quintiles. Although the most food insecure countries had reported higher

annual growth rates in health infrastructure, the level of the health expenditures per capita and

the percentage of people with access to proper sanitation were still lower compared to their

counterparts.

Variable Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5

Share of animal protein in diet 1.61 4.15 1.33 1.84 3.32

Average daily calorie intake 0.51 0.81 0.78 0.81 0.58

Food deficit 2.57 1.42 -3.98 -3.06 1.57

Undernourishment 1.39 -1.00 -0.60 -2.90 -0.64

Child mortality -3.04 -2.33 -2.71 -2.59 -3.71

Value added per worker in agriculture 0.78 1.93 3.28 1.70 5.25

Food production per capita 0.80 1.49 1.08 1.19 1.36

Health expenditures per capita 15.71 11.78 7.31 7.70 8.61

Sanitation 2.12 1.65 1.33 0.90 0.57

22

6. Classification of countries using median scores

The classification of countries using median scores facilitates the grouping of countries when

combining several thematic profiles7. Using the median as a benchmark, the countries are

divided into a higher and lower group compared to the median. The multiple indicator clusters

are then based on how good or bad a country scores on the thematic profiles. We have decided to

use the median scores rather than the mean to eliminate any bias from extreme values (although

this is already largely taken care of by relying on standardized scores for the different variables

entering the thematic scores).

The raw results showing the median scores for each country, in alphabetical order are presented

in the Section F of the Appendix. In the Tables 3-5 below, we present a clustering of the

countries based on the median scores. The first table clusters the countries around their median

scores for food security and its determinants, the second table clusters them around the median

scores for nutrition security and its determinants, and finally the third table clusters them around

the obesity score relative to the median in the sample and its determinants. We organized the

table in such a way that the FNS outcome index is presented on the left of the tables (rows),

together with its determinants on which policy can have the most immediate impact. The

determinants which are presented on the top of the tables (columns) are those which are largely

“exogenous”, in the sense that policy cannot affect them directly, or at least only with difficulty.

The two types of indices presented in the columns are “agricultural potential”, formed built

around agro-environmental conditions (growing period, precipitations, soil type), and the

political profile (democracy, stability and corruption).

In order to increase the coverage of countries under this clustering approach, we have reported in

the tables below all countries with a complete set of profiles (i.e. an index could be computed for

each of the profiles), as well as countries for which only one profile is missing (i.e. data

availability allowed to compute only six of the seven indices reported in each table). The latter

countries are mentioned in grey in the tables. Our coverage of countries is not as comprehensive

as in Yu et al. (2010), who cover more than 130 developing countries. Indeed, data availability

was an issue throughout the entire process of typology building in this paper. Our broader

conceptualization of determinants of food and nutrition security has come at this cost.

Nonetheless, the tables below cluster over 80 developing countries, and should help guide case

study selection under the FoodSecure project.

Finally, in Table 6 we show countries with their respective food security, nutrition security and

obesity scores. According to our index definitions, of the 108 developing countries with a score

for each of these three profiles, five countries compile “low” (relative to the sample median)

7 When using quintile scores, we would end up with 25 potential different combinations already for a pairwise

thematic comparison. Using all (seven) thematic scores would lead to 78’125 possible combinations, compared to

128 if we rely on the median conditional split. To reduce the complexity, we make use of median scores, as it only

results in 4 different groups.

23

food and nutrition security with “high” obesity among women (Bolivia, Botswana, Lesotho,

Swaziland and Yemen), 39 countries combine “low” food and nutrition security with “low”

female obesity, and 37 countries combine “high” food and nutrition security with “high” female

obesity. Thus in our sample the majority of countries with a female obesity issue have “high”

food and nutrition security. Cape Verde, China, Malaysia, Sao Tome et Principe, Thailand and

Vietnam are the best cases in our typology, with “high” food and nutrition security and “low”

female obesity. Again, our analysis is based on country averages and thus do not point to issues

in FNS which may be much more dramatic when looked at sub-nationally. Nonetheless, the next

four groups of countries offer an interesting point: they seem to correlate high female obesity

with high food (Gabon, Guyana, Maldives, Mauritania, Vanuatu) or nutrition security

(Dominican Rep., Ecuador, Guatemala, Nicaragua, Peru, Solomon Islands, Suriname), and “low”

female obesity with high food (Ghana, Mali, Nigeria, Philippines, Turkmenistan, Uzbekistan) or

nutrition security (Indonesia, Mongolia, Sri Lanka). It is beyond the scope of this paper, and

indeed beyond the power of our data and methodology, to discuss potential causal relationships

between the three indices of food and nutrition security presented here. Nonetheless, looking at

all three indices in conjunction might help refining our selection of case studies, or model

scenarios.

24

Table 3: Clustering of countries based on median scores - food security and its determinants

Low Political Quality High Political Quality Low Political Quality High Political Quality

High Health

Infrastructures

Azerbaijan, Belarus,

China, Iran,

Kazakhstan, Lebanon

Argentina, Armenia,

Chile, Mexico, South

Africa, Tunisia,

Turkey, Ukraine Venezuela, Fiji

Brazil, Columbia,

Costa Rica, Malaysia,

Panama, Paraguay,

Uruguay, Thailand,

Cuba, Guyana,

Jamaica

Low Health

Infrastructures Philippines Thailand

High Health

Infrastructures Syria, Uzbekistan

Low Health

Infrastructures

High Health

Infrastructures

Egypt, Kyrgyz Rep.,

Lebanon Jordan Fiji

Cuba, Guyana,

Jamaica

Low Health

Infrastructures Morocco

High Health

Infrastructures Syria, Uzbekistan Vietnam

Low Health

Infrastructures Morocco

High Health

Infrastructures El Salvador, Georgia

Low Health

Infrastructures

High Health

Infrastructures

Low Health

Infrastructures Mauritania Honduras

High Health

Infrastructures

Low Health

Infrastructures

High Health

Infrastructures

Low Health

Infrastructures Mauritania Mali Ghana

Low

Innovation

Low Agricultural Potential High Agricultural Potential

Hig

h F

ood S

ecuri

ty

Hig

h A

gri

cultura

l per

form

ance

Hig

h E

conom

ic P

erfo

rman

ce

High

Innovation

Low

Innovation

Low

Eco

nom

ic P

erfo

rman

ce

High

Innovation

Low

Innovation

Low

Agri

cultura

l P

erfo

rman

ce

Hig

h E

conom

ic P

erfo

rman

ce

High

Innovation

Low

Innovation

Low

Eco

nom

ic P

erfo

rman

ce

High

Innovation

25

Low Political Quality High Political Quality Low Political Quality High Political Quality

