![](media/image1.wmf){width="2.645138888888889in" height="0.78125in"}

## November 2005 Series No 3 

**MIGHT WOMEN'S GARDENING ACTIVITIES HAVE AN UNINTENDED NEGATIVE IMPACT
ON CHILDREN'S SCHOOL ATTENDANCE?**

**Summary:**

A recent survey conducted by the Regional Food for Peace Office in the
Abéché region of eastern Chad concluded that more than 50% of women
active in vegetable gardening activities in 2004 used child labor and
that only 7% hired extra labor of any kind. The survey also found that a
high number of children are not attending school. The study concluded
that child work might explain in part the low school attendance rate,
and that gardening programs such as the one in the Ouaddaï department
might discourage women from sending these free laborers to school. The
present study aimed to look more closely at these issues by posing the
following research question: 1) Is child labor associated with market
gardening an important contributor to low school attendance in Abéché or
are other variables (lack of or inadequate schools, lack of teachers or
some cultural phenomena) the key determinants of low school attendance?
We have tried to answer these questions through a review of the
literature and an analysis of the relationship between gardening and
school attendance. **The findings suggest that, while other factors such
as presence of schools and school and teacher quality are important,
gardening in Ouaddaï has a negative impact on school attendance as
garden size and labor demands (and household income) increase.** A
logistic regression model was used to provide a better understanding of
the interrelationship between women gardening and schooling. This paper
is divided into three sections: literature review, regression results
and, conclusions and policy recommendations.

**Review of literature**

Research on school attendance in developing countries has shown that low
levels of school participation are not completely explained by the lack
of schools (Mamadou Thiam, April 2004). Although governments are the
primary providers of schooling in Africa, parents, household
characteristics, other family members, community and tradition play a
part in the decision as to whether children attend school (Lloyd and
Blanc, 1996). Some children do not attend despite the availability of
schools. Often, children do not go to school because they work, for a
variety of reasons (the most important of which is probably poverty).
Children are engaged in economic employment and domestic work (Shapiro
et al 2003). In developing countries, children serve as major
contributors to household welfare through significant participation in
the running of the home, for example by caring for younger children or
fetching water and wood or by working in the family farm or business .

Many studies in Africa have concluded that parents' education is one of
the most important determinants of children's participation in school.
In Ghana, fathers with relatively high levels of education are unlikely
to have children who participate in child labor (Cananagarajah/Coulombe
1997) and more likely to be in school. The high child labor rates and
the low school enrollment rates in Ghana generally are the consequence
of the perceived low quality of schooling (Ray 2000).

School attendance by a child is also highly correlated with family
income (Ilon and Mook 1991) and household wealth influences the demand
for schooling. Poor families are often not able to bear the cost of
sending their children to school. Children's participation in economic
employment and domestic work is a common survival strategy for poor
families. Girls do more home-related work than boys and thus may be less
likely to attend school. Urban and rural area disparities can impact
child school attendance.

The effect of poverty is always significant but the order of magnitude
is moderate (Sudharshan, Canagarajah, Helena Skyt Nielsen, 1999).

Schooling problems also *contribute* to child labor (Faraaz Siddiqi et
al.). Sometimes children seek employment simply because there is no
access to schools (distance, no school at all). When there is access,
the low quality of the education often makes attendance a waste of time
for the students. Schools in many developing countries suffer from
problems such as overcrowding and apathetic teachers. As a result,
parents often conclude that there is no point in sending their children
to school when they could be home learning a skill (for example,
agriculture).

Traditional factors and religion are also important determinants of
child school attendance in Africa. In certain countries girls will not
go to school because they will not fit into traditional roles if they
become educated (Bequele and Boyden 1988). Parents think that an
educated girl will not get married. In Islamic African countries, some
parents prefer sending children to Koranic school than to formal school.

