• TABLE OF CONTENTS
HIDE
 Front Cover
 Table of Contents
 List of tables, figures, and...
 Foreword
 Acknowledgement
 Introduction: John Hoddinott
 Measuring nutritional dimensions...
 Choosing outcome indicators of...
 Rapid appraisal techniques for...
 Constructing samples for characterizing...
 Targeting: Principles and practice:...
 Designing methods for monitoring...
 Reference
 Contributors
 Back Cover






Group Title: Food security in practice
Title: Methods for rural development projects
CITATION PAGE IMAGE ZOOMABLE PAGE TEXT
Full Citation
STANDARD VIEW MARC VIEW
Permanent Link: http://ufdc.ufl.edu/UF00053861/00001
 Material Information
Title: Methods for rural development projects
Series Title: Food security practice technical guide series
Physical Description: vii, 118 p. : ill. ; 22 x 25 cm.
Language: English
Creator: Hoddinott, John
International Food Policy Research Institute
Publisher: International Food Policy Research Institute
Place of Publication: Washington D.C
Publication Date: c2001
 Subjects
Subject: Food supply -- Statistical methods -- Developing countries   ( lcsh )
Rural development projects -- Developing countries   ( lcsh )
Genre: bibliography   ( marcgt )
statistics   ( marcgt )
non-fiction   ( marcgt )
 Notes
Bibliography: Includes bibliographical references (p. 115-117).
Statement of Responsibility: edited by John Hoddinott.
Funding: Food security practice technical guide series.
 Record Information
Bibliographic ID: UF00053861
Volume ID: VID00001
Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: oclc - 49225641
lccn - 2002022204

Table of Contents
    Front Cover
        Page i
        Page ii
    Table of Contents
        Page iii
    List of tables, figures, and boxes
        Page iv
        Page v
    Foreword
        Page vi
    Acknowledgement
        Page vii
        Page viii
    Introduction: John Hoddinott
        The links between development interventions, household food security, and nutrition
            Page 1
            Page 2
            Page 3
            Page 4
            Page 5
            Page 6
        Introduction to the chapters
            Page 7
        The chapters in brief
            Page 8
            Page 9
            Page 10
    Measuring nutritional dimensions of household food security: Saul S. Morris
        Background: The role of nutritional assessment in meeting the challenge of hunger and poverty
            Page 11
            Page 12
        Using nutritional assessment to improve the impact of rural development projects
            Page 13
            Page 14
            Page 15
            Page 16
            Page 17
            Page 18
            Page 19
        Case study of the rural development plan for the western region, Honduras
            Page 20
            Page 21
            Page 22
            Page 23
            Page 24
            Page 25
            Page 26
            Page 27
            Page 28
            Page 29
            Page 30
    Choosing outcome indicators of household food security: John Hoddinott
        Introduction
            Page 31
        Outcome measure of household and individual food security
            Page 32
            Page 33
            Page 34
            Page 35
            Page 36
            Page 37
            Page 38
            Page 39
        Exploring associations between different outcome measures of food security
            Page 40
            Page 41
            Page 42
            Page 43
        Developing and using outcome indicators of household food security in development projects
            Page 44
            Page 45
            Page 46
    Rapid appraisal techniques for the assessment, design, and evaluation of food security interventions: Gilles Bergeron
        Page 47
        Page 48
        Instruments guide
            Page 49
            Page 50
            Page 51
            Page 52
            Page 53
            Page 54
            Page 55
            Page 56
            Page 57
        Example of conceptual map
            Page 58
            Page 59
            Page 60
            Page 61
            Page 62
            Page 63
            Page 64
            Page 65
            Page 66
            Page 67
        Appendix 4A: Methods for local concept
            Page 68
            Page 69
            Page 70
            Page 71
        Appendix 4B: Impact evaluation instruments
            Page 72
        Apendix 4C: Summary of impact evaluation
            Page 72
            Page 73
            Page 74
            Page 75
            Page 76
    Constructing samples for characterizing household food securtiy and monitoring and evaluating food security interventions: Calogero Carletto
        Introduction
            Page 77
        Why random samples?
            Page 77
            Page 78
        Steps in constructing a random sample
            Page 79
            Page 80
            Page 81
            Page 82
            Page 83
            Page 84
        A worked example
            Page 85
            Page 86
            Page 87
            Page 88
    Targeting: Principles and practice: John Hoddinott
        Introduction
            Page 89
        The principles of targeting
            Page 89
            Page 90
            Page 91
            Page 92
            Page 93
            Page 94
            Page 95
            Page 96
        The practice of targeting
            Page 97
            Page 98
            Page 99
            Page 100
            Page 101
            Page 102
    Designing methods for monitoring and evaluating food security and nutrition interventions: Calogero Carletto and Saul S. Morris
        Introduction
            Page 103
            Page 104
            Page 105
            Page 106
        Case studies
            Page 107
            Page 108
            Page 109
            Page 110
            Page 111
            Page 112
            Page 113
            Page 114
    Reference
        Page 115
        Page 116
    Contributors
        Page 117
    Back Cover
        Page 118
Full Text
FOOD SECURITY IN PRACTICE



Edited by John Hoddinott


International Food Policy Research Institute
Washington, DC









































ISBN 0-89629-713-6
Library of Congress Cataloging-in-Publication data available.

Copyright2001 International Food Policy Research Institute.
All rights reserved. Sections of this report may be reproduced
without the express permission of, but with acknowledgement
to, the International Food Policy Research Institute.










Foreword .............. ................... ... ...vi
Acknowledgments ................................vii
1. Introduction: John Hoddinott .................. .1
The Links Between Development Interventions,
Household Food Security, and Nutrition ........... .1
Introduction to the Chapters ................... .7
The Chapters in Brief .............................8
2. Measuring Nutritional Dimensions of
Household Food Security: Saul S. Morris ..........11
Background: The Role of Nutritional Assessment
in Meeting the Challenge of Hunger and Poverty ....... .11
Getting Familiar with Measures of Nutritional Status .... 11
Using Nutritional Assessment to Improve the Impact
of Rural Development Projects ................. 13
Case Study of the Rural Development Plan
for the Western Region, Honduras .................. .20
3. Choosing Outcome Indicators of Household Food
Security: John Hoddinott ..........................31
Introduction .............. ............ 31
Outcome Measure of Household
and Individual Food Security .................... .32
Exploring Associations Between Different Outcome
Measures of Food Security .................... 40
Developing and Using Outcome Indicators of Household
Food Security in Development Projects ................ 44


4. Rapid Appraisal Techniques for the Assessment,
Design, and Evaluation of Food Security
Interventions: Gilles Bergeron ................... 47
Introduction ............... ... ...........47
RA Methods for Local Needs Assessment,
Intervention Design, and Impact Evaluation ...........47
Instruments Guide .........................49
Example of Conceptual Map .......................58
Appendix 4A-Methods for Local Concept Definition ..... 68
Appendix 4B-Impact Evaluation Instruments ..........72
Appendix 4C-Summary of Impact Evaluation ..........72
5. Constructing Samples for Characterizing Household
Food Security and Monitoring and Evaluating Food
Security Interventions: Calogero Carletto .........77
Introduction .................................... 77
Why Random Samples? ..........................77
Steps In Constructing a Random Sample ..............79
AWorked Example ..............................85
6. Targeting: Principles and Practice: John Hoddinott ...89
Introduction ...................... ............ 89
The Principles of Targeting ........................ 89
The Practice of Targeting ............ ......... .97
7. Designing Methods for Monitoring and Evaluating
Food Security and Nutrition Interventions: Calogero
Carletto and Saul S. Morris ........................ 103
Introduction ............ ... .......... .103
Case Studies .................................107
References ......................... ..............115
Contributors ..................................... 117


Contents











1.1 Uses of this material at different points in the project cycle .. .6
2.1 Commonly used anthropometric indices ................ 13
2.2 Nutritional indicators for needs assessment exercises ...... 15
2.3 Nutrition indicators for monitoring and impact assessment .17
2.4 Time reference of different nutritional indicators ........ 17
2.5 Severely stunted first graders per 100 hectares, and
proportion of severely stunted first graders in the 18
departamentos of Honduras .......... ........ .21
2.6 Nutritional indicators, western Honduras ............. 22
2.7 Frequency of severe stunting among first graders, and severe
stunting score of beneficiary households, western Honduras .24
2.8 Mean anthropometric status of children under five by survey
year and program status, western Honduras ............ 25
2.9 Change in anthropometric status between July/August 1997
and March/April 1998, adjusted for changes in the age
structure of the survey populations, western Honduras .... 26
2.10 Height and weight velocities of children living in the
PLANDERO 96 and PLANDERO 97 study communities,
western Honduras, 1997-98 ..... ...................27
3.1 Comparison of methods in terms of costs, time, and skill
requirements, and susceptibility to misreporting ..........39
3.2 Pearson and Spearman correlation coefficient between
caloric availability and two alternatives ............. ....40
3.3a Contingency table of caloric availability and weighted
dietary diversity ...................................41
3.3b Contingency table of caloric availability and weighted
coping strategy index .............. ........... 41


3.4 The relationship between (log) per capital caloric
acquisition and two alternative measures of food security,
controlling for (log) household size and location ........ 43
3.5a Contingency table of actual and predicted per-person caloric
availability (dietary diversity) .. .................. 43
3.5b Contingency table of actual and predicted per-person caloric
availability (coping strategies) .. .................. 43
3.5c Comparison of predictive power of dietary diversity and
coping index .............. ............... 43
4.1 Realization of the village map ....................... .50
4.2 Matrix of household demography, assets, and food security
rating: Partial listing from Tomba .. ................ 52
4.3 Model used for coding compound and family numbers .... .53
4.4 Food security rating ............... ............ 54
4.5 Conceptual map of food sources and threats to food
security ............... .................... .57
4.6 Matrix of threats to food acquisition, with possible actions
and their likelihood ........... ...........60
4.7 Seasonal food security timelines ................. ..... 62
4.8 Development projects: Multiple timelines form ...........63
4.9 Monitoring and evaluation of impact ................. .65
4A.1 Concepts to define, approaches to use, and outputs to
obtain ......... ............... ............. 71
4C.1 Summary of impact evaluation .. ................. 73
4C.2 Individuals viewing intervention positively on dimensions
of food security, by gender .......................... 74
6.1 Example of data necessary for calculating PO, P1, and P2 .. .90
6.2 Errors of inclusion and exclusion ......................92


Tablles, Figure~s, and Boxtes










6.3 Errors of inclusion and exclusion under random draw .... .93
6.4 Errors of inclusion and exclusion under perfect targeting .. .93
6.5 Errors of inclusion and exclusion under "worst case"
targeting ................................. .. ..94
6.6 The impact of alternative targeting mechanisms on the
percentage and severity of food insecurity .............. 96
6.7 Ranking 10 Zone Lacustre villages by percentage of,
absolute numbers of, and severity of food insecurity ...... 98
6.8 Household-targeting mechanisms ......................99



1.1 The determinants of household food security .......... .2
1.2 The impact of development interventions on household
food security ............ ........................ .4
2.1 Nutrition security ........... ............ ..........12
2.2 Percentage of severely stunted first graders, Honduras, 1996 .20
2.3 Density of severely stunted first graders per 100 hectares,
Honduras, 1996 ........ ...................... 21
2.4 Percentage of severely stunted first graders, western Honduras,
1996................. ............. ....... 23
2.5 Distribution of PLANDERO beneficiary households and
malnourished first graders, western Honduras, 1996-98 .. 24


2.6 Prevalence of stunting in two municipios of western
Honduras, 1994-97 .. . ...... .............. 25
2.7 Average height-for-age Z-scores in March/April 1998, by
program status ........... ... ...........27
4.1a Zoning of the conceptual map into quadrants ............56
4.1b Nodes and pathways in conceptual map ..............58
4.1c Threats to food pathways ........... ........ 58
4A.1 Scree plot of core items .... .................... 69
4A.2 SWOT matrix ..... ................. ........... 72
6.1 Stylized distribution of food security ................. 90
6.2 Thebenefits of targeting ............................91
6.3 Leakage and undercoverage with perfect targeting ........93
6.4 Leakage and undercoverage under "worst case" targeting .. .94



3.1 Energy content per 100 grams of edible portions,
selected foods................... ................ 32
3.2 Recommended daily caloric intakes .............. 33
5.1 A glossary of sampling terms ........................ 79










he International Food Policy Research Institute (IFPRI), like
many development practitioners, often finds that insufficient
information constrains its research efforts with partner
organizations in developing countries. Solid data are often lacking
on the nature of poverty, food insecurity, and malnutrition; the
location of food insecure areas; and the causal links between
potential interventions and outcomes of interest. This absence of
information adversely affects the design, implementation,
monitoring, and evaluation of interventions, including those
designed to ameliorate food insecurity and malnutrition.
This book, based on IFPRI's field experience and interaction with
a variety of partner organizations, aims to assist development
practitioners in overcoming these constraints. The principal audience
is an operational one-multilateral or bilateral aid agencies,
nongovernmental organizations (NGOs), developing-country
governments, and other development practitioners actively engaged
in food security and nutrition issues. The book provides a framework
for thinking about what projects would be most appropriate in a
given situation and indicates what types of information are needed in
order to maximize project impact. It can also assist by making
development practitioners more fully conversant with food security
and nutrition concepts.


The authors have sought to make this book "fieldwork friendly,"
so it is not intended as an exhaustive survey of all issues or methods
with respect to food security and nutrition. Rather, the material
presented here is designed to provide a suite of useful methods
relevant at different points in the project cycle. Although each chapter
stands alone, I encourage readers to begin with the introduction,
which provides an overview of the key issues.
IFPRI's mission is to search for policies to feed the world and
protect the environment. I hope that this guide will assist others who
share our goal by facilitating the targeting and design of
interventions for maximum effect on food insecurity and nutrition,
and by facilitating the development of better methods for monitoring
and evaluation.

Per Pinstrup-Andersen
Director General, IFPRI


vi Food Secnrily in Practice


Fo rewo~ord










uch of the material presented here was originally
produced for the International Fund for Agricultural
Development (IFAD) in the form of a series of technical
guides for operationalizing household food security. Specifically,
funding for data collection and analysis was supported by IFAD's
Technical Assistance Grant 301-IFPRI. In addition, this work has
been aided by comments and advice we received from a number of
IFAD staff: Mona Fikry, Shantanu Mathur, Annina Lubbock, Enrique
Murguia Oropeza, SanaJatta, Gary Howe, and David Kingsbury. The
authors gratefully acknowledge this funding.
Some of the material also draws on work undertaken for The
United States Agency for International Development (USAID) under
grants 688-C-00-98-00151-00 and 698-0478-G-00-5-272-00. In
particular, we would like to acknowledge, with thanks, the support of
Roger Bloom and Kevin Sturr.
We would also like to record our appreciation to the research staff
who worked with us in the field. It is a pleasure to commend the


outstanding work of Juan Manuel Medina Banegas (Honduras), Luc
Christiaensen (Mali), Sidi Guindo (Mali), Abdourhamane Maiga
(Mali), and Charles Masangano (Malawi). In Washington, we were
S ably assisted by Lynette Aspillera, Ginette Mignot, Jay Willis, and
Yisehac Yohannes. We would also like to acknowledge the guidance
and enthusiasm of Lawrence Haddad and the valuable contribution
of Detlev Puetz.
Most important, the data used in this volume came from
hundreds of men and women in Honduras, Malawi, and Mali who,
with good humor, patiently answered many questions on their own
food security and nutrition. We recognize that the hours they spent
with us represented a genuine opportunity cost; we hope that in some
way they will benefit from this work.
The ideas and opinions presented in this volume are the sole
responsibility of the authors.


Food Seculily in Practice vii


llolm ~ ~ ~ ~ ~ ~ ~ ........~.






1. Introduction

John Hoddinott


his book is principally aimed at individuals in multilateral or
bilateral aid agencies, nongovernmental organizations
(NGOs), developing-country governments, and other
development practitioners who are actively engaged in food security
or nutrition issues. These practitioners often are knowledgeable about
general development issues and have substantial managerial prowess,
but lack materials that could provide a bridge between the academic
literature on these issues and the operational concerns associated
with designing, implementing, monitoring, and evaluating projects.
The purpose of this book is to help bridge this gulf between
theory and practice. To begin with, project staff often face
information and resource constraints. That is, information is often
lacking on the nature of the food security and nutrition problems
facing a country, or region within a country; the location of food-
insecure areas; and the causal links between potential interventions
and food security outcomes. Further, there is neither the time nor
money to launch detailed, lengthy, quantitative household surveys.
Even if such surveys could be launched, it is simply not feasible to
apply sophisticated statistical analyses to these data.
The material presented here recognizes these constraints. One of
our objectives is to outline a number of relatively quick methods for
obtaining information on food security and nutrition. A second is to
keep the statistical requirements associated with using these data to a
minimum. To apply the material and methods discussed in this
report, all that is needed is access to a spreadsheet program such as
Excel or Quattro and a rudimentary understanding of a few statistical
techniques, such as computing means, testing hypotheses, and


estimating simple linear regression models. Third, we show how
project designers can use this information to understand the nature
of food security and nutrition problems, target interventions more
effectively, and develop simple but effective tools for monitoring and
evaluation. Fourth, we try to avoid using jargon or technical
language; where we do, we define these terms in a way we hope is
accessible. Finally, alongside the presentation of these methods, we
present examples to make the material more accessible.
This introduction attempts to do two things. First, it provides a
brief introduction to the concept of food security. (An introduction to
nutrition concepts and issues is found in Chapter 2.) It outlines the
links between a variety of development projects and their impact on
food security and nutrition. By doing so, it provides a framework for
thinking about what projects would be most appropriate in a given
situation and indicates what types of information are needed in order
to maximize impacts on food security. Second, it introduces the
material in this book, showing how it can assist staff in easing the
information constraints they often face. By doing so, it should be
possible to improve the targeting of interventions, understand their
likely effects, and develop improved monitoring and evaluation
methods.

THE LINKs BETWEEN DEVELOPMENT INTERVENTIONS,
HOUSEHOLD FOOD SECURITY, AND NUTRITION

Having established the relevant dimensions of food security, the next
step is to outline a framework that links the concepts of food security


Food Securilt in Practice 1






Figure 1,1 The determinants of household food security


VIJ.,king capitall,
...................... ................ .................. ....... ............... .........

Othe.i
Food pruoductior I nc0M6- 3l,0t:Pg Cam; beahvaov
activitis


.. .. .. .....t ........


......Hou d hn~althi


Prices 0 o dsaffoc t'/
"ood socow uith
--- --- --- - ----------- ----------

]........ odcusewld n
OOthior c--vds ksod arQ-uisit,'Don


rs ..........b
Pubrbrc I'le 1-th
enviror~merc




I P, nss

I /NItritlOnlalStatujs
food utilization

/ ....... ..... .
.. .. .. .. .. -- -- -- --


/


- -


Source. Developed by aIuthor from rolama lnd Franonberger (992., 2).

to typical development interventions. This is shown in Figures 1.1
and 1.2. We begin with Figure 1.1. As it is a little complicated, it is
helpful to consider it in several steps.


1. The diagram is "framed" by the physical, policy, and social
environment. The purpose of this framing is to remind the
analyst that household food security issues cannot be seen in
isolation from broader factors. Examples of these
"environmental" issues are as follows:


* The physical environment plays a large role in determining the
type of activities that can be undertaken by rural households.
* Government policies toward the agricultural sector will have a
strong effect on the design and implementation of household
food security interventions. For example, a pricing policy that is
hostile toward agriculture will discourage production.
Interventions that ignore this fact are unlikely to succeed. The
presence of social conflict, expressed in terms of mistrust of
other social groups or even outright violence, is also an
important factor in the design and implementation of


2 Food Securily in Practice


P
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........................................ .. .............................. ............... ............................................. ..... ... .. ....... .. ........... ........ ............................ .... ... ...... ....... .... ... ....... .. ...........


1 '~ ii~






interventions. In such circumstances, maximizing beneficiary
participation becomes especially problematic. For example,
wealthier groups may take control of projects for their own
benefit, to the exclusion of poorer members. Alternatively, social
conflict may encourage groups excluded from an intervention
to take active steps to subvert it. A certain degree of social
cohesion is necessary if group activities, such as group-based
microcredit schemes or collective work on an infrastructure, are
to succeed.
2. The resources, or endowments, of households can be divided into
two broad categories: labor and capital. Labor refers to the
availability of labor for production. It incorporates both a
physical dimension-how many people are available to work-
as well as a "knowledge" or human capital dimension. For an
agricultural household, this knowledge includes formal
schooling and formal training in agricultural production. It also
includes informal knowledge obtained via trial and error, past
farming experiences, discussions with friends and relatives,
observations made about practices on neighbors' farms, and so
on. Capital refers to those resources-such as land, tools for
agricultural and nonagricultural production, livestock, and
financial resources-that, when combined with labor, produce
income.
3. Households allocate these endowments across different activities
such as food production, cash crop production, and
nonagricultural income-generating activities (such as wage
labor, handicrafts, food processing, services, and so on) in
response to the returns each activity generates. In addition,
households may receive transfer income from other households,
from some public body such as the state, or from an NGO.


Together, these four sources determine household income.
4. Households face a set of prices that determine the level of
consumption that can be supported by this level of income.
5. Consumption is divided between those goods that affect
household and individual food security and all other goods.
6. Goods that affect food security include food consumption at the
household level (referred to as food access in much of the food
security literature); goods directly related to health care, such as
medicines; and goods that affect the health environment, such as
shelter, sanitation, and water. These three goods, together with
knowledge and practice of good nutritional and health practices
(called "care behaviors") and the public health environment (for
example, the availability of publicly provided potable water),
affect illness and individual food intake, which in turn generates
nutritional status or food utilization. Note that this part of the
diagram is exactly the same as a diagram describing the causes
of malnutrition found in Maxwell and Frankenberger (1992, 25).
Stars are placed beside the household food acquisition, food
intake, and food utilization boxes to emphasize that these are
food security and nutrition outcomes.
7. Finally, note that food security is not static over time. There are
second-round, or feedback effects, denoted by the dashed lines in
Figure 1.1. Suppose a donor funds a project that improves the
provision of agricultural extension. This can be thought of as a
project that increases the human capital of the household. In
turn, this raises income. Some of this income might be used to
acquire additional capital stock, such as agricultural implements.
In turn, this raises household income in subsequent years.
Allocations of food, expenditures on education, and health will
affect the level and distribution of human capital within the


Food Security in Practice 3






Figure 1.2 The impact of development interventions on household food security


N__w fechn_!oqes Aq__icuaI extmsicrn


AgrSkiclts uraietenio


Envlironment~ -- --~k~
c0 UA Infitutt onal development
Village association


Nutritialon a
.. ....... .car ......... ...education ) Water and
OIsanitation
....................... ....................... ........ ....... .
acome eer1g Cae behaviors Public health
............. ......................................... .env io nm e nt
Private/
public transfers
Sn ss...................
Household health I 11
nv onme n ............................................
/rNutritional status -
......... ...... --- ---------- food utilization
f Goods affecting /
food security Health care
j .................................................. : .................................................. /
r -- ---..........-------... ------Food Int~O ke
fHousehor d
food acquisition|
------------------ --- -- -------


p
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I. there goods


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So.rce: Developed by author from Maxwel! and Frankenbergrt (1992 251)
household. These investments will also affect the household's
ability to generate income in subsequent years. In other words, a
well-designed intervention has the potential to set in train a
virtuous circle of development, whereby increased income
generates greater wealth, which in turn generates higher levels of
income, consumption, food security, and nutrition. But it is also
worth noting that not all these feedback effects are benign.
Increased income generation may induce an offsetting reduction
in private transfers received from other households, a
phenomenon known as "crowding out."


It is now possible to uncover the links between development projects
and household or individual food security. In Figure 1.2, these
interventions (written in bold) are superimposed on Figure 1.1. They
are placed within the diagram at the point where their direct impact
is observed.
A. A series of interventions are designed to improve the broader
environments that affect household food security. Examples of
these include: in the environment area, field operations such as
soil, water, and forest management; in the policy area, providing
an appropriate institutional environment for private agriculture;


4 Food Securily in Practice


P
H
Y
S

C
A
L


-- ------------


_. .._------------- i..


New technorrlogies
Ir Irigati onI r~i






and, in the social area, strengthening small farmers'
associations.
B. There are interventions that increase the level of and returns to
capital. Examples include the rehabilitation of irrigation
facilities, the provision of credit, and the development of new
technologies.
C. There are interventions that increase the stock of knowledge or
human capital. Examples include literacy training or extension
services that provide new technical skills in the nonagricultural
sector.
D. There are interventions that improve rural infrastructures,
notably roads. Reducing transport costs improves household food
security in two ways: by increasing the returns from production
activities and by reducing the costs of obtaining food and other
goods for consumption.
E. There are interventions to improve knowledge of good health care
and nutrition practices.
E There are interventions that improve the health environment,
such as improved access to safe drinking water and health
services.

It is worth noting that many development interventions attempt
to improve the broad environment in which households exist or to
raise levels of human or physical capital. These do not directly affect
food security outcomes. Instead, they raise incomes. One should not
assume, however, that there is invariably a strong link between
higher income and food security and nutrition outcomes.
In the case of nutritional status or food utilization, food is not the
only input. Increased food access will not necessarily improve food
utilization when other factors, such as the health environment, are


not favorable. A second cause is ignorance. Households and
individuals may simply not be aware of all the components of a
healthy diet or of good health practices. The third reason for these
weak links is that households, and individuals, face many competing
demands for their limited financial resources. They may want to
increase the level or quality of their food consumption, but they may
S also want to reduce labor drudgery, be better dressed, be able to send
their children to school, and so on. In those projects that emphasize
beneficiary participation, beneficiaries might choose interventions
that have their largest impact on an outcome other than food
security or nutrition.
An attraction of the framework here is that it provides some prior
indications as to which interventions are most likely to have such an
impact. For example, interventions directed at strengthening local
institutions are unlikely to have a direct impact on nutritional status.
Further, greater beneficiary involvement in project selection, design,
and implementation may also result in interventions that do not
address food security and nutrition concerns. Put another way, the
principal concerns of beneficiaries may relate to objectives that differ
from those of the project designer who seeks to improve food security
and nutrition. Such observations do not necessarily invalidate
approaches such as greater beneficiary participation, but do highlight
the challenges associated with linking these to food security and
nutrition.
It is also important to note that the strength of these links is not
constant across all households within a given population. As many
development practitioners are aware, women often face particularly
severe constraints or have access to weaker productive assets. There is
reasonable evidence to suggest that they devote a larger share of
resources under their control to food security and nutrition objectives.


Food Security in Practice 5







This provides the potential for a clear win-win scenario. Interventions

directed toward women both relieve constraints on a particularly

disadvantaged group and have maximal impact on food security and

nutrition indicators.

Accordingly, an attraction of this conceptual framework is that it

encourages project staff to consider carefully the likely impact of a

proposed intervention on food security and nutrition. A second

attraction is that it indicates that staff, when designing interventions,


need to obtain and interpret information on the following questions:

Who is food-insecure or at nutritional risk? Or, where should

this intervention be located in order to maximize impact on

these indicators?

Why are they food-insecure or at risk? Or, what interventions

will have maximal impact on improving these indicators?

How best can this intervention be monitored and evaluated?

Or, how can staff assess how well the project is working?


Table 1.1 Uses of this material at different points in the project cycle


Points in the project cycle


Title

Measuring nutritional dimensions of
household food security

Choosing outcome indicators of household
food security

Rapid appraisal techniques for the assess-
ment, design, and evaluation of food secu-
rity interventions

Constructing samples for characterizing
household food security and monitoring
and evaluating food security interventions

Targeting: Principles and practice

Designing methods for monitoring and
evaluating food security and nutrition
interventions


Brief description

Outlines different measures of nutrition and explains how
these can be implemented

Outlines different measures of food security and explains
how these can be implemented

Outlines community-based methods for the assessment and
monitoring of food security


Reviews different methods of selecting a sample for needs
assessment, monitoring, and evaluation


Reviews different methods for targeting interventions

Outlines rigorous, yet simple to implement, methods for
project evaluation


Chapter

2,


4,


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00S
0 4


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0 E
0r~ ~
0v15.
o~9 ot
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Source: Compil, d by autho.




6 Food Secuil l in Practice


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.. . .. .. .


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i i






The next section introduces the material that provides answers to
these questions.

