## ![](media/image1.wmf){width="2.645138888888889in" height="0.78125in"}December 2006 Series No 4 

**An Exploratory Analysis of Child Nutritional Status in the Sahel, the
Goundam Circle Case Study - Timbuktu Region -- Mali**

**By**

Dramane Mariko[^1]

Carolyn Hughes[^2]

Abstract

The malnutrition of children (0-59 months) is a public health concern in
Africa, particularly in the Sahelian countries. In spite of better agro
climatic conditions and agricultural production in many of these
countries, the nutritional status of children under-five continues to
deteriorate. This working paper summarizes the first results of a series
of exploratory analyses initiated by the regional office of FFP in Dakar
to understand the underlying causes of child malnutrition in West
Africa. Anthropometric and socio-economic data (at village level) from
the Goundam Food Security Initiative (GFSI) project carried out by
Africare/Mali were used. For this first exploratory phase of analysis,
descriptive statistics and factor analyses (Principal Component
Analysis - PCA, Multiple Correspondences Analysis - MCA) were used. The
results from the analysis reveal that reducing the number of underweight
children under-five continues to be a challenge in the study zone with
an average rate of stunting varying between 37% and 44% depending on the
month and year. However, it becomes worse (severe malnutrition) because
of the lack of food. Nutritional status, food access and agricultural
production systems are the three main village factors that influence the
rate of underweight children under-five. Regarding chronic malnutrition
of children under-five, the results of the analyses show that in the
Goundam area, seven socio-economic variables are strongly related to
stunting, including, by order of importance: 1) Presence of a school in
the village, 2) Irrigated Village Perimeter, 3) Improved wells, 4)
Practice of market gardening, 5) Production of millet, 6) Distance from
the village to the city, and 7) Production of sorghum. The first four
variables are negatively correlated to stunting, while the last three
variables are positively correlated with the rate of chronic
malnutrition.

**Keywords**: food security, child under-five malnutrition, factor
analysis

**Introduction**

The malnutrition of children (0-59 months) is a public health concern in
Africa, particularly in Sahelian countries. In spite of better agro
climatic conditions and agricultural production in many Sahelian
countries, the nutritional status of children under-five continues to
deteriorate. Within the framework of its food security projects in West
Africa, the USAID/FFP is financing food security activities with child
nutrition components. However, in many of these Sahelian countries, the
impact of these food security projects on the malnutrition of children
under-five is below target. This working paper summarizes the first
results of a series of exploratory analyses initiated by the regional
office of FFP in Dakar to understand the underling causes of child
malnutrition in West Africa. Anthropometric data from the Goundam Food
Security Initiative (GFSI) project carried out by Africare Mali were
used.

**Context**

In 2005, Mali ranked 174^th^ out of 177 countries according to UNDP's
Human Development Index. Seventy-one percent of its adult population is
illiterate and 91% of the population lives on less than \$2 per day. The
Malian economy relies on subsistence agriculture, with millet/sorghum,
rice, and maize as staple crops. Mali is one of the continent\'s biggest
cotton producers.

  According to the latest Demographic and Health Survey (DHS III - Mali
2001), four out of 10 children (38%) in Mali suffered from some kind of
chronic child malnutrition and half of them (19%) suffered from severe
chronic malnutrition (- 3 standard deviation). About 11% of children
were wasted and 33% underweight. Infant mortality due to malnutrition
and diseases was 219 per 1,000 births and 51% of this rate is due to
malnutrition (DHS III- Mali 2001). Per capita food availability in Mali
is highly variable. Mali's agricultural production system is largely
dependant on rainfall. Mali is a food insecure country, in particular in
the northern regions of Gao, Timbuktu, and Kidal

Located in the northern region of Mali, the circle of Goundam is one of
the largest circles[^3] in Mali. It covers a total surface of 92,688
km^2^ with a total population of 161,988 (census of 1998). The
population density is less than 2 people per km^2^. The circle of
Goundam where the GFSI project has been implemented is located in the
Sahel-Sahara zone of the country with an average rainfall varying
between 100 and 300 mm of rain per year. In 2004/2005, the recorded
rainfall in the town of Goundam was 114 mm against an average of 140 mm
in 2003/2004, and 300 mm in 2002/2003. It is a zone of recessional
farming system around lakes. Recently, these lakes have started to dry
up during the off-season because of silting irrigation canals. One notes
a massive departure of the population from the villages around these
lakes towards the bordering zones of the Niger River and the south.