High Health

Infrastructures Mongolia, Namibia Ecuador

Dominican Rep.,

Guatemala, Peru,

Georgia

Low Health

Infrastructures Bolivia

High Health

Infrastructures

Low Health

Infrastructures

High Health

Infrastructures Namibia

Low Health

Infrastructures

High Health

Infrastructures Indonesia

Low Health

Infrastructures India Côte d'Ivoire Nicaragua, Indonesia

High Health

Infrastructures

Low Health

Infrastructures

High Health

Infrastructures Sri Lanka

Low Health

Infrastructures

Kenya, Angola,

Nepal, Malawi, Sierra

Leone, Benin,

Lesotho, Botswana Sri Lanka

High Health

Infrastructures

Low Health

Infrastructures Burundi, Togo

High Health

Infrastructures Tajikistan Cambodia

Low Health

Infrastructures

Ethiopia, Pakistan,

Sudan, Yemen,

Angola, Burundi,

Nepal, Togo, Malawi,

Sierra Leone, Benin,

Lesotho, Botswana

Burkina Faso,

Mozambique, Senegal,

Tanzania, Zambia

Bangladesh,

Cameroon, Guinea,

Lao PDR, Uganda,

Cambodia Madagascar, Rwanda

Low Agricultural Potential High Agricultural PotentialL

ow

Food S

euri

ty

Hig

h A

gri

cultura

l per

form

ance

Hig

h E

conom

ic P

erfo

rman

ceHigh

Innovation

Low

Innovation

Low

Eco

nom

ic

Per

form

ance

High

Innovation

Low

Innovation

Low

Agri

cultura

l P

erfo

rman

ce

Hig

h E

conom

ic P

erfo

rman

ce

High

Innovation

Low

Innovation

Low

Eco

nom

ic P

erfo

rman

ce

High

Innovation

Low

Innovation

26

Table 4: Clustering of countries based on median scores - nutrition security and its determinants

Low Political Quality High Political Quality Low Political Quality High Political Quality

High Health

Infrastructures

Azerbaijan, China,

Iran, Kazakhstan,

Lebanon, Belarus

Argentina, Armenia,

Chile, Mexico,

Mongolia, South

Africa, Tunisia,

Turkey, Ukraine

Ecuador, Venezuela,

Fiji

Brazil, Columbia,

Costa Rica,

Dominican Rep.,

Guatemala, Malaysia,

Panama, Paraguay,

Peru, Uruguay,

Thailand, Cuba,

Jamaica

Low Health

Infrastructures Thailand

High Health

Infrastructures Syria

Low Health

Infrastructures

High Health

Infrastructures

Egypt, Kyrgyz Rep.,

Lebanon Jordan Fiji Cuba, Jamaica

Low Health

Infrastructures Morocco

High Health

Infrastructures Syria Vietnam Indonesia

Low Health

Infrastructures Morocco Nicaragua, Indonesia

High Health

Infrastructures El Salvador, Georgia

Low Health

Infrastructures

High Health

Infrastructures Sri Lanka

Low Health

Infrastructures Honduras, Sri Lanka

High Health

Infrastructures

Low Health

Infrastructures

High Health

Infrastructures

Low Health

Infrastructures SudanLow

Inovation

Low Agricultural Potential High Agricultural Potential

Hig

h N

utr

itio

n S

ecu

rity

Hig

h A

gri

cultu

ral p

erfo

rman

ce

Hig

h E

con

om

ic P

erfo

rman

ce

High

Innovation

Low

Inovation

Lo

w E

con

om

ic

Per

form

ance

High

Innovation

Low

Inovation

Lo

w A

gri

cultu

ral P

erfo

rman

ce

Hig

h E

con

om

ic

Per

form

ance

High

Innovation

Low

Inovation

Lo

w E

con

om

ic

Per

form

ance

High

Innovation

27

Low Political Quality High Political Quality Low Political Quality High Political Quality

High Health

Infrastructures Belarus Namibia, Ukraine Guyana

Low Health

Infrastructures Bolivia Philippines

High Health

Infrastructures Uzbekistan

Low Health

Infrastructures

High Health

Infrastructures Namibia Guyana

Low Health

Infrastructures

High Health

Infrastructures Uzbekistan

Low Health

Infrastructures India Côte d'Ivoire

High Health

Infrastructures Botswana

Low Health

Infrastructures

High Health

Infrastructures

Low Health

Infrastructures

Kenya, Angola,

Mauritania, Nepal Malawi Sierra Leone Benin, Lesotho

High Health

Infrastructures Botswana

Low Health

Infrastructures Burundi, Togo

High Health

Infrastructures Tajikistan Cambodia

Low Health

Infrastructures

Ethiopia, Pakistan,

Yemen, Angola,

Mauritania, Nepal,

Sudan

Burkina Faso, Mali,

Mozambique,

Senegal, Tanzania,

Zambia, Malawi

Sierra Leone,

Bangladesh, Uganda

Ghana, Madagascar,

Rwanda, Benin,

Lesotho

Low Agricultural Potential High Agricultural PotentialL

ow

Nu

tritio

n S

euri

ty

Hig

h A

gri

cultu

ral p

erfo

rman

ce

Hig

h E

con

om

ic

Per

form

ance

High

Innovation

Low

Inovation

Lo

w E

con

om

ic

Per

form

ance

High

Innovation

Low

Inovation

Lo

w A

gri

cultu

ral P

erfo

rman

ce

Hig

h E

con

om

ic

Per

form

ance

High

Innovation

Low

Inovation

Lo

w E

con

om

ic P

erfo

rman

ce

High

Innovation

Low

Inovation

28

Table 5: Clustering of countries based on median scores - obesity and its potential determinants

Low Political Quality High Political Quality Low Political Quality High Political Quality

