Thematic Guidelines

Household Food Security Profiles

April 2005

VAM Analytical Approach

ODAV (VAM) – WFP, Rome

Prepared by Annalisa Conte For any questions, queries and feedback please contact the following: Annalisa Conte, Chief of VAM Unit annalisa.conte@wfp.org

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Table of Contents

Introduction

Section I – Concepts and definitions 1

1.1 Defining food (in)security 1

1.2 Defining vulnerability 1

1.3 Poverty, vulnerability and food insecurity 2

Section II - WFP/VAM Analytic approach 3

2.1 Guiding questions 3

2.2 Measuring food insecurity and vulnerability 3

2.3 The VAM Analytic approach: Overview and Rationale 3

Section III – Creating household food security profiles 5

3.1 What information to household food security profiles provide? 5

3.2 Constructing household food security profiles 53.2.1 Principal Component Analysis: analyzing relationships among variables 53.2.2 Cluster Analysis: exploring the distribution of principal components 7

Section IV – Indicators used in household food security profiles 9

4.1 Selecting indicators: general guidance 9

4.2 Food access: consumption and food sources 94.2.1 Food consumption indicators as proxy measures of food access 94.2.2 Measuring dietary diversity and food frequency 104.2.3 Meal frequency 10

4.3 Food and non-food expenditures 114.3.1 Measuring and interpreting food and non-food expenditures 114.3.2 Comparing food and non-food indicators 12

4.4 Income 12

4.5 Assets: availability and ownership 13

Section V – Analysis of indicators for household food security profiles 15

5.1 Analyzing food consumption data to produce clusters 155.1.1 Setting up the data in the data set: food consumption matrix 155.1.2 Analyzing food consumption data using Principal Component Analysis 165.1.3 Analyzing food consumption using cluster analysis 17

5.2 Interpretation of food consumption analysis 175.2.1 Determining the minimum food intake threshold/benchmark 175.2.2 Comparing cluster to the minimum food intake threshold/benchmark 175.2.3 Why use PCA and cluster analysis when food intake scores can be calculated? 185.2.4 Incorporating data on food sources 19

5.3 Analyzing food and non-food expenditures to produce clusters 205.3.1 Setting up the dataset: food and non-food expenditures 205.3.2 Analyzing food and non-food expenditures using PCA 205.3.3 Analyzing food and non-food expenditures using cluster analysis 215.3.4 Interpreting food and non-food expenditure analysis 21

5.4 Combining expenditure profiles with food consumption profiles 225.4.1 Constructing quintiles for absolute expenditure data 225.4.2 Accounting for food availability from own production 23

5.5 Incorporating other factors into HFSPs: income and asset data 235.5.1 Sources of income and their share of contribution to household’s total income 245.5.2 Assets and durable goods ownership 24

II

Introduction

In early 2004, there was an external audit of WFP’s Vulnerability Analysis and Mapping(VAM) Unit activities around the world. From this audit came several observations andrecommendations that provided valuable inputs on how VAM can standardize and improveits work within a de-centralized management system and across a range of contexts withvaried information requirements. In April 2004, VAM convened a Global Meeting in Dakar,Senegal. During the meeting, VAM and program staff from all regional offices and severalcountry offices agreed that there was a need for guidelines to be produced from VAM HQ inseveral topical areas. One of these thematic areas was household food security profiling,being this the basis for an analysis of the role of food aid.

From some of the food security and vulnerability surveys carried out between 2001 and2003, it was recognized that data collected at household level was providing a range ofdetailed information helping VAM to identify several features of food security.

Although the focus of WFP-VAM studies remains:

1. Who are the food insecure?

2. How many are they?

3. Where do they live?

4. Why are they food insecure?

5. Does food aid have a role to play?

The comprehensive nature of the data collected through these surveys provides usefulinsights also on the characteristics of the food secure households, and allows for an easycomparison between different household typologies/profiles. Clearly, the comparativeanalysis of household typologies contributes to identify the underlying causes of theprevailing food insecurity among some households as well as the factors of success amongothers.

What these guidelines can do:

• Provide the definition of food security and its components.

• Provide the definition of vulnerability and its relationship with poverty.

• Identify the constraints in measuring food security and vulnerability.

• Explain why it is important for VAM to create and analyse household profiles.

• Present the fundamentals of multivariate analysis and how these techniques can beapplied for food security and vulnerability analysis.

• Present the key indicators used to create household food security profiles.

• Explain how to analyse these indicators using Principal Component Analysis andCluster Analysis.

• Provide hints on how to interpret the results of Cluster Analysis.

• Provide guidance on how to link the results of separate Cluster analyses in order tobetter characterize household groups.

What these guidelines cannot do:

• Substitute a statistics manual and teach the reader multivariate analysis.

• Provide prescriptive steps on how to conduct Principal Component Analysis and ClusterAnalysis.

• Create expertise in household food security data collection and analysis.

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Section I – Concepts and definitions

In this section, the basic concepts and definitions of food security and vulnerability will bepresented. A discussion of the relationship between these concepts and poverty is alsoprovided.

1.1 - Defining food (in)security

At the 1996 World Food Summit (WFS) it was agreed that food security exists when:

“all people, at all times, have physical and economic access to sufficient, safe andnutritious food to meet their dietary needs and food preferences for an active andhealthy life.” (CFS, 1996)

This definition of food insecurity incorporates three dimensions or elements:

• Food Availability is the amount of food that is physically present in a country or areathrough all forms of domestic production, commercial imports and food aid. (WFP, EFSAHandbook, 2004)

• Food Access is a household’s ability to regularly acquire adequate amounts of foodthrough a combination of their own stock and home production, purchases, barter,gifts, borrowing or food aid. (WFP, EFSA Handbook, 2004)

• Biological Utilization of Food1 refers to: (a) households’ use of the food to which theyhave access, and (b) individuals’ ability to absorb nutrients – the conversion efficiencyof food by the body. (WFP, EFSA Handbook, 2004)

The FIVIMS initiative uses similar definitions in its definitions of food insecurity and food-insecure people:

“Food insecurity exists when people are undernourished as a result of the physicalunavailability of food, the lack of social or economic access to adequate food,and/or inadequate food utilization. “

“Food-insecure people are those individuals whose food intake falls below theirminimum calorie (energy) requirements, as well as those who exhibit physicalsymptoms caused by energy and nutrient deficiencies resulting from an inadequateor unbalanced diet or from the body's inability to use food effectively because ofinfection or disease.”

1.2 - Defining vulnerability

The term food security (defined above) describes a condition at a given point in time. Bycontrast, the term vulnerability is used to describe the level of risk for future foodinsecurity.

FIVIMS defines vulnerability as:

“the full range of factors that place people at risk of becoming food-insecure. Thedegree of vulnerability of individuals, households or groups of people is determinedby their exposure to the risk factors and their ability to cope with or withstandstressful situations.”

Recent studies on vulnerability suggest that the concept can be expanded to capture amore complex relationship between risks, ability to cope, and actions taken before, duringand after shocks that affect food security. Vulnerability, when viewed in relation to theprobability of experiencing welfare loss caused by uncertain events, not only depends onexposure to risks and the ability to cope with and withstand stressful situations (FIVIMSdefinition), but also on the ability to reduce risks before a shock occurs (proactive) andrespond effectively during and after they occur (reactive). Not surprisingly, poor people

1 Refer to VAM Guidelines – Nutrition and Health

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tend to be more vulnerable because they have fewer means, resources and options torespond proactively to reduce risks and respond reactively once shocks occur.

1.3 - Poverty, vulnerability, and food insecurity

It is important to recognize that, although often related, the terms poverty and foodinsecurity describe different conditions that may have different causes. A commonassumption is that addressing poverty will reduce vulnerability to future food insecurity oralleviate current food insecurity. Despite often being true, this relationship betweenpoverty and vulnerability/food insecurity is not always direct (i.e. cause and effect) asincome poverty does not always result in reduced food availability or access and,therefore, not all poor are food insecure.

Furthermore, several recent World Bank papers on social protection2 highlight the fact thatthe poor are not a homogeneous group. For WFP’s programming purposes it is necessaryto carefully distinguish between the two types of poverty:

• Chronically poor (or the hungry poor) are those that are poor and food insecure forwhich long-term policies and programs should be designed. Food-aid has acomparative advantage in addressing food insecurity among this group.

• Transient poor (or vulnerable) for whom safety net programs are appropriate and foodaid might have a role to play as a supplementary transfer to boost current consumptionand enhance future productivity.

2 Holzmann and others, World Bank, 2000

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Section II – WFP/VAM Analytic Approach

In this section, the approach used by WFP/VAM to analyze food security and vulnerabilityis described in terms of guiding questions and complexity of measurement. An overview ofthe approach and the rationale for its use are also provided.

2.1 - Guiding questions

The VAM approach to food security and vulnerability analysis incorporates five guidingquestions with the aim of providing WFP decision makers with the information they need todesign appropriate programmatic responses.