The NGO Africare has been in Goundam since 1984 with various
intervention activities. From the USAID funding, Africare carried out
the Community Health Project of Goundam (PSCOM-G) in the circle of
Goundam. This project provided to the communities a nutritional control
of children, a prevention of diarrhea diseases, services of immunization
and medical education.

**Limits of the study**

This work has been limited by the quality and the quantity of data
available at the GFSI project level. For the first phase of the project,
only part of the data of the final evaluation has been found. Regarding
data on chronic malnutrition (stunting), the survey sampling method has
not been applied throughout the different studies conducted (Baseline,
mid-term and final evaluation). Furthermore, it has been noticed that in
97% of cases, survey enumerators estimated the age parameter of the
children. This means that the results measured for nutritional indexes
related to stunting and underweight are not totally reliable
(Africare/Mali Goundam Food Security Initiative -- Household Survey
Results, August 2001, page 5). This anthropometric data quality problem
is not unique to the GFSI project. The same problem has been found with
the Title II project in Burkina Faso (Africare), Mauritania (WVI), and
Niger (PVO consortium).

**Objectives and Methodology**

The general goal of these analyses is to evaluate the malnutrition
status of children under-five in the zones of the food security projects
financed by USAID/FFP in West Africa.

**Specific objectives**:

\- To evaluate the trends of the malnutrition status of children under
five, from the project monitoring /impact indicators; and

\- To identify the independent variables which may impact the
malnutrition status of children under-five in West African countries,
case of the GFSI project in the Timbuktu region - Mali.

**Methods and materials**

**Data collection**: analyzed data were collected from the GFSI project.
The following indicators have been analyzed:

\- Underweight (percent of children of a given age range with
weight-for-age z score less than -2 or less than -- 3 standard
deviation);

\- Stunting (percent of children of a given age range with
height-for-age z score less than -2 standard deviation).

These two indicators are part of the USAID/FFP Title II MCH programs
impact indicators. The Africare Chad/Mali project is not collecting data
on wasting. The data on the underweight variables are collected monthly
at the time of the child's growth monitoring. The results are reported
at the end of each quarter. For chronic malnutrition "stunting", data
were collected during the survey of the final evaluation of the
project's first phase (in 2001), during the baseline studies (in 2003),
and the mid-term evaluation (in 2005) of the second phase of the
project. This information was supported by secondary data collected in
the project zone on the food security activities carried out by the
project. The statistical unit of analysis is the village. The data
provided by the project GFSI are aggregated numbers (i.e. percent per
village) from individual household data. The EPINFO software was used by
the project to analyze and standardize the collected anthropometric
data. Village data were transferred onto the SPSS software at the
RFFP/Senegal office for appropriate analyses.

**Statistical analysis:** .for this first exploratory phase of analysis,
descriptive statistics ands factor analyses[^4] (Principal Component
Analysis - PCA, Multiple Correspondences Analysis - MCA) were used. The
PCA is an exploratory technique of multidimensional data analysis. It
was used for the analysis of underweight children data. For the analysis
of the stunted[^5] children under-five data, the use of the MCA was
chosen. For that, the original variable of stunting (continuous
variable) was transformed into a qualitative variable with the following
three modalities:

\- Village slightly affected by the chronic malnutrition of children:
less than 20% of children in the village are stunted;

\- Village moderately affected by the chronic malnutrition of children:
20% - 39% of children are stunted in the village; and

\- Village severely affected by the chronic malnutrition of children:
more than 40% of children are stunted.

**Results**

**Descriptive statistics**

The analysis of anthropometric data collected from the GFSI project on
the prevalence of underweight children (under five) over the period from
December 2000 to September 2005 shows that this form of malnutrition
continues to be a challenge (with rates \> 20%) in the study zone. In
general, it reaches its peak in June with an average rate varying from
43% to 44% of weighed children. June is considered part of the hungry
season (June - September).