High Health

Infrastructures

Azerbaijan, Belarus,

Iran, Kazakhstan,

Lebanon

Argentina, Armenia,

Chile, Mexico, South

Africa, Tunisia,

Turkey, Ukraine

Ecuador, Venezuela,

Fiji

Columbia, Costa

Rica, Dominican Rep.,

Guatemala, Panama,

Paraguay, Uruguay,

Cuba, Guyana,

Jamaica

Low Health

Infrastructures Bolivia

High Health

Infrastructures Syria

Low Health

Infrastructures

High Health

Infrastructures Egypt, Lebanon Jordan Fiji

Cuba, Guyana,

Jamaica

Low Health

Infrastructures Morocco

High Health

Infrastructures Syria

Low Health

Infrastructures Morocco Nicaragua

High Health

Infrastructures Botswana El Salvador, Georgia

Low Health

Infrastructures

High Health

Infrastructures

Low Health

Infrastructures Mauritania Honduras, Lesotho

High Health

Infrastructures Botswana

Low Health

Infrastructures

High Health

Infrastructures

Low Health

Infrastructures Yemen, Mauritania LesothoLow Inovation

Low Agricultural Potential High Agricultural Potential

Hig

h O

bes

ity

Hig

h A

gri

cultura

l per

form

ance

Hig

h E

conom

ic P

erfo

rman

ce

High

Innovation

Low Inovation

Low

Eco

nom

ic

Per

form

ance

High

Innovation

Low Inovation

Low

Agri

cultura

l P

erfo

rman

ce

Hig

h E

conom

ic

Per

form

ance

High

Innovation

Low Inovation

Low

Eco

nom

ic

Per

form

ance

High

Innovation

29

Low Political Quality High Political Quality Low Political Quality High Political Quality

High Health

Infrastructures China Mongolia, Namibia

Brazil, Malaysia,

Peru,

Thailand

Low Health

Infrastructures Philippines Thailand

High Health

Infrastructures Uzbekistan

Low Health

Infrastructures

High Health

Infrastructures Kyrgyz Rep. Namibia

Low Health

Infrastructures

High Health

Infrastructures Uzbekistan Vietnam Indonesia

Low Health

Infrastructures India Cote d'Ivoire Indonesia

High Health

Infrastructures

Low Health

Infrastructures

High Health

Infrastructures Sri Lanka

Low Health

Infrastructures Kenya, Angola, Nepal Malawi Sierra Leone Sri Lanka, Benin

High Health

Infrastructures

Low Health

Infrastructures Burundi, Togo

High Health

Infrastructures Tajikistan Cambodia

Low Health

Infrastructures

Ethiopia, Pakistan,

Sudan, Angola,

Nepal

Burkina Faso, Mali,

Mozambique,

Senegal, Tanzania,

Zambia, Malawi

Bangladesh,

Cameroon, Guinea,

Lao, Uganda,

Cambodia, Burundi,

Togo, Sierra Leone

Ghana, Madagascar,

Rwanda, Benin

Low Agricultural Potential High Agricultural PotentialL

ow

Obes

ity

Hig

h A

gri

cultura

l per

form

ance

Hig

h E

conom

ic P

erfo

rman

ce

High

Innovation

Low Inovation

Low

Eco

nom

ic

Per

form

ance

High

Innovation

Low Inovation

Low

Agri

cultura

l P

erfo

rman

ce

Hig

h E

conom

ic

Per

form

ance

High

Innovation

Low Inovation

Low

Eco

nom

ic P

erfo

rman

ce

High

Innovation

Low Inovation

30

Table 6: Clusters of countries - food, nutrition and obesity profiles

Country Food Security

Nutrition

Security Obesity Country Food Security

Nutrition

Security Obesity Country Food Security

Nutrition

Security Obesity

Bolivia 1 1 2 Algeria 2 2 2 Dominican Republic 1 2 2

Botswana 1 1 2 Argentina 2 2 2 Ecuador 1 2 2

Lesotho 1 1 2 Armenia 2 2 2 Guatemala 1 2 2

Swaziland 1 1 2 Azerbaijan 2 2 2 Nicaragua 1 2 2

Yemen, Rep. 1 1 2 Belize 2 2 2 Solomon Islands 1 2 2

Angola 1 1 1 Chile 2 2 2 Suriname 1 2 2

Bangladesh 1 1 1 Colombia 2 2 2 Indonesia 1 2 1

Benin 1 1 1 Costa Rica 2 2 2 Mongolia 1 2 1

Burkina Faso 1 1 1 Cuba 2 2 2 Peru 1 2 1

Burundi 1 1 1 Dominica 2 2 2 Sri Lanka 1 2 1

Cambodia 1 1 1 Egypt, Arab Rep. 2 2 2 Guyana 2 1 2

Cameroon 1 1 1 El Salvador 2 2 2 Maldives 2 1 2

Central African Republic 1 1 1 Fiji 2 2 2 Mauritania 2 1 2

Chad 1 1 1 Honduras 2 2 2 Vanuatu 2 1 2

Comoros 1 1 1 Iran, Islamic Rep. 2 2 2 Gabon 2 1 1

Cote d'Ivoire 1 1 1 Jamaica 2 2 2 Ghana 2 1 1

Djibouti 1 1 1 Jordan 2 2 2 Mali 2 1 1

Eritrea 1 1 1 Kazakhstan 2 2 2 Nigeria 2 1 1

Ethiopia 1 1 1 Kiribati 2 2 2 Philippines 2 1 1

Gambia, The 1 1 1 Lebanon 2 2 2 Turkmenistan 2 1 1

Guinea 1 1 1 Libya 2 2 2 Uzbekistan 2 1 1

Guinea-Bissau 1 1 1 Mauritius 2 2 2 Sudan 1 1

Haiti 1 1 1 Mexico 2 2 2 Zimbabwe 1 1

India 1 1 1 Morocco 2 2 2 West Bank and Gaza 1

Kenya 1 1 1 Panama 2 2 2 Belarus 2 2

Korea, Dem. Rep. 1 1 1 Paraguay 2 2 2 Ukraine 2 2

Lao PDR 1 1 1 Samoa 2 2 2 Georgia 2 2

Liberia 1 1 1 South Africa 2 2 2 Afghanistan 1

Madagascar 1 1 1 St. Kitts and Nevis 2 2 2 Bhutan 1

Malawi 1 1 1 St. Lucia 2 2 2 Equatorial Guinea 1

Mozambique 1 1 1 Syrian Arab Republic 2 2 2 Myanmar 1

Namibia 1 1 1 Tunisia 2 2 2 Papua New Guinea 1

Nepal 1 1 1 Turkey 2 2 2 Somalia 1

Niger 1 1 1 Uruguay 2 2 2 Iraq 2

Pakistan 1 1 1 Venezuela, RB 2 2 2 Marshall Islands 2

Rwanda 1 1 1 Brazil 2 2 1 Micronesia, Fed. Sts. 2

Senegal 1 1 1 Cape Verde 2 2 1 Palau 2

Sierra Leone 1 1 1 China 2 2 1 Tonga 2

Tajikistan 1 1 1 Kyrgyz Republic 2 2 1 Congo, Dem. Rep.

Tanzania 1 1 1 Malaysia 2 2 1 Congo, Rep.

