Measuring Household Food Consumption: A Technical Guide

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Food and Nutrition Technical Assistance Project (FANTA)
Academy for Educational Development 1825 Connecticut Ave., NW Washington, DC 20009-5721
Tel: 202-884-8000 Fax: 202-884-8432 E-mail: fanta@aed.org Website: www.fantaproject.org
Measuring Household Food
Consumption: A Technical
Guide
Anne Swindale
Punam Ohri-Vachaspati
2005 Edition

This publication was made possible through support
p
rovided by the Office of Health, Infectious Disease
and Nutrition of the Bureau for Global Health of the
U.S. Agency for International Development
(USAID), under the terms of Award No. HRN-A-00-
98-00046-00 of the Food and Nutrition Technical
Assistance (FANTA) Project. Additional support
was provided by USAID’s Office of Food for Peace
of the Bureau for Democracy, Conflict and
Humanitarian Assistance. Earlier drafts of the guide
were developed with funding from the Food and
N
utrition Monitoring Project (IMPACT) (Contract
N
o. DAN-5110-Q-00-0014-00, Delivery Order 16),
managed by the International Science and
Technology Institute, Inc. and the Food Security Unit
of the LINKAGES Project (Cooperative Agreement:
HRN-A-00-97-00007-00), managed by the Academy
for Educational Development. The opinions
expressed herein are those of the author(s) and do not
necessarily reflect the views of the U.S. Agency for
International Development. It may be reproduced
without written permission by including the full
citation of source.
Revised August 2005. First published December
1999.
Recommended citation:
Swindale, Anne and Punam Ohri-Vachaspati.
Measuring Household Food Consumption: A
Technical Guide. Washington, D.C.: Food and
N
utrition Technical Assistance (FANTA) Project,
Academy for Educational Development (AED),
2005.
Copies of the publication can be obtained from:
Food and Nutrition Technical Assistance Project
Academy for Educational Development
1825 Connecticut Avenue, NW
Washington, D.C. 20009-5721
Tel: 202-884-8000
Fax: 202-884-8432
Email: fanta@aed.org
Website: www.fantaproject.org

ACKNOWLEDGMENTS
This Guide was written by Anne Swindale and Punam Ohri-Vachaspati. The authors wish to
thank the reviewers for their thoughtful comments during the development of this guide.
Eunyong Chung, of USAID’s Office of Health, Infectious Disease and Nutrition of the Bureau
for Global Health, provided insight and support for the guide and her efforts are appreciated. The
USAID Office of Food for Peace has also encouraged and supported the development of the
guide.
A number of people assisted in the development of this guide. Patricia Bonnard wrote and
revised sections of the guide. Her input is greatly appreciated. Rosalind Gibson, Suzanne
Murphy, Patrick Diskin, Phil Harvey, Penny Nestel, and Bruce Cogill provided extensive
comments and support. The authors dedicate this guide to the Title II Cooperating Sponsors who
were essential to its development.
ABOUT THIS SERIES
This series of Title II Generic Indicator Guides was developed by the Food and Nutrition
Technical Assistance Project, and its predecessors (IMPACT, LINKAGES), as part of USAID’s
support for its Cooperating Sponsors in the development of monitoring and evaluation systems
for use in Title II programs. The guides are intended to provide the technical basis for the
indicators and recommended methods for collecting, analyzing, and reporting on the generic
indicators developed in consultation with PVOs during 1995/1996. The guides are available on
the project website http://www.fantaproject.org.
Below is the list of available guides:
! Agricultural Productivity Indicators Measurement Guide by Patrick Diskin
! Anthropometric Indicators Measurement Guide by Bruce Cogill
! Food for Education Indicator Guide by Gilles Bergeron and Joy Miller Del Rosso
! Food Security Indicators and Framework for Use in the Monitoring and Evaluation of Food
Aid Programs by Frank Riely, Nancy Mock, Bruce Cogill, Laura Bailey, and Eric Kenefick
! Household Food Consumption Indicators Measurement Guide by Anne Swindale and Punam
Ohri-Vachaspati
! Infant and Child Feeding Indicators Measurement Guide by Mary Lung’aho
! Sampling Guide by Robert Magnani
! Water and Sanitation Indicators Measurement Guide by Patricia Billig, Diane Bendahmane
and Anne Swindale

TABLE OF CONTENTS
1. INTRODUCTION..........................................................................................................................1
2. IMPACT INDICATORS FOR IMPROVED HOUSEHOLD NUTRITION...............................................1
2.1. Increased Number of Eating Occasions...................................................................2
2.2. Increased Number of Different Foods or Food Groups Consumed.........................2
2.3. Increased Percentage of Households Consuming Minimum Daily
Caloric Requirements...............................................................................................3
3. COLLECTING AND ANALYZING THE DATA................................................................................3
3.1. Increased Number of Eating Occasions...................................................................4
3.2. Increased Number of Different Foods or Food Groups Consumed.........................5
3.3. Increased Percentage of Households Consuming Minimum Daily
Caloric Requirements...............................................................................................8
3.3.1. 24-hour Food Intake Recall ..........................................................................8
3.3.2. Filling in the Questionnaire ........................................................................10
3.3.3. General Measurement Techniques..............................................................17
3.4. Recording Household Composition.......................................................................22
3.4.1. Age..............................................................................................................23
3.4.2. Gender.........................................................................................................23
3.4.3. Physiological Status of Women of Reproductive Age (14 - 60 years)........24
3.4.4. Current Activity Level.................................................................................24
3.4.5. Current Household Residents ......................................................................24
4. ANALYZING THE DATA ...........................................................................................................25
APPENDICES
1. Sample Ingredient Codes........................................................................................................26
2. Sample Ingredient Form Codes..............................................................................................28
3. Sample Unit of Measure Codes..............................................................................................29
4. Conversion Factors for Common Honduran Foods ...............................................................30
5. Sample Activities Grouped by Activity Level for Males and Females..................................35
6. Using SPSS/PC to Calculate Household Calorie Intake ........................................................37
7. Row Numbers for FAO Member Countries...........................................................................51
8. Daily Calorie Requirement for an Adult Equivalent..............................................................52
9. Calorie Requirements for Children Under10 Years of Age by Sex.......................................53
10. Average Weight in Kg by Age and Sex for FAO Member Countries ...................................55
11. Dietary File.............................................................................................................................59
12. Dietary File.............................................................................................................................59

13. Dietary File.............................................................................................................................60
14. Dietary File.............................................................................................................................60
15. Imputing Average Recipes for Dishes with No Recipes........................................................61
15a. Dietary File.............................................................................................................................67
15b.Household Recipe Proportions...............................................................................................67
16. Dietary File.............................................................................................................................70
17. Adult Equivalent File .............................................................................................................71
18. Population Distribution (proportions) by Age and Sex for Selected Countries, 1997 ...........72
19. Sample Calculation of Weighted Average Adult Equivalent Ratios for Guest Categories ...80
20. Dietary File.............................................................................................................................81
21. Command File Containing Nutritional Value of Foods .........................................................82
22. Dietary File.............................................................................................................................83
23. Aggregated Dietary File (aggregated, case = dish .................................................................84
24. Aggregated Dietary File (aggregated, case = household) ......................................................84
25. Aggregated Dietary File (aggregated, case = household) ......................................................84
26. List of Title II Generic Indicators ..........................................................................................85
27. Setting Food Diversity Targets...............................................................................................86

Measuring Household Food Consumption: A Technical Guide
1
1. INTRODUCTION
Many private voluntary organizations (PVOs) are engaged in projects aimed at improving food
security and household nutrition worldwide. The U.S. Agency for International Development
(USAID) supports many of these projects through the provision of Title II food aid to PVOs
designated as “Cooperating Sponsors.” Increasingly Cooperating Sponsors (CS) are being asked
to monitor and evaluate the impact of their interventions, and USAID is generating materials to
help them in this process. USAID Missions, in collaboration with PVOs and technical staff from
Regional and Central USAID Bureaus have identified a set of generic impact indicators for
household food consumption, to facilitate the monitoring and reporting process.
This technical guide was developed to systematize this information. It is based around the three
impact indicators defined by the PL480 Title II program: increased number of eating occasions,
increased dietary diversity and increased percentage of households consuming minimum daily
caloric requirements. This guide demonstrates how to measure and quantify this information.
The guide describes the process and procedures for collecting the information to assess the food-
intake requirements of a household and a step-by-step analysis of the nutritional impact of the
food consumed. The process begins with the design of a questionnaire; a model is provided here,
but is subject to modification depending on the particular information that a given CS seeks to
reveal. Filling in the questionnaire involves detailed interviews with a “respondent”(the
household member responsible for food preparation) to obtain data on household composition
and food consumption. The latter is gathered using a “24-hour recall” methodology, according to
which the respondent is asked to recall the ingredients of each dish prepared during the previous
day and the amount of that dish consumed by the household. The guide provides ideas for
approximating the size of different dishes and their weight or volume and defining who is a
“household member.”
Once the basic information has been gathered, the methodology requires fairly complex data
processing and analysis to convert information on household composition and consumption into
standard formats that can be compared across households. Detailed information about analyzing
household food consumption data is available in the Appendices. Topics covered in the
Appendices include: sample ingredient codes, caloric requirement tables and sample activities
grouped by activity level for males and females.
2. IMPACT INDICATORS FOR IMPROVED HOUSEHOLD NUTRITION
The three PL 480 Title II impact indicators developed to measure improvements in household
food consumption1 are:
• Increased number of eating occasions per day
• Increased number of different foods or food groups consumed (dietary diversity)
• Increased percentage of households consuming minimum daily caloric requirements.
1See Appendix, Section 26, Summary of P.L. 480 Generic Title II Performance Indicators.

Measuring Household Food Consumption: A Technical Guide
2
The suitability of a given indicator depends on the program objectives, environment, and
technical and financial capacity of the PVO executing the program. Advantages and
disadvantages can be cited for each indicator with regard to both collecting the data and
interpreting the results.
2.1. Increased Number of Eating Occasions
The number of daily eating occasions is a proxy indicator for gauging the adequacy of household
macronutrient (calories and protein) intake. An advantage in selecting this as an indicator of
household food security is that data are relatively easy and inexpensive to collect. Data on the
size and composition of meals are not required to calculate indicator values.
However, while the number of eating occasions may be a good indicator of household strategies
to cope with transitory food insecurity, it is less sensitive as an indicator of changes in situations
of chronic food insecurity or of micronutrient imbalances in the diet.
Moreover, interpreting data derived from this indicator is often complicated by cultural factors.
In cultures where consumption of three meals per day is customary, household rationing in the
face of food shortages can take the form of a reduction in the number of meals consumed.
However, in cultures where households consume one primary meal per day, the volume, rather
than the frequency, of meals tends to decline as food shortages develop. Thus measuring only the
number of eating occasions will not yield significant information on household food
consumption.
Another complication inherent in this indicator is the definition of a “meal,” which often varies
across cultures. For some, a meal is defined according to the volume and type of food consumed.
For others, the time of day it is consumed is important in defining a meal. While using the term
“eating occasions” helps to eliminate difficulties caused by different definitions of “meal,” the
term still requires careful attention to cultural factors when interpreting results. The same is true
of attempts to make cross-cultural comparisons of results. Because of these complicating factors,
it is recommended that the “eating occasions” indicator be used in conjunction with the dietary
diversity indicator described below.
2.2. Increased Number of Different Foods or Food Groups Consumed
The number of different foods or food groups consumed in a household provides a measure of
the quality of the diet by reflecting dietary diversity, thus serving as an important complement to
the eating occasions indicators. To accurately capture dietary diversity, this indicator should be
evaluated in terms of the variety of food groups (meats, milk, fruits, and vegetables) consumed,
rather than by simply totaling all types of foods consumed. The division of food into different
groups should focus on those nutrients stressed in a PVO’s program strategy.
As a food-security indicator, dietary diversity is usually highly correlated with such factors as
caloric and protein adequacy, percentage of protein from animal sources (high quality protein),
and household income. Even in very poor households, increased food expenditure resulting from
additional income can serve to increase the quantity and quality of the diet. Calculating dietary

Measuring Household Food Consumption: A Technical Guide
3
diversity requires only marginally more detailed information than is required to assess the
number of daily eating occasions. Therefore, the data are still relatively easy and inexpensive to
collect and analyze.
2.3. Increased Percentage of Households Consuming Minimum Daily Caloric
Requirements
The wording of the indicator included in the list of Title II Core Indicators is “increased percent
of households consuming minimum daily caloric requirements.” This indicator needs to be
defined more sharply to accurately measure the nutrient of focus in a particular PVO program.
The primary interest is generally calories. Thus this guide describes the processes required to
gather information to measure average caloric intake at the household level, as well as rough
estimates of protein adequacy. PVO programs aiming to improve household intake of other
nutrients, such as Vitamin A or iron, should consult either the Micronutrient Operational
Strategies and Technologies (MOST) Project or the International Vitamin A Consultative Group2
for specialized methodologies.
The percentage of minimum daily calorie requirements consumed provides a good indication of
overall household food security. This indicator can also be used in conjunction with a measure of
dietary diversity, which can be easily calculated using data collected on caloric consumption.
Despite these advantages, measuring the “caloric requirements indicator” is more costly than
using other indicators, as it requires a much higher level of technical expertise and more time to
collect and analyze data. While it is ideal for measuring food security, a host of factors such as
the difficulties in calculating food quantities and potential changes in consumption behavior due
to the presence of an interviewer make the caloric requirements indicator difficult to use in
practice.
For most PVOs, a preferred alternative might be to estimate the household’s consumption of
minimum daily requirements, based on the ingredients of each eating occasion during the
previous 24 hours, and then calculate the number of eating occasions and food diversity
indicators using this detailed information. Section Three offers a suggested methodology for
carrying out such a survey.
3. COLLECTING AND ANALYZING THE DATA
The first phase of information collection calls for familiarity with local consumption patterns, to
ensure that the survey tool developed is appropriate. Informal, exploratory approaches are the
most useful at this stage. Information should be gathered on traditional forms and frequencies of
eating occasions, standard ingredients, and household and market measuring units. Customary
behavior should be identified, as should typical variations in behavior, particularly among
targeted or food-insecure groups. With this information, the survey team can develop a set of
2Micronutrient Operational Strategies and Technologies (MOST), International Science and Technology Institute, Inc., 1820
North Fort Myer Dr., Suite 600, Arlington, VA 22209; International Vitamin A Consultative Group (IVACG), The Nutrition
Foundation, 1126 16th St., NW, Washington, D.C. 20036.

Measuring Household Food Consumption: A Technical Guide
4
appropriate interviewer aids, including code lists for common dishes, tools for direct
measurement, and food models. Once the survey tool is complete, interviewers must be trained
in the techniques described below.
Information on household food consumption should be collected using the previous 24-hour
period as a reference (24-hour recall). Lengthening the recall period beyond this time often
results in significant error due to faulty recall. Subsequent data collection (mid-term and final
evaluations, for example) should be undertaken at the same time of year, in order to avoid
conflicting results due to seasonal differences. To most accurately capture improvements in
household food security, a Cooperating Sponsor (CS) should collect food consumption
information during the season of greatest food shortages (such as immediately prior to the
harvest).
A single 24-hour recall is usually adequate to quantify performance indicators of a program’s
impact overtime, when the indicators are calculated as group averages; that is, the average
number of eating occasions of the recipient population. However, information from several days
is necessary to obtain robust estimates of household-level consumption patterns. If the CS seeks
to correlate household consumption with other household variables, as well as to analyze
consumption patterns and their determinants, at least four days of recall per household are
recommended.
When using the 24-hour recall method, the interviewer should first ascertain whether the
previous day was "usual" or "normal" for the household. If it was a special occasion, such as a
funeral or feast, or if most household members were absent, another day should be selected for
the interview. If this is not possible, it is better to select another household rather than conduct
the interview using an earlier day in the week.
The first few steps for collecting information on the nutrient adequacy indicator provide the data
necessary for other indicators, namely the number of food groups and frequency of eating
occasions. Information for these indicators can also be collected using a simplified
methodology, which appears below.
3.1. Increased Number of Daily Eating Occasions
In order to simplify data collection for this indicator, survey implementers can predefine up to
seven eating occasions and ask the respondent whether or not food was consumed during these
periods. An example of this method appears below.

Measuring Household Food Consumption: A Technical Guide
5
Interviewer: During the previous 24-hour period, did you or anyone in your household
consume...
Eating Occasion Yes No
Any food before a morning meal 1 0
A morning meal 1 0
Any food between morning and midday meals 1 0
A midday meal 1 0
Any food between midday and evening meals 1 0
An evening meal 1 0
Any food after the evening meal 1 0
The sum of “yes” responses quantifies the indicator for each household, which can then be
averaged over the population of interest. Because the sum is actually the total of all household
members’ eating occasions, the sum will probably be larger than the number of eating occasions
for any individual household member. For example, a household may report five eating
occasions, whereas each individual household member may have eaten no more than three times
that day.
An alternative, perhaps simpler, way of analyzing this indicator, is to calculate the percentage of
households that eat “x” or more times a day. The numerator would represent the sum of
households with “x” or more “yes” responses, and the denominator would represent the total
number of households. This indicator can easily be modified to reflect the different number of
meals consumed within a given cultural context; for example the percentage of households
eating two or more times a day. The indicator should always correspond to the specific cultural
context of the project.
3.2. Increased Number of Different Foods or Food Groups Consumed
For ease of analysis, the number of different food groups consumed should be calculated, rather
than the number of different foods. Knowing that households consume, for example, an average
of four different food groups implies that their diets offer some diversity in both macro- and
micronutrients. This is a more meaningful indicator than knowing that households consume four
different foods, which might all be cereals. The U.N. Food and Agriculture Organization (FAO)
uses the following set of food groups in its food balance sheets:
1. Cereals 7. Fish and seafood
2. Root and tubers 8. Oil/fats
3. Pulses/legumes 9. Sugar/honey
4. Milk and milk products 10. Fruits
5. Eggs 11. Vegetables
6. Meat and offal 12. Miscellaneous

Measuring Household Food Consumption: A Technical Guide
6
These groups can be adapted to the local context to reflect both cultural and economic patterns in
food selection (e.g., “high” and “low” status foods). The list can also be expanded to specify
foods of particular nutritional value, such as those high in Vitamin A or iron. The groups used
for a particular survey should be meaningful with respect to the CS’s program objectives and
project-level interventions. For example, while the addition a soft drinks group to the list may
not indicate improved nutritional status, it may be associated with increased income. This would
be important to measure if the project goal is “improved food security through increased
income.” Nonetheless, the total number of groups included in this indicator should not be too
large, as interpretation of results becomes difficult.
Once the set of food groups has been defined, data for the “number of food groups” indicator can
be collected by asking each respondent a series of yes-or-no questions. This allows the
interviewer to list the predominant products from each food group consumed by the respondent’s
household, and thus provide relevant examples for each of the food groups.
The respondent should include the food groups consumed by household members in the home, or
prepared in the home for consumption by household members outside the home (e.g., at
lunchtime in the fields.) As a general rule, foods consumed outside the home that were not
prepared in the home should not be included. While this may result in an underestimation of the
dietary diversity of individual family members (who may, for example, purchase food in the
street), the indicator is designed to measure household diversity, on average, across all members.
Including food purchased and consumed outside the household by individual members increases
the risk of overestimating the dietary diversity of household members overall. However, in
situations where consumption outside the home of foods not prepared in the household is very
common, survey implementers may decide to include those foods when measuring this indicator.
Such decisions should be clearly documented, so subsequent surveys can use the same method.
The following is an example of data collection for number of food groups:
Fine-Tuning Indicators
Based on dietary patterns in Honduras, where corn and sorghum constitute the basic, grain-
based starch sources and rice, bread, and other grains are added as incomes increase, an
indicator could separate the “cereals” group into “basic grains” (corn and sorghum.) and
“other cereals” (rice, wheat, and the remaining cereals).
In programs where increased consumption of Vitamin A-rich fruits and vegetables is encouraged, an
appropriate diversity indicator could separate fruits and vegetables high in Vitamin A to form another
group.

Measuring Household Food Consumption: A Technical Guide
7
Interviewer: “Yesterday, did you or anyone in your household consume...”
Food Group Yes No
Cereals 1 0
Roots/tubers 1 0
Legumes 1 0
Milk/milk products 1 0
Eggs 1 0
Meat/offal 1 0
Fish/seafood 1 0
Oil/fat 1 0
Sugar/honey 1 0
Fruits 1 0
Vegetables 1 0
Other (spices, sodas, etc.) 1 0
The sum of the “yes” responses quantifies the indicator for each household, which can then be
averaged over the target population.
For a sample among three households (A, B, and C), the responses might look something like
those in the box below. An answer of “yes” takes the value of 1; a “no”answer takes the value of
0. Food
Group ABC
Yes No Yes No Yes No
Cereals 1 1 1
Roots/tubers 1 0 0
Milk/milk
products 01 1
Eggs 1 0 1
Meat/offal 1 0 1
Fish/seafood 0 1 1
Oil/fat 1 0 1
Sugar/honey 1 0 1
Fruits 1 1 1
Vegetables 0 0 1
Other (spices,
sodas, etc) 001
TOTAL 7 4 10

Measuring Household Food Consumption: A Technical Guide
8
In this example, household C has the greatest dietary diversity, with a score of 10; household B
has the least diversity, with a score of 4. The average diversity of the sample is (7+4+10)
divided by 3, or 7. (See also Appendix 27, “Setting Food Diversity Targets.”)
3.3. Increased Percentage of Households Consuming Minimum Daily Caloric
Requirements
Two data components are necessary to quantify household caloric adequacy: intake and
minimum requirements. The caloric intake estimate is obtained through recall of consumption of
all significant sources of calories during the previous day (24-hour recall). This includes data on
exactly what was consumed and who consumed it. An estimate of caloric requirements is
calculated based on the age, sex, physiological status, and activity levels of household members
consuming the calories.
3.3.1. 24-Hour Recall of Food Intake
The 24-hour recall gathers information on:
• Eating occasions (definition of meals/snacks or time food was consumed)
• Household members present at each meal
• Visitors consuming each dish
• Type of dish
• Ingredients of dish
• Quantities prepared of foods that are a significant source of calories
• Quantities of food not consumed by household members or guests
• Source of each ingredient (home production, purchase, gift)
If it is of interest to the CS, the 24-hour recall method can also provide information on the intake
of individual household members, for example, for gender-disaggregation purposes. This
requires estimating individual consumption through individual portion sizes. This guide does not
provide detailed instructions for measuring individual intake.
A 24-hour recall of food consumption collects information on food intake over the previous 24-
hour period. The household member responsible for food preparation is the preferred survey
respondent. Others rarely know what food was consumed by individual household members.
Nor are others likely to be able to identify or recall the ingredients used in meal preparation. For
ease and accuracy of data collection and analysis, the reference period for 24-hour recalls should
be the day before the interview. This provides the respondent with a clearly defined beginning
and end of the reference period. The interviewer should ask about all foods consumed in the
household the previous day, beginning when the first person in the household woke up, and
using that as a reference point to start the day’s recall. The respondent is then asked about all
foods prepared and/or consumed until the last person in the household went to bed.

Measuring Household Food Consumption: A Technical Guide
9
Sample Interview (I = Interviewer, R = Respondent)
I: Who was the first person in the household to wake up yesterday?
R: I was.
I: After you woke up, what was the first thing prepared or consumed in the household?
R: I always make coffee first.
I: Did you make coffee yesterday?3
R: Yes.
I: At what time?
R: At about 5 a.m.
I: Did you consume the coffee with something else or only had the coffee?
R: Alone.
I What were the ingredients in the coffee?
R: Coffee and sugar.
I: Do you sweeten all the coffee at once, or does each person sweeten their own cup?
R I sweeten the whole thing.
I: What was the next thing prepared or consumed after the coffee?
R: I made breakfast: plantains and eggs.
I: (Asks for and writes down all the ingredients of each dish consumed at breakfast). Was
there any beverage with breakfast?4
R: No.
I: What was the next thing prepared or consumed after breakfast?
R: Lunch.
I: Did anyone in the household eat anything between breakfast and lunch? For example, a
fruit or cracker or milk for the baby?
R: Oh yes, the kids ate mangoes.
I: (After requesting information on the ingredients of each dish after lunch). What was the
next thing prepared after lunch?
R: We had rice and beans for dinner.
I: (Notes all the ingredients of each dish consumed at dinner) Was any beverage served with
dinner?
R: No.
I: Did anyone in the household eat or drink anything after dinner? For example, a cup of
coffee or a piece of fruit or milk for the baby?
R: No, we just went to bed.
I: Did you all go to bed at the same time, or did some household members stay up later than
others?
R: I am the last one to go to bed.
I: Did you eat or drink any last thing before going to bed?
R: No.
The interviewer will first lead the respondent through the entire day, recording the dishes and
ingredients consumed. This permits the respondent to follow a logical memory sequence all the
way through the day, without constantly changing focus from what was consumed to how much
3Always remember that the information being gathered refers to the day before the interview, i.e., yesterday. There is a tendency
for respondents to speak in terms of what is usually, commonly, or even ideally consumed. Interviewers must continually remind
respondents that the period of reference is yesterday.
4Note that the interviewer did not ask about a specific meal (e.g., breakfast), which would imply that the respondent ate that meal.
This can embarrass respondents when the household was not served three meals. Once a respondent mentions a meal, the
interviewer can refer to it.

Measuring Household Food Consumption: A Technical Guide
10
was consumed. Then the interviewer will return to the beginning of the 24-hour period to obtain
information on the quantity of the ingredients that are important contributors of calories.
3.3.2. Filling in the Questionnaire
Figure 1 presents a sample questionnaire for recording 24-hour food consumption recall
information. Detail is provided in this section on how to fill in the various columns of the
questionnaire.
Column 1: Eating occasions are recorded in Column 1. The information is used to identify
household members present during the time the food was consumed. An eating occasion is
identified when food is prepared for, or distributed to, one or more household members for their
consumption. Eating occasions are numbered consecutively, starting with 1, regardless of
whether they were a “meal” or “snack” and of how many people were present. If a pot of
porridge was prepared at 6 a.m., and the first household members were served at 6 am, another at
6:30, and the final member at 7:30, this should be recorded as one eating occasion.
Column 2: Columns 2 through 8 list information on the people who did, or did not, consume the
food served at each eating occasion. Column 2 lists the codes of those household members not
present during the eating occasion. The cell of column 2 corresponding to a specific eating
occasion can contain multiple household ID codes. These codes should not be entered vertically,
(one per row); accounting for multiple codes takes place at data entry. If a household member
was present during the meal, but did not eat, or did not eat all dishes served, that member’s code
is not recorded in Column 2. If a household member was not present, but took food to consume
outside the home, that person’s code is not recorded in Column 2.

Measuring Household Food Consumption: A Technical Guide
11
Figure 1. Sample Questionnaire Layout
Visitors
Adults 18 yrs Adolescents
12-17 yrs Children either
sex
(1)
Eating
occasion
(2)
ID of
household
Members
not
present &
not eating
(3)
male (4)
female (5)
male (6)
female (7)
5-11
yrs
(8)
0-4
yrs
(9)
Dish # (10)
Dish (11)
Dish
code
(12)
Ingred
-ient
(13)
Ingred.
code
See list 1 See list 2
(14)
Total prepared
quantity
(15)
Unit of measure
(16)
Unit code
(17)
Leftover
quantity
(18)
Source
(19)
Source
Code
9999 = don’t know See list 2 9999 =
don’t
know 01. Purchase
02. Home
consumption
03. Gift
04. Govt. program
05. Wild food
06. Other
07. Leftovers from
same day
08. Leftovers from
previous day
99. Don’t know
Notes:
1) Due to space limitations, this sample questionnaire has been split into two parts. In an actual
questionnaire the information in each row would be continuous across all columns.
2) If possible, codes should be included at the bottom of the relevant column. The codes in Figure 1 are an
example. The appropriate list of codes is determined by the thematic interests of the survey designers and
should be refined during the pre-tests. Long lists of codes, such as dish/ingredient, and unit of measure,
should be referenced at the bottom of the appropriate columns, and made available in a separate
document.

Measuring Household Food Consumption: A Technical Guide
12
All of the following examples are cases in which a household member should be considered
“present and eating” during the eating occasion. In other words, the member’s code should not
appear in Column 2.
• Household member 01 takes a home-prepared lunch to the fields, and member 02 takes a
lunch to school. Remaining members consume the same (or different) dishes at lunchtime at
home. Neither member 01 nor 02 should be noted in Column 2 when the dishes served at
lunch to the remaining members at home are recorded. The food prepared for 01 and 02 in
the morning is recorded, the food prepared at lunch is recorded, and the total amount of food
is divided among all household members.
• Household member 02 is sick at home and does not eat any lunch.
• Household member 02 doesn’t like eggs and only eats tortillas and beans at breakfast.
• Each household member eats a separately prepared breakfast at different times during the
morning. For example, member 02 eats breakfast at 7:00 am and leaves for school, member
03 eats at 8:00 am and leaves for work, and member 01 breakfasts at 8:30 am. Therefore all
members breakfasted; all were present and ate, even though at different times. The
breakfasts are all considered as the same eating occasion.
Columns 3 - 8 list the number of visitors in each age/sex category who ate each dish. While
household members are recorded by eating occasion or meal, visitors are recorded by dish.
Visitors are broken down into age/sex categories that cover a range of adult equivalents. During
data analysis, a weighted “average adult equivalent” will be assigned to each of these categories.5
Columns 9 - 11: The name of each dish prepared is recorded in Column 10 and coded in
Column 11. A “dish” can either be a cooked combination of ingredients or an uncooked food (in
the latter case, the dish is essentially equivalent to the ingredient). Dishes for which a liquid is
mixed with a solid before serving (such as milk and bread, broth and rice, milk and tortilla)
should be noted as a single dish; the liquid and the solid are listed as ingredients. This will
facilitate the measurement of leftovers. For ease of subsequent data analysis, dishes are
numbered consecutively in Column 9.
Columns 12 and 13 repeat the dish and its code. A measure of the total quantity of the dish is
recorded in the same row. The ingredients of the dish are then recorded under the dish name in
consecutive rows down Column 12, leaving two spaces between the last ingredient of one dish
and the first ingredient of the next dish listed. When the dish and the ingredient are the same, it
is not necessary to repeat the ingredient, unless precise information on the weight of the food
would be lost if it were not repeated as an ingredient.
A four-digit coding scheme is used for dishes and ingredients, allowing for greater flexibility in
determining the easiest and most accurate method of measurement. A given ingredient may pass
through several stages before being cooked. For example, it may start out raw, then be soaked,
then ground, then boiled. An estimate of the quantity prepared may be obtained at any stage,
although it may be easiest to estimate quantity when the ingredient is raw or after it has been
5The adult equivalent used for each age/sex range will be an average of the age and sex specific adult equivalents, weighted by
the proportion of the population in each age/sex range.