The rate of severely malnourished children (Weight/Age \<-3 SD) follows
the same trends as that of the rate of global malnutrition. It is
largely influenced by the food availability/access at the household
level (see graph 1). The minimum rate of severe malnutrition is observed
in September. The maximum recorded rate is 11% in June 2005. That means
that in June 2005, 11 children out of 100 in the project zone should
have been referred to hospitals as emergency cases due to severe
malnutrition problems.

**Graph 1: Evolution Child Malnutrition and Food Availability**

![](media/image2.wmf){width="5.998611111111111in"
height="3.3826388888888888in"}

The rate of underweight children under-five (table 2) is more important
among the sedentary population than the pastoralists. The average rate
of global (severe + moderate) underweight children under-five in the
pastoral zone is 37.5% (CV=38%) against 36%, 40 %( CV=26%) and 41%
(CV=37%) for the children under-five in the zones of river, Irrigated
Village Perimeters (IVP), and of the Lakes respectively (see table 1).
However the average rate of severely underweight children is higher
among the children of pastoralists (8.84%) than those of sedentary IVP
cereal producers (7.60%), and flooded rice growers (6.50%). The rate of
severely underweight children in the livestock zone remains lower than
the rate of malnourished children living in the villages located around
the lakes (9.91%)[^6].

As shown in graph 2, the average rate of stunted children (0-59 months)
in 1998 is 34%. In 2001, the first phase of the project impact
evaluation covered only focal villages[^7] (16 villages). The results of
this evaluation reveal an improvement of the stunting rate (26.9%). It
should be noted that part of these focal villages has benefited from the
support of USAID/Mali funded health project in Goundam (PSCOMG). The
second phase of the GFSI project started in 2003 with fifty villages
covered which included 10 focal villages of the first phase. The
baseline studies of this second phase gave a rate of chronic
malnutrition of children (24-59 months) of 36.6%. Furthermore, the
results of the midterm evaluation of this second phase show an increase
of the rate of stunted children compared to the baseline data. The rate
of stunted children increased to 38.5% in 2005 (midterm evaluation
results). This increase in the rate of chronic malnutrition can be
explained by several factors: the drought, the progressive draining of
the Faguibine lake, the early drying of wells, the closing of the
Goundam health project (PSCOMG), the lack of sufficient health services
in the study zone, the absence of adequate financing for the first two
years of the GFSI project' second phase. Nevertheless, the stunting rate
was reduced to 32.6% in FY 2006 in the study area. Compared to the
mid-term evaluation data, the stunting rate was reduced by 6 points. At
the same time, the chronic malnutrition rate was 28.4% for the first
generation villages of the project against 34% for the new (second
generation) villages (Africare Chad Mali FY 2006 Results Report). In
2001, the national rate of stunted children (0-59 months) in Mali was
38% while the average rate in the northern regions (Gao/Kidal/Timbuktu)
was 40.1% (DHS III- Mali 2001). However, according to the results of
"Enquête globale sur la Sécurité alimentaire et la
Nutrition "(AGSVA)[^8] -- December 2005, the Timbuktu region had a child
chronic malnutrition rate of 24%. For the Gao and Kidal regions this
rate was 25% and 14% respectively. The rate of stunted children was 46 %
for the Sikasso region, 41% for Koulikoro and 38% for the Segou region,
which are the main cereal and cotton production regions of the country.
The World Health Organisation (WHO) critical level for stunting rate is
30%.

**Graph 2: Evolution of the Chronic Child Malnutrition (Stunting) Rate
in Goundam**

![](media/image3.wmf){width="5.997222222222222in"
height="2.7805555555555554in"}

As for the rate of underweight children under-five, the delay in child
growth is more concentrated among sedentary farmers than among nomadic
pastoralists in the study zone. The rate of chronic malnutrition (24-59
month) was 32.29% in 2005 for the pastoralists as opposed to 35% to 42%
for cereal growers (see annex table 7)[^9].