• Who are food insecure? (What are their characteristics?)

• How many are they?

• Where do they live?

• Why are they food insecure? (causal analysis)

• Does food aid have a role to play?

2.2 - Measuring food insecurity and vulnerability

Given the complexity and multi-dimensional nature of food security and vulnerability(described in Section I), no single indicator provides a comprehensive measure of eithercondition.

Each element of food security (availability, access, and utilization) is also difficult tocapture in a single, definitive measure as each of these three factors are themselvescomplex and multi-dimensional. Moreover, many of the indicators used to measure eachof these elements of food security lack benchmarks (or agreed upon cut-off values) fordetermining whether food is definitively available/not available, accessible/not accessible,or utilized/not utilized. As a result, several proxy and outcome indicators are used incombination to measure each element of food security.

Like food security, vulnerability is difficult to measure. In addition to difficulties in definingthe measures to capture the complexity and multi-dimensional nature of vulnerability andthe lack of benchmarks for these measures (e.g. the same difficulties encountered inmeasuring food security), the range of shocks and the risks related to them furthercomplicate the measurement of this condition. A simple typology of shocks (covariate vs.idiosyncratic) is helpful for conceptualizing key categories within this range.

Covariate shocks affect a population within a defined area (e.g. droughts, floods, civil war,etc.). However the effect of such an event on people/households within the affected areavaries considerably owing to the response options/capacity corresponding with theirlivelihoods.

Idiosyncratic shocks are selective and affect only some households or individuals in acommunity. Examples include changes in market prices or terms of trade,morbidity/mortality among primary or secondary income earners, or border closures thatcut-off trade routes for a particular livelihood group within a larger community. Researchsuggests that, with the exception of large-scale disasters, these types of shocks pose aneven greater threat to household/individual food security than covariate shocks3.

2.3 – The VAM analytic approach - Overview and Rationale

The VAM analytic approach describes household food security and vulnerability bydeveloping household food security profiles (HFSPs) for different groups (within a largerpopulation) that share similar characteristics and outcomes related to food security. Theintent is to describe household food security and vulnerability in terms of itscharacteristics, rather than attempting to rank different situations of food insecurity.

3 Morduch (1991) showed that even in highly risk-prone semi-arid tropics in south India, as much as75 to 96% of the variance in household income is attributable to idiosyncratic shocks. Tesliuc andLindert (2002) reported the covariate and idiosyncratic risky events in Guatemala, where for mostshocks only 10 percent of the households reported experiencing the shock.

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Once groups that are similar in terms of key characteristics and outcomes related to foodsecurity have been identified, the severity of food insecurity among each group isestimated. Furthermore, the clustering of households into groups that share key foodsecurity characteristics and outcomesallows WFP/VAM to make causalinferences about the underlying causes offood insecurity in each group.

This approach recognizes that theseverity, causes, and threats to foodinsecurity within a population are rarely, ifever, uniform across all households. Theuse of household food security profiles notonly captures the diversity of experiencein a given population, but providesessential information for designingappropriate responses to food insecurityfor meaningful sub-groups within apopulation. A combination of qualitativeand quantitative methods and informationare used to develop and analyze the foodsecurity profiles.

The use of an index

Although food security/vulnerability indexeshave been used for geographic targeting(particularly in the 1980’s), VAM does notconsider this approach a best practice.

The creation of an index is inherently subjectiveand does not provide a meaningful andobjective benchmark to delineate the foodsecure from the food insecure, the vulnerablefrom those who are not vulnerable.

Furthermore, the use of such indices does notprovide analytic leverage for understanding theunderlying causes of food insecurity/vulnerability and, as a result, fails to answerthe fundamental programmatic question forWFP and its partners:

Does food aid have a role to play?

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Section III - Creating household food security profiles

This section describes the techniques (multivariate statistics) used to develop thehousehold food security profiles (HFSPs) described in Section II. The description isintended to provide a conceptual overview of the approach and the steps involved so thatreaders can interpret HFSPs and understand how they are created. Readers wishing toapply these techniques will require appropriate technical assistance.

3.1 - What information do household food security profiles provide?

Despite the complexities involved in measuring food security and vulnerability (Section II)it is relatively easy to identify the worst-off households (the most food insecure) and thebest-off households (most food secure) using a combination of indicators (e.g.convergence of indicators, meaning all indicators suggest a household is food insecure orall indicators suggest a household is food secure). However, the majority of householdswithin a given population are likely to fit somewhere in between these extremes (e.g.indicators provide a mixed picture of a household’s food security status). Furthermore,this middle group is rarely, if ever, homogenous in terms of food security characteristicsand outcomes.

WFP/VAM HFSPs provide a means of identifying meaningful sub-groups within thepopulation by clustering households that share similar food security characteristics andoutcomes (e.g. livelihoods, access to food, utilization of food, etc.), as well as therelationships between these characteristics and outcomes. As a result, HFSPs provide ameans of differentiating among the middle group described in the previous paragraph, aswell as identifying the most and least food insecure groups and their characteristics.

Once developed, HFSPs describe and tell a story about each of these groups; the structuralconstraints they face (e.g. chronic malnutrition, illiteracy, disabilities, etc.), what they do(e.g. livelihoods), where they live and why they are characterized by specific weaknessesor strengths. By providing a comprehensive description of each group, HFSPs provide ameans for comparative analysis; identifying key differences between groups and makinginferences about the relationship between these differences and variable food securityoutcomes.

3.2 - Constructing household food security profiles (HFSPs)

Household food security profiles are created in a two-step process. First, the indicatorsselected for inclusion in the food security analysis are processed using principal componentanalysis (PCA) techniques. Second, cluster analysis is used to translate this informationinto clusters or groups of households that share key characteristics and outcomes relatedto food security.

3.2.1 - Principal Component Analysis: analyzing relationships among variables

A domain of statistics called factor or multivariate analysis offers several techniques formulti-dimensional data analysis in order to capture the essence of the relationship amongvarious indicators of food security4.

Principal Component Analysis (PCA) is one technique of multivariate analysis that appliesto continuous variables. The objective of PCA is to identify and describe the underlyingrelationships amongst the variables by creating new indicators (called ‘factors’ or ‘principalcomponents’) that capture the essence of the associations between variables.

Although a single PCA can be applied to food security indicators in general (covering foodavailability, access, utilization, and even risk/vulnerability), the objective of the WFP/VAMapproach (identifying the optimal description of household food security status byexamining three dimensions of food security: availability, access, and utilization) requiresthat each of these dimensions of food security (and even sub-categories within them, suchas food consumption) are treated separately using PCA.

4 This type of analysis can be applied to all sorts of data (e.g. agriculture production, expenditures,nutrition, etc.) and to various aggregations or units of analysis (e.g. geographic area, households,individuals, etc.). For WFP/VAM, the primary unit of analysis used is households.

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Example of Principal Component Analysis (PCA)

Suppose you have several different variables relevant to food security. If you couldsimultaneously envision all variables, then there would be little need for ordinationmethods. However, with more than three dimensions, we usually need a little help.PCA takes the cloud of data points that depict the relationship between variables, androtates it such that the maximum variability is visible.

In this example, we take a simple set of 2-D data and apply PCA to determine theprincipal axes. Although the technique is used with many dimensions, 2 dimensionaldata makes it simpler to visualize (Graph 1). The Principal Component Analysis isperformed on these data and the correlation matrix is calculated. The PrincipalComponents are calculated from the correlation matrix.

Principal Components Analysis chooses the first PCA axis as that line that goes throughthe centroid, but also minimizes the square of the distance of each point to that line.Graphically, the first principal component lies along the line of greatest variation and it isas close to all of the data as possible (red line in Graph 2). The second PCA axis alsomust go through the centroid, and also goes through the maximum variation in the data,but with a certain constraint. It must be completely uncorrelated i.e. at right angles, or"orthogonal" to PCA axis 1 (green line in Graph 2).

PCA is essentially a process of data reduction. A series of variables measuring a particularcategory of behavior (e.g. food consumption) are optimized into principal componentscapturing the essence of the relationships among initial variables of this behavior. Eachprincipal component is thus a new indicator that represents the “best” summary of thelinear relationship among the initial variables.

PCA yields as many principal components as there are initial variables. However, thecontribution of each principal component in explaining the total variance found amongsthouseholds will progressively decrease from the first principal component to the last. As aresult, a limited set of principal components explain the majority of the matrix variabilityand principal components with little explanatory power can be removed from the analysis.The result is data reduction with relatively little loss of information.

Graph 1 -Correlationbetween twovariables

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Cluster Analysis (from Statsoft, Inc.)

Cluster Analysis is the name given to a collectionof diverse techniques (algorithms and methods)for grouping objects of similar kind intorespective categories.

It is an exploratory data analysis tool which aimsat sorting different objects into groups in a waythat the degree of association between twoobjects is maximal if they belong to the samegroup and minimal otherwise.

Most standard clustering methods fall into one oftwo categories: a) partition methods; and b)hierarchical methods.