Timor-Leste 1 1 1 Sao Tome and Principe 2 2 1

Togo 1 1 1 Thailand 2 2 1

Uganda 1 1 1 Vietnam 2 2 1

Zambia 1 1 1

31

7. Conclusion

The aim of this typology is to guide FoodSecure partners in their case study selection. The

typology will allow the FoodSecure members to mark countries where work has already been

planned and to identify gaps in coverage. Identifying the gaps should encourage partners to

develop case studies in relation to these gaps.

In this paper, we applied two different approaches to cluster countries. The first approach

grouped countries according to their quintile score on one of the nine thematic indices. This

method provides the reader with a detailed categorization under each profile and makes

comparison among countries easier. The second approach relies on median scores under each

profile and defines each country as a weak or strong performer under each profile. This approach

allows to cluster countries on food, and nutrition security or obesity scores with its determinants.

32

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36

9. Appendix

Principal Component Analysis

Table 7: Results PCA for the food security profile

Table 8: Results PCA for the nutrition security profile

Number of obs = 113

Number of comp. = 3

Trace = 3

Rho = 1

Component Difference Proportion Cumulative

Comp1 1.85069 0.7795 0.7795

Comp2    .313993 0.1626 0.9421

Comp3 .   0.0579 1

Variable Comp2 Comp3 Unexplained

Average daily calorie intake -0.2912 0.7388 0

Share animal protein in daily intake  0.8433 -0.1018 0

Food deficit 0.4518  0.6662 0

0.5278

 -0.5934

 .487769

 .173776

Principal Components  (eigenvectors)

Comp1

0.6078

Principal components/correlation

Rotation: (unrotated = principal)

Eigenvalue

2.33845

Number of obs = 109

Number of comp. = 3

Trace = 3

Rho = 1

Component Eigenvalue Difference Proportion Cumulative

Comp1  1.95416  1.22392 0.6514 0.6514

Comp2 0.73024 0.41464 0.2434   0.8948

Comp3  .3156 . 0.1052 1

Variable Comp1 Comp2 Comp3 Unexplained

Undernourishment 0.4891 0.8397  0.2359 0

Under five mortality rate 0.6411 -0.1627   -0.7500 0

Female Aneamia 0.5914 -0.5181  0.6179 0

Principal components/correlation

Rotation: (unrotated = principal)

Principal Components (eigenvectors)

37

Table 9: Results PCA for the agricultural potential profile

Table 10: Results PCA for the agricultural performance profile

Table 11: Results PCA for the economic profile

Principal components/correlation Number of obs = 107

Number of comp. = 3

Trace = 3

Rotation: (unrotated = principal) Rho = 1.0000

Component Eigenvalue Difference Proportion Cumulative

Comp1 1.97683 1.0494 0.6589 0.6589

Comp2 0.927431 0.831688 0.3091 0.9681

Comp3 0.0957422 . 0.0319 1

Principal Components (eigenvectors)

Variable Comp1 Comp2 Comp3 Unexplained

Length of growing period 0.6882 -0.1268 -0.7144 0

Average percipitation 0.6707 -0.2644 0.693 0

% area without major soil constraints 0.2768 0.956 0.097 0

Principal components/correlation Number of obs = 113

Number of comp. = 3

Trace = 3

Rotation: (unrotated = principal) Rho = 1.0000

Component Eigenvalue Difference Proportion Cumulative

Comp1 1.64182  .802698 0.5473 0.5473

Comp2  .839124 0.320071 0.2797 0.8157

Comp3  .519053 . 0.173 1

Principal Components (eigenvectors)

Variable Comp1 Comp2 Comp3 Unexplained

Agricultural import share -0.5377  0.6990  0.4715 0

Food production per capita 0.6545 -0.0066  0.7560 0

Growth of agricultural value added per worker 0.5316 0.7151 -0.4539 0

Principal components/correlation Number of obs = 68

Number of comp. = 3

Trace = 3

Rotation: (unrotated = principal) Rho = 1.0000

Component Eigenvalue Difference Proportion Cumulative

Comp1 1.99593 1.20304 0.6653 0.6653

Comp2 0.792892 0.581717 0.2643 0.9296

Comp3 0.211175 . 0.0704 1

Principal Components (eigenvectors)

Variable Comp1 Comp2 Comp3 Unexplained

GDP per captita 0.6152 -0.4416 0.6531 0

Gini 0.4347 0.8811 0.1863 0

Women's economic opportunity index 0.6578 -0.1693 -0.734 0

38

Table 12: Results PCA for the health infrastructure profile

Table 13: Results PCA for the political profile

Table 14: Results PCA for the innovation profile

Principal components/correlation Number of obs = 113

Number of comp. = 4

Trace = 4

Rotation: (unrotated = principal) Rho = 1.0000

Component Eigenvalue Difference Proportion Cumulative

Comp1 2.58605 1.91598 0.64650 0.6465

Comp2 0.670071 0.211166 0.1675 0.814

Comp3 0.458905 0.173928 0.1147 0.9288

Comp4 0.284977 . 0.0712 1.0000

Principal Components (eigenvectors)

Variable Comp1 Comp2 Comp3 Comp4 Unexplained

Health expenditures per capita 0.488 -0.4365 0.734 0.1806 0

Sanitation infrastructure 0.5514 -0.1166 -0.2416 -0.7899 0

Water infrastructure 0.5278 -0.1762 -0.5977 0.5772 0

Hospital beds 0.4234 0.8746 0.2137 0.1011 0

Principal components/correlation Number of obs = 109

Number of comp. = 3

Trace = 3

Rotation: (unrotated = principal) Rho = 1.0000

Component Eigenvalue Difference Proportion Cumulative

Comp1 2.02397 1.36363 0.6747 0.6747

Comp2 0.660345 0.344662 0.2201 0.8948

Comp3 0.315683 . 0.1052 1

Principal Components (eigenvectors)

Variable Comp1 Comp2 Comp3 Unexplained

Democracy index 0.5623 -0.6468 0.5152 0

Control of corruption 0.6322 -0.0653 -0.772 0

Political stability 0.533 0.7598 0.3722 0

Principal components/correlation Number of obs = 88

Number of comp. = 4

Trace = 4

Rotation: (unrotated = principal) Rho = 1.0000

Component Eigenvalue Difference Proportion Cumulative

Comp1 2.88267 2.21596 0.7207 0.7207

Comp2 0.666713 0.42284 0.1667 0.8873

Comp3 0.243872 0.0371299 0.061 0.9483

Comp4 0.206742 . 0.0517 1.0000

Principal Components (eigenvectors)

