# Household Size and Access to Education in Rural Burundi: The Case of Mutaho Commune

Bosco Ndayishimiye, René Manirakiza, Christian Kakuba, Jean François Régis Sindayihebura, Emmanuel Barankanira

<figure><img src="image_1.png" /></figure>

## To cite this version:

Bosco Ndayishimiye, René Manirakiza, Christian Kakuba, Jean François Régis Sindayihebura, Emmanuel Barankanira. Household Size and Access to Education in Rural Burundi: The Case of Mutaho Commune. Open Education Studies, 2024, 6 (1), 10.1515/edu-2024-0019. halshs-04613078

HAL Id: halshs-04613078

https://shs.hal.science/halshs-04613078

Submitted on 15 Jun 2024

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Distributed under a Creative Commons Attribution 4.0 International License
# Household Size and Access to Education in Rural Burundi: The Case of Mutaho Commune

Bosco Ndayishimiye, René Manirakiza, Christian Kakuba, Jean François Régis Sindayihebura, Emmanuel Barankanira

► To cite this version:

Bosco Ndayishimiye, René Manirakiza, Christian Kakuba, Jean François Régis Sindayihebura, Emmanuel Barankanira. Household Size and Access to Education in Rural Burundi: The Case of Mutaho Commune. Open Education Studies, 2024, 6 (1), 10.1515/edu-2024-0019. halshs-04613078

HAL Id: halshs-04613078

https://shs.hal.science/halshs-04613078v1

Submitted on 15 Jun 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Research Article

Bosco Ndayishimiye*, René Manirakiza, Christian Kakuba, Jean François Régis Sindayihebura, Emmanuel Barankanira

# Household Size and Access to Education in Rural Burundi: The Case of Mutaho Commune

https://doi.org/10.1515/edu-2024-0019

received April 25, 2023; accepted May 17, 2024

**Abstract:** While education is recognized as a fundamental human right and an important factor in improving economic and social conditions, child schooling rates in Burundi show that there are still children of school-age who are not attending school and those who drop out very prematurely. This scientific study aims to highlight the relationship between household size and access to education in Mutaho commune (rural Burundi) by documenting the socio-demographic factors of the head of household likely to influence the schooling status of children aged 7–17. The hypothesis put forward in this study is that children’s schooling is influenced by household size. Data from a survey of 215 households in the Mutaho commune were analyzed using binomial logistic regression. The analysis shows that a large household size (OR = 5.463; *p* < 0.05; 95% CI: 1.311–22.771) is negatively associated with the education of children aged 7–17 living in Mutaho commune. Reducing fertility should be an integral concern for all those involved in the fields of population, education, and development.

**Keywords:** household size, education, school attendance, Mutaho commune, Burundi

## 1 Introduction

Education is an integral part of human rights. The first paragraph of Article 26 of the Universal Declaration of Human Rights states that education for all is a right and that it should be free, at least in the elementary and fundamental stages. It emphasizes the compulsory nature of elementary education and recommends that technical and vocational education should be made generally available and that access to higher education be open to all on the basis of merit (Nations Unies, 1948). Education is therefore at the heart of major international, regional, and national priorities.

International conferences have been organized by the United Nations to establish international guidelines for education. These include the World Conference on Education for All (EFA) held in Jomtien (Thailand) in March 1990, the World Education Forum held in Dakar in April 2000, the Declaration of the Millennium Development Goals in September 2000, the Special Session of the United Nations General Assembly on Children in May 2002, the Agenda 2030 setting the Sustainable Development Goals for 2015, the Incheon World Forum (South Korea, 2015), etc. In all these conferences, education, especially primary education, has been recognized as a key factor in sustainable economic development, improved social welfare, and gender equality (UNESCO, 2017).

At regional and sub-regional levels, Africa's Agenda 2063, “Africa We Want,” aims for a prosperous Africa, based on inclusive growth and sustainable development, with the goal of having “well-educated citizens and a revolution in skills supported by science, technology and innovation” (African Union Commission, 2015). In the education sector, the East African Community's Vision 2050 aims to build capacity in new information and communication technologies in member countries in order to encourage innovation and increase competitiveness (East African Community, 2016).

At the national level, the Burundian government has put in place national and sectoral policies and strategies to

* Corresponding author: Bosco Ndayishimiye, Department of Geographic, Environment and Population Sciences, Doctoral School of the University of Burundi (UB), Bujumbura, Burundi; Center for Research and Studies on the Development of Societies in Reconstruction (CREDSR), Bujumbura, Burundi, e-mail: bosco.ndayishimiye@student.ub.edu.bi

René Manirakiza, Jean François Régis Sindayihebura: Department of Geographic, Environment and Population Sciences, University of Burundi (UB), Bujumbura, Burundi; Center for Research and Studies on the Development of Societies in Reconstruction (CREDSR), Bujumbura, Burundi

Christian Kakuba: Department of Planning and Applied Statistics, Makerere University (MAK), Kampala, Uganda

Emmanuel Barankanira: Department of Natural Sciences, Ecole Normale Supérieure du Burundi (ENS), Bujumbura, Burundi
ORCID: Bosco Ndayishimiye 0009-0001-4142-8260

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>
Research Article

Bosco Ndayishimiye*, René Manirakiza, Christian Kakuba, Jean François Régis Sindayihebura, Emmanuel Barankanira

# Household Size and Access to Education in Rural Burundi: The Case of Mutaho Commune

https://doi.org/10.1515/edu-2024-0019

received April 25, 2023; accepted May 17, 2024

**Abstract:** While education is recognized as a fundamental human right and an important factor in improving economic and social conditions, child schooling rates in Burundi show that there are still children of school-age who are not attending school and those who drop out very prematurely. This scientific study aims to highlight the relationship between household size and access to education in Mutaho commune (rural Burundi) by documenting the socio-demographic factors of the head of household likely to influence the schooling status of children aged 7–17. The hypothesis put forward in this study is that children’s schooling is influenced by household size. Data from a survey of 215 households in the Mutaho commune were analyzed using binomial logistic regression. The analysis shows that a large household size (OR = 5.463; *p* < 0.05; 95% CI: 1.311–22.771) is negatively associated with the education of children aged 7–17 living in Mutaho commune. Reducing fertility should be an integral concern for all those involved in the fields of population, education, and development.

**Keywords:** household size, education, school attendance, Mutaho commune, Burundi

## 1 Introduction

Education is an integral part of human rights. The first paragraph of Article 26 of the Universal Declaration of Human Rights states that education for all is a right and that it should be free, at least in the elementary and fundamental stages. It emphasizes the compulsory nature of elementary education and recommends that technical and vocational education should be made generally available and that access to higher education be open to all on the basis of merit (Nations Unies, 1948). Education is therefore at the heart of major international, regional, and national priorities.

International conferences have been organized by the United Nations to establish international guidelines for education. These include the World Conference on Education for All (EFA) held in Jomtien (Thailand) in March 1990, the World Education Forum held in Dakar in April 2000, the Declaration of the Millennium Development Goals in September 2000, the Special Session of the United Nations General Assembly on Children in May 2002, the Agenda 2030 setting the Sustainable Development Goals for 2015, the Incheon World Forum (South Korea, 2015), etc. In all these conferences, education, especially primary education, has been recognized as a key factor in sustainable economic development, improved social welfare, and gender equality (UNESCO, 2017).