Measuring Household Food Consumption: A Technical Guide
13
ground. The first digit of the four-digit code corresponds to the state in which the quantity
estimate was obtained, not to how the ingredient was ultimately prepared. The next three digits
are used to identify the ingredient.
Survey implementers must determine the appropriate items to include under “form of
preparation.” If more than nine forms are listed, a five-digit code can be used, of which the first
two digits should be for coding the form of preparation.
Sample: Form of Preparation Codes
Code Form Code Form Code Form
0
1
2
Raw
Boiled
Fried
3
4
5
Stewed
Broiled
Baked
6
7
8
Ground
Juice
Soup
Sample: Ingredients Codes
Code Food Code Food Code Food
001
002
003
004
005
006
007
008
Dry white corn kernel
New white corn kernel
White corn tortilla
White corn on the cob
White corn unhusked
1st quality rice
2nd quality rice
3rd quality rice
080
081
082
083
084
100
101
102
Potato
Sweet potato
Cassava
Squash whole
Squash sliced
Liquid whole milk
Powdered whole milk
Powdered baby formula
160
161
162
170
171
172
220
221
Veg. shortening
Lard (pig)
Vegetable oil
Refined white sugar
Refined brown
Raw sugar
Garlic
Onion

Measuring Household Food Consumption: A Technical Guide
14
Coding Different Ingredients
Corn provides a good example of the issues involved in codifying forms of preparation and measuring
quantity. The corn used to make tortillas passes through several stages. Generally, dried corn kernels are
cooked, and then ground into a crude cornmeal. It may be easiest to estimate the quantity of dried kernels
the respondent took from a sack, or the quantity of cooked kernels taken to the mill, or the quantity of
ground corn made into tortillas. For example, 450 ml. of dried corn expands to 1300 ml. after cooking,
then reduces to 700 ml. after grinding. The survey respondent can demonstrate the amount of any of
these forms, depending on which is easiest to measure. In all cases, the interviewer will record the dish as
“tortilla” and the ingredient as “corn.” What will vary is the coding of the ingredient, to indicate the form
in which it was measured.
(10)
Dish
(11)
Dish code
(12)
Ingredient
(13)
Ingred.
code
(14)
Quantity
(15)
Unit of
measure
(16)
Unit code
Tortilla 1003 Tortilla 1003 35 B2 19
Dry white corn 0001 450 ml 06
Tortilla 1003 Tortilla 1003 35 B2 19
Cooked white corn 1001 1300 ml 06
Tortilla 1003 Tortilla 1003 35 B2 19
Ground cooked white
corn 6001 700 ml 06
Another example of the intricacies of coding is soup. Broth from soup is a common weaning food.
Nutrition education programs often encourage mothers to thicken the consistency of the soups they serve
their infants. If a child is served soup or broth at a separate eating occasion6, the interviewer must verify
whether the soup served to a child included solid ingredients, or just broth. The soup form of preparation
code (8) should be reserved for soup with solid ingredients. A separate dish/ingredient code should be
identified for broth (See Appendix 1: Sample Ingredient Form Codes, code 406).
Columns 14-16 are for listing the quantity of the dish prepared and selected ingredients.7 If the
pot or container in which the dish was prepared is available and empty, estimating the amount of
the dish is relatively straightforward. If the pot is unavailable, or the total amount of the dish is
too large, the interviewer may ask the respondent to measure the portion served to each
individual and estimate the amount remaining in the pot. The interviewer can then add up the
individual servings plus leftovers, and enter the sum as the total amount of the dish prepared.
The leftover measure would also be entered separately in Column 17.
If large amounts of a dish are prepared for several days at a time, it is impractical to try to
measure the total amount of the dish prepared, and then measure the amount remaining in the pot
after each meal. In this case, the interviewer would not record and measure individual
ingredients. Instead, the respondent should be asked to demonstrate the amount of the cooked
6For estimation of household averages, it does not matter what the child ate during the same eating occasion when the soup was
prepared, because individual intake is not being estimated.
7Ingredients to be measured include all important sources of calories: grains and grain products, legumes, meats, milk and dairy
products, eggs, oils, sugar, roots/tubers/musacea, nuts, fruits with high oil content (such as avocados and coconuts).

Measuring Household Food Consumption: A Technical Guide
15
dish served from the pot to each individual.8 In this case leftovers are not estimated, since
leftovers at the household level refer to leftovers in the pot, not on each member’s plate. Given
that the objective of the study is to calculate average household consumption, obtaining details
on individual leftovers is too demanding and time-consuming to be worth the additional
precision gained. Clearly, however, individual leftovers should be estimated when individual
intake is of interest to the survey implementer.
The quantity of the dish and its ingredients are recorded separately. If the respondent states, “I
cooked one pound of rice,” the quantity is “1,” and the unit of measure is “pounds.” The
quantity (number of units) is recorded in Column 14, and the unit of measure in Column 15. The
unit of measure recorded should correspond to one on the precoded unit-of-measure list. (See
Appendix 3 for a sample listing of measurement codes.) Common household units of measure
(cup, glass, spoon, recycled can, bottle, bowl, or gourd) should not be recorded. For example, if
the respondent used a coffee-cup full of sugar to make juice, the interviewer must not record “1
cup of sugar” because the size and shape of coffee cups vary, as do the levels to which a
respondent may have filled the cup. The interviewer can determine the volumetric equivalent of
the amount of sugar by asking the respondent to fill the same coffee cup with rice to demonstrate
the amount of sugar used, and then recording the quantity of milliliters.
It is not necessary to estimate the amount of water in coffee, tea, reconstituted milk/formula,
juice, etc. The interviewer need only obtain quantity estimates for ingredients that are significant
sources of calories (such as powdered milk, formula, or sugar) and the total amount of the dish.
Column 17 notes the quantity of the dish not consumed during the eating occasion. This
“leftover” amount may include portions sent to neighbors, fed to animals, or discarded, as well as
portions set aside for subsequent consumption by household members. The measurement of
leftovers must always use the same unit of measurement as the dish. If a different unit of
measure is used, the data analyst will not be able to estimate what proportion of each ingredient
in the dish was not consumed.
One or more days worth of foods, such as flat breads and rolls, may have been made during the
recall period. For example, in Honduras some housewives grind enough corn and make enough
tortillas for the entire day at one sitting, while others grind corn and prepare tortillas before each
meal. When the whole day’s tortillas are prepared at once, it is often difficult for the survey
respondent to recall the total number of tortillas prepared. In such cases the interviewer can
prepare a matrix (as in the example below); the respondent is more likely to recall how many
tortillas were served to each person at each meal. The columns of the matrix can then be added
together to provide the total number of tortillas prepared, the amount left over and consumed at
subsequent meals, and the amount not consumed that day.
8Household, cluster or domain average recipes will be needed to impute the caloric content of dishes measured in this way.

Measuring Household Food Consumption: A Technical Guide
16
Columns 18 and 19 reflect the source and code(s) of food prepared and consumed in the
household. The level of detail in the code list depends on the objectives of the study. However,
at a minimum, it is useful to use at least five “source” categories: purchased, home produced,
Creating a Matrix
The respondent prepared tortillas for the entire day at breakfast time. All household members ate all meals, and
there were no visitors. The interviewer creates a matrix of meals consumed by household members, and asks
the respondent to recall how many tortillas each member ate at each meal. The interviewer then asks if any
tortillas were eaten as snacks, given to animals, given away, sold, or uneaten (left over).
The respondent recalls that Pedro ate four tortillas at breakfast and dinner and five at lunch. Maria ate two at
each meal. Juan ate three at each meal and three for a snack. Elsa ate one tortilla at lunch. Six tortillas were
given to the pigs, and 3 tortillas were left over at the end of the day.
Breakfast Lunch Dinner Snacks Animals Leftover Total
Pedro 454
Maria 222
Juan 3 3 3 3
Elsa 1
Total 911936341
The interviewer notes the total number of tortillas prepared (not the number consumed) at breakfast, which is
the sum of the total of all columns in the matrix. The interviewer then records the total number of tortillas not
consumed at breakfast as leftovers. The difference between the total number prepared and the number left over
is the number consumed. The interviewer must not record the amount of leftovers again; for each subsequent
occasion of tortilla consumption, only the amount consumed is recorded.
On the questionnaire, the sum of tortilla quantities from column 14 minus the sum of tortilla quantities should
yield the total number of tortillas consumed in the household that day (after subtracting leftovers and animal
feed).
(1)
Eating
Occa-
sion
(10)
Dish
(11)
Dish
code
(12)
Ingredient
(13)
Ingred
code
(14)
Total
prepared
quantity
(15)
Unit of
measure
(16)
Unit
code
(17)
Leftover
quantity
(18)
Source
(19)
Source
code
1 Tortilla 1003 Tortilla 1003 41 B1 18 32
Dry corn
kernel
cooked
1001 1200 Ml 06 Home
prod 03
2 Tortilla 1003 Tortilla 1003 11 B1 18 - Leftover
same day 07
3 Tortilla 1003 Tortilla 1003 3 B1 18 - Leftover
same day 07
4 Tortilla 1003 Tortilla 1003 9 B1 18 - Leftover
same day 07

Measuring Household Food Consumption: A Technical Guide
17
private gifts, government programs, freely gathered, and other. The source of the food also
includes leftovers from the same day or previous days. The code “leftover from same day” helps
the data analyst identify pre-cooked dishes for which household-specific recipes should be
available. Leftovers from other days will have household-specific recipes imputed if available; if
not, cluster- or domain-specific recipes will need to be calculated for commonly cooked dishes.
Methods for carrying out these calculations are described in Appendix 6.
3.3.3. General Measurement Techniques
Food intake can be estimated in four different ways:
1. Recorded Weight
2. Volume
3. Two-dimensional Food Models
4. Linear Dimensions
Each of these methods has an important and specific role to play, and different foods are
measured differently. Methods 1 and 2 are preferable, but not always feasible. Method 3 uses
preselected, pretested models that reflect the local context in terms of the types of foods available
and the form in which they are generally acquired and consumed. Success in implementing these
techniques in the field is highly dependent on the quality and depth of interviewer training.
Recorded Weight
Ideally, the interviewer will be able to record the weight of the food prepared or consumed. This
will be easiest when the respondent purchased a pre-measured amount of a food and prepared it
in its entirety during the recall period. For example, the respondent bought one-half pound of
sugar and used it all to make lemonade, or bought a 350-gm. bag of rice and cooked it all at
once. The respondent may know the exact weight or volume of a product if it was pre-packaged,
or if it was bought by the pound and weighed on a scale at the time of purchase. If a product was
purchased prepackaged, but the respondent does not know the weight, the interviewer should ask
to see the package. Cans and bags are often kept for reuse. If the package or container is no
longer available but was purchased at a local retail outlet, the interviewer can visit the store after
the interview, identify the same brand and price, and directly ascertain the weight of the product.
If the net weight on the can or container includes water (such as canned peas), the weight from
the container should not be used. Instead, the interviewer should estimate the volume of the
drained product (see next section).
In many countries respondents may imply that products have been weighed, when in fact they
have not. For example, in the Dominican Republic beans are commonly sold in the market by
the canful. Sellers use a can to measure the beans, which is commonly referred to as “one
pound.” Samples taken of the measure, however, averaged only three-quarters of a pound. In
Honduras people commonly refer to a prepackaged bag of rice as “1 pound,” even though the
package clearly states the weight as 350 grams. Thus when respondents provide an oral account

Measuring Household Food Consumption: A Technical Guide
18
of the weight of a product, interviewers should always ask if the product was actually weighed.
It is important that these types of distortions be identified during questionnaire design and pre-
testing and highlighted during training.
Many other factors may prevent respondents from providing reliable information on the weight
of a food prepared or consumed. For example, if the food: (a) came from the household’s own
agricultural production; (b) was bought without being weighed; (c) was a gift of raw or cooked
food; (d) was purchased by weight, but not prepared or consumed in its entirety; or (e) is a
cooked dish or an individual portion, then the interviewer must estimate the amount prepared or
consumed. Several techniques are available to do so. They require that interviewers carry with
them aids such as rice, clay, beakers with graduated measurements, and in some cases, cardboard
models.
Volume
To convert household measures to volume, the respondent is first asked to demonstrate the
amount of the product prepared or consumed using the household measure (cup, spoon) she
actually used. Then water or rice is used to substitute for the product. The interviewer will carry
four or five pounds of rice to be used to demonstrate the amount of dry ingredients, especially
those that tend to mound when measured (such as flour, powdered milk, and sugar). Rice can
also be used to estimate portions of an already-cooked, non-liquid dish; for example, if a
neighbor sent over a plate of rice and beans, or if leftover porridge from a previous day was
consumed. Water can be used to substitute for all liquid ingredients, as well as ingredients
measured with a level surface (such as a level teaspoon of sugar or liquid milk).9 The total
amount prepared can also usually be estimated by volume.
After the respondent replicates the amount prepared or consumed in the container used, the
interviewer transfers the rice or water to a measuring beaker. The beaker should always be the
smallest possible, because smaller beakers tend to have finer gradations (by 5 or 10 ml., instead
of 25 or 50 ml.), so the amount can be read with greater precision.10 After placing the beaker on
a level surface, at eye level, the interviewer reads the volume and records the amount in
milliliters.
9The interviewer uses rice and water to substitute for the ingredients, rather than the ingredients themselves, for hygienic and
practical reasons, and to minimize the imposition on respondents. Respondents may become reluctant to participate in the study
if they are constantly asked to use their own food to demonstrate quantities.
10Ideally, interviewers should have a set of 5 beakers: 1000, 500, 250, 100, and 50 ml.

Measuring Household Food Consumption: A Technical Guide
19
Another way to measure volume is by water displacement. This is particularly useful when the
ingredient or dish prepared or consumed is measured in individual units, such as a roll, piece of
meat, or block or slice of cheese. Interviewers request that respondents use clay to model the
shape and size of the food. Then the interviewer fills a beaker with water to a level high enough
to cover the modeled product, and notes the level of water in milliliters. Finally, the interviewer
places the clay model in the water, and notes the new water level. The difference between the
two levels is recorded in millileters on the questionnaire.
Measuring the Volume of Coffee and Sugar
The respondent has a sack of sugar and a small cup that she uses to remove sugar from the sack before
adding it to coffee. The interviewer asks the respondent to demonstrate using the same cup and rice for the
amount of sugar she used yesterday in the morning coffee. The respondent fills the cup with rice to where
it was filled with sugar; the interviewer empties the rice into a beaker and records the quantity in milliliters.
Then the interviewer asks the respondent to fill the coffeepot used yesterday with water to the level it was
filled with coffee. This amount is measured in the beakers and recorded as the total amount of the dish
prepared. The interviewer asks if any coffee was left in the pot after everyone had been served; if so, the
respondent is asked to demonstrate by placing water to the level of leftover coffee in the coffeepot. The
interviewer records this amount in the total dish leftover column.
(10)
Dish
(11)
Dish
code
(12)
Ingredient
(13)
Ingred.
Code
(14)
Total
prepared
quantity
(15)
Unit of
measure
(16)
Unit
code
(17)
Leftove
r
quantit
y
Coffee 1220 Coffee 1220 1050 Ml 06 200
Coffee 0220
White sugar 0170 240 Ml 06
Another example comes from a study in Honduras, where vegetable shortening (manteca) is commonly
used for cooking. The product is usually squeezed from a plastic tube into the frying pan, then heated. In
this case, respondents were asked to estimate the amount of manteca after it had melted in the pan by
adding water to the empty pan until the quantity replicated the amount of melted manteca. The water was
measured in the beaker, and milliliters of manteca recorded on the questionnaire. This technique can be
used with any solid fat that is melted before cooking.

Measuring Household Food Consumption: A Technical Guide
20
Conversion factors for all foods measured by volume will need to be obtained. Some such factors
are available from nutrient composition tables that list, for example, the volume of a standard 8-
ounce measuring cup: the standard 8-oz. cup contains 236.6 ml. The weight of one cup of the
product divided by 236.6 will give the conversion factor to grams for one milliliter of volume of
the product. Some volumetric conversion factors for common foods in Honduras, used in a 1994
survey, are included in Appendix 4. For conversion factors of foods not included in nutrient
composition tables or in Appendix 4, survey implementers will need to calculate survey-specific
conversion factors. To do so, the implementers should purchase a sample of different weights of
the product of interest. The volume of each sample should be measured, using the most
appropriate technique (directly for dry or liquid ingredients, water displacement for solid
ingredients, if possible). The volume-to-gram conversion factor for each sample is then averaged
to obtain a milliliter-to-gram conversion factor for the product.
Two-dimensional Food Models
Some foods are consumed unweighed, and cannot be easily measured through volumetric
conversion or clay models. In such cases, a two-dimensional cardboard model can serve as a
measurement tool. A common example is bananas; two-dimensional models are necessary for
Measuring the Volume of Cheese by Water Displacement
If a respondent purchased a portion of cheese but did not serve all of it yesterday, the interviewer can
estimate the amount of cheese consumed by asking the respondent to make a clay model similar to the
size and shape of the cheese when originally purchased. Having filled a 1000-ml. beaker up to the 600
ml. mark, the interviewer places the clay model in the beaker and notes that the water level has risen to
850 ml. Thus the volume of the original portion of cheese was 250 ml. The interviewer then asks for a
model demonstrating the amount of cheese not served. Making sure that the beaker still has 600 ml.
(the water level may drop as the clay models are removed), the unconsumed cheese model is placed in
the water, which rises to the 700 ml. mark, allowing the interviewer to calculate the amount of cheese
consumed the previous day.
(10)
Dish
(11)
Dish
code
(12)
Ingredient
(13)
Ingred.
code
(14)
Quantit
y
(15)
Unit of
measure
(16)
Unit
code
(17)
Leftover
quantity
Fresh
cheese 0104 Fresh cheese 0104 250 ml 06 100
Fresh cheese 0104 1 lb 01
Note: Strictly speaking, repeating fresh cheese on the second line is not necessary, because the
conversion factor for milliliters to grams for fresh cheese would be available from secondary data or
survey implementer calculations. However, when exact and direct information is available, for
example, on the weight of the 250 ml. of cheese purchased by the household, it is preferable to record it
for subsequent use by data managers in calculating a household-specific conversion factor of milliliters
to grams.

Measuring Household Food Consumption: A Technical Guide
21
most fruits, vegetables, roots, tubers, and some meat and dairy products. Two-dimensional
cardboard models should be developed for these foods prior to initiation of the field activities.
A cardboard model is created for each of a series of common sizes and shapes of a given
product, and each interviewer is given a full set. When the models are made, the gross and net
weight of the edible portion of a sample of each food model must be calculated for data-
processing purposes. For example, in the case of bananas, five to ten bananas are selected that
are the same shape and size as the models. Each banana is weighed with skin, and the gross
weight noted. Then each banana is peeled and weighed without skin to measure the weight of the
edible portion. Finally, the gross weight and edible portion weights are averaged and recorded
for use during data analysis.
When the interviewer determines that models are necessary, he or she will demonstrate the range
of models available for the particular food item, and ask the respondent to indicate which size
best corresponds to the amount of the food prepared or consumed.
Most food models are two-dimensional; that is, they show the length and width of the product,
but not its thickness. It is possible, however, to develop cardboard food models to measure
thickness. Flatbreads, such as tortillas, may vary widely in both diameter and thickness in
different regions of a country. Using cardboard that is approximately as thick as the thinnest
commonly observed bread, survey implementers can create a set of models covering several
different thicknesses. Interviewers can then ask respondents to indicate both the size of bread or
tortilla and the thickness, using the different cardboard models. Model sizes can be coded using
letters, and the number of models coded by number. For example, if a respondent selects two
thicknesses of model size B, the interviewer would record “B2” with the corresponding code for
the list of units of measure. These food models should be included on the unit of measure code
list (see the series of tortilla models listed in Appendix 3).
Roots and tubers, such as cassava, pose a special challenge. They are often obtained from the
household’s own agricultural production, so the respondent does not have a reliable weight to
report. Moreover, the size and shape of roots varies enormously, and it may be difficult to
produce a sufficient range of food models to cover all possibilities. Finally, when prepared, the
root may be cut into several pieces of varying shapes and sizes, and individuals may eat varying
number of these pieces.
Food models for roots and tubers should be developed to cover three-to-five sizes and one-to-
three shapes. To estimate the quantity of the ingredient, the respondent is asked to select the size
and shape closest to that prepared. The respondent may select several models to demonstrate the
range of shapes and sizes prepared. The respondent is then asked how many pieces each root was
cut into, the sum of which is recorded as the total amount of the dish. Individual portions will
then be defined as the number of pieces. When the data is analyzed, the total weight of the sum
of the food models (ingredients) is divided by the total number of pieces to calculate an average
weight per piece.

Measuring Household Food Consumption: A Technical Guide
22
Estimating the Quantity of Cassava Consumed
The respondent prepared four cassava roots, two of which correspond to the large food model, and one to
the medium model; the fourth root was approximately half again as big as the medium food model (i.e.,
1.5 medium). She cut each root into six pieces and then into 24 smaller pieces before cooking. Two
pieces were fed to the pigs.
(10)
Dish
(11)
Dish
code
(12)
Ingredient
(13)
Ingred.
Code
(14)
Total
prepared
Quantity
(15)
Unit of
measure
(16)
Unit
code
(17)
Leftover
quantity
Boiled
cassava 1082 Boiled cassava 1082 24 piece 08 2
Cassava 0082 2 large 11
Cassava 0082 2.5 med. 10
Linear Dimensions
The amount of some foods—most commonly already cooked square or rectangular foods
received as gifts or purchased—can be estimated using their dimensions. One Latin American
example is the tamale. The respondent can be asked to draw a rectangle to estimate the length
and width of the food, and to indicate the height with the distance between two fingertips. The
interviewer records the information as “cubic centimeters.”
However, if the respondent prepared tamales in the home during the reference period, it is not be
necessary to estimate the dimensions of the finished tamales in this manner. Rather, the
interviewer should record all the ingredients and their respective quantities. To obtain the total
amount of the dish, the interviewer records the total number of tamales made, using the
slice/piece unit-of-measure code.
3.4. Recording Household Composition
Caloric requirements of household members are based on their gender, height, weight,
physiological status, and level of activity. For the purposes of quantifying the Title II caloric
adequacy indicator, average heights and weights for the country should be used. Figure 2
presents the layout of a sample questionnaire for collecting the additional information required to
calculate caloric requirements for each household member.

Measuring Household Food Consumption: A Technical Guide
23
Figure 2. Sample Questionnaire for Household Composition
Member Age Physiological status Activity Current
ID
(1)
Name
(2)
Sex
(3)
Number
(4)
Unit
(5)
(women 14-60 yrs
only)
(6)
level
(7)
Resident
of
household
?
(sleeping/
eating)
(8)
1.
2.
3.
Etc. 1. Male
2. Female 1. Years
2.
Months
(children
< 1 year
only)
1. Not pregnant or
lactating
2. Pregnant
3. Breastfeeding
(child under 6 mo.)
4. Breastfeeding
(child 6 mo. or older)
5. Pregnant and
breastfeeding (child
under 6 mo.)
6. Pregnant and
breastfeeding (child 6
mo. or older)
1. High
2.
Medium
3. Light
1. Yes
2. No
3.4.1. Age
For the purposes of the caloric adequacy indicator, age in years completed is collected for all
household members over one year of age. Age in months is needed for children younger than one
year.11
3.4.2. Gender
The gender of each household member is recorded. Females do not need to be identified here as
pregnant or lactating, as this is recorded in the column on physiological status.
11If nutritional status or child feeding indicators are also being collected in the survey, the age in months of all children under five
will be necessary. Please refer to the appropriate IMPACT indicator guides (“Anthropometry Indicators Measurement Guide and
Infant and Child Feeding Indicator Measurement Guide”) for a discussion of recording and calculating age for those indicators.
Since the level of detail and accuracy of the age calculation is higher for nutritional status and child feeding indicators, those age
data requirements should be used, if available, rather than the less detailed requirements for the caloric adequacy indicator
detailed in this guide.

Measuring Household Food Consumption: A Technical Guide
24
3.4.3. Physiological Status of Women of Reproductive Age (14 - 60 years)
Women of reproductive age should be asked whether they are: pregnant but not breastfeeding,
breastfeeding but not pregnant, pregnant and breastfeeding, or not pregnant or breastfeeding. A
woman may be unaware that she is pregnant, especially during the first trimester. It is not
necessary for interviewers to probe further (such as asking the date of the woman’s last
menstrual period). The level of error that would be introduced by miscoding a pregnant woman
as not pregnant, especially in the first trimester, is not significant in relation to the relatively high
level of error in this indicator of household average caloric adequacy.
3.4.4. Current Activity Level
Current activity levels of household members 10 years and older are determined by the
interviewer, based on each member’s daily activities during the period that 24-hour recall data
is being gathered. Interviewers must not assume a level of activity based on the member’s
occupation. It cannot be assumed, for example, that all farmers always have high activity levels.
The survey may be being implemented during the off-season, when no agricultural activities are
taking place and no alternative employment options are available. In this case, farmers may not
be engaged in strenuous physical activity. During the week or two that the interviewer is visiting
the household, he or she should determine, based on observation and conversation with
household members, each individual’s activity level during the period. Appendix 5 contains
examples of light, moderate, and high activity levels.
3.4.5. Current Household Residents
The information recorded in this column is necessary because household members included in
the calculation of average household caloric adequacy should be limited to those who are
currently consuming from the household food supply. While ideally only such household
members will be mentioned by the respondent, it is not uncommon for respondents to list
individuals as household members even when they are not currently residing at home. For
example, a respondent may list a daughter who is attending school in the capital city and living
with a relative. For the respondent, the daughter is still considered to be a member of the
household. Rather than insult a respondent by not recording the daughter’s name, the interviewer
can record her information, but code her as ‘2’--not currently residing in the household. If the
daughter returns for a visit during the period of interviews, she should be recorded as a “visitor”
in the appropriate columns of the questionnaire. Additional motives for collecting household
composition data include the need to calculate income per capita or household labor supply. The
criteria for listing an individual as “present” or “absent” will differ according to the motive of the
survey. For the purposes of calculating caloric adequacy, household members should be included
only when currently residing in the household.

Measuring Household Food Consumption: A Technical Guide
25
4. ANALYZING THE DATA
Calculating the percentage of households meeting the minimum standards of daily nutrient
requirements entails significant manipulation of data. This section summarizes the steps to be
taken to perform the calculations. A more detailed guide to the SPSS/PC programming
procedures to be followed is provided in the Appendix 6. The procedures have been designed for
ease and convenience; nonetheless, the CS will probably have to employ or train staff in
SPSS/PC so that programs can be debugged and modified as needed.
Once data on the amount of food consumed and the people consuming the food has been
collected, the information must be converted to the two data components necessary to quantify
household caloric adequacy: intake and requirements. Caloric intake is estimated based on the
data on consumption of all significant sources of calories during the previous day (see Appendix
6). Caloric requirements for household members are calculated based on their age, sex,
physiological status, and activity levels (see Appendices 6, 8, 9, and 10), and the resulting
calculation of individual caloric requirements.
Computing caloric adequacy requires a detailed analysis of the composition of each dish
consumed by the household, which involves converting ingredients to standard weights;
establishing putative recipes for dishes with no recipes; and accounting for leftovers. Using the
survey data, the data analyst then proceeds to compute the number of people that consumed each
dish and the calories consumed by the household. The average intake of calories is then
compared with calorie requirements, to calculate the adequacy of average calorie intake for each
household.