Variable Comp1 Comp2 Comp3 Comp4 Unexplained

Education and skills 0.5233 -0.3619 0.0938 0.7658 0

Economic incentive regime 0.3957 0.9016 0.0996 0.1435 0

Innovation 0.5352 -0.0986 -0.7767 -0.3172 0

Information infrastructure 0.5322 -0.2154 0.6148 -0.5407 0

39

Quintile Scores

Table 15: Quintile scores by thematic profile

Country Food Security Nutrition Security Obesity Agricultural Potential Agricultural performance Economic Performance Health Infrastructure Political Index Innovation Index

Afghanistan 1 2 1 1 1

Algeria 4 4 3 1 2 2 3

Angola 1 1 2 2 2 2 2 1

Argentina 5 5 4 2 5 5 5 5 5

Armenia 4 5 4 2 5 3 4 3 5

Azerbaijan 4 3 4 2 3 3 5 2 4

Bangladesh 2 3 1 5 1 1 2 2 1

Belarus 5 4 3 5 4 5 3 5

Belize 4 4 5 4 5 4

Benin 3 1 2 3 1 1 4 2

Bhutan 1 4 4 3 5

Bolivia 2 3 4 3 4 4 2 4 3

Botswana 1 3 3 1 2 4 5 4

Brazil 5 5 3 4 5 5 5 5 5

Burkina Faso 2 1 1 2 1 1 1 3 2

Burundi 1 1 1 3 1 1 2 1

Cambodia 2 2 1 4 3 2 2 1

Cameroon 2 2 2 4 3 1 2 2 2

Cape Verde 4 4 2 1 3 5 3

Central African Republic 1 1 1 3 2 1 1

Chad 1 1 1 1 2 1 1

Chile 5 5 5 2 5 5 5 5 5

China 4 5 1 2 4 4 3 2 4

Colombia 4 4 3 5 4 5 4 4 4

Comoros 1 1 1 1 3 2

Congo, Dem. Rep. 1 1 1

Congo, Rep. 2 2

Costa Rica 4 5 4 5 5 5 5 5 5

Cote d'Ivoire 3 2 2 4 3 1 2 1 1

Cuba 5 5 4 4 4 5 5 4

Djibouti 2 2 2 1 1 3 1

Dominica 5 5 5 5 5 5

Dominican Republic 3 4 4 4 4 5 4 4 4

Ecuador 2 3 4 5 5 4 4 3 3

Egypt, Arab Rep. 5 4 5 1 3 2 4 2 3

El Salvador 3 4 4 4 3 4 3 5 4

Equatorial Guinea 2 5 3 3 1

Eritrea 1 1 1 1 1 2 1

Ethiopia 1 1 1 2 1 1 1 2 1

Fiji 5 4 5 5 4 4 3 4

Gabon 4 3 3 5 2 3 3

Gambia, The 3 1 2 3 1 3 3

Georgia 3 4 3 3 4 5 4 4

Ghana 4 3 2 3 3 3 2 5 2

Guatemala 2 3 4 5 4 5 3 4 3

Guinea 3 1 1 4 1 2 1 1 1

Guinea-Bissau 2 1 2 3 1 1

Guyana 4 3 4 5 5 4 4 4

Haiti 1 1 2 4 1 2

Honduras 3 5 4 5 3 4 3 3 2

India 2 2 1 3 3 2 2 4 3

Indonesia 3 3 1 5 3 2 3 3

Iran, Islamic Rep. 4 4 4 1 4 3 4 1 4

Iraq 5 1 3 1

Jamaica 4 5 5 4 3 4 5 5

Jordan 5 4 5 1 3 3 4 4 4

Kazakhstan 5 4 4 1 5 4 5 3 4

Kenya 1 2 1 2 2 3 1 2 2

Kiribati 4 4 5 1 2

Korea, Dem. Rep. 1 2 1 3 1

Kyrgyz Republic 4 3 3 2 4 2 4 2 3

Lao PDR 2 2 1 4 2 2 2 2 2

Lebanon 5 5 4 2 5 5 2 4

Lesotho 2 2 4 3 2 2 5 2

Liberia 1 1 1 5 1 1 3

Libya 5 4 5 1 5 2

40

Country Food Security Nutrition Security Obesity Agricultural Potential Agricultural performance Economic Performance Health Infrastructure Political Index Innovation Index

Madagascar 1 2 1 4 2 2 1 3 1

Malawi 2 2 1 3 2 2 4 2

Malaysia 5 5 2 5 5 5 4 5 5

Maldives 5 3 4 1 5

Mali 3 1 1 1 2 2 1 4 1

Marshall Islands 5 5

Mauritania 4 2 3 1 1 1 3 1

Mauritius 4 5 3 4 5 5 5

Mexico 5 5 5 2 4 5 5 4 5

Micronesia, Fed. Sts. 5 3

Mongolia 2 4 3 1 4 3 3 5 4

Morocco 5 4 3 2 4 3 3 3

Mozambique 1 1 2 3 1 2 1 4 1

Myanmar 1 4 2 1 1

Namibia 2 2 2 1 4 3 5 3

Nepal 2 2 1 3 2 3 2 1

Nicaragua 2 5 4 5 3 3 2 3 2

Niger 3 1 1 1 1 1 3

Nigeria 3 1 2 3 4 1 2

Pakistan 2 3 2 1 3 1 2 1 2

Palau 5 5

Panama 3 3 4 5 4 5 4 5 5

Papua New Guinea 3 5

Paraguay 3 4 3 3 5 4 3 3 3

Peru 3 3 3 4 4 4 3 4 4

Philippines 3 3 2 5 4 3 3 3 3

Rwanda 1 1 1 4 2 3 2 4 1

Samoa 5 5 5 3 4

Sao Tome and Principe 3 3 2 1 2

Senegal 3 2 2 2 1 2 2 4 2

Sierra Leone 1 1 2 5 1 1 3 1

Solomon Islands 3 3 5 5 2 2

Somalia 1 1

South Africa 5 4 5 2 5 5 5 5 5

Sri Lanka 2 3 1 5 2 3 4 3

St. Kitts and Nevis 3 5 5 1 5

St. Lucia 4 4 4 2 4

Sudan 2 2 1 3 1 1 1 1

Suriname 3 4 5 5 3 4 5

Swaziland 2 2 5 3 3 2 3

Syrian Arab Republic 5 4 5 2 4 3 1 2

Tajikistan 1 2 2 2 2 1 4 1 3

Tanzania 1 1 1 3 2 2 1 4 1

Thailand 4 5 2 4 5 4 4 5

Timor-Leste 1 2 1 1 4

Togo 2 2 1 3 2 1 1 2

Tonga 5 2 4

Tunisia 5 5 4 1 5 4 4 3 4

Turkey 5 5 5 2 5 4 5 4 5

Turkmenistan 4 3 2 1 5 4 1

Uganda 1 2 1 3 2 2 2 2 2

Ukraine 5 3 3 5 4 5 4 5

Uruguay 4 5 4 4 5 5 5 5 5

Uzbekistan 3 2 3 1 4 4 1 3

Vanuatu 4 3 5 4 3

Venezuela, RB 4 4 5 4 4 5 5 2 4

Vietnam 3 4 1 4 4 2 3 3 3

West Bank and Gaza 2

Yemen, Rep. 1 2 3 1 1 1 1 1 2

Zambia 1 1 1 2 2 3 2 5 2

Zimbabwe 1 2 2 1 1 2

41

Quintile Score Maps

Figure 4: Quintile score maps by thematic profile

42

Summary Statistics for Nutrition Security Profile

Table 16: Average value of profiles and underpinning variables by nutrition security quintile