INTRODUCTION TO THE CHAPTERS

In addition to this introductory material, this book contains six
chapters on different aspects of operationalizing food security and
nutrition in development projects. Table 1.1 provides a list of these
chapters and indicates where, within the project cycle, they can be
used.
Some explanation of the particular topics chosen is warranted.
Our focus on food security and nutrition reflects, in part, our own
background and experience with development projects. But this does
not mean that this book is only for practitioners in these fields. We
hope that readers with a related interest, such as livelihood security,
will also find this book useful. The material we present is the
outcome of our interactions with project staff in multilateral and
bilateral donor organizations, NGOs, officials in developing country
governments, and project beneficiaries over the last three years.
Working with these groups on project design and implementation
helped improve our understanding of the largest gaps between theory
and practice. This material attempts to fill these gaps. However, it
does not pretend to be comprehensive. For example, although we
discuss the design of survey instruments to obtain information on
food security and nutrition (Chapters 2 and 3) and sampling
methods for the implementation of such surveys (Chapter 6), we do
not discuss the logistics of survey implementation because (1) our
sense is that this is well-known territory for many development
practitioners and (2) there are a number of excellent reference
materials already in widespread circulation. Being selective rather


than comprehensive also enabled us to write a shorter, and we think
more manageable, volume with chapters that can be read either as
stand-alone pieces or as a whole.
Set against these advantages are several disadvantages. First, we
have no doubt missed some topics that at least one development
practitioner would have included.
Second, the selective nature of this material might make it appear
somewhat disjointed. However, we partly rectify this concern by noting
that the chapters that follow can be grouped by their basic function in
terms of assisting project staff in obtaining food security and nutrition
and aiding in interpreting this information. Chapters that extensively
discuss issues and techniques for obtaining information are Chapters 2
(nutritional dimensions of food security), 3 (choosing outcome
indicators of food security), 4 (rapid appraisal techniques), and 5
(constructing samples). Chapters that emphasize the interpretation,
use, and analysis of this information are 2 (nutritional dimensions of
food security), 3 (choosing outcome indicators of food security), 6
(targeting), and 7 (designing methods for monitoring and
evaluation).
Alternatively, these chapters can be grouped according to the
questions, listed at the end of the previous section, that they answer.
Specifically, the following chapters can be used to:
identify who is food-insecure or at nutritional risk-chapters 2,
3, 4, and 5;
identify causes of food insecurity and nutritional risk and the
interventions that will alleviate these causes-this
introduction, plus chapters 2 and 4;
design monitoring and evaluation mechanisms-chapters 2, 3,
4, 5, 6, and 7.


Food Security in Practice 7






THE CHAPTERS IN BRIEF

Chapter 2: Measuring Nutritional Dimensions of
Household Food Security
Many development projects intended to improve nutrition are
constrained by a limited knowledge base. In particular, it is not clear
whether the constraining factor to improved nutrition is poor access
to food; weaknesses in the provision of health care, child care, or in
the general health environment; or some combination of these. This
chapter explains how such knowledge bases can be expanded using
the principles of nutritional assessment. It answers the following
questions: What is nutritional assessment? How can nutritional
assessment assist the process of targeting projects to those most in
need? How can nutritional assessment direct the selection and
sequencing of interventions? How can nutritional assessment guide
project monitoring and evaluation?

Chapter 3: Choosing Outcome Indicators of Household.
Food Security
Any commitment to improve food security and nutrition carries with
it an important implication, namely the need to measure food
security outcomes at household and individual levels. Measurement
is necessary to characterize the severity of the food security problem
and to provide a basis for measuring impact. This chapter shows how
to construct four measures of household and individual food security:
individual intakes, household caloric acquisition, dietary diversity,
and coping indices. For each, an explanation is given regarding what
this indicator measures, how the data is collected, and how indicators
of food security are calculated. Each description ends with a
commentary on the strengths and weaknesses of the method. This is


followed by an explanation of how these different measures can be
compared, illustrated using data collected in the Zone Lacustre
region of Mali. The guide also proposes a possible sequence of
activities that would use these indicators at different stages of the
project cycle.

Clmapter 4: Rapid Appraisal Techniques for the
Assessment, Design, and Evaluation of Food Security
Interventions
Participatory appraisal techniques are "a family of approaches and
methods to enable rural people to share, enhance, and analyze their
knowledge of life and conditions, to plan and to act" (Chambers
1994). These include mapping activities, transect walks, seasonal
calendars, wealth ranking, and analytical diagramming. Unlike
traditional, more extractive data-gathering methods, participatory
rural appraisal (PRA) techniques are premised on the notion that
local people have an enormous amount of local knowledge. Rather
than merely appropriating this information, in PRA local people
dominate the agenda, decide how to express and analyze
information, and plan and evaluate.
This chapter outlines the advantages and disadvantages of rapid
appraisal techniques in the context of food security interventions.
These techniques are low-cost; provide information quickly; require
little equipment; and by deliberately seeking local opinions, provide
insights that might be missed by more conventional methods. But
they require highly skilled personnel and are not suitable for
targeting purposes. Six rapid-appraisal methods are outlined: concept
definition; community mapping; household food security ratings;
seasonal time lines; conceptual mapping of threats to food security;
and the evaluation of interventions.


8 Food Security in Practice






Chapter 5: Constructing Samples for Characterizing
Household Food Secrity and Monitoring and Evaluating
Food Securityv Intlervenions
Reliable information on household food security is a prerequisite for
the accurate and effective design, monitoring, and evaluation of
projects. But collecting data is not a costless exercise. This chapter
discusses how random-sampling techniques-methods that use
some mechanism involving chance to determine which farms,
households, or individuals are to be studied-can economize on the
costs of gathering information while increasing the likelihood that it
will be both accurate and available in a timely fashion.

Chapter 6: T1argeting: Principles and Iractice
Many development agencies have a mandate to direct their
investments toward the poor; that is, there is an explicit requirement
that projects are targeted. This chapter introduces the principles
underlying targeting, stressing that targeting only makes sense when
the additional costs of doing so are outweighed by the additional
benefits in terms of reduction in poverty or food insecurity. It also
introduces the practice of targeting, beginning by distinguishing
between two forms of targeting: administrative and self-targeting.
Administrative targeting is the process by which project staff
determine eligibility criteria. Under self-targeting, the intervention is,
in principle, open to anyone who wishes to take part. However, it is
designed in such a way that it is only attractive to certain households.
The chapter explains how these methods can be implemented as well
as their strengths and weaknesses.


Chapter 7: Designing Methods for Monitoring and
Evaluating Food Security and Nutrition Interventimns
In recent years, many development agencies have made intensive
efforts to improve their efficiency and increase their impact on rural
poverty. At the heart of this new strategic management process is the
measurement of performance. But poorly thought-out evaluations
may inadvertently act as an incentive to target better-off groups,
which offer higher returns and promise faster disbursement of project
resources. In addition, there is a clear danger of placing a higher
priority on more easily measurable outcomes or indicators, which fail
to provide the information necessary to address broader objectives or
to enhance the effectiveness of rural development projects for "the
poorest of the poor."
This chapter emphasizes the design of quantitative impact
evaluation exercises for household food security and nutrition.
It provides development practitioners with the basic principles on
why, when, and how to choose and implement a particular
evaluation system. Two key features of a good impact evaluation
study are stressed: the availability of accurate baseline information
and a properly thought-out control group, which allows before-after
and with-without comparisons. The chapter also illustrates why the
involvement of the evaluation team in the earliest stages of project
design is the most suitable way to ensure a proper and accurate
evaluation without having to rely on more complicated statistical
techniques, as well as permit a sound learning process to ensue from
the evaluation exercise.


Food Security in Practice 9




2. Measuring Nutritional Dimensions of Household Food Security
Saul S. Morris


development projects and practitioners can play a critical
catalytic role in overcoming the nutrition problems of the
rural poor, either by strengthening the household resource
base for food and good health or by enhancing target groups' control
and management of these resources.
Unfortunately, many practitioners do not have all the
information they need to maximize the nutrition impact of rural
development projects. This chapter outlines methodologies that will
assist practitioners to improve the nutrition impact of development
activities. The methodologies described here are jointly referred to as
nutritional assessment. The chapter begins by explaining what is
meant by nutritional assessment and how it can reinforce linkages
between nutrition and agricultural development. It then considers
how nutritional assessment can be used in rural development
projects for beneficiary targeting and project formulation, as well as
for practical project monitoring and evaluation.
Nutritional assessment has great potential for geographical
targeting at little additional cost. In addition, it is also a useful input
into project formulation. It is invaluable at the monitoring and
evaluation stage because it offers the possibility of directly measuring
the human-welfare impact of development activities, and also
because the information generated cannot easily be manipulated by
interested parties. In the final section of the chapter, the theoretical
discussions are illustrated using data from Honduras.
Those interested in reading about these topics in more detail
should consult Gibson (1990) and WHO (1995).


BACKGROUND: THE ROLE OF NUTRITIONAL
ASSESSMENT IN MEETING THE CHALLENGE OF HUNGER
AND POVERTY

Nutritional assessments are measurements of body size, body
composition, or body function intended to diagnose single or
multiple nutrient deficiencies. Sometimes nutritional assessments
consist of highly controlled technical measurements, while in other
circumstances, they may be conducted in a participatory manner that
fosters community involvement and ownership of the project as a
whole. Findings may be interpreted at the level of the individual, but
are commonly aggregated over a community, district, or subnational
region.
Frankenberger et al. (1993) have shown that measures derived
from nutritional assessments may be viewed as the biological
manifestation of Nutrition Security, a "condition that combines
having access to adequate food, being well cared for, and enjoying a
healthy environment." The conceptual model developed by
Frankenberger and colleagues is reproduced in Figure 2.1. In this
model, rural development projects attempt to directly influence the
household's resource base, and thus household food security.

GETTING FAMILIAR WITH MEASURES OF
NUTRITIONAL STATUS

There are numerous different measures of nutritional status, varying
with respect to their ease of measurement, relation to dietary intake,


Food Security in Practice 11






Figure 2.1 Nutritional security


OUTCOME
physiological)
O ON UTRITIONAL STATUS
OUTCOME.
O U T C O- -------------------
(social)
Bio/ogisca factors

INDIVIDUAL LEVEL
N. UT IT" N SEC URI1Y
Adeqae Aeuate w t
.L. lary anti
IN T R A H O U S EH O D ................................................ ....................... ......
(processes O
Aiocation i resources among
different needs aInd distribution
within household to ensure
individual food security and
satisfaction of heaihi-related
needs of individual members
CONTROL AND MANAGEMENT
OF RESOURCES
WITHIN THE HOUSEHOLD

ACCESS TO RESOURCES AT; .---..-..-..--...-
A SHOUSEHOLD LEVEL Capacity to obtain adequate
resources for livelihood needs
HOUSEHOLD FOOD SECURITY
AND OTHER BASIC NEEDS

HOUSEHOLD RESOURCE
BASE
Access to productive assets
Employment opportunities
income generation
pubiO: services
social support me-hanisms
.- .. ... .


and velocity of change following shocks or improvements in the
individual's environment. There are three major classes of measures.
The first class uses clinical examinations to detect signs and
symptoms of advanced nutritional depletion. Examples are surveys of
goiter to detect iodine deficiency, or eye examinations to detect
vitamin A deficiency. With appropriate training, lay inquirers can be
used to determine levels of these conditions in the community.
In contrast, laboratory methods are usually invasive (involve
taking samples from sites of the body that are not immediately
accessible) and therefore poorly suited to routine use in a program
situation. These methods are used to detect decreased levels of
nutrients in body tissues or fluids, or decreased activity of an enzyme
that is nutrient-dependent. One example of a laboratory method with
potential for more general use is the detection of anemia by
hemoglobinometry (see below).
The third class, and the main focus of this chapter, is
anthropometry, or the measurement of body size and gross body
composition. The basic principle of anthropometry is that prolonged
or severe nutrient depletion eventually leads to retardation of linear
(skeletal) growth in children and to loss of, or failure to accumulate,
muscle mass and fat in both children and adults. These problems
can be detected by measuring body dimensions, such as standing
height or upper-arm circumference or total body mass (weight).
All of these measures are expected to vary by the age and sex of the
person measured, so that there is a need for the measurements to be
standardized for age and sex before they can be interpreted.' Easy-to-
use computer applications are available for these conversions.
The five most commonly used anthropometric indices are
described in more detail in Table 2.1. There is a strong emphasis
here on children under five years of age, because children are


12 Food Security in Practice


31, f 9






especially vulnerable to adverse environments and respond rapidly to
changes. In particular, when children do not receive the nutrients
they need, their growth is rapidly compromised, with long-term


Table 2.1 Commonly used anthropometric indices

Indicator Age group Requirements

HeigIt-for-age/LLngth-for-age Extensive training req uired
"Height" measured as recumbent for measurement of recurn-
length for under 2-year-olds. bent length of infants and
Measure referred to standard for Up to puberty young children. Accurate
well-nourished individual of same age information required
age and sex (usually National (often misreported in non-
Center for Health Statistics (NCHS]. literate societies).

Weight-far-age Accurate age information
Measure referred to standard for required (often misreported
well-nourished individual of the U to berty in nonliterate societies),
same age and sex (usually
NCHS).

Weiqhtfor-heiqhtfWeight-for-lejgth Extensive training required
Weight measure referred to for measurement of recum-
standard for well-nourished individ- bent length of infants and
ual of same height and sex (usually Infancy and young children. Two differ-
NCHS). "Height" measured as childhood ent body measurements
recumbent length for under required,
2-year-olds.


Mlid-upper arm circmference Relatively little training
Special insertion tape used to required.
identify midpoint of upper arm and All ages
measure circum,.ference at this
point,


Body mass index
Weight (kilograms) divided by
height (meters) squared,


Adult


Two different body
measurements required.


implications for their future productivity. On the other hand,
although adults also lose weight in response to severe energy deficit,
this effect can be very difficult to distinguish from their genetic
potential. The selection of appropriate measures for different
programmatic purposes is described in later sections of this guide.
Anthropometric measurements are subject to a number of sources
of error, including instrument error, investigator error, and recall
error (for measures based on age). These sources of error need to be
controlled, since they can easily lead to overestimates of the
frequency of malnutrition or underestimates of the effectiveness of
interventions. Special standardization procedures have been
developed to minimize measurement error (Habicht 1974).


USING NUTRITIONAL ASSESSMENT TO IMPROVE THE
iMPACT OF RURAL DEVELOPMENT PROJECTS


In the following sections, we show how nutritional assessment
methods may be used to improve project formulation, beneficiary
targeting, monitoring, and evaluation. Many of the approaches take
advantage of the increase in the availability of nutritional data that
has occurred since 1990 (see Chapter 4). Other approaches require
collection of new data; interested readers are strongly advised to
consult Chapter 6 before undertaking data collection activities.


Country Srattegyx Project Inception, atd Formulation
Currently available data on nutritional status may be especially
valuable at the country strategy and project inception stages, both for
targeting subnational regions and for needs assessment.
Where nutrition security is a priority, identifying the geographic
areas of a country most in need of rural development interventions is


Source: Compled by author;


Food Securily in Practice 13






facilitated by reference to existing sources of nutrition data. The
principal nutritional indicator for targeting subnational regions is
proportion (absolute numbers) of children under 5, or
of school age (6-10 years), with low height-for-age
(stunting).
This indicator, more than any other, is recommended for
identifying areas of greatest need for targeting economic and health
interventions (WHO Expert Committee 1995). Weight-based measures
are, in general, too sensitive to illness and specific childcare practices,
and are subject to seasonal variations. Data on stunting are available
at the subnational level for virtually all poor countries, from surveys
(such as the Demographic and Health Surveys) and from school
height censuses. The usefulness of the measure for project targeting
can be enhanced by expressing the numbers on a per-kilometer-
squared basis.
Regions/communities with large numbers of children
characterized by low height-for-age are found in Africa, Asia, and
Latin America and the Caribbean, though the condition is most
common in South Asia (UN ACC/SCN 1998). In Latin America and
the Caribbean, the prevalence of low weight-for-age (underweight)
can be used as a proxy measure for low height-for-age (stunting),
since the two indicators are highly correlated in this region where low
weight-for-height (wasting) is not seen.
Another measure that may be of use in contexts of extreme
poverty is proportion (absolute numbers) of adults and adolescents
with low body mass index (BMI). This indicator identifies areas of
severe food insecurity. Data are sometimes available at the
subnational level from surveys (such as the Demographic and Health
Surveys, which commonly assess the nutritional status of women of
reproductive age). The usefulness of the measure for project targeting


can be enhanced by expressing the numbers on a per-kilometer-
squared basis. Caution should be exercised when using this measure
in areas where advanced HIV disease is prevalent, since individuals
with HIV disease are thin, but not as a result of food insecurity.
Regions/communities with large numbers of adults/adolescents
characterized by low BMIs are found in Asia and Africa. Small mid-
upper arm circumference is sometimes used as a proxy measure for
low body mass. It should be noted that the presence of significant
numbers of adults (say, 10 percent) with very low BMIs normally
indicates a need for emergency relief rather than rehabilitation or
development.

Needs Assessment
Also at the project formulation stage, nutritional measures can be
reviewed to assess the needs of project beneficiaries. Normally this
process is carried out for the project area as a whole, but where
possible, it is informative to disaggregate by variables known to be
linked to nutrition security, such as landownership, gender of
household head, sanitary/health care resources, and so on. Nutrition
indicators for needs assessment are described in Table 2.2.
The needs assessment process should start by collating nutritional
data from as many different population-based sources as possible (data
collected at health centers or from currently operating selective programs
are much more difficult to interpret because of the inevitable biases).2
The information should be arranged by indicator, age group studied, and
year of collection. Conflicting evidence from different sources should be
carefully reviewed with the help of local experts to identify the source of
the discrepancy. Subsequently, it may be helpful to rank the different
problems identified according to their frequency in the population.
It is useful to compare the same indicator across different age/sex


14 Food Secu.trill 1it1 Practice





Table 2.2 Nutritional indicators for needs assessment exercises

Indicator interpretation

Prevalence of low height-for- Children's skeletal (linear) growth compromised
age (stunting) in preschool or due to constraints to one or more of nutrition,
school-age children health, or mother-infant interactions. In some
populations, these constraints are already
apparent in utero. Quality of diet a more frequent
limitation than inadequate quantity,
Prevalence of low weight-for- Children suffer thinness resulting from energy
height (wasting) in preschool deficit and/or disease-induced poor appetite,
or school-age children malabsorption, or loss of nutrients.
Prevalence of1 low weight-for- This indicator confuses the two processes
age (underweight) in preschool described above and is therefore not a good
or school-age children indicator for needs assessment purposes.
Prevalence of low body mass Adults suffer thinness as a result of inadequate
index (BMI) in adults or energy intake, an uncompensated increase in
adolescents physical activity, or (severe) illness.
Prevalence of low mid-upper As above. Restricting analysis to the arm has the
arm circumference in advantage of reflecting the mass of just three tis-
adults/adolescents sues-bone, muscle, and fat-the last two of
which are particularly sensitive to body weight
gain/loss.
Prevalence of low serum Children suffer vitamin A deficiency, either as a
retinol in preschool children result of low intake of vitamin A in the diet, or
because there is a high frequency of infection,
leading to sequestering of vitamin A from the
blood.
Prevalence of low hemoglobin Children suffer from anemia, either as a result of
anemiai) in preschool or lowl iron intakes or poor absorption, or as a result
school-age children of illness. Severe protein-energy malnutrition
and vitamin B12/folate deficiency can also lead
to anemia.
Prevalence of low hemoglobin Women suffer from anemia as a result of low
(anemia) in nonlactating, non- iron intakes, poor absorption, illness, or exces-
pregnant women sive losses of blood. Severe protein-energy mal-
nutrition arnd vitamin B12/folate deficiency can
also lead to anemia,
Prevalence of low hemoglobin As above. Anemia is rare in adult F en except in
(anemia) in men conditions of extreme iron-deficient diets.
Source Compihled by auPtho


groups. For example, stunting in children where adults are also short
may be more suggestive of intergenerational deprivation effects than
of current food access or health problems; similarly, while wasting in
children may indicate poor feeding practices or health problems, the
combination of wasting in children and low body mass in adults
indicates a crisis in entitlements to food. Specific nutrient deficiencies
(for example, iron or vitamin A) are not uncommon in children as a
result of poor feeding practices; however, when they are also found in
adults, a problem of food access should be suspected.
It is also important to contrast different indicators. Substantial
childhood wasting in the absence of stunting, for example, indicates
a nutritional crisis of very recent advent. Stunting in the absence of
wasting, on the other hand, indicates a complex and deep-rooted
nutritional problem, sometimes not directly related to food
availability at the household level. Similarly, specific nutrient
deficiencies in the absence of stunting or wasting may indicate either
poor feeding practices or a general problem of dietary quality, while,
combined with stunting and/or wasting, they are more likely to
indicate profound poverty of resources at many levels.


Project Implemtentation
Just as nutrition data can assist with targeting and needs assessment
at the project formulation stage, so they can also be of assistance for
small-area targeting and sequencing of interventions in the
implementation phase.
The potential of small-area targeting is discussed in Chapter 6.
This procedure is greatly facilitated when nutritional data are
available at a fine level of disaggregation, permitting the
identification of priority-need small areas (usually districts or
municipalities) within the overall area of influence of the project.


Food Securily in Practice 15






School height censuses are an obvious source of such data, but
detailed nutritional surveys are also occasionally available. Where
such data are available, their use and interpretation are exactly as
described in the previous section (see above). Where these data are
not available, conducting a large-scale nutritional survey for the
purpose of small-area targeting is likely to be cost-ineffective; other
indicators should therefore be used (see Chapter 3).
Only in exceptional circumstances are nutritional data available
at a level of disaggregation sufficiently fine to permit community-
level targeting. Often, however, socioeconomic data can be collected
that permit the estimation of the expected rate of malnutrition in the
community.
For a number of reasons, it is unwise to use nutritional measures
for household-level targeting in rural development projects. These
reasons include the following:
Many of the measures that have been discussed in the previous
sections are dependent on the presence of a household member
of a particular age and/or sex, and thus exclude a priori
households of a different composition.
Most nutritional measures are age-sensitive; for example, a
two-year-old child is much more likely to be stunted than a
one-year-old, even though the conditions of the household are
identical.
Some measures of nutritional status change in a relatively short
time, so that a child who has just been ill can easily be wasted,
even when the household's conditions are generally good.
Many other measures reflect past conditions, or even
intergenerational effects, more strongly than current conditions.
The cutoffs used to determine the presence or absence of
malnutrition are arbitrary, so that a child with a height-for-age


Z-score of -2.1 is classified as stunted while one with a Z-score
of -2.0 is not, even though there is little reason to include the
first family in a development program and not the second.
Finally, there have been instances where families in areas with
projects using individual targeting-based nutritional status
have actually withheld food from children so that their
nutritional status will deteriorate and the family will be entitled
to participate in the project.

The nutritional needs assessment described above is expected to
identify the broad features of an appropriate nutrition strategy for the
project area. Beyond this, the search for interventions should be
guided by an analysis of the constraints to nutrition security in each
of its contributing areas: household food security, health, and
mother-infant interaction. As nutritional indicators represent the
joint outcome of all of these factors, there is only a limited amount
of information that they can provide on the causes of, and solutions
to, nutrition insecurity.

Monitoring and Evaluation
Nutritional assessment can be an extremely valuable element of the
monitoring and evaluation process in rural development projects for
a number of different reasons:
Nutritional measurements provide a measure of human welfare
that is sensitive to changes in food supply, as well as to other
community development processes.
Nutritional measurements provide a nonsubjective, quantitative
assessment of progress toward a fixed goal (the elimination of
malnutrition).
Nutritional measurements cannot easily be falsified by


16 Food Seciuri' in Practice






individuals with vested interests in the outcome of the
interventions (including the subjects themselves).
*Nutritional measurements are relatively easy to obtain, either
in sentinel sites for the purpose of ongoing monitoring, or in a
sample of the entire study area for the purpose of evaluation.


In order to assess whether project interventions have improved
nutrition security among beneficiaries, it is first necessary to identify
which nutritional indicators could plausibly have been altered by
project interventions and which subgroups of the population are
most likely to have benefited. For example, a project that has as its
sole aim the promotion of home gardening should not be expected to
produce an impact on adult BMI, since vegetables are, in general,




Table 2.3 Nutrition indicators for monitoring and impact
assessment


Improved availability of food
(dietary energy) at the house-
hold level, in areas where
dietary energy intake is initially
constrained.
Improved availability of food at
the individual level, plus
improvements in other basic
needs, especially health
Increased intake of animal
products
Increased intake of fruits and
leaves
Source: Compiled by author:


Most relevant nutritional indicators

Body mass index (BMI) (adults)
Weight.for-height Z -score (two to five year olds)
Weight-4or-age Z.score (two to five year olds)
Height-for-age Z.score (long-term evaluations
only; two to five year olds)
Height-for-age Z-score (under fives)
Weight-for-age Z-score (under fives)
Weight-for-height Z-score (under fives)

Anemia (hemoglobin)
Serurr vitamin A (retinol)
Serum vitamin A (retinol)


rich in micronutrients but not in energy.' Similarly, a project aimed
at increasing basic grain production in rural Africa is unlikely to
affect the nutritional status of infants less than six months of age,
since these infants usually consume only breast milk and are
therefore unaffected by changes in the family diet. Relevant
nutritional indicators for assessing the impact of a variety of different
interventions are shown in Table 2.3.
The length of time that an intervention has been in place is also
an important variable to take into account when selecting nutrition
indicators and study populations, since different indicators reflect
events in the recent and distant past with different intensities, and take
different amounts of time to respond to such changes (Table 2.4).






Table 2.4 Time reference of different nutritional indicators


Indicator

Serum vitamin A


Hemoglobin
Weight-for-height, body mass
index (BMI)
Height-for-age


Weight-for-age

Source:Comnipeihd by author


Time reference for dietary influences

Essentially, consumption over recent days, which
can be influenced by consumption events up to
four months in the past
Consumption over recent weeks and months
Consumption over recent weeks

Cumulative life-time consumption, especially
influenced by events occurring in first two years
of life and prenatally
SMixture of weight-for-height and height-for-age
effects


Food Securil' in Practice 17






There are many different ways of using nutritional assessment to
determine whether project interventions are improving, or have
improved, the nutrition security of the beneficiary population. In the
following sections, we examine four such methods: (1) the use of
sentinel sites for the monitoring of nutritional impact, (2) the
examination of changes in nutritional status of populations before
and after implementation of project activities, (3) the analysis of
changes in the nutritional status of individuals before and after
implementation of project activities, and (4) the comparison of
achieved nutritional status across beneficiary and nonbeneficiary
populations.

Sentinel sites for monitoring lutritional status
Sentinel sites (a few purposively selected "representative" locations
where data collection and analysis activities are concentrated) have
frequently played a major role in project monitoring activities. For
project management, the advantage of setting up a sentinel site
system is that a relatively small number of people can be intensively
trained to provide needed information in a timely and systematized
manner. On the other hand, there is always the danger that the
sentinel sites selected may not be representative of the project area as
a whole, and that the data collected may become more or less
reliable over time as those charged with the data collection master
the techniques or, alternatively, lose interest in the monitoring
process.4
The most important element of a successful monitoring system is
a mechanism for ensuring that the data are promptly collated and
analyzed so that they can feed into decisionmaking processes without
delay. Nutritional indicators should be selected on the basis of the
simplicity of measurement: weight-for-age would be the indicator of


choice in many communities, although mid-upper arm
circumference may be as good or better in communities where acute
or seasonal food shortages are known to occur and to result in
fluctuations in body mass. The analysis of the data should focus on
obtaining moving averages5 that reflect important changes in
nutritional status without being excessively dominated by short-term
'Iblips." It is likely to be necessary to control for the effect of the
aging of the study cohort over time, as this leads to apparent
improvements in nutritional status that are-sadly-illusory.
Samples of approximately 100 individuals are likely to be sufficient
for the monitoring of trends over time, with measurements perhaps
every two or three months. Ongoing monitoring may be linked to the
evaluation strategies described in the following sections, but it is
important to realize that it does not, in and of itself, provide evidence
of any impact of project activities. Rather, it indicates that within the
intervention area, changes are or are not occurring in the direction
expected, and are or are not of the desired magnitude (see Chapter 7).

Evaluating changes in nutritional status of populations
before and after implementation of project activities
One popular way of determining the impact of project activities on
nutrition security is to conduct one survey prior to implementation
and another at the end of the evaluation period, examining changes
in the nutritional profile of the population over the two points in
time. This type of evaluation is credible if it can be demonstrated that
the population surveyed is the same at each period in time (for
example, a representative sample of all adult women in the project
zone of influence). It is not necessary for the individuals in the
survey to be the same; indeed, often it is unavoidable that the
individuals are different, such as when the nutritional status of


18 Food Security in Practice






children under five is measured before and after a five-year
development project. The comparison may be strongly influenced by
factors specific to the timing of the two surveys. This is particularly
the case when nutritional measures are used that are sensitive to
short- or medium-term fluctuations in intake (for example, serum
vitamin A). It is less of a problem when using measures such as
height-for-age Z-scores, which reflect cumulative influences over a
substantial period of time.
When the beneficiary population alone is studied, the evaluation can
determine whether the observed changes in nutritional status are of the
expected direction and magnitude, but is unable to causally link
program activities to observed changes. When a "control" group is also
measured at the same time points as the intervention group (see Chapter
7), it is possible to infer whether changes in nutritional status appear to
be more beneficial in the intervention group than in the control group.
The before-after comparison is usually expressed as the change in mean
values of the nutritional indicator, but can also be expressed as the
relative (or absolute) change in the proportion of the population with
values below some critical measure. The latter comparison may be more
relevant from a human welfare perspective, but requires larger sample
sizes than the comparison-of-means approach.
One factor specific to studies with nutritional status as outcomes is
that the interpretation of the results will be strongly influenced by the
age composition of the study population. If the age composition has
changed between initial and final surveys, or if the intervention group
has a different age structure from the control group, be it ever so slight,
this must be taken into account in the analysis. Since adjusting for age
effects requires some knowledge of statistical methods, the utmost care
should be taken to ensure comparability of the initial and final samples.