Appendix 1: Sample Ingredient Codes
26
APPENDIX 1. SAMPLE INGREDIENT CODES
Basic grains
1. Dry white corn kernel
2. New white corn kernel
3. Tender white corn kernel
4. White corn tortilla
5. White corn on the cob
6. Unhusked white corn on cob
7. Dry yellow corn kernel
8. New yellow corn kernel
9. Tender yellow corn kernel
10. Yellow corn tortilla
11. Yellow corn on the cob
12. Unhusked yellow corn on cob
13. Sorghum kernel
14. Sorghum tortilla
15. Consumption rice
16. Parboiled rice
17. Unhusked rice (granza)
18. Other grain
Legumes
40. Beans in general
41. Red bean
42. Black bean
43. Soy bean
44. Cashew nut
45. Other legume
Other cereals/cereal products
60. Wheat flour
61. Wheat tortilla
62. Pancake mix
63. Whole wheat flour
64. Corn flour
65. Rice flour
66. Other flour
67. Sandwich bread
68. Sweet bread roll
69. Homemade sweet bread
70. Whole wheat bread
71. White bread roll
72. Homemade white bread
73. French bread
74. Other white bread
75. Sweet cracker
76. Salt cracker
77. Corn flakes
78. Oatmeal
79. Thin egg noodles
80. Spaghetti
81. Cannelloni
82. Lasagna
83. Macaroni
84. Shell macaroni
85. Wide noodles
86. Honduran pasta
87. Elbow macaroni
88. Other cereal
Bananas, roots, tubers
100. Ripe banana
101. Green banana
102. Butuco banana
103. Datil banana
104. Green plantain
105. Ripe plantain
106. Potato
107. Cassava
108. Sweet potato
109. Squash (whole)
110. Squash (slice)
111. Other roots, tuber, banana
Milk, dairy products
130. Liquid whole milk
131. Liquid skim milk
132. Evaporated milk
133. Condensed milk
134. Powdered whole milk
135. Powdered skim milk
136. Powdered milk for babies
137. Soy milk for babies
138. Other milk
139. Cream cheese
140. Fresh cheese
141. Hard cheese
142. American processed cheese
143. Parmesan cheese
144. Pepper cheese
145. Quesillo
146. Cuajada
147. Requesón
148. Other cheese
149. Cream ‘rala’
150. Cream ‘crema’
151. Yellow cream
152. Yogurt
153. Other milk product
Eggs
170. Chicken egg
171. Duck egg
172. Turtle egg
173. Other egg
Meat, poultry, fish, seafood
180. Beef with bone
181. Beef without bone
182. Beef bone (soup)
183. Beef ribs
184. Pork with bone
185. Boneless pork
186. Pork ‘tajo’
187. Pork ribs
188. Pork chop
189. Pig feet
190. Liver
191. Kidneys
192. Heart
193. Tongue
194. Tripe with bone
195. Boneless tripe
196. Chicken (general)
197. Chicken breast
198. Chicken thigh/leg
199. Chicken giblets
200. Patio chicken (general)
201. Patio chicken breast
202. Patio chicken thigh/leg
203. Patio chicken giblets
204. Rabbit
205. Baloney (mortadela)
206. Ham
207. Chorizo extremeño (sausage)
208. Hot-dog
209. Copetines (sausage)
210. Longaniza (sausage)
211. Salami
212. Fish filet
213. Whole fish
214. Dried fish
215. Shrimp
216. Crab (river)
217. Crab (ocean)
218. Caracol (shellfish)
219. Canned sardines
220. Other meat, sea food

Appendix 1: Sample Ingredient Codes
27
Fats
240. Veg. shortening
241. Lard (pig)
242. Vegetable oil
243. Other oil
244. Margarine
245. Mayonnaise
246. Other fat
Sugars
260. Refined white sugar
261. Refined brown sugar
262. Raw sugar
263. Sugar cane
264. Honey (bee)
265. Honey (sugar cane)
266. Other sugar
Fruit
300. Avocado
301. Coconut
302. Anona
303. Cherry
304. Peach
305. Strawberry
306. Granada
307. Granadilla
308. Guanábana
309. Guava
310. Lichies
311. Lima
312. Lemon
313. Mamones
314. Tangerine
315. Mango
316. Apple
317. Small apple variety
318. Passion fruit
319. Mazapán
320. Peach
321. Melon
322. Membrillo
323. Raspberry
324. Nance
325. Sweet orange
326. Sour orange
327. Papaya
328. Pear
329. Pineapple
330. Rambután
331. Watermelon
332. Suncuya
333. Tamarind
334. Grapefruit
335. Grapes
336. Zapote
337. Other fruit
Vegetables
360. Garlic
361. Celery
362. Eggplant
363. Broccoli
364. Onion
365. Cauliflower
366. Cilantro (castilla)
367. Cilantro (pata)
368. Sweet pepper
369. Hot pepper
370. Spinach
371. Unripe red beans
372. Lettuce
373. Malanga
374. Mustard leaves
375. Oregano
376. Pataste
377. Cucumbers
378. Parsley
379. Pipian
380. Radishes
381. Beets
382. Cabbage
383. Tomato
384. Carrot
385. Other vegetable
Other products
400. Achiote
401. Sesame
402. Cinnamon
403. Coffee toasted
404. Coffee bean not toasted
405. Coffe bean unpeeled
406. Broth
407. Bouillon cubes
408. Hot sauce
409. Cocoa
410. Chips
411. Spices
412. Ice cream
413. Juice (boxed)
414. Juice (canned)
415. Ketchup
416. Corn starch
417. Mustard
418. Dried oregano
419. Tomato paste
420. Coagulant
421. Soda
422. Baking soda
423. Salt
424. Tomato sauce
425. Worcestershire sauce
426. Dried soup mix
427. Sweet n Low
428. Vinegar
429. Other misc. prods
Local Dishes
540 Meatballs
541 Rice with shrimp
542 Rice with pork
543 Rice with milk
544 Rice with corn
545 Rice with chicken
546 Rice and beans
547 Cordon blue
548 Chop suey
549 Stew
550 Other local dishes
Note: Conversion factors were calculated only for ingredients with codes 1 through 301. These products
are significant contributors of calories and protein, and were the only foods for which quantity estimates
were obtained. (See tables on following pages.)

Appendix 2: Sample Ingredient Form Codes
28
APPENDIX 2. SAMPLE INGREDIENT FORM CODES
Code Form
0Raw
1 Boiled
2Fried
3Stewed
4 Broiled
5 Baked
6 Ground
7Juice
8 Soup

Appendix 3: Sample Unit of Measure Codes
29
APPENDIX 3. SAMPLE UNIT OF MEASURE CODES
1. Pound 2. Ounce
3. Kilogram 4. Gram
5. Liter 6. Milliliter
7. Unit 8. Slice, piece
*80. Tiny loaf *9. Small model
*10. Medium model *11. Large model
*81. Very large model *12. Small (rolls/crackers)
*13. Medium (rolls/crackers) *14. Large (rolls/crackers)
15. Centimeter 16. Centimeter squared
82. Centimeter cubed 17. Gallon
#18. 2 liter Coke bottle #19. 1 liter Coke bottle
#20. ½ liter Coke bottle #21. Small Coke bottle
#22. Large bottle salsa #23. Small bottle salsa
#24. Large Flor de Caña bottle #25. Small Flor de Caña bottle
#26. Small Ron Botrán bottle #27. Large Ron Botrán bottle
#28. Large vinegar bottle 29. Liter box of milk
39. Anega 40. Arroba
#41. Bag #42. Box
#43. Truckload #44. Canasto
#45. Carga #46. Carretada
47. Cuartillo #50. Gavilla
51. Mano 52. Medida
53. Matate #54. Mazo
55. Medio #56. Paca
57. Palo 58. Quintal
59. Racimo #60. Sack
61. Tercio 62. Man/day
63. Piece 64. Other unit of measure
*65. Tortilla A1 *66. Tortilla A2
*67. Tortilla A3 *68. Tortilla B1
*69. Tortilla B2 *70. Tortilla B3
*71. Tortilla C1 *72. Tortilla C2
*73. Tortilla C3 *74. Tortilla D1
*75. Tortilla D2 *76. Tortilla D3
*77. Tortilla E1 *78. Tortilla E2
*79. Tortilla E3 99. Doesn’t know
* Conversion factors not included. Should be calculated when food models are developed prior to field
work.
# Use of these units of measure is not recommended, because they are not standardized. They were
included in the list as a second-best solution when the interviewer was unable to collect the information
using a standardized units. For example, if a household purchased milk from a producer using a large
rum bottle, the interviewer should always ask whether the bottle is available and, if so, ask the respondent
to fill it with water to the level it had been filled with milk. The quantity can then be recorded in
milliliters. If, however, the bottle is not available, then an appropriate rum bottle code (24-27) can be
used.

Appendix 4: Conversion Factors for Common Honduran Foods
30
APPENDIX 4. CONVERSION FACTORS FOR COMMON HONDURAN
FOODS
The table below presents conversion factors for common foods from a 1994 survey in Honduras. The
gross and edible portion weights for the food models are not included. Food model weights will be
specific to each survey, and calculated at the time that each model is developed (prior to the start of
survey field work). The table does include, however, some common units of measure (e.g., arroba,
medida), that are unique to the Honduran setting and should not be used in other countries without prior
verification that the weights are the same.
The columns in the table contain:
(1) Ingredient code (see Appendix 1)
(2) Unit of measure (see Appendix 3)
(3) Ingredient form in which the quantity is estimated
0 Raw 5 Baked
1 Boiled 6 Soup
2Fried 7Juice
3 Stewed 8 Ground/blended
4 Grilled 9 Other
(4) Edible portion weight, in grams of raw ingredient per 1 unit of unit of measure
(5) Gross weight, in grams of raw ingredient per 1 unit of unit of measure
Thus the conversion factors include two transformations. All forms of the ingredient are converted to the
equivalent in the raw ingredient, and all units of measures are converted to grams.
Two examples based on the table:
(1) The second line in the first column of the table is 001 01 1 00259.00100 00259.00100
The ingredient is 001 (dry white corn kernel)
The unit of measure is 01 (pound)
The form is 1 (boiled)
The equivalent weight in edible portion of raw white corn kernels is 259.001 grams.
Since corn kernels do not have any wastage, the equivalent gross weight of raw white corn
kernels is also 259.001 grams.
(2) The first line in the sixth column of the table is 100 07 0 00100.00000 00150.00000
The ingredient is 100 (ripe banana)
The unit of measure is 07 (unit)
The form is 0 (raw)
The weight of the edible portion of the banana is 100 grams.
Since the peel of the banana is not consumed, the equivalent gross weight of the banana is 150
grams.

Appendix 4: Conversion Factors for Common Honduran Foods
31
(1) (2)(3) (4) (5)
001 01 0 00453.59250 00453.59250
001 01 1 00259.00100 00259.00100
001 01 8 00480.80805 00480.80805
001 02 0 00028.34950 00028.34950
001 02 1 00016.18700 00016.18700
001 02 8 00030.05047 00030.05047
001 03 1 00571.00000 00571.00000
001 05 0 00907.20000 00907.20000
001 06 0 00000.90720 00000.90720
001 06 1 00000.60400 00000.60400
001 06 8 00000.56699 00000.56699
001 40 0 11339.81300 11339.81300
001 52 0 02267.96200 02267.96200
001 52 1 02267.96200 02267.96200
001 58 0 45358.25000 45359.25000
002 01 0 00453.59250 00453.59250
002 06 0 00000.90320 00000.90320
002 06 1 00000.82790 00000.82790
003 01 0 00453.59250 00453.59250
003 01 8 00480.80805 00480.80805
003 02 0 00028.34950 00028.34950
003 06 0 00001.00000 00001.00000
003 06 1 00000.82790 00000.82790
003 06 8 00000.59430 00000.59430
003 07 0 00100.00000 00100.00000
003 07 1 00057.10000 00057.10000
003 07 8 00100.00000 00100.00000
003 51 0 00500.00000 00500.00000
003 51 8 00500.00000 00500.00000
003 52 0 02267.96200 02267.96200
004 01 0 00316.60757 00316.60757
004 01 1 00316.60757 00316.60757
004 02 1 00019.78790 00019.78790
004 07 0 00023.26660 00023.26660
004 07 1 00023.26660 00023.26660
005 01 8 00216.36359 00216.36359
005 07 0 00100.00000 00100.00000
007 01 0 00453.59250 00453.59250
007 01 1 00259.00100 00259.00100
007 01 8 00480.80805 00480.80805
007 06 0 00000.90720 00000.90720
007 06 1 00000.60400 00000.60400
007 06 8 00000.56699 00000.56699
007 40 0 11339.81300 11339.81300
007 52 0 02267.96200 02267.96200
008 06 0 00001.00000 00001.00000
008 06 1 00000.82790 00000.82790
008 06 8 00000.59430 00000.59430
010 01 0 00316.60757 00316.60757
011 01 0 00204.11660 00204.11660
011 07 4 00100.00000 00100.00000
013 01 0 00453.59250 00453.59250
013 02 0 00028.34950 00028.34950
013 04 0 00001.00000 00001.00000
(1) (2)(3) (4) (5)
013 06 0 00000.58060 00000.58060
013 06 1 00000.34840 00000.34840
013 06 8 00000.43550 00000.43550
013 40 0 11339.81300 11339.81300
013 52 0 02267.96200 02267.96200
013 58 0 45358.25000 45359.25000
014 07 0 00023.26660 00023.26660
015 01 0 00453.59250 00453.59250
015 01 1 00141.74766 00141.74766
015 01 3 00141.74766 00141.74766
015 02 0 00028.34950 00028.34950
015 02 1 00008.85915 00008.85915
015 02 3 00008.85920 00008.85920
015 03 0 01000.00000 01000.00000
015 04 0 00001.00000 00001.00000
015 04 3 00000.31250 00000.31250
015 05 0 01225.00000 01225.00000
015 06 0 00001.22500 00001.22500
015 06 1 00000.23500 00000.23500
015 06 2 00001.14230 00001.14230
015 06 3 00000.23500 00000.23500
015 06 8 00001.29850 00001.29850
015 40 0 11339.81300 11339.81300
015 58 0 45358.25000 45359.25000
016 01 0 00453.59250 00453.59250
016 02 0 00028.34950 00028.34950
016 02 3 00008.85915 00008.85915
016 04 0 00001.00000 00001.00000
016 04 3 00000.53720 00000.53720
016 06 0 00001.39000 00001.39000
016 40 0 11339.81300 11339.81300
018 06 0 00000.72200 00000.72200
040 00 1 00210.46690 00210.46690
040 01 0 00453.60000 00453.60000
040 02 0 00028.35000 00028.35000
040 02 1 00013.15416 00013.15416
040 02 8 00013.15416 00013.15416
040 04 0 00001.00000 00001.00000
040 06 0 00000.83300 00000.83300
040 06 1 00000.38650 00000.38650
040 06 2 00000.38650 00000.38650
040 06 8 00000.48400 00000.48400
040 41 0 01133.98130 01133.98130
040 58 0 45358.25000 45359.25000
041 01 0 00453.59250 00453.59250
041 01 1 00210.46692 00210.46692
041 01 2 00210.46692 00210.46692
041 01 8 00210.46620 00210.46620
041 02 0 00028.31950 00028.31950
041 02 1 00013.15416 00013.15416
041 02 8 00013.15416 00013.15416
041 03 0 01000.00000 01000.00000
041 03 1 00464.00000 00464.00000
041 04 1 00000.86400 00000.86400
(1) (2)(3) (4) (5)
041 04 8 00000.86400 00000.86400
041 06 0 00000.83300 00000.83300
041 06 1 00000.31200 00000.31200
041 06 2 00000.48400 00000.48400
041 06 3 00000.31200 00000.31200
041 06 8 00000.42400 00000.42400
041 40 0 11339.81300 11339.81300
041 52 0 02267.96300 02267.96300
041 58 0 45359.25000 45359.25000
042 01 0 00453.59250 00453.59250
042 01 1 00212.73488 00212.73488
042 02 0 00028.34950 00028.34950
042 02 1 00013.29590 00013.29590
042 02 8 00013.89590 00013.89590
042 06 0 00000.85400 00000.85400
042 06 1 00000.37100 00000.37100
042 06 2 00000.37100 00000.37100
042 06 8 00000.37100 00000.37100
042 52 0 02267.96300 02267.96300
042 58 0 45358.25000 45359.25000
043 01 0 00453.59250 00453.59250
043 01 1 00210.46600 00210.46600
043 01 8 00210.46600 00210.46600
043 02 0 00028.34950 00028.34950
043 02 3 00013.15410 00013.15410
043 02 8 00013.15410 00013.15410
043 06 0 00000.39625 00000.39625
045 01 0 00453.59250 00453.59250
060 01 0 00453.59250 00453.59250
060 01 8 00453.59250 00453.59250
060 02 0 00028.34950 00028.34950
060 02 8 00028.34950 00028.34950
060 06 0 00000.60000 00000.60000
060 06 8 00000.60000 00000.60000
061 01 0 00294.83510 00453.59250
062 01 0 00453.59250 00453.59250
062 01 8 00453.59250 00453.59250
062 02 0 00028.34950 00028.34950
062 06 0 00000.60000 00000.60000
063 01 0 00453.59250 00453.59250
063 02 0 00028.34950 00028.34950
063 06 0 00000.60000 00000.60000
064 01 0 00480.80800 00480.80800
064 01 1 00480.80800 00480.80800
064 01 8 00480.80800 00480.80800
064 02 0 00030.05000 00030.05000
064 02 8 00030.05000 00030.05000
064 06 0 00000.63000 00000.63000
064 06 1 00000.63000 00000.63000
064 06 8 00000.63000 00000.63000
064 07 0 00023.00000 00023.00000
064 07 1 00023.00000 00023.00000
064 07 5 00023.00000 00023.00000
065 06 0 00000.56074 00000.56074

Appendix 4: Conversion Factors for Common Honduran Foods
32
(1) (2)(3) (4) (5)
065 06 8 00000.56074 00000.56074
066 01 0 00453.59250 00453.59250
066 02 0 00000.63000 00000.63000
067 01 5 00453.59250 00453.59250
067 08 0 00021.00000 00021.00000
067 08 5 00021.00000 00021.00000
067 41 0 00348.75000 00348.75000
067 41 5 00348.75000 00348.75000
068 01 0 00453.59250 00453.59250
068 02 5 00028.34950 00028.34950
068 03 5 01000.00000 01000.00000
068 04 5 00001.00000 00001.00000
068 07 0 00047.70000 00047.70000
068 07 5 00047.70000 00047.70000
068 08 5 00023.58700 00023.58700
068 41 5 00272.15505 00272.15505
069 07 0 00043.09360 00043.09360
069 07 5 00043.09360 00043.09360
069 16 5 00001.99900 00001.99900
069 16 6 00001.99900 00001.99900
069 16 7 00001.99900 00001.99900
069 16 8 00001.99900 00001.99900
070 07 0 00020.70000 00020.70000
070 08 5 00021.00000 00021.00000
070 13 5 00043.09360 00043.09360
071 02 5 00028.34950 00028.34950
071 04 0 00001.00000 00001.00000
071 07 0 00020.70000 00020.70000
071 07 5 00020.70000 00020.70000
071 08 5 00021.00000 00021.00000
071 12 0 00028.34950 00028.34950
071 12 5 00028.34950 00028.34950
071 13 0 00043.09360 00043.09360
071 13 5 00043.09360 00043.09360
071 14 0 00105.83800 00105.83800
071 14 5 00105.83800 00105.83800
071 41 5 00310.50000 00310.50000
072 01 0 00453.59250 00453.59250
072 01 5 00453.59250 00453.59250
072 04 0 00001.00000 00001.00000
072 07 0 00043.09360 00043.09360
072 07 5 00043.09360 00043.09360
072 08 5 00021.00000 00021.00000
072 12 0 00028.34900 00028.34900
072 12 5 00028.34900 00028.34900
072 13 0 00043.09360 00043.09360
072 13 5 00043.09360 00043.09360
072 14 0 00105.83805 00105.83805
072 14 5 00105.83805 00105.83805
072 16 5 00001.99900 00001.99900
074 12 5 00028.34900 00028.34900
075 07 0 00043.09360 00043.09360
075 07 5 00043.09360 00043.09360
075 12 0 00028.34900 00028.34900
(1) (2)(3) (4) (5)
075 12 5 00028.34900 00028.34900
075 13 0 00043.09360 00043.09360
075 13 5 00043.09360 00043.09360
075 14 0 00105.85300 00105.85300
075 14 4 00105.85300 00105.85300
075 14 5 00105.85300 00105.85300
075 41 5 00215.46800 00215.46800
077 01 0 00453.59250 00453.59250
077 02 0 00028.34950 00028.34950
077 02 5 00028.34950 00028.34950
077 04 0 00001.00000 00001.00000
077 06 0 00000.10570 00000.10570
077 06 1 00000.10570 00000.10570
078 01 0 00453.59250 00453.59250
078 02 0 00028.34950 00028.34950
078 04 0 00001.00000 00001.00000
078 04 8 00001.00000 00001.00000
078 06 0 00000.57000 00000.57000
078 06 1 00000.57000 00000.57000
078 06 8 00000.57000 00000.57000
078 41 0 00057.00000 00057.00000
079 01 0 00453.59250 00453.59250
079 02 0 00028.34950 00028.34950
079 04 0 00001.00000 00001.00000
079 06 0 00000.33330 00000.33330
079 41 0 00453.59250 00453.59250
080 01 0 00453.59250 00453.59250
080 02 0 00028.34950 00028.34950
080 04 0 00001.00000 00001.00000
080 41 0 00453.59250 00453.59250
081 01 0 00453.59250 00453.59250
081 41 0 00453.59250 00453.59250
082 01 0 00453.59250 00453.59250
083 01 0 00453.59250 00453.59250
083 02 0 00028.34950 00028.34950
084 01 0 00453.59250 00453.59250
084 02 0 00028.34950 00028.34950
084 04 0 00001.00000 00001.00000
084 41 0 00453.59250 00453.59250
085 01 0 00453.59250 00453.59250
085 02 0 00028.34950 00028.34950
085 04 0 00001.00000 00001.00000
085 41 0 00453.59250 00453.59250
086 01 0 00453.59250 00453.59250
086 02 0 00028.34950 00028.34950
086 04 0 00001.00000 00001.00000
086 41 0 00453.59250 00453.59250
087 01 0 00453.59250 00453.59250
087 02 0 00028.34950 00028.34950
088 01 0 00453.59250 00453.59250
088 02 0 00028.34950 00028.34950
088 04 0 00001.00000 00001.00000
088 04 8 00001.00000 00001.00000
100 01 0 00302.39500 00453.59250
(1) (2)(3) (4) (5)
100 07 0 00100.00000 00150.00000
100 07 4 00100.00000 00150.00000
101 01 0 00302.39500 00453.59250
101 02 0 00001.88996 00028.34950
101 07 0 00100.00000 00150.00000
101 07 1 00100.00000 00150.00000
101 07 2 00100.00000 00150.00000
102 01 0 00151.18000 00453.59250
102 07 0 00060.00000 00180.00000
102 07 1 00060.00000 00180.00000
102 51 0 00300.00000 00900.00000
103 07 0 00017.35000 00023.13000
105 01 0 00344.73030 00453.59250
105 07 0 00190.00000 00250.00000
105 52 0 00950.00000 01250.00000
106 01 0 00388.87000 00453.59250
106 01 1 00388.87000 00453.59250
106 01 2 00388.87000 00453.59250
106 02 0 00024.30430 00028.34950
106 04 0 00000.85730 00001.00000
106 07 0 00120.00000 00140.00000
106 07 1 00120.00000 00140.00000
106 58 0 45358.25000 45359.25000
107 01 0 00366.04900 00453.59250
107 02 0 00022.87800 00028.34950
107 07 0 00460.00000 00570.00000
107 08 0 00120.00000 00135.00000
108 01 0 00376.48000 00453.59250
108 07 0 00170.00000 00190.00000
109 01 0 00318.10422 00453.59250
109 04 0 00000.70000 00001.00000
109 07 0 00250.00000 00450.00000
109 08 0 00250.00000 00450.00000
110 01 0 00328.74000 00453.59250
110 08 0 00080.00000 00100.00000
111 01 0 00331.20000 00453.59250
111 02 0 00020.69500 00028.34950
111 07 0 00100.00000 00150.00000
130 01 0 00227.27000 00227.27000
130 01 1 00227.27000 00227.27000
130 02 0 00014.20450 00014.20450
130 02 1 00014.20450 00014.20450
130 03 0 00499.99400 00499.99400
130 04 0 00000.49999 00000.49999
130 05 0 01000.00000 01000.00000
130 05 1 01000.00000 01000.00000
130 06 0 00001.00000 00001.00000
130 06 1 00001.00000 00001.00000
130 17 0 03785.60000 03785.60000
130 19 0 01000.00000 01000.00000
130 20 0 00500.00000 00500.00000
130 21 0 00354.00000 00354.00000
130 24 0 00750.00000 00750.00000
130 25 0 00375.00000 00375.00000

Appendix 4: Conversion Factors for Common Honduran Foods
33
(1) (2)(3) (4) (5)
130 26 0 00375.00000 00375.00000
130 27 0 00750.00000 00750.00000
131 01 0 00227.27000 00227.27000
131 01 8 00227.27000 00227.27000
131 02 0 00014.20450 00014.20450
131 05 0 01000.00000 01000.00000
131 05 1 01000.00000 01000.00000
131 06 0 00001.00000 00001.00000
131 06 1 00001.00000 00001.00000
131 19 0 01000.00000 01000.00000
131 20 0 00500.00000 00500.00000
131 21 0 00354.00000 00354.00000
131 24 0 00750.00000 00750.00000
134 01 0 00453.59250 00453.59250
134 02 0 00028.34950 00028.34950
134 03 0 01000.00000 01000.00000
134 04 0 00001.00000 00001.00000
134 06 0 00000.52900 00000.52900
134 06 1 00000.52900 00000.52900
134 34 0 01800.00000 01800.00000
135 01 0 00453.59250 00453.59250
135 04 0 00001.00000 00001.00000
136 01 0 00453.59250 00453.59250
136 01 1 00453.59250 00453.59250
136 02 0 00028.34950 00028.34950
136 03 0 01000.00000 01000.00000
136 04 0 00001.00000 00001.00000
136 06 0 00000.52900 00000.52900
136 06 1 00000.52900 00000.52900
137 06 0 00000.52900 00000.52900
138 04 0 00000.49999 00000.49999
138 05 0 01000.00000 01000.00000
139 01 0 00453.59250 00453.59250
139 02 0 00028.34950 00028.34950
139 04 0 00001.00000 00001.00000
140 01 0 00453.59250 00453.59250
140 02 0 00028.34950 00028.34950
140 04 0 00001.00000 00001.00000
140 07 0 00453.59250 00453.59250
141 01 0 00453.59250 00453.59250
141 02 0 00028.34950 00028.34950
141 04 0 00001.00000 00001.00000
141 06 0 00000.52900 00000.52900
142 01 0 00453.59250 00453.59250
142 02 0 00028.34950 00028.34950
142 04 0 00001.00000 00001.00000
142 08 0 00023.13300 00023.13300
143 01 0 00453.59250 00453.59250
143 02 0 00028.34950 00028.34950
143 06 0 00000.52900 00000.52900
144 01 0 00453.59250 00453.59250
144 02 0 00028.34950 00028.34950
144 06 0 00000.52900 00000.52900
145 01 0 00453.59250 00453.59250
(1) (2)(3) (4) (5)
145 01 1 00453.59250 00453.59250
145 02 0 00028.34950 00028.34950
145 02 1 00028.34950 00028.34950
145 03 0 01000.00000 01000.00000
145 05 0 00529.00000 00529.00000
145 06 0 00000.52900 00000.52900
145 07 0 00453.59250 00453.59250
146 01 0 00453.59250 00453.59250
146 01 1 00453.59250 00453.59250
146 02 0 00028.34950 00028.34950
146 02 1 00028.34950 00028.34950
146 05 0 00529.00000 00529.00000
146 06 0 00000.52900 00000.52900
147 01 0 00453.59250 00453.59250
147 01 1 00453.59250 00453.59250
147 02 0 00028.34950 00028.34950
147 02 1 00028.34950 00028.34950
148 02 0 00028.34950 00028.34950
148 06 0 00000.52900 00000.52900
149 01 0 00453.59250 00453.59250
149 01 1 00453.59250 00453.59250
149 02 0 00028.34950 00028.34950
149 02 1 00028.34950 00028.34950
149 04 0 00001.00000 00001.00000
149 04 1 00001.00000 00001.00000
149 05 0 01120.00000 01120.00000
149 06 0 00001.12000 00001.12000
149 06 1 00001.12000 00001.12000
150 01 0 00453.59250 00453.59250
150 02 0 00028.34950 00028.34950
150 02 1 00028.34950 00028.34950
150 03 0 01000.00000 01000.00000
150 04 0 00001.00000 00001.00000
150 06 0 00001.16600 00001.16600
150 07 0 00113.39800 00113.39800
151 01 0 00453.59250 00453.59250
151 04 0 00001.00000 00001.00000
152 01 0 00453.59250 00453.59250
152 04 0 00001.00000 00001.00000
153 01 0 00453.59250 00453.59250
153 02 8 00028.34950 00028.34950
153 05 0 01000.00000 01000.00000
153 06 0 00001.00000 00001.00000
153 06 8 00001.00000 00001.00000
170 07 0 00001.00000 00001.00000
170 07 1 00001.00000 00001.00000
170 07 2 00001.00000 00001.00000
170 07 5 00001.00000 00001.00000
170 07 6 00001.00000 00001.00000
170 48 0 00012.00000 00012.00000
171 07 0 00001.00000 00001.00000
171 07 5 00001.00000 00001.00000
171 48 0 00012.00000 00012.00000
173 07 0 00001.00000 00001.00000
(1) (2)(3) (4) (5)
173 07 1 00001.00000 00001.00000
173 07 2 00001.00000 00001.00000
173 48 0 00012.00000 00012.00000
180 01 0 00290.29900 00453.59250
180 01 6 00290.29900 00453.59250
180 02 0 00018.14360 00028.34950
181 01 0 00453.59250 00453.59250
181 01 2 00453.59250 00453.59250
181 01 3 00453.59250 00453.59250
181 01 4 00453.59250 00453.59250
181 01 8 00453.59250 00453.59250
181 02 0 00028.34950 00028.34950
181 02 2 00028.34950 00028.34950
181 02 3 00028.34950 00028.34950
181 02 4 00028.34950 00028.34950
181 02 8 00028.34950 00028.34950
181 06 0 00000.94347 00000.94347
181 06 3 00000.94347 00000.94347
181 06 4 00000.94347 00000.94347
181 06 8 00000.94347 00000.94347
182 01 0 00278.27900 00453.59250
183 01 0 00290.29900 00453.59250
184 01 0 00358.33800 00453.59250
184 01 2 00358.33800 00453.59250
184 02 0 00022.40000 00028.34950
184 02 2 00022.40000 00028.34950
184 02 3 00022.40000 00028.34950
185 01 0 00453.59250 00453.59250
185 01 1 00453.59250 00453.59250
185 01 2 00453.59250 00453.59250
185 01 3 00453.59250 00453.59250
185 01 5 00453.59250 00453.59250
185 02 0 00028.34950 00028.34950
185 02 2 00028.34950 00028.34950
186 01 0 00453.59250 00453.59250
186 01 1 00453.59250 00453.59250
186 01 8 00453.59250 00453.59250
186 02 0 00028.34950 00028.34950
186 02 8 00028.34950 00028.34950
187 01 0 00358.33800 00453.59250
187 02 0 00022.39610 00028.34950
188 01 0 00385.55830 00453.59250
188 01 2 00385.55830 00453.59250
188 02 0 00024.09700 00028.34950
188 04 0 00000.85000 00001.00000
189 01 0 00340.19430 00453.59250
190 01 0 00408.23700 00453.59250
190 01 3 00408.23700 00453.59250
190 02 0 00025.51455 00028.34950
191 01 0 00453.59250 00453.59250
192 01 0 00453.59250 00453.59250
193 01 0 00453.59250 00453.59250
194 01 0 00204.11660 00453.59250
195 01 0 00453.59250 00453.59250