VAM uses the partition methods where everydata sample (e.g. household) is initially assignedto a cluster in some random way. Samples (e.g.households) are then iteratively transferred fromcluster to cluster until some criterion function isminimized. This method creates separate,compact, mutually exclusive clusters.

By multiplying the original data-set by the principal components, the data is rotated so thatthe components form the new perpendicular axes and the objects lying exactly on the axeshave now only one coordinate (e.g. are now captured by one variable). PCA reduces thedimensionality of the data while retaining the most information.

(Source http://www.eng.man.ac.uk/mech/merg/Research/datafusion.org.uk/techniques/pca.html)

3.2.2 - Cluster analysis: exploring the distribution of principal components amonghouseholds

The second phase of the analysis consists of exploring the distribution of the principalcomponents among the units of analysis. Although the units of analysis can beadministrative or geographic regions,individuals, or households, for WFP/VAMthe unit of analysis is usuallyhouseholds.

Cluster analysis provides a means ofidentifying and clustering householdscharacterized by very similar patterns asdescribed by the principal componentindicators developed in the previousstep. Clustering methods use thesimilarities or distances between objects(i.e. households) when forming theclusters. These similarities are a set ofrules that serve as criteria for groupingor separating households and can bebased on a single principle component ormultiple principle components. Eachprincipal component included in thecluster analysis represents a rule orcondition for grouping households.

The most straightforward way of computing similarities between households in a multi-dimensional space (defined by principle components included in the analysis) is to computeEuclidean distances. If the space is two or three dimensional, the Euclidean distance is theactual geometric distance between households (as if measured with a ruler).

The highest similarity possible is zero distance between households (e.g. households areexactly the same). However, in practice clustering only those households that are exactly

Graph 2 -Creation ofthe principalcomponents

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the same would result in a large number of clusters of very small size. It is much moreuseful to identify a limited number of clusters that contain households that are similar, butnot exactly the same. To this end, cluster analyses (performed by statistical software)involve a series of iterations that creates mutually exclusive clusters by obtaining thelowest dispersion among households belonging to each cluster (e.g. grouping togetherhouseholds that are similar as indicated by the small geometric distance between them).

3.2.2.1 - How many clusters are there in the data?

A given dataset does not contain a definitive number of clusters. First, because clusteranalysis involves a series of iterations performed by statistical software, there will be somevariance in the number of clusters and assignment of particular households to clusterseach time the analysis is run. Second, several different methods and algorithms can beused to produce clusters and the number of clusters produced will vary depending on thetype of clustering method used. VAM uses the partition method with a random selection ofthe initial centers. The best two or three partitions are then cross-tabulated to createstable clusters, i.e. groups of households that consistently group together.

3.2.2.2 - Measuring the dispersion or compactness of each cluster (inertia)

The measurement of dispersion or compactness of each cluster is inertia. The degree ofinertia within clusters and among clusters provides a useful means of deciding the finalnumber of clusters to use in developing HFSPs.

There are no standard thresholds indicating what level of inertia is good, acceptable orpoor and the final decision remains with the analyst. However, the analyst will bear inmind that the ratio between the inertia of the overall cloud (the dispersion found among allhouseholds in the sample) and the inertia of each cluster should be maximized. By doingso, it is ensured that the similarly among households belonging to a same cluster (e.g.within clusters) is high, while the similarity between clusters is very low (e.g. maximizingintra-cluster homogeneity and inter-cluster heterogeneity).

3.2.2.3 - Clusters and household food security profiles

The final output of a cluster analysis is a table presenting for each cluster the “average”values of the initial indicators. These averages should be interpreted as a profile ratherthan one by one as they represent the average characteristics of the units belonging to thecluster.

Once the final clusters have been identified, they then serve as a basis for developinghousehold food security profiles (HFSPs). A HFSP is developed for each cluster bycomputing the mean, median, or prevalence (above or below a threshold or cut-off point)for various indicators associated to the households included in that cluster. In this way,the HFSP describes the average or typical experience among similar households (e.g.households that are similar in terms of food security characteristics and outcomes asdefined by the PCA and Cluster analyses). Clusters can then be validated by comparingthe profiles that correspond with each cluster.

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Section IV - Indicators used in household food security profiles

This section provides an overview of the indicators typically used in conducting theprincipal component analysis (PCA) in order to create the household food security profiles(HFSPs).

4.1 - Selecting indicators: general guidance

The selection of indicators to include in a household survey is one of the most crucial stepsinvolved in the development of WFP/VAM household food security profiles. Given thecomplexities of measuring food security and vulnerability (see Section 2.2), the choicesbetween the right and wrong indicators are contingent on the context in which the surveywill be conducted and, as a result, indicators must be assessed for appropriateness on acase-by-case basis.

The food security definition (Section 1.1) provides a basis for selecting an appropriaterange of indicators needed to capture availability, access, and utilization. Yet the choice ofa particular indicator or indicators among the range of possible indicators that can be usedto measure each of these elements of food security relies on the expertise of the personsdesigning the survey instrument.

For example, limited access to food can be measured in terms of quantity (Kcal intake) orquality (lack of diversity5) or both. Access to limited quantity can be due to irregularmarket supplies, difficult physical access to markets, and inadequate purchasing power orown production in the absence of financial means. Poor dietary diversity, food taboos orother social food restrictions can contribute to limited quality of food intake even in thepresence of abundant food availability and access. Similarly, lack of hygiene, sanitationand clean water and/or disease can lead to negative food security and nutritional outcomeseven where food is available and accessible.

This example underscores the fact that the range of indicators needed to measure foodsecurity is great. Furthermore, the range of indicators used in a particular survey ofteninvolve different levels of measurement. For example, food access may be assessed at thehousehold level while food utilization is often measured at the individual level.

The following sections provide detailed information on indicators that are often included insurveys aimed at developing household food security profiles.

4.2 - Food access: consumption and food sources

4.2.1 - Food consumption indicators as proxy measures of food access

In an ideal world, food consumption would be measured through a detailed foodconsumption survey measuring by type and quantity of foods (e.g. 24 hour food intakerecall). This type of method yields valuable data on both caloric (macro-) and micro-nutrient intake. However, there are several constraints that prevent widespread use ofthis method in WFP/VAM food security surveys: it is very expensive and time consuming tocollect for a large enough sample of households to yield reasonably precise populationestimates and requires high levels of technical skills both in data collection and analysis(FANTA, 2002).

A recent study conducted by FANTA (2002) demonstrated that the inclusion of dietarydiversity and food frequency indicators in a household survey provide a faster and lessexpensive alternative to detailed food consumption surveys6. Furthermore, the dataproduced by these indicators require less technical skill to collect and analyze. The

5 Indicates poor micro-nutrient intake6 Both dietary diversity and food frequency (measures of the number and frequency of different foodsor food groups consumed) were shown to be highly correlated with food consumption and caloricavailability (FANTA, 2002)

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WFP/VAM has adopted this approach to analyze food consumption, assess food shortfallsand measure food access7

.

4.2.2 - Measuring dietary diversity and food frequency

To develop HFSPs information on dietary diversity and the consumption frequency of stapleand non-staple foods is collected at household level. Data on the main sources of accessto these foods are also gathered in order to obtain a more comprehensive understandingof the household food availability and access.

Box 1 provides an example survey module designed to collect data on a household’stypical food consumption diversity and frequency, as well as the sources of staple and non-staple foods8. Particular attention should be paid to the number of food items that areconsumed within each of the six main food groups (carbohydrate, animal products, oilsand fats, fruits and vegetable, legumes and oilseeds, tubers and roots. and the number ofdays these items were consumed (a measure of frequency/regularity of consumption).

Changes in dietary diversity (defined as the number of unique foods consumed) provide anindication of changes in household per capita consumption and household per capita caloricavailability. These, in turn, provide a proxy measure of changes in food access, one of thethree components of food security (FANTA, 2002). As a result, dietary diversity not onlyprovides a good proxy measure of food access in cross-sectional surveys (one point intime), but may also be used in monitoring activities (measuring change by time seriescomparisons).

4.2.3 - Meal frequency

In addition to dietary diversity and food frequency, the number of meals consumed byadults and by children provides an important indicator of food consumption. Care must betaken to avoid assumptions about normal meal frequency and the indicator for mealfrequency (normally expressed as the percentage of households falling below a defined

7 Dietary diversity and food frequency can act as a proxy indicator of food access under a variety of

circumstances including poor and middle income countries, rural and urban areas, and across seasons(FANTA, 2002)8 Additional examples are provided in the WFP/VAM Nutrition and Health Guidelines

Box 1: Example module on food consumption

Food item

DAYS eaten inpast week(0-7 days)

Source of food(code)

83a Rice

83b Maize, sorghum, bulgar

83c Cassava

83d Sweet potatoes/tubers

83e Groundnuts, legumes

83f Fish

83g Chicken, beef, bush meat, etc.