Mamadou Thiam (April 2004) conducted a child work and schooling study in
Niger. Using the MICS-2[^1] survey data collected in 2000, this study
shows that child work does not affect school attendance. However, it
significantly increases grade repetition and school dropout. The study
also finds that children from poor households are less likely to attend
school. The more educated the caretaker, the higher probability of
children attending school. Thiam finds that in Niger, urbanization and
gender can affect children's school attendance. Boys and urban children
are twice as likely to attend school as girls and rural children.

A common opinion in the African context is that the presence of
under-five children in the household has a negative effect on children's
school attendance, especially on girls' attendance. Thiam reported that
Chernichovsky (1985) conducted a school enrollment and attendance study
in rural Botswana and concluded that the presence of very young siblings
was detrimental to older children's school attendance. He said that
Canagarajah and Coulombe (1997) did not find such effect in a recent
study of child labor and schooling in Ghana. The Niger study finds that
the number of children under 5 residing in the household is not related
to children's likelihood of attending school.

The household's decision to enroll a child in school also may depend on
the perceived cost and benefit associated with schooling. Parents'
willingness to send children to school will be affected by their beliefs
about the profitability of schooling relative to other activities and
their ability to afford schooling. The expected returns from education
are therefore an important factor (Peter Matz).

High costs of schooling can serve to pull children away from school as
was found to be the case in Ghana (Canagarajah /Coulombe 1997). In Chad,
the non-governmental organizations (NGOs) are promoting community
schools where teachers are hired and paid by the community from the
parents' contribution (1,500 FCFA or \$3 per month per child). This fee
is having the effect of excluding children from school whose parents can
not afford the fees.

The lack of teaching materials and housing for teachers can lead to
teacher absences, reduced attendance and poor quality education as an
example from the Siedougou school in Burkina Faso shows. In
Siedougou[^2], an international NGO is supporting a school of 150
children (45% girls) with a school lunch program and take home rations
for girls. The school was created in 1998 and has three teachers for
three classrooms. Children are divided by grade, and each teacher
teaches two different grades in the same classroom. Only the school
director has a house in the village; the two other teachers live in
another village 6 km from Siedougou. According the chief of the village,
this situation has negatively impacted the village schooling because
there is a high absentee rate among teachers. Teaching materials are
also lacking. For example, in 2003, the fifth grade children did not
receive any books from the Government. The lack of teachers and teaching
materials has clearly translated to poor student achievement. (The
success rate on the CEP (6^th^ grade) exam in 2000 was only 45% (8 out
of 18 students passed) while the national rate was 65% (RFFP/Senegal,
Burkina Faso trip report - 2004).

A comparative study on child labor and schooling in Africa concluded
that one way to reduce child labor and increase incentives to keep the
children in the educational system is to improve the access to credit
(Canagarajah/Nielsen 1999). However, Peter Matz proposes income transfer
programs in order to stimulate demand.

**Background Information on Chad**

Chad, a landlocked country is Africa\'s fifth-largest country. It is
among the world\'s poorest and least-developed nations. Currently the UN
Human Development Index ranks Chad 167 out of 177 countries. About 80
percent of the population survives on agriculture - subsistence farming,
fishing and herding livestock. The country is regularly subject to
climatic disturbances and natural disasters (drought, floods, and
locusts) that lead to food shortages (World Food Program 2005). The most
recent occurrence is the entry of refugees from the war-ravaged Dafur
region in Sudan.

In 2000, UNICEF estimated that 65.5 percent of children ages 5 to 14
years in Chad were working (World Bank, World Development Indicators
2003, Washington, D.C., 2003). In southern Chad, children are contracted
to nomadic herders to tend animals. These children are often abused and
provided little monetary compensation for their work. (U.S. Department
of State, Country Reports- 2002: Chad, Section 6d). Children also work
for little compensation as domestic servants in the households in
cities. 

**Data and methods**

Data were collected during the Ouaddaï women gardening survey and were
complemented with secondary data and interviews with Africare Chad field
agents working in the study zones. A total of 125 women were interviewed
for the gardening survey[^3]. The questionnaire covers modules relating
to the socio-economic characteristics, gardening activities and the
impact of these activities on household food security. The questionnaire
was not designed with the intention to study gardening activities and
child school attendance. Using a logistics regression model (logit), we
attempt to understand the socio-economic characteristics and kinds of
gardening activities that might affect a woman's likelihood of sending
her children to school.