According to the United Nations Children's Fund's framework for the
causes of child malnutrition (UNICEF 1990, 1998) and the subsequent
extended models. The determinants of child malnutrition can be divided
into three levels of causality: immediate, underlying, and basic (Lisa
C. Smith, and Lawrence Haddad). The immediate determinants are dietary
intake and health status. They are influenced by underling determinants
-- food security, care of mothers and children, and health environment
quality. These underlying causes are, in turn, influenced by basic
determinants. They include the potential resources available to a
country or community which are a function of natural resources, access
to technology and the quality of human resources. This paper analyses
the influence of some of these underlying determinants (agricultural
production system, health environment, and education) on child
malnutrition in the context of the Sahel region, case of Goundam in
Mali.

**Socio-economic determinants of underweight children under-five**

Child malnutrition is a complex phenomenon whose causes depend on
several factors such as structural, economic and socio-cultural. To
analyze the pattern of relationships between these factors (variables)
and child malnutrition, the factor analysis (PCA) technique was used.
For this first exploratory phase the underweight variables - percentages
per village of the various groups of malnourished children: severely
malnourished (PCTROUGE), moderately malnourished (PCTJAUNE), and well
nourished (PCTVERT) - are put in relation with the socio-economic
variables of the villages.

The results of the analyses show that the data analyzed can be grouped
into five factors or components (see annex table 3). These components
account for 67% of the total variance created by the variables analyzed.
The interpretation of these components shows that the first represents
the "**Production System**". It opposes an agro-pastoral system (bovine,
camel, sheep, millet/sorghum production) to an irrigated farming system
(irrigated rice production, Irrigated Village Perimeter (VIP), vegetable
market gardening). The agro pastoral zone is characterized by a high
rate of severely malnourished children. The second factor "**Village
infrastructure"** shows that infrastructure like schools, markets, tube
wells (boreholes) and health centers are very strongly correlated. In
general, they are located in the same area (large villages, urban
centers). According to the results of the analyses, village
infrastructure does not have significant direct influence on the
variance of the underweight child under-five rate in the short run. The
third factor, \"**Accessibility to food**" shows that access to food by
the household is strongly related to the presence of IVP, improved wells
(water availability) and the presence of food secure households in the
village. The degree of correlation between the \"**Accessibility to
food**\" component and the variable Percentage of severely malnourished
children under-five is weak (0.28). One can conclude that food
accessibility by the household in the study zone has a moderate
influence on the rate of underweight children under-five in the village.
This might be explained by the quality of food offered to the children
under-five especially during the hungry season. The fourth factor
represents the **\"Village nutritional status\"**. It highlights the
importance of access to drinking water, medical services, and the
education of women in the reduction of the rate of underweight children
under-five. The sign of loading scores (see annex table 3) indicates
that an improvement of access to potable water by the population, more
specifically children under-five, can reduce the rate of underweight
children under-five. This finding has been confirmed by the
Africare/Mali project midterm evaluation results (June 2005) that
concluded that 4 out of 10 children (39.5%) in the project area had
diarrhea during the last two weeks before the survey. Also, a reduction
of the distance from the village to the health service delivery point
can lower the rate of underweight children under-five. The variable
Village nutritional volunteer is positively correlated with the
\"Village Nutritional Status\" component whereas one expected a negative
relation. A high number of village nutritional volunteers well trained
in the execution of their activities in the village should result in a
reduction of the prevalence of the children under-five malnutrition.
Probably the variable Village nutritional volunteer has captured other
phenomena which positively influence the malnutrition of children
under-five. There is a need for more investigation on this issue. These
findings corroborate with the results of the GFSI project final
evaluation survey (July 2006). These results show that child underweight
is strongly related to bloody diarrhea (Chi-2 test value=3.4, p=0.000).
Overall, 5% of surveyed mothers have stated that their child under five
had bloody diarrhea during the two last weeks before the survey. This
rate was 12% among mothers with underweight children. Also, the number
of mothers who don't have a sanitation facility (latrine) at home is
important among mothers with underweight children (68%) than the global
percentage of mothers who don't have a latrine (57%). Also, these
findings suggest that the rate of child underweight in the Goundam area
is influenced by episodes of fever (Chi-2 test value=2.06, p=0.020) and
diarrhea (Chi-2 test value=1.73 p=0.042) of children.