At regional and sub-regional levels, Africa's Agenda 2063, "Africa We Want," aims for a prosperous Africa, based on inclusive growth and sustainable development, with the goal of having "well-educated citizens and a revolution in skills supported by science, technology and innovation" (African Union Commission, 2015). In the education sector, the East African Community's Vision 2050 aims to build capacity in new information and communication technologies in member countries in order to encourage innovation and increase competitiveness (East African Community, 2016).

At the national level, the Burundian government has put in place national and sectoral policies and strategies to

* Corresponding author: Bosco Ndayishimiye, Department of Geographic, Environment and Population Sciences, Doctoral School of the University of Burundi (UB), Bujumbura, Burundi; Center for Research and Studies on the Development of Societies in Reconstruction (CREDSR), Bujumbura, Burundi, e-mail: bosco.ndayishimiye@student.ub.edu.bi

René Manirakiza, Jean François Régis Sindayihebura: Department of Geographic, Environment and Population Sciences, University of Burundi (UB), Bujumbura, Burundi; Center for Research and Studies on the Development of Societies in Reconstruction (CREDSR), Bujumbura, Burundi

Christian Kakuba: Department of Planning and Applied Statistics, Makerere University (MAK), Kampala, Uganda

Emmanuel Barankanira: Department of Natural Sciences, Ecole Normale Supérieure du Burundi (ENS), Bujumbura, Burundi
ORCID: Bosco Ndayishimiye 0009-0001-4142-8260

<figure><img src="image_2.png" /></figure>
meet its commitments in the field of education. These
include free primary education since 2005, the First and
Second-Generation Strategic Framework for Growth and Poverty
Reduction (CSLP), Burundi Vision 2025, and Burundi
National Development Plan (PND) 2018–2027. Nevertheless,
EFA in Burundi remains an unachieved goal, with low lit-
eracy rates (ISTEEBU & ICF, 2017; MENRS, 2020).

The basic education system in Burundi is character-
ized by a high number of repeaters, resulting in gross
enrolment ratios (GER) of over 100, or 111.0% for children
aged between 7 and 12. In reality, this GER masks short-
comings, as many children who enrol in primary education
(i.e., the first three levels of fundamental schooling, from
first to sixth year) leave school before completing this
level. According to the Ministry of National Education
and Scientific Research (MENRS), the net primary school
attendance rate is 85.1%. However, only 41.1% of children
attend secondary school, while the survival rate at the end
of secondary school is only 27.5% (MENRS, 2020). These
latest rates show that there are still children of school-
age who are not attending school and those who are leaving
school very early (MENRS, 2020). The average length of
schooling in Burundi is 3.0 years, whereas the expected
length of schooling is 11.7 years, according to the United
Nations Development Programme (UNDP, 2018). In a nation
of exceptionally young people, where the under-18s repre-
sent 50% of the total population and the under-15s 49%, with
an average fertility rate of 5.5 children per woman (ISTEEBU
& ICF, 2017), it is important to understand whether demo-
graphic variables, especially household size, have any sig-
nificant impact on access to education.

The data were collected in the Commune of Mutaho,
which, according to administrative data for the 2019–2020
school year, had 825 children enrolled in nursery educa-
tion, 16,213 pupils in the fundamental level, and 1,274 pupils
in post-fundamental education. The general educational
situation, beyond these enrolments, is characterized by a
shortage of schools and a low completion rate due to drop-
outs and repetition (UNESCO, 2021). For example, during
the same school year, 80 pupils dropped out of nursery
schools, representing a dropout rate of 9.7%. In the funda-
mental level, there were 2,127 dropouts, representing a
dropout rate of 13.1%, while the same rate was 15.5% in
the post-fundamental education, representing 198 drop-
outs for the 2019–2020 school year.<fnref n="1" />

However, scientific studies have shown that household
size is a determining factor in access to education for

school-age children. On the one hand, several studies
have shown that large household size has a negative impact
on the schooling of children (Bougma, 2014; Kravdal, Kodzi,
& Sigle-Rushton, 2013; Lachaud, 2015; Vogl, 2022). On the
other hand, empirical studies show that children from small
households are less likely to attend school than children
from large households (Chabi & Attanasso, 2015). However,
most of these studies used secondary data collected during
censuses or household surveys.

The above considerations make it particularly inter-
esting to study the relationship between household size
and access to education. Is there a relationship between
household size and children's schooling? If yes, what impact
might household size have on access to schooling in rural
Burundi, particularly in the Mutaho commune? It is with a
desire to answer this twofold central question that this study
is undertaken, in order to obtain evidence-based answers on
the likely differential access to education based on house-
hold size.

In this study, we postulate that school attendance of
children in Mutaho commune is influenced by household
size and that large household size decreases the chances of
its children attending school. On the one hand, this study
uses a bivariate analysis of data from the household survey
we conducted. On the other hand, we attempt to identify
and show the share of family-related factors in explaining
school attendance in the area of this study.

Researchers agree that education is a key factor in
improving productivity through the acquisition of new
skills, improving the health of mothers and children in the
Third World, reducing fertility and mortality, accelerating
the demographic transition, and achieving the demographic
dividend (Bambara & Wayack-Pambè, 2019; Becker, 1985;
Guengant, Kamara, & collab, 2011; Kamuragiye & Buzingo,
2019; United Nations, 2003). Indeed, the population of Bur-
undi has grown very rapidly since the 1960s. However,
around 90% of this population still lives in rural areas,
mainly producing subsistence agriculture, which intensifies
the pressure on available resources. This strong demo-
graphic growth is the result of high fertility over more
than 50 years, coupled with a significant decline in mor-
tality. With an average Burundian household size of 5
people and a total fertility rate of 5.5 children per woman
(ISTEEBU & ICF, 2017), Burundi has a high fertility rate com-
pared with the sub-Saharan African average of 4.7 children
per woman (Tabutin & Schoumaker, 2020). The country is
currently facing many challenges in terms of demographic
growth, improving its human capital (in health, education,
and professional training), employment, and governance
(UNESCO, 2021). This is why a scientific study is needed to
show the relationships between household size and access

<footnote n="1">1 These data were collected in the Statistics and Planning Department of the Mutaho Communal Direction of Education (DCE), 2020.</footnote>
meet its commitments in the field of education. These
include free primary education since 2005, the First and
Second-Generation Strategic Framework for Growth and Poverty
Reduction (CSLP), Burundi Vision 2025, and Burundi
National Development Plan (PND) 2018–2027. Nevertheless,
EFA in Burundi remains an unachieved goal, with low lit-
eracy rates (ISTEEBU & ICF, 2017; MENRS, 2020).