The explanatory variables used in the model were: interviewed woman's
age, matrimonial status (non married, married, widowed, divorced),
education level, number of children, presence of school in the village,
use of child labor for gardening activities, use of gardening income
(buying cereal for household, credit reimbursement, buying small animal,
commerce), distance to the regional capital Abéché, and geographic
locations (cantons).

**Results**

Children's school attendance rate is very low in the survey area. Only
32% of married women have at least one child in school. Of 236 children
who were greater than 6 years old, only 54 (23%) are currently attending
school. More than 70% of these students are from only three
cantons[^4]\--where school attendance rates are high: Ouaddaï Hamra
(68%), Koniere (56%) and Mandjabo (50%). School attendance rates in the
other 6 cantons are between 0 and 21%. The sex ratio of school
attendance is 1 girl for 3 boys. The results of the regression analysis
are presented in table 2.

Although, much of the literature on the determinants of schooling
indicated that the educational level of the household head and the
caretaker are important determinants of children's school attendance,
there was no relationship observed between women's education level and
child school attendance in the present study. This is probably due to
the very high illiteracy rate (98%) among the women surveyed. Only 3
women out of 122 women have a primary education level.

Contrary to the conventional wisdom in Africa that the presence of
children under-five in the household has a negative impact on children's
school attendance, this study finds that the number of children under
five does not affect a woman's probability of having children in school.
Similar conclusions were reached in Niger (Thiam, 2004) and Ghana
(Canagarajah and Coulombe, 1997). On the other hand, the number of
school age children (6-14 years old) is an important determinant of a
woman's likelihood of having children in school. The logistic regression
coefficients of two variables (number of children 6- 10 years old, and
number "more than 10 years old" children) are statistically significant
(p=0.008, p=0,032). However, the variable "number of children 6-10
years" is more highly positively correlated with the likelihood of a
woman having children in school (32% higher) than is the second variable
("number of children more than ten years old"). Possible explanations
for this observed relationship include the fact that in a harsh
environment like the Ouaddaï department in Chad, a woman's primary
source of assistance in her daily housework is her children, especially
girls. It is not probable that a woman will send her only child to
school if she has to walk every day more than 10 km to look for drinking
water, mill cereal, fetch firewood, prepare and bring food to the field.
In general in the Sahelian society, children 6-10 years old are the
closest to their mother in the household. Children 10 years or older
have a tendency already to leave the family either to get married
(girls) or to look for a job in the cities (boys). The regression
results imply that the more help a woman has, the higher likelihood
there is that she will send at least some children to school.

Nevertheless, Nankhuni and Findeis in August 2003, using a 2SCML model
for children's schooling in Malawi concluded that the presence of more
children above age 6 is associated with lower children's school
attendance, while the presence of more 1- 5 year olds is not
statistically significant. According to them, this is likely due to the
increased financial demands that children place on a household. However
they do add, seemingly in concordance with this study findings, that
presence of more young girls (6-10 years) and women all age categories,
is correlated with increased school attendance.

The results suggest that the presence of a school in the village does
not necessary imply a high likelihood of school attendance. The variable
presence of public school in the village was used.
![](media/image2.jpeg){width="4.25in" height="3.1354166666666665in"} The
coefficient of this variable is not statistically significant (p=0.349)
implying there is no difference between public school and community
school in the study area. This is probably due to the GOC's primary
school policy which gives priority to the construction of new a woman
filling a 20 liter container with drinking water, after six Kilometers

walking -- Ouaddaï -- Chad

schools rather than school quality. For example, through the World Bank
and other funding sources, there is a proliferation of new schools in
Chad, especially in rural areas. This policy is reinforced by the
development of community schools in the villages where the Government
does not intervene. For example, from a sample of 25 villages surveyed,
16 schools were counted. Five of these schools were constructed and
managed by the communities. In these surveyed villages, only 23% of
school age children (6-14 years old) are attending school. This rate
seems to be very low compared to the national rate of 34% (2000) in the
rural areas of Chad. This low school attendance is probably due to many
factors: quality of school, the relevance of teaching materials to
children, the opportunity costs of schooling for children's parents and
cultural and religious factors. It has also been reported that many non
professional teachers who were teaching in the community schools in the
study area have left teaching for humanitarian work when NGOs, which pay
better salaries, came to Chad in support of the Sudanese refugees.