A positive correlation was found between the underweight, chronic
malnutrition and wasting indicators in Mali (EDS-III Mali 2001, page
158). The socio-economic variables related to child chronic malnutrition
available for the project are much more qualitative than quantitative;
therefore, the Multiples Correspondences Analysis (MCA) as an analysis
tool is preferred.

**Socio economic determinants of chronic malnutrition (Height /Age) of
children under-five**

The MCA is used to measure the degree of liaison between the qualitative
variable of stunting and the other qualitative socio-economic variables
of the villages. The interpretation of the different variables'
contribution to the formation of the factors makes it possible to
understand the patterns that exist between these socio-economic
variables and the stunting level in the village.

As a starting point, the stunting variable was characterized using all
the socio-economic variables of the village that could have a
significant influence on the growth of the child. This analysis will
explore the different relationships between village socio-economic
variables and stunting. This analysis uses the crossing tables of the
stunting and the other variables[^10]. Among the thirty qualitative
variables analyzed with the stunting variable, 22 socio-economic
variables have significant relationships (Chi-2 test value \> 2 or p =
0.000) with the chronic malnutrition of children. Seven variables among
those have maximum test values (99.9). These 7 variables are much
related to stunting. By order of importance, these variables are:

Presence of school in the village, Irrigated Village Perimeters (IVP),
improved wells for potable water, market garden, millet crop production,
distance of the village to the urban town of Goundam, and sorghum crop
production.

The first 5 factors generated following the MCA explains 62% of the
total variance created by the original variables. According to the plan
of the first two factors, about 40% of the variance created by the
original variables is explained by these factors.

The first factor represents the livestock breeding area (bovine, ovine,
goat, camel). This area is characterized by the existence of operating
tube wells (boreholes), health services with incomplete personnel in
general It corresponds to the desert part of the project zone where the
only possible activity is livestock breeding. **This area is less
affected by chronic malnutrition of children than the agricultural
production area**. These results are confirmed by other studies carried
out in Mali which showed that the malnutrition of children under-five is
concentrated more in the sedentary zones than in the pastoral zones. One
possible explanation of these results is that in the livestock breeder
communities, the milk of livestock is directly managed by women. The
main use of this milk is for feeding children. In the cereal producer
area, the low quality of child and pregnant women's diet is an important
determinant of stunting.

The second factor (Component) represents the irrigated crop production
system area. It opposes the recessional crop production area (zone of
lakes) to the irrigated crops (rice and wheat). **The zone of lakes is
characterized by villages where more than 40% of children are considered
chronically malnourished (stunted).** This chronic malnutrition can be
explained as one of the logical consequences of the environmental
degradation of the lakes. For a few years, it has been noted the
progressive drying of the Faguibine Lake which constitutes the "economic
lung" of the zone. More and more, the irrigation canal of the Faguibine
Lake is filled with sand thus preventing the water of the Niger River
from reaching the lake. The wells of the villages around these lakes
dried up very precociously because of the low level of the water table.
During the off-season, the sumps dug in the lakes are the principal
sources of drinking water for the population. The irrigated crop
production area is where Irrigated Village Perimeters exist. Women grow
vegetable crops during the off-season. The villages of the irrigated
zone are not far from health delivery points. The improved wells in
these villages have water all year round. They are located in a radius
of 30-50 km of the town of Goundam. In FY 2005, the average rate of
chronic malnutrition among children under-five in these villages was 34%
against 43% in villages around the lakes.

As the results of the MCA show, the chronic malnutrition of children is
a structural problem whose solutions require multi-sector interventions
such as access to food, health services, hygiene, drinking water, and,
the education of women. These findings are validated by the results of
the crossing of the stunting variable with the socio-economic variables
of the village. The modalities of the seven identified variables have
been strongly related to the variable stunting, and all are correlated
with the irrigated farming system factor (second component).

**Conclusion**

The malnutrition of children under-five is a complex and
multidimensional phenomenon whose causes depend on several factors
(structural, socio-economic and cultural). Results from the analyses
reveal that underweight children under-five is a quasi permanent problem
in the study zone with an average rate varying between 37 to 44%
depending on the months and years. The underweight child rate reaches
its peak during the hungry period (June - August) when household food
becomes limited. The global underweight of the child under-five is
related to the life style of the parents, especially mothers. However,
it becomes worse (severe malnutrition) by the lack of food as observed
in the study zone. The level of social services in the village
(existence of health facilities, access to potable water for children)
is also important. The results of the PCA show that nutritional status,
food access and production system are the three main factors that
influence the rate of underweight children under-five.