The basic education system in Burundi is character-
ized by a high number of repeaters, resulting in gross
enrolment ratios (GER) of over 100, or 111.0% for children
aged between 7 and 12. In reality, this GER masks short-
comings, as many children who enrol in primary education
(i.e., the first three levels of fundamental schooling, from
first to sixth year) leave school before completing this
level. According to the Ministry of National Education
and Scientific Research (MENRS), the net primary school
attendance rate is 85.1%. However, only 41.1% of children
attend secondary school, while the survival rate at the end
of secondary school is only 27.5% (MENRS, 2020). These
latest rates show that there are still children of school-
age who are not attending school and those who are leaving
school very early (MENRS, 2020). The average length of
schooling in Burundi is 3.0 years, whereas the expected
length of schooling is 11.7 years, according to the United
Nations Development Programme (UNDP, 2018). In a nation
of exceptionally young people, where the under-18s repre-
sent 50% of the total population and the under-15s 49%, with
an average fertility rate of 5.5 children per woman (ISTEEBU
& ICF, 2017), it is important to understand whether demo-
graphic variables, especially household size, have any sig-
nificant impact on access to education.

The data were collected in the Commune of Mutaho,
which, according to administrative data for the 2019–2020
school year, had 825 children enrolled in nursery educa-
tion, 16,213 pupils in the fundamental level, and 1,274 pupils
in post-fundamental education. The general educational
situation, beyond these enrolments, is characterized by a
shortage of schools and a low completion rate due to drop-
outs and repetition (UNESCO, 2021). For example, during
the same school year, 80 pupils dropped out of nursery
schools, representing a dropout rate of 9.7%. In the funda-
mental level, there were 2,127 dropouts, representing a
dropout rate of 13.1%, while the same rate was 15.5% in
the post-fundamental education, representing 198 drop-
outs for the 2019–2020 school year.<fnref n="1" />

However, scientific studies have shown that household
size is a determining factor in access to education for

school-age children. On the one hand, several studies
have shown that large household size has a negative impact
on the schooling of children (Bougma, 2014; Kravdal, Kodzi,
& Sigle-Rushton, 2013; Lachaud, 2015; Vogl, 2022). On the
other hand, empirical studies show that children from small
households are less likely to attend school than children
from large households (Chabi & Attanasso, 2015). However,
most of these studies used secondary data collected during
censuses or household surveys.

The above considerations make it particularly inter-
esting to study the relationship between household size
and access to education. Is there a relationship between
household size and children's schooling? If yes, what impact
might household size have on access to schooling in rural
Burundi, particularly in the Mutaho commune? It is with a
desire to answer this twofold central question that this study
is undertaken, in order to obtain evidence-based answers on
the likely differential access to education based on house-
hold size.

In this study, we postulate that school attendance of
children in Mutaho commune is influenced by household
size and that large household size decreases the chances of
its children attending school. On the one hand, this study
uses a bivariate analysis of data from the household survey
we conducted. On the other hand, we attempt to identify
and show the share of family-related factors in explaining
school attendance in the area of this study.

Researchers agree that education is a key factor in
improving productivity through the acquisition of new
skills, improving the health of mothers and children in the
Third World, reducing fertility and mortality, accelerating
the demographic transition, and achieving the demographic
dividend (Bambara & Wayack-Pambè, 2019; Becker, 1985;
Guengant, Kamara, & collab, 2011; Kamuragiye & Buzingo,
2019; United Nations, 2003). Indeed, the population of Bur-
undi has grown very rapidly since the 1960s. However,
around 90% of this population still lives in rural areas,
mainly producing subsistence agriculture, which intensifies
the pressure on available resources. This strong demo-
graphic growth is the result of high fertility over more
than 50 years, coupled with a significant decline in mor-
tality. With an average Burundian household size of 5
people and a total fertility rate of 5.5 children per woman
(ISTEEBU & ICF, 2017), Burundi has a high fertility rate com-
pared with the sub-Saharan African average of 4.7 children
per woman (Tabutin & Schoumaker, 2020). The country is
currently facing many challenges in terms of demographic
growth, improving its human capital (in health, education,
and professional training), employment, and governance
(UNESCO, 2021). This is why a scientific study is needed to
show the relationships between household size and access

<footnote n="1">1 These data were collected in the Statistics and Planning Department of the Mutaho Communal Direction of Education (DCE), 2020.</footnote>
to education in Burundi, particularly in rural areas, and
why Mutaho Commune was chosen as the study area.

## 2 Literature Review

There is a rich and extensive literature on the relationship
between education, population, and development. Several
theories have been formulated on these concepts, particularly
showing the interrelationships between them (Bambara &
Wayack-Pambè, 2019; Becker, 1960; Guengant et al., 2011;
Kamuragiye & Buzingo, 2019; Livenais & Vaugelade, 1993;
United Nations, 2003). In this study, we focus on socio-econ-
nomic theories and the theory of the demographic dividend.

Through socio-economic theories, the impacts of high
population growth and its pressure on resources have been
widely discussed and addressed in the studies of various
researchers, particularly those of the twentieth century.
Such is the case of the American economist (Becker, 1960),
which, through its quantity–quality economic model, has
shown that family size is negatively associated with house-
hold income. In addition, Blake (1989) has also shown that
family size has a negative influence on school outcomes and
employment; the reason why Mincer (1975) proposed that it
is necessary to take into account not only the number of
children but also their quality, assessed by their level of
education.

The demographic dividend is defined as all the eco-
nomic opportunities that a country obtains as a result of
declining fertility. However, to benefit from the demo-
graphic dividend, it is necessary to invest in the develop-
ment of human capital (especially in health and education),
in economic policies that encourage job creation, and in
good governance. According to Livenais and Vaugelade
(1993), a certain level of education is required to under-
stand the importance of vaccination, oral rehydration,
bottle-feeding precautions, breast-weaning, circumcision,
the harmful implications of female circumcision, and to
assimilate the concepts of family planning. In this way, Pilon
(2006) concluded that “education is a key factor in changing
mentalities and adopting new demographic behaviours,
because according to this author, education, especially for
women, contributes to later marriage, reduced fertility and
mortality (infant, child and maternal); yet women are the
most educated, marry later, give birth to fewer children,
care for them and themselves, etc.”

Recognizing that education is an important interna-
tional human right and an important factor in the improve-
ment of economic and social conditions (United Nations,
2003), several studies on the determinants of education

have been carried out in both developed and developing
countries. Certain determinants have been considered sig-
nificant by most researchers. These include institutional
factors, generally based on the demand and/or supply of
education (Livenais & Vaugelade, 1993; Pilon, 2006). They
also include socio-demographic factors such as household
size (Chabi & Attanasso, 2015; Kakuba, 2014; Kobiane, 2002),
sex of household head (Mba Oyono, 2009; Pilon, 1996), age of
head of household (Pilon & Yaro, 2001), and marital status of
household head (Nganawara, 2016; Pilon, 1995; Pilon & Yaro,
2001). Finally, socio-cultural and economic factors associated
with schooling include the education of the head of house-
hold (Bahri, 2024; Pilon, 1995), religion of the head of
household (Nganawara, 2016; Pilon, 1995), the place of
residence (Bahri, 2024; Kakuba & Pilon, 2023), household
income (Chabi & Attanasso, 2015; Kakuba & Pilon, 2023;
Kakuba, Nzabona, Asiimwe, Tuyiragize, & Mushomi,
2021), and main activity of household head (Mba Oyono,
2009; Pilon & Yaro, 2001; Pilon, 1995).

In summary, this review of the literature shows that
children's schooling is influenced by household size and
other socio-demographic and economic factors of the house-
holds in which they live, the reason this study was carried
out in a Burundian context.