In addition, many teachers are reluctant to work in remote rural areas.
In terms of school quality, public versus communal school, the
regression results reveal that there is no quality difference between
public and communal schools that impact a woman's likelihood of having a
child in school.

In order to address the question of the relationship between school
attendance and women's gardening activities in Chad, a dummy variable
was used to assess the impact of the use of child labor by woman on
schooling. The variable was not statistically significant (p=0.885),
meaning that a mother using her child in a gardening activity is not
less likely to have a child in school compared to a woman who is not
using her child (when all levels of gardening income are examined
together). The Niger study (Thiam 2003) confirms that, even after
controlling other factors. Many studies argue that owning a non farm
business could positively impact attendance. Non working children still
do not significantly differ on school attendance.

The most important and perhaps surprising finding is that a woman's net
gardening income has a negative effect on a woman's likelihood to have
her children in school. The logistic regression coefficient for revenue
is statistically significant at 10% level (p-value = 0.08). Holding
other factors constant, this implies that an increase in a woman's
income will decrease her odds of sending her children to school. When
the net income variable was transformed into class
income[^5]![](media/image3.jpeg){width="4.5in"
height="3.6354166666666665in"} variables, only the coefficient of the
highest class (class 4) is negatively significant (p=0.044). For example
a woman with an income higher or equal to 300,000 F CFA (\$500) is less
likely to have children in school than a woman with 50,000 F CFA (\$100)
net income. This finding is confirmed by the results of the multiple
correspondences analysis (see table 2) using the same data. Canagarajah
and Nielson reported that studies which estimate a bivariate probit
model generally indicate

A community School constructed by the community using WFP food aid in
Chad that the effect of the income measure on school attendance is
always significantly positive, whereas the effect on child labor is less
clear. Our regression results have confirmed some field evidences. The
canton of Ouaddaï Chok is known as the most important vegetable crop
production area in the Ouaddaï department. On average each woman works
on 1,196 m^2^ against an average of 250 m^2^. The mean income in this
zone is \$368,630 or \$736 per woman per season. Eighty six per cent
(86%) of women surveyed use their children as laborers in gardens while
only 5% of school age children are going to school. On the other hand,
in the Ouaddaï Harma canton, where school attendance is the highest
(68.18%), average net gardening income per woman is 89,678 F CFA or
\$179. Forty percent of women surveyed use their children in gardening
activities, however, most of these children are both going to school and
helping their mothers in the garden. The agricultural field agents
report that they have often been contacted by teachers and asked to
sensitize mothers regarding the importance of allowing their children to
go to school.

This finding---that as a woman's income from gardening increases, her
tendency to send her children to school decreases (with the implicit
assumption that income increases with increasing hectarage farmed and
that, in turn, increasing hectarage means an increase in labor demands,
and hence child labor)\--might be explained by the opportunity costs and
the immediate benefit that women can get from gardening and sending
children to school. In rural areas of Cote d'Ivoire, Coulombe (1998)
finds the probability of school attendance decreases proportionally with
the number of acres of land owned by the household. The negative
relationship between women's gardening and school attendance seems to be
explained by the high demand for labor for gardening---especially as
gardening incomes increase.

Among the matrimonial status variables introduced in the model, only the
divorced variable is statistically significant. A divorced woman is less
likely than a widowed, married or non married woman of having her
children in school. This finding is probably due to the marginalized and
vulnerable status of divorced women in Chadian society. Most divorced
rural mothers are extremely poor and cannot afford to send their
children to school. However, this variable is not stable in our model
and is influenced by other variables i.e. geographic locations,
activities.