Analysis of changes in nutritional status of individuals
before and after implementation of project activities
In some situations, it is possible to track individuals over time and to
examine associations between project activities and changes in
nutritional status at the individual level. This approach to measuring
project impact is expected to be far more sensitive than the approach
outlined above.' An individual's final height minus initial height is
referred to as gain in height, while their final weight minus initial
weight is referred to as weight gain. Since the amount of gain in
height and/or weight is dependent on the time elapsed between the
two measures, it may be appropriate to express these measures as
gain per unit time, usually referred to as height or weight velocity.
It is very important to realize that height and weight velocity are both
sex- and (especially) age-dependent, so that analysis must take
account of different age structures of intervention and control groups.
One other complication that should also be borne in mind is that
many individuals will not be able to be traced at the time of the
second survey. Since these individuals are always different from those
who remain traceable, the picture of project impact obtained may be
unrepresentative.
It is not a good idea to calculate an individual's change in
Z-score from one time period to another, since, for example, a half
Z-score deterioration in nutritional status in an infant can have very
different physiological implications from a half Z-score deterioration
in an older child. Such comparisons are also confounded by
technical problems with the customarily used National Center for
Health Statistics (NCHS) reference. A new NCHS reference has been
available since December 2000.


Food Securi/ly' i Practice 19






Comparison of achieved nutritional status across
bineficihrvy and nonbeneficiarv communities
In the absence of data on nutritional status prior to intervention, it is
possible to directly compare the attained nutritional status of
children of project beneficiaries with the attained nutritional status of
children of nonbeneficiaries. In order to be able to interpret the
results of such a comparison, it is necessary either to assume that
beneficiaries and nonbeneficiaries were comparable prior to the
project intervention, or to adjust statistically for variables known to
affect beneficiary status. The many dangers inherent to both
approaches are explained in detail in Chapter 7.
If all concerns about using these methods are satisfied,
beneficiaries may be compared with nonbeneficiaries using either
average (mean) nutritional status, or the proportions falling below a
critical cutoff point. If the latter method is used, it is particularly
important to select an indicator that can reasonably be expected to be
sensitive to dietary intake and changes in the household environment
over the period of evaluation. Some degree of internal control may be
obtained by comparing the experience of two subgroups of the
population. The first subgroup was expected to respond to the project
interventions, while the second subgroup was not expected to
respond to the particular kind of interventions implemented, or
within the time frame under consideration.


CASE STUDY OF THE RURAL DEVELOPMENT PLAN FOR
THE WESTERN REGION, HONDURAS

Project Placement
Figure 2.2 shows the prevalence of severe stunting (height-for-age
below -3 standard deviations of the NCHS median) in the 18
departamentos of Honduras. The data are taken from the Sixth
Census of First-Graders' Heights (Republic of Honduras, Secretary for
Education 1996). The prevalence of severe stunting exceeds 21
percent in four departamentos of the West (South-West) of Honduras:
Copan, Intibuca, La Paz, and Lempira. The Rural Development Plan
for the Western Region (PLANDERO) project7 covers Copain and
Lempira, but also Ocotepeque, where the prevalence of severe
stunting is half that of Intibucai.

Figure 2.2 Percentage of severely stunted first graders,
Honduras, 1996


Note,: Sevtre iSf I rjdicpted idwsted i hiqhlfr- a g a Z-s7cores ;s tlim ii 3


20 Food Seci/uif' in Practice






Figure 2.3 shows the number of severely stunted first graders per
100 hectares of land area. Intibuca, La Paz, and Lempira have the
highest densities of malnourished children in the country, followed by
Francisco Morazan and Cortes, areas where high population densities,
rather than high prevalences of malnutrition, result in high
concentrations of malnourished children. Copan is the sixth of 18

Figure 2,3 Density of severely stunted first graders per
100 hectares, Honduras, 1996


m".. Source: Compiled by author.
Niote: Severe stunting ts idicated by heiht-for-age Z-scores less than 3.

departamentos when ranked by density of malnourished children,
and Ocotepeque is the eleventh of 18. Table 2.5 shows the
correspondence between the rankings based on the prevalence of
malnutrition, and those based on the density per unit land area.
It appears that the location of the PLANDERO project is generally
appropriate for a project aiming to affect nutrition security in
Honduras, although it could be argued that it would have been
preferable to exclude the departamento of Ocotepeque from the
project's zone of influence.


Table 2.5 Severely stunted first graders per 100 hectares,
and proportion of severely stunted first graders in the 18
departamentos of Honduras


Severely stunted first graders
Department per 100 hectares
(count)
Intibuca '1.83
Lempira 1.79
La Paz 1.56
Francisco Morazan 1.40
Cortes 1.32
Copon | 1.23
Comayagua 1.05
Santa Ba rbara .92
Yoro .91
Valle .84
Ocotepeque .73
Atlantida .67
Cholouteca .58
Colon .50
El Paraiso .45
Gracias a Dios .34
Oiancho .29
Islas de la Bahia .26
Source Compied by author from survey data.


Proportion of severely
stunted first graders
(percent) frank)
30.70 1
27.22 2
21,11 4
8.24 14
7,95 15
21.27 3
13,77 7
17.95 5
12.26 9
7,79 16
14.52 6
8.51 12
9,88 10
8.46 13
12.80 8
4,49 17
9.52 11
1.18 18


Needs Assessment
Nutritional parameters for the project area are given in Table 2.6.
The information has been collated from three different surveys and
censuses conducted in recent years. Childhood stunting is the major
nutritional problem in the area: The levels recorded, around 60
percent of all children, are among the highest in the world. There is
virtually no wasting in this population, so that the relatively high
levels of underweight can be attributed entirely to stunting. Similarly,


Food Securilty in Practice 21







there is very little chronic energy deficiency in adults: Although 8
percent of mothers of young children had BMIs below 18.5, virtually
none had values below 17 (severe energy deficiency).
These sources reveal that in the area of influence of PLANDERO,
the proportion of stunted preschool children rises from 33 percent in
the highest (national) income quartile to 62 percent in the lowest


Table 2.6 Nutritional indicators, western Honduras


quartile, and from 39 percent in households with high caloric
adequacies to 67 percent in those with the lowest. The fact that
stunting does not fall to low levels, even among those who are
relatively well-off, may be attributed to (1) environmental features
(for example, illness), which no one living in the region is protected
from, and (2) intergenerational effects, reflecting the low stature of


Severe stunting (HAZ <-3)

Stunting (HAZ < 2)


Age group

First graders

First graders


Stunting HAZ < --2) Cilden five years


Underweight (WAZ < ..2)


Wasting (WHZ < -2)


Severe stunting {HAZ < .3)
Stunting (HAZ ..2)1
Underweight (WAZ < -2)
Wasting (WHZ < -..2)
Low body mass index
(BMI< 18,5)
Low serum retinol < 20 g/d!)
Anemia (Hemoglobin < 11 g/dl)


Anemia (Hemoglobin < 11 g/dl)


Children < five years


Children < five years


Child.re. n 12..71 months
Children 12--71 months
Children 12-71 months
Children 12-71 months
Mothers of children
12-71 months
Children 12-71 months
Children 12 -71 months


Percent
Detected

22.6


60.0


32.8


3.5


30.3
6217


37.6
1.5

8,3
18.7
29.7


Mothers of children
12---71 months 26,7


Geographical area

Copan, Lempira, and Ocotepeque

Copan, Lempira, and Ocotepeque

Rural areas of cOcotepnqrire, La Paz, Le rpira, and Inrrhibnc


Rural areas of Ocotepeque, La Paz, Lempira, and Intibuca


Rural areas of Ocotepeque, La Paz, Lernpira, and Intibuca


Rural areas of Ocotepeque, La Paz, Lempira, and Intibucai
Rural areas of Ocotepeque, La Paz, Lempira, and Intibuch


Rural areas of Ocotepeque, La Paz, Lemrpira, and Intibuca
Rural areas of Ocotepeque, La Paz, Lernpira, aid InribuchI

Rural areas of Ocotepeque, La Paz, Lemprira, and IntibucI
Rural areas of Ocotepeque, La Paz, Lempira, and Intibucai
Rural areas of Ocotepeque, La Paz, Lempira, and Intiburca

Rural areas of Ocotepeque, La Paz, Lemrnpira, and intibuc.


Source

Sixth Census of Height of First
Graders, 1996
Sixth Census of Height of First
Graders, 1996


National Household Consumption,
Income, Expenditure, and
Nutrition Survey 1994
National Household Consumption,
Income, Expenditure, and
Nutrition Survey 1994
-.....-........ .... ...............
National Household Consumption,
Income, Expenditure, and
Nutrition Survey 1994
National Micu.nutrnient Survey, 1996
National Miconutrient Survey, 1996
National Miconutrient Survey, 1996

National Miconutrient Survey, 1996

National Miconutrient Survey, 1996


National Miconutrient Survey, 1996
National Miconutnent Survey, 1996

National Miconutrient Survey, 1996


'. 1.1 ': C !&( Eyi I ;V i
?01c i d Ii t(ititkblqG )fz', ri~.. s'i&'C.!S"Cp [. npnf'r-nqo L)0ns


22 Food Seciuri' in Practice


-- ------------- I


-----------,


I


-I-


-L


-t-


-----------------


-L






the children's mothers (with an average height of only 148
centimeters) and growth retardation in utero.
With respect to specific nutritional deficiencies, vitamin A
deficiency (as measured by low serum retinol) constitutes a public
health problem of "moderate" importance, according to
international guidelines (WHO/UNICEF 1994). It is strongly
associated with raised acute phase proteins (indicating infection),
suggesting that it may result more from illness than from a lack of
vitamin A in the diet per se (Republic of Honduras, Secretary for
Health 1996). Anemia, on the other hand, is more common, both in
children and in their mothers. There is also a strong association
between anemia and infection, but the direction of causality cannot
be determined.
These features suggest a population where ill health, poor care-
giving practices, and perhaps dietary quality are likely to be major
constraints to nutrition security, but an absolute deficit of dietary
energy is not likely to be common. In these circumstances, increasing
agricultural productivity alone cannot produce marked changes in
nutrition security, even in the very long term. In order to affect
nutrition security, PLANDERO might therefore choose to work in
close coordination with health- and education-sector collaborators
and invest in breaking down the isolation and poverty of the region
in the longer term.


targetingg at the Mlah icmipio Level
Figure 2.4 shows the prevalence of severe stunting (height-for-age
below -3 standard deviations of the NCHS median) in the 66
municipios of western Honduras. The data are taken from the Sixth
Census of First-Graders' Heights (Republic of Honduras, Secretary for
Education 1996). The prevalence of severe stunting exceeds 30


Figure 2.4 Percentage of severely stunted first graders,
western Honduras, 1996


Percent
3. 3-12
1 12-21
S21.-29
m 9....57


Sot,,ice: ,i ow:,PId by author.
No te Sp, oe stuntig is
z.. lC osstan3


percent in 13 municpios of the center, northeast, and northwest of
Lempira, and the center-east of Copan, and is below 10 percent in 9
municipios of Ocotepeque, Southern Lempira, and the far south of
Copan.
In order to assess the ability of PLANDERO to target its activities
to the areas with the worst nutritional problems, each beneficiary
family was given a score based on its municipio of residence.
Families living in municipios with the highest levels of stunting were
given the highest scores, while those living in the municipios with
the lowest levels of stunting were given the lowest scores.8
The distribution of severely stunted first graders and of the
project's beneficiary families in the first three project years (estimated
numbers for 1988) is shown in Figure 2.5. The average stunting and
severe stunting scores for beneficiary families in 1996, 1997, and


Food Security in Practice 23


'i






Figure 2.5 Distribution of PLANDERO beneficiary households
and malnourished first graders, 1996-98, western Honduras


Beneficiary households 1997


Severely stunted first graders 1996


Source: Compiled by author from survey data.
Note: PI.ANfiEROf stands for the Rural Developatent Plan for the Western Regjior. Each dot represetss
two houstehds in she first three maps, and 2 first gradersf in the fourth map.

1998 (estimated) are shown in Table 2.7. The project was
geographically neutral in its targeting in the first and second years of
enrollment. In the third year (1998), new project beneficiaries were
somewhat more likely to live in areas with more severe nutritional
problems, so that the average scores of the new households were 63.5
(stunting) and 27.5 (severe stunting). However, the number of new


Beneficiary households 1996


Table 2.7 Frequency of severe stunting among first graders, and
severe stunting score of beneficiary households, western Honduras
Number of Stunting Severe stunting
Region/Proram year individuals score score
Ail first graders
Western Honduras 1996 23,129 56.4 22.6
Program beneficiaries
PLANDERO 1996 1,632 56.2 22,8
PLANDERO 1997 3,930 56.3 22.1
PLANDERO 1998b 5,109 57.9 23.4
Source: Compilred by author from survey data.
Note: PLADVER O stands for t ae Rural D-velopment Plan for the Western Region.
a. See the discussion of stuniin scores under a!geiting at the Municipio Level on page 2.
b. Data for 1998 are estimates given no definitive rdata on the new 19,S8 beneficiary households was
available at the time of waiting.


beneficiary households anticipated in 1998 was small relative to the
number already included in the project (30 percent of 1996
numbers), with the result that the overall targeting of project
activities remained essentially neutral.


Moitoriiig
Figure 2.6 shows the proportion of first graders stunted for each year
from 1994 to 1997 in two almost adjacent municipios in western
Honduras. In the municipio of La Labor, which had a relatively
strong institutional presence in 1993 (17 percent of farmers receiving
technical assistance and 25 percent receiving credit) and was one of
the first municipios to have groups assisted by PLANDERO in 1996,
the rate of stunting remained almost unchanged throughout the
period at approximately 40 percent. On the other hand, in Dolores
Merend6n, which had limited institutional presence in 1993
(7 percent coverage of technical assistance and 6 percent of farmers
receiving credit) and did not receive any assistance from PLANDERO
in 1996 or 1997, the rate of stunting in first graders increased


Beneficiary households 1998
(estimated)


24 Food Secrilty in Practice






dramatically from just over 45 percent in 1994 to nearly 75 percent
in 1997. Although far from providing conclusive evidence of project
impact, this example shows how it is possible to take advantage of
existing data collection activities and extract potentially useful
information about the evolution of nutritional status in the project
zone of influence and in selected control areas. The analysis would
have been strengthened if a finer level of geographical disaggregation
could be achieved, making it possible to examine the experience of
communities with a high coverage of project activities; alternatively,
PLANDERO could have undertaken its own data collection activities
in selected sentinel sites and compared the experience of its own
study population with that of the universe of first graders in the same
m1u)licipios.

Figure 2,6 Prevalence of stunting in two municipios of western
Honduras, 1994-97


Fr1rntnt of
first gr-aders.
stfuntefI
80

70

60


40


-- Delores Merendon
-AMLH a Laabor


3 0 ............................................................................................... ............................. .................... ....... ...............
1994 1995 1996 1997
Sou';e: Compiled by author from survey data ,


Evaluation
Table 2.8 compares the nutritional status of children from birth to 60
months of age in July/August 1997, and the same group (plus new
births) seven to nine months later in March/April 1998. Results are
shown separately for children living in PLANDERO 96 households
and for those living in PLANDERO 97 households. The seven-to-nine-
S month interval between the two survey rounds encompassed the
1997-98 growing and harvest season, during which time both sets of
households received technical assistance and credit from PLANDERO.
The control community households could not be included in this
analysis because they were not assessed prior to the final survey
round.


Table 2.8 Mean anthropometric status of children under five, by
survey year and program status, western Honduras

Indicator/Year PLANDERO 96 PLANDERO 97
(Z. scores) (Z-..scores)
Height-or-age 1997 -2.09 (1,75) n:243 -212 (1,69) rn215
1998 -1,99 (1.51) n=245 -2.18 (1,53) n=250
Change 1997. 98 +010 (0.15) P)0.51 -0.06 (0.15) P=0.69
Weight-for-.height 1997 -0.17 (0.98) n=243 -0.17 (1.17) n=217
1998 -0.07 (1,07) n=243 -0.04 (0,97) n=249
Change 1997.--98 +0.10 (0.09) P= 0.27 +0.13 (0.10) P=0.19
Weight-for-age 1997 -1.39 (1.28) n=243 -1.42 (1.32) n=214
1998 -1.29 (1,06) n-.-243 -1.35 (1.13) ri :.250
Change 1997-98 11 (0.11) P=0,31 -0.06 (0.11) P=0.57
Source: Complied by author fuom survey date.
Note: PLANDERO stands for the Rural Developmentr! P/ar for sr Western Region. Change is adjusted
change in eran values; figures i.'n brackts indicate standard diatiion r, in the case of rows shovwin
change, standard errors; n denotes sample number; and P denotes P-value.


The analysis shows that over this period, there was little change
in anthropometric status either among PLANDERO 96 children or


Food Securilv in Practice 25






PLANDERO 97 children, for any of the three indices examined.
Furthermore, there was scarcely any evidence of differences between
the experience of the two groups of children, although PLANDERO 96
children performed very slightly better than PLANDERO 97 children
on the height-for-age indicator. In cases such as these, formal
statistical hypothesis testing has little to add to the analysis.
One factor that should always be borne in mind when evaluating
data in which the nutritional status of a given population or
subpopulation is assessed on more than one occasion is the
possibility of some change in the age structure of the populationss,
which might invalidate uncontrolled comparisons. In the case of
western Honduras, the average age of the children surveyed in the
PLANDERO 96 communities was slightly different in July/August
1997 from that of the children in the PLANDERO 97 communities:
29.5 months versus 31.4 months, respectively. By March/April 1998,
both study groups had aged somewhat, but this effect was more
marked in the PLANDERO 97 communities, so that the average ages
of children surveyed at this time were 33.5 months and 36.7 months,
respectively. Changes in average anthropometric indices, adjusted
statistically for changes in the age structure, are shown in Table 2.9.


Table 2.9 Change in anthropometric status of children under
five between July/August 1997 and March/April 1998, adjusted
for changes in the age structure of the survey populations,
western Honduras
Indicator PLANDERO 96 PLANDERO 97
Height-faor.age +0.13 (0.14) P = 0.36 +0.06 (0.15) P : 0.67
Weight-for-height +0.06 M009) P = 0.52 .0.09 (0.10) P = 0,37
Weig ht-f or-age ,+0.09 (0.10) P P 0.38 4+0.09 (0.11) P -.0,41
Source: Compiled by auhorr from survey data.
Note: PLANDFO ,r stfds for the R!al DBvelopnmert Pln f for the Western Region. Change is aiiusted
change i3n me1an valios; figures in brackets indicate s.an;dard deviation; and P denotes P-vai;e.


This adjustment is sufficient to reverse the apparent direction of the
evolution of height-for-age status in the PLANDERO 97 communities,
so that their experience became comparable with that of the
PLANDERO 96 communities. Thus, although technically demanding,
age adjustment can be important to ensure the correct interpretation
of results.
Many of the children in this data set were measured both in 1997
and in 1998, making it possible to examine changes at the individual
level. Table 2.10 shows that when this approach is taken, there
appears to be a rather substantial (and almost statistically
significant, at the 5 percent level) difference in weight gain between
children living in PLANDERO 96 communities and those living in
PLANDERO 97 communities, in favor of the former. However, this
difference is attenuated when differences in the age composition of
the two groups are taken into account as described above. The
approach that focuses on individual change has the advantage of not
confusing the impact of changes in individual status with the impact
of modifications in the composition of the group studied. On the
other hand, it is marred by the (possibly major) biases inherent in
studying only those children present in both surveys. Evaluators
therefore need to carefully weigh the benefits and costs that would
result from adopting this "cohort" approach.
In the absence of data on the anthropometric status of children
in the control communities in 1997, any inference about the impact
of PLANDERO on nutritional status relative to areas not included in
the program must be drawn entirely from the post-intervention
observations of March/April 1998. In order to extract the maximum
possible amount of information from these data, it is useful to graph
average height-for-age Z-scores recorded at this time by program
status (PLANDERO 96, PLANDERO 97, and controls). Such a graph is


26 Food Security in Practice







Table 2.10 Height and weight velocities of children under five living in
the PLANDERO 96 and PLANDERO 97 study communities, Western
Honduras, 1997-98


Indicator I Diiference 1


Height velocity








Weight velocity


unadjusted difference

age-adjusted difference






unadjusted difference


age-adjusted difference


PLANDERO 96 PLANDERO 97
(centimeters per month)
0.70 (0,42) 0,67 (0.36)
n-=179 n=178
0.03 (0,04)
P=0.46
-0,01 (0.03)

---------- P = 0.... '. 8 0 2 -- ----------. --.^-------------
(grams per month)
193(119) 169(123)
n=183 n=183
24 (13)
P=0,055
15(11)
P-=0.17


years of age in these communities, we can safely assume that the

status of children four years and older represents the effects of

variables that exerted their effect prior to the advent of PLANDERO in

mid-1996. The experience of the younger children suggests a

negative effect of PLANDERO's activities on stunting, but only in the
year that each community first started to receive technical assistance

and credit from the program. It can be seen that this analytic strategy

is convenient when there are no baseline data available, but results

are prone to the vagaries of sampling variation and interpretation

can be somewhat subjective.



Figure 2,7 Average height-for-age Z-scores in March/April 1998,
by program status

Heaight-ftr-
age Z-score


Source: Cnopiled by author from survey data.
Note: PLANDERO stands for the Rural UDvelopmen't Pian for the Westerni region. Figures in brIackets
indicate standard deviation ufor height and weight veloci fy and standard error for defferinces; ; denotes
sanple numbsoe and P denotes P -value.



shown in Figure 2.7. Infants under six months of age were excluded

because (1) they had been only briefly exposed to the project, and (2)

most of their energy intake came from breast milk, which was

unlikely to have been affected by the project activities. It can be seen

that between six and 24 months of age, children from PLANDERO 97

communities were more stunted than PLANDERO 96 or control

community children (who were similar to each other). From two

years to 42 months, the PLANDERO 96 children were the ones to

show most stunting. In this age group, the PLANDERO 97 and

control community children had similar, higher height-for-age

Z-scores. Above 48 months of age, the control community children

were the most stunted. As stunting is basically established by two


-f
:i:p~
tiib
~
~j
'':8x
:ri
":""
i:


PLANDERO 96
. PLANDERO 97
Control group


8 12 18 24 30 36
Age months)


42 48 54 60


SUrrct C;O inpiled byt' c idot fr o m Suv ey datai
Not PANDER stcaN-ds for thp, talDevek'pmc.nt Plan for the o Awitn Region.


Food Security in Practice 27


::::2,


-u-:
~~ehb .....:~:rm.......
:~ ~~~'-~
5~:~.;1....::~::::::::i:::: ~o~ci~~
::`
''"'
2:~;~:Y35~:: ::;:.~.:.:;
:ii : ~:r~
P:~'









1. In order to allow for the normal variation in body size that is due to age
and sex, observed measures are contrasted with the expected value for an
individual of the same age and sex. For most commonly used
anthropometric measures, these expected values are taken to be the average
(median) value in the U.S. population, as determined by the National
Center for Health Statistics (NCHS). The NCHS database is referred to as the
reference population. How far above or below the reference median a
particular value lies is measured in multiples of the standard deviation in
the reference population, with the resulting quantity being referred to as a
Z-score. Thus, Z-scores are calculated as
(oaiseveld- (lSa.s..


where pNCHS denotes the reference median, and -NCHS denotes the age-
specific standard deviation in the reference population. There is currently
much debate about the appropriateness of using the a single reference
database to assess the growth of children and adolescents from different
ethnic backgrounds, but it has generally been found that children from all
countries and races can grow equally well up to 7 years of age (Habicht et
al. 1974). At this age, height differentials within a race between children
from different socioeconomic groups can reach 10 centimeters, while
differences between races among children of high socioeconomic status do
not exceed 1 centimeter.
2. The health profile of those attending health facilities is generally quite
unrepresentative of the population as a whole, since people go to health
facilities because they are sick. Similarly, those benefiting from selective
programs are also unrepresentative, since such programs often target (or
are self-targeted) towards the most needy Alternatively, certain segments of
the population may have characteristics that make it easy for them to
access programs and services; such characteristics are likely to be associated
with better health outcomes.


3. It is, of course, possible that the consumption of vegetables could displace
other energy-rich items in adults' diets.
4. The intensive "training effect" may itself alter the nutritional status of the
population, particularly if it is accompanied by increased awareness of
nutritional issues.
5. Moving averages are averages of community-level nutritional status over a
number of different time points (often up to five or more). These averages
are recalculated every time measurements are made, so that short-term
variations are "smoothed" by combining them with other measurements
from different time-points. Medium-term trends are, however, reflected.
6. Why this should be the case may be understood by considering the case of
an indicator such as height-for-age Z-score. At the initial survey, children's
height reflects the sum of all environmental influences they have been
exposed to since conception. On the other hand, their height at the final
survey will reflect the sum of all the environmental influences they were
exposed to from conception to project baseline, plus influences experienced
during the course of project implementation. For the youngest children, the
influences experienced during the implementation period will dominate
the final measure; however, these children may have been buffered against
external influences by their mothers. The final height of older children, on
the other hand, will be dominated by events that occurred before the
beginning of the project, and therefore is not particularly informative with
respect to project impact.
7. See Report and Recommendation of the President to the Executive Board
on a Proposed Loan to the Republic of Honduras for the Rural
Development Plan for the Western Region (PLANDERO). International
Fund for Agricultural Development 1993.
8. The scores assigned to each beneficiary household were equal to the rate of
malnutrition in the municipio where the family resided. Thus, beneficiary
families living in a municipio where 60 percent of all first graders were
stunted were assigned a score of 60 each, while beneficiary families living
in municipios where only 30 percent of first graders were stunted were
assigned a score of just 30. The summary score for the whole project at any
given point in time is calculated as the average of the scores assigned to
each beneficiary household. The project may be described as neutral in its


28 Food Security in Practice






geographical targeting if the average score thus derived is the same as the
prevalence of stunting in the area as a whole. If, on the other hand, the
average score is higher than the prevalence of stunting in the region, then
the project is targeting areas with more severe nutritional problems;


similarly, if the score is lower than the prevalence of stunting, then the
project is targeting areas with less severe problems. The process was
repeated using rates of severe stunting (height-for-age Z-score <-3)
instead of rates of total stunting (Z-score <-2).


Food Security in Practice 29




3. Choosing Outcome Indicators of Household Food Security
John Hoddinott


Introduction
Many development agencies consider household food secu-
rity a guiding principle for designing interventions in
rural areas. A commitment to food security-defined as
the condition in which a population has the physical, social, and
economic access to safe and nutritious food over a given period to
meet dietary needs and preferences for an active life-carries with it
an important implication for development practitioners, namely the
need to measure food security outcomes at the household and indi-
vidual levels. Measurement is necessary at the outset of any develop-
ment project to identify the food-insecure, to assess the severity of
their food shortfall, and to characterize the nature of their insecurity.
Further, an initial measurement provides the basis for monitoring
future progress and assessing the impact of these projects on the ben-
eficiaries' food security.

The concept of food security has evolved considerably over time,
as have food security indicators. There are approximately 200
definitions and 450 indicators of food security. One volume on
household food security by Maxwell and Frankenberger (1992) lists
25 broadly defined indicators. Riely and Moock (1995) list 73 such
indicators, somewhat more disaggregated than those found in
Maxwell and Frankenberger. Chung et al. (1997) note that even a
simple indicator such as a dependency ratio can come with many
different permutations. They list some 450 indicators. With this
abundance of indicators, an important methodological problem for
development practitioners is to determine which indicators are
appropriate, given the project being proposed.


Maxwell and Frankenberger (1992) make a distinction between
"process indicators," which describe food supply and food access, and
"outcome indicators," which describe food consumption. Many
studies have found that process indicators are insufficient to
characterize food security outcomes. Chung et al. (1997) found that
there is little correlation between a large set of process indicators and
measures of food security outcomes. This finding echoes the
conclusion of some development agencies, namely that there is little
correlation between area-level food production and household food
security (IFAD 1997, 13).' For these reasons, this guide focuses only
on outcome indicators.
The practical circumstances in the field are another factor that
influence the choice of indicators. Development agencies and their
local collaborators face significant financial and time constraints.
Undertaking detailed household and individual surveys on an
ongoing basis to characterize, monitor, and measure impact is not
feasible, either because (1) the time spent on these activities does not
fit into the standard project cycle, (2) the skills to implement and
analyze such data are not available, or (3) purchasing these skills-
by contracting to outside consultants, for example-is prohibitively
costly. Mindful of this constraint, this guide shows how simple
measures of food security outcomes can be constructed and
compared. These methods are accessible to anyone with a basic
grounding in statistics and access to a spreadsheet software program
such as Microsoft Excel.
The next section outlines four ways of measuring household food
security outcomes: (1) individual intakes, (2) household caloric


Food Security in Practice 31






acquisition, (3) dietary diversity, and (4) indices of household
coping strategies.2 In each case, there is a brief explanation of
what this indicator measures, how data can be collected, and
how indicators of food security can be calculated. Each
description ends with a commentary on the strengths and
weaknesses of the method.
It is possible that project designers may wish to use
some combination of these indicators. For example,
project goals might be specified in terms of improving
caloric availability at the household level, yet there may
not be sufficient resources to monitor this outcome on
an ongoing basis. Section 3 explains how using simple
measures of statistical association, together with these
indicators, can overcome problems such as these. The
final section proposes a possible sequence of activities
that would use these indicators at different stages of a
project cycle.
Readers of this chapter should note that the
methods presented here are complemented by material
in the introduction (on concepts of food security), in
Chapter 2 (nutritional dimensions of food security), in
Chapter 4 (on obtaining information on food security
status using rapid appraisal techniques), and in
Chapter 5 (sampling techniques for household surveys).