## 3 Methods

In order to test our hypothesis and achieve our research
objectives, we carried out quantitative data collection. The
choice of this quantitative approach is motivated by the
fact that it enables us to test the influence of household size
on access to schooling by testing the influence of the other
variables used, thanks to the statistical processing of the
information collected (Giordano & Jolibert, 2016).

Our target population is children of school-age living
in households in the Commune of Mutaho (Burundi). These
are children whose ages are between 7 and 17. The choice
of this age group is based on the Burundian education
system, which officially sets the age of admission to the first
year of fundamental school at 6 years (MENRS, 2020). Given
that fundamental school lasts 9 years and post-fundamental
school 3-4 years, the secondary school-leaving age is 18,
hence the choice of 7-17 year olds.

Aware that this study focuses on the relationship
between household size and access to education and that
the phenomenon is likely to be observed at the household
level, it was decided to survey households. Thus, given that
the total number of households in the 18 hills of Mutaho
Commune amounts to 18,449 households spread over the
to education in Burundi, particularly in rural areas, and
why Mutaho Commune was chosen as the study area.

## 2 Literature Review

There is a rich and extensive literature on the relationship
between education, population, and development. Several
theories have been formulated on these concepts, particularly
showing the interrelationships between them (Bambara &
Wayack-Pambè, 2019; Becker, 1960; Guengant et al., 2011;
Kamuragiye & Buzingo, 2019; Livenais & Vaugelade, 1993;
United Nations, 2003). In this study, we focus on socio-econ-
nomic theories and the theory of the demographic dividend.

Through socio-economic theories, the impacts of high
population growth and its pressure on resources have been
widely discussed and addressed in the studies of various
researchers, particularly those of the twentieth century.
Such is the case of the American economist (Becker, 1960),
which, through its quantity–quality economic model, has
shown that family size is negatively associated with house-
hold income. In addition, Blake (1989) has also shown that
family size has a negative influence on school outcomes and
employment; the reason why Mincer (1975) proposed that it
is necessary to take into account not only the number of
children but also their quality, assessed by their level of
education.

The demographic dividend is defined as all the eco-
nomic opportunities that a country obtains as a result of
declining fertility. However, to benefit from the demo-
graphic dividend, it is necessary to invest in the develop-
ment of human capital (especially in health and education),
in economic policies that encourage job creation, and in
good governance. According to Livenais and Vaugelade
(1993), a certain level of education is required to under-
stand the importance of vaccination, oral rehydration,
bottle-feeding precautions, breast-weaning, circumcision,
the harmful implications of female circumcision, and to
assimilate the concepts of family planning. In this way, Pilon
(2006) concluded that “education is a key factor in changing
mentalities and adopting new demographic behaviours,
because according to this author, education, especially for
women, contributes to later marriage, reduced fertility and
mortality (infant, child and maternal); yet women are the
most educated, marry later, give birth to fewer children,
care for them and themselves, etc.”

Recognizing that education is an important interna-
tional human right and an important factor in the improve-
ment of economic and social conditions (United Nations,
2003), several studies on the determinants of education

have been carried out in both developed and developing
countries. Certain determinants have been considered sig-
nificant by most researchers. These include institutional
factors, generally based on the demand and/or supply of
education (Livenais & Vaugelade, 1993; Pilon, 2006). They
also include socio-demographic factors such as household
size (Chabi & Attanasso, 2015; Kakuba, 2014; Kobiane, 2002),
sex of household head (Mba Oyono, 2009; Pilon, 1996), age of
head of household (Pilon & Yaro, 2001), and marital status of
household head (Nganawara, 2016; Pilon, 1995; Pilon & Yaro,
2001). Finally, socio-cultural and economic factors associated
with schooling include the education of the head of house-
hold (Bahri, 2024; Pilon, 1995), religion of the head of
household (Nganawara, 2016; Pilon, 1995), the place of
residence (Bahri, 2024; Kakuba & Pilon, 2023), household
income (Chabi & Attanasso, 2015; Kakuba & Pilon, 2023;
Kakuba, Nzabona, Asiimwe, Tuyiragize, & Mushomi,
2021), and main activity of household head (Mba Oyono,
2009; Pilon & Yaro, 2001; Pilon, 1995).

In summary, this review of the literature shows that
children's schooling is influenced by household size and
other socio-demographic and economic factors of the house-
holds in which they live, the reason this study was carried
out in a Burundian context.

## 3 Methods

In order to test our hypothesis and achieve our research
objectives, we carried out quantitative data collection. The
choice of this quantitative approach is motivated by the
fact that it enables us to test the influence of household size
on access to schooling by testing the influence of the other
variables used, thanks to the statistical processing of the
information collected (Giordano & Jolibert, 2016).

Our target population is children of school-age living
in households in the Commune of Mutaho (Burundi). These
are children whose ages are between 7 and 17. The choice
of this age group is based on the Burundian education
system, which officially sets the age of admission to the first
year of fundamental school at 6 years (MENRS, 2020). Given
that fundamental school lasts 9 years and post-fundamental
school 3-4 years, the secondary school-leaving age is 18,
hence the choice of 7-17 year olds.

Aware that this study focuses on the relationship
between household size and access to education and that
the phenomenon is likely to be observed at the household
level, it was decided to survey households. Thus, given that
the total number of households in the 18 hills of Mutaho
Commune amounts to 18,449 households spread over the
55 sub-hills,<fnref n="2" /> the sample size calculation led to a total of 215 households, based on the formula proposed by Gumucio (2011)

$$n = \frac{Z^2 \times p \times (1 - p)}{c^2},$$

where *n* is the sample size, *Z* is the value corresponding to a given confidence level (1.96 for a 95% confidence level, generally accepted in the social sciences), *p* is the percentage of the main indicator (89.4% value corresponding to the net enrolment rate in Gitega Province, in 2019), and *c* is the standard error, expressed in decimals (0.05 for the 5% margin of error). In addition, for this formula to be valid in our work area, it was necessary to take into account the sampling effect. Knowing that the sampling effect was not available for the Mutaho Commune, we selected that of Gitega province, which is equal to 1.4 according to the Burundi Demographic and Health Survey 2016–2017 (ISTEEBU & ICF, 2017).

By replacing the numbers in the above formula, we obtained

$$n = \frac{1.96^2 \times 0.894(1 - 0.894)1.4^{\frac{1}{0.95}}}{0.05^2} = 215.$$

The sample was selected in several stages. In the first stage, it was necessary to select a number of 10 sub-hills (reasoned choice) from the 55. First, all the sub-hills in the Mutaho commune were listed in alphabetical order. All households were then aggregated according to sub-hill. The sub-hills were selected with unequal probability. Once the sub-hills had been selected, the household sample size was allocated in proportion to the number of households in each sub-hill (Table 1 and Table A1). The sample of households was selected with equal probability (Agresti, 2018).

With these methodological considerations in mind, the survey was carried out on a sample of 215 households, randomly selected from 10 sub-hills spread over 8 census hills in the Commune of Mutaho (Burundi). In this survey, data were collected using a specially prepared questionnaire and entered into the CSPro application. They were then exported to SPSS for processing and analysis.

