MEASUREMENT OF FOOD DEPRIVATION

Jorge Mernies

FAO Statistics Division

**[TABLE OF CONTENTS]{.underline}**

1.  INTRODUCTION

2.  METHODOLOGICAL FRAMEWORK

3.  ESTIMATION PROCEDURES

4.  ARE THERE OTHER VALID OPTIONS TO THE FAO'S APPROACH?

5.  FOOD DEPRIVATION, CHILD MALNUTRITION AND INCOME POVERTY INDICATORS

> BIBLIOGRAPHY

# 1. Introduction

The FAO measure of food deprivation, which is referred to as the
prevalence of undernourishment, is based on a comparison of usual food
consumption expressed in terms of dietary energy (kcal) with minimum
energy requirement norms. The part of the population with food
consumption below the minimum energy requirement is considered underfed.

For the purpose of monitoring progress towards the target of halving the
number of undernourished, the need had arisen to regularly up-date such
estimates at the global as well as country level. FAO has been
undertaking this task in its annual report on "The State of Food
Insecurity in the World" (SOFI), which was first issued in 1999. SOFI
2003, which is the latest report, was issued in October 2003. The
estimates cover 45 developing countries in Africa, 30 in Asia, 19 in
Latin America and 5 in the Caribbean; the 12 Eastern European countries,
the 12 CIS countries and the 3 Baltic States

In the following sections the basic methodological framework, the data
sources and the procedures used by FAO for deriving the country
estimates are described. Then, the alternatives to the FAO's approach
and the need for complementary indicators are discussed. The last
section examines the relationship between food deprivation, child
malnutrition and income poverty indicators.

# 

# 

# Methodological Framework

The estimate of the proportion of the population below minimum level of
dietary energy consumption has been defined within a probability
distribution framework:

$P(U)\  = \ P(x < \ r_{L}\ )\  = \ \int_{x < r_{L}}^{}f(x)\ \text{dx}\  = \ F_{x}\ (r_{L})$
(1)

where

*P (U) is the proportion of undernourished in total population*

*(x)* refers to the dietary energy consumption

*r~L\ ~*is a cut-off point reflecting the *minimum energy requirement*

*f(x)* is the density function of dietary energy consumption. and

*Fx* is the cumulative distribution function

The next two sections discuss the estimation of *f(x)* and *r~L\ ~*.

###### 

###### 3. ESTIMATION PROCEDURES

The graph below illustrates the framework for the estimation procedures.

**Proportion of population undernourished**

In the graph the curve *f(x)* depicts the proportion of the population
corresponding to different per caput dietary energy consumption levels
*(x)* represented by the horizontal line. The area under the curve up to
the cut-off point, *r~L~*, represents the proportion of the population
undernourished.

The density function, *f(x)*, is assumed to be lognormal so that the
parameters *µ* and *σ²* can be estimated on the basis of the
mean,$\overline{x}$, and the coefficient of variation, *CV(x)*. A
summarised description of the procedure for calculating the prevalence
of undernourishment on the basis of $\overline{x}$, *CV(x)* and rL is
given below.

***3.1* Estimation *of the mean and CV of f(x)***

***3.1.1* *Estimation of the mean,*** $\overline{x}$

There are two options for estimating the mean: using Food Balance Sheet
(FBS) data or Household Budget Survey (HBS) data. The first can be used
to prepare annual estimates for monitoring progress in food security for
the country as a whole. The second one allows the derivation of
sub-national estimates. The latter estimates can not be prepared on a
yearly basis, as they depend on the survey frequency, in general ranging
from 5 to 10 years.

***3.1.1.1 Dietary energy consumption from the Food Balance Sheet***

The mean is represented by the *per caput* Dietary Energy Supply
(**DES**) which refers to the food available for human consumption
during the course of the reference period, expressed in terms of energy
(kcal/person/day). The estimate is derived from the Food Balance Sheets
compiled on the basis of data on the production and trade and of food
commodities. Using these data and the available information on stock
changes, losses between the level at which production is recorded and
the household gate, and types of utilization a supply/utilization
account is prepared for each commodity in weight terms. The food
component, which is usually derived as a balancing item, refers to the
total amount of the commodity available for human consumption during the
year. The total DES is obtained by aggregating the food component of all
commodities after conversion into energy values.

The procedure of using the *per caput* DES derived from the food balance
has some advantages as indicated below.