OUTCOME MEASURE OF HOUSEHOLD AND
INDIVIDUAL FOOD SECURITY


This section outlines four ways of measuring household and
individual food security: individual intakes (either directly


measured or 24-hour recall), household caloric acquisition, dietary
diversity, and indices of household coping strategies. This ordering of
methods is deliberate, moving from methods that are time- and skill-
intensive, but are regarded as being more accurate, to those that can
be implemented quickly, are relatively undemanding in terms of the
skills required for their implementation, but are more impressionistic.


Box 3.1 Energy content per 100 grams of edible portions, selected foods
----~ -- --- --- ------ ----- -------------


Food


Kilocalories


Cereals and grains
Maize, yellow immature on cob
Maize, white whole kernel, dried
Maize, flour, 60----80 percent extraction
Maize meal
Millet, finger, flour
Millet, bullrush, whole grain
Rice, milledl
Sorghum, whole grain
Sorghum flour
Wheat flour
White bread
Brown bread
Starchy roots, tubers
Cassava meal
Plantain, ripe, raw
Sweet potato, raw
Taro/cocoyam
Yam, fresh
Yam, flour
Sugars
Sugar
Milk and milk products
Milk, cow, whole
Milk powder, cow, whole
Milk, goat
Source: CTAE'CSA i987.
........... ......................... ...... .............. .......... ... ................................. .


166
345
334
341
315
339
333
343
337
340
240
233

318
128
109
94
111
310

375

79
357
84


Food Kilocalories

Grain legumes
Beans/peas, fresh, shelled 104
Beans, dried 320
Chickpea, whole seeds, raw, dried 327
Cowpea, mature pods, dried 318
Mung bean, dried 322
Pigeon pea, dried 309
Nuts and seeds
Baambara groundnut, fresh 346
Cashew nut, dried 560
Coconut, mature kernel, fresh 392
Groundnut, dry 572
Meat, poultry and eggs
Beef, mooderately fat 234
Egg, hen 140
G;oat, moderately fat 171
Mutton, moderately fat 257
Poultry 138
Fish, dried 255
Oils and fats
Butter from cow's milk 699
Coconut oil 900
Ghee, clarified butter 884
Lard/animal fats 891
Margarine 747
Red palm oil 892


3 2 Food Security in Practice






Individual Food Intake Data
Description. This is a measure of the amount of calories, or
nutrients, consumed by an individual in a given time period, usually
24 hours.
Methods for generating these data. There are two basic
approaches used to collect these data. The first is observational.
An enumerator resides in the household throughout the entire day,
measuring the amount of food served to each person. The amount of
food prepared but not consumed ("plate waste") is also measured. The
enumerator also notes the type and quantity of food eaten as snacks
between meals as well as food consumed outside the household. The
second method is recall. The enumerator interviews each household

Box 3,2 Recommended daily caloric intakes

Age group Kilocalories per day
Young children
<1 820
1-2 1,150
2-3 1,350
3-- -5 1,550
Older children Boys Girls
5-7 1,850 1,750
7-10 2,100 1,800
10-12 2,200 1,950
12-14 2,400 2,100
14-16 2,650 2,150
16-18 2,850 2,150
Men Light activity Moderate activity Heavy activity
18-30 2,600 3,000 3,550
30-60 2,500 2,900 3,400
>60 2,100 2,450 2,850
Women Light activity Moderate activity Heavy activity
18-30 2,000 2,100 2,350
30-60 2,050 2,150 2,400
>60 1,850 1,950 2,150
Source: World Hesth Orm..-aion ?,,5 .


member regarding the food they consumed in the previous 24-hour
period. This covers the type of food consumed, the amount consumed,
food eaten as snacks, and meals outside the household.
Method of calculation. Data collected on quantities of food are
expressed in terms of their caloric content, using factors that convert
quantities of edible portions into calories. A useful reference point for
these conversion factors is found on the Web site for the United States
Department of Agriculture (USDA), http://www.nal.usda.gov/fnic/
foodcomp, also available in hardcopy form (USDA 1999). Another
source is CTA/ECSA (1987). A sample of these conversion factors is
found in Box 3.1. These intake data are compared against a
definition of food needs. It should be noted that "food needs" is a
contested concept. Individual caloric requirements reflect individual
characteristics such as age, sex, weight, body composition, disease
states, genetic traits, pregnancy, and lactation status, and activity
levels, as well as other factors such as climate. A typical approach is
to begin with a reference person, say a 60-kilogram man aged
somewhere between 30 and 60 years undertaking "moderate activity."
This yields a caloric requirement of approximately 2,900 kilocalories
per day. Individual requirements for children are made on the basis
of their age and sex to yield "adult equivalents." These are reported
in Box 3.2. A minimum requirement for a low-activity existence-
8 hours sleeping, 1 hour walking, 15 hours standing or sitting
quietly-is 2,030 kilocalories, or 70 percent of that required to
undertake moderate activity. For this reason, this lower figure is often
used as a cutoff to determine whether an individual is consuming
enough to meet their food needs. However, it should be stressed again
that there is no universal agreement on these figures; estimates of
"basic requirements to meet food needs" range from 1,885 to 2,500
kilocalories Games and Schofield 1990; Smil 1994).


Food Securil' in Practice 33






Advantages and disadvantages of this method. This
method has two principal advantages. First, when implemented
correctly, it produces the most accurate measure of individual caloric
intake (and other nutrients) and therefore the most accurate
measure of an individual's food-security status. Second, because the
data are collected on an individual basis, it is possible to determine
whether food security status differs within the household. It may be
that sufficient calories are being consumed at the household level,
but inequalities within the household result in some members
consuming in excess of their requirements, while others do not
obtain sufficient food.
Set against these significant advantages are a large number of
disadvantages. These measures of intakes need to be made
repeatedly-ideally for seven nonconsecutive days-in order to
account for within-person and within-household day-to-day
variations in nutrient intake (for example, those resulting from
religious prohibitions on the consumption of certain foods on certain
days of the week or seasonal changes in diet). The method requires
highly skilled enumerators who can observe and measure quantities
quickly and accurately-and in a fashion that does not cause
households to alter typical levels of food consumption and
distribution within the household. The recall method requires
enumerators to interview carefully every household member until
they have established the exact makeup (food types, ingredients, and
quantities) of every meal and snack, an extremely difficult task. This
method generates an enormous amount of data that needs to be
entered, checked, and aggregated before it is usable.


Household Caloric Acquisition
This is the number of calories, or nutrients, available for
consumption by household members over a defined period of time.

Description. The principal person responsible for preparing meals
is asked how much food was prepared for consumption over a period
of time. After accounting for processing, this is turned into a measure
of the calories available for consumption by the household.

Method for generating these data. A set of questions
regarding food prepared for meals over a specified period of time,
usually either 7 or 14 days, is asked to the person in the household
most knowledgeable about this activity.3 In constructing these
questions, the following considerations should be borne in mind.
First, it is extremely important that the list of foods specified in the
questionnaire is detailed and exhaustive. Experience has shown that
using short lists typically leads to an understatement of consumption
by 25 to 75 percent (Deaton and Grosh 1998). Second, the questions
need to unambiguously distinguish between the amount of food
purchased, the amount prepared for consumption, and the amount
of food served. Third, it is not uncommon for individuals to report
consumption in units other than kilograms or liters. In such cases, it
is necessary to obtain information on the size of a "heap," or the
quantity contained in a "sawal," or whatever units are used locally.
Following is an excerpt from the questionnaire used in northern
Mali to obtain information on food consumption in the last seven days.


34 Food Seciurity' in Practice






We would like to ask you some questions about food
consumption in this household in the last seven days.
These questions pertain to the quantity of foods
prepared for consumption.

Food Unit Quantity
Millet
Sorg urn
R;c
Maize
Bread
Note: O.uan!ty. consumed (unrs)-. 'Bow' 2. "Sack'; 3. "Swai'"; ., "Pot' ;. '"Caeba ,s"
6, Kiograum.


Method of calculation. Converting these data into calories
requires three steps:
1. Converting all quantities into a common unit such as a
kilogram.
2. Converting these into edible portions by adjusting for
processing.
3. Converting these quantities into kilograms using the standard
caloric conversions.
Sample data for five households consuming millet are reported
below. Measurements undertaken as part of this survey determined


that millet was typically measured in "sawal" and "pots." Both were
obtained and the amount stored in these was weighed. One sawal
contained 6 pots, and a pot was approximately 0.77 kilograms,
implying that a sawal was 5 kilograms. The ratio of unground to
processed millet, 0.61, was obtained by providing several women with
1 kilogram measures of millet, having the millet crushed using local
technologies, and measuring what remained. The number of calories
available in the previous seven days was computed by taking this
quantity and multiplying it by the number of calories (3,390
kilocalories) in 1 kilogram of edible millet.


Advantages and disadvantages. This measure produces a
crude estimate of the number of calories available for consumption
in the household. It is not obvious to respondents how they could
manipulate their answers. Because the questions are retrospective,
rather than prospective, the possibility that individuals will change
their behavior as a consequence of being observed is lessened. The
level of skill required by enumerators is less than that needed to
obtain information on individual intakes. In this locality, it took, on
average, around 30 minutes per household to obtain these data, an
amount of time considerably less than that required to obtain
information on individual intakes.


Quantity consumed
Conversion
into kilograms
15 x5 75
10x 5 =50
14 x 5 70
12 x 077 = 9,24
20 x 0.77 15.4


Adjustment for
processing
75 x 0.61 = 45.75
50 x 0,61 30.5
70 x 0.61 42.7
9.24 x 0.61 = 563
15.4 x 0.61 9,39


Number of calories
available from consumption
in the previous seven days
45.75 x 3,390 155,093
30.5 x 3,390= 103,395
42.7 x 3,390 1= 44,753
5.63 x 3,390 = 19,086
9.39 x 3,390 31,832


Food Securil' in Practice 35


Household
1
2
3
4
5


Unit


s a. vv a
sOf, C v


p ot


Number


15
10
14
12
20


_I i






Set against these advantages are a number of disadvantages. This
method generates a large quantity of numerical data that needs to be
carefully checked both in the field and during data entry. Relative to
the methods described below, data processing requirements are also
higher. It is not as accurate as dietary intake data. The use of a recall
period puts considerable reliance on recollection of events that may
not be remembered accurately, with respondents either forgetting
about particular foods or "telescoping"-including foods that had
been used in a period prior to the preceding seven days. It is not
especially accurate in capturing any food eaten outside of the
household. It does not incorporate considerations of wastage, nor is it
possible to uncover differential allocations of food among household
members. Just as with the dietary intake method, it is necessary to
convert quantities into calories and compare these against some
standard, which, as already discussed, remains controversial.


Dietary Diversity
Description. This is the sum of the number of different foods
consumed by an individual over a specified time period. It may be a
simple arithmetic sum, the sum of the number of different food
groups consumed, the sum of the number of different foods within a
food group, or a weighted sum-where additional weight is given to
the frequency by which different foods are consumed.
Method for generating these data. One or more persons
within the household are asked about different items they have
consumed in a specified period. Where it is suspected that there may
be differences in food consumption among household members,
these questions can be asked of different household members.
Experience implementing this method has shown that
comprehensive lists with 100 to 120 different food items perform


better than shorter lists in distinguishing better-off from poorer
households. Determining which items should appear on these lists
can be done via rapid appraisal exercises (see Chapter 5), discussions
with key informants, and references to previous survey work. Below is
an excerpt from a questionnaire used in northern Mali, to illustrate
this approach.4

I would like to ask you about all the different foods that
you have eaten in the last 30 days. Could you please tell
me whether you ate the following foods: 16 to 30 days in
the last month--that is, at least every other day if not
more frequently than that (J); 4 to 15 days in the last
month .that is, once or twice a week (S); 1 to 3 days in
the last month (M); 0 days--not at all (R).

Frequency Frequency
item a J S M.... R Item S M MRa


Cereals
Millet
Sorghum
Rice
Maize
Bread
Wheat
Other cereals
Tubers
Sweet potato
Manioc
Groundnuts
Other tubers
Vegetables
Tomatoes
Onions
Beans
Carrots
Okra
Other vegetables


Fruits
Bananas
Mangoes
Lemons
Pineapple
Other fruits
Meat
Beef
Chicken
Sheep!goat
Fish
Dried
Smoked
Milk products
Cows milk
Goats milk
Other items
Butter
Tea
Salt


36 Food Securiy in Practice






Methods of Calcuslation
There are two possible methods of calculation: (1) calculating a
simple sum of the number of different foods eaten by that person over
the specified time period, and (2) calculating a weighted sum, where
the weights reflect the frequency of consumption and not merely the
number of different foods. Here, the following weights are assigned:
foods consumed at least every other day, if not more frequently: 24;
foods eaten once or twice a week: 10; foods eaten infrequently (1-3
times per month): 3; and foods never eaten: 0.
Sample data for five households, together with these two
measures, are presented below.


Household
1
2
3
4
5


Millet
J

S
S
J


Sorghum
J
J
R
R
R


Rice


R

R


Beef
M
M
R
R
R
R3
R3


Salt



S

M


Tea

S
R
R
J


Simple
sum
5
5
2
2
3


Weighted
sum
99
64
34
20
51


Advantages and disadvantages. The use of this measure stems
from the observation made in many parts of the developing world
that as households become better-off, they consume a wider variety of
foods. It is easy to train enumerators to ask these questions. In
addition, individuals generally find them easy questions to answer.
Asking these questions typically takes about 10 minutes per
respondent. Field-testing indicates that this measure is correlated
with levels of caloric acquisition; tracks seasonal changes in food
security (measures of dietary diversity are highest just after harvest
time and lowest during the hungry season); and also appears to
capture differences in distribution within the household. In northern


Mali, for example, women reported that they were more likely than
their husbands to reduce their own food consumption during periods
of stress, and this was reflected in lower scores for women than for
men on measures of dietary diversity. Finally, a diverse diet is a valid
welfare outcome in its own right-the nutritional literature is
placing increasing emphasis on the importance of consuming a wide
Variety of foods to enhance dietary quality in addition to addressing
longer-standing concerns regarding quantities of consumption.
The disadvantage of this measure is that simple form does not
record quantities. If it is not possible to ask about frequency of
consumption of particular quantities, it is not possible to estimate the
extent to which diets are inadequate in terms of caloric availability
(but see footnote 4).


Indices of Household Coping Strategies
Description. This is an index based on how households adapt to
the presence or threat of food shortages. The person within the
household who has primary responsibility for preparing and serving
meals is asked a series of questions regarding how households are
responding to food shortages. In the nutrition literature, these first
appeared in Radimer, Olson, and Campbell (1990). Coping strategies
themselves are discussed in Maxwell and Frankenberger (1992).
Maxwell (1996) proposed a method for taking consumption-related
strategies and constructing a numerical index.


Food Securi/y inz Practice 37






Method for generating these data. The most knowledgeable
woman in the household regarding food preparation and distribution
within the household is asked a series of questions of the following
form.


In the last seven days:

1. Has the household consumed less preferred foods?
(Circle the best response.)
1, Never 2, Rarely (once)
3, From timetime o ime (2 or 3 times) 4, Often (5 or more times)

2. Have you reduced the quantity of food served to men in this
household?
1. Never 2. Rarely (once)
3. From time to time (2 or 3 times) 4. Often (5 or more times)

3. Have you reduced your own consumption of food?
1. Never 2. Rarely (once)
3, From time to time (2 or 3 times) 4, Often (5 or more times)

4. Have you reduced the quantity of food served to children in
this household in the last seven days?
1. Never 2. Rarely (once)
3. From time to time (2 or 3 times) 4. Often (5 or more times)

5. Have members of this household skipped meals in the last
seven days?
2. Never 2. Rarely (once)
3. From time to time (2 or 3 times) 4. Often (5 or more times)

6. Have members of this household skipped meals for a
whole day?


Method of calculation. A sample of responses to these questions,
taken from a survey of households in the Zone Lacustre region of
Mali, are reproduced below.

Question
Household #1 #2 #3 # #5 #
..................................... ................................. ............................................................ .............................. .........................................
1 3 3 3 3 1 1
2 3 3 3 3 2 2
3 2 2 3 2 2 2
4 3 3 4 3 3 3
5 2 1 2 2 1 1

There are several ways of summarizing the information obtained
from these questionnaires into a single number.
Counting the number of different coping strategies used by the
household. Here, this is the number of strategies that the
household used often, from time to time, or rarely. The higher
the sum, the more food-insecure the household.
Calculating a weighted sum of these different coping strategies,
where the weights reflect the frequency of use by the household.
A simple way of doing this is to make the weights consecutive,
so that "often" is counted as a 4, "from time to time" is
counted as a 3, "rarely" is counted as a 2, and "never" is
counted as a 1. The higher the sum, the more food-insecure the
household.
Calculating a weighted sum of these different coping strategies,
where the weights reflect the frequency of use-as described
above-and the severity of the household's response. A simple
way of doing this is to ascribe a weight of 1 to the use of
strategies such as eating less preferred foods (question 1) and
reducing portion sizes served to men, children, and women
(questions 2, 3, and 4); a weight of 2 to skipping meals


38 Food Securill' in Practice






(question 5); and a weight of 3 to skipping eating all day
(question 6). The first household on this list would obtain a
score of 17 = 1 x (3 + 3 + 3 +3) + 2 x (1) + 3 x (1).
Again, the higher the sum, the more food-insecure the
household.


Number of
different
strategies used
4
6
6
6
3


Weighted sum
reflecting
frequency of use
14
16
13
19
9


Weighted sum
reflecting frequency
and severity of use
17
22
19
28
12


Advantages and disadvantages of this measure. There are
three attractive features of this measure. First, it is easy to implement,
typically taking less than three minutes per household. Second, it
directly captures notions of adequacy and vulnerability (is there
enough food to eat in this household?), as well as the vulnerability of
households. Those households, using a larger number of coping
strategies or using more severe strategies, are more likely to be poor
and more vulnerable to destitution. Third, the questions asked are
easy to understand, both by respondents and by analysts and project
designers.
There are also several disadvantages. As it is a subjective measure,
with different people having different ideas as to what is meant by
"eating smaller portions," comparison across households or localities
is problematic. In particular, as part of the field tests for these
measures, men and women were asked what constituted a "food-
secure" diet. Poorer households tend to report smaller quantities of
food than richer households. This has two implications. First, this


measure can be somewhat misleading-a richer and a poorer
household may both report eating smaller quantities, but this does
not imply an equal increase in food insecurity. Second, evaluating
the impact of an intervention solely in terms of this measure risks
setting a lower target for poorer households than for richer ones.
Second, this measure's simplicity makes it relatively easy to
misreport a household's circumstances. For example, households
might perceive that they are more likely to receive assistance when
they report greater use of these coping strategies. Finally, it is
necessary to decide what weights should be applied to different
questions and to different levels of response. The rapid appraisal
techniques described in Chapter 5 could be used to obtain this
information.


A Cmmparisoin of Methods
Table 3.1 provides a summary table that qualitatively compares these
four methods in terms of costs, time and skill requirements, and
susceptibility to misreporting.


Table 3.1 Comparison of methods in terms of costs, time, and skill
requirements, and susceptibility to misreporting
i Household Isndex of
Individual caloric Dietary coping
MIethod Details intake acquisition diversity strategies
Data collection costs High Moderate Low Low
Time required for analysis High i Moderate Low Low
Skill level required High Moderately high Moderately low Low
Susceptibility to misreportig Low Moderate Low High
Source: Compiled by al'thor friomn survey da.ta.


Food Securit' in Practice 39


Household
1
2
3
4
5






EXPLORING ASSOCIATIONS BETWEEN DIFFERENT
OUTCOME MEASURES OF FOOD SECURITY

Each of the four measures described above are valid indicators of
different dimensions of food security. However, there may be
occasions when project designers, managers, or evaluators want to
compare these indicators. For example, suppose that a project
objective includes increasing levels of caloric availability at the
household level, but there are insufficient financial resources to
monitor this outcome on an ongoing basis. In such a circumstance,
it would be useful to know whether changes in, say, dietary diversity
are associated with increases in household caloric availability.
Comparing these indicators may also provide insights into the
distribution of project benefits within the household. For example, a
finding that household caloric availability is rising, yet information
on coping strategies indicates that women, but not men, are
continuing to reduce food consumption during periods of stress,
would be consistent with a project providing benefits, but these are
being accrued primarily by men within the household.
Comparing these indicators in the manner described here
requires the use of statistical techniques that measure the strength of
association, or correlation, between these indicators. Below, three
methods are discussed: correlation coefficients, contingency tables,
and regression-prediction methods. All are illustrated using data
collected in a project in northern Mali. The techniques are a little
more technically demanding than the material presented in the
previous section, but only a little. They can be implemented by
anyone who has competently completed a basic undergraduate
course (not degree) in statistics and has access to a spreadsheet
computer package such as Microsoft Excel.


Correlation Coefficients
A simple approach to examining the validity of alternative measures
of food security is to calculate measures of correlation such as
Pearson or Spearman correlation coefficients. These are index
numbers that show to what extent two variables are linearly related.
They can take on values that range from -1 to 1. A priori, it is
expected that the dietary diversity index and per capital calorie
consumption are positively related, that is, both increase in value
together. By contrast, the indices of coping strategies and per capital
caloric availability should be negatively related. One would expect
that increased reliance on coping strategies would be associated with
lower food availability.
Examples are reported in Table 3.2. The measure of dietary
diversity is the weighted measure based on data provided by women
in these Malian households. The index of coping strategies is doubly
weighted by the frequency of use of this strategy and the severity of
the strategy.

Table 3.2 Pearson and Spearman correlation coefficient between
caloric availability and two alternatives
Correlation between calories available per person and
Weighted female Doubly weighted
MIieasure dietary diversity coping strategy index
.i !........ ............................................................. .I^ ^ ............. .............................................................. .............................................
Pearson0.17 ---0.17"
S p e a rm a n 0.22" ,17
Source: Compilru dh by ae'uhor from sirv.e data.
Notoe: denofs st..istkcal'y significant at thte 1 percent' !Iee

Note that prior expectations are borne out: there is a positive
correlation between dietary diversity and caloric availability and a
negative correlation between the coping index and caloric
availability. All four correlation coefficients are statistically
significant at the 1 percent level. A more difficult question is how to


40 Food Securit' in Practice






interpret the magnitude of these coefficients, which are all roughly
the same. It would appear that there is little to choose between these
two measures. Both provide some correlation with the benchmark,
but not at an especially high level.
A drawback to the use of correlation coefficients is that the
correlation could be driven by just one part of the distribution of joint
variables. Suppose that for most households, there is little correlation
between dietary diversity and calorie consumption, but for very rich
households, the correlation is quite high. As a consequence, the
calculated coefficient might prove to be statistically significant. An
additional problem is that of false correlation where some other
variable is correlated with both measures, producing a false
correlation between the two variables that are observed. A reasonable
conclusion, therefore, is that these correlation coefficients are a good
exploratory tool, but should not be the only method used.


Contingency Tables
Contingency tables cross-classify two variables by two or more
attributes. In the tables below, households are classified by whether
per-person caloric availability is above or below 2,030 kilocalories
per-person per-day. Approximately one-third of households did not
have access to even this minimal amount of food. Households are
separately ranked by the alternative indicators and grouped
according to whether or not they are in the bottom tercile for that
ranking. Within these tables, there are three numbers of interest:
specificity, the fraction of food-insecure households also classified by
the alternative as food-insecure; sensitivity, the fraction of food-secure
households also classified by the alternative indicator as food-secure;
and a chi-squared test of whether there is a statistically significant
association between these attributes.


Table 3.3a Contingency table of caloric availability and
weighted dietary diversity
Household is in bottom Household is not in bottom
tercile when ranked by tercile when ranked by
Attribute dietary diversity dietary diversity


Per capital
caloric availability
< 2,030 kilocalories


45


Pel capita
caloric availability 39
> 2,030 kilocalnes ht
m'-~tas $4
Sptcificity: 45193 0.48
Semsitivity: 134/173 =0.177
Chi-squared tcest = 1830""
$ource: Compiied by/ a'loitb. fi rom Survey datia
Note.- "" 'i~de s stotistifzahnY sittificait at tih I p in;oemnt tdl.


48


134

182


Total


93


173

266


Table 3.3b Contingency table of caloric availability and weighted
coping strategy index


Ho
ter
Attribute ce
Per capital
caloric availability
< 2,030 kilocalories
Per capital
caloric availability
> 2,030 kilocalories
Totals
Specificity: 26/93 0,28
Sensitivity: 93/173 = 0.54
Chi-squared test = 8.44**


usehold is in bottom
cile when ranked by
apiing strategy index
I............................................................


80


106


Household is not in bottom
tercile when ranked by
coping strategy index

67


93


160


Total

93


173


266


Sousa. Coi wdbutttfor fom ,vet'y -datai.
Niotae: dernote, s:;tatuisrhal sit;ficant, at thes I prcerrint level


Food Securili' in Practice 41


-------------- --






These contingency tables indicate that there is a statistically
significant correlation between these attributes. The dietary diversity
measure performs better than the index of coping strategies in
identifying food-secure and -insecure households as measured by
caloric availability. This can be seen when comparing the measures
of specificity and sensitivity in Tables 3.3a and 3.3b.
There are, however, two problems associated with using
contingency tables. First, there is the issue of choosing the cutoffs for
the attributes. Second, the method becomes demanding in a
statistical sense when more than a handful of alternatives are
considered. Specifically, repeating the exercise several times increases
the likelihood of obtaining a significant association that results
purely by chance. This can be rectified by setting a higher critical
level for the chi-squared statistic (see Chung et al. 1997).


Regression-Prediction Aethods
In light of the difficulties associated with correlation coefficients and
contingency tables, a third method is outlined here that combines
their advantages while minimizing their drawbacks. There is no
formal name for this approach, which is described here as the
"regression-prediction" method.
We begin with the observation that the two methods described
above do not use all the information available. Specifically, in order
to calculate per capital caloric consumption, it is necessary to
determine how many people are in the household. Additionally, the
location of the household will also be known. Consequently, these
data can be added to the analysis at no additional cost. Further, there
are good reasons for using this information. First, cross-country
studies consistently reveal a negative association between food access
and household size, although the reasons for this are not well


understood (Deaton and Paxson 1998). Second, consider the
following case. There are two localities: one is centrally located with a
weekly food market; the second is remote from any markets. One
would expect that the more remote village would face higher food
prices and less access to a variety of foods. Failing to control for this
might lead to a misleadingly strong association between dietary
diversity and caloric consumption. The obvious way of incorporating
these variables is to use them in a regression where the benchmark
indicator is the dependent variable, and household size, location, and
the alternative indicator appear as right-hand-side or explanatory
variables.


Table 3.4 The relationship between (log) per capital caloric
acquisition and two alternative measures of food security,
controlling for (log) houshousold size and location
Variable Coefficient t statistic Coefficient statistic


Log household size
Dietary diversity
Coping strategies
Location
To mb a
Mangourou
Gouaty
N'goro
Tomi
Ha makoira
Goun d a rTo uskel
0uaki
Ang uira


Constant
Adjusted R-squared


---0.403
0.002


0.045
0,299
0.165
0,115
0,092
..0, 54/
0,155
0,286
---0212


4,071' '


0.300
1,861
0,738
0,830
0,467
--0,872
0.836
2.028
-1,283


8,017"~
0.24


-0.339


-0,53 1,764


0.048
0.229
-0,140
0,059
-.0,40
0,242
0,171
0,234
-0,329


8,495


0,308
1,398
0,656
0.422
0.202
-1.3.45
0,895
1,621
-1,976'


42,885""
0.17


Source : cajpLwed bV durhor irom strt-'ey Jeta.
Plow deo'nes" 4 4":. u Percent levef. IJ&'iotes stdtistica ly
significafI f.ii 5 ii re i!cernt ieeve.' l i.i re~t eplhicaeS


42 Food Securily in Practice


- - - i - - - - -






The results of using this method for the Mali data, collected at
the height of the hungry season, are presented in Table 3.4 (note that
the dependent variable and household size have been transformed
into their logarithmic values).
Controlling for household size and location, increased dietary
diversity is associated with higher per capital caloric availability. Every
additional point on the dietary diversity index is associated with an
increase of 0.2 percent in caloric availability. This association is
statistically significant at the 1 percent level. By contrast, once these
other factors are taken into account, there is no statistically
significant association between the coping index and the benchmark.
Also note that the adjusted R-squared, which indicates to what extent
the variance in the dependent variable is explained by the regression,
is considerably higher for the regression using dietary diversity as an
explanatory variable.
These estimated coefficients can be used to predict levels of log
per person caloric availability. For example, for households residing
in the village of Tomba, these predicted levels are
predicted log caloric availability per person
= 5.567 + 0.045 0.403 log hh size
+ 0.002 dietary diversity.
Taking antilogs yields predicted values in terms of caloric
availability per person. These can be used to construct the following
contingency tables in which the benchmark (actual caloric
availability) and predicted caloric availability are divided into three
categories: less than 2,030 calories per day (indicating severe food
insecurity); 2,030 to 2,900 calories per day (indicating some degree
of food insecurity); and greater than 2,900 calories per day. The
results of this exercise for the Mali data set are reported in Tables 3.5a
and 3.5b, with summary statistics reported in Table 3.5c.