Nine variables were identified as suitable for analysis. School attendance status was the dependent variable and eight independent variables. These were demographic factors (age of head of household, sex of head of household, and marital status of head of household), socio-cultural variables (religion of head of household and education of head of household), and socio-economic factors (household

<table><caption>Table 1: Sample size and distribution by sub-hill</caption><thead><tr><th>No</th><th>Hills</th><th>Sub-hills</th><th>Households by sub-hill</th><th>Number of households</th></tr></thead><tbody><tr><td>1</td><td>Masango</td><td>Gaheza</td><td>215</td><td>13</td></tr><tr><td>2</td><td>Gerangabo</td><td>Gerangabo</td><td>377</td><td>22</td></tr><tr><td>3</td><td>Mutaho</td><td>Hayiro</td><td>500</td><td>30</td></tr><tr><td>4</td><td>Nzove</td><td>Kibungere</td><td>241</td><td>14</td></tr><tr><td>5</td><td>Rurengera</td><td>Mibazi</td><td>530</td><td>31</td></tr><tr><td>6</td><td>Muririmbo</td><td>Muririmbo</td><td>606</td><td>36</td></tr><tr><td>7</td><td>Bigera</td><td>Ncaramba</td><td>334</td><td>20</td></tr><tr><td>8</td><td>Ngoma</td><td>Nyakabungo</td><td>158</td><td>9</td></tr><tr><td>9</td><td>Bigera</td><td>Nyarubuye</td><td>334</td><td>20</td></tr><tr><td>10</td><td>Bigera</td><td>Rukorobwa</td><td>333</td><td>20</td></tr><tr><td><strong>Total (n)</strong></td><td><strong>8</strong></td><td><strong>10</strong></td><td><strong>3,628</strong></td><td><strong>215</strong></td></tr><tr><td><em>N</em></td><td>18</td><td>55</td><td>18,449</td><td>3,628</td></tr></tbody></table>

Source: Authors, based on data from the Mutaho commune civil registry, 2019.

size, number of school-age children in household, and main activity of head of household). However, due to the collinearity effect between household size and the number of school-age children in the household (with a correlation of 0.75) and between sex and marital status of the head of household (with a correlation of 0.83), two variables were excluded from the analysis (sex of the head of household and number of school-age children) (Schoumaker, 2013).

All these variables had no non-response rate after filtering out children under 7. In fact, of the 215 households planned, it was deemed important to keep only those with children aged 7 or over (initially, households with pre-school children – aged 3–6 – were included), i.e., 180 households. With the six independent variables retained, the explanatory power of school attendance is 65.64% (area under the ROC curve = 0.6564). These results allow us to conclude that these variables provide a significant explanation for the phenomenon of school attendance.

The influence of household size on school attendance is analyzed from a multivariate perspective. Given the qualitative and binary nature of the dependent variable (coded 1 if all children in the household attend and 0 if the household has at least one child who does not), the method used was binomial logistic regression (Rizzi, 2013). The probability associated with the Chi-square test of the likelihood ratio to the complete model is less than 5% (LR chi2, df: 12; *p* < 0.05), confirming the validity of the multivariate modeling approach.

Data processing and variable recoding were carried out using SPSS 25 software. Logistic regression was performed using Stata 15. Microsoft Excel was used to format tables and present data and results.

<footnote n="2">2 Data collected in the Mutaho Commune's Civil registry office, Annual Report, 2019.</footnote>
55 sub-hills,<fnref n="2" /> the sample size calculation led to a total of 215 households, based on the formula proposed by Gumucio (2011)

$$n = \frac{Z^2 \times p \times (1 - p)}{c^2},$$

where *n* is the sample size, *Z* is the value corresponding to a given confidence level (1.96 for a 95% confidence level, generally accepted in the social sciences), *p* is the percentage of the main indicator (89.4% value corresponding to the net enrolment rate in Gitega Province, in 2019), and *c* is the standard error, expressed in decimals (0.05 for the 5% margin of error). In addition, for this formula to be valid in our work area, it was necessary to take into account the sampling effect. Knowing that the sampling effect was not available for the Mutaho Commune, we selected that of Gitega province, which is equal to 1.4 according to the Burundi Demographic and Health Survey 2016–2017 (ISTEEBU & ICF, 2017).

By replacing the numbers in the above formula, we obtained

$$n = \frac{1.96^2 \times 0.894(1 - 0.894)1.4^{\frac{1}{0.95}}}{0.05^2} = 215.$$

The sample was selected in several stages. In the first stage, it was necessary to select a number of 10 sub-hills (reasoned choice) from the 55. First, all the sub-hills in the Mutaho commune were listed in alphabetical order. All households were then aggregated according to sub-hill. The sub-hills were selected with unequal probability. Once the sub-hills had been selected, the household sample size was allocated in proportion to the number of households in each sub-hill (Table 1 and Table A1). The sample of households was selected with equal probability (Agresti, 2018).

With these methodological considerations in mind, the survey was carried out on a sample of 215 households, randomly selected from 10 sub-hills spread over 8 census hills in the Commune of Mutaho (Burundi). In this survey, data were collected using a specially prepared questionnaire and entered into the CSPro application. They were then exported to SPSS for processing and analysis.

Nine variables were identified as suitable for analysis. School attendance status was the dependent variable and eight independent variables. These were demographic factors (age of head of household, sex of head of household, and marital status of head of household), socio-cultural variables (religion of head of household and education of head of household), and socio-economic factors (household

<table><caption>Table 1: Sample size and distribution by sub-hill</caption><thead><tr><th>No</th><th>Hills</th><th>Sub-hills</th><th>Households by sub-hill</th><th>Number of households</th></tr></thead><tbody><tr><td>1</td><td>Masango</td><td>Gaheza</td><td>215</td><td>13</td></tr><tr><td>2</td><td>Gerangabo</td><td>Gerangabo</td><td>377</td><td>22</td></tr><tr><td>3</td><td>Mutaho</td><td>Hayiro</td><td>500</td><td>30</td></tr><tr><td>4</td><td>Nzove</td><td>Kibungere</td><td>241</td><td>14</td></tr><tr><td>5</td><td>Rurengera</td><td>Mibazi</td><td>530</td><td>31</td></tr><tr><td>6</td><td>Muririmbo</td><td>Muririmbo</td><td>606</td><td>36</td></tr><tr><td>7</td><td>Bigera</td><td>Ncaramba</td><td>334</td><td>20</td></tr><tr><td>8</td><td>Ngoma</td><td>Nyakabungo</td><td>158</td><td>9</td></tr><tr><td>9</td><td>Bigera</td><td>Nyarubuye</td><td>334</td><td>20</td></tr><tr><td>10</td><td>Bigera</td><td>Rukorobwa</td><td>333</td><td>20</td></tr><tr><td><strong>Total (n)</strong></td><td><strong>8</strong></td><td><strong>10</strong></td><td><strong>3,628</strong></td><td><strong>215</strong></td></tr><tr><td><em>N</em></td><td>18</td><td>55</td><td>18,449</td><td>3,628</td></tr></tbody></table>

Source: Authors, based on data from the Mutaho commune civil registry, 2019.

size, number of school-age children in household, and main activity of head of household). However, due to the collinearity effect between household size and the number of school-age children in the household (with a correlation of 0.75) and between sex and marital status of the head of household (with a correlation of 0.83), two variables were excluded from the analysis (sex of the head of household and number of school-age children) (Schoumaker, 2013).