Table 17: Annual growth rates between 1999-2009 by nutrition security quintile

Variable Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5

Undernourishment 29.47727 21.61818 14.13182 12.44091 11.41429

Anaemia women 49.22727 38.4 34.25 32.65455 27.67619

Child mortality 132.2045 85.8518 42.9227 29.179 16.59905

Food Security Profile 1.909091 2.666667 3.363636 3.636364 3.333333

Share of animal protein in diet 17.31818 22.95238 25.95455 26.72727 28.14286

Average daily calorie intake 2275.682 2417.182 2665.027 2848.1 2683.4

Food deficit 229 155 93.77273 80.81818 69.80952

Obesity 1.3636 1.8636 3.0455 4 3.9048

Female obesity 11.55455 19.49091 22.96364 22.76364 24.80476

Agricultural Potential Profile 3.333333 3 3 2.714286 3

Length of growing period 211.7268 184.3777 197.5021 180.5947 217.5671

Percentage without major soil constraints 26 28.21053 24.90476 24.33333 29.58824

Percipitation 1318.476 1183.455 1340.5 1089.455 1335.263

Agricultural Performance Profile 2.25 2.714286 3.380952 3.454545 3.05

Value added per worker in agriculture 1056.366 1882.574 4408.753 2549.184 3198.997

Import share of agriculture 17.64439 20.12637 13.99832 12.97146 17.79297

Food production per capita 171.2557 219.9552 255.5548 291.8263 299.8087

Economic Performance Profile 2.181818 3.076923 3.214286 3.25 3.142857

Gini 40.60217 44.14247 42.15437 42.84961 43.90819

GDP per capita 2981.585 4684.682 6856.106 6000.751 5604.159

Women economic opportunity index 42.03125 44.46667 44.13333 47.92 50.51333

Political Profile 2.6 3.4 2.95 3.3 3.277778

Political stability and violence -0.6652411 -0.2288324 -0.4791027 -0.4473278 -0.1703606

Control of corruption -0.5964118 -0.4732585 -0.6211579 -0.3476759 -0.342103

Democracy index 4.173 5.19 4.628 4.6255 5.461667

Innovation Profile 2.1875 2.8125 3.235294 3.368421 3.266667

Innovation system 2.633529 3.54875 3.608824 4.035789 3.763333

Economic incentive regime 2.812353 3.673125 3.167059 3.715789 4.598667

Education and skills 2.8375 3.050625 3.955882 3.657895 3.65

Information infrastructure 2.244706 3.623125 3.792353 3.841579 3.474

Health Infrastructure Profile 2.473684 2.9 3.3 3.210526 2.952381

Health expenditures per capita 83.71635 134.5641 191.4831 201.858 208.2055

Sanitation 50.79773 59.2 68.75455 63.32727 55.28571

Water supply 74.61136 82.40909 82.58413 81.94545 82.53016

Hospital beds 2.032142 1.958719 2.140283 2.159524 1.746945

Variable Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5

Undernourishment 4.54 5.97 1.17 7.77 -3.25

Child mortality -2.68 -2.54 -3.14 -3.16 -3.82

Share of animal protein in diet 5.10 1.32 2.39 1.74 4.03

Average daily calorie intake 0.63 0.60 0.83 0.76 0.76

Food deficit 6.96 1.37 0.51 18.23 -2.96

Value added per worker in agriculture 2.26 4.33 5.38 2.01 2.63

Food production per capita 1.29 0.92 0.97 0.77 1.85

Health expenditures per capita 13.53 10.20 8.36 12.34 4.40

Sanitation 2.54 0.78 1.10 0.98 0.72

43

Median Scores

Table 18: Median scores by thematic profile

Country Food Security Nutrition Security Obesity Agricultural Potential Agricultural Performance Economic Performance Health Infrastructure Political Index Innovation Index