Table 3.5a Contingency table of actual and predicted per-person
caloric availability (dietary diversity)


Number of households
by actual per person
daily caloric availability
Total


Number of households by predicted
per-person daily caloric availability Total
< 2,030 2,030-2,900 > 2,900
< 2,030 50 34 9 93
2,030----2,900 16 25 23 64
> 2,900 12 39 58 109
78 98 90 266


Table 3.5b Contingency table of actual and predicted per-person
caloric availability (coping strategies)
Number of households by predicted
per person daily caloric availability Total
< 2,030 2,030--2,900 > 2,900
Number of households < 2,030 1 46 33 14 93
by actual per person 2,030-2,900 12 34 18 64
daily caloric availability > 2,900 10 47 53 110
Total 68 114 85 267



Table 3.5c Comparison of predictive power of dietary diversity and
coping index
Attrilmte Dietary diversity Coping index
Chi-squared test of association 60.16 6 54.24~'
(porcenrt)
Households correctly categorized 50,0 50.0
Severely food-insecure households
classified as food secure 97 15.1
Predicted distribution of calories
per person by food-security status:
< 2,030 (actual distriiutiiol = 35 percent) 29,0 25.0
2,030-2,900 (actual distribution 24 percent) 37.0 43.0
> 2,900 (actual = distribution 41 percent) 34.0 32.0
Source: Compiled by author from survey data.
Note: deirOts significant at :ie percent level
in Table 3.5a, household size, location, and dietary diversity were used as regressors;
in Table 315b, household size, location, and measure of coping strategies were used as regressors.


Food Securily in Practice 43






The chi-squared tests indicate that the match between the actual
distribution of food acquisition and that predicted by both alternative
indicators is greater than would have occurred if these alternatives
had randomly assigned households to these different groups. Both
correctly classify about half the households in the sample. Whereas
the actual distribution across food security status is fairly constant,
both alternatives predict that it is more concentrated among
households experiencing moderate food insecurity. This is
particularly marked in the case of the coping strategies index, which
appears to especially underreport the number of severely food-
insecure households.

Summary
This section has presented three methods for examining the
associations among different outcome measures of food security. All
three can be implemented using a standard spreadsheet package.

DEVELOPING AND USING OUTCOME INDICATORS OF
HOUSEHOLD D OOD SECURITY N DEVELOPMENT
PROJECTS

The material presented thus far has outlined possible outcome
measures of food security and methods for evaluating these. This
section outlines a possible sequence of events by which project
designers can implement these methods. We are assuming that the
project area has been identified.


1. The first step is to review existing secondary literature on the
types of foods consumed in this area. In addition, rapid
appraisal techniques and discussions with key informants can
be used to establish a list of foods eaten in the area and coping
strategies used by these households during periods of food stress.
2. The next step is to develop a household questionnaire to
capture data on a variety of outcome measures of varying
degrees of complexity. The measures chosen will need to take
into account local conditions and resources (time, money, and
people) available for this work as well as the advantages and
disadvantages of each method.
3. Data on these outcome indicators are collected.' These can be
used to provide a characterization of the locality in terms of the
nature of the food security problem (is it lack of calories, poor
diversity, a problem of seasonal fluctuations in access, unequal
access within the household?), the identity of the food-insecure,
and the severity of the food insecurity. The methods described
above can be used to determine to what extent the simpler
measures mimic the more complex indicators.
4. If the association is considered strong, these simpler indicators
can be used not only as monitoring measures in their own
right, but also as a means of inferring changes in more
complex measures.
5. Both simple and more complex outcome indicators can be used
to measure impact.


44 Food 'Secrily in Practice









1. The discussion on how to choose indicators can also be applied to process
indicators.
2. A fifth method, group rating, is described in Chapter 4.
3. There is no consensus regarding the optimal recall period between 7 and 14
days. In the author's experience, 7 days seems to be the most appropriate. A
shorter recall period risks missing foods served infrequently, say on Fridays
(in Muslim areas) or Sundays (in Christian areas). A longer recall period
can be problematic as difficulties of remembering what was prepared
appear to increase. However, other organizations such as the World Bank
(in its Living Standard Measurement Surveys) have used the 14-day recall
period.


4. A variant of this approach, called a semiquantitative measure of dietary
diversity, involves showing respondents pictures or models of different
serving sizes of these foods. Respondents indicate whether they consumed
the item and in what quantity. From this information, it is possible to
obtain a rough estimate of caloric intake. For example, in Honduras,
respondents were shown five sizes of tortilla and asked how many of each
they had consumed.
5. Chapter 5, on sampling, provides an introduction to this.


Food Security in Practice 45




4. Rapid Appraisal Techniques for the Assessment, Design,
and Evaluation of Food Security Interventions

Gilles Bergeron


Introduction
project managers in charge of implementing activities that
address food-security problems need tools to (1) identify the
populations that are food-insecure, (2) design interventions
that address the causes of food insecurity, and (3) evaluate the
impact of their interventions on the food security status of project
beneficiaries. This chapter illustrates how rapid appraisal (RA)
techniques can provide useful insights into the research and design of
food-security interventions, as well as into the limitations of such
interventions. Many factors determine whether RA methods are
appropriate in any given case, including the degree of precision
required, the characteristics of the population being investigated, and
the ability of fieldworkers.
The first section of this chapter presents some general
observations about the advantages and disadvantages of RA methods
over survey-based methods. The second section presents a set of RA
tools that were tested in the field. The tools developed include
community mapping, household food security ranking, conceptual
mapping of food sources, seasonal food security timelines, and
evaluation of an intervention's impact on food security. Each
instrument is presented in a similar sequence. First, a brief
introduction presents the instrument and its relevance to the study of
food security. Second, the tool is described in terms of its specific
objectives, format, methods, and products expected. Third, examples
from fieldwork experiments are provided to illustrate its use.


RA METHODS FOR LOCAL NEEDS ASSESSMENT,
INTERVENTION DESIGN, AND IMPACT EVALUATION

Rapid appraisal techniques offer development workers a useful set of
research and appraisal tools to quickly obtain information from local
populations about their condition and their needs. RA methods also
enable local people and outsiders to plan together appropriate
interventions and evaluate the impact of development interventions.'
RA methods have distinct advantages over survey-based research
methods. They generally involve low costs; are highly adaptable to
different situations; and tend to facilitate rapport with local
communities, which can allow investigators to explore topics not
easily studied otherwise or to bring out qualitative aspects that would
be missed by surveys. They also favor analysis on the spot with local
people, enabling verification of findings and enhancing the local
relevance of results. However, RA methods present important
disadvantages over more conventional methods, including limited
ability to generalize findings, lack of clear validation procedures, and
susceptibility to manipulation by informants. In addition, the
qualitative focus of RA methods limits researchers' capacity to
transform the data, thus constraining the analysis to what is reported
by local informants. Besides, the quality of the information collected
depends to a high degree on the skills of field personnel.
The general belief that RAs are simple to apply is, in most cases,
not true. The selection and training of fieldworkers is much more
critical than for conventional enumerators. Finally, because of the


Food Security in Practice 47






use of "participatory-type" methods, RAs tend to raise expectations
among the population about program activities. Goals have to be
carefully explained from the outset to avoid misconceptions. For all
these reasons, the RA approach is viewed in this manual as a
complement rather than an alternative to survey-based methods.
RA is used to guide, inform the design of, and confirm findings from
formal surveys. A combination of formal and RA methods is the best
way to ensure the quality of final results.

General (Gidelines to the Use of RA Methods
Whenever using RA methods, a number of basic issues must be
considered, including:
Training and selection of personnel.2 As mentioned above,
the skill of fieldworkers is critical to the success of RA methods. These
skills are quite different from those required by formal surveys. For
example, social skills are important: Controlling dominant
personalities in group settings while seeking the participation of
silent participants-all of this without imposing one's opinions-
requires superior communication abilities. Another distinctive
attribute is that, unlike survey enumerators who collect data for
analysis by outside researchers, RA fieldworkers have to collect,
analyze, and validate the data themselves. They are the researchers.
Hence they need a sound understanding of the aim of the research so
they can, for instance, change the instrumentation used, if need be,
without losing sight of the final objectives. The importance of
selection and training of field personnel cannot be overstressed. (See
the references on training RA fieldworkers.)
Establishing contact. Community life is complex, and care must
be taken from the start not to unwittingly alienate groups or
individuals by associating too closely with the "wrong" personss.


It is useful to make unannounced visits to a village before the first
official visit3 in order to learn the basic "political language" of that
community. This can be done by sending one fieldworker to the
village to establish informal contact. Avoiding local authorities is
preferable, although not always possible. Free-flowing discussions are
initiated with the people encountered, leading to questions such as:
Who are the official representatives? How are they perceived? Are there
factions or rivalries (political, religious, economic) in the village?
Such early knowledge is invaluable when making the first official
visit, and helps avoid early missteps.
Then an official visit can be scheduled. In contrast to the first
informal visit, this one is well announced and involves local
authorities as well as high-ranking officials of the project. This visit
is preferably not used for working sessions. Rather, the aim is to
explain the project goals and the type of work to be done. Permission
is sought from local authorities, dates for workshops are established,
and an understanding is established on who will be invited to attend.
Timing of workshop and sequencing of instruments.
Project personnel must look for ways to minimize the disruption of
people's lives. If possible, the meeting is held in periods or seasons of
low activity; otherwise, field personnel must look for a time of day
when people are back from their daily activities. Besides showing
basic respect, this increases the likelihood that people will actually
respond to the invitation and attend the meeting.
The sequencing of instruments during the workshop should
normally follow the logical flow proposed in this manual. Some
exercises can be undertaken at different moments without affecting
the final results-for instance, transects and flow calendars may be
done at different times if it is more convenient.


48 Food Secirily in Practice






Choice of informants. Initially, all community residents are
viewed as potential informants. Some of the exercises-for example
mapping and concept definition-can be done without being
selective about informants, insofar as they know their community
well and are honest in their responses. As the groups most likely to
suffer from food insecurity are identified, individuals from these
groups soon must play the central role in the discussions. Besides,
within identified target groups, subgroups usually need to be
considered. Typical subgroups are stratified by gender, livelihood
strategy-for example, farmers versus ranchers, age group, and
ethnic/caste affiliation. It may be necessary to obtain contributions
separately from each group in order to capture all the relevant
information. Separating groups may also be necessary if putting
them together creates social tensions. The choice of method also
must take into account informants' profiles; for example, if the
literacy level is low, the method should not require reading skills.
Triangulation. Triangulation refers to the comparison of data
between sources to improve the data's validity and reliability. This is
particularly critical with RA data (many refer to RAs as "quick and
dirty" methods), which are easily manipulated by informants,
although group meetings tend to reduce this problem. The important
point is that no data should ever go unchecked if they are used for
making important decisions. The quality of RA information may be
verified in several ways: replicating the exercise with other groups,
exploiting alternative sources of information (for example, aerial
photos or prior surveys), comparing results against predicted values
from mathematical models, "ground truthing" by walking transects,
and so on.


INSTRUMENTS GUIDE


Concept Definitions
Eliciting local concepts is basic to establishing a common language
between fieldworkers and informants. One good time to do this is at
the start of each exercise, when the ideas used in this particular
workshop are first introduced. The content of each concept is then
discussed, so that it is defined in its local, cultural equivalent.
Another approach is to hold a special "Concept Definition" workshop
where all the notions used in the RA sessions are defined. Whichever
method is best depends on moderator preference and on the time
available. Appendix 4A provides further discussion.
Approaches proposed to define local concepts go from simple
ones, such as brainstorming and pile sorts, to complex ones, such as
Delphi methods and cultural consensus modeling. Since all these
techniques have the same objective (translating in local terms the
concepts used in the RA sessions), the simplest ones should always be
used unless compelling reasons require otherwise. Some of the
concepts to be defined are described below.
Community. The universe to be mapped has to be clearly defined,
so that all households in the village fall within its boundaries and
any unit falling outside of it is excluded. Special cases, such as with
nomad or pastoral societies that move in and out of the community,
have to be discussed and a decision has to be made as to whether or
not to include these in the potential target group.
Household. In Latin America, the nuclear family (a man, his wife,
and children) is the most common type of household, but in West
Africa, extended households (multiple generations/nuclear families
living together) are common. The definition of a household may also
change depending on whether the focus of the projected activity is


Food Seciri/l in Practice 49






production or consumption. If the project goal is production-
enhancing, then the targets are the productive units; if the intervention
is for food relief, then the targets are the consumption units.
Food security. From the project's point of view, food security is
defined as availability and access to food by all at all times.4


Availability and access, however, are notional constructs that are
sometimes difficult for local people to grasp. The following is a useful
shorthand for defining these ideas: availability relates to
communities; access relates to households. Availability is defined as
the capacity of communities to obtain the supplies of food required to


Table 4.1 Realization of the village map


Informants
Where
Time

Objective


Concepts to define

MIethod










Products







Validation Transects


All villagersiotherwise, selected representatives of the various stakeholder groups in the community.
Large open space. For 3-D maps, preferably outside so the area may be expanded if needed.
Varies with the size of the village and the degree of participation of villagers. On average, three hours should be sufficient to
complete the realization.
Have informants reproduce, at reduced scale, the distinct homes and important living areas of the village. Precision must be
sufficient so that all homesteads are clearly identifiable.
Depends on the type of map and intended durability. No need for fancy materials; instead, use only materials locally available, such
as sand, pebbles, sticks, and so on. These are less intimidating than paper and pen for firsttime participants. Once finished, the
output is copied to large paper sheets or cartons,
The concepts of community, household, and food security must be defined before starting this exercise. See section on concept
definitions in this chapter.
No single method exists for this exercise. Villagers are responsible for its realization and their spontaneous suggestions are
encouraged so villagers feel at ease with the instrument and its use. First, a decision is made as to whether a bidimensional or
tridimensional map will be done. A tridimensional map takes more time but is more precise, is easier, and is more enjoyable for
villagers. On the other hand, time may be short, or the weather may not favor working outside, in which case a bidimensional map
should be preferred. Whichever type is used, fieldworkers must ensure that the work proceeds systematically so it has the desired
precision. Guidelines to that effect are, first, identify well-known features, such as the central park, the mosque, and so on, and
place them on the map. Then, draw the outer limit of the inhabited space in relation to these main features. Next, proceed from the
center to the periphery in a concentric fashion. As work proceeds, readjustments to the initial placement of spatial features or to
the outer limits of the village are made as required. As households are represented on the map, they are identified by the name of
their head. Their characteristics (number of persons in the unit, presence of migrants, number of animals or fields owned, and so
on) can also be added at that point.
Two products are generated by this exercise. First (if a tridimensional map was done), the lay model is transcribed on a large sheet
of paper, with households properly numbered and identified (if possible, photos of the model should also he taken). All the elements
of information present on the map are reported on paper, including names and number of households (note: we assume this
requirement is already satisfied if a bidimensional format is used). The second product is a spreadsheet, which organizes the
information elicited by the mapping exercise in a matrix format. All items locally associated with food security (for example, fields
and animals) that were elicited for each particular household are reported as variables in the matrix. Families are listed as rows,
variables as columns. Particular attention goes into coding household identification numbers, especially in cases where extended
family units are common (see a model of coding in Table 4.2)
If high precision is required, an aerial photo may be used.


Source: Compiled by author


50 Food Security in Practice






feed everyone that lives there. In a famine situation, for instance, the
village's capacity to maintain food supplies collapses. Food becomes
unavailable even for people who are wealthy. This is a case where
food insecurity is due to low food availability. Access refers to the
capacity of households to obtain food. This dimension of food
security relates mainly to individual household wealth. For instance,
a household that has sufficient land to harvest grain for the full year
enjoys greater food security than a household whose land can provide
grain for only six months of the year.
Seasons. The Gregorian calendar's month names are not
necessarily known to local populations. The length of months or
seasons may also vary substantially. The seasons have to be defined
before construction of the timeline.

Ctomm Mity Mapping for Census Taking
Community mapping is a versatile tool used to cheaply gather
baseline information on a number of indicators-population
characteristics, wealth and asset distribution, labor availability, and
so on. This manual suggests considering the use of community
mapping instead of a formal census (Table 4.1). Besides being
quicker, this method may yield better results than a conventional
census (but not always-see Christiaensen, Hoddinott, and Bergeron
2001). Another good reason to use this tool is the high level of
participation it encourages: villagers usually enjoy mapping, as it is a
good way for them to communicate issues that have a spatial
dimension. The construction of a map is thus a good starting place
for social assessment studies. Note, however, that community
mapping is not always the most appropriate tool for census taking-
for instance in highly dispersed communities, in areas of low
population density, or in situations where the precise targeting of a


specific population is of particular concern, a formal census format is
preferred.

Example of Cotmmounity Mapping
Tomba is a community of northern Mali where development agencies
are financing the construction of irrigation infrastructure. We visited
local authorities, and informed them of our desire to conduct a series
of exercises in their village to better understand the local
characteristics of food insecurity. The local council accepted a request
to map the community, and agreed to invite villagers to participate in
this exercise. The time was set for the afternoon of the next day, after
they had returned from their daily occupations. A wide-open space,
used as a traditional meeting ground, was designated to hold the
mapping workshop. We also requested that a selected set of
informants meet a few hours before the construction of the
community map to conduct a "concept definition" workshop to elicit
local definitions of households, wealth, and food security.
The next day, arriving at the meeting place, we were surprised by
the level of attendance: all villagers-perhaps more than 200
people-were expecting them. The workshop was obviously seen as a
festive occasion, and everyone came in their finest clothes. Field
personnel, who spoke the local language, began by explaining the
objectives of the exercise to the villagers: reproduce their living space
on the ground as exactly as possible inorder to identify household
units and the people living in them. The mosque and the central
place were laid out first (since these stand in the geographic center of
the village), as well as the main paths leading to the central place.
Banco (wet clay) was proposed as material, and the staff built a few
hypothetical street walls to illustrate the idea.
At the beginning, only two or three men seemed to understand


Food Security in Practice 51







the aim. They proceeded to correct the model. Seeing them work,
bystanders quickly joined in and soon all people present, men and
women alike, were busy adding their own compound to the map.
Controlling the work of so many people soon became impossible, and
we were reduced to acting simply as resource persons, answering
people's questions about procedural aspects and making sure nothing
was left out. As delimitations between compounds were drawn,


vigorous discussions were heard all over as to how much of that wall
was owned by this compound versus its neighbor, where did this
pathway end, and so on. The level of participation, debate, and cross-
checking was such that we are confident no major mistakes were
made. People clearly counterbalanced one another in making the
assessments and little was left unchecked.


Table 4.2 Matrix of household demography, assets, and food security rating: Partial listing from Tomba
Domestic Name of head Number of Number of
Compound unit of domestic Gender domestic Ethnic household Number Owns Number Numbe
number number unit (HHH) of HHH units group members ofoxen a plow of cows ofgoat

1 1 Abdoulaye 4 1 10 4 1 1 1
Amadou Yatara
1 2 Issa Madiu 1 4 1 8 0 0 0 2
1 3 Mamadou Kabara 1 4 1 3 0 0 0 1
1 4 Aligui Madiou 1 4 1 4 0 0 0 1
2 1 Hamadou Mahamar 1 3 1 3 0 0 0 1
2 2 Mahamman Hamadou 1 3 1 6 0 0 0 1
2 3 Abdoulaye Hamadou 1 3 1 0 0 0 0 1
3 1 Boubacar Madio 1 2 1 10 0 0 0 2
3 2 Arsina Madio 1 2 1 2 0 0 0 1
4 1 Djougal Iko 1 1 1 4 0 0 0 1
5 1 Sidar Traore 1 1 1 5 0 0 0 2
6 1 Djoubalo Ahidji 1 1 1 7 0 0 0 2
7 1 Aisa Bocar 2 1 1 7 0 0 0 1
8 1 Ousmane Kouly 1 1 1 4 0 0 0 1
9 1 Ali Oumba 1 2 1 5 0 0 0 1
9 2 Hamadou Oumba 1 2 1 2 0 0 0 1
10 1 Brerna Ousmane 1 1 1 6 0 0 0 1
11 1 Hammadou Ahdoulaye 1 1 1 8 0 0 0 1


SO"Prie.. Coim"p'led hy au"Or f!*ror n srvoy daa.
IV c ldndeI "~eodtr p clr 0oed 0! i doO h'C h1u 0001 130se030f100 lle ood 7
a, See discussions, of fobod-securiifv g onr q 00, p 5,-?


52 Food Security in Practice


Irrigation
fields

1

1
I
1
1
1
1
1
1
1
1
1

1
1
1
1

1
1


3r
s


Non-
irrigation
fields

1

1
1
1
1
1
1I
1
1
1
1
1
1
1
1
1
1
1


Migrant
fields

0

1I
2
1
0
0
2
0
6
5
0
1
0
0
0
1
1I
3


Food
security
rating'

3


2
2

2
2
2
2
2
2
2
2
2
1
1
2
2
1
2


:: IOfa J fSo!moofdfid, uoider '"oworan a p1fmvAi i I .. yes 3o"d 0 rjo.






Once the main streets and family compounds had been laid out,
people began separating individual homes within compounds by
making little clay mounds, each one representing a home. We then
asked them to represent their domestic assets, including number of
persons present in the home. On each house mound, a number of
twigs were then planted to represent how many people lived there-
migrant members were represented by a bent twig. Other symbols
that represented the household assets were deposited in the yard
adjacent to each home. Symbols used included goat feces, to
represent the number of goats owned by the home; bean seeds, to
represent the number of non-irrigated fields; rice seeds, to represent
the number of irrigated fields; and so on.
Once the map was considered complete by informants, field staff
proceeded to record the information on a large sheet of paper and the
summary matrix was done (see Table 4.2). Particular care was taken
when recording family identity numbers, as extended families were
common in that village. Compounds were numbered first and


Table 4.3 Model used for coding compound and family
numbers


Compound number
001
001
001
col
001
002
002
003
004
004
004
Source: Compiled by author
Note: 01 indicates f~,ily head ('HH}.


Family number
01
02
03
04
01
02
01
01
02
03


domestic units second. Both compounds and domestic units were
numbered in ascending sequential order (1, 2, 3, 4. .), but the
numbering of domestic units began anew each time compounds
changed. It was also agreed that the first domestic unit named in
each compound (which received number 1) would systematically
correspond to the family head (Table 4.3). This way of coding was
used in order to allow later analysts to associate each domestic unit
with the compound it belongs to, a crucial piece of information,
given the importance of family networks for livelihood strategies in
this region.


Food Security Rating
Food security rating is part of a family of field research techniques
known as group informant ratings (GIR), which allow fieldworkers
to (1) quickly understand how units of interest (households, plots,
and so on) are different from each other on a particular aspect
(wealth, food security, and so on); and (2) classify them accordingly
(Table 4.4). The resulting classification can be used to identify target
groups for specific activities. The GIR provide a rapid and low-cost
assessment of unit characteristics. In wealth ranking exercises (a
popular GIR method), ratings by local informants are further
credited with removing the biases of conventional survey methods by
bringing intangible elements (such as status, and access to networks
of support) to the measurement of wealth and poverty, thus bridging
the gap between outsider and local perceptions of poverty.
There are problems with GIR methods, however. The first one is
the inability to do cross-community comparisons: Ratings produced
are, by definition, contingent on each setting. The GIRs may thus
have high internal validity but no external validity whatsoever. Some
attempts have been made to overcome this limitation, but no


Food Security in Practice 53






Table 4.4 Food security rating


Purpose
Informants




Format
Where
Materials
Method


convincing alternative has yet been offered. We recommend never
using a GIR scale outside the site where it was developed. Second, it
must be recognized when GIR is not useful. In communities where
everybody is subject to considerable stress, such as is the case with
refugee communities, GIR provides spurious or irrelevant details, as
differences in wealth or food security become increasingly marginal.
Also, the approach is not very useful in large communities where no


one can know everybody well. One may divide the larger community
into wards or neighborhoods, but then the problem of standard-
ization between subdivisions surfaces (see first point above).
Limitations are also noted where populations are highly mobile
(such as in pastoral societies), or where households are highly
scattered (as in the Amazon). Third, GIRs appear to be very
susceptible to error, both systematic and random. Tests of the


54 Food Secrity fin Practice


Classify households in a community according to their level of food security
Much care has to go into selecting informants. They must be long-standing members of the community, be knowledgeable, and be
honest. They should represent a cross-section of the community in terms of age, sex, ethnicity, or other locally relevant distinctions
(caste, productive orientation). The number of informants per focus group should be from four to six. Separate groups may be created
if members of different social status do not want to stand together in the same exercise, or if women remain silent in the presence of
men. Then, however, the ratings produced by each different group have to be reconciled and standardized.
Focus group session
In a calm, private area, inside or outside
Index cards (as many as there are households in the community plus five for labeling of piles/categories) and markers
Of all the methods proposed in the literature, the "index card" approach is preferred, for it is comprehensive and easy to control. In
this method, the name of each household head is written on a separate index card, Once the categories to be used are identified (see
"Prior steps" below}, a separate pile is created to represent each particular category. Informants talk arong themselves and decide
which category each household belongs to. If informants are unsure about one household, they put its card aside so the case can be
resolved later. Once all households are rated, the moderator takes each pile and reads the names back to the group to give them a
chance to review their classification. This may bring additional shuffling. New categories may also need to be created to
accommodate intermediary or uncertain cases. If so, all cards have to be read back again to the group, until no more discrepancies
are manifested. Once the final categories are made, their attributes are discussed anew, by empirically considering the
characteristics of the households falling in this group,
Define the concepts of the community, household, and food security. Define a rating system: irnformarts should be allowed to define
their own rating system, so that they feel comfortable with their assessment. Usually, three to five classes are proposed.
About one hour
A listing of all households in the community with their rating in terms of food security categories. A clear definition of what each
category of household food security (HFS) refers to.
Control with attributes of household obtained from mapping. Obtain second opinion from different focus group. Classification and
Regression Tree (CART) analysis.


Time
Products


S O C iOr napled C' atr~a;.






reliability of ratings suggested that the main sources of error are poor
informant selection and poor training of field personnel. This can be
remedied by exerting considerable care in the use of the method;
however, it has to be clear that it is less straightforward than it
initially appeared.
For all these reasons, GIR methods should be used with much
caution. They should be used strictly to classify populations within
single communities. Careful selection of key informants is required,
and careful training of field personnel is an absolute must.

Example of Food Security Rating
A food security rating exercise was conducted in San Marcos, a
community of western Honduras where a rural development project is
being implemented. The aim of the exercise was to examine how
food security varied in the group of farmers targeted by the project.
A listing of community members was provided by project managers.
We randomly selected various people from that list and visited them,
asking who in their opinion were the most reliable and
knowledgeable informants in the village. Five persons were repeatedly
pointed out by villagers. These five persons-three men and two
women-were invited to participate in a focus group session.
We explained to them that they would have to create a food security
rating of community members. The meeting was scheduled for the
next afternoon, and held in the schoolyard.
After informants had arrived at the meeting place, we explained
to them what was meant by "food security" and "households" (see
discussion above in "Concept Definition" section). They were asked
to add whatever they thought should form part of these concepts.
Next they were asked two questions: "Does everyone among villagers
have equal access to food? (Yes/No)," and "If there are differences,


how would you characterize these differences?" After some debate, a
two-way classification emerged from these discussions: (1) food-
secure, defined as families that never have food security problems;
and (2) food-marginal, defined as families that seem to have food
security problems every year.
The group was then asked to rate each household on the list in
relation to this categorization. The moderator read the names of
every household head in turn, asking in each case on which of the
two piles this household should be placed. Informants deliberated
and then took the card and put it on the appropriate pile. Many cards
created difficulties, so they were put aside for later categorization.
After the group had gone through all the cards, the moderator asked
them to consider again those that created problems. One informant
eventually mentioned that it seemed all of them did not fit in either
of the extreme categories; rather, they fell in between, not totally
food-secure nor totally food-insecure. A third, intermediary category
was thus added, which was defined as "families that occasionally
have food security problems but not every year." The moderator
added a new corresponding pile. He then read back the names that
had been put on the two first piles (Food-Secure and Food-Marginal)
and asked if they still agreed on this rating. Many of the households
from these piles were then reclassified to the intermediary category.
Once the review was completed, the moderator asked informants
to consider again each class and the households in it, and asked,
"What makes you think these households belong to this class?"
Responses to that question improved understanding of food security
differences in the community, and provided a point of entry for later
project design. Mentioned characteristics were as follows:


Food Secuirity in Practice 55






Food-secure group
They work at a large scale on their own lands.
They have good ideas.
They work hard.
They save their money.
They have the best lands.
They have public responsibilities.
They have cattle.


Food-insecure group
They do not have much land.
They have to work for wages occasionally.
Their families are large, and the little they produce is
consumed right away.
They sell their product before it is harvested.


Food-marginal group
They always have to work for wages.
They have no money, low revenues.
They do not make decisions; they do not have a view of the
future.
They are lazy or sick people.
They do not have a sense of responsibility.
They must buy all their food.
They do not have land, or their land is insufficient.