All these variables had no non-response rate after filtering out children under 7. In fact, of the 215 households planned, it was deemed important to keep only those with children aged 7 or over (initially, households with pre-school children – aged 3–6 – were included), i.e., 180 households. With the six independent variables retained, the explanatory power of school attendance is 65.64% (area under the ROC curve = 0.6564). These results allow us to conclude that these variables provide a significant explanation for the phenomenon of school attendance.

The influence of household size on school attendance is analyzed from a multivariate perspective. Given the qualitative and binary nature of the dependent variable (coded 1 if all children in the household attend and 0 if the household has at least one child who does not), the method used was binomial logistic regression (Rizzi, 2013). The probability associated with the Chi-square test of the likelihood ratio to the complete model is less than 5% (LR chi2, df: 12; *p* < 0.05), confirming the validity of the multivariate modeling approach.

Data processing and variable recoding were carried out using SPSS 25 software. Logistic regression was performed using Stata 15. Microsoft Excel was used to format tables and present data and results.

<footnote n="2">2 Data collected in the Mutaho Commune's Civil registry office, Annual Report, 2019.</footnote>
## 4 Results

The results of the binomial logistic regression show that of the six explanatory variables entered in the model, two variables are the only ones significantly associated with school attendance. These are the age of the head of household and household size. With regard to the age of the head of household, the results show that children living in households whose heads are aged 20–34 are 81.5% (OR = 0.185; *p* < 0.05; 95% CI: 0.046–0.722) less likely to attend school than those living in households whose heads are aged 35–49 or over. As for household size, the results show that children living in households of small size (1–4 members) are 5.46 times (OR = 5.463; *p* < 0.05; 95% CI: 1.311–22.771) more likely to attend school than those living in households of 5–6 or more members (Table 2).

## 5 Discussion

By cross-referencing demographic, socio-cultural, and economic factors with school attendance, our study in Mutaho

commune aimed to determine the role played by household size in access to schooling for children aged 7–17. The results of the study confirmed the significant influence of household size on school attendance in the Mutaho commune. Indeed, children living in small households, for example, with four or fewer household members, are 5.46 times more likely to attend school than children living in households with five or more household members.

These results converge with those of other authors, including Blake (1989) and Becker (1960), who, based on studies carried out in developed countries, demonstrated that household size is negatively associated with children's schooling. We should also mention the studies carried out in underdeveloped countries, including those by Bougma (2014), Kravdal et al. (2013), and Lachaud (2015) and Vogl (2022), which have proven that there is a negative relationship between family size and the children's average education.

Nevertheless, this relationship is far from being the model for every country in the world. Other researchers have found contradictory results. This is the case of studies carried out in some sub-Saharan African countries, which

<table><caption>Table 2: Binomial logistic regression results</caption><thead><tr><th rowspan="2">Variables and modalities</th><th rowspan="2">N = 180</th><th rowspan="2">Odds ratio</th><th rowspan="2">p > |z|</th><th colspan="2">95% Conf. interval</th></tr><tr><th>Lower terminal</th><th>Upper terminal</th></tr></thead><tbody><tr><td><strong>Household size</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>1–4 members</td><td>36</td><td>5.463</td><td>p < 0.05</td><td>1.311</td><td>22.771</td></tr><tr><td>5–6 members</td><td>73</td><td>1.000</td><td></td><td></td><td></td></tr><tr><td>7 members and more</td><td>71</td><td>1.155</td><td>p > 0.05</td><td>0.528</td><td>2.53</td></tr><tr><td><strong>Age of household head</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>20–34 years</td><td>38</td><td>0.185</td><td>p < 0.05</td><td>0.046</td><td>0.722</td></tr><tr><td>35–49 years</td><td>121</td><td>1.000</td><td></td><td></td><td></td></tr><tr><td>50 years and more</td><td>21</td><td>2.966</td><td>p > 0.05</td><td>0.746</td><td>11.788</td></tr><tr><td><strong>Religion of household head</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Catholic</td><td>122</td><td>1.000</td><td></td><td></td><td></td></tr><tr><td>Protestant</td><td>48</td><td>0.566</td><td>p > 0.05</td><td>0.264</td><td>1.213</td></tr><tr><td>Muslim/Jehovah's witness/Anglican</td><td>10</td><td>0.48</td><td>p > 0.05</td><td>0.118</td><td>1.951</td></tr><tr><td><strong>Education of household head</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yaga Mukama<fnref n="1" /></td><td>43</td><td>0.788</td><td>p > 0.05</td><td>0.334</td><td>1.858</td></tr><tr><td>Fundamental</td><td>95</td><td>1.000</td><td></td><td></td><td></td></tr><tr><td>Post-fundamental</td><td>32</td><td>1.126</td><td>p > 0.05</td><td>0.216</td><td>5.854</td></tr><tr><td>University</td><td>10</td><td>0.262</td><td>p > 0.05</td><td>0.326</td><td>2.105</td></tr><tr><td><strong>Main activity of household head</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Agriculture/livestock/artisan</td><td>102</td><td>1.000</td><td></td><td></td><td></td></tr><tr><td>Trader/entrepreneur</td><td>34</td><td>0.671</td><td>p > 0.05</td><td>0.264</td><td>1.704</td></tr><tr><td>State/private/NGO civil servant</td><td>44</td><td>1.119</td><td>p > 0.05</td><td>0.239</td><td>5.242</td></tr><tr><td><strong>Marital status of household head</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>In union</td><td>159</td><td>1.000</td><td></td><td></td><td></td></tr><tr><td>Out of union</td><td>21</td><td>0.651</td><td></td><td>0.232</td><td>1.824</td></tr></tbody></table>

Source: Authors.

<footnote n="1">¹Yaga Mukama, literally “Speak Lord,” is a Roman Catholic educational system that taught school-age children who did not attend school to read, write and recite the Church’s catechism. It is comparable to the Koranic school in Muslim-dominated countries.</footnote>
## 4 Results

The results of the binomial logistic regression show that of the six explanatory variables entered in the model, two variables are the only ones significantly associated with school attendance. These are the age of the head of household and household size. With regard to the age of the head of household, the results show that children living in households whose heads are aged 20–34 are 81.5% (OR = 0.185; *p* < 0.05; 95% CI: 0.046–0.722) less likely to attend school than those living in households whose heads are aged 35–49 or over. As for household size, the results show that children living in households of small size (1–4 members) are 5.46 times (OR = 5.463; *p* < 0.05; 95% CI: 1.311–22.771) more likely to attend school than those living in households of 5–6 or more members (Table 2).

## 5 Discussion

By cross-referencing demographic, socio-cultural, and economic factors with school attendance, our study in Mutaho

commune aimed to determine the role played by household size in access to schooling for children aged 7–17. The results of the study confirmed the significant influence of household size on school attendance in the Mutaho commune. Indeed, children living in small households, for example, with four or fewer household members, are 5.46 times more likely to attend school than children living in households with five or more household members.