The results of the analyses indicate that the rate of chronically
malnourished child under-five has been increasing in the study. The
Multiple Correspondence Analysis (MCA) results shows that in the Goundam
area, seven socio-economic variables are strongly related to stunting,
by order of importance: 1) Presence of a school in the village, 2)
Irrigated Village Perimeters, 3) Improved wells, 4) Practice of market
gardening, 5) Production of millet, 6) Distance from the village to the
city, and 7) Production of sorghum. The first four variables are
negatively correlated to stunting, while the last three variables are
positively correlated with the rate of chronic malnutrition. In other
words, chronic malnutrition of children under-five is very important
among children whose parents grow millet and sorghum as main crops i.e.
zone of lakes (43%). The stunting rate is less in the zones where
development potentials exist i.e. livestock zone (32%), village
irrigated perimeter zone (35%), river zone (37%). The stunting rate
decreases when one moves toward the city. Based on this result, the
diversification of agricultural production systems, for example the
cultivation of beans or sweet potato in the lakes for household food
consumption might be one of the solutions to child under-five
malnutrition in the study zone.

**Lessons learned**

The results of exploratory study confirm some of the previous findings
about child malnutrition causes in developing countries.

1\. Village socioeconomic environment (production system,
infrastructures, and health services) are the underlying determinants of
child malnutrition. They influence the nutritional status of children
under-five through immediate determinants (dietary intake and health
status of children).

3\. Access to potable water for children and women is a determinant
factor for improving children under-five\'s nutritional status in West
Africa.

4\. The access of households to food can positively impact children's
nutritional status in the Sahel region, but the impact is moderate
compared to health and child feeding factors.

5\. For the sustainability of nutritional programs, communities should
be at the heart of proposed interventions.

**Acknowledgements**

Authors are grateful to all those who contributed to the review of this
working paper. In particular, we would like to thank **Marie
Loustaunou**, Food For Peace Officer at USAID/Washington, for her
thorough review of the French and English versions of this paper, **Dr.
Gilles Bergon** FANTA for his methodological advice. Special thanks to
**Sounka N'Diaye,** Monitoring & Evaluation Specialist at USAID/Dakar,
for his comments and suggestions. We would like to send our appreciation
to **Dr. Mohamed Ag Bendech**, Nutrition Advisor at Helen Keller
Intentional/Dakar, for his highly valuable contributions which were key
to the comprehension and interpretation of statistical results. Without
the involvement and leadership of **Carolyn Hughes**, RFFP/Senegal team
leader, the RFFP/Senegal Professional Paper Series would not be a
reality. We are grateful for her leadership, and methodological advice
on this paper.

[References]{.underline}

1\. Africare Mali, Goundam Food Security Initiative (GFSI) - Household
Survey Results -- August 2001.

2\. Antonio L. Cunha, Peter A. Margolis, Steven Wing -- Community
Economic Development and acute lower respiratoty infection in Children.

3\. Carlos Larrea, Pedro Montalvo, Ana Maria Ricaurte - Child
Malnutrition, Social Development and Health Services in the Andean
Region

4\. Demographic & Health Survey III -- Mali 2001

5\. Jean Pierre LECH AUD, Université Montesquieux Bordeaux IV- France,
La dynamique de l'inégalité de la malnutrition des enfants en Afrique.
Une Analyse comparative fondée sur une décomposition de régression.

6\. Lisa C Smith, Lawrence Haddad, IFPRI, Explaining Child Malnutrition
in Developing Countries -- A Cross-Country Analysis.

7\. G. Phillippeau, STAT-ITCF, comment interpreter les resultas d'une
analyse en composantes principales ?

8\. Nepali Sah, Save the Children Japan-Nepal Office - Determinants of
Child Malnutrition in Nepal: A Case Analysis from Dhanusha, Central
Teral of Nepal.