83h Palm oil, vegetable oil, fats

83i Eggs

83j Milk

83k Vegetables (including leaves)

83l Fruits

83m Wild foods

From Sierra Leone CFSVA, 2003

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Box 2: Questionnaire module on food and non-foodexpenditures

Dépenses Totales(en FBu)DÉPENSES DU MOIS PASSÉ

Cash/crédit Troc

7.1 – Maïs/Sorgho/blé/Riz

7.2 – Manioc/Ignames/patate

7.3 – Légumineuse /haricot

7.4 – Huile végétale/de palme

7.5 – Viande/Volaille/poisson

7.6 – Autre nourriture

7.7 – Alcool & Tabac

7.8 – Transport

7.9 – Amendes ou dettes

7.10 – Eau/Electricité/Carburant/ pétrole

7.11 – Bois de chauffe

7.12 – Louer (maison)

7.13 – autre _____________

7.14 – autre _____________

From Burundi CFSVA, 2004

Engel’s Law

Engel’s law states that as total expenditure increases,the percentage of total expenditure spent on fooddecreases. However, there are several caveats to thislaw:

First, for household’s not currently meeting their foodneeds, food expenditure as a percentage of totalexpenditure will increase as total expenditure increasesuntil food needs are met.

Second, for households with high total expenditure,food expenditure as a percentage of total expendituremay increase as total expenditure increases owing tothe purchase of specialty/costly foods

threshold) must take account of country-specific meal consumption habits. The indicatormust also provide a local definition of the term ‘meal’ and ensure that this is reflected intranslation as the meaning of this term is not universal (e.g. sometimes includes snacks,sometimes does not).

4.3 - Food and non-food expenditures

4.3.1 - Measuring and interpreting food and non-food expenditures

Data on expenditures for food and basic non-food items, including education and health,are also collected to understand how a household budget limited resources to meethousehold priorities and needs.

Food and non-food expenditure alsoprovide a proxy indicator of foodaccess, as research on householdconsumption and expenditure hasshown that poor household’sexpend a larger proportion of theirresources on food. However, caremust be taken in interpreting foodexpenditure as a proportion ofoverall expenditure as a proxy offood access as the indicator can bemisleading if analyzed in isolation.

For example, WFP beneficiaries whoreceive food aid are able to divert

their limited resources to non-food or specialty food expenditure. Furthermore, in ruralareas poor subsistence farmers may have low food expenditures simply because theylargely rely on their own production. Similarly, better off households (particularly in urbanareas) may spend a highproportion of expenditure onfood because they can affordto purchase a wider range offood or specialty/costly fooditems.

Due to these potential analyticpitfalls, food expenditure datashould be analyzed incombination with informationo n h o u s e h o l d f o o dconsumption, sources of foodand (per capita) absolute foodand non-food expenditurevalues.

Box 2 provides an example ofa food and non-foodexpenditure modules that canbe included in a householdquestionnaire.

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4.3.2 - Comparing food and non-food expenditure indicators

Raw expenditure data (absolute figures) are prone to misstatement, recall problems andcomparability issues. Therefore, the ability to make valid comparisons betweenhouseholds in terms of food and non-food expenditure requires that expenditure data becollected and analyzed in comparable units.

For example, some households (primarily in rural areas, but also in urban areas) maypurchase food, particularly cereals, beans, and vegetable, once a month. Otherhouseholds may go to the market every week or even every day. In order to ensurecomparability between these households, data on food and expenditure should becollected for the last month.

For situations in which respondents have difficulty recalling food expenditures for the pastmonth (e.g. those who purchase daily or weekly may have difficulty in aggregating theirexpenditures), a 7 day recall period may be used. The choice of recall period forexpenditure data should be informed by the local context and will vary betweenurban/rural, accessibility to markets, and a number of other factors. Non-foodexpenditures are always collecting using a one month recall period as these purchases arelikely to be less frequent than food expenditures.

In these cases it is suitable to collect data on quantities purchased in order to assess howlong food purchased lasts (e.g. time coverage). Doing so will also allow the analyst toverify whether figures between households are comparable in contexts where somehouseholds purchase monthly and others purchase weekly or daily. If a householdindicates that no expenditures have occurred in the last week for staple foods, the datacollection team should be instructed to probe for an explanation: is this due to a purchasein a previous period for which food stores still exist or has the household consumed thisstaple during the last 7 days? The dietary diversity and food frequency data (Section 4.2)allows for cross-validation of expenditure data.

Furthermore, studies have demonstrated that households tend to overestimate theirexpenditures. Accordingly the analysis should be conducted using relative figures (e.g.percentages, rather than absolute values). Per capita food expenditure data and non-foodexpenditure data are normally analyzed using quintiles to reduce the possibility of makinginappropriate distinctions based on differences attributable to misstatement orincomparable recall periods.

4.4 - Income

Information on the household sources of income, their importance and relative contributionto the overall household’s budget will provide us with useful information to bettercharacterize the nature of their livelihoods. Unlike expenditure data, WFP/VAM does notcollect income data in absolute values as household income is a sensitive subject that isnotoriously prone to misstatement (intentional or unintentional)9. Because householdstend to substantially and variably underreport their income, income data collected in

absolute figures is likely to be unreliable/inaccurate even if relative values are used inanalysis. Experience (WFP/VAM and others) suggests that questions concerning theimportance of their main and secondary livelihood activities are significantly less sensitivethan questions about absolute income. Consequently, respondents are more at ease (andless prone to intentional/unintentional misstatement) when providing information on therelative contribution of each source of income.

Box 3 provides an example module designed to collect data on the main sources ofhousehold income, their respective contribution to meeting food and non-food needs, andwhich members of the household are involved in these activities. Proportional pilingexercises are often incorporated in the collection of this information.

9 Households involved in various livelihood activities are likely to underreport income to variabledegrees due to involvement in informal economy, taxation implications, cultural norms, or difficulty intranslating non-cash income into currency values. By comparison, expenditure data are more likely tobe underreported to a similar degree by households.

13

Generally, WFP/VAM does not calculate ahousehold wealth-index based on the ownedassets. Asset ownership is also NOT used whenassessing levels of resilience to shocks.

Choosing assets to include in thequestionnaire

The list of assets included in the survey toolshould only list those assets that are helpful fordistinguishing between households. Forexample, if every household (or almost everyhousehold) owns a bicycle, collecting thisinformation will not be useful (e.g. the variabledoes not vary!). Similarly, if no (or very few)households own cars, collecting this informationwill not be useful. Qualitative techniques (FGDs,key informant interviews) should be used toidentify assets for which ownership is variableand therefore useful in analysis. Assumptionsabout certain assets being ‘luxury items’ may beinappropriate and should be confirmed prior todata collection.

4.5 - Assets: availability and ownership

Another important set of data crucial to characterize households’ livelihoods, and thus foodsecurity and vulnerability, is asset ownership. Assets can be classified into two types:productive assets tied directly to ahousehold ’s l ive l ihood ( land,agricultural and fishing tools, livestockand labor) and non-productive assets.Information on the presence or absenceof non-productive assets, includinghousehold durable goods such as radio, TV set, beds, roofing material and bicycles, is alsocollected as these may provide proxy indicators of household well-being or wealth.

Despite the value of analyzing asset ownership as part of a food security and vulnerabilityanalysis, asset ownership alone does not provide an effective means of assessing foodinsecurity, vulnerability, or identifying which households or individuals require food aid.For example, land and/or livestock ownership can be used to measure household’s wealth,but other factors must be taken into account in assessing their food security as divestingthese assets to meet food needs is both costly for future productivity (increasing

vulnerability) and markets for thesegoods may be extremely unfavorable intimes of crisis, particularly duringcovariate shocks (see Section 1.3).Furthermore land and livestockownership may provide a means ofbuffering shocks, but they may also be aliability (e.g. preventing out-migrationduring times of severe crisis). Finally,asset quality (e.g. land fertility, thepresence/absence of irrigation, type andquality of animals, morbidity amongworking-age members of a household) isoften masked when simply countingassets.

Conventional wisdom indicates thatbetter off households tend to own more

“luxury” goods such as TV set, motorbike, beds, bicycles, and other prestige items.However, this is not always the case. For example, in some rural communities lack ofelectricity or security concerns may prevent purchase of high-profile, luxury items. In

Box 3: Questionnaire module on household main sources of income

Quelles sont les principalesactivités qui ont fait vivre votre

ménage au cours de l’année2004 ?

Quelle est lacontributionrelative de

chaque activitépour

l’autoconsommation du ménage ?

Quelle est lacontribution

relative de chaqueactivité pour les

dépensesalimentaires duménage (cash,

emprunt, troc) ?

Qui pratiquecette activité

La principaleactivité

|__|__| |__|__| |__|__|__|

La 2ème plusimportante activité

|__|__| |__|__| |__|__|

La 3ème plusimportante activité

|__|__| |__|__| |__|__|

La 4ème plusimportante activité

|__|__| |__|__| |__|__|

From Niger CFSVA, 2005

14

other cases the lack of beds may reflect custom (e.g. sleeping on rugs) rather thanpoverty. Therefore, effective asset analysis requires that assets directly related to foodsecurity be identified prior to conducting a household survey (through qualitative methodssuch as key informant interviews or focus group discussions).