These results converge with those of other authors, including Blake (1989) and Becker (1960), who, based on studies carried out in developed countries, demonstrated that household size is negatively associated with children's schooling. We should also mention the studies carried out in underdeveloped countries, including those by Bougma (2014), Kravdal et al. (2013), and Lachaud (2015) and Vogl (2022), which have proven that there is a negative relationship between family size and the children's average education.

Nevertheless, this relationship is far from being the model for every country in the world. Other researchers have found contradictory results. This is the case of studies carried out in some sub-Saharan African countries, which

<table><caption>Table 2: Binomial logistic regression results</caption><thead><tr><th rowspan="2">Variables and modalities</th><th rowspan="2">N = 180</th><th rowspan="2">Odds ratio</th><th rowspan="2">p > |z|</th><th colspan="2">95% Conf. interval</th></tr><tr><th>Lower terminal</th><th>Upper terminal</th></tr></thead><tbody><tr><td><strong>Household size</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>1–4 members</td><td>36</td><td>5.463</td><td>p < 0.05</td><td>1.311</td><td>22.771</td></tr><tr><td>5–6 members</td><td>73</td><td>1.000</td><td></td><td></td><td></td></tr><tr><td>7 members and more</td><td>71</td><td>1.155</td><td>p > 0.05</td><td>0.528</td><td>2.53</td></tr><tr><td><strong>Age of household head</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>20–34 years</td><td>38</td><td>0.185</td><td>p < 0.05</td><td>0.046</td><td>0.722</td></tr><tr><td>35–49 years</td><td>121</td><td>1.000</td><td></td><td></td><td></td></tr><tr><td>50 years and more</td><td>21</td><td>2.966</td><td>p > 0.05</td><td>0.746</td><td>11.788</td></tr><tr><td><strong>Religion of household head</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Catholic</td><td>122</td><td>1.000</td><td></td><td></td><td></td></tr><tr><td>Protestant</td><td>48</td><td>0.566</td><td>p > 0.05</td><td>0.264</td><td>1.213</td></tr><tr><td>Muslim/Jehovah's witness/Anglican</td><td>10</td><td>0.48</td><td>p > 0.05</td><td>0.118</td><td>1.951</td></tr><tr><td><strong>Education of household head</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yaga Mukama<fnref n="1" /></td><td>43</td><td>0.788</td><td>p > 0.05</td><td>0.334</td><td>1.858</td></tr><tr><td>Fundamental</td><td>95</td><td>1.000</td><td></td><td></td><td></td></tr><tr><td>Post-fundamental</td><td>32</td><td>1.126</td><td>p > 0.05</td><td>0.216</td><td>5.854</td></tr><tr><td>University</td><td>10</td><td>0.262</td><td>p > 0.05</td><td>0.326</td><td>2.105</td></tr><tr><td><strong>Main activity of household head</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Agriculture/livestock/artisan</td><td>102</td><td>1.000</td><td></td><td></td><td></td></tr><tr><td>Trader/entrepreneur</td><td>34</td><td>0.671</td><td>p > 0.05</td><td>0.264</td><td>1.704</td></tr><tr><td>State/private/NGO civil servant</td><td>44</td><td>1.119</td><td>p > 0.05</td><td>0.239</td><td>5.242</td></tr><tr><td><strong>Marital status of household head</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>In union</td><td>159</td><td>1.000</td><td></td><td></td><td></td></tr><tr><td>Out of union</td><td>21</td><td>0.651</td><td></td><td>0.232</td><td>1.824</td></tr></tbody></table>

Source: Authors.

<footnote n="1">¹Yaga Mukama, literally “Speak Lord,” is a Roman Catholic educational system that taught school-age children who did not attend school to read, write and recite the Church’s catechism. It is comparable to the Koranic school in Muslim-dominated countries.</footnote>
have proved that household size positively influences chil-
dren's school attendance (Chabi & Attanasso, 2015). The
reasons put forward to explain these results include the
intervention of older siblings in the schooling of their
younger siblings, which can contribute to improving edu-
cation levels in large families.

However, in the Mutaho commune, as in the Burundian
context in general, our finding is that an additional child in a
household reduces the chances of his or her siblings attending
school. Given that smaller households are more likely to send
their children to school than larger ones, this highlights the
negative effects of large families on the schooling of rural
children and young people. These findings also demonstrate
the negative influence of demographic pressure on children's
schooling in this rural environment, where around 90% of
Burundi's population lives, earning their living mainly from
subsistence farming and livestock (BCR, 2011; UNFPA, 2016). In
the context of climate change and land dwindling, households
are under the haunts of monetary poverty, famine, disease,
etc., and schooling suffers as a result (PNUD, 2019; Sindayihe-
bura & Nkunzimana, 2020).

The results of this study also show that the age of the
head of household is closely related to school attendance at
the 5% significance level. Nevertheless, as mentioned above,
the results are worrying insofar as we would expect younger
heads of household to understand the benefits of schooling
better than older ones and that as the age of the head of house-
hold increases, there are more children who fail and conse-
quently drop out of school for various reasons. However, these
results are in line with those of other researchers, including
Degan and Guezo (1986), who, based on a sample of 120 house-
holds and 526 school-age children in Benin, demonstrated that
young adults are more reluctant to send their children to school
than older people (Pilon & Yaro, 2001). Conversely, Mba Oyono
(2009) has shown that the age of the head of household is
among the social determinants of children's school attendance
with the lowest explanatory power.

In the rural context of Burundi, as in the Mutaho com-
mune, young household heads do not yet have sufficient
means to support their families. In fact, the over-exploita-
tion of agricultural land means that the work in the fields is
no longer sufficient to provide a livelihood, and these
heads of household often leave for the towns or other
regions or countries where there is a demand for labor (pro-
vinces of Bujumbura Mairie, Bujumbura, Bubanza, Makamba,
Western Tanzania, and Eastern Democratic Republic of Congo).
As a result, these heads have no opportunity to influence their
children's schooling, even if they would like to. The members
of their households often suffer from neglected diseases such

as anemia, chronic malnutrition of children, etc., as well as lack
of schooling (Kamuragiye & Buzingo, 2019; Manirakiza, 2008;
Sindayihebura, Manirakiza, & Nganawara, 2022).

6 Conclusions

The aim of this study was to identify the demographic,
socio-cultural, and economic factors likely to influence
school attendance in Mutaho commune for children aged
7–17 and to show the role played by household size in
access to education in Mutaho commune. These results
show that large household size is negatively associated
with school attendance among children in fundamental
and post-fundamental school (aged 7–17) in Mutaho com-
mune at the 5% significance level. The age of the household
head also has an influence on children’s school attendance
at the 5% significance level. School attendance rates increase
with the age of the household head. Nevertheless, this
finding is worrying insofar as young household heads
were expected to understand the benefits of schooling.
Conversely, it was expected that an increase in the age
of the household head would correspond to children
dropping out of school for a variety of reasons. The
results obtained in the present study allow us to raise
other questions. These include the role of other, non-
family factors in children’s school attendance. In future
studies, we will be looking to deepen our research, in
particular by documenting other non-family determi-
nants of school attendance. Given the impact of house-
hold size on children’s school attendance, fertility reduc-
tion should be a priority concern for all those involved in
population, education, and development.

**Funding information:** The authors state no funding involved.

**Author contributions:** All authors listed in this article have made a substantial and intellectual contribution to this study. They have also read and approved the final manuscript for publication.