Appendix 4: Conversion Factors for Common Honduran Foods
34
(1) (2)(3) (4) (5)
196 01 0 00303.90600 00453.59250
196 01 1 00303.90600 00453.59250
196 01 2 00303.90600 00453.59250
196 01 3 00303.90600 00453.59250
196 01 4 00303.90600 00453.59250
196 01 5 00303.90600 00453.59250
196 02 0 00016.98400 00028.34950
196 02 2 00016.98400 00028.34950
196 02 3 00016.98400 00028.34950
196 02 4 00016.98400 00028.34950
196 06 1 00000.53290 00000.88950
196 06 2 00000.53290 00000.88950
196 06 3 00000.53290 00000.88950
196 06 4 00000.53290 00000.88950
196 06 5 00000.53290 00000.88950
196 07 0 00759.76700 01133.98130
196 07 2 00759.76700 01133.98130
197 01 0 00362.87400 00453.59250
197 01 2 00362.87400 00453.59250
197 07 2 00157.92000 00188.00000
198 01 0 00304.18000 00453.59250
199 01 0 00391.22000 00453.59250
199 01 1 00391.22000 00453.59250
199 01 2 00391.22000 00453.59250
199 01 3 00391.22000 00453.59250
199 01 6 00391.22000 00453.59250
199 02 0 00024.45100 00028.34950
199 02 3 00024.45100 00028.34950
199 41 0 00391.22000 00453.59250
200 01 0 00303.90600 00453.59250
200 01 1 00303.90600 00453.59250
200 01 3 00303.90600 00453.59250
200 01 6 00303.90600 00453.59250
200 02 3 00018.99410 00028.34950
200 07 0 01063.67400 01587.57820
203 01 0 00391.22000 00453.59250
204 01 0 00245.00000 00453.59250
205 01 0 00453.59250 00453.59250
205 01 1 00453.59250 00453.59250
205 02 0 00028.34950 00028.34950
205 02 2 00028.34950 00028.34950
205 04 0 00001.00000 00001.00000
205 08 0 00045.35900 00045.35900
205 08 2 00045.35900 00045.35900
206 01 0 00453.59250 00453.59250
206 01 1 00453.59250 00453.59250
206 02 0 00028.34950 00028.34950
206 08 0 00037.79900 00037.79900
207 01 0 00453.59250 00453.59250
207 01 2 00453.59250 00453.59250
207 02 0 00028.34950 00028.34950
207 04 0 00001.00000 00001.00000
207 07 0 00100.79800 00100.79800
207 07 2 00100.79800 00100.79800
(1) (2)(3) (4) (5)
208 01 0 00453.59250 00453.59250
208 01 1 00453.59250 00453.59250
208 02 0 00028.34950 00028.34950
208 04 0 00001.00000 00001.00000
208 07 0 00030.23950 00030.23950
209 01 0 00453.59250 00453.59250
209 02 0 00028.34950 00028.34950
209 07 0 00010.53800 00010.53800
210 01 0 00453.59250 00453.59250
210 02 0 00028.34950 00028.34950
210 04 0 00001.00000 00001.00000
211 01 0 00453.59250 00453.59250
211 02 0 00028.34950 00028.34950
212 01 0 00453.59250 00453.59250
213 01 0 00367.40900 00453.59250
213 01 1 00367.40900 00453.59250
213 01 2 00367.40900 00453.59250
213 01 4 00367.40900 00453.59250
213 01 6 00367.40900 00453.59250
213 02 0 00022.96300 00028.34950
213 07 0 00300.00000 00580.00000
214 01 0 00453.59250 00453.59250
214 02 0 00028.34950 00028.34950
214 07 0 00175.00000 00175.00000
215 01 0 00340.19400 00453.59250
215 01 1 00340.19400 00453.59250
215 02 0 00021.26210 00028.34950
216 01 0 00226.79600 00453.59250
216 02 0 00014.17470 00028.34950
219 01 0 00453.59250 00453.59250
219 02 0 00028.34950 00028.34950
219 02 1 00028.34950 00028.34950
219 02 3 00028.34950 00028.34950
219 04 0 00001.00000 00001.00000
220 01 0 00362.87400 00453.59250
220 01 2 00362.87400 00453.59250
220 02 0 00022.67960 00028.34950
220 02 4 00022.67960 00028.34950
220 04 0 00000.80000 00001.00000
220 04 1 00000.80000 00001.00000
240 01 0 00453.59250 00453.59250
240 02 0 00028.34950 00028.34950
240 03 0 01000.00000 01000.00000
240 04 0 00001.00000 00001.00000
240 05 0 01166.00000 01166.00000
240 06 0 00001.16600 00001.16600
240 06 1 00001.16600 00001.16600
240 06 2 00001.16600 00001.16600
240 26 0 00437.25000 00437.25000
241 01 0 00453.59250 00453.59250
241 06 0 00001.16600 00001.16600
241 06 1 00001.16600 00001.16600
241 17 0 03785.60000 03785.60000
241 25 0 00375.00000 00375.00000
(1) (2)(3) (4) (5)
241 26 0 00375.00000 00375.00000
241 27 0 00874.50000 00874.50000
242 01 0 00453.59250 00453.59250
242 02 0 00028.34950 00028.34950
242 05 0 00951.90000 00951.90000
242 06 0 00000.95190 00000.95190
242 06 1 00000.95190 00000.95190
242 17 0 03603.51300 03603.51300
242 24 0 00750.00000 00750.00000
243 02 0 00028.34950 00028.34950
243 06 0 00000.95190 00000.95190
244 01 0 00453.59250 00453.59250
244 02 0 00028.34950 00028.34950
244 03 0 01000.00000 01000.00000
244 04 0 00001.00000 00001.00000
244 06 0 00001.16600 00001.16600
244 42 0 00453.59250 00453.59250
245 01 0 00453.59250 00453.59250
245 02 0 00028.34950 00028.34950
245 04 0 00001.00000 00001.00000
245 06 0 00000.93000 00000.93000
246 01 0 00453.59250 00453.59250
246 06 0 00001.16600 00001.16600
260 01 0 00453.59250 00453.59250
260 02 0 00028.34950 00028.34950
260 03 0 01000.00000 01000.00000
260 04 0 00001.00000 00001.00000
260 05 0 01088.60000 01088.60000
260 06 0 00001.08860 00001.08860
260 06 1 00001.08860 00001.08860
260 60 0 00453.59250 00453.59250
260 58 0 45358.25000 45359.25000
261 01 0 00453.59250 00453.59250
261 06 0 00001.08860 00001.08860
262 01 0 00453.59250 00453.59250
262 01 1 00453.59250 00453.59250
262 02 0 00028.34950 00028.34950
262 02 1 00028.34950 00028.34950
262 06 0 00000.72300 00000.72300
262 06 1 00000.72300 00000.72300
263 02 1 00018.42700 00018.42700
264 01 0 00453.59250 00453.59250
264 02 0 00028.34950 00028.34950
264 02 1 00028.34950 00028.34950
264 04 0 00001.00000 00001.00000
264 06 0 00001.43300 00001.43300
264 21 0 00507.28200 00507.28200
264 24 0 01074.75000 01074.75000
264 25 0 00537.37500 00537.37500
300 01 0 00359.74580 00453.59250
300 07 0 00230.00000 00290.00000
301 06 0 00000.33810 00000.33810
301 07 0 00396.76000 00763.00000

Appendix 5: Sample Activities for Males and Females, Grouped by Activity Level
35
APPENDIX 5. SAMPLE ACTIVITIES FOR MALES AND FEMALES,
GROUPED BY ACTIVITY LEVEL
Males: Activity Level
Light Moderate High
Activities
Lying
Sitting
Standing quietly
Cooking
Fishing with line
Fishing from canoe
Playing cards
Washing clothes
Making bows and arrows
Light recreational (billiards, golf,
cricket)
Office work
Driving bus, taxi, tractor
Flying helicopter
Sewing
Sorting crops, kneeling
Laboratory work
Weaving
Carving
Sorghum harvest - cutting ears
Tailoring
Cleaning kit (Army)
Strolling
Fishing with spear
Light or moderate cleaning
Tying fence posts
Walking slowly or at normal
pace
Walking downhill, at any pace
Weaving bamboo wall
Roofing house
Singing and dancing
Nailing
Hunting birds, flying fox, pigs
Walking with 10 kg load
Moderate recreation (dancing,
swimming, tennis)
Shoemaking
Kneading clay
Painting and decorating
Planting
Milking cows by hand
Making bricks, squatting
Electrical industry
Machine tool industry
Cutting bamboo
Joinery
Drill (Army)
Bricklaying
Paddling canoe
Jungle patrol (Army)
Uprooting timbers
Carpentry
Chemical industry
Feeding animals
Making a fence
Lifting grain sacks
Winnowing
Chopping firewood
Laying floor (LDC)
Walking uphill
Heavy recreational (jogging, athletics)
Putting coconuts in a bag
Brick breaking
Sharpening posts
Planting trees
Cutting palm tree trunks
Splitting wood for posts
Sawing and power sawing
Route marching (Army)
Shoveling mud
Collecting coconuts (incl. climbing
trees
Cutting grass with machete
Loading sacks
Cutting trees
Pushing wheelbarrow
Repairing fences
Digging holes for posts
Assault course (Army)
Laboring
Collecting and spreading manure by hand
Pulling cart
Digging irrigation channels
Digging earth to make mud
Shoveling
Jungle march (Army)
Mining
Earth cutting
Digging holes
Husking coconuts
Loading manure by hand
Cutting sugar cane
Forking
Pedaling rickshaw
Trimming branches of a tree
Felling tree with ax
Hand sawing

Appendix 5: Sample Activities for Males and Females, Grouped by Activity Level
36
Females: Activity Level
Light Moderate High
Activities
Lying down
Sitting quietly
Roasting corn
Ironing
Preparing vegetables
Sitting, sewing clothes
Podding beans
Sewing
Sewing pandanus mat
Weaving carrying bag
Preparing rope
Standing
Peeling taro
Washing dishes
Cooking
Squeezing coconut
Collecting leaves for flavoring
Breaking nuts e.g., peanuts
Spinning cotton
Preparing tobacco
Picking coffee
Winnowing
Office work
De-seeding cotton
Electrical industry
Beating cotton
Walking downhill
Strolling
Singing and dancing
Loading earth oven
Light cleaning
Light weeding
Sweeping house
Walking slowly or at normal
pace
Washing clothes
Sweeping yard
Moderate cleaning
Stirring porridge
Grinding grain on millstone
Catching fish by hand
Machine tool industry
Brewery work
Chemical industry
Harvesting grains
Harvesting vegetables
Harvesting root crops
Harvesting medicinal crops
Kneading clay
Milking cows/goats by hand
Making cheese
Feeding animals
Furnishing industry
Laundry work
Cutting fruit from trees
Clearing ground
Planting
Walking with load
Fetching water from well
Chopping wood
Catching crabs
Pounding grain
Walking uphill (w/ or w/o load)
Walking downhill (fast with load)
Sawing
Binding sheaves
Digging holes for planting
Hoeing
Digging ground
Threshing
Cutting grass with machete
Collecting fuel wood
Road construction
Digging irrigation ditches
Digging holes
Cutting sugar cane
Husking coconuts
Putting coconuts in a bag
Harvesting tree crops
Planting trees

Appendix 6: Using SPSS/PC to Calculate Household Calorie Intake
37
APPENDIX 6. USING SPSS/PC TO CALCULATE HOUSEHOLD
CALORIE INTAKE
To calculate household calorie intake and requirements, the data analyst(s) will need to make several
adjustments to the data collected using data from the food-intake questionnaire described in the text.
Dishes and many individual ingredients must be transformed into standard units, for which caloric
equivalents can be assigned. The caloric value of leftover food must be calculated and deducted. The
caloric requirements for the household must be calculated, based on several factors, and finally, a
calculation of the adequecy of caloric intake can be made. Procedures and SPSS/PC programs for making
these adjustments are presented and explained in detail below.
I. Calculating Adult Equivalent Ratio for Household Members
The adult equivalent ratio (AER) for each household member needs to be calculated and saved in a
separate file, for use in processing the 24-hour recall data. The AER is based on the individual’s caloric
requirements, which are calculated based on age, sex, imputed weight, current activity level, and
physiological status, as well as the caloric requirements of a standardized adult equivalent. Weight is
labeled “imputed” because the interviewer does not actually weigh household members or ask the
respondent to estimate weights. Instead, country specific averages are used (see section I.A. for further
details).
I.A. Caloric Requirements of Adult Equivalents
The denominator of the AER is the daily caloric requirement of an adult equivalent. An adult equivalent
can be defined by any combination of age, sex, and activity level. However, once defined, the adult
equivalent must be considered standard and fixed for all cases in a study. Appendix 8 contains caloric
requirements for suggested adult equivalents for FAO-member countries with populations greater than
300,000 (countries are listed in Appendix 7). The definition of an adult equivalent in the table is an adult
male, 30-60 years old, of moderate activity, and average weight for the respective country.
I.B. Caloric Requirements for Each Household Member
The numerator of the AER is the daily caloric requirement of each household member currently residing
in the household (code of ‘1’ in Column 8 of the Household Composition Questionnaire, figure 2). The
four steps outlined below should be followed to calculate individual caloric requirements for household
members aged 10 years and over. While it is possible to calculate requirements for individual children
under 10, the FAO/WHO Committee recommends using the standardized caloric requirements contained
in Appendix 9, tables 1-4. These requirements were estimated based on observed intakes of healthy
children growing normally. Figure 4 illustrates how AERs were calculated for a Kenyan household.
I.B.1. Estimate Household Member’s Weight
Appendix 10 contains weight data for FAO member countries to be used as a best estimate of average
weight in kilograms by age and sex. This is the “imputed weight.”
I.B.2. Calculate Basal Metabolic Rate (BMR) Caloric Requirements
The imputed weight (W) for each age/sex is included in the following equations to estimate the basal
metabolic rate (BMR) caloric, or energy, requirements of an individual while “at rest.” This formula is

Appendix 6: Using SPSS/PC to Calculate Household Calorie Intake
38
applied to all household members ten years old and older. Younger children are assigned caloric
requirements according to age and irrespective of weight. The appropriate tables are listed in Appendix 9.
Equations for Predicting BMR from Body Weight in kgs (W)
Age Range (in Years) Equation for Calories per Day
Male
10-17+ (17.5 x W) + 651
18-29+ (15.3 x W) + 679
30-59+ (11.6 x W) + 879
60+ (13.5 x W) + 487
Female
10-17+ (12.2 x W) + 746
18-29+ (14.7 x W) + 496
30-59+ (8.7 x W) + 829
60+ (10.5 x W) + 596
Source: WHO, 1985, Energy and Protein Requirements: Report of a
Joint FAO/WHO/UNU Expert Consultation, World Health
Organization: Geneva, 71.
I.B.3. Allow for Activity Level
Individual BMR requirements are multiplied by a factor to reflect his or her activity level. Although
BMR multipliers represent broad averages, they serve to increase total caloric requirements to reflect
relative rates of energy use. More detailed and precise BMR multipliers can be calculated if more detailed
information of time allocation is collected for each household member, but this level of detail is not
necessary, given the relative level of precision of the caloric adequacy indicator.
BMR Multipliers for Current Activity Level
Gender Activity Level
Light Moderate Heavy
Male 1.55 1.78 2.10
Female 1.56 1.64 1.82
Source: WHO, 1985, Energy and Protein Requirements: Report of a Joint
FAO/WHO/UNU Expert Consultation, World Health Organization: Geneva, p.
78.
I.B.4. Additional Requirements During Pregnancy and Lactation
Pregnancy and lactation increase a woman’s caloric requirements. If a household member is
pregnant, 285 calories should be added to her daily caloric requirement. Add 700 calories a day
if she is breastfeeding a child under 6 months of age, and 500 calories a day if the breastfed child
is six months or older. Combine the additional requirements if the woman is both pregnant and
breastfeeding.

Appendix 6: Using SPSS/PC to Calculate Household Calorie Intake
39
I.C. Calculating Adult Equivalent Ratios for Each Household Member
The AER is the daily caloric requirement of each household member divided by the caloric requirements
of the adult equivalent for the country of interest. Thus each household member’s AER represents the
proportion of the adult equivalent caloric requirements required by the household member. See Figure 4
for an example of an AER calculation.
I.D. Creating an Adult Equivalent Data File
The following example presents a partial set of SPSS/PC commands used to assign AER values to
household members in a Honduran data set. The commands assign AER for adult males and non-
pregnant, non-lactating females. Similar commands can be created for all possible groupings of
age/sex/physiological status/activity level for which there are separate AER calculations.
If (AGE ge 18 and AGE lt 30) and SEX = 1 and STAT = 1 and ACT = 1)AER = 1.374.
If (AGE ge 18 and AGE lt 30) and SEX = 1 and STAT = 1 and ACT = 2)AER = 1.164.
If (AGE ge 18 and AGE lt 30) and SEX = 1 and STAT = 1 and ACT = 3)AER = 1.014.
If (AGE ge 18 and AGE lt 30) and SEX = 2 and STAT = 1 and ACT = 1)AER = .951.
If (AGE ge 18 and AGE lt 30) and SEX = 2 and STAT = 1 and ACT = 2)AER = .857
If (AGE ge 18 and AGE lt 30) and SEX = 2 and STAT = 1 and ACT = 3)AER = .815
Key:
AGE Age in years
SEX Gender
1 = Male 2 = Female
STAT Physiological status
1. Not pregnant nor lactating
2. Pregnant
3. Breastfeeding child < 6 mo.
4. Breastfeeding child >= 6 mo.
5. Pregnant and breastfeeding child < 6 mo.
6. Pregnant and breastfeeding child >= 6 mo.
ACT Activity level
1 = High
2 = Moderate
3 = Light
Once the AER has been calculated for each household member, an adult equivalent data file
(ADEQUIV.SYS) should be created for use during processing of the dietary intake data. The
ADEQUIV.SYS will contain one line per household, with the AER of all household members listed as
separate variables. To create this file, the household composition file (HHCOMP.SYS) needs to be
transposed.

Appendix 6: Using SPSS/PC to Calculate Household Calorie Intake
40
For example:
HHCOMP.SYS
HHID MEMID SEX AGE STAT ACT AER
11145121
12241421.071
13212130.799
21239110.962
22118111.355
23215220.963
2 4 2 9 1 3 0.737
31282130.736
41127121.164
42224220.973
HHID = Household ID
MEMID = Member ID
If (MEMID = 1) AECAL1 = AER.
If (MEMID = 2) AECAL2 = AER.
If (MEMID = 3) AECAL3 = AER.
If (MEMID = 4) AECAL4 = AER.
If (MEMID = 5) AECAL5 = AER.
** The number of “if statements” should equal the maximum number of household members in
the data set. In the example from Honduras, there were 24.
The result of the above set of commands is: HHCOMP.SYS
HHID MEMID SEX AGE STAT ACT AER AECAL1 AECAL2 AECAL3 AECAL4 AECAL5
111451211 . . . .
1 2 2 41 4 2 1.071 . 1.071 . . .
1 3 2 12 1 3 0.799 . . 0.799 . .
2 1 2 39 1 1 0.962 0.962 . . . .
2 2 1 18 1 1 1.355 . 1.355 . . .
2 3 2 15 2 2 0.963 . . 0.963 . .
2 4 2 9 1 3 0.737 . . . 0.737 .
3 1 2 82 1 3 0.736 0.736 . . . .
4 1 1 27 1 2 1.164 1.164 . . . .
4 2 2 24 2 2 0.973 . 0.973 . . .
The next step is to reduce HHCOMP.SYS from one line per household member to one line per household.
The SPSS/PC AGGREGATE command is used, with “household” as the break variable.

Appendix 6: Using SPSS/PC to Calculate Household Calorie Intake
41
Figure 4. Sample AER calculation (Kenya)
AgeMember
ID Name Sex Number Unit Physiological status
( 14 - 60 yrs only) Activity
level
11451 1
224216 2
1. Male
2. Female 1. Years
2.
Months
(children
< 1 year
only)
1. Not pregnant nor lactating
2. Pregnant
3. Breastfeeding (child < 6 mos.)
4. Breastfeeding (child >= 6 mo.)
5. Pregnant and breastfeeding
(child < 6 mo.)
6. Pregnant and breastfeeding
(child >= 6 mo.)
1. High
2.
Medium
3. Light

Appendix 6: Using SPSS/PC to Calculate Household Calorie Intake
42
Sample Adult Equivalent Ratio Calculation
Member
ID Weight
(Appendix
10) BMR calculation
BMR
cal/day
requiremen
t
Activity
level
multiplier
BMR
requirement
adjusted for
activity
level
Pregnancy/
lactation
requirement
cals/day
Total caloric
requirement
Cals/day
Adult
equivalent
caloric
requirement
Member
Adult
Equivalent
Ratio (AER)
for calories
1 59.1 (11.6 x 59.1) +
879 1565 2.10 3286 0 3286 2840 1.16
2 52.8 (8.7 x 52.8) + 829 1288 1.64 2113 285+500 2328 2840 .82
aggregate file ‘ADEQUIV.SYS’ / break HHID / AECAL1 = sum(AECAL1)
/ AECAL2 = sum(AECAL2) / AECAL3 = sum(AECAL3)
/ AECAL4 = sum(AECAL4) / AECAL5 = sum(AECAL5) ...etc...
**There will be as many variable creation subcommands as the maximum number of household members in the data set.
*The result of the above set of commands: ADEQUIV.SYS.
HHID AECAL1 AECAL2 AECAL3 AECAL4 AECAL5
1 1 1.071 0.799 . .
2 0.962 1.355 0.963 0.737 .
30.736. . . .
4 1.164 0.973 . . .

Appendix 6: Using SPSS/PC to Calculate Household Calorie Intake
43
II. Calculating Household Food Intake
This section details the data-processing steps necessary to convert raw food intake data into a summary
variable of calories consumed per adult equivalent for each household. Examples of SPSS/PC command
language for each step are included.
II.A. Dietary File
Once food intake data has been collected and entered, the data file should look like the one shown below.
In this file, henceforth referred to as the “Dietary File,” each row represents either an ingredient that the
household used for preparing a dish, or the dish itself. Therefore, the number of rows (lines of data) in the
file will equal the number of dishes prepared, plus the number of ingredients in each dish that the
household prepared the previous day. Thus if a household used sugar in three dishes, sugar should appear
three times in the data for that household.
Sample Dietary File
Line
#HHID
1Meal
2Abst1
..
Abst
N*
3
18M
418F
5AdM/F
#
6
Chl4/1
1#
7
Dnum
8Dish
9Ingr
10 Quan
11 Unit
12 Lquan
13 Lunit
14 Src
15
1 21 1 1 0 0 0 0 1 1003 1003 35 19 4 19 1
2 21 1 1 0 0 0 0 1 1003 1001 1300 6 0 0 2
3 21 1 1 1 0 0 0 2 1403 1403 900 6 0 0 0
4 21 1 1 1 0 0 0 2 1403 403 .00 0 0 0 1
5 21 1 1 1 0 0 0 2 1403 260 110 6 0 0 1
6 21 2 0 0 0 0 0 1 2170 2170 5 7 0 0 0
7 21 2 0 0 0 0 0 1 2170 170 5 7 0 0 12
8 21 2 0 0 0 0 0 1 2170 240 70 6 0 0 1
* The number of Absent Member variables (Abst1...AbstN) will equal the maximum number of
household members in
the data set.
# There will be separate variables for Male and Female adolescent guests; and for 0-4 and 5-11 year old
categories.
Note: the number of columns had to be limited in the interest of space and clarity of presentation.
Where the variable labels are:
Variables Labels
HHID Household ID
MEAL Number of eating occasions
ABS1, ABS2 ...ABSN Member1 absent from meal, Member2 absent from
meal, ..... MemberN absent from meal
18M Number of male guests 18 and over
18F Number of female guests 18 and over
ADM Number of adolescent male guests
ADF Number of adolescent female guests
CHL11 Number of child guests 5-11 yrs
CHL4 Number of child guests 0-4 yrs

Appendix 6: Using SPSS/PC to Calculate Household Calorie Intake
44
DNUM Dish number for this eating occasion
DISH Dish code
INGR Ingredient code (include form of ingredient)
QUAN Quantity prepared
UNIT Unit of quantity prepared
LQUAN Left over quantity
LUNIT Unit of left over quantity
SRC Source
In the dietary file, the lines in which the dish and the ingredient have the same code are referred to as
“dish” lines. Line numbers 1, 3, and 6 in the dietary file shown are dish lines. A dish line is followed by
one or more ingredient lines, depending on the number of ingredients used in the preparation of a dish. In
the example, line 2 in the dietary file is an ingredient line; in this line the ingredient and the dish have
different codes. A dish line separates one dish from the next. For example, line 3 separates dish 1003
from dish 1403.
The first step in preparing the data for analysis is to label the dish and ingredient lines by putting a flag on
each line, since calories will be computed only for the ingredient lines. The flags also help to identify
dishes that do not have ingredients listed after them and dishes without recipes. The following SPSS/PC
commands are used to separate the dish and ingredient lines:
Do if (DISH = INGR)
Compute LINETYP = 1 *(dish line)
Else
Compute LINETYP = 2 *(ingredient line.
End if
Variable labels LINETYP ‘dish or ingredient’
Value labels LINETYP 1 ‘dish’ 2 ‘ingredient’
As a result of the above command, each line of data in the file will have a variable LINETYP, which will
be either 1 or 2, depending upon whether it is a dish or an ingredient line (see Appendix 11).
The next step is to ensure that the data are sorted by HHID, MEAL, DNUM, and LINETYP, so that the
data are in the correct order; meals are ordered by the number of eating occasion or hour; dishes at each
meal are ordered by dish number; and the ingredients in each dish follow the dish line to which they
belong.
sort HHID MEAL DNUM LINETYP
II.B. Convert Ingredient Quantity to a Standard Weight
At the time of data collection, the ingredients used to prepare food may have been measured using a
number of different units (milliliters, pounds, units, etc.). These measures have to be converted into a
uniform standard weight (grams in this example) before nutritional values can be calculated. In the
dish/ingredient coding system used, the ingredient (INGR) variable, includes codes for type (e.g., corn)
and form (e.g., boiled) of the ingredient. Ingredients are coded using a four-digit code in which the first
digit corresponds to the form, and the last three digits to the type of ingredient (referred to as PRODUCT).
In order to assign a standard weight to the quantity of a specific type of ingredient used in a certain form,
two new variables are created from the INGR variable, so that the type and form for each ingredient can
be easily distinguished. The following SPSS/PC commands are used to separate the FORM from the
PRODUCT in an ingredient code.