Finally, households need not own assets to have access to them. Households may accessproductive assets such as land, labor or livestock through exchange mechanisms, rent orfee-for-use, or as part of a community safety net or risk mitigation mechanism.Furthermore, access to community assets, such as forests or other common land, millingfacilities, and credit, must also be taken into account. Community or focus groupdiscussions may provide a more effective means of assessing the range and ability toaccess community assets.

Box 4 provides an example module for collecting data on household ownership ofproductive assets and durable goods. Note that the terms ownership and access should beclearly defined as the distinction between the two is often culturally contingent.Information on both ownership and access should be collected. However access toproductive assets is generally collected to analyze livelihoods (see WFP/VAM LivelihoodsAnalysis Guidelines).

Box 4: Questionnaire module on household asset ownership

42. How many of the following trees does your family own? (trees or acreage)

Trees Acreage Trees Acreage

42.1 Banana 42.6 Coffee

42.2 Cacao 42.7 Kola Nut

42.3 Cashew nut 42.8 Mango

42.4 Citrus 42.9 Oil palm

42.5 Coconut 42.10 Pineapple

Source: Sierra Leone CFSVA, 2003

40. Does your family own any ofthe following household/farmingassets? (Circle all that apply)

Bed……………………………….40.1

Table……………………………..40.2

Chair……………………………..40.3

Lantern……………….………..40.4

Big pot…………………………..40.5

Bicycle…………………………..40.6

Hoe………………………………..40.7

Axe………………………………..40.8

Sickle…………………………….40.9

Machete……………………….40.10

Boat/Canoe………………….40.11

Radio/Tape…………………..40.12

Sewing machine……………40.13

Watering can…………………40.14

15

Section V – Analysis of indicators for Household Food Security Profiles

This section describes how to use the indicators described in Section IV using PCA andcluster analysis to develop household food security profiles. This process involves severalsteps, including:

• Analysis of food consumption data (dietary diversity and food frequency) to produceinitial clusters;

• Categorization of each cluster in terms of consumption adequacy;• Refinement of food consumption clusters using food source information;• Analysis of food and non-food expenditure data;• Combining/cross-tabulating expenditure and food consumption clusters to produce

final clusters for Household Food Security Profiles (HFSPs);• Adding income and asset data to enhance HFSPs.

Each of these steps is outlined in the sections below.

5.1 - Analyzing food consumption data to produce clusters

5.1.1 - Setting up the data in the dataset: food consumption matrix

For food consumption data (dietary diversity and food frequency), the more food items ahousehold consumes from six main food groups (carbohydrate, animal products, oils andfats, fruits and vegetable, legumes and oilseeds, tubers and roots.) and the higher thefrequency of consumption of these items, the better food consumption and caloric intake.The first step in conducting the food consumption analysis is to create a matrix10 (examplein Box 5) in the data set, with each column representing either a specific food item or agroup of foods belonging to the same food group (e.g. manioc, cassava, sweet potato).

10 Data is entered with each row representing a household and each column representing a variable.One variable (column) should be added to provide a unique identifier for each household (usuallynamed ‘household ID’).

HH

ID

Sorg

hum

Mille

t

Oth

er

Cere

als

Puls

es

Meat

Vegeta

ble

Oil

Vegeta

ble

Fru

its

Milk

Eggs

Sugar

Wild F

ood

1 7 0 0 1 0 7 0 0 0 0 7 5

2 0 0 0 0 0 0 0 0 0 0 0 03 0 0 0 0 0 0 0 0 0 0 0 0

4 7 0 0 0 0 0 0 0 0 0 0 05 7 0 0 0 0 0 0 0 0 0 0 0

6 7 0 0 0 0 6 0 0 0 0 0 0

7 7 0 0 0 0 7 0 0 0 0 0 08 7 0 0 0 0 7 0 0 0 0 0 0

9 7 0 0 0 0 7 0 0 0 0 0 010 7 2 0 0 0 7 0 0 0 0 7 0

11 0 7 0 0 0 7 0 0 0 0 0 012 7 0 7 0 0 7 0 0 0 0 0 0

13 7 0 7 7 0 7 0 0 0 0 0 014 7 0 0 0 1 7 0 0 0 0 0 0

15 5 2 0 0 1 7 0 0 0 0 7 016 0 0 7 2 1 7 0 0 0 0 0 0

17 7 0 0 0 2 7 0 0 0 0 0 018 0 6 7 0 3 7 0 0 0 0 0 0

19 7 0 0 3 0 5 2 0 0 0 0 020 7 0 0 7 0 7 2 0 0 0 0 0

21 7 0 0 0 0 0 7 0 0 0 0 022 0 0 7 0 0 0 7 0 0 0 0 0

23 7 0 0 0 0 7 7 0 0 0 0 0

24 7 0 7 0 1 7 7 0 1 0 0 025 0 7 0 0 0 0 0 0 7 0 0 0

26 7 0 0 0 0 7 0 0 7 0 0 027 7 0 0 0 0 0 0 0 0 0 1 0

Box 5 – FoodConsumptionMatrix

16

PCA transforms a set of more or less correlatedvariables into a set of uncorrelated variableswhich are ordered by reducing variability. Sincethe last of these variables explain very smalldegrees of variability, they can be removed witha minimal loss of useful data.

Every principal component created has aneigenvalue associated with it that is thevariance captured by the component or factor.They are ranked from the highest to the lowestand their level is related to the amount ofvariation explained by the component.Eigenvalues are normally expressed as apercentage of the total variance explained.

Although the choice to analyze particular food items versus food groups will depend on thepurpose of the analysis. Food groups are normally used whenever the individual fooditems are equally interchangeable and the consumption of one or another does not reflectbetter or worse access to food (e.g. inferior food items). For example in some countriesbetter-off households eat rice instead of millet or sorghum. Therefore, despite the factthat millet, sorghum and rice are all cereals, rice is kept separate from the other two inorder to capture the distinction in purchase price - an indicator of a household’s ability topurchase higher prestige items and, therefore, a proxy indicator of household well-beingand wealth.

The data included in the sample matrix (Box 5) indicates how many times during the past7 days each household has consumed a particular food item or a group of foods. Thisvalue can range from 0 (never eaten) to 7 (eaten every day) while intermediate valuescorrespond to the number of days during which the food was consumed.

Staple and non-staple foods will vary from country to country. However in all situationsthe selected foods should cover the main six food groups: carbohydrates, animal products,oils and fats, fruits and vegetables, legumes and oilseeds, tubers and roots. Ideally, ahousehold should consume at least one food item from each main food group every day(food frequency = 7). Food diversity is captured by looking at the combination of thedifferent food items consumed during the 7-day recall period.

5.1.2 - Analyzing food consumption data using principal component analysis (PCA)

Once the matrix has been created in the dataset, the second analytical step involvesanalyzing the relationships between the various food consumption indicators. This isachieved through the application of principal component analysis (PCA)11.

PCA creates a set of new variables, alsocalled principal components. Each newvariable captures the relationship amongthe food consumption indicators(diversity and food frequency). Initially,PCA yields as many principal components(new indicators) as there are initialindicators12. However, the contribution of each principal component (new indicator) inexplaining the total variance found amongst households will progressively decrease fromthe first principal component to the last. As a result, a limited set of principal components(new indicators) will explain the majority of the matrix variability.

One of the main purposes of PCA is toreduce the dimensionality of the datasetby removing principal components thathave little explanatory power. However,there are many schools of thought abouthow many principal components shouldbe kept.

The main purpose of PCA in WFP/VAManalyses is to describe households on the basis of the relationships among selectedvariables. Data reduction is a secondary objective. Given this objective, it isrecommended that that analysts keep as many principal components needed to capture atleast 90% of the total variance13.

11 Several commercial statistical software packages perform both PCA and Cluster analysis. VAMtypically uses a software (ADDATI) developed explicitly for socio-economic and food security analysiswith the support of FAO. This software has been designed for the use of food security specialists, itincludes pre-selected algorithms proven to be suitable when analyzing socio-economic and nutritiondata for food security and vulnerability analyses, and it uses the output of the PCA as the input for theCluster analysis and facilitate the final interpretation of the outputs.12 Each food item constitutes an orthogonal axis.13 Analysis for which data reduction is the primary objective will remove more principal components.

17

5.1.3 - Analyzing food consumption using cluster analysis

The third step involves clustering households that share common underlying characteristicsin their food consumption patterns (as measured by the PCA analysis described in 5.1.2).The matrix created by the PCA is similar to the one presented in Box 5, except that thevariables corresponding to the original indicators collected during the household survey arereplaced by the principal components generated in the previous analytic step (e.g.indicators of the relationship among selected food items for each individual household).