The effect of distance to the regional capital was examined and as would
be expected, there was a negative relationship between distance from the
regional capital and school attendance (the coefficient of this variable
is significant p-value = 0.02). Women living long distances from
regional capitals are less likely to have children in school and
teachers resist working in remote schools.

Villages were classified according to their potential for wadi (river
beds) gardening activities. A dummy variable was used to assess the
impact of wadis on women's likelihood of having children in school. The
regression coefficient of this variable is negatively significant (p=
0,001) implying that women living in areas with wadis are less likely of
having children in school than women in villages where access to wadis
is difficult.

Many school attendance studies in Africa reported that the possession of
assets by a woman can significantly impact school attendance. Others
concluded that school attendance is highly correlated with family income
(Ilon and Mook 1991) and that household wealth influences the demand for
schooling. To test this hypothesis, several variables were used as asset
possession proxies. Only the coefficient of the variable "has bought
small animals" was significant and positive---suggesting that the
possession of small animals secures women in the case of food or other
financial emergency and increases her likelihood of having children in
school. For instance, animals may be sold to finance school expenses.

Regarding "location variables", the survey covered nine cantons
(districts) and each canton was considered as a dummy variable. Location
effects may reflect traditions and attitudes but also measurement errors
related to included or omitted variables describing for instance the
education system, infrastructure, demand for labor in the study area,
and credit constraints (Sudharshan, Canagarajah and Helena Skyt, Nielsen
1999).

[Conclusion]{.underline}

An analysis of the data in this study and a review of the literature
suggest that while other factors mainly related to the quality of
schooling are also important, **gardening in Ouaddaï has a negative
impact on school attendance as garden size and labor demands (and
household income) increase.**

The logistic regression results show that the Ouaddaï women's likelihood
of sending children to school increases as the number of children over 6
years old in the household increases probably in large part because
children in this age group are a woman's main source of assistance in
the household. Any activity or project - i.e. potable water in the
village, cereal milling that will reduce women's daily workload will
increase her likelihood of having children in school. Contrary to
conventional wisdom, the number of children under five does not affect
the probability of school attendance for the household's children. On
the other hand, the low quality of schooling in the Ouaddai - i.e. lack
of qualified teachers in remote villages, the high rate out of student,
local religious practices do not encourage women to send children to
school. There is no statistical difference between public and communal
school in the Ouaddaï department.

Finally, the study suggests that although there is a very profitable
vegetable gardening activity in the region, it is highly labor intensive
and that child labor is the main source for fulfilling that need.
Gardening activities appear therefore to negatively affect school
attendance when they reach the commercial stage. For low income class
women, vegetable gardening does not have an impact on children's school
attendance. To increase gardening income, women will increase the size
of their garden which requires more labor, which is mainly furnished
through child labor. **The findings suggest that gardening in Ouaddaï
has a negative impact on school attendance as garden size and labor
demands (and household income) increase.**

A widowed woman is very vulnerable in the Ouaddaï department because of
her social status. The likelihood that she will send her children to
school is very low compared to that of other women. She needs special
attention from a development perspective. The logistic regression
results reveal that the Ouaddaï department is not homogeneous in terms
of school attendance. Geographic location is an important determinant in
schooling.

This study is just a first attempt to look at the relationship between
market gardening, child labor and school attendance and its findings
need to be validated by other gardening and school attendance studies in
the Ouaddaï department (and elsewhere in West Africa where women's
gardening activities are funded by Title II resources such as in Burkina
Faso, Mali, and Guinea). Future studies in the Ouaddaï could be improved
over the present exercise through the use of a larger sample size and by
considering other possible determinant variables such as: household
characteristics, and credit availability, etc.

[Policy recommendations]{.underline}

1.  Given the high involvement of children in vegetable gardening
    production in Chad and elsewhere in the West Africa region and its
    possible consequences on school attendance, governments and their
    partners should make schooling and gardening activities more closely
    linked--- rather than attempting a radical step such as the
    elimination of child labor. One approach would be to promote the
    creation of vegetable gardens in schools where children could learn
    the most efficient gardening technologies. Parents could benefit
    from this experience which would also constitute an incentive for
    sending children to school.