Appendix 6: Using SPSS/PC to Calculate Household Calorie Intake
45
Compute PRODUCT = INGR - 1000 * trunc(INGR/1000)
Compute FORM = trunc((INGR-PRODUCT)/1000)
The dietary file (Appendix 12) now has information on the type of ingredient, its form, the unit of
measure, and the quantity of that unit prepared in the household. To convert the quantities of ingredients
measured in different units into a common unit (such as grams), a standard weight conversion file is used.
This file contains information on the equivalent weight (WGTFACT) in raw edible product of one unit of
measure for each form of the products in the data file. The sample file below has weight in grams of the
raw product (WGTFACT), for dry corn kernels (PRODUCT = 1) in three forms: raw, cooked and ground
(FORM = 0 or 1 or 8), measured in two units, pounds or milliliters (UNIT = 1 or 6). Note that
WGTFACT for cooked ingredients (e.g., 1 milliliter of cooked corn (line 3) calculates the weight of the
equivalent in raw product, not the weight per milliliter of cooked product. WGTFACT is in essence
carrying out 2 conversions: it converts the volume of a cooked (or ground etc.) product to its equivalent
volume of raw product, and then converts that raw volume to weight. This facilitates subsequent
calculation of the total amount consumed of each product.
Sample Standard Weights File
Line # PRODUCT FORM UNIT WGTFACT
1 1 0 1 453.59
2 1 0 6 0.91
3 1 1 6 0.60
4 1 8 1 480.81
5 1 8 6 0.57
The weight conversion file (INGRDWGT.SYS) is matched with the dietary file (DIETARY.SYS) by
PRODUCT, FORM and UNIT to insert the appropriate weight conversion factor (WGTFACT) in each
ingredient line. The total weight (WGT) of the PRODUCT used is then calculated by multiplying the
quantity (QUAN) of PRODUCT by WGTFACT. Appendix 13 shows a dietary file after these steps have
been taken.
Join match file ‘DIETARY.SYS’ /table ‘INGRDWGT.SYS’/by PRODUCT FORM UNIT.
Compute WGT = QUAN * WGTFACT.
II.C. Obtaining Recipes for Dishes with No Recipes
The interviewer will not obtain recipes for dishes consumed by the households when the food was a
leftover, a gift, or purchased outside the home for consumption in the home. Dishes with no recipe need
to be identified before proceeding further with the analysis. The following SPSS/PC commands can be
used to identify dishes that are not followed by any ingredient lines, which are those without recipes (see
Appendix 14).
Create LINETY_N = lead (LINETYP,1) * Create a variable LINETY_N
whose value is equal to the value of
the LINETYP variable in the next
case.
Variable label LINETY_N ‘value of linetyp for next case’
Compute NORECIPE = 0
If (LINETY_N = 1 and LINETYP = 1) NORECIPE = 1 * If the case with
LINETYP= 1 (dish line) is
followed by another dish line

Appendix 6: Using SPSS/PC to Calculate Household Calorie Intake
46
(LINETY_N = 1), it should be
marked as a case where dish has
no recipe.
Value label NORECIPE 0 ‘dish has recipe’ 1 ‘dish has no recipe’
Dishes that would not normally have ingredients must be excluded from the list of dishes with no recipes.
For example, a ripe banana or a slice of cheese would be “dishes” with no ingredients. This can be done
by listing the codes of DISH for all dishes with no recipe, and then manually selecting out those would
not be expected to have a recipe. For these codes, the nutritional value for the dish line itself will be
computed. LINETYP for these dishes should be recoded to 2, to flag these “dish-same-as-ingredient”
lines. For example, a ripe banana would have a DISH code of 0100. To recode LINETYP:
If (DISH=0100)LINETYP=3
Average recipes need to be calculated for dishes that have no recipe in the data, so that nutritional values
can be computed. Recipes are imputed either from the household itself or from the next level of sampling,
such as the cluster. Average recipes from the cluster or domain level can be used when household recipes
are not available. The program used for imputing the recipes, provided in Appendix 15, is complex and
lengthy. It requires that the different units in which the foods are measured be converted into standard
weights.
II.D. Accounting for Leftovers
At the time of data collection, information was obtained on the quantities left over from each dish
(LQUAN). In order to be able to subtract the leftover quantities from the total amount of dish prepared, it
is important for the interviewer to ensure that the leftovers are measured in the same units as the dish
itself. The fraction of dish left over is computed, and deducted from 1 to get the fraction of dish consumed
by the household.
Compute LFRAC = LQUAN/QUAN *Compute fraction left over
Compute CFRAC = 1-LFRAC *Compute fraction consumed
Variable label LFRAC ‘fraction left over’/
CFRAC ‘fraction consumed’
Since information on leftover quantities and, therefore, fraction consumed (CFRAC), is available only on
the dish line, it next has to be copied onto each of the INGR lines for that dish.
If (linetyp = 2) CFRAC = lag (CFRAC) * If the line is an ingredient line
(LINETY = 2), set fraction
consumed, CFRAC to be the same
as CFRAC for the previous case
The fraction of the dish consumed is then multiplied with the WGT of PRODUCT used in the DISH to
come up with the net amount (WGT1) of PRODUCT (see Appendices 15A and 15B).
Compute WGT1=WGT * CFRAC
Variable label WGT1 ‘net grams of ingredient’
This step should be taken after the recipes for dishes with no recipes have been imputed (see
Appendix 16).

Appendix 6: Using SPSS/PC to Calculate Household Calorie Intake
47
II.E. Computing Number of Adult Equivalents That Ate Each Dish
The dietary file contains information on the ID of household resident members who were not present at
the meal, as well as on guests who ate a particular dish. An adult equivalent has been computed for each
member, based on age, gender, physiological status, and activity level (see section I.). This information is
in the ADEQUIV.SYS file, which contains the household ID code and adult equivalent values for each of
the household members in the data. That file presents the data in the form shown Appendix 17. The adult
equivalent file is then matched with the dietary file, to include the adult equivalent information for each
member of the household in the dietary file. The sum of the adult equivalents for all members of the
household gives us the total adult equivalent number for the household.
Compute TOTADEQ = sum (AECAL1, AECAL2....)
Variable label ‘total number of adult equivalents in a household’
The next step is to calculate the number of adult equivalents who ate each dish. The dietary file contains
information on the ID of household members who did not eat a meal. The adult equivalent values for
these members are summed to get the total value of adult equivalents not eating a meal.
For example, let AECAL1, AECAL2... be the adult equivalent values for household member IDs 1,2....
and ABAECA1, ABAECA2.... be the adult equivalent values for the household members (IDs 1,2...)
absent from a meal. The adult equivalent value for each member is available from the adult equivalent
file, which was matched with the dietary file in the previous step. Next, if a member was absent from a
meal, the value for absent adult equivalent is set to be equal to the adult equivalent value for that member.
If (ABST1 = 1) ABAECA1 = AECAL1 * Find the adult equivalent values
If (ABST2 = 1) ABAECA2 = AECAL2 for IDs 1 and 2 (and all possible IDs). Note:
the absent adult equivalent is calculated only if
the member was not present at a meal and did
not take food for that particular meal from the
household to consume outside the household.
Compute TABSADEQ = sum (ABAECA1, ABAECA2 ......) * summing to get total
hh adult equivalents absent
Variable label TABSADEQ ‘total number of adult equivalent absent from a meal’
Next, calculate adult equivalents for guests. Weighted average adult equivalent ratios are calculated for
each guest age/sex category, based on population distribution by age and sex in the country. (See
Appendix 18 for population distributions by age and sex and Appendix 19 for a sample calculation of
weighted adult equivalent values for each guest category for Honduras). The weighted AERs for guests
are multiplied by the number of guests in each category, then summed to get total guest adult equivalents
who have eaten that dish (TGSTADEQ).
If (18M ge 1) GSTCAL1 = (18M * .970) *Using Honduras weighted average
If (18F ge 1) GSTCAL2 = (18F * .728) guest AERs from Appendix 19
If (ADM ge 1) GSTCAL3 = (ADM * .872) example
If (ADF ge 1) GSTCAL4 = (ADF * .743)
If (CHL11 ge 1) GSTCAL5 = (CHL11 * .642)
If (CHL4 ge 1) GSTCAL6 = (CHL4 * .445)
Compute TGSTADEQ = sum (GSTCAL1, GSTCAL2, *Sum of total guest adult
GSTCAL3, GSTCAL4, GSTCAL5, GSTCAL6). equivalents eating a meal

Appendix 6: Using SPSS/PC to Calculate Household Calorie Intake
48
The number of adult equivalents who have eaten a dish (DSHADEQ) can then be calculated by
subtracting adult equivalents absent from a meal (TABSADEQ) from total household adult equivalents
(TOTADEQ), and then adding guest adult equivalents (TGSTADEQ) to the result.
Compute DSHADEQ = TOTADEQ + TGSTADEQ - TGSTADEQ
The data file at this stage will look like the one shown in Appendix 20.
II.F. Calculating Nutritional Content
Nutritional values can be calculated once all of the measured ingredients in the data have been assigned
net weight consumed. Nutritional values of foods can be obtained from local or international sources.12 It
is important to keep track of different sources of nutritional values used, as there tend to be large
differences in reported values. Nutritional values are computed only for the ingredient lines, except in the
cases of dishes that do not normally have recipes, such as ripe bananas and cheese. Nutritional value data
can be prepared in several ways. It can either be in the form of a data file that can be matched with the
dietary file, or it can be written in the form of command language, as shown in Appendix 21. Either way,
once a conversion factor for nutrients (CALCON) is added to each line of data, the ingredient lines
(LINETYP = 2) are selected, and the nutritional value calculated.
If (LINETYP = 2) CAL = CALCON * WGT1 *If data line is for an ingredient, calculate
calories
Dishes that do not normally have recipes need to be selected, and the nutritional value for the dish lines
(LINETYP = 1) must be calculated.
If (PRODUCT = 100 and LINETYP = 1 and NORECIPE = 1) CAL = WGT1 * CALCON
The data (see Appendix 22) are then aggregated to calculate the total amount of calories per dish
consumed at the household level (DSHCAL).
Aggregate outfile = *
/break = HHID DAY MEAL DISH
/DSHCAL = sum (CAL)
/DSHADEQ = first (DSHADEQ)
This aggregated file now has dishes as a case; that is, one line of data will represent a single dish
consumed by the household (see Appendix 23). Using this aggregated file, DSHCAL is divided by
DSHADEQ to compute calories per adult equivalent obtained from each dish (DSHCALAE).
Compute DSHCALAE = DSHCAL/DSHADEQ
II.G. Calculating Household Calorie Consumption
At this stage information is available on the number of calories per adult equivalent obtained from each
dish that the household consumed. The next step is to aggregate the calories obtained from different
dishes consumed, and calculate the total number of calories per adult equivalent obtained during the 24-
hour recall period (DAYCALAE).
12A comprehensive list of food composition tables for most regions can be obtained from the International Network of Food
Systems (INFOODS) at http://www.crop.cri.nz/foodinfo/infoods /infoods.htm, or via email to infoods@crop.cri.nz.

Appendix 6: Using SPSS/PC to Calculate Household Calorie Intake
49
Aggregate outfile = *
/break = HHID
/DAYCALAE = sum (DSHCALAE)
A row in the resulting file contains the sum of calories per adult equivalent for the day of recall for each
household (see Appendix 24).
II.H. Average Daily Caloric Contribution from Breast Milk
Using the breastfeeding status of women, an estimation of the nutritional contribution from breast milk in
the diets of children should be added to the daily calories at this stage, because the amount of breast milk
consumed is usually estimated on a daily basis. Since surveys of this nature only collect information on
whether a woman is breastfeeding a child, the analysis is usually limited to computing average calories
obtained from breast milk for different age groups. The average amount of milk produced and the average
nutritional value of milk for different age groups can be obtained from literature for a similar ethnic,
cultural, and socioeconomic population.
In this example from Honduras, the data included children up to four years of age who were reported to
be breast-fed. It was decided that the contribution from breast milk would be computed for children who
were 18 months or younger, since that was the reported average duration of breastfeeding among children
in Honduras. Although children over this age may have been receiving some caloric contribution from
breast milk, it is more likely that after 18 months the actual intake of breast milk for most children was
limited, thus diminishing its nutritional contribution for these older children. The values noted below
were used to estimate the average number of calories derived from breast milk, based on average amounts
secreted and average nutritional value of breast milk for different age groups. These values, derived from
a low-income, rural Guatemalan sample, were obtained from a joint World Health Organization/Food and
Agriculture Organization report on breastfeeding.13
Households with a breastfeeding woman are identified using information from the household composition
file. The nutritional contribution of breast milk should be computed for the youngest child. Variables
needed for computing the caloric contribution of breast milk to the household calories include household
id (HHID), youngest child’s age in years (AGE), and adult equivalent value for the youngest child
(ADLTEQ).
If (AGE le .0833) BMCAL = 305
If (AGE gt .0833 and AGE lt .25) BMCAL = 344
If (AGE = .25) BMCAL = 384
If (AGE gt .25 and AGE lt .5) BMCAL = 389
If (AGE = .5) BMCAL = 337
If (AGE gt .5 and AGE lt .75) BMCAL = 341
If (AGE = .75) BMCAL = 344
If (AGE gt .75 and AGE lt 1.25) BMCAL = 341
If (AGE = 1.25)BMCAL = 339
If (AGE gt 1.25 and AGE lt 1.5) BMCAL = 332
If (AGE = 1.5) BMCAL = 325
13 WHO. The quantity and quality of breast milk, report on the WHO collaborative study on Breast Feeding. Geneva,
Switzerland: World Health Organization, 1985.

Appendix 6: Using SPSS/PC to Calculate Household Calorie Intake
50
The BMCAL (calories from breastmilk) variable is divided by the adult equivalent for the breastfeeding
child, to get the BMCALAE variable. From the above file, save HHID and BMCALAE to a file and
match them with the dietary file. In the dietary file, add the new variable BMCALAE to DAYCALAE to
get the total calories per adult equivalent (including breast milk) DAYCALA1 consumed by the
household.
III. Calculate Percentage of Caloric Adequacy
Once the average number of calories consumed per adult equivalent by each household in the sample has
been computed, it is compared to the calorie requirement of an adult equivalent to calculate the level of
caloric adequacy. The daily calorie requirements for an adult equivalent for different countries are
presented in Appendix 8. When the level of calorie requirement for an adult equivalent has been
established (for example, 2858 for Honduras), the average calories consumed per adult equivalent
(AVECALAE) is divided by the number of calories required, to compute the level of caloric adequacy. In
the Honduran example, the level of caloric adequacy (CALADEQ) of a household will be computed as:
Compute CALADEQ = (AVECALAE / 2858) *100
Variable label CALADEQ ‘% calorie adequacy’
The final step is to determine the percent of households that are at or above 100 percent of caloric
requirements.
If (CALADEQ ge 100)REQSMET = 100
If (CALADEQ lt 100)REQSMET = 0
Variable label REQSMET ‘Household meets caloric requirements’
Value labels REQSMET 100 ‘yes’ 0 ‘no’
For convenience, the code “100,” rather than “1,” is assigned to households meeting caloric requirements,
so that the average of the REQSMET variable over a group of interest will directly indicate the percent of
households meeting caloric requirements.14
For purposes of analysis it is often useful to categorize households into various levels of caloric adequacy
(Appendix 25).
If (CALADEQ le 60)CALCAT = 1
If (CALADEQ gt 60 and CALADEQ le 80) CALCAT = 2.
If (CALADEQ gt 80 and CALADEQ le 100) CALCAT = 3
If (CALADEQ gt 100 and CALADEQ le 120) CALCAT = 4
If (CALADEQ gt 120) CALCAT = 5
Variable label CALCAT ‘calorie adequacy category’
Value label CALCAT 1 ‘<= 60%’ 2 ‘60 - 80%’ 3 ‘80-100%’ 4 ‘100 - 120%’
5 ‘>120%’
14If a code of 1 were used, the average of REQSMET would give the proportion rather than the percent of households.

Appendix 7: Row Numbers for FAO Member Countries
51
APPENDIX 7. ROW NUMBERS FOR FAO MEMBER COUNTRIES
Country Row
#Country Row # Country Row
#
Africa
Algeria
Angola
Benin
Botswana
Burkina Faso
#Burundi
Cameroon
Cape Verde
C.A.R.
Chad
Comoros
Congo
*Côte d’Ivoire
Equatorial Guinea
*Ethiopia
Gabon
Gambia
Ghana
Guinea
Guinea-Bissau
Kenya
Lesotho
*Liberia
Madagascar
Malawi
Mali
Mauritania
#Mauritius
Morocco
*Mozambique
Namibia
Niger
Nigeria
#Rwanda
*Senegal
Sierra Leone
*Somalia
*Sudan
Swaziland
*Tanzania
Togo
*Tunisia
Uganda
Zaire
Zambia
Zimbabwe
55
3
3
3
4
1
6
4
6
8
11
6
3
8
2
3
4
3
3
3
11
11
4
6
11
8
8
5
54
6
11
8
4
7
8
4
9
10
11
11
3
54
11
6
3
3
Latin America/Caribbean
*Argentina
Barbados
*Bolivia
*Brazil
*Chile
*Colombia
*Costa Rica
*Cuba
Dominican Republic
Ecuador
El Salvador
*Guatemala
#Guyana
*Haiti
Honduras
*Jamaica
*Mexico
Nicaragua
*Panama
Paraguay
Peru
Surinam
#Trinidad and Tobago
*Uruguay
*Venezuela
Asia
*Bangladesh
Bhutan
Cambodia
*China
*India
*Indonesia
*Japan
Laos
*North Korea
#Malaysia
Mongolia
*Myanmar
Nepal
Pakistan
*Philippines
Sri Lanka
South Korea
*Thailand
Vietnam
36
45
37
38
39
40
41
42
42
39
43
43
20
44
43
45
46
43
47
36
39
48
48
49
50
12
14
18
14
15
16
17
18
18
19
14
13
14
15
21
15
18
22
22
Near East
Afghanistan
Bahrain
*Egypt
Iran
Iraq
*Jordan
Kuwait
*Lebanon
Libya
Oman
Qatar
Saudi Arabia
Syria
U.A.E.
Yemen, Arab Rep.
Yemen, P.D.R.
South Pacific
#Fiji
*Papua New Guinea
15
52
51
52
52
52
51
53
51
54
54
51
53
54
54
54
59
61
* Original data # Combined data from more than one study

Appendix 8: Daily Calorie Requirement for an Adult Equivalent
52
APPENDIX 8. DAILY CALORIE REQUIREMENT FOR AN ADULT
EQUIVALENT
An adult equivalent is defined as an adult male, 30-to-60 years old, of average weight and height
for the country, with moderate activity level. For country-specific adult equivalent requirements,
refer to Appendix 7 and identify the relevant Row number for this Appendix 8 table, where the
relevant adult equivalent figures will be found.
Row
number Weight (kg) adult
equivalent Daily caloric
requirement for
an adult
equivalent
Row
number Weight (kg) adult
equivalent Daily caloric
requirement for
an adult
equivalent
1 57.5 2752 32 72.5 3062
2 55.6 2713 33 71.4 3039
3 64.6 2898 34 72.5 3062
4 58.2 2766 35 72.8 3068
5 60.2 2808 36 69.2 2993
6 62.9 2863 37 57.7 2756
7 57.4 2750 38 59.1 2785
8 60.5 2814 39 58.2 2766
9 56.5 2731 40 57.5 2752
10 58.2 2766 41 60.7 2818
11 59.1 2785 42 61.1 2826
12 53.1 2661 43 60.0 2804
13 53.9 2678 44 62.6 2857
14 55.4 2709 45 66.9 2946
15 51.1 2620 46 61.1 2826
16 55.7 2715 47 63.0 2865
17 62.5 2855 48 62.5 2855
18 58.0 2762 49 67.5 2958
19 55.6 2713 50 57.5 2752
20 62.1 2847 51 61.7 2839
21 53.9 2678 52 57.5 2752
22 58.8 2779 53 67.2 2952
23 78.5 3185 54 61.4 2832
24 69.2 2993 55 72.1 3053
25 71.2 3035 56 72.9 3070
26 76.3 3140 57 78.1 3177
27 75.0 3113 58 70.0 3010
28 71.2 3035 59 68.1 2971
29 71.2 3035 60 69.3 2996
30 61.9 2843 61 59.2 2787
31 77.2 3159 62 70.4 3018
The formula used to calculate the daily caloric requirements for an adult equivalent in this table
has changed from previous versions. This version used the formula as presented in Appendix 6.

Appendix 9: Calorie Requirements for Children Under 10 Years of Age, by Sex
53
APPENDIX 9. CALORIE REQUIREMENTS FOR CHILDREN UNDER 10
YEARS OF AGE, BY SEX
Table 1. Children Under 6 Months of Age Calorie requirement
per day*
Age
(months) Calorie
Requirement
(per kg per day) Male Female
< 1 124 470 445
1 < 2 116 550 505
2 < 3 109 610 545
3 < 4 103 655 590
4 < 5 99 695 630
5 < 6 96.5 730 670
* Based on NCHS median weights at mid-point of month.
Source: WHO, 1985, Energy and Protein Requirements: Report of a Joint
FAO/WHO/UNU Expert Consultation, Geneva, World Health Organization, p.
91.
Table 2. Children 6 Months to 2 Years of Age
Calorie requirement per
kg per day* Calorie requirement per
day#
Age
(years) Male Female Male Female
.5 < .75 109 109 850 784
.75 < 1 109 109 1003 937
1 < 1.5 108 113 1102 1074
1.5 < 2 108 113 1242 1220
* Includes allowance for infection and desirable activity level.
# Based on NCHS median weights at mid-point of age range. Source: WHO, 1985,
Energy and Protein Requirements: Report of a Joint FAO/WHO/UNU Expert
Consultation. Geneva, World Health Organization, p.180.
Source: W.P.T. James and E.C. Schofield, 1990, Human Energy Requirements: A
Manual for Planners and Nutritionists, Oxford, Oxford Medical Publications, p. 74.
Table 3. Children 2-5 Years of Age
Calorie requirement per
kg per day* Calorie requirement per
day#
Age
(years) Male Female Male Female
2 < 3 104 102 1410 1310
3 < 4 99 95 1560 1440
4 < 5 95 92 1690 1540
5 < 6 92 88 1810 1630
* Based on NCHS median weights at mid-point of year.
# Based on estimated average daily energy intakes from data of Ferro-Luzzi & Durnin +
5 percent for desirable activity level.
Source: WHO, 1985, Energy and Protein Requirements: Report of a Joint
FAO/WHO/UNU Expert Consultation. Geneva. World Health Organization, pp. 94-95.

Appendix 9: Calorie Requirements for Children Under 10 Years of Age, by Sex
54
Table 4. Children 6-9 Years of Age
Calorie requirement per
day*
Age
(Years) Male Female
6 < 7 1822 1619
7 < 8 1901 1657
8 < 9 1948 1711
9 < 10 2023 1767
* Based on estimated average daily energy intakes
from data of Ferro-Luzzi & Durnin + 5 percent for
desirable activity level.
Source: WHO, 1985, Energy and Protein
Requirements: Report of a Joint FAO/WHO/UNU
Expert Consultation, Geneva, World Health
Organization, pp. 94-95.

Appendix 10: Average Weight by Age and Sex by FAO Member Countries (in kilograms)
55
APPENDIX 10. AVERAGE WEIGHT BY AGE AND SEX FOR FAO
MEMBER COUNTRIES (IN KILOGRAMS)
Note: See Appendix 7 to identify relevant row number for country of interest.
The growth curves provided in below are not newly developed local standards, but simply currently
available data from single studies made within some of the listed countries. The data sets vary in size and
quality; some are the result of national surveys and others are taken from surveys on smaller communities
within a country. Sampling techniques vary, and in many cross-sectional surveys, sample sizes have
changed from year to year, thus affecting the consistency of the growth curves which is shown by wide
fluctuations in percentile values between age bands. For comparative purposes, and for use in contexts
where no local data are available, the curves have been modified as described below. They therefore can
only be considered as ‘best estimates’ rather than statistically representative national data sets. Hence it is
recommended that, where possible, local data should be used rather than values provided in the following
paragraphs.
I. 0-17+ Years
1. Weight and height data for groups aged 0-17+ years have been used from a variety of sources,
currently gathered together by the Food Policy and Nutrition Division, FAO.
2. For comparative purposes the weight and height curves have been smoothed, matched with the
NCHS standards, and expressed as percentiles. To prevent bias, all measurements were allocated
to the nearest main percentile (i.e., 3rd, 5th, 10, 20, 30, 40 , 50, 60, 70, 80, 90, 95 and 97th
percentiles).
3. Thus a series of 62 modified curves has been established which is provided in Appendix 8.
II. Adult data
1. Complete growth curves covering the whole life span are available for only a few countries.
Therefore some established characteristics of growth and anthropometry had to be used when
estimating appropriate adult values. They are:
a. Females are regarded as having reached their maximum growth potential by 18 years.
b. In well nourished male populations full growth may be achieved by 18 years, but in less well
nourished populations, growth may continue for another 4-5 years so, in the absence of data,
heights must be derived.
c. A commonly observed feature of the relationship between male and female height is that in
many populations females are approximately 7 percent shorter than males. This relationship
was therefore used to obtain adult male heights.
d. The body mass index (BMI), which expresses a relationship between weight and height
(Wt/Ht2) can be used to calculate an actual desirable weight from height.
2. Height. Where adult measurements are unavailable, the actual heights of females at 18 years has
been treated as the adult height. Male heights have been estimated by calculating a value 7
percent higher than that of the females.
3. Weight. Similarly, weights of females at 18 years have been treated as adult weights. Male
weights have been calculated using BMIs and the estimated heights and then applying an estimate
of the BMI.
Source of male BMIs:
a. Studies on adults from the country itself; or
b. In the absence of a study, appropriate BMIs from a nearby country have been applied; or

Appendix 10: Average Weight by Age and Sex by FAO Member Countries (in kilograms)
56
c. Where no data are available for LDCs, a BMI within the range of 19-21 has been selected.
This range was found to apply to the LDC adult data provided by Eveleth and Tanner.15
4. Patterns of weight change. Lean body mass does not in general increase over the age of 24
years, but total body weight does, with a consequential increase in BMI. This process generally
occurs in western societies and in the urban populations of some LDCs. Evidence from studies
in the U.S.A., the U.K. and Belgium suggest that an increment of 2 BMI points could be added to
adult weights in the 30-59 years age group in order to allow for the extra energy required to
maintain the actual body weight.
Source: James, W.P.T. and Schofield, E.C. (1990). Human Energy Requirements: A Manual for
Planners and Nutritionists. Oxford University Press, Oxford, p. 116-117.
15 Eveleth, P.B. and J.M. Tanner. International Biological Programme 8: Worldwide variation in human growth. Cambridge,
U.K.: Cambridge University Press, 1984.

Appendix 10: Average Weight by Age and Sex by FAO Member Countries (in kilograms)
57
Male Female
Row
#10
yrs++ 11
yrs+ 12
yrs+ 13
yrs+ 14
yrs+ 15
yrs+ 16
yrs+ 17
yrs+ Adult 10
yrs+ 11
yrs+ 12
yrs+ 13
yrs+ 14
yrs+ 15
yrs+ 16
yrs+ 17
yrs+ Adult
1 27.8 30.8 32.2 34.8 39.8 44.9 49.4 53.1 57.5 25.2 28.0 30.0 33.5 36.8 40.0 41.9 44.9 45.4
2 24.9 27.5 29.3 33.4 38.4 43.4 47.8 51.0 55.6 25.2 28.3 31.7 33.5 38.8 41.6 43.5 44.4 44.7
3 30.6 34.3 36.5 41.4 43.3 48.5 49.9 56.3 64.6 29.8 33.6 37.6 41.6 45.3 48.1 49.8 50.4 52.0
4 29.0 32.4 36.5 38.0 43.3 48.5 53.1 56.3 58.2 31.7 35.7 40.0 44.1 47.8 50.7 52.3 52.8 53.3
5 26.7 29.7 33.4 38.0 43.3 48.5 53.1 56.3 60.2 27.3 30.7 37.6 41.6 45.3 48.1 49.8 52.8 53.0
6 30.6 34.3 36.5 41.4 45.8 49.2 53.1 56.3 62.9 33.3 35.7 40.0 44.1 47.8 52.9 54.4 54.8 55.2
7 27.7 30.6 32.1 34.6 39.7 44.7 49.2 52.8 57.4 25.2 28.1 30.0 33.5 36.8 40.0 41.9 44.8 45.4
8 29.0 29.7 33.4 35.2 40.3 45.4 52.1 60.3 60.5 27.3 30.7 34.4 38.2 45.3 48.1 52.3 52.8 53.5
9 26.7 29.7 30.9 35.2 38.4 43.4 49.9 51.0 56.5 29.8 33.6 34.4 41.6 45.3 48.1 49.8 50.4 50.5
10 24.9 27.5 33.4 35.2 40.3 45.4 53.1 56.3 58.2 27.3 30.7 34.4 38.2 41.7 48.1 52.3 52.8 53.2
11 24.9 27.5 33.4 35.2 40.3 45.4 53.1 56.3 59.1 27.3 28.3 34.4 38.2 41.7 48.1 49.8 52.8 52.8
12 23.7 26.1 29.3 33.4 38.4 43.4 47.8 51.0 53.1 23.8 26.7 30.0 33.5 36.8 39.7 41.6 42.7 42.9
13 23.7 26.1 29.3 33.4 38.4 43.4 47.8 51.0 53.9 23.8 26.7 30.0 33.5 38.8 41.6 43.5 44.4 44.7
14 26.7 29.7 30.9 35.2 43.3 45.4 49.9 53.1 55.4 27.3 30.7 34.4 38.2 41.7 44.6 46.4 47.2 48.0
15 23.7 26.1 29.3 33.4 38.4 43.4 47.8 51.0 51.1 23.8 26.7 30.0 33.5 36.8 39.7 41.6 42.7 42.9
16 23.7 26.1 29.3 33.4 38.4 43.4 47.8 51.0 55.7 23.8 26.7 30.0 35.3 36.8 39.7 41.6 42.7 44.4
17 30.6 34.3 38.6 43.8 49.5 52.3 57.0 60.3 62.5 29.8 35.7 40.0 44.1 47.8 50.7 52.3 52.8 52.8
18 29.0 32.4 36.5 38.0 43.3 48.5 53.1 56.3 58.0 29.8 30.7 37.6 41.6 41.7 44.6 46.4 47.2 49.0
19 25.0 26.7 30.0 34.2 41.6 45.4 49.9 51.9 55.6 25.3 29.3 31.9 36.5 40.0 44.6 46.4 47.2 48.1
20 27.8 29.7 33.4 38.0 43.3 47.0 53.1 56.3 62.1 28.5 32.1 37.6 41.6 45.3 48.1 49.8 52.8 53.0
21 23.7 26.1 29.3 33.4 38.4 43.4 47.8 51.0 53.9 23.8 28.3 31.7 35.3 38.8 41.6 43.5 44.4 45.7
22 23.7 26.1 29.3 33.4 38.4 43.4 47.8 51.0 58.8 23.8 26.7 30.0 35.3 36.8 41.6 43.5 44.4 45.0
23 33.3 37.5 40.5 45.9 53.8 59.5 62.2 65.5 78.5 33.3 39.2 43.8 48.3 52.1 55.0 56.4 56.7 56.9
24 35.3 39.8 42.3 47.8 51.7 55.0 57.0 60.3 69.2 33.3 37.5 42.0 46.3 50.0 52.9 54.4 54.8 55.2
25 33.3 37.5 42.3 47.8 53.8 59.5 64.4 67.8 71.2 34.7 39.2 43.8 48.3 52.1 55.0 56.4 59.7 61.5
26 33.3 35.9 40.5 45.9 51.7 57.3 62.2 65.5 76.3 31.7 35.7 42.0 46.3 52.1 52.9 54.4 56.7 57.3
27 32.0 35.9 38.6 43.8 49.5 57.3 59.8 63.1 75.0 31.7 35.7 40.0 46.3 50.0 50.7 52.3 52.8 53.5
28 33.3 37.5 40.5 45.9 51.7 59.5 62.2 65.5 71.2 33.3 37.5 43.8 48.3 52.1 55.0 56.4 56.7 57.4
29 30.6 34.3 38.6 43.8 49.5 55.0 59.8 63.1 71.2 33.3 39.2 46.7 51.3 52.1 55.0 56.4 56.7 56.7
30 29.0 39.7 33.4 38.0 43.3 48.5 53.1 56.3 61.9 27.3 33.6 37.6 41.6 45.3 48.1 52.3 54.8 56.0
31 33.3 35.9 40.5 43.8 51.7 57.3 62.2 65.5 77.2 33.3 37.5 42.0 48.3 52.1 55.0 56.4 56.7 58.2
32 33.3 35.9 40.5 45.9 51.7 57.3 62.2 65.5 72.5 33.3 37.5 42.0 48.3 52.1 52.9 56.4 56.7 58.0