Conceptual Map of Sources of (and Threats to) Food Security
Conceptual mapping is a relatively new technique in the
participatory rural appraisal (PRA) tool set, used to specify which
factors contribute to a particular outcome. It can be viewed as the


qualitative version of a functional equation in which the outcome
(dependent variable) is determined by a set of factors (independent
variables) that can be objectively specified and ranked in terms of
their respective contribution to total explained variance (Table 4.5
and supporting Figure 4.1a-c).

Table 4.1a Zoning" of the conceptual map into
quadrants
--------------- ----------


SOw production
........................................................


M onetary income


Food


[ Han.icrafts


IRemittances, transfers
.......... .... .. -... -.-.....-.... .........


Documented experience in the use of this technique is scarce. Our
field trials suggest that, although theoretically promising, obtaining
good empirical results is a challenging task. We noted two main
difficulties. First, the map is complex and requires a very skilled
moderator. Second, verification is problematic: Supporting evidence is
difficult to obtain and requires a better knowledge of the community


56 Food Secturil' i Practice






Table 4.5 Conceptual map of food sources and threats to food security


(1) To elicit the most important pathways by which households obtain their main staple food in that community, (2) identify the most
important threats to these food acquisition strategies, and (3) assign priorities to thesetheshreats.


Informants


Format
Materials
Methods


























Prior steps
Time
Products

Validation


Source: Compiled by author
a. Pairwise rankin, is a common RA technique in which every choice is iteratively compared with every, other choice by asking which of the two is most important: in this way all choices get
ordered in terms of their relative importance one to the otiier.


FoodSecurity in Practice 57


Purpose


Products include (1) a specification of main staple sources in the community and their relative importance, (2) an identification of the
main threats to these pathways, and (3) a list of threats in order of priority.
The only rapid way of validating the results is to repeat the exercise with another group and triangulate findings. A household-based
survey of food consumption may provide information about sources of food, but not about pathways or threats. A prolonged stay in
the community (six to seven days) is needed to verify the conclusions.


~I --------------------------------------


Optimal size of group is from 8 to 12 participants. Informants must be selected to represent the distinct farming strategies found in
that community. A balanced gender representation is also required.
Focus group session held in a quiet, private area.
Materials include a large sheet of paper and markers of distinct colors.
This exercise is easier when limited to main staple foods Pfor example, maize and beans).
(a) General aspects. The moderator explains to the participants that he/she wants to know the sources of their staples in this
community. A simple example (for example, "growing it") is usually sufficient for participants to understand what is expected from
them. Informants will mention that they get staples from their own production, donations, purchases, and so on. Always remind
informants to refer only to actual, nonhypothetical sources of food. Also, a minimal number of families-- for example, at least 25
percent of households---should use this strategy before it gets recorded on the map.
(W) MIapping food sources and their pathways. The moderator "holds the pen" during the whole session, so the product remains
organized as it fills up. The moderator mentally divides the map in "zones" to keep sources separate from one another. An example of
"conceptual map zoning" is presented in Figure 4.1 a. Once the main sources of staples are listed, each source is considered
individually. The main prior conditions to this source are elicited. For example, a prior condition to have "food from own production"
is that there be a harvest. To have a harvest the farmer must have land and buy inputs. Both of these require capital, which may
come from savings or loans; and so on. Each of the steps in this sequence corresponds to a node; the full sequence of nodes
associated with a particular source is called a pathway. The pathway and its nodes are reported on the map as in Figure 4.1b.
(c) Ranking food sources by order of importance. Conceptual maps generally turn out to be very similar from one village to another.
What makes them different is the relative importance of each pathway in the livelihood strategies of the villagers. Once all pathways
have been identified, a subjective weighting is made between them by drawing arrows of various sizes indicating their relative
importance in the community. The size of each figuratively corresponds to that vector's effect.
(d) Identify threats to each food source. The moderator next asks informants to identify the main threats that exist along each pathway.
The link between each node is examined, and elements that may threaten this link are elicited and written on the map, using a marker of
different color. Here again, it is important that the threats identified correspond to those that exist in this village, and not merely
theoretical ones. Since threats are usually different between sites, the map will also differ between villages at this level (see Figure 4.1c).
(e) Prioritize threats to address first. The final step is to rank threats by order of importance. Pairwise ranking is adequate for that
purpose.a To keep this manageable, a maximum of five threats per pathway is suggested. If three pathways are identified, that makes
a total of 15 threats to rank.
Identify main staples. Recruit informants.
Approximately two hours.


J-






Table 4.1b Nodes and pathways in conceptual map

Land accessed Local jobs grant jobs

.................Z.I.'
and rented j Land owned Sales age

I Own production Mnetary inc 1

I Consumpt.i i Food purch ases ar Nofood


Food
.. . . -


Handicrafts

SAccess to supli
.............. ............!....t...]


Remittances, transfers


Table 4,.c Threats to food pathways

-, Land accessed Local jobs MI grant jobs


Lad rented Land owned Sa es wages

Own-- :-- 3 -~r-- ...V1---- i

.... ...... .i.. ..
IConsumpti I LFood purchases Noifood


Food



Remittances, transfersi.
Handicrafts
Aesstesuplli
A Access to supplies


than the little time spent doing a rapid appraisal can actually
generate. Yet, this exercise can be very useful for assessing the sources
of (and threats to) food supply. For this reason, project managers
should be aware of its potential when exploring options for food
security interventions. Basic guidelines about its use are provided
below. It must be emphasized, however, that it should be used only if
qualified personnel and time are available.

EXAMPLE OF CONCEPTUAL MAP


iMain. Food Pathways and theirP Prior Conditio.ns
Santa Teresa is a mountain community of western Honduras. Staple


1. Availability of land for rent
2, Price of land
3. Rain, pests, access to capital
4. Storage losses
5. Capacity of household
members (HHM) to use food
6. Output prices
7. Total output
8. Demand for wage labor
Source: Comp:ed by author.


9. Availability to travel
10. Wage levels
1'. Prices of food
12. Prices of nonfood items
13. Arrangements to access
materials
14. Handicraft output prices
15. Presence of migrant member
or access to income transfer


foods are maize and beans. Villagers obtain these staples either by
producing them or through purchases. No food donation programs
are active in this community, and few households mention receiving
transfers. Staples include maize and beans, grown primarily for


58 Food Securily in Practice






subsistence with small quantities occasionally sold locally for cash.
Wheat was once important, but less of this crop is grown every year
due to genetic erosion, and the small amounts produced are grown
only for sale. The prior condition to production is access to land,
labor, and inputs. Land in this village is either owned or rented by the
producers. Labor depends on the family demographic cycle. Inputs
are generally bought, since organic fertilizers are little used locally.
The working capital for production comes from credit, savings, the
sale of produce, or from wage work.
Food purchases depend on income generated from two distinct
sources: The sale of one's own production and wage work on other
people's land. The conditions that determine sales are the same as
those determining production. Thus, land access is the key to how
much cash is derived from production. Wage work refers mainly to
temporary migration during the coffee harvest season.

Threats to Food( Acquisition
Pathway 1-own production. The local production of basic
grains is determined by many factors. Farmers say external inputs
are crucial to their production of food. Most of the money to buy
these inputs comes from loans; but to obtain a loan one has to own
land, be a member of a producer organization, and be free of debt. In
Santa Teresa, about half of the people own some land. They recently
formed a producer association, enabling them to access credit. For
them, the conditions to access credit are met-unless they have bad
loans. For those with no land, however, the situation is more difficult.
They may rent land, but rented land cannot be used as collateral and
does not give access to credit. Besides, land rental is insecure because
of the legal stipulation that a farmer who has worked a plot for more
than three years can claim ownership of that plot. Fearing loss of


their ownership rights, landowners prefer not to rent to the same
person from one year to another. Landless producers thus constantly
have to seek new land to sow their basic grains. This lowers the
incentive to land investment, and rented land is typically more
degraded and of poorer quality, making it (and the family that uses
it) more vulnerable to production shortfalls. There are few ways out
of this situation, as the land market is tight in this area, and buying
land is expensive.
Assuming land and capital are secured, the next problem
confronted by producers is the price of inputs, which is always
increasing. This complaint is certainly legitimate in the case of basic
grain producers. Other sources confirm that the cost/benefit ratio in
basic grain production has gone down in Honduras by up to 40
percent in the last two decades (compared with an increase of more
than 200 percent in nontraditional commercial crops). This is bound
to have severe effects on a community like Santa Teresa, where
people rely to a high extent on their own production to ensure their
supply of basic foods.
Going down the pathway, and assuming fertilizers are obtained,
farmers still have to face the hazards of erratic rainfalls, pest
outbursts, postharvest losses, and so on. Irrigation systems could
remedy rain shortages, but water sources are distant and would have
to be pumped, requiring a major infrastructure investment and high
operational costs. Pest incidence is relatively low in this community,
yet pesticides are needed at times, which again requires capital.
Storage losses, largely from rot and rodents, are reported to affect up
to 15 percent of stored grains.
Pathway 2-purchase of foods. The capacity to buy food is
related to the wealth of a household, which is a function of the
amount of land owned, sales from one's own production, access to


Food Securil' in Practice 59






savings, and/or earnings from wage work. The threats associated with
production were already described. To these, one must now add the
problem of output prices, which fluctuate quite dramatically on a
seasonal basis. With respect to wage work, the most important source
of employment is provided by coffee harvests. However, this source of
income is premised upon the availability of household members for
periods of out-migration and the effective demand for labor in the
coffee sector, which is a function of world coffee prices and climate.
Coffee harvests occur only in a short, seasonal fashion, but the
incomes provided are secure and stable. Yet farmers resent this


obligation to migrate, and they would rather stay at home if they
could. Also, they complain that salaries are low (although other
sources report that coffee wages have improved over the last few
years). A few alternate sources of employment exist locally, but they
are occasional and cannot serve as a main source of income. They
also pay less.
Finally, producers mentioned that the purchase of food is affected
by problems of local availability (nonexistence locally) and access
(high prices). Prices, they say, are particularly subject to
manipulation by intermediaries.


Table 4.6 Matrix of threats to food acquisition, with possible actions and their likelihood


Possible action


Likelihood of action


Inadequate tenure laws Change land tenure law Unlikely: Tenure laws are a national policy.
High land prices Change land market Unlikely: The market is already quite open.
Land reform Local land reform would provide no relief, as landowners in this community are
smallholders.
Production hazards Stabilize yields via technical Can be done. Technologies can be adapted to improve maize/beans/climate/pest
improvement tolerance.
Poor access to capital Offer credit without need Can be cone, but requires organization. Alternative credit guarantees for instance,
Sfor collateral group lending-must be explored.
Storage losses Provide silos Can be done: Simple, cheap technologies exist.
Poor or unstable output prices Diversify in high-value crops to Diversification into commercial-output-prices crops might be envisioned, although
deflect poor prices of basic grains this needs to be paired with irrigation and roads for market access.
Poor labor market Stabilize labor market Unlikely: Local outlets are saturated and there is no control over demand for labor in
coffee. _______lserda
Poor wages Improve wage levels Unlikely: Wage levels are determined nationally.


High food prices


Remove middlemen via
consumer co-op
Favor production of vegetables
in home gardens
Improve transport


Possible, but difficult. Consumer co-op requires much organization and training.
Can be done. Additionally, favors involvement of women and children in food
production and offers alternative source of income and sales
Possible, but costly. Could be paired with consumer co-op.


Source: Compiled by author from survey data.


60 Food Security in Practice


Problem






Analysis and ranking of f" '..
The threats identified above were listed for further discussion.
A matrix (Table 4.6) was drawn to discuss the possible action, and
whether any of these actions were in the project's and the
community's manageable interest.
A pairwise ranking was made to prioritize issues to be addressed
by development agencies. The following were listed in order of
preference:
1. Offer creative solutions that would provide credit funds without
need for collateral.
2. Make technical improvements for yield stabilization in basic
grains.
3. Construct storage silos.
4. Diversify production towards higher value crops.
5. Favor production of vegetables in home gardens.
6. Create a consumer coop to remove middlemen.

Seasonal Food Security Timelines
Diagrams such as pie charts, bar graphs, and timelines are very
popular among rapid appraisal workers seeking a chronological
representation of processes. Considerable documentation is available
on the various types of chronological instruments that have been
developed and their uses (see References). The timeline is a
particular version of these that models time-bound processes in a
linear fashion (Table 4.7). Timelines are very flexible: One can find
applications all the way from history manuals, where they are used to
describe long historical sequences, to software planning tools, where
they are used to describe sequential flows of activities in a project. In
this guide, the technique is used to better understand the sequence of
events leading to food insecurity. To do so, multiple timelines are


superimposed to illustrate the connections between production and
consumption flows, and cycles in asset availability and demand for
cash. The data thus provided can be used at distinct phases of project
design: in initial needs assessment ("When is the hungry season?"
"What food runs out first?"), project design ("What combination of
early/late maturation breeds could reduce the length of the hungry
season?" "When is labor available to realize projects?"), and
evaluation ("How do calendars compare between the beginning and
the end of the project?").

Example of' ':: ;:. ..
Data from the community of Santa Teresa in Honduras illustrates the
use of timelines (Table 4.8). As already noted, mountain wheat is
produced in this community in addition to the usual Honduran
staples of maize and beans.
Harvests. Food harvests go from August to January, but they are
divided in two distinct subperiods: August and September, and
November to January. The little wheat that is still harvested comes
mainly in September, although a few households also obtain small
amounts of wheat in August. Maize harvest begins in November,
increasing gradually until the peak month of January. Small
amounts of early maize (elote) may be harvested also in September
and October. Most beans are harvested in December, with small
amounts coming up in November.
Monetary revenues. Monetary incomes come mainly in the last
two months of the year (November and December) and in the first
three months of the year (January to March). Cash comes either from
the sale of one's own production (wheat in a few cases, which is sold
in September, and maize, in most cases, sold between December and
March, with sales culminating in the latter month), or from wage


-,,.f..' l .Pracfice 61






Table 4.7 Seasonal food security timelines


Purpose
Informants
.......................................................
Format
Materials















Method













Prior steps

Time
Products


Describe yearly cycles of food production, food consumption, cash, and labor use.
Two different groups are consulted: A set of community informants chosen from the whole village to develop a typical community
timeline; and a set of households viewed as most food-insecure, to develop timelines for food-deprived units.
Focus group sessions held in a sheltered, private area.
A redesigned matrix (months as columns and flows as rows). Six groups of seasonal flows are considered: harvests, income,
expenditures, labor, food, and cash. Each is further subdivided into single categories:
Harvests: Consider individually the three main crops grown locally, at least one of which is a staple (the other two may be cash crops
or staples). A rating of their relative importance in terms of the amount of labor they require is also provided.
Cash income: Distinguish between income sources from agricultural sales, wage work, and sales of handicrafts. Their relative
importance is also rated.
Cash expenditures: Distinguish between production and consumption expenditures. include only recurrent, important ones. For each,
consider the total amount of cash needed, for example, for production expenditures, Informants must add costs of inputs, hired labor,
animal medicine, and so on. For consumption, they must consider the need to buy food when supplies from their own production are
over, plus school materials. They then add all of these in deciding when more money is needed. The calendar reports on the total,
Labor: Includes mainly timing of female labor. Could be divided between labor in own farm versus labor or for wage.
Food ands cash: Describes periods when food and cash are scarce.
Markets are chosen to be representative of the cycle being described (coffee, maize seeds, bean seeds, and so on.)
The calendar is laid on the floor, and participants are invited to stand around it. The purpose of the exercise is explained, and the
moderator indicates how to use markers. The exercise begins with the harvest of the most important subsistence crop in that
community (first row). Say it is maize. The moderator asks participants, "In which month do you mostly harvest your maize?" One of
the participants is asked to put five maize grains in the cell corresponding to the designated month. The moderator next asks whether
harvests of this crop are obtained in other months. Another number of maize grains are deposited in the corresponding cell. It is
explained to participants that the number of grains corresponds to the relative amount obtained in each month, so that months with
greatest harvests have the largest numbers (five) and those with smaller harvests have the smallest number (one). Intermediary
months may receive from two to four grain markers. Months without harvests are left blank. Each timeline is revised in a similar
fashion, that is, the month of greatest importance receives the largest number of markers, with the exception of "months where food
arnd cash are scarce." Thiese are inversely classified to indicate periods of greatest scarcity monthss of greatest scarcity get more
markers). After the community workshop, the exercise is repeated with the three most food-vulnerable families (selected from Food
Security Rating results). In this case, however, the timeline is made specific to those households' situations. The objective here is to
assess how these households compare with the rest of the village.
Identify the main crops and income-earning activities in the community. Identify inforrriants from thie food-insecure group. Describe
seasons in local words.
Approximately one hour per group.
Once finished, project staff transcribes the result on a separate sheet, coding the size of mounds from 1 (smallest) to 5 (greatest).
Pictures are taken of the final calendar if possible. Relevant details that do not get reported on the timeline are collected by the
relator, to be reported later at the time of write-up.


Source: Compilued by author


62 Food Secir'it' in Practice






work during the coffee harvest season, beginning in November and
culminating in December and January. Some additional wage
earnings are obtained in February, mainly obtained from working in
coffee harvests, which implies seasonal migration. No other sources
of cash are reported; trade or handicrafts are not mentioned.
Women's labor. Women do not work in other people's fields. They
work only in their family's plots. Their involvement in agriculture
occurs in two periods: land preparation for maize in June, and maize
harvests in December and January.
Expenditures. Most production expenditures occur at the time of
land preparation, before the sowing of maize (May-June) and shortly
after fertilizer or weed killers are needed (August-September).
Consumption expenditures concentrate in the months from June to


August, with a culmination in the latter month, when foodstocks are
exhausted and school equipment has to be bought.
Food reserves and monetary savings. Food reserves usually
last until June. From that moment on and until September, when a
few early maize cobs can be harvested, people depend almost entirely
on their monetary savings to buy food. Monetary reserves reach their
lowest point between the months of June and August, but the period
of scarcity may begin as early as April or May. The early maize
harvests in September provide some relief at that point, if the season
is favorable.
Summary of the timeline. In summary, the timeline indicates
that the supply of food is at its highest between the months of
November and January. Starting between April and June, we note a


Table 4.8 Development projects: Multiple timelines form (example from Honduras fieldwork)


Community:
Category
Harvest of (main crop)
Maize harvest
Bean harvest
Income from production
Income from wage work
Income from other w ork
Women's work in own farm
Women's work outside
Production expenditures
Consumption expenditures
Low reserves of food
Low reserves of cash


Group: (Mixed, Males, Females, Individual)
Jan. Feb. Mar. Apr. JMay June


..Ju ... A- ug.


Date:
Sept


SoCrcc p;il d b Oy 3;Jihar
A-borA Re 'alYs e ro rhilar'o hrtw:,pcvvs! l Whhi oart-h catWqiygor


Food Security in Practice 63


Oct. Nov. Dec.






progressive decline of food and monetary resources, which
culminates in August when severe scarcity is mentioned. The small
harvests of maize recorded in September alleviate this situation; from
that point on, food access and food availability improve progressively
until the cycle begins again.
This sequence indicates a high level of dependence on the maize
harvests in September and afterwards. The total maize harvest can be
assessed by the end of January, and dispositions could be taken to
alleviate future food shortages based on an assessment of total
harvests at that date. Another indicator of future harvest performance
is the quality of the rainy season. Late or poor rainfall (which can be
assessed by July) can create a difficult situation for the coming
September and October, translating into a serious problem of food
access and/or availability. A combination of these two situations can
be disastrous. A monitoring of the situation at these two critical
points would be useful to forestall severe food security problems.
Production expenditures occur mainly in the rainy season (May
to August). Credit funds must be available in these months if they are
to affect the current growing season.
Comparison with poorer households. The same exercise was
carried out with three households identified as food-insecure by the
Food Security Rating exercise. Similar situations were reported by
those three households. Differences with the general village situation
were particularly evident along the following lines:
In all cases, fewer months of harvests were reported, no matter
the crop. In two cases, no maize of segunda (second crop cycle)
was obtained, and none grew wheat.
Income from sales of their own production came in fewer
months, if at all, and meant little. By contrast, income from
outside sources was important. Wage work in a traditional tile


factory was cited by one as a main source of income, day labor
in coffee farms by another.
The time spent by women working outside the home was much
greater in two of three cases. In both cases, women worked for
wages, not on their own farm. The third case corresponded to
an elderly couple, who reported no sources of income at all
(they subsisted on transfer income from charities).
The period for production expenditures was much shorter in all
cases.
The months of scarcity were approximately the same, but
extended for longer periods.

It was clear, from conversations with these households, that their
main problem was lack of access to land, but also to labor and other
productive resources. None of them owned land, and two rented small
plots on a yearly basis-thus the little amounts of produce reported,
either for consumption or sales. This lesser emphasis on agriculture
also explains the different timing and direction of expenditures-
little went to production, most went to subsistence. Women's work is
certainly of concern, as this may lower their ability to care for
younger children, without apparently bringing compensation in the
form of sufficient income.
The lesson from the timeline is that quite different strategies
might need to be envisioned if the project is truly interested in
dealing with food security issues. Alternatives to agricultural
production-for example, value-added transformation of locally
produced goods-may do more for those particular families than
agriculture-oriented interventions. The best strategy would be to
combine both.


64 Food Securill' in Practice






Monitoring and Evaluation Workshop
The last exercise aims at monitoring and evaluating the impact of
the project on local food security (Table 4.9). It is conducted at least
one year after the beginning of the project, so the activities have time


to manifest some impact. It may be done on a yearly basis thereafter,
to assess whether the project is on course and enable changes if
needed (monitoring function). It may also be realized at the end of
the activities, to draw lessons and guide the design of future activities


Table 4.9 Monitoring and evaluation of impact


Purpose

Informants




IViethod



















Prior steps


Monitor the progress of activities with respect to stated goals, and evaluate the overall impact of activities at completion to inform,
orient, and improve design,
Beneficiaries of project activities.
Focus group session including 8-10 informants, held in a quiet, private area,
Large chart prepared in advance, listing activities in rows, and whether they had an impact on income, food access, and food
availability in columns. The last column is left for explaining reasons for impact or lack thereof. See Appendix 4B for an example.
* List the activities undertaken by the project in that community (list only activities that have been implemented, and which had time
to have an impact; for instance, the impact of a tree nursery on community life will not be felt before some years, so this activity is
not evaluated). This list may be obtained from project officers working in the community. It is later validated with local informants in
the village to ensure that the activities noted in project paperwork indeed correspond to those deployed in the community, and that
no important activity is omitted (or added).
* Considering each activity in turn, ask villagers whether this activity had the effect of increasing income, food access, or food
availability in the community. A good definition has to be provided for each of these notions. Access refers to the food obtained at
the household level. Availability refers to the food found at the village level. Income refers to cash earnings associated with the
activity (see definition above).
SInformfants are asked about the reasons for the success (or failure) of the activity. For instance, if the activity is technical extension
in maize production and villagers report lack of impact on food access in the first year, this may be due to poor irrplementation of
the activity, but it may also be due to poor rainfall or to a pest outbreak. Likewise, the failure of a credit program may be due to a
late delivery of funds, but also to the unavailability of inputs locally. The actions listed in the project paperwork can be consulted to
augment this characterization (that is, each activity is supported by specific actions). In case the activity is not successful, we may
ask whether the actions were indeed taken, and the failure to do so may explain why the activity did not have any impact.
* The activities considered most successful (in terms of villagers' priorities) are listed, followed by the less successful ones, and so
on, until all the activities have been listed and ranked in relation to one another,
* This exercise is also undertaken with the technical staff in charge of the program. Comparing assessments between project
managers and beneficiaries validates the findings and provides a more complete and balanced evaluation of the activities,
Identify the main crops and income earning activities in the community. Identify informants from the food-insecure group. Describe
seasons in local words.


Time Approximately one hour per group,
Products Once finished, project staff transcribe the result on a separate sheet, coding the size of mounds from 1 (smallest) to 5 (greatest).
Pictures are taken of the final calendar if possible. Relevant details that do not get reported on the timeline are collected by the
relato-r to be reported later at the time of write up.
Validation Repeat the exercise with another set of informants and compare results. Plausibility should also be corroborated with external data.
Source: Compiled by author[ from survey data.


Food Securilj' in Practice 65






(evaluation function). Note that this exercise does not aim to replace
the monitoring and evaluation procedures based on the collection
and analysis of quantitative data by the project. Rather, the aim is to
ensure that the voice of local people is heard and that their opinions
on the activities and suggestions for improvements are taken into
account.
Here again the we found no documented experience in the
literature on this topic, but experimental trials in certain project sites
proved satisfactory. It is estimated that there are two crucial
requirements for a successful completion of this exercise. First, only
the direct impact of activities is evaluated. Second, the outcome
variables are the components of food security (that is, food access
and food availability). Income is also considered an outcome
variable, as many activities directly target income, and income
indirectly affects access or availability. These three dimensions are
defined to the participants as follows:
1. Increased income refers to additional sales resulting from
increased production.
2. Increased food access refers to the greater presence of food at
the household level, and results when more food grains are
produced as a result of project activity.
3. Increased food availability refers to the greater presence of food
at the village level, and obtains when the activity results in
additional food being sold in the village, thus augmenting the
amount of food available in the village as a whole.
For instance, technical assistance in coffee production may result
in increased income, but not in increased food access nor food
availability, as coffee is not eaten. Only through the increased income
generated by coffee sales may food access be improved-but it may
not have this result, since the increased income may not be spent on


food. Thus, it is important to identify only direct impacts. As another
example, if the project improves bean production, and this increased
production is both sold and consumed, then the assistance will have
an impact on incomes, on access, and (if beans are sold locally) on
availability.

Example 1: Using the Impact Evaluation Instrument
The example of Santa Teresa illustrates the use of the Impact
Evaluation tool. The community had been visited the previous year
by an NGO. This NGO had identified the following objectives for its
activities in that village: increase maize yields (no target specified),
increase bean yields, improve handling of minor species, train
villagers in environmental protection of water sources, train villagers
in proper use of credit, and implement a credit program.
Increase maize yields. Villagers say this goal was reached. Their
maize yields were higher this year than in previous ones, although
the precise improvement was not known. This yield increase had
positive effects on food access, mainly via the augmentation of
subsistence production. It had very little effect on either income or
food availability, however, since only a few households sold maize.
The increase in yield was due to (1) a favorable rainfall in that
season; (2) the training farmers had received from the NGO in
improved seed selection, better agronomic practices, and proper use
of fertilizer; and (3) the availability of credit for purchasing inputs.
Increase bean yields. Bean yields were reportedly higher this
year than in previous ones. This goal was reached, although again
the exact improvement is not known. The increase in bean yields had
positive effects on income (in Santa Teresa, beans are as much a
cash crop as a staple), on food access (households' production of this
staple went up), and on food availability (more of the production was


66 Food Securi/ in Practice






sold locally).
The reasons for improved yields were similar to maize: improved
agronomic practices, and better fertilization and pest control
practices. Farmers also received improved genetic materials through
the NGO. Favorable rains also helped production. Farmers also
received credit, which allowed them to buy the inputs they had been
taught to use by the NGO's agronomist.
Improve handling of minor species. No activities were
developed around this objective, so it had no effect on any of the
three outcomes. Villagers said they did not know why the NGO had
left aside this part of the work plan. When consulted, the NGO staff
said their contract with their funding agency had come to an end,
and no resources were available to develop this aspect.
Train villagers in environmental protection of water
sources. The same situation as for training in minor species was
reported on this activity. No training took place, and plans for
reforesting riverbanks were left undone. Here again, the NGO blamed
this on a miscommunication with their funding agency
representative.
Train villagers in proper use of credit, and implement a
credit program. Credit principles were taught, and villagers said it
was very useful. Part of the training consisted of creating a producer
association responsible for channeling and administering the
individual loans. The creation of this association had secondary
benefits, such as providing a conduit to farmers' requests for
technical assistance and providing a focal point for the realization of
public goods activities like road repairs, soil conservation structures,
and so on. Thus, although this training had no direct effect on
incomes, food access, or food availability, it was undeniably
beneficial to the long-term well-being and food security of Santa


Teresa's inhabitants, as it incited better community organization.
Credit was obtained in the last production season. The effects on
outcome indicators were indirect, but villagers say it had a critical
influence on final yields.

Example 2: Using the Impact Evaluatioi& hsstrunent
The impact evaluation instrument can also be used by project
managers to evaluate how well they are doing globally, how well
particular classes of activities serve the objectives of improving food
security, and how well particular NGOs are doing in implementing
their contract. To illustrate this, results were compiled from 10
communities of western Honduras where a number of NGOs
implement development activities. A total of 16 types of activities were
carried out across all communities-note, however, that none of the
communities hosted more than 8 activities in total. Table 4C.1 in
Appendix C reports on the results, breaking down by village
(columns) and activity type (rows), each type being, in turn, divided
by its impact on income (Y), food access (AA), and food availability
(DA). An additional line specifies the NGO in charge of this particular
community. Examination of the table offers the following insights.
The overall rate of success was 33 percent.
The three most successful types of intervention for improving
income were agronomic training in coffee production, credit
programs, and agronomic training in bean production.
The three most successful types of intervention for improving
food access were agronomic training in maize production,
training in care of minor species, and agronomic training in
bean production and diversification of production.