-   The FAO *per caput* DES database, which covers practically all
    countries of the world, is regularly revised and up-dated in
    connection with FAO's continuous work programme on
    supply/utilization accounts and food balance sheets. As a result the
    database represents a readily available source of information for
    the assessment and monitoring of the prevalence of food deprivation
    at the global, regional and country levels.

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-   The linkage of the *per caput* DES with a measure of inequality
    within a probability framework provides a mechanism for assessing
    the effect of short-term changes in food supply as well as its
    components (production, import, etc.) on the distribution of dietary
    energy consumption and hence the prevalence of food deprivation. In
    addition, the use of a probability model -- such as the log-normal
    function -- facilitates the assessment of expected changes in the
    prevalence of food deprivation as a result of the combined effect of
    food supply increase and inequality reduction.

***3.1.1.2 Dietary energy consumption from Household Budget Survey***

This option requires converting quantities of the different food items
consumed by the household into energy values. These data are usually
collected through budget surveys using large scale samples which may
allow mean estimates not only at the national level but also at
sub-national levels such as geographic areas and socio-economic
population groups.

***3.1.2 Estimation of the coefficient of variation**, **CV(x)***

The **CV** of the household *per caput* dietary energy consumption is
formulated as follows:

$\text{CV}(x)\  = \ \sqrt{\text{CV}^{2}(x|v)\  + \ \text{CV}^{2}\ (x|r)}$,

where **CV(*x*)** is the total CV of the household *per caput* dietary
energy consumption, **CV (*x\|v*)** is the component due to household
*per caput* income **(*v*)** and **CV (*x\|r*)** is the component due to
energy requirement **(*r*)**. **CV (*x\|r*)** is considered to be a
fixed component and is estimated to correspond to about 0.20. **CV
(x\|v)** is however estimated on the basis of household survey data.

For the purpose of estimation, **CV (x\|v)** is formulated as follows:

CV (x\|v) = σ (x\|v) / μ(x) .

The numerator of the ratio is derived as

$$\sigma(x|v)\  = \sqrt{\ \left\lbrack \sum_{j = 1}^{k}{f_{j}\ (x|v)_{j}^{2}\  - \ (}\sum_{j = 1}^{k}{f_{j}\ (x|v)_{j})^{2}\frac{\ }{\ }n} \right\rbrack\frac{\ }{\ }(n - 1)\ }$$

and the denominator, which is the overall average household *per caput*
dietary energy consumption, is derived as

$\mu(x) = \ \sum_{}^{}{(x\frac{)}{n}}$

where ***k*** is the number of income classes and ***f ~j~*** is the
number of sampled households and **(x\|v)~j~** is the average household
*per caput* dietary energy consumption of the ***jth*** income or
expenditure class.

Thus, the data required for estimating **CV (x\|v)** are the averages of
household *per caput* dietary energy consumption by household *per
caput* income or expenditure classes from **n** households and the
number of households in each class.

According to the sample size and design, the CV estimates can be
broken-down by geographic areas and socio-economic groups.

***3.2 Estimation of the minimum energy requirement (cut-off point), r
~L~***

The procedure for estimating the minimum energy requirement by sex and
age group begins with the specification of the reference body weight.
After specifying the reference body weight the procedure for arriving at
the corresponding energy requirement differs between children below age
10 on the one hand and adolescents and adults on the other. Therefore
the procedure for deriving the reference body weight is handled first,
followed by two separate subsections dealing with the derivation of
minimum energy requirements for children and adolescents and adults and
lastly a fourth subsection dealing with the derivation of the overall
minimum *per caput* energy requirement. Minimum energy requirements can
be estimated for geographic and socioeconomic groups, using survey data
on heights and demographic structure.

***3.2.1 Reference body- weight***

The reference body weights by sex and age groups are based on the
available weight-for-height reference tables. Thus given an estimate of
the actual height the acceptable weight corresponding to this height is
derived from these tables.

For **children below age 10 the** reference body weight is fixed at the
median of the range of weight-for-height given by the WHO reference
tables (WHO, 1983).

For **adolescents and adults** of age 10 and above, the reference body
weight is estimated on the basis of the fifth percentile of the
distribution of the Body Mass Index [^1] (WHO, 1995).

The actual heights by sex and age used are those estimated by national
anthropometric studies.