Appendix 10: Average Weight by Age and Sex by FAO Member Countries (in kilograms)
58
Male Female
Row
#10
yrs+ 11
yrs+ 12
yrs+ 13
yrs+ 14
yrs+ 15
yrs+ 16
yrs+ 17
yrs+ Adult 10
yrs+ 11
yrs+ 12
yrs+ 13
yrs+ 14
yrs+ 15
yrs+ 16
yrs+ 17
yrs+ Adult
33 35.3 37.5 42.3 47.8 53.8 57.8 62.2 65.5 71.4 34.7 39.2 43.8 48.3 50.0 52.9 54.4 54.8 56.4
34 33.3 37.5 42.3 45.9 51.7 55.0 59.8 60.3 72.5 33.3 37.5 42.0 46.3 52.1 55.0 56.4 56.7 58.0
35 32.0 35.9 40.5 45.9 51.7 57.3 59.8 63.1 72.9 33.3 37.5 43.8 48.3 52.1 55.0 56.4 56.7 56.7
36 35.3 37.5 42.3 45.9 51.7 57.3 59.8 63.1 69.2 34.7 39.2 43.8 46.3 50.0 52.9 54.4 54.8 55.6
37 29.0 32.4 33.4 38.0 40.3 43.4 49.9 53.1 57.5 27.3 30.7 34.4 38.2 45.3 48.1 52.3 52.8 53.0
38 30.6 32.4 33.4 35.2 40.3 48.5 53.1 56.3 59.1 29.8 35.7 37.6 41.6 45.3 48.1 49.8 50.4 50.9
39 30.6 32.4 36.5 41.4 46.9 52.3 53.1 56.3 58.2 31.7 35.7 40.0 41.6 47.8 48.1 49.8 50.4 51.0
40 30.6 32.4 36.5 41.4 43.3 48.5 53.1 56.3 57.5 25.2 28.3 31.7 35.3 38.8 44.6 49.8 50.4 50.9
41 29.0 32.4 36.5 41.4 46.9 48.5 53.1 56.3 60.7 29.8 33.6 37.6 41.6 45.3 48.1 49.8 50.4 50.5
42 29.0 32.4 36.5 38.0 43.3 48.5 53.1 56.3 61.1 29.8 33.6 37.6 41.6 45.3 48.1 49.8 50.4 50.5
43 29.0 32.4 33.4 38.0 40.3 43.4 49.9 53.1 60.0 27.3 30.7 34.4 38.2 45.3 48.1 52.3 52.8 53.0
44 32.0 35.9 38.6 43.8 46.9 52.3 53.1 60.3 62.6 33.3 37.5 43.8 46.3 50.0 52.9 54.4 54.8 55.1
45 26.7 29.7 33.4 35.2 38.4 43.4 49.9 53.1 66.9 27.3 30.7 34.4 41.6 45.3 48.1 49.8 52.8 53.2
46 30.6 32.4 36.5 41.4 46.9 52.3 53.1 56.3 61.1 31.7 35.7 40.0 41.6 47.8 48.1 49.8 50.4 51.0
47 30.6 32.4 36.5 41.4 43.3 48.5 53.1 56.3 63.0 25.2 28.3 31.7 35.3 38.8 44.6 49.8 50.4 52.0
48 28.1 29.7 33.4 38.0 43.3 46.6 53.1 56.3 62.5 28.8 32.4 37.6 41.6 45.3 48.1 49.8 52.8 53.0
49 30.6 34.3 38.6 47.8 49.5 55.0 57.0 60.3 67.5 33.3 35.7 40.0 46.3 50.0 52.9 56.4 56.7 57.6
50 26.7 29.7 33.4 38.0 40.3 48.5 53.1 56.3 57.5 29.8 33.6 37.6 41.6 45.3 48.1 49.8 50.4 52.8
51 29.0 32.4 36.5 41.4 46.9 52.3 57.0 60.3 61.7 29.8 33.6 37.6 44.1 50.0 52.9 54.4 56.7 56.7
52 26.7 29.7 33.4 35.2 40.3 45.4 49.9 53.1 57.5 25.2 28.3 31.7 38.2 41.7 48.1 52.3 54.8 55.7
53 30.6 34.3 38.6 43.8 46.9 55.0 57.0 60.3 67.2 31.7 33.6 40.0 44.1 47.8 52.9 54.4 54.8 56.1
54 29.0 29.7 33.4 38.0 43.3 48.5 53.1 56.3 61.4 27.3 33.6 37.6 41.6 45.3 48.1 52.3 54.8 56.0
55 35.3 39.8 45.0 50.7 56.9 59.5 64.4 67.8 72.1 37.0 41.9 46.7 51.3 52.1 55.0 56.4 56.7 56.8
56 30.6 32.4 38.6 43.8 46.9 52.4 57.1 60.3 72.9 31.7 35.7 40.0 44.1 47.8 50.7 52.3 54.8 55.9
57 33.3 37.5 42.3 47.8 53.8 59.5 64.4 67.8 78.1 34.7 39.2 43.8 48.3 52.1 55.0 56.4 56.7 56.7
58 33.3 37.5 42.3 45.9 51.7 57.3 62.2 65.5 70.0 33.3 37.5 43.8 48.3 50.0 52.9 54.4 54.8 55.3
59 27.9 31.1 33.4 38.0 43.3 48.5 53.1 58.3 68.1 28.6 32.2 37.6 41.6 46.6 50.5 53.1 56.4 56.8
60 35.3 39.8 45.0 50.7 56.9 57.3 59.8 63.1 69.3 34.7 39.2 43.8 48.3 50.0 52.9 56.4 56.7 56.7
61 23.7 26.1 29.3 33.4 38.4 43.4 47.8 53.1 59.2 23.8 26.7 30.0 33.5 36.8 39.7 43.5 47.2 47.3
62 35.1 38.9 43.2 47.5 52.4 57.3 62.1 65.6 70.4 34.5 38.6 42.4 47.1 50.3 52.2 52.8 54.0 61.4

Appendices 11, 12, 13, 14: Dietary Files
59
APPENDIX 11. DIETARY FILE
HHID
1Meal
2Abst1*
318M
418F
5AdM/F#
6Chl4/11#
7Dnum
8Dish
9Ingr
10 Quan
11 Unit
12 Lquan
13 Lunit
14 Src
15 Linetyp
16
21 1 1 0 0 0 0 1 1003 1003 35 19 4 A2 1 1
21 1 1 0 0 0 0 1 1003 1001 1300 6 0 0 2 2
21 1 1 1 0 0 0 2 1403 1403 900 6 0 0 0 1
21 1 1 1 0 0 0 2 1403 403 .00 0 0 0 1 2
21 1 1 1 0 0 0 2 1403 260 110 6 0 0 1 2
21 2 0 0 0 0 0 1 2170 2170 5 7 0 0 0 1
21 2 0 0 0 0 0 1 2170 170 5 7 0 0 12 2
21 2 0 0 0 0 0 1 2170 240 70 6 0 0 1 2
APPENDIX 12. DIETARY FILE
HHID
1Meal
2Abst1*
318M
418F
5AdM/F#
6Chl4/11#
7Dnum
8Dish
9Ingr
10 Quan
11 Unit
12 Lquan
13 Lunit
14 Src
15 Linetyp
16 Product
17 Form
18
21 1 1 0 0 0 0 1 1003 1003 35 19 4 19 1 1 3 1
21 1 1 0 0 0 0 1 1003 1001 1300 6 0 0 2 2 1 1
21 1 1 1 0 0 0 2 1403 1403 900 6 0 0 0 1 403 1
21 1 1 1 0 0 0 2 1403 403 .00 0 0 0 1 2 403 0
21 1 1 1 0 0 0 2 1403 260 110 6 0 0 1 2 260 0
21 2 0 0 0 0 0 1 2170 2170 5 7 0 0 0 1 170 2
21 2 0 0 0 0 0 1 2170 170 5 7 0 0 12 2 170 0
21 2 0 0 0 0 0 1 2170 240 70 6 0 0 1 2 240 0

Appendices 11, 12, 13, 14: Dietary Files
60
APPENDIX 13. DIETARY FILE
HHID
1Meal
2Abst1*
318M
418F
5AdM/F
#
6
Chl4/11
#
7
Dnum
8Dish
9Ingr
10 Quan
11 Unit
12 Lquan
13 Lunit
14 Src
15 Linety
p
16
Produc
t
17
For
m
18
Wgtfac
t
19
Wgt
20
21 1 1 0 0 0 0 1 1003 1003 35 19 4 19 1 1 3 1 33.92 1187.2
21 1 1 0 0 0 0 1 1003 1001 1300 6 0 0 2 2 1 1 .60 780
21 1 1 1 0 0 0 2 1403 1403 900 6 0 0 0 1 403 1 .# .
21 1 1 1 0 0 0 2 1403 403 .00 0 0 0 1 2 403 0 . .
21 1 1 1 0 0 0 2 1403 260 110 6 0 0 1 2 260 0 1.0886 119.74
6
21 2 0 0 0 0 0 1 2170 2170 5 7 0 0 0 1 170 2 1+ 5
21 2 0 0 0 0 0 1 2170 170 5 7 0 0 12 2 170 0 1+ 5
21 2 0 0 0 0 0 1 2170 240 70 6 0 0 1 2 240 0 1.166 81.62
# Coffee was not measured, as it does not contribute any calories to the diet.
+ For eggs, units are used instead of weights.
APPENDIX 14. DIETARY FILE
HHID
1Meal
2Dnum
3Dish
9Ingr
10 Quan
11 Unit
12 Lquan
13 Lunit
14 Src
15 Linetyp
16 Product
17 Form
18 Wgtfact
19 Wgt
20 Linetype_n
21 Norecipe
22
21 1 1 1003 1003 35 A2 4 A2 1 1 3 1 33.92 1187.2 2 0
21 1 1 1003 1001 1300 6 0 0 2 2 1 1 .60 780 1 0
21 1 2 1403 1403 900 6 0 0 0 1 403 1 .% . 2 0
21 1 2 1403 403 .00 0 0 0 1 2 403 0 . . 2 0
21 1 2 1403 260 110 6 0 0 1 2 260 0 1.0886 119.746 1 0
21 2 1 2170 2170 5 7 0 0 0 1 170 2 1+ 5 2 0
21 2 1 2170 170 5 7 0 0 12 2 170 0 1+ 5 2 0
21 2 1 2170 240 70 6 0 0 1 2 240 0 1.166 81.62 1 0
21 2 2 2040 2040 220 6 0 0 22 1 40 2 .3865 85.03 1 1
21 2 3 1403 1403 500 6 0 0 0 1 403 1 . . 2 0

Appendix 15: Imputing Average Recipes for Dishes without Recipes
61
APPENDIX 15. IMPUTING AVERAGE RECIPES FOR DISHES
WITHOUT RECIPES
To impute an average recipe for dishes without recipes in the data, start with the dietary file that has the
NORECIPE labels and weights (WGT) of ingredients converted into grams. The procedure described
below involves computing proportions of ingredients (by weight) used for preparing a certain amount of a
dish. First, recipes are calculated at the household level. If the household does not have a matching recipe,
the recipe should be calculated at the next level of sample stratification (for example, a cluster of
households, a block, or a region).
In the first step, the unit and quantity on the dish line is recoded as dish quantity (DSHQUAN) and dish
unit (DSHUNIT). This information is then copied onto all the ingredients belonging to that particular
dish. This information will be used for computing the ingredient proportions.
If (LINETYP = 1) DSHQUAN = QUAN
If (LINETYP = 1) DSHUNIT = UNIT
If (LINETYP = 2) DSHUNIT = lag (DSHUNIT)
If (LINETYP = 2) DSHQUAN = lag (DSHQUAN)
Then, the proportion of ingredients in each recipe (RECPROP) is calculated and aggregated to obtain a
mean recipe for households in the sample. RECPROP is aggregated on household id, dish id, dish unit,
and ingredient, to compute specific proportions for each unit of measurement of the dish. For example, if
bread was measured as a small loaf and a large loaf, specific proportions of flour and other ingredients
went into the preparation of small and large loaves. In this example the proportions are calculated based
on dish quantities, rather than dish weight, because the information on dish weight conversions in the
standard files was not complete.
Select if (NORECIPE = 0) *Select only those cases
If (DSHQUAN gt 0) and (LINETYP = 2) that have recipes
RECPROP = (WGT/DSHQUAN) *Use gross weight, which
aggregate outfile = * includes leftovers
/break = HHID DISH DSHUNIT INGR
/MRECPROP = mean(RECPROP)
Once the household-level average recipe proportions have been computed, the ingredients in each recipe
are numbered in consecutive order, in order to identify each ingredient in a recipe by a number, and to
know the maximum number of possible ingredients in any recipe in the data. The ingredient ordering
sequence does not matter (for fried eggs, oil could be numbered one and eggs numbered two, or vice
versa) as long as all ingredients in a recipe are identified by an ingredient number.
If (DISH = INGR) INGORD = 0
If (DISH ne INGR) INGORD = (lag(INGORD) + 1)
Var label INGORD ‘order of ingredient in a recipe’
Sort cases by HHID DISH DSHUNIT INGORD
Save outfile = ‘recprop.sav’
Ingredient number 1 for each dish is then saved in one file, ingredient number 2 in another file and so on.
This will enable the subsequent matching of the ingredients to their specific dishes in the file that contains
dishes with no recipes.

Appendix 15: Imputing Average Recipes for Dishes without Recipes
62
Get file = ‘recprop.sav’
Select if (INGORD = 1)
Sort case by HHID DISH DSHUNIT
Save outfile = ‘ing1.sav’
Get file = ‘recprop.sav’
Select if (INGORD = 2)
Sort case by HHID DISH DSHUNIT
Save outfile = ‘ing2.sav’
Get file = ‘recprop.sav’
Select if (INGORD = N)
Sort case by HHID DISH DSHUNIT
Save outfile = ‘ingN.sav’
Using the original dietary file, cases that do not have recipes are then selected to match the new recipes
with them.
Get file = ‘dietary.sav’
Select if (NORECIPE = 1)
Save outfile = ‘norecipe.sav’
Get file = ‘norecipe.sav’/drop = INGR LINETYP *Drop these, as we will be matching
new list of ingredients to these lines.
Sort cases by HHID DISH DSHUNIT
Save outfile = ‘norecipe1.sav’
Using the file just saved, match the different files containing the various ingredients with the recipes. The
output will be ingredient lines for different dishes for which recipe matches could be found. All
ingredients numbered 1 will be saved in one file, and all ingredients numbered 2 in the second file, and so
on.
Match file file = ‘norecipe1.sav’
/table = ‘ing1.sav’ /by HHID DISH DSHUNIT
/map
Sort cases by HHID DAY DSHNUM
Save outfile = ‘withrec1.sav’
Match file file = ‘norecipe1.sav’
/table = ‘ing2.sav’ /by HHID DISH DSHUNIT
/map
Sort cases by HHID DAY DSHNUM.
Save outfile = ‘withrec2.sav’
Match file file = ‘norecipe1.sav’
/table = ‘ingN.sav’ /by HHID DISH DSHUNIT
/map
Sort cases by HHID DAY DSHNUM
Save outfile = ‘withrecN.sav’

Appendix 15: Imputing Average Recipes for Dishes without Recipes
63
Files containing the ingredient lines are then added to the file containing no recipes. The ‘/BY’ qualifier
in the “add” command is used with the variables HHID DSHNUM so that each ingredient line is added
after the specific recipe to which it belongs.
Get file = ‘norecipe.sav’
Sort case by HHID DSHNUM
Save outfile = ‘norecipe.sav’
Add file file = ‘norecipe.sav’
/in = in0 *The in = in0 etc. allows us
/file = ‘withrec1.sav’ to put a flag on each line to
/in = in1 identify which file that particular
/file = ‘withrec2.sav’ line came from
/in = in2
/file = ‘withrecN.sav’
/in = inN
/by HHID DSHNUM.
Compute extra = 0
If (sysmis(INGR)) extra = 1 *There will be extra lines of data
because each dish will have the
maximum possible number of
ingredient lines added after it
Select if (extra = 0)
Save outfile = ‘hhrec.sav’
The file now has recipes for the dishes that had matches at the household level. To get the recipes for
others, the process is repeated for the next level of data stratificationCENTER, in this example. First,
the cases lacking matching household level recipes are separated out, using the commands that created the
NORECIPE variable.
Get file = ‘hhrec.sav’
Do if (DISH = INGR )
compute LINETYP = 1
Else
Compute LINETYP = 2
End if
Sort cases by HHID DSHNUM LINETYP
Create LINETY_N = lead (LINETYP, 1)
Var label LINETY_N ‘value linetyp nxt case’
Compute NOHHREC = 0
If (LINETY_N = 1 and LINETYP = 1) NOHHREC = 1
Var label NOHHREC ‘no hh recipe’
Value label NOHHREC 0 ‘with recipe’ 1 ‘no recipe’
Save outfile = ‘hhrec.sav’
Next, create average recipes at the cluster (or center) level.
Get file = ‘dietary.sav’
If (LINETYP = 1) DSHQUAN = QUAN
If (LINETYP = 1) DSHUNIT = UNIT
If (LINETYP = 2) DSHUNIT = lag(DSHUNIT)

Appendix 15: Imputing Average Recipes for Dishes without Recipes
64
If (LINETYP = 2) DSHQUAN = lag(DSHQUAN)
Select if (NORECIPE = 0).=
If (DSHQUAN gt 0) and (LINETYP = 2)
RECPROP = (WGT/DSHQUAN)
Aggregate outfile = *
/break = CENTER DISH DSHUNIT INGR
/CRECPROP = MEAN(RECPROP)
Save outfile = ‘crecprop.sav’
Once again, the ingredients in these average recipes are ordered.
Get file = ‘crecprop.sav’
Do if (DISH = INGR )
Compute LINETYP = 1
Else
Compute LINETYP = 2
End if
Sort cases by CENTER DISH DSHUNIT LINETYP
If (DISH = INGR) INGORD = 0
If (DISH ne INGR) INGORD = (lag(INGORD) + 1)
Sort cases by CENTER DISH DSHUNIT INGORD
Save outfile = ‘crecprop.sav’
The ingredients ordered number 1 for all recipes are saved in one file, and ingredients ordered number 2
in the second file, and so on.
Get file = ‘crecprop.sav’
Select if (INGORD = 1)
Sort case by CENTER DISH DSHUNIT
Save outfile = ‘ing1.sav’
Get file = ‘crecprop.sav’
Select if (INGORD = 2)
Sort case by CENTER DISH DSHUNIT
Save outfile = ‘ing2.sav’
Get file = ‘crecprop.sav’
Select if (INGORD = N)
Sort case by CENTER DISH DSHUNIT
Save outfile = ‘ingN.sav’
Using the file in which the household-level recipes were matched, separate out the dishes that still do not
have a recipe.
Get file = ‘hhrec.sav’/drop = in0 to extra wgt *Drop these variables, as this file will
be used to match the center-level
recipes, which will have new values
for these variables

Appendix 15: Imputing Average Recipes for Dishes without Recipes
65
Select if (NOHHREC = 1)
Save outfile = ‘nohhrece.sav’
Next, this file is prepared so that the ingredient lines can be matched to the dish line, by dropping the old
variables, for which there will be new values in the matched file.
Get file = ‘nohhrece.sav’/drop = INGR INGORD LINETYP
Sort cases by CENTER DISH DSHUNIT
Save outfile = ‘nohhrec1.sav’
Each file containing ingredients of the dishes is matched, one at a time.
Match file file = ‘nohhrec1.sav’
/table = ‘ing1.sav’ /by CENTER DISH DSHUNIT
/map.
Sort cases by CENTER DSHNUM
Save outfile = ‘withrec1.sav’
Match file file = ‘nohhrec1.sav’
/table = ‘ing2.sav’ /by CENTER DISH DSHUNIT
/map.
Sort cases by CENTER DSHNUM
Save outfile = ‘withrec2.sav’
Match file file = ‘nohhrec1.sav’
/table = ‘ingN.sav’ /by CENTER DISH DSHUNIT
/map.
Sort cases by CENTER DSHNUM
Save outfile = ‘withrecN.sav’
Get file = ‘nohhrece.sav’
Sort cases by CENTER DSHNUM
Save outfile = ‘nohhrece.sav’
Add file = ‘nohhrece.sav’
/in = in0
/file = ‘withrec1.sav’
/in = in1
/file = ‘withrec2.sav’
/in =in2
/file = ‘withrecNsav’
/in = inN
/by CENTER DSHNUM.
Compute EXTRA = 0
If (sysmis(INGR)) EXTRA = 1
Select if (EXTRA = 0)
Save outfile = ‘centrec.sav’
At the end of this step, once again separate out cases lacking center-level recipes, and repeat the iterations
as above for the next level of sample stratification (e.g., region). Once recipes have been found for all the
cases, the information in these files is added to the dietary file.

Appendix 15: Imputing Average Recipes for Dishes without Recipes
66
Get file = ‘dietary.sav’
Select if (NORECIPE = 0) *All dishes with recipe
Save outfile = ‘first.sav’
Get file = ‘hhrec.sav’/drop = IN0 to EXTRA WGT CENTER INGORD LINETY_N
select if (NOHHREC = 0) *Dishes with household recipes
If (LINETYP = 2)WGT = MRECPROP*DSHQUAN
Save outfile = ‘second.sav’
Get file = ‘centrec.sav’/drop= IN0 to EXTRA
CENTER INGORD LINETY_N
Select if (NOCREC = 0) *Dishes with center-level recipes,
last
level in this example
If (LINETYP = 2) WGT = CRECPROP*DSHQUAN
Save outfile = ‘third.sav’
If recipes were imputed at other levels of data stratification, those files should also appear here.
Next, the dishes that normally do not have recipes are selected and given a new LINETYP code so that
their nutritional values can be calculated.
Get file = ‘dietary.sav’
Select if (NORECIPE = 1)
Sort cases by HHID DSHNUM
Save outfile = ‘fourth.sav’
Match file file = ‘fourth.sav’
/table = ‘third.sav’/by = HHID DSHNUM *Last level at which the
/map. recipes were imputed
Select if (nocrec = 1). *Select those dishes for which we
did
not find any recipes
If (nocrec = 1 and (DISH = 100 or DISH = 139 or .......)) LINETYP = 3.
*Dishes that normally lack recipes
Save outfile = ‘c:\temp\fourth.sav’
Add file file = ‘first.sav’
/file = ‘second.sav’
/file = ‘third.sav’
/file = ‘fourth.sav’.
Save outfile = ‘recepall.sav’

Appendix 15A and 15B: Dietary File and Household Recipe Proporations
67
APPENDIX 15A. DIETARY FILE
HHID
1Meal
2Dnum
8Dish
9Ingr
10 Quan
11 Unit
12 Linetyp
16 Product
17 Form
18 Wgtfact
19 Wgt
20 Linety_n
21 Norecip
e
22
Dshqua
n
23
Dshunit
24
21 1 1 1003 1003 35 19 1 3 1 33.92 1187.2 2 0 35 19
21 1 1 1003 1001 1300 6 2 1 1 .60 780 1 0 35 19
21 1 2 1403 1403 900 6 1 403 1 . . 2 0 900 6
21 1 2 1403 403 .00 0 2 403 0 . . 2 0 900 6
21 1 2 1403 260 110 6 2 260 0 1.0886 119.74
61 0 900 6
21 2 1 2170 2170 5 7 1 170 2 1 5 2 0 5 7
21 2 1 2170 170 5 7 2 170 0 1 5 2 0 5 7
21 2 1 2170 240 70 6 2 240 0 1.166 81.62 1 0 5 7
21 2 2 2040 2040 220 6 1 40 2 .3865 85.03 1 1 220 6
21 2 3 1403 1403 500 6 1 403 1 . . 2 0 500 6
APPENDIX 15B. HOUSEHOLD RECIPE PROPORTIONS
HHID
1Dish
2Dshunit
3Ingr
4Mrecprop
5Ingord
21 1003 19 1003 33.92 0
21 1003 19 1001 22.28 1
21 1403 6 1403 . 0
21 1403 6 403 . 1
21 1403 6 260 .13305 2
21 2170 7 2170 1 0
21 2170 7 170 1 1
21 2170 7 240 16.324 2
21 2040 6 2040 .3865 0
21 2040 6 040 .2272 1
21 2040 6 240 .04545 2
21 1403 6 1403 . 0

Appendix 15A and 15B: Dietary File and Household Recipe Proporations
68
To show the computation steps, the proportions in this table were computed assuming that every time the household prepared the above dishes,
they used the recipes given in Appendix 15-A. In practice, this may not be the case.
File containing ingredient number 1 from all recipes ING1.SAV
HHID
1Dish
2Dshunit
3Ingr
4Mrecprop
5Ingord
6
21 1003 19 1 22.28 1
21 1403 6 403 . 1
21 2170 7 170 1 1
21 2040 6 40 .2272 1
File containing ingredient number 2 from all recipes ING2.SAV
HHID
1Dish
2Dshunit
3Ingr
4Mrecprop
5Ingord
21 1403 6 260 .13305 2
21 2170 7 240 16.324 2
21 2040 6 240 .04545 2
File containing dishes with no recipes NORECIPE.SAV
HHID
1Meal
2Dnum
8Dish
9Ingr
10 Quan
11 Unit
12 Linetyp
16 Product
17 Form
18 Wgtfact
19 Wgt
20 Linety_n
21 Norecipe
22 Dshquan
23 Dshunit
24
21 2 2 2040 2040 220 6 1 40 2 .3865 85.03 1 1 220 6
21 2 5 5476 5476 4 7 1 476 5 . . 1 1 4 7
File containing dishes with no recipes NORECIPE1.SAV without INGR and LINETYP Variables
HHID
1Meal
2Dnum
8Dish
9Quan
11 Unit
13\2 Form
18 Wgtfact
19 Wgt
20 Linety_n
21 Norecipe
22 Dshquan
23 Dshunit
24
21 2 2 2040 220 6 2 .3865 85.03 1 1 220 6
21 2 5 5476 4 7 5 . . 1 1 4 7

Appendix 15A and 15B: Dietary File and Household Recipe Proporations
69
File containing ingredient 1 for all recipes WITHREC1.SAV
HHID
1Meal
2Dnum
8Dish
9Norecipe
22 Dshquan
23 Dshunit
24 Ingr
25 Mrecprop
26 Ingord
27
21 2 2 2040 1 220 6 040 .2272 1
22 3 4 1435 1 12 2 180 .5646 1
File containing ingredient 2 for all recipes WITHREC2.SAV
HHID
1Meal
2Dnum
8Dish
9Norecipe
22 Dshquan
23 Dshunit
24 Ingr
25 Mrecprop
26 Ingord
27
21 2 2 2040 1 220 6 240 .04545 2
22 3 4 1435 1 12 2 105 .245 2
File containing household recipes HHREC.SAV
HHID
1Meal
2Dnum
8Dish
9Norecipe
22 Dshquan
23 Dshunit
24 Ingr
25 Mrecprop
26 Ingord
27
21 2 2 2040 1 220 6 2040 .
21 2 2 2040 1 220 6 040 .2272 1
21 2 2 2040 1 220 6 240 .04545 2
22 3 4 1435 1 12 2 180 .5646 1
22 3 4 1435 1 12 2 105 .245 2
Files for cluster-level recipes will be similar to those displayed above.

Appendix 16: Dietary File
70
APPENDIX 16. DIETARY FILE
HHI
D
1
Mea
l
2
Dnum
8Dish
9Ingr
10 Qua
n
11
Unit
12 Lqua
n
13
Luni
t
14
Src
15 Linetyp
16 Produc
t
17
For
m
18
Wgtfac
t
19
Wgt
20 Linety_n
21 Norecipe
22 Lfrac
23 Cfrac
24 Wgt1
25
21 1 1 1003 1003 35 A2 4 A2 1 1 3 1 33.92 1187.2 2 0 .11429 .88571 1051.34
21 1 1 1003 1001 1300 6 0 0 2 2 1 1 .60 780 1 0 .11429 .88571 690.854
21 1 2 1403 1403 900 6 0 0 0 1 403 1 . . 2 0 0 1 .
21 1 2 1403 403 .00 0 0 0 1 2 403 0 . . 2 0 0 1 .
21 1 2 1403 260 110 6 0 0 1 2 260 0 1.0886 119.74
61 0 0 1 119.746
21 2 1 2170 2170 5 7 0 0 0 1 170 2 1 5 2 0 0 1 5
21 2 1 2170 170 5 7 0 0 12 2 170 0 1 5 2 0 0 1 5
21 2 1 2170 240 70 6 0 0 1 2 240 0 1.166 81.62 1 0 0 1 81.62
21 2 2 2040 2040 220 6 0 0 22 1 40 2 .3865 85.03 1 1 0 1 85.03
21 2 3 1403 1403 500 6 0 0 0 1 403 1 . . 2 0 0 1 .

Appendix 17: Adult Equivalent File
71
APPENDIX 17. ADULT EQUIVALENT FILE
HHID aecal1* aecal2 aecal3
21 1.18 .959 .339
22 .871 .924 .410
23 .718 .888 .482
24 .838 1.106 .871
*There will be as many variables in the data as there are
maximum numbers of household members.