The cluster analysis will group households that have a similar relationship among the foodconsumption indicators as expressed in the principal components. As indicated in 3.2.2.1,the number of clusters produced is not predefined and will be equal to the number neededto classify all households in the sample. In theory, this will vary, both by analysis and byanalytic technique, from a minimum of one cluster to a maximum equal to the totalnumber of households included in the sample (i.e. each household is a cluster). Inpractice, the analysis will propose a number of potential clusters (all mathematically valid)somewhere between the minimum and maximum. Ultimately, it is up to the analyst tomake a decision on the number of clusters to be used, balancing the increasedheterogeneity within clusters associated with a smaller number of clusters and thereduction in analytic utility associated with a large number of clusters (e.g. analyticallycumbersome and not very useful to program decision-makers). As indicated in Section III,the following criteria should be used in deciding the final number of clusters:

Maintain a reasonable level of similarity (maximize intra-cluster homogeneity)within each cluster as measured by low cluster inertia;

En su r e significant differences between clusters (maximize inter-clusterheterogeneity).

5.2 - Interpretation of food consumption analysis

5.2.1 - Determining the minimum food intake threshold/benchmark

The interpretation of the food consumption data for each cluster (identified in 5.1.3) is acritical step in the developing the overall household food security profile for each cluster.The overall objective is to describe the different levels of food access among the clustersas measured by food consumption.

In order to differentiate between clusters, benchmarks (or thresholds) must be identifiedthat indicate which cluster have/do not have adequate food consumption, as measured byproxy indicators: dietary diversity and food frequency. Building from FANTA’s research ondietary diversity, and taking into account the characteristics of the typical diet of thepopulation under analysis and the recommended WFP food basket, a minimum level offood intake, in terms of diversity and frequency of food intake, must be developed.

In most cases, this threshold will correspond to a daily intake of cereals or tubers (orboth), vegetable proteins and fats (cooking oil or animal fat), as well as frequent (2 to 4days a week) consumption of locally relevant items such as vegetables, fruits, or leaves.Condiments such as salt, chili pepper, or Maggi cubes do not make significant contributionsto caloric intake and are therefore not considered. However, in some cases sugar may beincluded in the ‘minimum level of food intake’. Other important (and protein/calorieintensive) food items such as meat, poultry, fish, eggs and dairy products are consideredas additional items and, therefore, are not included in the staple food basket unless thetypical local diet indicates otherwise.

5.2.2 - Comparing cluster to the minimum food intake threshold/benchmark

Once the threshold for the ‘minimum level of food intake’ has been specified, a weeklyminimum food intake score can be calculated. This score is the sum of seven daysconsumption of cereals/tubers, pulses, cooking oil, and three days consumption of freshfood items (leaves or vegetables/fruits). The weekly minimum food intake score will beequal to 24 [(3 x 7) + 3] and represents a conservative threshold for minimum food

18

intake14. As a result, any household or group of households falling below this level of foodconsumption is characterized by inadequate food consumption.

To compare the clusters identified using PCA, calculate the weekly food intake score foreach of the clusters and compare it with the benchmark weekly minimum food intake scoredeveloped in the previous step. Each cluster score is calculated as an average scoreamong all households included in the cluster. As a result, each cluster will be categorizedas exceeding, equaling, or falling short of the weekly minimum food intake score. Clustersfalling in the last category are characterized by inadequate food consumption.

5.2.3 - Why use PCA and cluster analysis when food intake scores can be calculated forindividual households?

The fact that the household level data allow for food intake scores to be calculated for eachhousehold, and therefore each household can be classified in reference to the definedbenchmark, may hide the need for multivariate data analysis to derive cluster profiles.

The individual household food intake score aggregates a number of different consumptionindicators, and despite being very useful for screening purposes, it masks differentconsumption patterns. Similarly to many other synthetic indicators/indices, it informs on

the result but it fails explaining the reasons or causes of that particular outcome.

Consequently, it is difficult to make the analytic leap from individual household food intakescores to level of food access (e.g. at the household level, the food intake score does notprovide a good proxy of food access).

For example, one household consuming maize 4 days a week, tubers 3 days a week, riceonce, beans 5 days, meat once, and cooking oil every day and leaves 3 days a week,would have a weekly food intake score of 24. Another household which consumes rice(superior item) every day, tubers twice, meat (superior item) 5 days, cooking oil everyday, vegetables 3 days a week, would also have a weekly food intake score of 24. Despitehaving both the same score the latter appear to enjoy a better food access with a regularconsumption of meat rather than beans and rice rather than maize. Classifying these twohouseholds as the same would mask the fact that the latter can access two superior fooditems on a regular basis.

PCA and cluster analysis overcome this problem by first clustering households that sharesimilar food consumption patterns, thus allowing food intake scores for each cluster to beinterpreted with reference to the food consumption pattern describing that cluster. Table 1and the corresponding analysis provide an example of the type of consumption patternanalysis that can be performed for each cluster and used to complement cluster foodintake scores.

Table 1 – Cluster food consumption data

Clu

ster

HHs %

Sorg

hum

Mill

et

Puls

es

Meat

Veg O

il

Vegeta

ble

s

Milk

Eggs

Sugar

Wild

Foods

1 26 3.7 2 5 3 3 7 3 5 3 7 1

2 58 8.2 3 4 2 7 7 4 2 0 7 0

3 374 53.0 5 0 1 1 7 3 0 0 7 1

4 132 18.7 5 3 4 2 7 2 5 0 7 2

5 77 10.9 6 0 1 0 7 0 1 0 6 2

6 38 5.4 5 0 1 0 2 1 0 0 1 5

Analysis: To effectively interpret the results, the analyst must take into account the overallprofile of each cluster. Colors are very useful in helping to summarize the characteristics ofthe final clusters.

14 In some cases this can be even more conservative including only the daily consumption of cerealsor tubers, pulses and vegetable oil.

19

• Households in cluster 6 have both low dietary diversity and low frequency ofconsumption. Households belonging to this cluster consume sorghum and wild foods ona frequent basis (both five times per week), but have extremely poor consumption ofprotein (measured by pulses), vegetable oil, vegetables and sugar which are consumedonce or twice a week. All other foods are not consumed.

• Households in cluster 5 are also characterized by poor food consumption. However,their overall access to food is better than households in cluster 6 as households incluster 5 have a regular consumption of three staple foods: sorghum, vegetable oil andsugar.

• Households grouped in Cluster 2 have frequent access to the staple foods (sorghum,pulses, vegetable oil and sugar), but also to millet (superior to sorghum) meat andvegetables.

• Households in Cluster 1 have the best dietary diversity despite a lower consumptionfrequency of meat.

For the presentation and programmatic purposes, it is useful to group the seven clustersproduced in the PCA into a reduced number of qualitative categories that describe theadequacy of their consumption: “very poor and poor”, “borderline”, “adequate” foodconsumption. Clusters in Table 2 can thus be re-grouped as follows:

• Very poor and poor food consumption: Cluster 5 and 6

• Borderline food consumption: Cluster 3

• Adequate food consumption: Clusters 1, 2 and 4

5.2.4 - Incorporating data on food sources

The final analytic step is to incorporate data on the food sources with the foodconsumption analysis to produce an overall picture of food access for each cluster’shousehold food security profile. This is an important step because households with similarfood consumption pattern may, in fact, utilize a diverse set of methods in acquiring theirfood.

In order to capture this feature, household food sources are analyzed separately for eachcluster defined in the previous steps (see 5.1.3). For each cluster, food items consumedby all households in the cluster are cross-tabulated with data on the primary source ofeach food item. This allows the analyst to differentiate among households that arecharacterized by a similar profile/pattern in terms of food frequency and dietary diversity,but that rely on different strategies to acquire food (e.g. self-production, purchase, foodaid).

Households totally reliant on market purchases are separated from those combining ownproduction and purchases, or those relying on food aid. The number of food acquisitionoptions will vary by context. This information can then be used to divide each clusterdefined by food consumption into sub-clusters defined by both consumption and foodsources. This refinement provides a more comprehensive picture of food access.

Where there are a large number of potential food sources, the number of clustersdescribing food access (e.g. both the different consumption frequency and diversity) willincrease from the number of clusters describing consumption patterns alone. Conversely,if the food consumption patterns are strongly associated with the acquisition optionsavailable (e.g. high diversity associated with market purchases, and low diversityassociated with own production), the information on food sources may not result in anincrease in the number of household groups.

Table 2 presents the three food consumption profiles (very poor, borderline, andacceptable) described in 5.2.3 by the two primary sources of acquiring food available tohouseholds15.

15 This example provides a simplified version of the type of analysis that will be required in most casesas the number of food sources will usually exceed two.