2.  PVOS, USAID and the governments should make it a priority to provide
    new technologies to women working in the gardening sector to reduce
    labor demand. School gardens could be used as a base for the
    diffusion of more efficient technologies such as drip irrigation.

3.  Parents make recourse to child labor because they don't have other
    alternatives. Making microfinance loans available to women working
    in the vegetable gardening sector might result in a reduction in the
    demand for child labor and increase school attendance, as women are
    able to hire labor. Microfinance school loans could encourage women
    to send children to school and increase incentives to keep children
    in the education system.

4.  Household work load is one of the most important burdens for rural
    women in the Sahel region. In addition, it is depriving children of
    access to education when they are a woman's only source of
    assistance. Assisting women to reduce their daily household work
    load could significantly increase children school attendance.
    Examples include but are not limited to digging village wells,
    increasing incomes, providing milling machines, improving child
    health, helping women obtain more assets: activities in all of these
    areas could substantially reduce women's workload and therefore
    improve the likelihood that children will be freed up to attend
    school.

5.  Title II food distributions, for example through school feeding
    programs or take home rations should be used as a form of incentive
    for parents to send their children to school.

Table 1: Logit Regression coefficients

+----------------+----+----+-------+------+----+------+-------+------+
| ** **          | *  |    |       |      | *  |      |       |      |
|                | *M |    |       |      | *  |      |       |      |
|                | od |    |       |      | ** |      |       |      |
|                | el |    |       |      |    |      |       |      |
|                | I  |    |       |      | *  |      |       |      |
|                | ** |    |       |      | *M |      |       |      |
|                |    |    |       |      | od |      |       |      |
|                |    |    |       |      | el |      |       |      |
|                |    |    |       |      | II |      |       |      |
|                |    |    |       |      |    |      |       |      |
|                |    |    |       |      | ** |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
| **Variable**   | *  | ** | **z-S | *    | *  | **   | **z-S | *    |
|                | *C | St | tatis | *Pro | *C | Std. | tatis | *Pro |
|                | oe | d. | tic** | b.** | oe | Err  | tic** | b.** |
|                | f. | E  |       |      | f. | or** |       |      |
|                | ** | rr |       |      | ** |      |       |      |
|                |    | or |       |      |    |      |       |      |
|                |    | ** |       |      |    |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
|                |    |    |       |      |    |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
| C              | 7  | 5  | 1.283 | 0    | 1  | 2    | 0.404 | 0    |
|                | .6 | .9 |       | .200 | .1 | .794 |       | .686 |
|                | 43 | 58 |       |      | 28 |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
|                |    |    |       |      |    |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
| Age            | 0  | 0  | 0.964 | 0    | 0  | 0    | 0.912 | 0    |
|                | .0 | .0 |       | .335 | .0 | .043 |       | .362 |
|                | 41 | 43 |       |      | 39 |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
|                |    |    |       |      |    |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
| Education      | -1 | 2  | -     | 0    | -1 | 2    | -     | 0    |
|                | .2 | .7 | 0.458 | .647 | .7 | .924 | 0.612 | .540 |
|                | 40 | 05 |       |      | 90 |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
|                |    |    |       |      |    |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
| Matrimonial    |    |    |       |      |    |      |       |      |
| Status         |    |    |       |      |    |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
| Non married    | \  | 1  | \--   | \--  |    |      |       |      |
|                | -\ |    |       |      |    |      |       |      |
|                | -- |    |       |      |    |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
| Divorced       | -0 | 2  | -     | 0    | -1 | 2    | -     | 0    |
|                | .6 | .1 | 0.308 | .758 | .1 | .241 | 0.497 | .619 |
|                | 56 | 33 |       |      | 13 |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
| Married        | -0 | 1  | -     | 0    | -0 | 1    | -     | 0    |
|                | .3 | .3 | 0.244 | .807 | .4 | .386 | 0.354 | .723 |
|                | 35 | 69 |       |      | 91 |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
| Widow          | -0 | 1  | -     | 0    | -0 | 1    | -     | 0    |
|                | .0 | .6 | 0.044 | .965 | .3 | .657 | 0.187 | .852 |
|                | 72 | 35 |       |      | 10 |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