Appendix 18: Population Distribution (Proportions) by Age and Sex for Selected Countries, 1997
72
APPENDIX 18. POPULATION DISTRIBUTION (PROPORTIONS) BY AGE AND SEX FOR
SELECTED COUNTRIES, 1997
AGE IN YEARS
COUNTRY SEX 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18-29 30-59 60+ Total
Population
Male .038 .034 .032 .031 .030 .030 .029 .028 .027 .027 .025 .025 .024 .023 .023 .022 .022 .021 .209 .255 .046 12,223,573Afghanistan
Female .038 .035 .033 .032 .030 .031 .029 .029 .028 .027 .026 .025 .025 .024 .023 .023 .022 .021 .208 .248 .044 11,514,512
Male .023 .023 .023 .023 .024 .025 .026 .025 .025 .025 .024 .024 .024 .024 .023 .023 .022 .021 .167 .323 .084 1,580,997Albania
Female .019 .019 .020 .020 .020 .021 .022 .022 .022 .021 .021 .021 .021 .020 .020 .019 .018 .019 .204 .335 .096 1,712,255
Male .027 .027 .026 .026 .026 .026 .026 .026 .025 .025 .024 .027 .028 .027 .026 .025 .024 .023 .226 .254 .054 15,067,956Algeria
Female .027 .026 .026 .026 .026 .026 .025 .025 .025 .025 .024 .027 .027 .027 .026 .025 .024 .023 .223 .257 .062 14,762,414
Male .040 .036 .035 .034 .033 .032 .031 .030 .029 .028 .026 .025 .024 .023 .022 .021 .021 .019 .194 .251 .043 5,317,767Angola
Female .039 .036 .035 .034 .032 .032 .031 .030 .028 .027 .026 .025 .024 .023 .022 .021 .021 .020 .200 .243 .051 5,231,080
Male .020 .020 .020 .019 .019 .019 .019 .019 .019 .019 .019 .019 .018 .018 .018 .019 .019 .020 .196 .342 .120 17,679,895Argentina
Female .019 .019 .019 .018 .018 .018 .018 .018 .018 .018 .018 .017 .017 .017 .017 .018 .018 .019 .187 .335 .155 18,117,641
Male .017 .016 .015 .015 .017 .019 .021 .021 .021 .021 .021 .022 .022 .021 .020 .019 .019 .019 .196 .352 .107 1,694,695Armenia
Female .015 .014 .014 .014 .015 .018 .019 .019 .019 .019 .019 .020 .020 .019 .018 .018 .017 .017 .179 .370 .135 1,770,916
Male .022 .021 .021 .022 .022 .024 .024 .024 .023 .023 .023 .023 .023 .022 .020 .020 .020 .019 .214 .308 .084 3,770,958Azerbaijan
Female .020 .020 .020 .020 .020 .022 .022 .022 .021 .020 .021 .021 .020 .019 .018 .018 .018 .017 .193 .334 .115 3,964,960
Male .028 .026 .026 .025 .026 .026 .026 .025 .025 .025 .024 .024 .025 .025 .025 .025 .024 .023 .222 .273 .054 64,360,139Bangladesh
Female .028 .027 .026 .026 .026 .026 .026 .026 .025 .025 .024 .024 .025 .025 .025 .025 .025 .023 .227 .268 .049 60,980,122
Male .013 .013 .012 .012 .013 .014 .015 .016 .017 .017 .017 .018 .017 .018 .017 .016 .016 .016 .181 .405 .136 4,914,444Belarus
Female .011 .011 .011 .011 .011 .012 .012 .013 .014 .014 .015 .015 .015 .015 .015 .014 .014 .014 .161 .383 .220 5,525,472
Male .013 .012 .012 .012 .013 .013 .013 .013 .013 .012 .012 .012 .012 .012 .012 .013 .013 .013 .163 .425 .187 4,991,829Belgium
Female .011 .011 .011 .011 .012 .012 .012 .012 .012 .011 .011 .011 .011 .011 .011 .012 .012 .012 .152 .399 .244 5,211,854
Male .044 .041 .039 .037 .036 .035 .034 .033 .031 .030 .029 .028 .027 .026 .025 .024 .023 .022 .215 .189 .033 2,882,399Benin
Female .041 .038 .037 .035 .034 .033 .032 .031 .030 .028 .027 .026 .025 .024 .024 .023 .022 .021 .200 .225 .041 3,019,779
Male .035 .032 .031 .030 .029 .028 .027 .027 .026 .025 .024 .023 .023 .022 .021 .021 .020 .020 .202 .272 .062 961,767Bhutan
Female .035 .032 .031 .030 .029 .028 .027 .026 .025 .024 .024 .023 .022 .021 .021 .020 .020 .019 .201 .278 .064 903,424
Male .032 .030 .029 .029 .028 .028 .027 .027 .027 .026 .026 .025 .025 .024 .024 .023 .023 .022 .207 .257 .059 3,783,842Bolivia
Female .030 .029 .028 .027 .027 .027 .026 .026 .026 .025 .025 .024 .024 .023 .023 .022 .022 .021 .208 .270 .068 3,886,026
Male .007 .006 .006 .007 .008 .011 .015 .016 .017 .018 .018 .018 .018 .018 .018 .018 .019 .018 .173 .392 .178 1,275,669Bosnia and
Herzegovina Female .006 .006 .006 .006 .007 .008 .013 .014 .015 .015 .015 .015 .016 .016 .016 .016 .017 .017 .140 .421 .216 1,332,065

Appendix 18: Population Distribution (Proportions) by Age and Sex for Selected Countries, 1997
73
Male .033 .032 .031 .031 .031 .030 .030 .028 .028 .028 .028 .028 .028 .027 .027 .026 .026 .025 .227 .211 .045 726,402Botswana
Female .030 .029 .029 .028 .028 .028 .027 .026 .026 .026 .026 .025 .025 .025 .025 .024 .024 .023 .227 .237 .062 774,363
Male .020 .020 .020 .020 .020 .019 .019 .020 .021 .022 .022 .021 .021 .021 .022 .023 .022 .022 .235 .329 .062 81,417,819Brazil
Female .019 .019 .019 .019 .019 .018 .018 .019 .020 .021 .020 .020 .019 .020 .021 .021 .021 .021 .226 .337 .084 83,093,547
Male .009 .009 .009 .010 .011 .011 .012 .012 .013 .013 .013 .014 .014 .013 .014 .014 .014 .015 .186 .402 .191 4,239,177Bulgaria
Female .008 .008 .008 .009 .010 .010 .011 .011 .012 .012 .012 .012 .012 .012 .013 .013 .013 .013 .172 .394 .232 4,413,568
Male .044 .041 .039 .037 .036 .035 .034 .033 .032 .031 .029 .028 .027 .026 .025 .024 .023 .023 .207 .181 .043 5,298,042Burkina Faso
Female .041 .038 .036 .035 .033 .033 .032 .031 .030 .029 .028 .027 .026 .025 .024 .023 .022 .021 .196 .219 .053 5,593,117
Male .028 .027 .027 .026 .026 .026 .025 .025 .024 .024 .024 .023 .023 .022 .022 .022 .021 .021 .217 .290 .058 23,495,319Burma
Female .027 .026 .026 .026 .025 .025 .024 .024 .024 .023 .023 .022 .022 .022 .021 .021 .020 .020 .211 .298 .069 23,326,624
Male .040 .038 .037 .035 .034 .033 .033 .032 .031 .030 .029 .028 .027 .026 .025 .024 .023 .022 .196 .223 .034 2,978,722Burundi
Female .038 .036 .036 .034 .033 .032 .032 .031 .030 .029 .028 .027 .026 .025 .024 .023 .022 .021 .188 .235 .049 3,073,892
Male .042 .039 .037 .036 .035 .034 .033 .032 .031 .029 .028 .027 .026 .025 .024 .023 .022 .020 .198 .220 .039 5,385,225Cambodia
Female .037 .035 .034 .033 .031 .031 .030 .029 .028 .027 .026 .025 .024 .023 .022 .021 .020 .018 .183 .271 .054 5,778,636
Male .040 .038 .036 .035 .034 .033 .032 .030 .029 .028 .027 .027 .026 .025 .024 .023 .022 .022 .192 .231 .048 7,320,234Cameroon
Female .039 .037 .035 .034 .033 .032 .031 .030 .029 .028 .027 .026 .025 .025 .024 .023 .022 .022 .190 .232 .055 7,357,276
Male .036 .035 .035 .035 .034 .034 .034 .033 .033 .032 .031 .030 .029 .027 .026 .025 .024 .020 .179 .198 .070 188,871Cape Verde
Female .032 .032 .032 .032 .031 .031 .031 .030 .030 .029 .028 .027 .026 .025 .024 .022 .021 .018 .173 .230 .098 204,972
Male .037 .035 .033 .032 .032 .031 .030 .030 .029 .028 .028 .027 .026 .025 .024 .024 .023 .022 .205 .230 .050 1,651,857Central African
Republic Female .036 .033 .032 .031 .030 .030 .029 .029 .028 .028 .027 .026 .025 .024 .024 .023 .022 .021 .200 .243 .058 1,690,194
Male .041 .038 .036 .034 .033 .031 .030 .029 .028 .027 .026 .025 .025 .023 .023 .022 .021 .021 .196 .247 .044 3,536,034Chad
Female .039 .036 .035 .033 .032 .030 .029 .028 .027 .026 .025 .025 .024 .023 .022 .021 .021 .020 .198 .252 .053 3,629,989
Male .018 .018 .018 .019 .020 .020 .020 .021 .021 .020 .019 .018 .018 .018 .018 .020 .019 .018 .200 .369 .087 7,157,848Chile
Female .017 .018 .018 .019 .019 .020 .020 .019 .020 .019 .018 .018 .018 .018 .018 .019 .018 .017 .190 .368 .112 7,350,320
Male .017 .017 .017 .017 .017 .017 .018 .019 .019 .020 .020 .018 .016 .016 .017 .018 .015 .016 .224 .373 .090 629,862,051China Mainland
Female .016 .016 .016 .016 .016 .016 .017 .018 .018 .019 .020 .017 .016 .015 .016 .018 .015 .016 .225 .370 .104 591,729,727
Male .021 .021 .021 .021 .022 .022 .022 .022 .022 .022 .022 .022 .021 .021 .021 .020 .019 .019 .230 .325 .063 18,485,758Colombia
Female .020 .020 .020 .020 .021 .021 .021 .021 .021 .021 .021 .021 .020 .020 .020 .019 .019 .018 .221 .340 .076 18,932,532
Male .043 .041 .039 .037 .036 .035 .033 .032 .031 .029 .028 .027 .026 .025 .024 .023 .022 .022 .209 .198 .041 293,115Comoros
Female .042 .040 .038 .037 .035 .034 .033 .031 .030 .029 .028 .026 .025 .024 .024 .023 .022 .021 .201 .215 .043 296,682
Male .037 .034 .033 .031 .031 .030 .029 .029 .028 .027 .027 .026 .026 .025 .027 .026 .025 .024 .220 .222 .044 1,270,882Congo
Female .035 .033 .031 .030 .029 .029 .028 .028 .027 .026 .025 .025 .024 .024 .026 .025 .024 .023 .214 .233 .060 1,312,316
Male .023 .023 .023 .023 .023 .023 .023 .023 .023 .023 .023 .023 .022 .022 .022 .021 .020 .020 .213 .315 .067 1,787,974Costa Rica
Female .023 .023 .023 .023 .023 .023 .023 .023 .023 .023 .023 .022 .021 .021 .021 .020 .019 .019 .206 .323 .076 1,746,200
Male .039 .036 .035 .034 .033 .032 .031 .031 .030 .029 .029 .028 .027 .026 .025 .024 .023 .022 .201 .230 .036 7,630,421Côte d’Ivoire
Female .040 .037 .036 .035 .034 .033 .032 .032 .031 .030 .029 .028 .027 .026 .026 .025 .024 .023 .202 .216 .035 7,355,797
Male .010 .010 .011 .011 .011 .011 .012 .013 .013 .013 .013 .013 .014 .014 .014 .014 .014 .015 .176 .435 .164 2,449,551Croatia
Female .009 .009 .009 .010 .010 .010 .011 .011 .011 .012 .012 .012 .012 .013 .013 .013 .013 .013 .162 .405 .229 2,577,444

Appendix 18: Population Distribution (Proportions) by Age and Sex for Selected Countries, 1997
74
Male .013 .014 .014 .014 .014 .015 .017 .017 .017 .017 .016 .016 .016 .015 .015 .013 .011 .011 .220 .394 .122 5,509,856Cuba
Female .013 .013 .013 .013 .014 .014 .016 .016 .016 .016 .015 .015 .015 .014 .014 .013 .011 .011 .212 .402 .136 5,489,185
Male .038 .035 .033 .032 .030 .029 .028 .027 .026 .025 .024 .024 .024 .020 .020 .021 .021 .021 .192 .285 .046 224,091Djibouti
Female .040 .037 .035 .034 .032 .031 .030 .029 .028 .027 .026 .025 .025 .021 .022 .022 .022 .022 .198 .251 .044 210,025
Male .022 .022 .022 .022 .023 .023 .023 .023 .023 .023 .023 .022 .022 .022 .021 .021 .021 .020 .226 .316 .060 4,168,603Dominican
Republic Female .022 .022 .022 .022 .022 .023 .023 .023 .023 .023 .023 .022 .022 .022 .021 .021 .021 .020 .225 .312 .067 4,059,548
Male .024 .024 .025 .026 .026 .027 .026 .026 .026 .025 .025 .024 .024 .023 .023 .023 .022 .022 .223 .276 .060 6,029,971Ecuador
Female .023 .023 .024 .025 .025 .026 .025 .025 .025 .024 .024 .023 .023 .022 .022 .022 .022 .021 .226 .285 .067 6,075,153
Male .027 .026 .026 .025 .025 .025 .025 .025 .025 .025 .025 .023 .023 .023 .022 .022 .022 .021 .225 .288 .053 32,747,611Egypt
Female .026 .025 .025 .025 .024 .024 .024 .024 .024 .024 .025 .023 .023 .022 .022 .022 .022 .021 .212 .299 .064 32,076,855
Male .028 .028 .027 .027 .027 .027 .027 .027 .026 .026 .025 .025 .025 .025 .024 .024 .024 .024 .219 .247 .069 2,755,845El Salvador
Female .025 .025 .025 .025 .025 .024 .024 .024 .024 .023 .023 .023 .023 .022 .022 .022 .022 .022 .224 .274 .077 2,905,982
Male .038 .035 .034 .033 .032 .031 .031 .030 .029 .028 .027 .026 .025 .024 .023 .023 .022 .021 .209 .224 .056 214,844Equatorial
Guinea Female .035 .033 .032 .031 .030 .029 .029 .028 .027 .026 .025 .024 .023 .023 .022 .021 .021 .020 .196 .260 .063 227,672
Male .040 .036 .033 .032 .030 .029 .028 .028 .028 .027 .026 .026 .025 .022 .025 .025 .025 .024 .231 .212 .049 1,800,522Eritrea
Female .040 .036 .034 .032 .030 .029 .028 .028 .027 .027 .026 .025 .025 .022 .024 .024 .024 .024 .216 .234 .047 1,789,165
Male .012 .011 .011 .011 .012 .013 .014 .016 .017 .017 .017 .016 .016 .016 .016 .016 .016 .015 .189 .402 .147 673,194Estonia
Female .010 .009 .009 .009 .010 .011 .012 .013 .014 .014 .014 .014 .014 .014 .014 .013 .013 .013 .156 .390 .234 771,527
Male .042 .038 .036 .035 .033 .032 .031 .030 .029 .028 .027 .025 .023 .024 .024 .023 .022 .021 .198 .236 .042 29,405,683Ethiopia
Female .041 .038 .036 .035 .033 .032 .031 .030 .029 .028 .027 .025 .023 .024 .024 .023 .022 .021 .196 .232 .047 29,326,894
Male .023 .023 .023 .023 .023 .023 .023 .024 .024 .024 .024 .024 .024 .024 .024 .024 .023 .023 .211 .314 .052 398,433Fiji
Female .022 .022 .022 .022 .022 .023 .023 .023 .023 .023 .023 .023 .023 .023 .023 .023 .022 .022 .206 .328 .056 394,008
Male .026 .025 .024 .024 .023 .023 .022 .022 .022 .021 .021 .021 .020 .020 .020 .019 .019 .019 .191 .328 .090 599,291Gabon
Female .026 .025 .024 .024 .024 .023 .023 .022 .022 .022 .021 .021 .020 .020 .020 .020 .019 .019 .195 .321 .089 590,868
Male .041 .039 .037 .035 .034 .033 .032 .030 .029 .028 .027 .026 .024 .023 .023 .022 .021 .020 .193 .239 .045 622,844The Gambia
Female .041 .038 .036 .035 .034 .033 .031 .030 .029 .028 .026 .025 .024 .023 .022 .022 .021 .020 .197 .244 .040 625,241
Male .049 .046 .044 .043 .041 .040 .038 .035 .032 .029 .027 .025 .025 .025 .024 .023 .022 .021 .193 .183 .035 499,002Gaza Strip
Female .047 .045 .043 .042 .040 .039 .037 .034 .031 .029 .027 .025 .024 .024 .023 .022 .021 .020 .181 .198 .050 488,867
Male .014 .013 .012 .012 .013 .015 .017 .018 .017 .017 .018 .018 .018 .018 .017 .017 .017 .017 .197 .370 .145 2,445,260Georgia
Female .012 .011 .010 .010 .011 .013 .015 .015 .015 .015 .015 .016 .016 .015 .015 .015 .015 .015 .168 .383 .201 2,729,382
Male .033 .032 .031 .031 .031 .031 .031 .030 .029 .029 .028 .028 .026 .025 .023 .021 .020 .020 .214 .239 .048 8,972,930Ghana
Female .032 .031 .031 .030 .030 .030 .030 .029 .029 .028 .028 .027 .026 .024 .022 .021 .020 .020 .213 .249 .051 9,127,773
Male .018 .018 .018 .018 .018 .019 .019 .019 .018 .017 .016 .016 .016 .016 .016 .016 .017 .017 .238 .348 .102 202,608Guadeloupe
Female .016 .016 .016 .016 .017 .017 .017 .018 .017 .016 .015 .015 .015 .016 .016 .015 .016 .016 .225 .353 .130 209,215
Male .032 .032 .031 .031 .030 .030 .030 .029 .029 .028 .028 .027 .026 .025 .024 .024 .023 .023 .214 .236 .051 5,816,751Guatemala
Female .031 .031 .030 .030 .029 .029 .029 .028 .028 .027 .027 .026 .025 .024 .023 .023 .023 .022 .211 .247 .057 5,741,656
Male .039 .036 .034 .033 .032 .031 .030 .030 .029 .028 .027 .026 .025 .024 .024 .023 .022 .021 .203 .245 .039 3,637,064Guinea
Female .037 .035 .033 .032 .031 .031 .029 .029 .028 .027 .026 .025 .024 .023 .023 .022 .021 .020 .199 .254 .050 3,768,311
Guinea Bissau Male .037 .035 .033 .032 .031 .031 .030 .029 .028 .027 .027 .026 .025 .025 .024 .024 .023 .022 .221 .225 .044 571,760

Appendix 18: Population Distribution (Proportions) by Age and Sex for Selected Countries, 1997
75
Female .035 .032 .031 .030 .029 .029 .028 .027 .026 .026 .025 .024 .024 .023 .023 .022 .022 .021 .205 .267 .048 606,824
Male .018 .019 .019 .019 .020 .020 .021 .021 .022 .023 .023 .024 .024 .025 .025 .026 .025 .025 .248 .291 .061 354,882Guyana
Female .018 .018 .018 .019 .019 .020 .020 .021 .022 .022 .023 .023 .023 .024 .025 .025 .024 .024 .229 .312 .071 351,234
Male .032 .030 .029 .029 .029 .029 .030 .030 .031 .031 .031 .031 .029 .028 .027 .026 .024 .023 .201 .218 .063 3,254,586Haiti
Female .030 .028 .027 .027 .027 .028 .028 .029 .029 .029 .029 .029 .028 .027 .026 .025 .023 .021 .194 .255 .063 3,356,821
Male .032 .032 .031 .031 .031 .030 .030 .029 .028 .027 .027 .026 .026 .026 .025 .025 .024 .023 .222 .227 .049 2,880,644Honduras
Female .031 .030 .030 .030 .030 .029 .029 .028 .027 .026 .026 .026 .025 .025 .024 .024 .023 .023 .218 .243 .053 2,870,740
Male .025 .024 .024 .024 .023 .023 .023 .024 .023 .023 .023 .022 .022 .022 .022 .021 .021 .021 .216 .306 .068 500,005,495India
Female .025 .025 .025 .024 .024 .023 .023 .024 .024 .023 .023 .023 .022 .022 .021 .021 .020 .020 .212 .308 .070 466,777,676
Male .023 .022 .022 .021 .021 .021 .021 .021 .021 .021 .021 .020 .021 .021 .022 .022 .022 .022 .233 .325 .058 104,696,028Indonesia
Female .022 .021 .021 .021 .021 .021 .020 .020 .020 .020 .020 .020 .020 .021 .021 .021 .021 .021 .229 .329 .071 105,078,110
Male .041 .039 .037 .036 .035 .034 .034 .033 .031 .029 .028 .027 .026 .025 .024 .023 .023 .022 .218 .197 .040 11,233,719Iraq
Female .040 .038 .037 .036 .034 .033 .033 .032 .030 .029 .028 .026 .025 .025 .024 .023 .023 .022 .211 .203 .046 10,985,570
Male .022 .022 .023 .022 .022 .022 .022 .022 .021 .021 .022 .022 .022 .022 .022 .021 .020 .020 .239 .288 .083 1,300,893Jamaica
Female .021 .021 .021 .021 .021 .021 .021 .021 .020 .020 .021 .021 .021 .021 .021 .020 .019 .019 .230 .301 .100 1,314,689
Male .019 .019 .019 .019 .020 .021 .021 .022 .022 .023 .023 .023 .022 .021 .020 .020 .019 .019 .209 .344 .077 8,146,209Kazakstan
Female .017 .017 .016 .016 .017 .018 .019 .019 .020 .021 .021 .021 .020 .019 .018 .018 .018 .017 .188 .350 .129 8,752,363
Male .031 .031 .031 .030 .030 .029 .030 .030 .030 .030 .030 .029 .028 .028 .027 .026 .025 .025 .230 .212 .037 14,426,891Kenya
Female .031 .030 .030 .029 .029 .029 .029 .029 .029 .029 .029 .029 .028 .027 .027 .026 .025 .024 .224 .220 .045 14,376,194
Male .025 .024 .024 .024 .025 .027 .027 .027 .027 .027 .027 .026 .025 .024 .023 .022 .021 .020 .213 .272 .072 2,215,507Kyrgyzstan
Female .023 .023 .023 .022 .024 .025 .025 .025 .025 .025 .025 .024 .023 .022 .021 .021 .020 .019 .199 .282 .106 2,324,678
Male .039 .037 .036 .035 .034 .033 .032 .031 .030 .029 .028 .027 .026 .025 .024 .023 .022 .022 .204 .218 .047 2,527,748Laos
Female .037 .035 .034 .033 .032 .031 .031 .030 .029 .028 .027 .026 .024 .023 .023 .022 .021 .021 .200 .241 .054 2,589,211
Male .012 .011 .011 .011 .012 .014 .015 .016 .017 .017 .017 .017 .017 .017 .017 .015 .015 .015 .178 .407 .149 1,123,120Latvia
Female .010 .009 .009 .009 .010 .011 .012 .013 .013 .014 .014 .014 .014 .014 .014 .013 .012 .012 .147 .393 .242 1,314,529
Male .023 .022 .022 .022 .021 .021 .020 .020 .020 .020 .020 .021 .021 .022 .022 .023 .023 .024 .285 .238 .089 1,668,581Lebanon
Female .021 .020 .020 .019 .019 .019 .018 .018 .018 .018 .018 .019 .019 .019 .020 .020 .021 .022 .260 .294 .095 1,780,997
Male .031 .030 .029 .029 .029 .028 .028 .028 .028 .027 .027 .027 .026 .025 .025 .024 .024 .023 .217 .237 .058 980,040Lesotho
Female .029 .028 .028 .027 .027 .027 .027 .026 .026 .026 .026 .025 .025 .024 .024 .023 .022 .022 .205 .256 .075 1,027,774
Male .038 .037 .035 .033 .033 .031 .033 .028 .029 .027 .026 .024 .025 .024 .022 .021 .020 .020 .192 .253 .051 1,318,162Liberia
Female .039 .037 .035 .034 .033 .032 .033 .028 .030 .028 .026 .024 .025 .024 .023 .022 .020 .021 .196 .237 .053 1,283,906
Male .014 .013 .013 .013 .014 .015 .016 .016 .016 .017 .017 .017 .017 .017 .016 .015 .015 .015 .189 .396 .138 1,712,193Lithuania
Female .012 .011 .011 .011 .012 .013 .014 .014 .014 .014 .015 .015 .015 .014 .014 .013 .013 .013 .161 .387 .214 1,923,739
Male .013 .013 .013 .014 .015 .016 .015 .014 .015 .015 .016 .016 .016 .016 .016 .016 .017 .016 .185 .407 .135 1,066,660Macedonia
(former Yugo.) Female .013 .012 .013 .013 .014 .015 .014 .014 .014 .015 .015 .015 .016 .016 .016 .016 .016 .016 .177 .399 .161 1,047,206
Male .040 .037 .035 .034 .033 .032 .031 .030 .029 .028 .027 .026 .025 .024 .024 .023 .022 .021 .204 .228 .048 7,025,577Madagascar
Female .039 .036 .035 .033 .032 .031 .030 .029 .028 .027 .026 .025 .025 .024 .023 .022 .022 .021 .201 .239 .052 7,036,050
Male .038 .035 .033 .033 .032 .032 .031 .031 .030 .030 .029 .029 .028 .027 .027 .026 .025 .024 .223 .200 .037 4,750,059Malawi
Female .036 .034 .032 .032 .031 .031 .030 .030 .029 .029 .028 .028 .027 .026 .026 .025 .024 .023 .208 .221 .049 4,859,022

Appendix 18: Population Distribution (Proportions) by Age and Sex for Selected Countries, 1997
76
Male .027 .027 .026 .026 .026 .025 .025 .024 .024 .024 .024 .024 .023 .022 .021 .021 .020 .018 .209 .309 .055 10,280,096Malaysia
Female .026 .025 .025 .025 .025 .024 .023 .023 .023 .023 .023 .023 .022 .021 .021 .020 .020 .017 .203 .320 .068 10,211,207
Male .049 .044 .040 .038 .036 .034 .033 .031 .030 .029 .027 .026 .025 .025 .024 .024 .023 .022 .197 .193 .049 4,833,839Mali
Female .045 .041 .038 .036 .034 .032 .031 .030 .029 .028 .026 .025 .024 .024 .023 .023 .022 .021 .190 .227 .051 5,111,544
Male .017 .017 .017 .016 .016 .016 .016 .016 .016 .016 .015 .015 .015 .015 .014 .015 .015 .016 .238 .361 .117 197,296Martinique
Female .016 .016 .016 .015 .015 .015 .015 .015 .015 .015 .015 .014 .014 .013 .013 .013 .014 .015 .221 .365 .149 205,688
Male .045 .042 .039 .038 .036 .035 .034 .032 .031 .030 .029 .027 .026 .025 .024 .024 .023 .022 .202 .205 .032 1,188,141Mauritania
Female .042 .039 .038 .036 .035 .034 .033 .031 .030 .029 .028 .027 .026 .025 .024 .023 .022 .021 .199 .219 .041 1,223,176
Male .019 .019 .019 .019 .019 .020 .019 .019 .018 .017 .017 .016 .017 .017 .018 .021 .021 .021 .207 .382 .075 570,904Mauritius
Female .018 .018 .018 .018 .019 .018 .018 .018 .017 .016 .016 .015 .016 .016 .017 .020 .020 .020 .197 .383 .099 583,368
Male .026 .026 .026 .026 .025 .025 .025 .025 .025 .024 .024 .024 .024 .023 .023 .023 .023 .022 .233 .266 .062 48,072,941Mexico
Female .024 .024 .024 .024 .024 .024 .024 .023 .023 .023 .023 .022 .022 .022 .022 .022 .022 .021 .228 .289 .070 49,490,433
Male .017 .016 .015 .015 .016 .017 .018 .019 .020 .020 .021 .021 .021 .020 .020 .018 .018 .018 .189 .370 .112 2,134,589Moldova
Female .015 .014 .014 .013 .014 .015 .015 .016 .017 .018 .019 .019 .018 .018 .017 .016 .016 .016 .169 .379 .160 2,340,643
Male .024 .024 .024 .024 .024 .025 .027 .028 .028 .028 .027 .026 .025 .024 .023 .023 .022 .022 .233 .268 .049 1,269,575Mongolia
Female .023 .023 .023 .023 .023 .024 .026 .027 .027 .027 .027 .026 .025 .024 .023 .022 .022 .022 .229 .272 .062 1,268,636
Male .042 .039 .037 .035 .033 .031 .029 .028 .027 .027 .026 .026 .025 .025 .024 .023 .023 .021 .222 .224 .034 8,873,787Mozambique
Female .039 .037 .035 .034 .033 .031 .029 .028 .027 .027 .026 .026 .025 .025 .024 .023 .023 .021 .202 .243 .043 9,291,689
Male .036 .035 .034 .033 .032 .031 .030 .029 .028 .028 .027 .027 .026 .026 .025 .024 .023 .022 .208 .228 .050 852,424Namibia
Female .034 .033 .032 .031 .030 .029 .028 .027 .027 .026 .026 .026 .025 .024 .024 .023 .022 .022 .210 .239 .060 874,759
Male .035 .033 .031 .030 .030 .029 .028 .028 .027 .026 .026 .025 .025 .024 .024 .024 .023 .022 .210 .252 .048 11,548,384Nepal
Female .034 .032 .031 .030 .029 .029 .028 .028 .027 .026 .026 .025 .025 .024 .024 .023 .023 .022 .208 .256 .049 11,092,677
Male .033 .032 .032 .032 .031 .031 .030 .030 .029 .029 .028 .028 .027 .027 .026 .025 .024 .023 .219 .224 .038 2,162,353Nicaragua
Female .031 .030 .030 .030 .030 .030 .029 .029 .028 .028 .027 .027 .027 .026 .025 .025 .024 .023 .219 .236 .046 2,224,046
Male .050 .045 .040 .038 .036 .034 .033 .031 .030 .028 .027 .026 .025 .024 .024 .023 .022 .021 .193 .211 .041 4,694,658Niger
Female .049 .043 .039 .036 .034 .032 .031 .030 .028 .027 .026 .025 .024 .023 .022 .021 .021 .020 .203 .229 .036 4,694,201
Male .040 .037 .035 .034 .032 .031 .030 .029 .028 .027 .026 .025 .024 .024 .023 .023 .022 .021 .196 .246 .047 54,217,739Nigeria
Female .040 .038 .036 .034 .033 .032 .031 .030 .029 .027 .026 .025 .025 .024 .024 .023 .022 .021 .197 .237 .047 52,911,730
Male .023 .023 .023 .022 .022 .022 .021 .021 .020 .019 .019 .018 .018 .018 .017 .017 .017 .017 .240 .352 .052 12,042,483North Korea
Female .021 .021 .021 .021 .021 .021 .020 .019 .019 .018 .018 .017 .017 .017 .016 .016 .016 .016 .230 .351 .084 12,274,521
Male .022 .022 .021 .022 .022 .022 .023 .022 .022 .022 .022 .022 .021 .021 .020 .020 .020 .019 .225 .313 .078 1,363,852Panama
Female .022 .021 .021 .021 .022 .022 .022 .022 .022 .021 .021 .021 .021 .020 .020 .020 .019 .019 .223 .315 .083 1,329,565
Male .031 .030 .029 .028 .028 .027 .027 .026 .026 .025 .025 .024 .024 .024 .023 .023 .023 .022 .231 .261 .044 2,320,792Papua New
Guinea Female .031 .030 .029 .029 .028 .028 .027 .026 .026 .026 .025 .025 .024 .024 .024 .023 .023 .023 .224 .253 .052 2,175,429
Male .030 .030 .029 .029 .029 .028 .028 .027 .027 .026 .026 .026 .025 .025 .024 .023 .022 .021 .201 .268 .056 2,844,648Paraguay
Female .029 .029 .028 .028 .028 .027 .027 .027 .026 .026 .025 .025 .024 .024 .023 .023 .022 .021 .200 .274 .065 2,806,986
Male .023 .023 .023 .023 .024 .024 .023 .023 .023 .023 .023 .023 .023 .023 .023 .023 .022 .022 .227 .296 .064 12,552,649Peru
Female .023 .022 .022 .023 .023 .023 .023 .023 .023 .023 .022 .022 .022 .022 .023 .023 .021 .021 .224 .298 .074 12,396,863
Philippines Male .029 .028 .028 .028 .028 .027 .027 .026 .025 .025 .024 .024 .023 .023 .023 .023 .022 .022 .223 .272 .050 37,869,476