Food Security in Practice 67






The three most successful types of intervention for improving
food availability were diversification of production, training in
care of minor species, and agronomic training in bean
production.

This information suggests that the overall rate of success is rather
low. This assessment is tempered by many factors, however, as
revealed by detailed consideration of the data. First, it seems that
agricultural production-oriented interventions usually work well.
Other types of interventions by contrast-improving
commercialization, inciting alternative income-generating activities,
protecting the environment-do poorly. Project managers should
thus consider whether to emphasize these types of activities in the
future, or (given their poor rate of success) abandon them altogether.
In making this decision, due consideration should be given to the
guidelines emitted earlier to direct NGO work, and whether the
conceptual tools were available to them to develop this type of
activity.
Other elements may explain the poor overall rate of success. First,
many activities have been implemented only recently, and have not
had time to manifest their impact yet. Thus the same assessment
should be made again at a later date-say, in one year-to see if the
patterns documented here hold over time. Second, and unlike the
example in Santa Teresa, many communities suffered from adverse
climatic conditions in the last production year, and this may have
thwarted any beneficial influence from the interventions.


APPENDIX 4A: METHODS FOR LOCAL CONCEPT
DEFINITION

In this appendix, we review a few of the most important techniques
used to identify and define local concepts. Three techniques are
examined: cultural domain identification (or free listing and pile
sort), Delphi analysis, and cultural consensus modeling.

Cultural Domain Ilentification
Practically speaking, to define a cultural or cognitive domain is to
make a list of its elements. Such a definition is needed when one has
a general idea of the domain, but does not know exactly which items
belong to it in the particular society under study. To determine this,
anthropologists commonly use free listing techniques (akin to
brainstorming sessions), in which a set of respondents is requested to
name all items matching a given description.5 For example, if
interested in the domain of "food vulnerability," one asks each
informant to individually identify all the elements he or she
associates with that term (it may be working for wages, or lacking
land, but also may refer to traits that are specific to that culture, for
instance, being in a casted group, or not having a camel, and so on).
Once the brainstorming has elicited all the attributes associated with
the term of interest, the list is further processed using particular
techniques, such as "pile sorts" and "ratings." They consist in simply
counting up the number of times each item is mentioned, and
sorting the list in order of decreasing frequency. A well-understood
concept (for example, one that informants easily associate with their
daily lives) will typically have a core set of items that are mentioned
by many respondents, plus a large number of items that are
mentioned by few or just one person. It is assumed that the core set of


68 Food Securili in Practice






items reflects the existence of a shared cultural norm regarding that
concept, while the additional items represent the idiosyncratic views
of individuals (Borgatti 1993). The shared cultural norm is what is of
interest.
The first step in distinguishing the "shared" from the
"idiosyncratic" is a distribution of the frequency with which
brainstorming items are mentioned. If represented in a scree plot, the
cutoff point between shared and idiosyncratic items should be
indicated by a drop (or "elbow") in the plot. In Figure 4A.1, for
instance, items 1 and 2 are mentioned 10 times each, and others with
declining frequency. The elbow method suggests a natural cutoff point
between item 6 (mentioned 7 times) and item 7 (mentioned twice).
The concept here is thus formed by the six first items. If no clear elbow
shows, then one can pick the top n items, or items that are listed by
more than x respondents, as the cultural definition of the domain.
Whatever the rule used to eliminate noncore items, one should
always ask why some respondents did not mention items that were


Scree plot of core items


Elbow


1 2 3 4 5
t~ern numbnjer


commonly mentioned, or that were theoretically expected to be
associated with the domain. In many cases, the reason why an
informant does not mention a particular criterion may not be that it
is irrelevant, but that it did not occur to him or her at the time of the
questioning. Such "informant blanking" can be rectified through
more discussion. If the variation in frequencies is due to real
individual differences in opinion, however, then more steps are
needed. The researcher should first make sure that the concept is
clear to the informants. A concept like "food security," for instance,
may be diffused, and need to be reformulated before consensus is
reached on its local meaning. It may also be that the concept per se
is unfamiliar to local people. An example of this situation arose in
Guatemala when indigenous people were asked about their natural
resources conservation methods. The informants did not understand
the question because conservation exists as an intrinsic part of the
farming system, not as a set of activities independent of it. If it is
concluded, as in that example, that the lack of concordance on a
concept is due to the absence of a precise cognitive referent, then the
researcher should resort to one of the other strategies listed below
that rely more on "specialists" (people who understand this problem
because of their particular situation or knowledge).


Delphi Method
The so-called "Delphi method" is an iterative definition process
designed to achieve consensus among a group of persons considered
experts on a particular topic as to the criteria used in evaluating this
topic. This is especially useful in situations where no standard criteria
yet exist for doing this evaluation. The method is well documented
and has been used in a wide number of applications.


Food Securilt in Practice 69


Figure 4A. 1
Number of
items
mentioned




5
4
3
2
1
1 __






The procedure consists of the following steps. Begin by identifying
a set of "experts," or individuals who have a vested interest in the
issue. Then each is asked a few questions, following a standard
format. For instance, assuming that the two areas of interest are
criteria for evaluating food security, and criteria for evaluating
causes for loss of food security, these questions could take the
following form:
Question 1. Assume you are in the middle of the dry season.
Please list the five most important criteria you would use in
assessing your food security situation on that day from your
own point of view (that is, as a cattle rancher or as a coffee
grower). Once you have made your list, please rank each of
these criteria from one to five, with five being the most
important factor. Give reasons for the importance given to each
factor. Also, give an an opinion as to how each could be
measured.
Question 2. What are, in your opinion, the five most
important reasons for deterioration in food security? Once you
have made your list, please rank each of these criteria from one
to five, with five being the most important factor. Give reasons
for the importance given to each factor. Also, give opinion as to
how each could be measured.

The next step is to reduce the quantity of information provided to
a manageable number of criteria. This is necessary because of the
large number of responses that may be elicited. A large number may
be useful in terms of domain mapping, but it is impractical in terms
of establishing streamlined evaluation criteria. To reduce the impact
of too many responses (and also to reduce the impact of informant
blanking), a second round of questioning is done, using the same


cues, but asking respondents to select among the list of criteria
elicited in tlW first round. Respondents are also informed that they do
not have to list the same ones as before; rather they should consider
whether any of the criteria mentioned by others would be a better
criterion than any of those they originally proposed. This procedure
has been demonstrated to drastically cut the number of criteria
mentioned. Finally, the most important criteria are isolated using
individual criterion scores, ranking them from most important to
least important, using a five-point Likert scale. The final list of
valuation criteria may be finally reduced to the five or 10 most
important ones, according to this last ranking exercise.

Cultural :Cosensus ~odeling
Cultural consensus modeling describes and measures the amount
and distribution of cultural knowledge among a group of informants
(Romney, Weller, and Batchelder 1986). Consensus analysis has two
goals: first, to determine the culturally correct answers to questions
relative to a particular domain and, second, to evaluate the "cultural
competence" of each informant (Borgatti 1993). The first goal is that
which is most relevant to our work.
Romney, Weller, and Batchelders' cultural consensus theory is
based on the insight that informants who agree with one another
about some item of cultural knowledge tend to know more about the
domain than informants who disagree with each other. The idea is
illustrated in West Africa on manioc classification. Researchers
walked 58 women through a manioc garden and asked them to
identify the various plants. They found that the more women agreed
with each other on the identification of the plant, the more they were
likely to know what the plant actually was. In other words, as
cultural competence increased, so did cultural consensus (Ryan and


70 Food Securil' in Practice






Martinez 1996). As for the Delphi method, a focus group of
"specialized" informants is required to conduct these exercises.


Which Method?
The choice among the three approaches presented above should be
informed by the concept to be defined. This project requires that the
concepts of wealth, poverty, food security, and food vulnerability be
defined in their local meaning. Table 4A.1 suggests guidelines to the
exploration of those concepts.
Once the meaning of those concepts has been elicited, some
additional exploration may be appropriate. For instance, in the
normative diet, a rank ordering of essential foods could be obtained
through pairwise scoring or contingent valuation. These tools will be
reviewed later.


Alternative Methods for Impact Evaluation: The SWOT
Analysis
SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis is
a common tool used by program managers to elicit and analyze the
relative merits and deficiencies of particular activities, and
possibilities for improvement. This instrument was initially developed
t for use by specialists, but it can easily be adapted to an RA setting as
its realization is well developed and very straightforward. SWOT
analysis is easily explained to participants using a matrix (Figure
4A.2) where the time frame (present/future) is placed on one axis,
and evaluations (positive/ negative) are on the other.
This framework is particularly well-suited to examine the present
performance of single development activities for food security (say,
credit or technical extension) and evaluate their future implications.
Considering the present, what works well and why (strengths) is first
examined. Informants work in a brainstorming mode, where all


Table 4A.1 Concepts to define, approaches to use, and outputs to obtain


Concept
-- -- -- -- -- -- -- .--- -- -- -- -- -- -- -- -- ---... . .... ...... . .
Wealth and poverty

Household configurations

Food security


Indicator for food security
Food vulnerability

Normative diets

Source: Compied by author:


Approach
Cultural domain

Cultural domain

Delphi


Format/pparticipants
Focus group/cross section of all villages

Focus group/cross section of all villages

Separate focus groups of men, women,
project staff


Cultural domain Focus group/cross section of all villages
Cultural domain Focus group/cross section of all villagers


Delphi or cultural
corlsernsus


Focus group/senior women of households


Output
List of attributes associated with wealth and poverty in
that community
List of household forms (extended, nuclear, gender of
household heads) and their relative occurrence
List of attributes associated with food-secure and food-
insecure situations; may also include a list of graded
responses to food insecurity (to be used as indicators)
Ordered list of responses to food insecurity
List of local livelihood strategies arind of threats to these
strategies
Minimum list of foods and their quantity needed by
average adult to lead a healthy life


Food Security in Practice 71






Figure 4A.2 SWOT matrix


STi.me
'I snt^ __j


..... .... [


Va~~ualtioo


I .iNegative r ee0^ |[

iNote: SW;O' stands for st:rengths, w-eakwnsses, opportunities, and threat


comments are welcomed and listed. The same is done
working, and why, in present implementation plans (w
examination of future opportunities may refer to ways
present weaknesses, or new initiatives that may be add(
enhance the present strengths. Future threats refer to p
impacts of the activity on food security or the emergent
that may impede the continuation of identified strength
realization of future opportunities. Programmatic imp
naturally follow from these considerations.


APPENDIX 4B: IMPACT EVALUATION INSTRUMENTS
(EXAMPLE FROM HONDURAS)


Coooorlity:
Activities/goaks;


GroupT : hRapportae r
impact s ( onditi
-N AA D A
..... ..- -- -- -


.. .. .. -- -- -- .. .. ... .. ----- - -

....---- --- ------------ ------- -


I APPENDIX 4C: SUMMARY OF IMPACT EVALUATION


Sixteen types of interventions were carried out in total (Table 4C.1).
Fure. _] Interventions were not always the same from village to village, as (1)
the choice of activity was defined by community members
poiiites themselves; (2) the service provider varied from village to village; and
(3) programs were generally directed either at men or at women, and
thais levels of participation varied by gender between communities. Global
..............evaluations of the programs are thus difficult to make, and we can
only offer crude measures of the general performance of the activities
promoted by PLANDERO in the ten communities. Disaggregating
with what is not measures by gender, by service provider, by intervention type, and by
weaknesses The community can, however, improve the evaluation. The analysis is
to improve on supported by a review of the reasons invoked by informants as to the
2d that would reason for success or failure of each activity.
possible negative Respondents felt that about one of every three (32.8 percent) of
ce of constraints PLANDERO activities improved the food security of their income. This
hs or the rate of approval differs by gender, with women positively viewing the
locations contribution of activities to food security 41 percent of the time, and
men 25 percent of the time. The various dimensions of food security
were also rated differently by gender. Overall, 24 percent felt it
improved their income, 50 percent felt it improved the local
availability of foods, and 25 percent felt it improved their access to
food. When contrasted by gender, however, men viewed positively the
contribution to income in 16.8 percent of cases; to food availability,
ons
in 36.9 percent of cases; and to food access in 23.2 percent of cases,
-while women viewed positively the contribution to income in 36.6
percent of cases; to food availability in 56.6 percent of cases; and to
food access in 29 percent of cases (Table 4C.2).


72 Food Securi/ in Practice






Figure 4A.2 SWOT matrix


STi.me
'I snt^ __j


..... .... [


Va~~ualtioo


I .iNegative r ee0^ |[

iNote: SW;O' stands for st:rengths, w-eakwnsses, opportunities, and threat


comments are welcomed and listed. The same is done
working, and why, in present implementation plans (w
examination of future opportunities may refer to ways
present weaknesses, or new initiatives that may be add(
enhance the present strengths. Future threats refer to p
impacts of the activity on food security or the emergent
that may impede the continuation of identified strength
realization of future opportunities. Programmatic imp
naturally follow from these considerations.


APPENDIX 4B: IMPACT EVALUATION INSTRUMENTS
(EXAMPLE FROM HONDURAS)


Coooorlity:
Activities/goaks;


GroupT : hRapportae r
impact s ( onditi
-N AA D A
..... ..- -- -- -


.. .. .. -- -- -- .. .. ... .. ----- - -

....---- --- ------------ ------- -


I APPENDIX 4C: SUMMARY OF IMPACT EVALUATION


Sixteen types of interventions were carried out in total (Table 4C.1).
Fure. _] Interventions were not always the same from village to village, as (1)
the choice of activity was defined by community members
poiiites themselves; (2) the service provider varied from village to village; and
(3) programs were generally directed either at men or at women, and
thais levels of participation varied by gender between communities. Global
..............evaluations of the programs are thus difficult to make, and we can
only offer crude measures of the general performance of the activities
promoted by PLANDERO in the ten communities. Disaggregating
with what is not measures by gender, by service provider, by intervention type, and by
weaknesses The community can, however, improve the evaluation. The analysis is
to improve on supported by a review of the reasons invoked by informants as to the
2d that would reason for success or failure of each activity.
possible negative Respondents felt that about one of every three (32.8 percent) of
ce of constraints PLANDERO activities improved the food security of their income. This
hs or the rate of approval differs by gender, with women positively viewing the
locations contribution of activities to food security 41 percent of the time, and
men 25 percent of the time. The various dimensions of food security
were also rated differently by gender. Overall, 24 percent felt it
improved their income, 50 percent felt it improved the local
availability of foods, and 25 percent felt it improved their access to
food. When contrasted by gender, however, men viewed positively the
contribution to income in 16.8 percent of cases; to food availability,
ons
in 36.9 percent of cases; and to food access in 23.2 percent of cases,
-while women viewed positively the contribution to income in 36.6
percent of cases; to food availability in 56.6 percent of cases; and to
food access in 29 percent of cases (Table 4C.2).


72 Food Securi/ in Practice






Table 4C.1 Summary of impact evaluation


Activity

Augment maize
production of
(wheat)
Augment.
production of
beans
Augment
production of
coffee
Aug ment
production of
horticultural
Diversify
production

Built conservation
infrastructures and
agroforestry systems
Protect and delimit.
sources of water

Stop slash-and-
burn practices

Involve primary
school in
environmental
activities
Credit education
and programs

Extension in
environmental
protection


Affected

Y
AA
DA
Y
AA
DA
Y
AA
DA
Y
AA
DA
Y
AA
DA
Y
AA
DA
Y
AA
DA
Y
AA
DA

AA
DA

Y
AA
DA
Y
AA
DA


Villages
1 2 3 4 5 6 7 8 9 10 11 12

i01 0/o 0 0 0 0 0/0 0/1 i01 0/ 1 1/0
0/1 0/1 1 1 0 1 1/0 1/1 1/1 1/ 1 1/1
0/1 0/1 0 0 0 0 1/0 0!0 0/1 1 1 0/1
-!- 0 0 0 1 1- 110 -!- 0/- 1 /1
........ 1 .... 1 0 .... li/ .1.. .. 1 1/1
/........ 0 0 0 1.1 .1/0 I /- O--. I 1/1
-- -!- -i I- -!-
.... ... .... I .... .... 1 .... .... .... ....... .... /.... .... .... ....
......... ........ 1 .. ... .. ........... .... .... .... ....
1 1 0/ .......
1 01 1 01 ; 0 0
-i- 0 0 0/i- 0/- 0
....0 0 .. ... .. .... .... / .
.... ....... 0 .... .i o ...... 0 i..O .
--



1 0 0 0/- 0/- 1


0,.... ....i.... .... .... .... ....... 1 .. 0 1. .0 .1..
i/- ... .... .... .. /. 0 / ..0 ......
'... .... ..... .. ...... 0. ... 0i'.... I... .... ... .



1 10 /..0/ .. /







0 0 1 1 0 1 10 /1 Ol 0 1 1/1

11 0/1 0 1 0 1 1/0 !1 0/1 0 1 1/1
-- 0
/ 0 .... 1 .. 1 1 .. .. 1... .... ....
li --... .... i.. .... .... i ... l! .... .... i .... .. !...



..i ....... .. ....o. i ... ... .. /0 .... .... ... .. ....


0 0 I 1/01 1 1!i ...!I 0/1 0 1 1/1


0-i. -... .. .. .... .... .- 0 ........
0--- 0 .... .... .... .. i .... ....


Number of
communities
where
activity
is deployed,
PM/F groups


12/111
12/11
12/11


8!7
8/7
8/7
3,2
3/2
3/2
5,3
5/3
5/3

21
52/
5/2
5/2
512
3/3
3/3
3/3
3/1
3/1

-12
12
..../2
.... ,2


11/12
11/12
11/12
2/2
2/2
2/2


Number of
positive
impacts, M/F
groups


2/4
9/9
3/5


4/3
5/5
4/3
2/2
2/2
2/2
0/1
0/1




1/0
0i/





2/0
1/0
2/0


0/0
0/0
1/1
2/1
1/0
-/11
2/1
/1


6/9
9/10
6/7


Percent of
positive
impacts, M/F
groups


17/36
75/82
25/45


50/43
63/71
50/43
87/1(00
67/100
67/100
0/33
0/33
0/33
0/-
50/ ....
-...........- --- .o 5...
20/0
40/0
20/0
o0io
0/0
0/0
3/0!0


33/100
67/100
33/0
-/50
-!50
-/50


55/75
82/83
55/58


MVean rate of
positive
impact
across /F
groups


27
79
35


47
67
47
84
84
84
..................... ..................
18
18
0
0
0
0
10
20
10
0
0
0


67
84
17
50
50
50


65
83
57


0/0 0 0
0 0 0
0 0 0


Food Security in Practice 73


soul-of": C"t"Impilod by alithor ftofi-l v d;. ta.


~_______


i


-r


----- -- ---







Table 4C.1 Summary of impact evaluation (cont.)


Activity

Extension in handling
minor species
(also value -added
production)
Improve
commercial lization

Family/school
garden

improve women/
youth participation

Foment handicraft
industries


Villages
Affected 1 2 3 4 5 6 7 8 9 10 11 12

Y / 00 /-0 -!- -10 0 --
AA -- 1 -- -1 0 -- 0 -/0-- 1 0 --
DA j- 0 1 .. .1 0 -/0 ./ 0 0 /-

S -!- 0 -!- 0 -- -... ..


AA
DA
Y
AA
DA

AA
DA
Y
AA
DA


.... ... .... ... .... 0 0 -.... .... ....


.. -. / ......../.. ..... ........ ... .............. ... .... -. ,,. : ...... .... ............... ... ... -....... .... ... ..... -. / -...
... i .... s ... .. i .. ... ... ...
_s_ -. .!- -!0 -i- -1-


/ 0 1 1
---;, -. .... 10 -.j $
-...- / .. ..... .... ..... .... ..... ..... -- .. ..... .... .. ..


./1 0/.
-/ .. .... .... .. ... 0 -!-
...i.... .... ... .... .... .... .... .... ... ....- .. i .-.. .. i ..- .
/-- -- -!0 0!- --
# #


Source;: Compiled by author from survey data.
Note. Data are distingued bv'y ender (m.ne,=malem / when a.ppr;opr.ae. Y = income. AA A foo 'P access; DA food availability f 12 columns 1nde3 r wi1iiges, 0 means "had no positive impact; 1
means "had positive impact"; and me-ns!"n.' activity wA s repoed." M = male; F = female.1 "Mean rte of positvf e fimpct" is a simple average o1f the proportions of r!male and female groups report-
ing a positive iol a Elmpac
Key to Viiages: 1: El Auacate 3: Soca del Monte 5: El Mora 7: Pan El ncho : San Marcos : B ina


2: aisni San Juan


4: La Mohaga


6: Nueva Vitud


:: El Rosnrio 10 Tepezc uinte 12: Lamu Sca


Table 4C.2 Individuals viewing intervention positively on dimensions of food security, by gender

Dimensions of food security All informants IIale informants
.................................................................................................................................. ..........................................................................................................................................................................................................................
Household income 24,1 16.8
Availability of food in commnl unity 50.4 36.9
Access of food by household 25.2 23.2
Improved food security 32 23

Source: Compiled by au.,hor fro'i ; survey data.


Female informants


56.6
28.5
40


74 Food Security in Practice


Number of
communities
where activity
is deployed,
M/F groups

4/7
4/7
4/7
4/P7

2/2
2/2
2/2
/I
1
................................................
-/1

i/1
1/1
1/1
1/1


Number of
positive
impacts, M/F
groups

1/1
2/3
2/2

0/0
0/0
0/0
/1

-'10
--/I

-/0
--/o1
./1

0/0
0/1
0/0


Percent of
positive
impacts, M/F
groups

25/14
50/43
50/29

0/0
0/0
0/0


-/100
0/0
..--/0



0/100
0/0


Mean rate of
positive
impact across
M/F groups

20
47
40

0
0

100
100
0
0
100
0
0.
50
0









1. Rapid appraisal (RA) techniques and participatory rural appraisals (PRA)
are often thought to be the same: They seek local input using similar
techniques and assuming a similar attitude on the part of project staff.
There are differences, however. The ultimate goal of PRA is community
empowerment. This involves intensive community participation and
assumes an open research agenda. This can hardly be done quickly. RA
methods, by contrast, are meant to provide researchers with data quickly.
RA requires the participation of community members but the research
agenda is predefined and the time frame is short. Use of the word
"participatory" here is thus in reference to a methodological style rather
than an epistemological posture.
2. For the purpose of the exercises described in this manual, a typical team is
composed of one "moderator," who explains the activities, channels the
interactions, and so on; and one "relator" who takes notes and keeps track
of all the information that is provided, including that which does not get
transcribed on the final group output. One such team is required for each
working group.


3. It is assumed that the situation here is one in which no previous contacts
exist and no activities have yet been programmed for that community. The
situation will obviously be different if the community graduates from a
previous development program, or if development activities have already
been defined.
4. There are several definitions of food security that can be found in the
literature. USAID for instance includes food utilization (in addition to food
availability and food access) as part of the definition of food security,
whereas FAO, IFAD and UNDP include only food access and food availability.
Since this chapter was done under commission for IFAD, its definition of
food security, which includes only access and availability, is used.
5. This method is quite tolerant about choice of respondents: In fact, it is
preferable to avoid selecting respondents, as the concept should have as
wide a currency as possible among inhabitants of the target village. It is
thus best carried on in a workshop setting where all villagers are invited.


Food Securil' in Practice 75




5. Constructing Samples for Characterizing Household Food

Security and Monitoring and Evaluating Food Security Interventions

Calogero Carletto


Introduction


WHY RANDOM SAMPLES?


Reliable information on household food security is a
prerequisite for the accurate and effective design,
monitoring, and evaluation of development projects. In
marginalized areas, where many development agencies work, this
information is often either not available or grossly out-of-date. But
collecting data is not a costless exercise. This chapter discusses how
random sampling techniques-methods that use some mechanism
involving chance to determine which farms, households, or
individuals are to be studied-can economize on the costs of
gathering information while increasing the likelihood that it will be
both accurate and available in a timely fashion.
The chapter begins with a brief explanation of why random
sampling techniques are a powerful means of obtaining information
on household characteristics such as food security. It then takes the
reader through a step-by-step process of constructing a random
sample. Having outlined these issues, a worked example is then
presented. Readers interested in pursuing the issues raised in this
guide are encouraged to consult Bernard (1988), Casley and Lury
(1987), Casley and Kumar (1988), Devereux and Hoddinott (1992),
and Newbold (1988). More technical discussions are found in Kish
(1965) and Cochran (1977).


S Randono Sam& ples Rather than Censuses
One alternative to a random sample would be to obtain information
on all observations in a population census or a census of agriculture.
The advantage of a census is that it seemingly provides an accurate
"snapshot" of the population at a particular moment in time. It also
ensures that numerically small groups, which might be missed in a
survey, are counted. Censuses are characterized by (1) individual
enumeration (each unit of observation, say farm household, is
measured separately); (2) universality within a defined territory or
domain (information is obtained on everyone in a certain area); and
(3) simultaneity (everyone is interviewed at the same point in time).
The key criterion is simultaneity. The census should be conducted
within a short and well-defined period of time to reduce omissions
and duplications.
There are a number of drawbacks to conducting a census. First, it
is usually much more expensive than conducting a survey. (This is not
true, of course, where the population is very small.) Second, the
processing and cleaning of a census is enormously time consuming.
Further, a smaller sample allows the researcher to devote extra effort to
ensure the information obtained is accurate. The gains from a smaller,
more accurate survey could well outweigh the benefits of obtaining less
accurate information on a much larger group. Finally, many topics,
such as those involving detailed transactions of individuals or firms,
require extensive interviews or observations that cannot be carried out


Food Securil)' in Practice 77




5. Constructing Samples for Characterizing Household Food

Security and Monitoring and Evaluating Food Security Interventions

Calogero Carletto


Introduction


WHY RANDOM SAMPLES?


Reliable information on household food security is a
prerequisite for the accurate and effective design,
monitoring, and evaluation of development projects. In
marginalized areas, where many development agencies work, this
information is often either not available or grossly out-of-date. But
collecting data is not a costless exercise. This chapter discusses how
random sampling techniques-methods that use some mechanism
involving chance to determine which farms, households, or
individuals are to be studied-can economize on the costs of
gathering information while increasing the likelihood that it will be
both accurate and available in a timely fashion.
The chapter begins with a brief explanation of why random
sampling techniques are a powerful means of obtaining information
on household characteristics such as food security. It then takes the
reader through a step-by-step process of constructing a random
sample. Having outlined these issues, a worked example is then
presented. Readers interested in pursuing the issues raised in this
guide are encouraged to consult Bernard (1988), Casley and Lury
(1987), Casley and Kumar (1988), Devereux and Hoddinott (1992),
and Newbold (1988). More technical discussions are found in Kish
(1965) and Cochran (1977).


S Randono Sam& ples Rather than Censuses
One alternative to a random sample would be to obtain information
on all observations in a population census or a census of agriculture.
The advantage of a census is that it seemingly provides an accurate
"snapshot" of the population at a particular moment in time. It also
ensures that numerically small groups, which might be missed in a
survey, are counted. Censuses are characterized by (1) individual
enumeration (each unit of observation, say farm household, is
measured separately); (2) universality within a defined territory or
domain (information is obtained on everyone in a certain area); and
(3) simultaneity (everyone is interviewed at the same point in time).
The key criterion is simultaneity. The census should be conducted
within a short and well-defined period of time to reduce omissions
and duplications.
There are a number of drawbacks to conducting a census. First, it
is usually much more expensive than conducting a survey. (This is not
true, of course, where the population is very small.) Second, the
processing and cleaning of a census is enormously time consuming.
Further, a smaller sample allows the researcher to devote extra effort to
ensure the information obtained is accurate. The gains from a smaller,
more accurate survey could well outweigh the benefits of obtaining less
accurate information on a much larger group. Finally, many topics,
such as those involving detailed transactions of individuals or firms,
require extensive interviews or observations that cannot be carried out


Food Securil)' in Practice 77






in a census. So issues of cost, time, precision, and quantity of data all
suggest that a survey is preferred over a census.
There is a further reason. Censuses are unnecessary. You can
learn all you need to know about a given population with a random
sample of that population. This is referred to as inference. You draw a
sample of a certain number of observations from a given population
then calculate parameters of interest such as means and proportions
that, by inference, represent the characteristics of the underlying
population.