Afghanistan 1 1 1 1 1

Algeria 2 2 2 1 1 1 2

Angola 1 1 1 1 1 1 1 1

Argentina 2 2 2 1 2 2 2 2 2

Armenia 2 2 2 1 2 2 2 2 2

Azerbaijan 2 2 2 1 2 2 2 1 2

Bangladesh 1 1 1 2 1 1 1 1 1

Belarus 2 2 1 2 2 2 1 2

Belize 2 2 2 2 2 2

Benin 1 1 1 2 1 1 2 1

Bhutan 1 2 2 1 2

Bolivia 1 1 2 1 2 2 1 2 2

Botswana 1 1 2 1 1 2 2 2

Brazil 2 2 1 2 2 2 2 2 2

Burkina Faso 1 1 1 1 1 1 1 2 1

Burundi 1 1 1 2 1 1 1 1

Cambodia 1 1 1 2 1 1 1 1

Cameroon 1 1 1 2 1 1 1 1 1

Cape Verde 2 2 1 1 1 2 1

Central African Republic 1 1 1 2 1 1 1

Chad 1 1 1 1 1 1 1

Chile 2 2 2 1 2 2 2 2 2

China 2 2 1 1 2 2 2 1 2

Colombia 2 2 2 2 2 2 2 2 2

Comoros 1 1 1 1 1 1

Congo, Dem. Rep. 1 1 1

Congo, Rep. 1 1

Costa Rica 2 2 2 2 2 2 2 2 2

Cote d'Ivoire 1 1 1 2 2 1 1 1 1

Cuba 2 2 2 2 2 2 2 2

Djibouti 1 1 1 1 1 2 1

Dominica 2 2 2 2 2 2

Dominican Republic 1 2 2 2 2 2 2 2 2

Ecuador 1 2 2 2 2 2 2 1 2

Egypt, Arab Rep. 2 2 2 1 2 1 2 1 2

El Salvador 2 2 2 2 1 2 2 2 2

Equatorial Guinea 1 2 1 2 1

Eritrea 1 1 1 1 1 1 1

Ethiopia 1 1 1 1 1 1 1 1 1

Fiji 2 2 2 2 2 2 1 2

Gabon 2 1 1 2 1 1 1

Gambia, The 1 1 1 1 1 1 1

Georgia 2 2 2 1 2 2 2 2

Ghana 2 1 1 2 1 1 1 2 1

Guatemala 1 2 2 2 2 2 2 2 2

Guinea 1 1 1 2 1 1 1 1 1

Guinea-Bissau 1 1 1 2 1 1

Guyana 2 1 2 2 2 2 2 2

Haiti 1 1 1 2 1 1

Honduras 2 2 2 2 1 2 1 2 1

India 1 1 1 1 2 1 1 2 1

Indonesia 1 2 1 2 2 1 2 1

Iran, Islamic Rep. 2 2 2 1 2 2 2 1 2

Iraq 2 1 1 1

Jamaica 2 2 2 2 2 2 2 2

Jordan 2 2 2 1 2 1 2 2 2

Kazakhstan 2 2 2 1 2 2 2 1 2

Kenya 1 1 1 1 1 2 1 1 1

Kiribati 2 2 2 1 1

Korea, Dem. Rep. 1 1 1 2 1

Kyrgyz Republic 2 2 1 1 2 1 2 1 2

Lao PDR 1 1 1 2 1 1 1 1 1

Lebanon 2 2 2 1 2 2 1 2

Lesotho 1 1 2 2 1 1 2 1

Liberia 1 1 1 2 1 1 2

Libya 2 2 2 1 2 1

44

Country Food Security Nutrition Security Obesity Agricultural Potential Agricultural Performance Economic Performance Health Infrastructure Political Index Innovation Index

Madagascar 1 1 1 2 1 1 1 2 1

Malawi 1 1 1 1 1 1 2 1

Malaysia 2 2 1 2 2 2 2 2 2

Maldives 2 1 2 1 2

Mali 2 1 1 1 1 1 1 2 1

Marshall Islands 2 2

Mauritania 2 1 2 1 1 1 1 1

Mauritius 2 2 2 2 2 2 2

Mexico 2 2 2 1 2 2 2 2 2

Micronesia, Fed. Sts. 2 2

Mongolia 1 2 1 1 2 2 2 2 2

Morocco 2 2 2 1 2 1 1 2

Mozambique 1 1 1 1 1 1 1 2 1

Myanmar 1 2 1 1 1

Namibia 1 1 1 1 2 2 2 2

Nepal 1 1 1 1 1 1 1 1

Nicaragua 1 2 2 2 2 1 1 2 1

Niger 1 1 1 1 1 1 1

Nigeria 2 1 1 1 2 1 1

Pakistan 1 1 1 1 1 1 1 1 1

Palau 2 2

Panama 2 2 2 2 2 2 2 2 2

Papua New Guinea 1 2

Paraguay 2 2 2 2 2 2 2 2 2

Peru 1 2 1 2 2 2 2 2 2

Philippines 2 1 1 2 2 2 1 1 2

Rwanda 1 1 1 2 1 1 1 2 1

Samoa 2 2 2 1 2

Sao Tome and Principe 2 2 1 1 1

Senegal 1 1 1 1 1 1 1 2 1

Sierra Leone 1 1 1 2 1 1 1 1

Solomon Islands 1 2 2 2 1 1

Somalia 1 1

South Africa 2 2 2 1 2 2 2 2 2

Sri Lanka 1 2 1 2 1 2 2 1

St. Kitts and Nevis 2 2 2 1 2

St. Lucia 2 2 2 1 2

Sudan 1 1 1 1 1 1 1 1

Suriname 1 2 2 2 2 2 2

Swaziland 1 1 2 1 2 1 1

Syrian Arab Republic 2 2 2 1 2 2 1 1

Tajikistan 1 1 1 1 1 1 2 1 1

Tanzania 1 1 1 1 1 1 1 2 1

Thailand 2 2 1 2 2 2 2 2

Timor-Leste 1 1 1 1 2

Togo 1 1 1 2 1 1 1 1

Tonga 2 1 2

Tunisia 2 2 2 1 2 2 2 2 2

Turkey 2 2 2 1 2 2 2 2 2

Turkmenistan 2 1 1 1 2 2 1

Uganda 1 1 1 2 1 1 1 1 1

Ukraine 2 2 1 2 2 2 2 2

Uruguay 2 2 2 2 2 2 2 2 2

Uzbekistan 2 1 1 1 2 2 1 1

Vanuatu 2 1 2 2 1

Venezuela, RB 2 2 2 2 2 2 2 1 2

Vietnam 2 2 1 2 2 1 2 1 1

West Bank and Gaza 1

Yemen, Rep. 1 1 2 1 1 1 1 1 1

Zambia 1 1 1 1 1 1 1 2 1

Zimbabwe 1 1 1 1 1 1

45

Data

Criteria for variable selection

The selection of variables entering a principle component analysis is bound to be subjective, as it

does not follow theory but the assessment of the researcher as to the information content of

variables and how they link, or not, with the concept to be illustrated. There are of course many

variables not presented in this typology, which can describe characteristics of a countries

agricultural sector or the functioning of its political/governance system. As PCA leads to very

unconvincing results when applied to a non-discriminative set of variables, we tried to apply

some method in our final variable selection.

The variables entering the various indices of FNS determinants were selected based on: (1)

economic rationale. (2) data availability; (3) the rule of thumb which suggests to limit the

number of variables entering the analysis to somewhere between 1/10 and 1/5 the number of

observations (Hair et al., 2006); (4) the need to avoid including variables who duplicate

information content (e.g. agricultural land per capita and crop land per capita are very similar for

most countries); (5) our perception that in most cases, relative variables were providing better

information than absolute ones (hence elements such as size of the country or its population are

only indirectly captures in other variables expressed in relative terms such as per hectare or per

capita, growth rates, shares of total, etc.); (6) our perception that in the absence of a solution to

describe the dynamic evolution of the different variables across countries (in a panel way), we

should work from average values of a given period of time8.

8 This has the added benefit to increase data availability: our definition of a 5 year average in this context is often

simply that of the average of the available observations over the last 5 years. Further, this also helps to mitigate the

impact of extreme values in specific years.

46

Definition and source of the variables selection

Food Security Profile Definition Available

Years Data Source

Average daily calorie intake The total available supply during that year expressed

in kcal/per person/ a day.

2005-2009 FAO (2013) - FAOSTAT

Average daily calorie per capita deficit

of undernourished

The depth of the food deficit (kcal/caput/day)

indicates how many calories would be needed to lift

the undernourished from their status, everything else

being constant.