|                |    |    |       |      |    |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
| Number of      |    |    |       |      |    |      |       |      |
| Children       |    |    |       |      |    |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
| Under 5        | 0  | 0  | 1.211 | 0    | 0  | 0    | 1.346 | 0    |
|                | .3 | .3 |       | .226 | .4 | .308 |       | .178 |
|                | 71 | 07 |       |      | 14 |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
| 6 -10 years    | 0  | 0  | 2     | 0    | 0  | 0    | 2     | 0    |
| old            | .8 | .3 | .640\ | .008 | .9 | .355 | .626\ | .009 |
|                | 88 | 36 | *\*\* |      | 33 |      | *\*\* |      |
+----------------+----+----+-------+------+----+------+-------+------+
| \> than 10     | 0  | 0  | 2.14  | 0    | 0  | 0    | 2.34  | 0    |
| years old      | .5 | .2 | 9\*\* | .032 | .6 | .258 | 7\*\* | .019 |
|                | 60 | 61 |       |      | 05 |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
|                |    |    |       |      |    |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
| Child work     | -0 | 0  | -     | 0    | -0 | 0    | -     | 0    |
|                | .0 | .6 | 0.144 | .885 | .2 | .693 | 0.311 | .756 |
|                | 98 | 80 |       |      | 15 |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
|                |    |    |       |      |    |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
| Log Net income | -0 | 0  | -1.   | 0    | \- | \-   | \-    | \-   |
|                | .7 | .4 | 725\* | .085 |    |      |       |      |
|                | 19 | 17 |       |      |    |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
|                |    |    |       |      |    |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
| Presence of    | -1 | 1  | -1.   | 0    | -2 | 1    | -2.01 | 0    |
| Ouaddis        | .9 | .0 | 861\* | .063 | .1 | .065 | 8\*\* | .044 |
|                | 31 | 38 |       |      | 49 |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
|                |    |    |       |      |    |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
| Income class   |    |    |       |      |    |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
| 1st quarter    | \  | \  | \--   | \--  | \  | \--  | \--   | \--  |
|                | -- | -- |       |      | -- |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
| 2nd quarter    | \  | \  | \--   | \--  | -2 | 1    | -     | 0    |
|                | -- | -- |       |      | .0 | .225 | 1.644 | .100 |
|                |    |    |       |      | 14 |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
| 3rd quarter    | \  | \  | \--   | \--  | -1 | 1    | -     | 0    |
|                | -- | -- |       |      | .9 | .243 | 1.545 | .122 |
|                |    |    |       |      | 20 |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
| 4th quarter    | \  | \  | \--   | \--  | -3 | 1    | -2.01 | 0    |
|                | -- | -- |       |      | .0 | .533 | 8\*\* | .044 |
|                |    |    |       |      | 94 |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
|                |    |    |       |      |    |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
| School         |    |    |       |      |    |      |       |      |
| presence       |    |    |       |      |    |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
| Public school  | -0 | 0  | -     | 0    | -0 | 0    | -     | 0    |
|                | .7 | .8 | 0.853 | .394 | .6 | .859 | 0.800 | .424 |
|                | 11 | 34 |       |      | 87 |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
| Community      | \  | \  | \--   | \--  | \  | \--  | \--   | \--  |
| school         | -- | -- |       |      | -- |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
|                |    |    |       |      |    |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
| Buy animal     | 1  | 0  | 2.37  | 0    | 1  | 0    | 1.89  | 0    |
|                | .5 | .6 | 8\*\* | .017 | .2 | .679 | 0\*\* | .059 |
|                | 34 | 45 |       |      | 82 |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
|                |    |    |       |      |    |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
| Credit payment | -0 | 0  | -     | 0    | -0 | 1    | -     | 0    |
|                | .6 | .9 | 0.690 | .490 | .6 | .154 | 0.554 | .580 |
|                | 84 | 91 |       |      | 39 |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
|                |    |    |       |      |    |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
| Distance to    | -0 | 0  | -     | 0    | -0 | 0    | -1.93 | 0    |
| Abéché         | .0 | .0 | 1.985 | .047 | .0 | .013 | 3\*\* | .053 |
|                | 25 | 13 |       | \*\* | 25 |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
|                |    |    |       |      |    |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+
| D              | 0  | 0  | 2.488 | 0    | 0  | 0    | 2.55  | 0    |
| istance/health | .1 | .0 |       | .013 | .1 | .066 | 6\*\* | .011 |
| center         | 63 | 65 |       | \*\* | 68 |      |       |      |
+----------------+----+----+-------+------+----+------+-------+------+