Appendix 18: Population Distribution (Proportions) by Age and Sex for Selected Countries, 1997
77
Female .027 .027 .027 .027 .026 .026 .026 .025 .024 .024 .023 .023 .022 .022 .022 .022 .022 .021 .218 .286 .059 38,234,088
Male .024 .024 .024 .024 .024 .024 .024 .023 .023 .022 .021 .020 .020 .019 .018 .018 .017 .017 .218 .322 .073 341,978Reunion
Female .022 .023 .023 .023 .023 .022 .022 .022 .021 .020 .020 .019 .018 .017 .017 .016 .016 .016 .212 .333 .095 350,226
Male .010 .010 .010 .011 .011 .012 .012 .015 .016 .016 .017 .016 .016 .015 .015 .016 .017 .018 .211 .372 .165 10,437,409Romania
Female .009 .009 .009 .010 .010 .010 .011 .013 .015 .015 .015 .015 .014 .013 .013 .015 .016 .016 .194 .368 .208 10,961,705
Male .011 .011 .011 .011 .011 .013 .014 .016 .017 .018 .018 .018 .018 .018 .018 .017 .016 .016 .185 .421 .121 69,197,422Russia
Female .009 .009 .009 .009 .010 .011 .012 .013 .014 .015 .016 .015 .015 .015 .015 .014 .014 .014 .159 .399 .212 78,789,679
Male .035 .034 .030 .029 .028 .030 .031 .032 .031 .031 .031 .030 .029 .030 .029 .028 .026 .024 .210 .214 .036 3,835,879Rwanda
Female .034 .033 .029 .029 .028 .030 .030 .031 .030 .030 .030 .029 .029 .029 .029 .028 .026 .024 .203 .219 .049 3,901,658
Male .044 .041 .039 .038 .036 .035 .034 .033 .031 .030 .029 .028 .026 .025 .024 .023 .022 .021 .190 .207 .044 4,580,770Senegal
Female .041 .039 .037 .036 .035 .033 .032 .031 .030 .029 .028 .027 .026 .024 .023 .022 .021 .020 .194 .229 .043 4,822,776
Male .014 .014 .014 .014 .013 .014 .014 .015 .015 .015 .015 .015 .016 .016 .016 .015 .016 .016 .183 .388 .161 4,969,668Serbia
Female .013 .013 .013 .013 .012 .013 .013 .013 .014 .014 .014 .014 .014 .014 .014 .014 .015 .015 .170 .380 .204 5,047,726
Male .043 .039 .037 .036 .034 .033 .031 .030 .028 .027 .026 .025 .024 .023 .022 .021 .020 .020 .201 .229 .052 2,377,218Sierra Leone
Female .041 .038 .036 .035 .034 .032 .030 .029 .028 .026 .025 .025 .023 .022 .022 .021 .020 .020 .203 .241 .049 2,514,328

Appendix 18: Population Distribution (Proportions) by Age and Sex for Selected Countries, 1997
78
Male .013 .013 .013 .013 .014 .014 .015 .015 .015 .016 .016 .017 .017 .017 .017 .017 .018 .018 .199 .394 .127 2,625,227Slovakia
Female .012 .012 .012 .012 .013 .013 .014 .014 .014 .014 .015 .015 .015 .016 .016 .016 .016 .017 .182 .385 .179 2,767,789
Male .009 .009 .009 .010 .010 .011 .011 .012 .013 .013 .013 .013 .014 .014 .015 .015 .016 .016 .185 .440 .152 944,720Slovenia
Female .008 .008 .008 .009 .009 .010 .010 .011 .011 .012 .012 .012 .012 .013 .013 .014 .014 .014 .168 .411 .221 1,001,278
Male .036 .035 .035 .034 .033 .032 .031 .030 .029 .029 .028 .027 .026 .025 .025 .024 .023 .023 .217 .213 .045 216,844Solomon
Islands Female .036 .035 .034 .033 .033 .032 .031 .030 .029 .028 .028 .027 .026 .025 .025 .024 .023 .023 .216 .214 .046 210,011
Male .041 .036 .033 .030 .029 .031 .032 .032 .030 .027 .025 .024 .023 .023 .022 .021 .020 .019 .227 .234 .041 3,315,514Somalia
Female .041 .036 .033 .030 .029 .031 .032 .032 .031 .027 .025 .025 .024 .023 .023 .023 .021 .020 .205 .238 .050 3,274,811
Male .026 .026 .025 .025 .025 .024 .024 .023 .023 .023 .023 .023 .023 .022 .022 .022 .021 .021 .222 .300 .058 20,962,238South Africa
Female .025 .025 .024 .024 .024 .024 .023 .022 .022 .022 .022 .022 .022 .022 .021 .021 .021 .020 .213 .303 .078 21,365,220
Male .018 .018 .018 .018 .018 .018 .018 .019 .019 .019 .019 .020 .020 .021 .021 .021 .022 .020 .222 .345 .088 9,348,019Sri Lanka
Female .017 .017 .017 .017 .017 .017 .017 .018 .018 .018 .018 .019 .019 .020 .020 .020 .021 .019 .215 .366 .092 9,414,056
Male .038 .036 .035 .034 .033 .033 .032 .031 .028 .027 .028 .027 .026 .025 .024 .024 .023 .022 .212 .220 .041 16,519,468Sudan
Female .038 .036 .034 .034 .033 .032 .031 .030 .028 .027 .028 .027 .026 .025 .024 .023 .022 .021 .203 .244 .034 16,074,660
Male .023 .023 .023 .023 .023 .023 .023 .023 .023 .023 .022 .022 .022 .021 .021 .020 .020 .020 .232 .298 .071 215,238Suriname
Female .023 .023 .023 .023 .023 .023 .023 .023 .022 .022 .022 .021 .021 .020 .020 .019 .019 .019 .224 .303 .083 209,331
Male .041 .038 .036 .035 .034 .033 .033 .031 .030 .029 .027 .026 .026 .025 .024 .024 .023 .022 .214 .214 .035 500,694Swaziland
Female .038 .036 .034 .033 .032 .032 .031 .030 .028 .027 .026 .025 .025 .024 .023 .023 .022 .022 .208 .237 .043 530,906
Male .037 .036 .036 .035 .034 .033 .032 .031 .030 .029 .028 .027 .027 .026 .025 .024 .024 .022 .211 .211 .043 8,248,230Syria
Female .037 .036 .035 .035 .034 .033 .032 .031 .030 .029 .028 .027 .026 .026 .025 .024 .024 .022 .209 .213 .046 7,889,669
Male .032 .029 .028 .027 .027 .030 .033 .032 .030 .030 .030 .029 .027 .025 .024 .023 .021 .021 .208 .235 .059 2,987,232Tajikistan
Female .031 .029 .028 .027 .027 .029 .031 .030 .029 .029 .029 .028 .026 .024 .023 .022 .021 .020 .204 .240 .073 3,026,623
Male .039 .036 .034 .033 .032 .031 .031 .030 .029 .028 .028 .027 .027 .026 .025 .025 .024 .024 .221 .209 .043 14,493,583Tanzania
Female .037 .035 .033 .032 .031 .030 .030 .029 .028 .028 .027 .027 .026 .025 .025 .024 .024 .023 .213 .224 .048 14,967,170
Male .017 .017 .017 .017 .017 .017 .017 .017 .015 .015 .016 .017 .018 .019 .020 .020 .020 .020 .234 .366 .083 29,370,158Thailand
Female .016 .016 .016 .016 .016 .016 .016 .016 .014 .014 .015 .016 .017 .018 .019 .019 .019 .019 .227 .378 .097 30,080,660
Male .044 .041 .039 .037 .036 .035 .034 .033 .031 .030 .029 .028 .027 .026 .025 .024 .023 .022 .204 .199 .033 2,330,105Togo
Female .041 .039 .037 .036 .035 .034 .033 .032 .030 .029 .028 .027 .026 .025 .024 .023 .022 .021 .193 .228 .038 2,405,505
Male .015 .015 .016 .016 .017 .017 .017 .018 .019 .021 .022 .024 .024 .024 .024 .023 .021 .021 .185 .370 .090 577,591Trinidad
& Tobago Female .015 .016 .016 .017 .017 .018 .017 .018 .019 .021 .023 .024 .024 .023 .023 .023 .021 .020 .172 .364 .109 552,746
Male .023 .023 .023 .023 .023 .022 .022 .021 .022 .023 .024 .024 .023 .022 .023 .023 .022 .021 .227 .285 .081 4,658,302Tunisia
Female .022 .022 .022 .022 .022 .021 .021 .020 .021 .022 .023 .023 .023 .022 .022 .022 .022 .021 .224 .301 .081 4,524,795
Male .028 .027 .027 .026 .027 .028 .028 .028 .028 .028 .028 .027 .026 .024 .023 .022 .021 .021 .217 .266 .051 2,083,239Turkmenistan
Female .026 .025 .025 .024 .025 .026 .027 .027 .026 .026 .026 .026 .024 .023 .022 .021 .020 .020 .206 .280 .074 2,142,112
Male .042 .040 .039 .037 .036 .035 .034 .033 .032 .031 .030 .029 .027 .026 .025 .024 .023 .022 .200 .199 .036 10,309,459Uganda Female .042 .040 .038 .037 .036 .035 .034 .033 .032 .031 .030 .029 .027 .026 .025 .024 .023 .021 .196 .203 .038 10,295,415
Male .012 .012 .011 .011 .012 .013 .014 .014 .015 .016 .017 .017 .017 .017 .016 .016 .016 .016 .182 .408 .148 23,516,163Ukraine
Female .010 .010 .009 .009 .010 .011 .011 .012 .013 .013 .014 .014 .014 .014 .014 .013 .013 .013 .154 .392 .235 27,168,472

Appendix 18: Population Distribution (Proportions) by Age and Sex for Selected Countries, 1997
79
Male .018 .017 .017 .017 .017 .017 .017 .018 .017 .017 .017 .017 .015 .016 .017 .017 .018 .018 .196 .344 .152 1,590,527Uruguay
Female .016 .016 .016 .016 .016 .016 .016 .016 .016 .015 .015 .015 .014 .015 .015 .016 .016 .016 .181 .347 .193 1,671,180
Male .028 .027 .027 .026 .027 .028 .028 .028 .028 .028 .028 .027 .026 .024 .023 .022 .021 .021 .208 .266 .057 11,807,968Uzbekistan
Female .027 .026 .026 .025 .026 .027 .027 .026 .026 .027 .027 .026 .025 .023 .022 .021 .020 .020 .203 .269 .079 12,052,484
Male .024 .024 .024 .024 .024 .026 .026 .023 .023 .023 .022 .022 .022 .022 .022 .022 .021 .021 .222 .302 .060 11,298,958Venezuela
Female .023 .023 .023 .023 .023 .024 .025 .022 .022 .022 .021 .021 .021 .021 .021 .021 .021 .021 .219 .314 .070 11,097,449
Male .023 .023 .023 .024 .025 .026 .026 .027 .026 .024 .023 .025 .026 .026 .025 .023 .022 .022 .225 .274 .063 36,834,391Vietnam
Female .021 .021 .021 .022 .023 .023 .024 .024 .023 .022 .021 .023 .024 .024 .023 .021 .020 .020 .215 .299 .086 38,289,489
Male .037 .037 .036 .036 .035 .035 .034 .032 .028 .027 .026 .025 .025 .024 .023 .023 .022 .022 .214 .221 .040 756,222West Bank
Female .036 .036 .035 .035 .034 .034 .033 .031 .028 .026 .025 .024 .024 .023 .022 .022 .021 .021 .201 .232 .057 739,461
Male .043 .040 .039 .037 .036 .034 .033 .033 .031 .030 .029 .027 .026 .025 .024 .023 .022 .021 .220 .190 .037 7,038,728Yemen
Female .042 .039 .038 .036 .035 .033 .032 .031 .029 .028 .027 .026 .025 .024 .023 .022 .022 .021 .207 .211 .050 6,933,749
Male .045 .041 .039 .037 .036 .034 .033 .032 .031 .030 .029 .028 .027 .025 .024 .023 .023 .022 .205 .202 .036 23,372,417Zaire
Female .043 .040 .037 .036 .034 .033 .032 .031 .030 .029 .028 .027 .026 .025 .024 .023 .022 .021 .200 .215 .046 24,067,945
Male .042 .039 .037 .036 .035 .034 .034 .033 .032 .032 .031 .030 .029 .028 .027 .026 .025 .025 .205 .183 .037 4,639,894Zambia
Female .040 .038 .036 .035 .034 .033 .033 .032 .031 .031 .030 .029 .028 .027 .026 .025 .024 .024 .198 .201 .042 4,710,081
Male .031 .030 .029 .029 .029 .030 .030 .030 .031 .030 .030 .029 .028 .028 .027 .029 .027 .026 .238 .196 .042 5,682,082Zimbabwe
Female .030 .029 .028 .028 .029 .029 .029 .030 .030 .030 .029 .029 .028 .027 .026 .029 .027 .026 .228 .216 .044 5,741,093

Appendix 19: Sample Calculation of Weighted Average Adult Equivalent Ratios for Guest Categories
80
APPENDIX 19. SAMPLE CALCULATION OF WEIGHTED AVERAGE ADULT EQUIVALENT RATIOS
FOR GUEST CATEGORIES
Steps:
1) Find proportional distribution of population by age and sex for country of interest in Appendix 18.
2) Calculate the population in each age/sex category by multiplying the proportion of the population in each age/sex category by the total
population by sex (2,880,664 males and 2,870,740 females for Honduras).
3) Calculate daily caloric requirements for each age/sex category (see section I.B.).
4) Calculate the AER for each age/sex category (see section I.). The caloric requirement for an adult equivalent in Honduras is 2858.
5) Calculate the weight of each age/sex category within each guest age/sex category. Divide the population in each age/sex category by the total
population in each guest age/sex category. For example, 10.3 percent (.103) of Honduran children 0-4 years old are one-year- old males
(90,854/883,643).
6) Multiply the AER for each age/sex category by its weight, and sum for weighted average AER for each guest age/sex category.
AGE IN YEARS
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18-29 30-59 60+
Step 1
Proportion of Male .032 .032 .031 .031 .031 .030 .030 .029 .028 .027 .027 .026 .026 .026 .025 .025 .024 .023 .222 .227 .049
population Female .031 .030 .030 .030 .030 .029 .029 .028 .027 .026 .026 .026 .025 .025 .024 .024 .023 .023 .218 .243 .053
Step 2
Population Male 92,513 90,854 89,857 89,057 88,184 87,252 85,419 82,724 80,065 78,280 77,018 75,759 74,620 73,550 72,397 70,968 69,282 67,368 639,578 654,668 141,231
Female 88,661 87,321 86,458 85,760 84,978 84,138 82,444 79,925 77,429 75,778 74,473 73,327 72,305 71,345 70,256 68,857 67,178 65,234 624,980 696,697 153,196
Step 3
Caloric Male 772 1172 1410 1560 1690 1810 1822 1901 1948 2023 2062 2168 2199 2342 2414 2511 2713 2813 2843 2804 2309
requirement Female 712 1147 1310 1440 1540 1630 1619 1657 1711 1767 1770 1838 1912 1988 2130 2186 2270 2280 2091 2116 1890
Step 4
Adult equivalent Male 0.270 0.410 0.493 0.546 0.591 0.633 0.638 0.665 0.682 0.708 0.722 0.759 0.769 0.820 0.845 0.878 0.949 0.984 0.995 0.981 0.808
ratio (AER) Female 0.249 0.401 0.458 0.504 0.539 0.570 0.566 0.580 0.599 0.618 0.619 0.643 0.669 0.696 0.745 0.765 0.794 0.798 0.732 0.740 0.661
Step 5
Weight w/in Male 0.105 0.103 0.102 0.101 0.100 0.078 0.077 0.074 0.072 0.070 0.069 0.068 0.174 0.172 0.169 0.166 0.162 0.157 0.446 0.456 0.098
guest category Female 0.100 0.099 0.098 0.097 0.096 0.076 0.074 0.072 0.070 0.068 0.067 0.066 0.174 0.172 0.169 0.166 0.162 0.157 0.424 0.472 0.104
Step 6
Weighted average 0.445 (Children 0 - 4 yrs) 0.642 (Children 5 - 11 yrs) 0.872 (Males 12 - 17 yrs) 0.970 (Males 18+ years)
4AER by category 0.743 (Females 12 - 17 yrs) 0.728 (Females 18+
years)

Appendix 20: Dietary File
81
APPENDIX 20. DIETARY FILE
HHID
1Meal
3Abst1*
218M
3Dnum
8Dish
9Aecal1*
26 Totadeq
27 Abaeca1*
28 Gstcal1*
29 Tgstadeq
30 Dshadeq
31
21 1 1 0 1 1003 1.18 7.36 1.18 0 0 6.18
21 1 1 0 1 1003 1.18 7.36 1.18 0 0 6.18
21 1 1 1 2 1403 1.18 7.36 1.18 1.014 1.014 7.194
21 1 1 1 2 1403 1.18 7.36 1.18 1.014 1.014 7.194
21 1 1 1 2 1403 1.18 7.36 1.18 1.014 1.014 7.194
21 2 0 0 1 2170 0 7.36 0 0 0 7.36
21 2 0 0 1 2170 0 7.36 0 0 0 7.36
21 2 0 0 1 2170 0 7.36 0 0 0 7.36
21 2 0 0 2 2040 0 7.36 0 0 0 7.36
21 2 0 0 3 1403 0 7.36 0 0 0 7.36
*There will be as many variables as there are maximum number of members/guests in the data.

Appendix 21: Command File Containing Nutritional Value of Foods
82
APPENDIX 21. COMMAND FILE CONTAINING NUTRITIONAL VALUE
OF FOODS
(Taken from USAID, Commodity Reference Guide, Washington, D.C.USAID/FFP)
Do if (product = 90)
Compute calcon = 3.8
Compute prtcon = .18
Compute vitacon = .6
Compute fatcon = .06
end if.

Appendix 22: Dietary File
83
APPENDIX 22. DIETARY FILE
HHID
1Meal
2Dnum
8Dish
9Linetyp
16 Product
17 Norecipe
22 Wgt1
25 Dshadeq
31 Calfact
32 Cal
33
21 1 1 1003 1 3 0 1051.34 6.18 2.037 .
21 1 1 1003 2 1 0 690.854 6.18 3.6 2487
21 1 2 1403 1 403 0 . 7.194 . .
21 1 2 1403 2 403 0 . 7.194 . .
21 1 2 1403 2 260 0 119.746 7.194 2.8333 339.276
21 2 1 2170 1 170 0 5 7.36 72 .
21 2 1 2170 2 170 0 5 7.36 72 360
21 2 1 2170 2 240 0 81.62 7.36 8.000 652.96
21 2 2 2040 1 40 1 85.03 7.36 1.60 .
21 2 3 1403 1 403 0 . 7.36 . .

Appendix 23, 24, 25: Aggregated Dietary Files
84
APPENDIX 23. AGGREGATED DIETARY FILE
(aggregated, case = dish)
HHID
1Meal
2Dish
3Dshadeq
4Dshcal
5Dshcalae
6
21 1 1003 6.18 2487 402.427
21 1 1403 7.194 339.276 47.161
21 2 2170 7.36 1012.92 137.25
21 2 2040 7.36 . .
APPENDIX 24. AGGREGATED DIETARY FILE
(aggregated, case = household)
HHID
1Daycalae Numdays
21 2816.33 3
22 2140 2
23 2948.33 3
24 1784 2
APPENDIX 25. AGGREGATED DIETARY FILE
(aggregated, case = Household)
HHID
1Avecalae Numdays Caladeq Calcat
21 2816.33 3 98.53 3
22 2140 2 74.88 2
23 2948.33 3 103.15 4
24 1784 2 62.42 2

Appendix 26: List of Title II Generic Indicators
85
APPENDIX 26. LIST OF TITLE II GENERIC INDICATORS
Category Level Indicator
% stunted children 24-59 months (height/age Z-score)
% underweight children by age group (weight/age Z-score)
% infants breastfed w/in 8 hours of birth
% infants under 6 months breastfed only
% infants 6-10 months fed complementary foods
% infants continuously fed during diarrhea
Impact
% infants fed extra food for 2 weeks after diarrhea
% eligible children in growth monitoring/promotion
% children immunized for measles at 12 months
% of communities with community health organizations
Health,
nutrition,
and MCH
Annual
monitoring
% children in growth promotion program gaining weight in past 3 months (by
gender)
% infants with diarrhea in last two weeks
Liters of household water use per person
% population with proper hand washing behavior
Impact
% households with access to adequate sanitation (also annual monitoring)
% households with year-round access to safe water
Water and
sanitation
Annual
monitoring % water/sanitation facilities maintained by community
% households consuming minimum daily food requirements
Number of meals/snacks eaten per day
Household
food
consumption
Impact
Number of different food/food groups eaten
Annual yield of targeted crops
Yield gaps (actual vs. potential)
Yield variability under varying conditions
Value of agricultural production per vulnerable household
Months of household grain provisions
Impact
% of crops lost to pests or environment
Annual yield of targeted crops
Number of hectares in which improved practices adopted
Agricultural
productivity
Annual
monitoring
Number of storage facilities built and used
Imputed soil erosion
Imputed soil fertility
Impact
Yields or yield variability (also annual monitoring)
Number of hectares in which NRM practices used
Natural
resource
management
Annual
monitoring Seedling/sapling survival rate
Agriculture input price margins between areas
Availability of key agriculture inputs
Staple food transport costs by seasons
Volume of agriculture produce transported by households to markets
Impact
Volume of vehicle traffic by vehicle type
Kilometers of farm to market roads rehabilitated
FFW/ CFW
roads
Annual
monitoring Selected annual measurements of the impact indicators

Appendix 27: Setting Food Diversity Targets
86
APPENDIX 27. SETTING FOOD DIVERSITY TARGETS
An increase in the average number of different foods or food groups consumed provides a quantifiable
measure of improved household food security. However, to use this indicator to assess improvements in
food security, the changes in consumption diversity must be compared to some meaningful target level of
diversity. Unfortunately, data on ‘ideal’ or ‘target’ levels of diversity are usually not available.
Several options are available to determine appropriate targets. One method is to use the consumption
patterns of wealthier households as targets, with the assumption that poorer households will diversify
their food expenditures as incomes rise, and thereby mirror the consumption patterns of wealthier
households. Because projects using the dietary diversity indicator usually include interventions aimed at
household income, baseline surveys generally collect some income or economic status information, in
addition to the dietary data. If income data are available, the sample should be divided into four income
groups (quartiles of income), and the average number of food groups consumed should be calculated for
the richest income quartile. The average dietary diversity in the richest 25 percent of households can then
serve as a target level of dietary diversity for the purpose of performance monitoring. Where income data
are not available, income groups can be defined using proxies, such as possession of assets or other items
found to be highly correlated with income in the project population.
In the absence of income or economic data from the baseline survey, a food-diversity target can be
established by taking the average diversity of 25 percent of households with the highest diversity (upper
quartile of diversity). Because most food security projects aim to increase household incomes as a means
to improve food security, income-based targets are preferable to this diversity- based target.
Instructions on how to code income quantities and calculate average diversity using SPSS appear below.
The program can also be used to calculate diversity quartiles, by substituting diversity for income. In
either case, the descriptive statistics need to be run on the diversity variable. Using the Windows 95
version of SPSS, locate in the pull down menu TRANSFORM. “Rank Cases” creates new variables
containing ranks, normal, and savage scores, as well as percentile values for numeric variables. New
variable names and descriptive variable labels are automatically generated by SPSS, based on the original
variable name and the selected measure(s). A summary table lists the original variables, the new variable,
and the variable labels.
Cases can be ranked either in ascending or descending order. Organize rankings into subgroups by
selecting one or more grouping variables for the By list. Ranks are computed within each group. Groups
are defined by the combination of values of the grouping variables. For example, if you select GENDER
and MINORITY as grouping variables, ranks are computed for each combination of GENDER and
MINORITY.
Use the “Rank Types” button to select multiple ranking methods. A separate ranking variable is created
for each method. Ranking methods include simple ranks, savage scores, fractional ranks, and percentiles.
Rankings can also be created based on proportion estimates and normal scores.
RANK
VARIABLES=3Dincome (A) /RANK /NTILES (4) /PRINT=3DYES
/TIES=3DMEAN
-----------------------------------

Appendix 27: Setting Food Diversity Targets
87
Example:
DATA FILE
Food Group Household ID # (HHID#)
12345678
Cereals 1 1 1 1 1 1 1 1
Roots/tubers 0 0 1 0 0 0 0 1
Milk/milk
products 01110010
Eggs 01101011
Meat/offal 0 1 1 1 0 0 1 1
Fish/seafood 0 0 0 0 1 0 0 0
Oil/fat 1 1 1 1 1 1 1 1
Sugar/honey 1 1 1 1 1 1 1 1
Fruits 00100010
Vegetables 1 1 1 1 1 0 1 1
Other (spices,
sodas, etc) 01111111
DIVERSE (total #
of food groups
consumed)
481077498
INCOME 250 700 1500 540 630 180 980 760
Frequency variable = INCOME /format=notables /ntiles=4.
FREQUENCY COMMAND OUTPUT
INCOME
Percentile Value Percentile Value Percentile Value
25 322.5 50 665.0 75 925.0
IF STATEMENT TO CREATE QUARTILE VARIABLE:
If (INCOME <= 322.5) QUARTILE = 1.
If (INCOME > 322.5 and INCOME <= 665.0) QUARTILE = 2.
If (INCOME > 665.0 and INCOME <= 925.0) QUARTILE = 3.
If (INCOME > 925.0) QUARTILE = 4.

Appendix 27: Setting Food Diversity Targets
88
DATA FILE RESULT
HHID# 12345678
DIVERSE 4 8 10 7 7 4 9 8
INCOME 250 700 1500 540 630 180 980 760
QUARTILE 1 3 4 2 2143
CALCULATE AVERAGE DIVERSITY (DIVERSE) FOR HOUSEHOLDS IN
QUARTILE 4
Select if (QUARTILE = 4).
Descriptives variable DIVERSE.
OUTPUT OF DESCRIPTIVES COMMAND
Number of valid observations (listwise) = 2.00
Variable Mean StdDev Minimum Maximum Valid N
DIVERSE 9.50 .71 9 10 2
CALCULATE AVERAGE DIVERSITY FOR HOUSEHOLDS IN SAMPLE
Descriptives variable DIVERSE.
OUTPUT OF DESCRIPTIVES COMMAND
Number of valid observations (listwise) = 8.00
Variable Mean StdDev Minimum Maximum Valid N
DIVERSE 7.13 2.17 4 10 8
The average dietary diversity among the 25 percent richest households is 9.50. Current diversity for the
sample as a whole is 7.13. The PVO can use this data to establish baseline (7.13) and target (9.50)
diversity levels for the target population.