***3.2.2 Minimum energy requirements for children below 10***

The minimum energy requirement per person for children is obtained by
multiplying the reference body weight by the recommended energy
requirement per kilogram of body weight for each sex/age group. The
energy requirements per kilogram of body weight are based on the
recommendations of the report of the FAO/WHO/UNU Expert Consultation on
Energy and Protein Requirements (FAO/WHO/UNU, 1985).

***3.2.3 Minimum energy requirements for adults and adolescents aged 10
and above***

The minimum energy requirements per person for adults and adolescents
are derived by first estimating the Basic Metabolic Rate (**BMR)** on
the basis of the reference body weight and using the sex and age
specific regression parameters of the Schofield equations (James and
Schofield, 1990).

Then, the minimum energy requirements are derived by multiplying BMR by
sex specific Physical Activity Level factors.

***3.2.4 The overall minimum per caput energy requirement***

The overall minimum *per caput* dietary energy requirement, which is
used as the cut-off point, **r** ~L~, for estimating the prevalence of
undernourishment, is derived by aggregating the sex-age requirements
weighted by the proportion of each sex and age group in the total
population.

Finally a pregnancy allowance (**PA**) in *per caput* terms for the
whole population is added to the overall requirement. The PA is
estimated by multiplying the birth rate by 75 kcal (assuming an
estimated daily requirement of 100 kcal during pregnancy over 75 per
cent of the year).

Thus, the overall minimum *per caput* energy requirement is derived as

**r ~L\ =~ Σ ~ij~ (MER ~ij~ \* P ~ij~ ) + PA**

where

**MER =** minimum energy requirement per person

**P~ij\ =\ ~**proportion of each sex and age group in the total
population

**PA** = pregnancy allowance

**i** = age group

**j =** sex

***3.3**. **Estimation of the proportion and number of undernourished
***

The density function of dietary energy consumption, **f(x),** is assumed
to be lognormal with parameters **µ** and **σ ².** These are estimated
on the basis of $\overline{x}$and *CV*(*x*) as follows:

**σ = \[ log ~e~ (CV ² (x) + 1) \] ^0.5^**

and

**μ = log ~e~** $\overline{x}$ **-- σ ² / 2**

The proportion of population below **r~L~** is then evaluated as
follows:

**Ф \[ (log~e~ r~L~ --μ) / σ) \]**

Where **Ф =** standard normal cumulative distribution.

**4.** **ARE THERE OTHER VALID OPTIONS TO THE FAO'S APPROACH?**

The need for finding alternatives to FAO's parametric approach for
estimating food deprivation, was widely discussed during the
International Scientific Symposium (ISS) held in Rome in June 2002. As
mentioned in previous sections, the mean of the food consumption
distribution can be estimated using data from national food accounts or
national household budget surveys, as both allows for the measurement of
a similar concept of food consumption, but using different procedures.
The use of one or the other source depends on the purpose and scope of
the measurement. As regards the second parameter of the distribution,
the situation is different: the inequality in access to food can be
derived from national household budget survey data only. For the scope
of global monitoring of WFS and MDG hunger reduction targets, the FAO
estimates are based on food consumption levels derived from food
accounts. The current national household survey programmes do not
provide an adequate global and regional coverage on a regular basis.
However, for the purpose of targeting sub-national areas and population
groups, FAO approach allows for the estimation of both parameters of the
food consumption distribution from household survey data.

During the last two years, several efforts for finding better
alternatives for estimating the prevalence of undernourishment have been
fruitless. For example, some authors have proposed a non-parametric
approach based on the classification of each sampled household as
undernourished / not undernourished. This procedure is methodologically
incorrect because it is not possible -- for reasons already explained in
the document describing the FAO measurement of undernourishment
presented at the ISS - to derive adequate estimates of food consumption
and needs *for each individual household*. Therefore, it cannot be
considered as a valid alternative. In view of the above, FAO's approach,
in spite of signalled limitations, is still the best available way for
measuring undernourishment. However, as a follow-up of the ISS
discussions, FAO is actively working on the integration of a suite of
complementary indicators reflecting not only the food situation
(nourishment) but also other key dimensions such as income, health,
environment and nutritional status. Furthermore, FAO is undertaking case
studies using qualitative measures of food insecurity as complementary
approaches to enhance the assessment of the food situation.