9\. Programme Alimentaire Mondial (PAM), Système d'alerte Précoce (SAP)
Mali -- Suivi de la sécurité Alimentaire à Travers les sites sentinelles
Résultats du premier passage des enquêtes -- Juin 2005

10\. Programme Alimentaire Mondial (PAM), Système d'alerte Précoce (SAP)
Mali - Enquêtes médico -nutritionnelles et socio--économiques dans les
zones nomades de Ber( Tombouctou), N' Tillit, Bamba et Temera (GAO),
Adjelhok et Anafif (KIDAL) et Dans le Sahel Occidental -- Juillet 2005

11\. Systeme SPAD pour Window, Version 5, SPAD Base -- Aide à
l'interprétation.

12\. V. Kelly, J. Teff, J.Oehmke, and John Staatz, Staff Paper --
Identifying Policy Relevant Variables for Reducing Childhood Malnutrtion
in Rural Mali -- December 2004

13\. Understanding Factor Analysis R.J. Rummel

14\. StatSoft, Inc. (2006). Electronic Statistics Textbook. Tulsa, UK:
StatSoft. WEB: http://www.statsoft.com/textbook/stathome.html

**ANNEX**

Table 1: Descriptive Statistic of Underweight Children under-five rate

+---------+----------+------+-------+-------+-------+-------+--------+
|         |          | Ave  | S.D   | Confi |       | Mi    | M      |
|         |          | rage |       | dence |       | nimum | aximum |
|         |          |      |       | int   |       |       |        |
|         |          |      |       | erval |       |       |        |
|         |          |      |       | at    |       |       |        |
|         |          |      |       | 95%   |       |       |        |
|         |          |      |       | for   |       |       |        |
|         |          |      |       | the   |       |       |        |
|         |          |      |       | av    |       |       |        |
|         |          |      |       | erage |       |       |        |
+---------+----------+------+-------+-------+-------+-------+--------+
| Form of | Pr       | \%   |       | Lower | H     |       |        |
| Unde    | oduction |      |       | limit | igher |       |        |
| rweight | system   |      |       |       |       |       |        |
|         |          |      |       |       | Limit |       |        |
+---------+----------+------+-------+-------+-------+-------+--------+
| M       | L        | 3    | 14.22 | 36.54 | 38.39 | 0.00  | 77.78  |
| oderate | ivestock | 7.46 |       |       |       |       |        |
+---------+----------+------+-------+-------+-------+-------+--------+
|         | Lake     | 4    | 10.82 | 40.58 | 41.15 | 7.69  | 73.91  |
|         |          | 0.86 |       |       |       |       |        |
+---------+----------+------+-------+-------+-------+-------+--------+
|         | VIP      | 4    | 15.05 | 39.90 | 40.80 | 6.90  | 100.00 |
|         |          | 0.35 |       |       |       |       |        |
+---------+----------+------+-------+-------+-------+-------+--------+
|         | Flooded  | 3    | 13.95 | 36.12 | 36.78 | 0.00  | 77.78  |
|         | rice     | 6.45 |       |       |       |       |        |
+---------+----------+------+-------+-------+-------+-------+--------+
|         | Total    | 3    | 13.51 | 38.65 | 39.05 | 0.00  | 100.00 |
|         |          | 8.85 |       |       |       |       |        |
+---------+----------+------+-------+-------+-------+-------+--------+
| Severe  | L        | 8.84 | 8.19  | 8.31  | 9.38  | 0.00  | 36.36  |
|         | ivestock |      |       |       |       |       |        |
+---------+----------+------+-------+-------+-------+-------+--------+
|         | Lake     | 9.91 | 6.26  | 9.74  | 10.07 | 0.00  | 33.33  |
+---------+----------+------+-------+-------+-------+-------+--------+
|         | VIP      | 7.60 | 5.63  | 7.43  | 7.77  | 0.00  | 26.30  |
+---------+----------+------+-------+-------+-------+-------+--------+
|         | Flooded  | 6.50 | 4.96  | 6.38  | 6.62  | 0.00  | 33.33  |
|         | rice     |      |       |       |       |       |        |
+---------+----------+------+-------+-------+-------+-------+--------+
|         | Total    | 7.96 | 5.93  | 7.88  | 8.05  | 0.00  | 36.36  |
+---------+----------+------+-------+-------+-------+-------+--------+