20

Table 2: Household Food Consumption distribution by main source of food

HouseholdsFoodConsumption Total

Relying onFood Aid

Relying onPurchases

Acceptable 12% 6% 6%

Borderline 64% 49% 15%

Very poor 24% 15% 8%

The result is six distinct clusters based on food consumption and food sources:

1. households with acceptable food consumption that rely primarily on food aid

2. households with acceptable food consumption that rely primarily on purchase

3. households with borderline food consumption that rely primarily on food aid

4. households with borderline food consumption that rely primarily on purchase

5. households with poor food consumption that rely primarily on food aid

6. households with poor food consumption that rely primarily on purchase

5.3 - Analyzing food and non-food expenditure to produce clusters

Food and non-food expenditure data provide a means characterizing household foodaccess that complements the measures provided by food consumption and source data(Section 5.1). Accordingly, a second set of clusters are produced through PCA and clusteranalysis based on food and non-food expenditure. This second set of clusters is thencross-tabulated with the clusters produced in the food consumption and source analysis togenerate a third set of clusters that reflect both food consumption and source patterns and

food and non-food expenditure patterns.

The process of setting up the data for analysis for food and non-food expenditure, as wellas conducting PCA and cluster analysis is very similar to the process used for foodconsumption (Section 5.1). Accordingly, the description of the process is streamlined inthis section. For more procedural detail refer to Section 5.1.

5.3.1 - Setting up data in the dataset: food and non-food expenditure

The first step in conducting the food and non-food expenditure analysis is to create amatrix16 (example in Table 3) in the data set, with each column representing either aspecific food or non-food expenditure. It is important at this stage to bear in mind thatshares of food expenditures despite providing a good idea of the resources allocation tomeet basic needs, they do not tell about the household’s cash availability. Furthermore,relative figures (e.g. % of total expenditure) will not distinguish between households withvastly different total expenditure amounts when creating clusters. To overcome this issue,the absolute total monthly household expenditure will be used as a weight in the nextstep17.

5.3.2 - Analyzing food and non-food expenditure data using principal component analysis(PCA)

Once a matrix is created (see Table 3), the second step is to carry out a PCA to capturethe relationship among different expenditures incurred by the households included in thesample. Each principal component will indicate an association among expenditures, thusproviding an initial hint about the expenditure behavior of the sampled households. As forfood consumption principal components, the number of principal components to be savedfor the subsequent analysis should be the number required to explain approximately 90%of the total variance.

16 Data is entered with each row representing a household and each column representing a variable.One variable (column) should be added to provide a unique identifier for each household (usuallynamed ‘household ID’).17 Not all statistical packages that perform PCA have this feature. The software ADDATI does allows forthis type of “weighting” scheme.

21

5.3.3 Analyzing food and non-food expenditure using cluster analysis

The third step utilizes clustering analysis based on the principal components produced inthe second step (5.3.2). Similar to the process described for the food consumptionanalysis, households will be grouped together based on their similarity and this similarity iscalculated using analytical geometry. Average expenditure shares are then calculated foreach cluster with the sum of all shares (all expenditures) equal to 100. An example outputmatrix of expenditure shares for nine clusters is provided in Table 4.

Table 3: Food and non-food expenditures matrix for PCA

HHID

To

tal

Exp

en

dit

ure

Fo

od

Mil

lin

g

Med

ical

Tra

nsp

ort

Ren

t

Deb

ts

Eq

uip

men

t

Ed

uca

tio

n

Clo

thin

g

90 6406 66.9 1.9 0.0 0.0 0.0 31.2 0.0 0.0 0.0

98 20991 62.3 2.0 35.7 0.0 0.0 0.0 0.0 0.0 0.0

172 8283 62.1 0.5 32.6 4.8 0.0 0.0 0.0 0.0 0.0

182 8886 68.5 3.2 0.0 16.9 0.0 0.0 10.1 1.4 0.0

230 17189 67.3 1.2 2.3 0.0 0.0 29.1 0.0 0.0 0.1

233 23479 58.0 0.6 0.0 31.9 0.0 0.0 3.0 6.4 0.0

246 15836 65.0 3.5 0.0 0.0 0.0 0.0 0.0 0.0 31.6

256 33171 66.5 1.4 3.5 0.0 0.0 0.0 0.0 13.6 15.1

260 17881 64.7 3.6 16.8 0.0 0.0 0.0 0.0 6.7 7.4

266 22986 57.8 1.7 15.2 0.0 0.0 0.0 6.5 11.7 7.0

312 19394 67.6 1.4 20.6 5.2 0.0 0.0 0.0 5.2 0.0

313 3350 67.2 3.0 29.9 0.0 0.0 0.0 0.0 0.0 0.0

322 6579 65.8 3.8 30.4 0.0 0.0 0.0 0.0 0.0 0.0

326 42183 65.5 0.6 8.3 0.0 0.0 23.7 0.0 0.0 1.9

348 2206 58.3 5.4 0.0 36.3 0.0 0.0 0.0 0.0 0.0

351 28179 62.2 0.5 1.8 0.0 0.0 35.5 0.0 0.0 0.0

358 32620 61.6 0.4 0.0 0.0 0.0 30.7 0.0 1.2 5.5

366 27857 67.7 0.7 1.1 0.0 0.0 26.9 0.0 3.6 0.0

369 43500 57.9 0.7 23.0 6.9 0.0 0.0 0.0 11.5 0.0

388 12686 57.4 0.8 0.0 31.5 0.0 0.0 7.9 2.4 0.0

Table 4: Example of final expenditure profiles

Expenditure SharesCluster

HHs Food Health House Educ Cloth Trans Equip Fest Other

1 12 32.8 4.3 10.1 1.8 4.3 0.4 3.3 2.3 41.0

2 42 48.4 6.0 5.2 0.9 9.0 0.4 20.7 6.8 2.0

3 127 46.4 14.4 2.2 2.6 12.9 0.8 4.9 14.2 1.7

4 31 60.6 6.4 2.3 1.1 9.4 11.8 2.3 5.5 0.0

5 215 66.5 9.8 0.8 0.4 12.1 0.7 1.6 6.0 1.5

6 151 87.0 3.7 1.6 0.0 5.5 0.1 1.0 0.7 0.6

7 42 57.1 12.5 2.7 13.7 6.2 0.6 1.5 4.0 1.4

8 38 55.8 2.6 0.5 0.1 31.1 0.4 1.3 7.8 0.4

9 56 59.8 4.7 14.4 0.4 16.1 0.0 1.4 1.4 1.3

5.3.4 - Interpreting food and non-food expenditure analysis

To be useful, the interpretation of these clusters must take into account other householdcharacteristics, similar to the way in which the food consumption analysis took intoaccount food sources. A range of variables on production, nutritional outcomes, or otherfactors may be appropriate and included on a case by case basis. See 5.5 for a descriptionof how to incorporate these factors in refining clusters.

When working in rural settings where many households are subsistence farmers, lowshares of food expenditures can be misleading as this may indicate a high level of relianceon a few self-produced staple crops. Conversely, high shares of food expenditure can also

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be misleading as it may, in fact, result in a more diversified diet (see 4.3.1 for a morethorough description). Accordingly, one of the most important analytic steps ininterpreting both food consumption data and food and non-food expenditure data involvescombining the clusters produced in the food consumption analysis with the clustersproduced in the food and non-food expenditure analysis.

5.4 - Combining expenditure profiles with food consumption profiles

Simple cross-tabulations between expenditure clusters and food consumption clusters willallow for a more thorough analysis of both by producing a convergence of evidence amongthe following factors:

a) access to food

b) main source(s) of the food consumed

c) ratio of food expenditure

d) ratio of non-food expenditure.

If the data are reliable, a strong relationship should exist between (b) and (c), as typicallyhouseholds relying on markets have comparatively higher shares of food expendituresthan those relying on own production or those using mixed sources (e.g. market and ownproduction). Intuitively, a strong relationship should also exist between (c) and (d) asthose with high shares of food expenditure must concurrently have low shares of non-foodexpenditure.

5.4.1 - Constructing quintiles for absolute food expenditure data

Given the expenditure data’s constraints indicated in Section 4.3, i.e. comparison betweenfood and non-food expenditures, large degree of variation in expenditure amounts existingamong households18, an alternative way to analyze expenditure data is through thecalculation of per capita food and non-food figures. Per capita cash expenditures for foodand for non-food provide an indication of the household cash availability. However, asthese figures should be treated as relative rather than absolute values, per capita food andnon-food expenditures are aggregated into per-capita quintiles. Quintiles yield a relativemeasure of the cash availability at household level in a format that is comparable acrosshouseholds. Furthermore, it also allows for comparisons between quintiles of weekly cashfood expenditures and monthly cash non-food expenditures to be made.

Through this process, each household will be assigned to its respective quintile for bothfood and non-food expenditures, thus creating two new categorical variables19. These twovariables are then cross-tabulated with the food consumption typologies in order toidentify how many of the households with poor food consumption are also characterized bylow per capita food and non-food expenditures (i.e. first and second quintile). A certaindegree of relationship between food consumption typology and per capita food expenditurequintile should be found.