**5. FOOD DEPRIVATION, CHILD MALNUTRITION AND POVERTY INDICATORS**

The World Food Summit (WFS) in 1996 set the goal - to reduce the number
of hungry people in the world by half before the year 2015. Four years
later, that goal was echoed in the first of the Millennium Development
Goals (MDGs), which set targets of reducing by half both the proportion
of people who suffer from hunger and the proportion living on less than
US\$1 per day. These targets are closely related; neither can be
achieved without the other, and achieving both is essential to success
in reaching the rest of the MDGs.

While poverty is undoubtedly a cause of hunger, hunger can also be a
cause of poverty. Hunger often deprives impoverished people of the one
valuable resource they can call their own: the strength and skill to
work productively. Hunger seriously impairs the ability of the poor to
develop their skills and reduces the productivity of their labour.

Hunger in childhood impairs mental and physical growth, crippling the
capacity to learn and earn. Evidence from household food surveys in
developing countries shows that adults with smaller and slighter body
frames caused by undernourishment earn lower wages in jobs involving
physical labour. And they are frequently unable to work as many hours or
years as well-nourished people, as they fall sick more often and have
shorter life spans.

Therefore, any effort to achieve sustainable development demands a
concerted effort to reduce poverty, including finding solutions to
hunger and malnutrition. Alleviating hunger is a prerequisite for
sustainable poverty reduction since better nourishment improves labour
productivity and the earning capacity of individuals. In order to make
poverty reduction strategies work, they must address adequate food
access. However, this is not a sufficient condition to ensure a good
nutritional status. Malnutrition can be the outcome of a range of
circumstances such as bad health and environmental conditions.

The assessment of progress made to halve between 1990 and 2015 the
proportion of people who suffer from hunger, is based on two related
aspects: food deprivation or \"undernourishment\" and child
malnutrition. The analysis on undernourishment is based on the FAO
estimates. These figures are estimates of the proportion of the
population below the minimum level of dietary energy consumption. The
data on child malnutrition are based on the estimates of underweight
prevalence (low weight-for-age) prepared by UNICEF. Both indicators are
related but they are not measuring the same thing and are not referring
to the same population. The first indicator refers to the prevalence of
food deprivation in the whole population. The second indicator refers to
the prevalence of underweight in the child population and, as mentioned
before, is related not only to food deprivation but also to other
factors such as infections, adequate family care and environmental
conditions.

In view of the above, there is a clear need to assess separately each
one of the three dimensions: income poverty, food deprivation and child
malnutrition. This would allow for the analysis of their relationship
and the differences in trends at the global, regional and country
levels.

**BIBLIOGRAPHY**

**FAO,** *1977. The Fourth World Food Survey*, Rome

**FAO***, 1987. The Fifth World Food Survey,* Rome

**FAO,** *1996. The Sixth World Food Survey,* Rome

**FAO,** *1999. The State of Food Insecurity in the World,* Rome.

**FAO,** *2000. The State of Food Insecurity in the World,* Rome.

**FAO,** *2001. The State of Food Insecurity in the World,* Rome.

**FAO,** *2002. The State of Food Insecurity in the World,* Rome

**FAO,** *2003. The State of Food Insecurity in the World,* Rome

**FAO/WHO/UNU***, 1985. Energy and Protein Requirements.* Report of a
joint FAO/WHO/UNU ad hoc Expert Consultation. Geneva, WHO, WHO Tech.
Rpt. Ser. 724.

**James, W.P.T. & Schofield, E.C.,** 1990. Human energy requirements.
Oxford, Oxford University Press.

**Naiken, L.,** *1998. On Certain Statistical Issues Arising from the
Use of Energy Requirements in Estimating the Prevalence of Energy
Inadequacy (Undernutrition)*. Journal of the Indian Society of
Agricultural Statistics, Vol. L1, N. 2.3, pp. 113-128.

**Naiken, L.,** *2002. FAO methodology for estimating the prevalence of
undernourishment* Key note paper presented at the International
Scientific Symposium on Measurement of Food Deprivation and
Undernutrition (26-28 June, 2002).

**WHO,** *1983. Measuring change in nutritional status.* WHO, Geneva.

**WHO,** *1995. Physical Status: the use and interpretation of
anthropometry.* WHO Technical Report Series 854. Geneva.

[^1]: The BMI refers to weight (kg) divided by height² (m).