Random Versus Nonrandom Sampling
It is not necessary to obtain information on all units of observation.
Is it necessary, however, to choose those households or farms to be
studied in a random, or probabilistic, fashion? Why not use
nonrandom, or nonprobabilistic, methods instead?
Nonprobabilistic methods are those in which the analyst
consciously chooses who will be interviewed. Examples of these
include the following. One is accidental sampling. This involves
interviewing respondents as they are found, for example, walking
down a track or road and interviewing whoever you happen to meet.
A second is quota sampling. Here, enumerators are instructed to
contact a specified number of observations possessing certain
characteristics (for example, 15 farms with no livestock; 10 farms
with 1 to 3 head; 5 farms with more than 3 head of cattle). These
quotas are assigned on the basis of what is known about the
underlying population. However, the actual selection of observations
is left up to the enumerator. A third method is purposive sampling.
Here, individual units of study are chosen on the basis of some
judgment criteria. Suppose you want to learn about long-term processes
of environmental change in a rural area. To obtain this information,


you could choose a sample of "wise old men." A fourth method is
referred to as networking. Here, you find one person to interview and ask
them to name others who are also suitable candidates, given the topic of
interest. If the population is small, this can be a useful means of
building up a sample. However, in larger populations every person or
unit of observation does not have an equal chance of being sampled-
that is, the sample selected is not representative of the underlying
population. However, networked samples are very useful when exploring
networks (you want to find people who know each other) or when
dealing with hard-to-find groups.
Under a number of circumstances, nonprobabilistic sampling
methods are appropriate. For example, if the population is
homogeneous ("describe one unit of observation and you describe
them all"), these methods produce information identical to that
derived from probabilistic techniques. They are also appropriate if
there is no intention to extrapolate the results to the larger
population (for example, where the objective is to describe a village
in general terms rather than obtain a statistically representative
picture). Finally, such methods are useful where a sampling frame is
unavailable or too costly to obtain.
But there are also significant drawbacks to these methods.
Statements made on the basis of observations found in these ways
must be limited to the sample itself-it is not possible to make
legitimate inferences about the wider population. Further, standard
statistical techniques-such as comparing means of two groups-
cannot be used either. For these reasons, the use of nonprobabilistic
methods by development practitioners is strongly discouraged. They
should only be used where probabilistic methods are infeasible.


78 Food Securif in Practice






STEPS IN CONSTRUCTING A RANDOM SAMPLE


There are five steps involved in constructing a random sample:
(1) determining the sample unit, (2) determining the "universe,"
(3) constructing a sampling frame, (4) deciding on the sample size,
and (5) choosing the sample. Although these are discussed
sequentially, it should be noted that it is often necessary to iterate
back and forth among these. So, for example, practical
considerations associated with choosing a sample may have an
impact on the manner in which the sample frame is constructed and
the calculation of the sample size. A glossary of sampling terms is
provided in Box 5.1.

Box 5.1 A glossary of sampling terms


Universe The location or population or group that the
analysis seeks to describe.
Sampling units The unit of observation of the study, such as
farms, households, individuals, and so on.
Sampling frame The list of sampling units. It must contain all units
within the universe.
Self-weighting A sample in which all units have an equal
samples probability of selection.
Sampling The ratio between t e sample size arnd the
fractions population size. Also called the selection probability.
Domain A part of the population for which separate
estimates are sought. Examples are farms of a
certain size, individuals of a particular age group.r
Cluster The aggregation of sampling units, often based
on geographic proximity. Examples are a village
or a section of village.
Take The number of sampling units drawn from a
selected cluster.

Source: Comnpiled by author.


Determiinii g the Samliping Unit
The appropriate sampling unit-or unit of observation-is guided
largely by the objectives of the survey and the project. For example,
where a project seeks to increase farm yields, the relevant sampling
unit for evaluation purposes would be the farm household. If the
objective was to improve the nutritional status of children under five,
the relevant unit of observation would be children in that age
bracket. What is important here is that the definition of the sampling
unit should be unambiguous and conform to local understanding
and acceptance. The most common ultimate sampling unit in
multipurpose socioeconomic studies is the household, even if
individual-specific estimates are sought. In some countries, there
may be a generally accepted definition of what constitutes a
household-for example, the definition adopted by the central
statistical office. Even where such a definition exists, it should be
validated locally before proceeding with the listing exercise.


Determining the iUniverse
The "universe" is the location or population or group that the study
seeks to describe. Again, this is likely to be determined by the
objectives of the project. If the project is located in, say, western
Honduras, then western Honduras would be the location of the study.
However, it is not always practical to survey the entire location. The
discussion below on "choosing the sample" and the worked example
from Malawi illustrate solutions that are available when this
problem arises.

GConstructing a Sample Frame
The use of probabilistic methods to select a sample requires a
sample frame.


Food Security in Practice 79






The frame for a sample is a list of the units in the population (or
universe) from which the units that will be enumerated in the
sample area are selected. It may be an actual list, a set of index
cards, a map, or data stored in a computer. The frame is a set of
physical materials (census statistics, maps, lists, directories, records)
that enables us to take hold of the universe piece by piece (Casley and
Lury 1987, 52.)
Examples of lists that can be used as sample frames include lists
of administrative areas, census materials, ordinance survey maps, tax
listings, land registries, and lists of project beneficiaries. In practice,
there are a number of dangers when working with such materials.
Take, for example, a list of households. First, the frame may be
inaccurate. This could result from errors in recording information-
names might have been misspelled, adults were listed as children,
households contain people who are not recorded, and so on.
Alternatively, these errors might have occurred because the
information was collected from neighbors because household
members were absent or unavailable when the frame was created.
Second, the frame might be incomplete. Households or groups of
households may have been omitted. This might have occurred
because the frame is out-of-date (for example, households have
subdivided or migrated in or out) or because of poor enumeration
when the frame was created. There might have been difficulties in
determining the location of boundaries, with the result that certain
households were missed. Third, there might be duplication. Some
households are included twice, possibly because (1) the lists were
compiled by more than one person; (2) there is confusion over
names; or (3) disputes over land claims exist. Devereux and
Hoddinott (1992) provide a good example of some of these problems
in this description of surveying households in northern Ghana.


"When I first arrived in my chosen village of Pusiga I introduced
myself to the subchiefs in the two sections, Terago and Tesnatinga (or
Teshie), in which I planned to work. These sub-chiefs had recently
compiled lists of households for their sections, which were used by
the District Administration to distribute small quantities of
government food aid (following the two successive poor harvests
mentioned above). Had these lists been compiled for an unpopular
purpose such as tax collection, I would have had reservations about
their accuracy. But since everybody had an incentive to register for
food aid, I decided to use the sub-chiefs' lists as a basis for household
enumeration.
Nonetheless, these lists were inaccurate in several respects....
Over-reporting occurred mainly in large, complex compounds, and
typically took the form of young men claiming to be separate
households when they were, in fact, still farming with their brothers
or father. The explanation for this was simple. When the household
lists had been drawn up, local residents were well aware that the
purpose was to distribute food aid. People in large compounds
reasoned that if each household was to receive free food, it was to
their advantage to exaggerate the number of separate households in
their compound. When I made my first round of interviews, the
expectation that I would be bringing some kind of free or subsidized
assistance to the village was high, and overreporting was standard
practice. During the year I gradually discovered which compounds
had overreported household numbers, and simply crossed them off
my list. (A clear indicator was when I asked several 'household
heads' in a compound about planting, harvests, and asset ownership,
and received identical or near-identical figures-since they were
each listing, in fact, the same (joint) production and assets.) ...
Under-reporting of households occurred most commonly with old


80 Food Securiil)' in Practice






women, especially widows. Although most old widows are looked after
by a son or son-in-law, this is not always the case, and some old
women constitute separate households, either because they insist on
retaining their independence by farming their own land, or because
they have been cast adrift to fend for themselves. In my sampling
frame there were three such single-person 'households,' one in the
first category and two in the second, all of which I missed until it was
too late to incorporate them in the lists of households from which my
samples were randomly selected.
The reason why these widows were missed is to be found in the
local conceptualisation of a household, which corresponds broadly to
the Western notion of a 'production-consumption unit.' A man is
said to constitute a separate household if he is 'farming separately'
(from his father and brothers) and 'feeding himself' (and his wives
and children)-that is, both a production entity and a consumption
entity. The two widows living on their own were virtually beggars,
being too infirm to work and having no one to help them with
farming. In fact, they were dependent on handouts from relatives and
neighbours. So they did not strictly qualify as households in terms of
the local definition because they were neither 'farming separately'
nor 'feeding themselves'" Deveraux and Hoddinot (1992, 50-53).
It follows that survey designers should always plan to have any
existing list checked. In monitoring and evaluation exercises, the
population under study, or at least a domain of it, is generally
composed of the beneficiaries of a certain project or program. In
many instances, the lists of project beneficiaries are readily available
with the project management. However, even in these apparently
favorable conditions, it is imperative to check these lists for
inconsistencies, omissions, and duplications. By no means should
their accuracy be taken for granted.


Where no such listing of households, or units of observation, is
available, or where such lists are so outdated or inaccurate as to be
useless, two possibilities remain. These are (1) to create a list or (2)
to derive a sample without a frame. These are discussed in turn.
Creating a sample frame can be a time-consuming and expensive
exercise. For this reason, there may be practical advantages
associated with using multistage sampling (described below) or
restricting the universe to be studied. For example, an evaluation of a
project might be limited to certain localities rather than all areas in
which a project has operated. It is important to note that by adopting
such strategies, probabilistic samples are representative of a restricted
universe and as such any extrapolation of the results should be
confined to it.
One approach is to start with a list, even one that is known to be
inaccurate. For example, in northern Mali, survey work began with
lists of households that had been compiled several years previously
for the distribution of food aid. The survey team, accompanied by
village leaders, walked through the villages matching names on the
list to households, adding new names, and deleting those no longer
resident in the village.
Where even rudimentary lists are unavailable, maps can be used
as a starting point. A first step in area sampling may involve the use
of a map providing a graphical representation of the universe, for
example, a region or a province. Using easily identifiable natural
boundaries, the map can then be partitioned into approximately
equal-sized segments. Once all the segments/villages have been
delimited and some chosen, sketch maps can be easily produced in a
relatively short time without need of much expertise. Of course, the
amount of time and resources going into this mapping exercise
should be suited to the objective at hand. In most cases, very rough


Food Securifl in Practice 81






sketches describing the main roads and pathways and some
landmarks (for example, a church, a mosque, a borehole, a river and
so on) clearly delimiting different sub-areas of the segment/village
could suffice. In most cases, however, the inclusion in the sketch of
the individual domiciles, properly numbered, may be necessary. As in
the case of working with listings, it is important to verify that no area
or sampling unit of the universe has been missed and that no
overlapping occurs between different maps, since this would
obviously result in unequal probability of selection for the elements
of the chosen population.
There may be instances where it is simply not feasible to
construct a sampling frame. In such circumstances, the following
two-stage technique-EPI Cluster Survey Design, developed
originally to monitor and evaluate the Expanded Programme on
Immunization (EPI)-can be used.
The original design, used for the monitoring of immunization
coverage of children within a target age (generally 12-23 months),
involves the selection of 30 primary sampling units or clusters
(villages or other types of area units), and the subsequent drawing of
seven ultimate units (children) from each cluster, for a total sample
size of 210 children. The clusters are selected from a comprehensive
list of villages or area units with probability proportional to estimated
cluster size. Census information and administrative records may be
used to generate the list containing the estimated size of the cluster.
The second-stage selection of seven children in each cluster was
originally envisioned as a random selection from a list of children in
the target age living within the cluster. However, enumeration
difficulties have led to the development of more simplified
procedures. A commonly used variant of the original scheme suggests
choosing a random direction from a central point in the village/area


unit by spinning a pen or bottle. Only the households along this
direction up to the edge of the cluster are enumerated, and one is
chosen at random. Starting from the chosen households, and along
the direction line, seven adjacent households with children in the
target age are selected and interviewed.
A plethora of variants have been tried in recent years to partly
overcome some of the limitations associated with the standard
design. Choosing seven adjacent households in the case of a restricted
target group (such as children between 12-23 months) may actually
result in a quite spread-out sample within the cluster. In another
circumstance in which eligibility criteria are not so stringent, the
selection is likely to identify a highly concentrated conglomerate of
households. Under the plausible assumption that adjacent
households exhibit very similar socioeconomic characteristics, it is
evident why the standard design does not perform well in multi-
indicator socioeconomic surveys.
With the goal of selecting more heterogeneous elements within
the cluster, a possible variant to the standard design would be to
select the third, fourth, or fifth household, starting from a central (or
randomly chosen) location after a direction is picked. From this last
selected household, you repeat the procedure and proceed in a
random-walk fashion until the quota is met. Alternatively, the village
could be split into smaller areas and from the center of each unit (or
any randomly chosen point), a direction picked, and the nth
household meeting the eligibility criteria interviewed.

Deciding on the S3amplv Size
Calculating sample sizes is one of the most technically demanding
aspects of survey design. Although a number of software packages-
such as Epi-Info and STATA-automate these calculations, it is still


82 Food Security in Practice






necessary to understand what information is required in order to run
these routines. This subsection provides an overview of these issues.
Abstracting from practical issues such as the time and
resources available to undertake a survey, a decision regarding
sample size is strongly linked to the required level of precision in
the variables we seek to measure. Precision-or sampling error-
is described in terms of a margin of error and a confidence level.
For example, you might want to estimate sample maize yields
within 3 percent of the true mean (the mean we would obtain if
we measured all maize yields). This statement implies that if you
were to take 100 samples, you would expect that the sample
means would be within 3 percent of the true mean at least 95
times. Three other factors will also play a role. One is the
distribution of the variable of interest. If maize yields are identical
across all households, then you would only need to sample one
household in order to determine the average level of maize yields.
By contrast, more dispersed distributions require a larger sample
size. Second, sample sizes are affected by the particular sampling
design chosen. Multistage designs require larger samples than
single-stage designs in order to achieve the same degree of
precision. Third, increasing the number of variables that you seek
to estimate may also affect the sample size needed to attain a
certain degree of precision.
Finally, there is a widespread but erroneous belief that sample
size depends on the size of the population and therefore on the
sampling fraction. The size of the population only marginally
affects the precision of the estimate. The precision of the estimate
is directly related to the absolute size of the sample, but much less
so to the sampling fraction. A sample of 100 units drawn from a
population of 1,000 (sampling fraction 10 percent) is highly


unlikely to produce more precise estimates than a sample of 200
from population of 10,000 (sampling fraction 2 percent).

Choosing the Sample
Armed with a suitable sample frame that lists units of study and
knowledge regarding the desired sample size, the last step is to select
the sample in a random or probabilistic fashion. There are four types
of probabilistic methods: systematic, simple random sampling,
stratified, and multistage.
A relatively straightforward method of selection is systematic
sampling, where draws are made at fixed intervals through the list
starting from a random unit. For example, suppose you want to
extract the same sample of 10 households from the list of 150
households. You randomly select a number between 1 and 15 (150
divided by 10) and, starting from that unit, select every 15th
household. If 5 were the randomly selected number, then the sample
would be composed of households 5, 20, 35, 50, 65, 80, 95, 110, 125,
and 140.
Note that in addition to being a random selection method, this
method has another advantage when the list is ordered on the basis
of some feature related to the variable of interest. Suppose you want
to estimate crop yield, and the list is ordered based on farm-size class;
then systematic selection would guarantee that a wider spectrum of
farm-size classes are represented in the sample. Following this
systematic method, you can be almost certain that the first sample
element (household 5) belongs to a different class of, say, element 8
(household 110) or 10 (household 140). Set against this advantage is
a potential danger. If there is some subtle, difficult-to-observe
ordering of the sample (resulting, for example, in small farms never
having numbers ending in zero or five), the observations drawn will


Food Securit' in Practice 83






not be a random sampling of the population.
A second method is systematic random sampling (SRS). A simple
illustration of this is the following. Write all the farm identification
numbers on individual slips of paper and throw these in a hat. Shake
the hat vigorously. Pick out the number of farms you want to
interview and that is your sample. However, in large populations this
is a rather tedious operation (and might require a very large hat!).
An alternative method is to use a table of random numbers.
There is a potential weakness with both systematic and systematic
random sampling. Suppose you are drawing a sample of 100 farms
from a population of 1,000. You know from the census that 30
percent of these have more than 10 acres of land, so the sample
should contain 30 such farms. However, this is only true on average.
Though the likelihood is high that the sample will contain 30 large
farms, it is also possible that it will contain 20, 25, or 40. Suppose it
contains only 15 such farms. Other things being equal, if larger
farms have better access to formal-sector credit than smaller farms,
and given that the larger farms have been underrepresented, you
might feel that inferences regarding credit will not be reliable. One
tempting possibility would be to pick two or three other samples and
choose the one you thought was most representative. The difficulty
with this approach is that the sampling procedure being used-the
population is sampled until you find a sample you like-can no
longer be justified and the results are no longer suitable for the
purposes of inference.
There is a solution to problems such as these: random stratified
sampling. The first step is to divide the population into groups or
strata. Here, the division would be between the 300 large farms and
the 700 smaller ones. Using the random number method, select 10
percent of farms in each category, so the resultant sample contains


30 large farms and 70 small ones. The proportions in the sample are
identical to those in the underlying population.
Random stratified sampling is an attractive means of obtaining a
sample. However, it is helpful to note two potential problems. First,
the relevant stratification variables must be known in advance.
Second, you must know the underlying population proportions of
each stratum. Addressing these problems requires additional
information on each unit of observation. For example, lists of farms
may contain only the name of the household head. A short survey
may be necessary to obtain information to stratify and this may be
too time-consuming or expensive.
A fourth form of sampling is multistage or cluster sampling.
Whenever the universe from which you want to draw the sample is
geographically spread out, single-stage procedures such as SRS or
systematic sampling may not be logistically feasible, since they are
both likely to generate equally dispersed samples. The necessity to
lower transportation and organizational costs, as well as reduce
nonsampling errors enumeratorss working on a large area may be
more difficult to supervise, increasing the likelihood for errors),
suggests that a multistage design may be more appropriate. In
addition, multistage designs can produce substantial savings in terms
of time and financial resources that must be allocated to the listing
operations.
A two-stage design would generally call for the selection of
geographically delimited nonoverlapping primary sampling units
(PSU), also known as clusters (examples of clusters are a region, a
district, a village), the selection of a limited number of clusters, and
within each cluster, the random selection of a certain number of
ultimate sampling units. Given that a two-stage design is chosen, a
number of issues arise. How do you select the clusters from the


84 Food Securii/f in Practice






universe? How many clusters do you select? How many ultimate units
do you draw from each cluster?
The way clusters are selected depends primarily on the
availability and accuracy of a complete sample frame. In the simplest
case scenario in which such a list is available and the clusters are of
equal size, we can select a number of them using simple random
sampling and, within each, draw an equal number of ultimate units.

A WORKED EXAMPLE

This example outlines how a random sample of farmers was obtained
in order to assess the impact of two projects directed toward
smallholders in Malawi. As is discussed in Chapter 7, it was necessary
to survey participants in both projects, as well as households enrolled
in neither ("control households"). The example illustrates practical
difficulties encountered in sampling, the solutions adopted, as well as
the time requirements of the different steps.

Sdleclig the Sampling Unit
Both projects targeted smallholder farmers. Consequently, the
sampling unit was a smallholder farm household, classified using a
local definition as a rural household with less than 10 hectares of
land.

Selecting the Universe
The next step was to select the areas) for the data collection. Based
on a classification by the Ministry of Agriculture, the country of
Malawi is divided into three regions (North, Central, and South),
further divided into extension planning areas (EPAs). Although it
would have been ideal to work in all three regions, time and


budgetary constraints made it necessary to restrict the survey to a
single region. Field visits conducted in the regions (one or two days
for each visit, combined with extensive talks with key informants)
revealed significant differences between these regions. For this reason,
a random selection of one region was not appropriate. Instead, it was
decided to select an EPA in Central region. To facilitate the contrast of
the two projects and rule out differences in location-specific features
(or having to control for them during data analysis), it was decided
to select an EPA in which both projects were active. This restricted the
choices to a pool of only two EPAs with very similar characteristics;
one EPA was randomly selected. An implication of this decision was
that it was not possible to extrapolate any findings from this region to
the whole country.

Constructing the Sampling Frame and Selecting
the Sample
Given that the objective of the study was to compare the two projects
against each other, and against the control group, it was necessary to
sample households in both projects as well as households in neither
project. These three groups constitute separate "domains."
(Technically, the universe-the EPA-was stratified by domain.)
One way of doing this would have been to enumerate all households
in this area and select households from each domain in proportion to
their number in the EPA. However, given the relatively low coverage
of both projects, this technique would have led to the selection of an
insufficient number of observations among the two beneficiary
groups. Since the main objective of the study was to compare these
groups and not to extrapolate the group or domain estimates to the
EPA as a whole, the research team chose to select an approximately
equal number of observations in each of the three strata, namely the


Food Securiti' in Practice 8 5






food security and agriculture development project beneficiary group
and the control group.
Smallholder farmers belonging to either project were organized
into clubs of variable size between 10 and 30 households. The club
was selected as the primary sample unit. The lists of clubs belonging
to each project were available with the project management units.
But because membership in these groups changed radically over
relatively short periods of time, these lists were not considered
reliable. Further investigation (one or two days talking to key
informants) revealed the existence of several such lists. In some
instances, a list would differ from the others quite substantially.
We spent several days trying to reconcile the different sources in an
attempt to come up with a unique list that reflected actual project
membership.
The first step in the verification process was to clearly define
membership for each project. Given the objective of the stratification
(to enhance the group contrast and measure project impact within
each domain), a club was considered a beneficiary of project A if it
had been active within the project for at least two seasons and it had
never belonged to project B. "Active" meant that it had produced and
sold tobacco in both seasons and had participated in the project's
activities. Based on the definition, some clubs were excluded from the
list, either because of dual membership or because they had not
produced and sold any tobacco.
In the case of the food security project, determining membership
was slightly easier since it could be related to access to the credit
package being disbursed by the project. A club was considered a food
security beneficiary if it had received a full or partial credit package
in both of the last two seasons. The main difficulty with this group
was represented by the common practice for members to use different


names in joining the club. Local key informants-field assistants
and village headman-helped screen "hidden" duplications.
Once the research team accounted for these sources of omissions
and duplications, they ended up with a list of 14 clubs for the food
security project and 71 for the agricultural development project. The
next step was to enumerate all the members of each club. These lists
were not available. The only information readily accessible was the
total number of members at the year of club formation. Given the
dynamic nature of membership, these figures were not considered
reliable. Therefore, enumeration of the clubs was deemed necessary.
To reduce the amount of time necessary for this operation, it was
decided to select only a limited number of clubs from the agricultural
development list. To this end, 30 agricultural development clubs were
selected, using a fixed probability of selection.
Once enumeration for these 30 clubs had been completed,
because of the variable size of the clusters the research team drew
from each cluster a number of households proportional to the size of
the cluster. The procedure resulted in a self-weighting sample within
the agriculture development domain. Due to the already limited
number of food security clubs eligible for inclusion in the sample, a
full census was considered appropriate for this domain.
The selection of the control group called for a different
methodology altogether. Available census data were more than 10
years old, and hence suspect. Alternative administrative records were
not available. Tight time constraints made complete enumeration of
the selected villages infeasible. In addition, to reduce transportation
costs and to avoid selecting households from villages where neither
project was active, it was decided to select a control household for
every other beneficiary household in the village in which the
beneficiary household resided. One complication was that the exact


86 Food Securii' in Practice






village of residence of the beneficiary household was not known until
the household was actually visited, so enumeration of selected
villages in advance was not possible. In addition, time constraints
would not have allowed for it. For the selection of the control
households, a variant of the EPI cluster design was used.
Once the research team visited a village in which selected
beneficiary households lived, they randomly selected one
nonbeneficiary household for every other beneficiary household in
the sample. For example, assume that a total of eight beneficiary
households belong to village x. A total of four nonbeneficiary
households were to be drawn from this village. The first step was to
roughly sketch the village to locate a central point. From this central
point, a team of enumerators, jointly with a supervisor, chose a
random direction by spinning a pen on a flat surface. Once a
direction was selected, the enumerators were asked to follow that
direction and, starting from the 4th dwelling, interview the first
household that met the eligibility criteria to belong to the group, that
is, they owned less than 10 hectares of land and they had never
belonged to either the food security or the agriculture development
project. If the same team had been assigned another control
household, the supervisor would again spin the pen in front of this
first selected household, choose a new direction and, starting from
the 4th dwelling, identify the next household to be interviewed along


this "random walk." If, instead, another team was required to select
an additional control household in the same village, the random
walk would start again from the center of the village by randomly
choosing a new direction.
One of the potential problems with this variant of this selection
design is that it tends to underrepresent households located in more
remote areas from the village center. To partly prevent this problem,
bigger villages were often divided into sub-areas and a center chosen
in the sub-area in which the selected beneficiary household fell.
Another potential problem was that the selection must follow a
natural path, restricting the number of options in terms of the
direction an enumerator can take from the central point. Whenever
possible, the enumerators were instructed to cut through fields and
follow as closely as possible the direction chosen.

Calculating the Snample Size
Sample size calculations took into account the degree of precision
required, statistical power, design effects, and estimated nonresponse
rates. A total of 202 households per stratum, for a total sample size of
approximately 600 households, were pursued. The value corresponds
to a prevalence of about 0.7, a difference in the magnitude of 0.15, a
one-tailed statistical significance of 95 percent, a statistical power of
80 percent, and a design effect of 2.


Food Security in Practice 87




6. Targeting: Principles and Practice
John Hoddinott


introduction


THE PRINCIPLES OF TARGETING


Targeting refers to the practice of limiting access to an
intervention to a select group of individuals. Generally, this
can be accomplished by: explicitly applying criteria for
participation that include some groups, but exclude others (variants
of this are described as categorical targeting and individual
assessment); allowing, in principle, anyone to participate but setting
up the intervention in such a fashion so as to discourage
participation by certain groups (typically described as self-selection);
or by some combination of the two. It is widely praised as an attempt
to reach the poorest of the poor, yet it is not always straightforward to
implement. A poorly targeted intervention can be more costly and less
effective than one that is randomly allocated or made available to all
households. To avoid costly mistakes, development practitioners must
understand the principles and practice of targeting.
This chapter considers three principles underlying targeting:
Targeting should never be undertaken for its own sake, but
should be assessed against a benchmark, such as its impact on
reducing the severity of food insecurity.
Targeting is not costless. It is effective only when the benefits
associated with additional reductions in food insecurity
outweigh the additional costs associated with doing so.
Where resources are limited, there is a strong case for
categorical targeting, for example by using geographical
criteria. However, even in this case, regional rankings can be
very sensitive to the criteria used in the identification process.
Where resources are limited, the case for individual assessment
is considerably weaker.


Defihing the Objective
S Many development agencies seek to improve household food security,
which is generally defined as adequate access to food at all times,
throughout the year, and from year to year. Suppose this general
definition is specified more narrowly. Specifically, a hypothetical
female person is food-secure if the number of calories available for
her to eat exceeds her requirements. If caloric availability is less than
nutritional requirements, she is described as food-insecure.
Accordingly, it is tempting to assume that the objective of targeting is
to produce the greatest decrease in the percentage of individuals who
are food-insecure.
Unfortunately, matters are not quite so simple. Consider Figure
6.1. The horizontal axis is a ranking of individuals from least to most
food-secure. The vertical axis shows individual caloric availability.
The horizontal line indicates requirements. Note that the number of
calories available to person A is just below her requirements, whereas
caloric availability for person B is significantly below her
requirements. Suppose enough calories were "transferred" from B to
A so that A can now meet her requirements. The measure of food
insecurity-percentage insecure-would register an improvement,
even though the poorest person has been made worse off. This is
presumably not the intention of interventions designed to reduce food
insecurity.


Food Seciu'ry in Practice 89




6. Targeting: Principles and Practice
John Hoddinott


introduction


THE PRINCIPLES OF TARGETING


Targeting refers to the practice of limiting access to an
intervention to a select group of individuals. Generally, this
can be accomplished by: explicitly applying criteria for
participation that include some groups, but exclude others (variants
of this are described as categorical targeting and individual
assessment); allowing, in principle, anyone to participate but setting
up the intervention in such a fashion so as to discourage
participation by certain groups (typically described as self-selection);
or by some combination of the two. It is widely praised as an attempt
to reach the poorest of the poor, yet it is not always straightforward to
implement. A poorly targeted intervention can be more costly and less
effective than one that is randomly allocated or made available to all
households. To avoid costly mistakes, development practitioners must
understand the principles and practice of targeting.
This chapter considers three principles underlying targeting:
Targeting should never be undertaken for its own sake, but
should be assessed against a benchmark, such as its impact on
reducing the severity of food insecurity.
Targeting is not costless. It is effective only when the benefits
associated with additional reductions in food insecurity
outweigh the additional costs associated with doing so.
Where resources are limited, there is a strong case for
categorical targeting, for example by using geographical
criteria. However, even in this case, regional rankings can be
very sensitive to the criteria used in the identification process.
Where resources are limited, the case for individual assessment
is considerably weaker.


Defihing the Objective
S Many development agencies seek to improve household food security,
which is generally defined as adequate access to food at all times,
throughout the year, and from year to year. Suppose this general
definition is specified more narrowly. Specifically, a hypothetical
female person is food-secure if the number of calories available for
her to eat exceeds her requirements. If caloric availability is less than
nutritional requirements, she is described as food-insecure.
Accordingly, it is tempting to assume that the objective of targeting is
to produce the greatest decrease in the percentage of individuals who
are food-insecure.
Unfortunately, matters are not quite so simple. Consider Figure
6.1. The horizontal axis is a ranking of individuals from least to most
food-secure. The vertical axis shows individual caloric availability.
The horizontal line indicates requirements. Note that the number of
calories available to person A is just below her requirements, whereas
caloric availability for person B is significantly below her
requirements. Suppose enough calories were "transferred" from B to
A so that A can now meet her requirements. The measure of food
insecurity-percentage insecure-would register an improvement,
even though the poorest person has been made worse off. This is
presumably not the intention of interventions designed to reduce food
insecurity.


Food Seciu'ry in Practice 89




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