\*0.10 level; \*\* 0.05 level; \*\*\* 0.001 level or better

[Table 2: Result of Multiple Correspondence Analysis -- Income class vs
child school attendance]{.underline}
![](media/image4.png){width="7.001388888888889in"
height="4.872916666666667in"}

Table 3: Women's gardening activity and School attendance in the Ouaddaï
department

  -------------- ------------ ----------- ------------ ----------- ---------
  Cantons        Total area   Average Net School       Percent of  Average
                 cultivated   Income per  attendance   women using number of
                 by woman     woman       rate (%)     child labor school
                                                                   per
                                                                   village

  Mandjobo       639.07       178589.32   41.94        57.89       0.74

  Mabrone        465.00       130026.60   13.64        53.33       1.00

  Ouadi Chock    1196.60      368630.00   5.00         85.71       0.50

  Barbé          457.38       141612.50   15.91        36.84       0.50

  Moulou         465.23       128434.62   0.00         66.67       0.31

  Koniéré        241.20       210429.20   55.56        66.67       1.00

  Marfa          181.44       122152.40   0.00         80.00       1.00

  Korio          473.60       133083.16   21.05        44.44       0.50

  Ouadi Hamra    207.00       89677.50    68.18        40.00       1.00

   Total                                  22.88        56.88        
  -------------- ------------ ----------- ------------ ----------- ---------

[Bibliograph]{.underline}y

Faraaz, Siddiqui and Harry Anthony, Patrinos, "Child labor: Issues,
causes and interventions".

ILO/IPEC, 0ctober 2002 "Costs and Benefits of education to replace child
labor"

Sudharshan, Canagarajah and Helena Skyt Nielsen, July 1999 "Child labor
and Schooling in Africa: A comparative study"

USAID/RFFP/Senegal, Professional Paper Series, Series No 2, June 2005
"How Market Gardens can alleviate Sahelian Small Holder Food Insecurity
Case of Africare in Abeché, Chad"

Mamadou Thiam, UNESCO Institute for Statistics, April 2004 International
Conference on "Promoting Human Rights and Social Policies for Children
and Women: Monitoring and Achieving the Millennium Development Goals"

F. Francavilla, S. Lyon, March, 2002 "Children's Work in Cote d'Ivoire:
An Overview"

[^1]: Multiple Indicator Cluster Survey

[^2]: Siedougou is located in the Gnagnan province (300 km from the
    capital city) is one of the most food insecure provinces of Burkina
    Faso.

[^3]: A sample of 25 villages was randomly selected from the 41 villages
    that practice gardening. Similarly, the number of women carrying out
    vegetable gardening was listed for each village and a sample of five
    (5) women per village was randomly selected. Thus a total of 125
    women were interviewed for the survey by Africare Chad's field
    agents.

[^4]: A geographic location of the country, it is constituted by a group
    of village

[^5]: class 1: net income \< 50,000 FCFA; class 2: 50,000 FCFA \>= net
    income \< 150,000 FCFA; class 3: 150,000 FCFA \>= net income \<
    300,000 FCFA; class 4 : net income \>= 300,000 FCFA