Table 5 - Households by food consumption and per-capita weekly food expenditure quintiles

FoodConsumption

Relying on Quintile I Quintile II Quintile III Quintile IV Quintile V

Food Aid 11% 23% 18% 20% 28%Acceptable

Purchases 2% 10% 21% 24% 43%

Food Aid 29% 22% 20% 18% 11%Borderline

Purchases 7% 18% 29% 26% 20%

Food Aid 59% 25% 8% 3% 5%Very Poor

Purchases 35% 29% 15% 17% 4%

Table 5 provides an example of the cross-tabulation between the three food consumptionprofiles depicted in 5.2.3 by main source of food (depicted in 5.2.4, Table 2) and thequintiles of the per capita weekly food expenditures describe above. Shaded cells depict

18 Weekly food expenditures are likely to refer for some households to quantities of food purchased forthe weekly needs, and for some others for longer term consumption (e.g. one month).19 The two new categorical variables are respectively calculated for the per-capita food and non-foodexpenditures. In both cases the first quintile refers to the bottom 20% households of the per-capitaexpenditure (food or non-food), the second quintile to the following 20% households, etc.

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strong evidence of the expected convergence of the three variables, whereas bold figuresindicate discrepancies with the expected results.

5.4.2 - Accounting for food availability from own production

In some cases, particularly when staple food availability from own production is verycommon, indicators on production should be incorporated into the food and non-food(cash) expenditure PCA and cluster analysis.

The nine clusters presented in Table 4 appear quite different in Table 6, where expenditureshares are analyzed together with the per capita cereal availability from own production.In fact, by including this variable five more clusters are obtained, for a total of 14. In acontext where own production plays a key role in accessing staple food(s), it may beimportant to differentiate between households with high dependency from own productionand other sources of access to food, but sharing a similar expenditure pattern.

Table 6: Example of expenditure shares and food availability

Expenditure SharesCluster HHs

CerealAvail20

Food Health House Edu Cloth Trans Equip Fest Other

1 12 117.9 32.8 4.3 10.1 1.8 4.3 0.4 3.3 2.3 41.0

2 63 108.9 40.6 16.8 1.8 0.7 14.3 0.7 5.7 17.4 2.3

3 10 56.6 47.4 12.8 2.2 5.9 12.2 0.0 4.5 14.7 0.0

4 42 88.5 48.4 6.0 5.2 0.9 9.0 0.4 20.7 6.8 2.0

5 38 43.2 55.8 2.6 0.5 0.1 31.1 0.4 1.3 7.8 0.4

6 27 130.5 56.7 10.4 2.5 3.8 8.6 3.1 4.1 8.6 2.4

7 42 75.1 57.1 12.5 2.7 13.7 6.2 0.6 1.5 4.0 1.4

8 27 526.2 57.4 12.4 3.2 2.2 13.4 0.4 3.3 6.0 1.7

9 56 86.2 59.8 4.7 14.4 0.4 16.1 0.0 1.4 1.4 1.3

10 31 105.6 60.6 6.4 2.3 1.1 9.4 11.8 2.3 5.5 0.0

11 150 86.8 63.6 10.6 0.4 0.5 12.0 0.3 0.9 8.8 1.8

12 65 97.1 69.5 8.9 1.2 0.4 12.2 1.1 2.3 3.1 1.1

13 140 48.5 83.0 5.1 0.8 0.1 8.1 0.2 0.6 1.4 0.7

14 11 47.5 90.9 2.3 2.4 0.0 3.0 0.0 1.5 0.0 0.5

5.5 - Incorporating other factors into the household food security profiles:income and asset data

A large number of factors contribute to making a household more or less successful inattaining food security. These factors are wide-ranging and include, but are not limited to,household size, sex and age composition, level of education, labour skills, geographiclocation of the community and of the household within the community, as well as theability to undertake investments.

20 This is the percentage ratio between the per-capita net production and per-capita consumptionneeds based on the typical consumption in the country under analysis. Values greater than 100indicate a surplus and those below 100, a deficit.

Interpreting clusters in Table 6

Households belonging to Clusters 13 and 14 have very low cereal availability from own productionand are characterized by very high food expenditure shares. Looking at other data through simplecross-tabulations, it was found that food expenditure among households in these clusters is mainlyallocated to acquiring cereals and that the prevalence of stunting (a measure of chronicmalnutrition) among these households is very high.

Conversely, households in Clusters 6 and 8 have large cereal availability from own production andtheir shares of food expenditures are moderate. Through a series of simple cross-tabulations it wasfound that their food expenditure is mainly allocated to purchasing rice and condiments.Furthermore, despite their good food availability, the prevalence of stunting (chronic malnutrition)remains high, but they are characterized by a low prevalence of wasting (a measure of acutemalnutrition).

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Over-simplified AnalysesBecause so many endogenous and exogenousfactors affect a household’s food security status,analyses that assume that householdsperforming a same economic activity (e.g. samelivelihood sources) within a defined geographicarea will have similar food security outcomes areoverly simplistic and misleading.

5.5.1 - Sources of income and their share of contribution to household’s cash and in-kindincome

Data on the household sources of income and their contribution to the overall household’sbudget are often combined with the household food consumption and expenditure profilesdescribed in Sections 5.1 and 5.2because income information providesadditional information that can be usedto complement information on theprevious analysis that describe foodaccess.

Incorporating data on income sourcesmay provide insights into how livelihoodsources and livelihood diversification are related to food security in a particular context.By cross-tabulating the sources of income with the food consumption profiles, the analystcan assess whether good, average, or poor food consumption is related to specific sourcesof income, particular combinations of income sources (e.g. contingent) or whom (e.g. men,women, both, or children) is performing the specific economic activity.

Furthermore, data on the relative contribution of income sources to overall income can beused to generate clusters/profiles using PCA and cluster analysis. For example, eachcluster in Table 7 provides an indication of the income strategies pursued by householdsand of the share of each income strategy as a contribution to overall household revenue.Households may rely on only one source of income or on several (normally two or three)with one being generally more important than the others, i.e. contributes for more than 50percent.Given the large number of sources of income combinations, the number of final clusterswhen analyzing these data will also be quite large. Results including many clusters areoftentimes difficult to interpret. However, to overcome this problem, clusters can be sortedand re-grouped on the basis of their main source of income. The fifteen clusters in Table 7can be summarized as follows:

• Households in cluster 1 rely on only one activity: agriculture. This activity generates onaverage 90% of their resources. Conversely households in cluster 2, 3, 4, 7, 8, 9, 10and 12 combine agriculture, their main source of income, with a second economicactivity, respectively livestock, horticulture, handicraft, migration, domestic wagelabour, fishing, trade, wage labour.

• Households belonging to cluster 5, 6, 13, 14 and 15 have one main source of incomecontributing on average for about 2/3 of the overall household revenue. The remainingis met through several activities with small contributions.

Table 7: Example of income shares and food availability

Cluster # HHs

Cere

al

Availability

21

Agriculture

Liv

esto

ck

Fis

hin

g

Tra

de

Hort

iculture

Wage labour

Handic

raft

Mig

ration

Dom

estic

Wild F

ood

Gam

e

Oth

er

Percentages

1 167 124 92 2 0 0 1 0 3 1 0 1 0 0

2 98 102 67 25 0 1 1 0 3 0 1 1 0 0

3 68 130 62 3 0 1 30 0 1 0 0 3 0 0

4 60 84 58 4 1 0 2 0 25 1 1 6 0 0

5 46 54 18 72 0 1 1 2 3 1 1 1 0 0

6 36 28 8 2 0 1 0 1 78 2 5 3 0 0

21 Values greater than 100 indicate a surplus and those below 100 a deficit.

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7 35 107 60 6 1 0 1 1 3 26 1 0 0 0

8 33 89 62 7 0 0 1 1 3 1 24 1 0 0

9 28 152 66 5 24 0 0 1 1 1 0 1 0 0

10 26 150 64 5 0 26 1 0 1 0 1 1 0 0

11 23 85 39 17 1 2 6 1 6 13 2 2 0 0

12 17 94 54 12 0 1 3 24 3 1 0 1 0 0

13 17 73 18 3 77 2 0 0 0 0 0 0 0 0

14 15 38 17 6 0 0 0 0 10 66 1 0 0 0

15 12 22 13 2 0 1 2 77 1 0 3 1 0 0

These different fifteen profiles can then be cross-tabulated with the consumption clustersin order to identify likely relationships between food consumption patterns and incomegeneration activities.

5.5.2 - Assets and durable goods ownershipA similar process can be applied to analyze asset ownership by household foodconsumption and food and non-food expenditure clusters. In the same way data onincome sources and relative contribution are incorporated into the analysis, data aboutassets are used to further characterize the clusters with the aim of developingcomprehensive household food security profiles. The inclusion of asset data is particularlyimportant because there is often a strong correlation between food consumption and thepresence/absence of assets, particularly productive assets.

Productive assets such as land and livestock are particularly important, especially whendata on size and quality of these assets is collected. Composite indexes can be createdassociating housing characteristics such as type of roof, wall, water, sanitation, fuel andlighting, and durable goods such as furniture, radio and bicycles.