Table 2: Descriptive Statistic of chronic malnutrition of children
(24-59 months) rate

  ----------------- --------- ------- ------------ ------------ ----------------- -------- -------------- --------- --------------- --------- ------------- ----
  Production system Average   S.D                  Confidence                                             Minimum                   Maximum                 
                                                   interval at                                                                                              
                                                   95% for the                                                                                              
                                                   average                                                                                                  

                    \%                             Lower limit                    Higher                                                                    
                                                                                  limit                                                                     

  Livestock         32.29     3.99                 31.47                          33.11                   25.00                     36.00                   

  Lake              42.74     9.22                 41.98                          43.50                   21.00                     58.00                   

  PIV               35.34     18.25                33.49                          37.20                   7.00                      69.00                   

  Flooded rice      37.09     14.26                35.54                          38.65                   8.00                      75.00                   

  Total             38.64     13.77                37.90                          39.37                   7.00                      75.00                   

  Table 3 :                                                                                                                                                 
  Component Matrix                                                                                                                                          
  after rotation                                                                                                                                            

                                      Components                                                                                                            

                                      Production                Village                    Food                     Village                   Nutritional   
                                      system                    infrastructures            availability             accessibility             status        

  PCT severely                        0.30                                                 0.28                                               0.42          
  malnourished                                                                                                                                              

  PCT moderate                                                                                                                                0.70          
  malnourish.                                                                                                                                               

  CSCOM                                                         0.80                                                                                        

  Improved wells                      0.31                                                 -0.62                                              -0.34         

  Borehole                                                      0.80                                                                                        

  School                                                        0.83                                                                                        

  Market                                                        0.91                                                                                        

  Village                                                                                                           -0.82                                   
  accessibility                                                                                                                                             

  Distance/health                                               -0.47                                                                         0.46          
  center                                                                                                                                                    

  Vegetable garden                    -0.63                                                                         0.39                                    

  Nutritional                         -0.35                                                                         0.56                      0.47          
  volunteers                                                                                                                                                

  Sorghum/corn                        0.30                      0.32                       0.61                                                             
  production                                                                                                                                                

  Camel                               0.84                                                                                                                  

  Bovine                              0.85                                                                                                                  

  PCT category III                                                                         0.84                                                             
  HH                                                                                                                                                        

  Goat                                0.82                                                                          0.36                                    

  Distance/Town                       0.73                                                                                                                  

  Nb of month of                      -0.38                                                -0.66                                                            
  food availab.                                                                                                                                             

  Extraction                                                                                                                                                
  method: Principal                                                                                                                                         
  Component                                                                                                                                                 
  Analysis.                                                                                                                                                 

  Rotation method :                                                                                                                                         
  Varimax with                                                                                                                                              
  Kaiser                                                                                                                                                    
  normalization.                                                                                                                                            

  Rotation                                                                                                                                                  
  converged in 5                                                                                                                                            
  iterations.                                                                                                                                               
  ----------------- --------- ------- ------------ ------------ ----------------- -------- -------------- --------- --------------- --------- ------------- ----

[^1]: Agricultural Economist, Food Aid Development Specialist, RFFP
    Dakar

[^2]: Agricultural Economist, Team Leader, RFFP Dakar

[^3]: A territorial or administrative division of the country

[^4]: **Principal factors vs. principal components.** The defining
    characteristic then that distinguishes between the two factor
    analytic models is that in principal components analysis we assume
    that *all* variability in an item should be used in the analysis,
    while in principal factor analysis we only use the variability in an
    item that it has in common with the other items. (from the StatSoft,
    Inc. (2006). Electronic Statistics Textbook)

[^5]: Most variables used to characterize the stunting variable are
    categorical variables.

[^6]: The t tests computed are significant at 5% level.

[^7]: Focal villages= villages that benefited from the project's support

[^8]: Food Security and Vulnerability Assessment organized and funded by
    WFP and UNICEF Mali

[^9]: The t tests computed are significant at 5% level.

[^10]: The Chi-2 statistics associated with each crossing of two
    variables and the probability of exceeding this value are calculated
    (test of Chi-2). Each probability is assigned a "test value".