2007-2009

FAO, WFP and IFAD (2012b)

Share of animal protein in average daily

calorie intake diet

The share on animal proteins is calculated in the total

daily calorie intake.

2007-2009

FAO (2013) - FAOSTAT

Nutrition Security Profile

Under-five mortality rate Child mortality rate before the age of 5 (per 1,000 live

births)

2005-2009 UN Inter-agency Group for Child

Mortality Estimation (2013)

Percentage of the population who are

undernourished

Proportion of the population estimated to be at risk of

caloric inadequacy.

2007-2009

FAO, WFP and IFAD (2012b)

Percentage of women who suffer from

anemia

% of women at reproductive age with iron deficiency

(Hg < 120 g/L)

2008 WHO (2008) - Global database on

Anaemia

Obesity Profiles

Obesity of female adults The percentage of female adults with a BMI greater

than or equal to 30.

2008 WHO (2012) - Global Database on

Body Mass Index: Source

Agricultural Potential Profile

Length of growing period

Number of days in a year in which the moisture

availability and temperature are favorable for growth

of crop and pasture.

2013 FAO (2013) - Global Agro-

Ecological Zones (GAEZ)

47

Soil fertility Percentage of soil that has no major soil constraints

based on 8 FCC criteria.

2002 FAO (2002)

Precipitation Total yearly precipitation (in mm) 2005-2009 FAO (2013) - Global Agro-

Ecological Zones (GAEZ)

Agricultural Potential Profile

Value added per worker in agriculture

Value added in agriculture is measured as the output

of the agricultural sector less the value of

intermediate inputs in constant U.S. Dollars.

2005-2009

World Bank (2013a) - WDI

Import share of agriculture products Percentage of agricultural imports to total

merchandise exports

2005-2009 FAO (2013) - FAOSTAT

Food production per capita Total value of annual food production divided by the

total population (Int $)

2005-2009 FAO (2013) - FAOSTAT

Economic Performance Profile

GINI

The GINI measures to which extent the distribution of

income diverges from a perfectly equal income

distribution. A score of 0 represents perfect equality,

while a score of 100 implies perfect inequality.

2005-2009

World Bank (2013a) - WDI

GDP per capita Gross Domestic Product per capita expressed in $. 2006-2010 Heston et el. (2012) - Penn World

Tables

Women economic opportunity index

The Women’s economic opportunity index measures

specific attributes of the environment of women

employees and entrepreneurs. The index ranges from

0 to 100.

2011

Economist Intelligence Unit

(2011a)

Political Profile

Control of corruption

Control of Corruption captures perceptions of the

extent to which public power is exercised for private

gain, including both petty and grand forms of

corruption, as well as "capture" of the state by elites

and private interests. A country's score ranges from

approximately -2.5 to 2.5.

2007-2011

World Bank (2013b) - Worldwide

Governance Indicators

48

Political stability and absence of

violence and terrorism

Political Stability and Absence of Violence/Terrorism

captures perceptions of the likelihood that the

government will be destabilized or overthrown by

unconstitutional or violent means, including

politically-motivated violence and terrorism. A

country's score ranges from approximately -2.5 to 2.5.

2007-2011

World Bank (2013b) - Worldwide

Governance Indicators

Democracy index

The Democracy Index provides a snapshot of the state

of democracy and is based on electoral process and

pluralism, functioning of government, political

participation, political culture, and civil liberties. The

EIU democracy index ranges from 0 to 10.

2011

Economist Intelligence Unit

(2011b)

Innovation Profile

Innovation system

Innovation system is a composite index based on

royalty payments and receipts, patent count and

journal articles. The index ranges from 0 t 10.

2012 World Bank (2012b) – Knowledge

Assessment Methodology

Economic incentive regime

Economic incentive regime is a composite index

based on tariff and non-tariff barriers, regulatory

quality and the rule of law. The index ranges from 0

to 10.

2012

World Bank (2012b) – Knowledge

Assessment Methodology

Education and skills

Education and skills is a composite index based on

adult literacy rates, secondary school enrolment, and

tertiary school enrolment. The index ranges from 0 to

10.

2012

World Bank (2012b) – Knowledge

Assessment Methodology

Information infrastructure

Information infrastructure is a composite index based

on telephone, computer, and internet penetration. The

index ranges from 0 to 10.

2012 World Bank (2012b) – Knowledge

Assessment Methodology

Health Infrastructure Profile

Health expenditures

Total health expenditure is the sum of public and

private health expenditures as a ratio of total

population. Data are in current U.S. dollars.

2005-2009

World Bank (2013a) - WDI

49

Sanitation The percentage of the population with access to

adequate sanitation facilities

2005-2009 World Bank (2013c) - Health

Nutrition and Population statistics

(HNP)

Water The percentage of the population with access to

improved water resources

2005-2009 World Bank (2013c) - Health

Nutrition and Population statistics

(HNP)

Hospital beds

Hospital beds (per 1,000 people) include inpatient

beds available in public, private, general, and

specialized hospitals and rehabilitation centers. In

most cases beds for both acute and chronic care are

included.

2005-2009

World Bank (2013a) - WDI

This project is funded by the European Union under the 7th Research Framework Programme (theme SSH) Grant agreement no. 290693

The FOODSECURE project in a nutshell Title FOODSECURE – Exploring the future of global food and nutrition security

Funding scheme 7th framework program, theme Socioeconomic sciences and the humanities

Type of project Large-scale collaborative research project

Project Coordinator Hans van Meijl (LEI Wageningen UR)

Scientific Coordinator Joachim von Braun (ZEF, Center for Development Research, University of Bonn)

Duration 2012 - 2017 (60 months)

Short description

In the future, excessively high food prices may frequently reoccur, with severe

impact on the poor and vulnerable. Given the long lead time of the social

and technological solutions for a more stable food system, a long-term policy

framework on global food and nutrition security is urgently needed.

The general objective of the FOODSECURE project is to design effective and

sustainable strategies for assessing and addressing the challenges of food and

nutrition security.

FOODSECURE provides a set of analytical instruments to experiment, analyse,

and coordinate the effects of short and long term policies related to achieving

food security.

FOODSECURE impact lies in the knowledge base to support EU policy makers

and other stakeholders in the design of consistent, coherent, long-term policy

strategies for improving food and nutrition security.

EU Contribution

€ 8 million

Research team 19 partners from 13 countries

FOODSECURE project office LEI Wageningen UR (University & Research centre) Alexanderveld 5 The Hague, Netherlands

T +31 (0) 70 3358370 F +31 (0) 70 3358196 E foodsecure@wur.nl I www.foodscecure.eu