**Conflict of interest:** The authors state no conflict of interest.

**Declaration for human participants:** Data were collected from household heads who signed a consent form allowing their responses to be processed for scientific purposes, and the results of the study have been approved by the thesis committee at the University of Burundi. The principles of
have proved that household size positively influences chil-
dren's school attendance (Chabi & Attanasso, 2015). The
reasons put forward to explain these results include the
intervention of older siblings in the schooling of their
younger siblings, which can contribute to improving edu-
cation levels in large families.

However, in the Mutaho commune, as in the Burundian
context in general, our finding is that an additional child in a
household reduces the chances of his or her siblings attending
school. Given that smaller households are more likely to send
their children to school than larger ones, this highlights the
negative effects of large families on the schooling of rural
children and young people. These findings also demonstrate
the negative influence of demographic pressure on children's
schooling in this rural environment, where around 90% of
Burundi's population lives, earning their living mainly from
subsistence farming and livestock (BCR, 2011; UNFPA, 2016). In
the context of climate change and land dwindling, households
are under the haunts of monetary poverty, famine, disease,
etc., and schooling suffers as a result (PNUD, 2019; Sindayihe-
bura & Nkunzimana, 2020).

The results of this study also show that the age of the
head of household is closely related to school attendance at
the 5% significance level. Nevertheless, as mentioned above,
the results are worrying insofar as we would expect younger
heads of household to understand the benefits of schooling
better than older ones and that as the age of the head of house-
hold increases, there are more children who fail and conse-
quently drop out of school for various reasons. However, these
results are in line with those of other researchers, including
Degan and Guezo (1986), who, based on a sample of 120 house-
holds and 526 school-age children in Benin, demonstrated that
young adults are more reluctant to send their children to school
than older people (Pilon & Yaro, 2001). Conversely, Mba Oyono
(2009) has shown that the age of the head of household is
among the social determinants of children's school attendance
with the lowest explanatory power.

In the rural context of Burundi, as in the Mutaho com-
mune, young household heads do not yet have sufficient
means to support their families. In fact, the over-exploita-
tion of agricultural land means that the work in the fields is
no longer sufficient to provide a livelihood, and these
heads of household often leave for the towns or other
regions or countries where there is a demand for labor (pro-
vinces of Bujumbura Mairie, Bujumbura, Bubanza, Makamba,
Western Tanzania, and Eastern Democratic Republic of Congo).
As a result, these heads have no opportunity to influence their
children's schooling, even if they would like to. The members
of their households often suffer from neglected diseases such

as anemia, chronic malnutrition of children, etc., as well as lack
of schooling (Kamuragiye & Buzingo, 2019; Manirakiza, 2008;
Sindayihebura, Manirakiza, & Nganawara, 2022).

6 Conclusions

The aim of this study was to identify the demographic,
socio-cultural, and economic factors likely to influence
school attendance in Mutaho commune for children aged
7–17 and to show the role played by household size in
access to education in Mutaho commune. These results
show that large household size is negatively associated
with school attendance among children in fundamental
and post-fundamental school (aged 7–17) in Mutaho com-
mune at the 5% significance level. The age of the household
head also has an influence on children’s school attendance
at the 5% significance level. School attendance rates increase
with the age of the household head. Nevertheless, this
finding is worrying insofar as young household heads
were expected to understand the benefits of schooling.
Conversely, it was expected that an increase in the age
of the household head would correspond to children
dropping out of school for a variety of reasons. The
results obtained in the present study allow us to raise
other questions. These include the role of other, non-
family factors in children’s school attendance. In future
studies, we will be looking to deepen our research, in
particular by documenting other non-family determi-
nants of school attendance. Given the impact of house-
hold size on children’s school attendance, fertility reduc-
tion should be a priority concern for all those involved in
population, education, and development.

**Funding information:** The authors state no funding involved.

**Author contributions:** All authors listed in this article have made a substantial and intellectual contribution to this study. They have also read and approved the final manuscript for publication.

**Conflict of interest:** The authors state no conflict of interest.

**Declaration for human participants:** Data were collected from household heads who signed a consent form allowing their responses to be processed for scientific purposes, and the results of the study have been approved by the thesis committee at the University of Burundi. The principles of
the Declaration of Helsinki, such as consent, confidentiality
anonymity, etc., were respected.

**Data availability statement:** All data are included in the content of the article.

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MENRS. (2020). *Annuaire Statistique Scolaire 2019-2020. Tome 1: Statistiques de l'enseignement préscolaire et fondamental*.

Mincer, J. (1975). *Education, experience and distribution of earnings and employment: An overview* (pp. 71-94). National Bureau of Economic Research, Education (NBER). doi: 10.2307/1057807.

Nations Unies. (1948). *Déclaration universelle des droits de l'homme*.

Nganawara, D. (2016). *Famille et scolarisation des enfants en âge obligatoire scolaire au Cameroun: Une analyse à partir du recensement de 2005, Québec: Observatoire démographique et statistique de l'espace francophone/Université Laval*.

Pilon, M. (1995). Les déterminants de la scolarisation des enfants de 6 à 14 ans au Togo en 1981: Apports et limites des données censitaires. *Cahiers des Sciences Humaines*, 31(3), 697-718.

Pilon, M. (1996). Genre et scolarisation en Afrique Sub-Saharienne. *Genre et développement, des pistes à suivre* (pp. 25-47). Paris, France: Textes d'une rencontre à Paris.

Pilon, M. (2006). *Défis du développement en Afrique subsaharienne. L'éducation en jeu*. Les Collections du CEPED (Centre Population et Développement). Groupement d'intérêt Scientifique INED-IRD-Paris 1-Paris 5-Paris X.

Pilon, M., & Yaro, Y. (2001). *La Demande d'Education en Afrique. Etat des connaissances et perspectives de recherche*. France: UAPS Thematic Research Networks. Network on Family and Schooling in Africa.

PNUD. (2019). *République du Burundi: Rapport National sur le Développement Humain 2019: Cohésion sociale, dividende démographique et développement humain durable* (p. 153). https://www.bi.undp.org/content/burundi/fr/home/library/mdg/RNDH.html.

Rizzi, E. (2013). La régression logistique. In G. Masuy-Stroobant & R. Costa (Eds.), *Analyser les données en Sciences sociales: De la préparation des*
the Declaration of Helsinki, such as consent, confidentiality
anonymity, etc., were respected.

**Data availability statement:** All data are included in the content of the article.

References

African Union Commission. (2015). *Agenda 2063-The Africa we want*.

Agresti, A. (2018). *Statistical methods for the social sciences* (5th ed., global edition). Pearson.

Bahri, N. (2024). Les inégalités d'accès à l'éducation des enfants en Tunisie. *GPH International Journal of Educational Research*, 7(1), 31–41.
https://zenodo.org/records/10638813.

Bambara, A., & Wayack-Pambè, M. (2019). Pauvreté, scolarisation des enfants et sexe du chef de ménage au Burkina Faso: Une analyse à partir de deux indicateurs de niveau de vie. *Genre Éducation Formation*, 3, 3. doi: 10.4000/gef.585.

BCR, B. C. des R. (2011). *Recensement Général de la Population et de l'Habitat 2008, Vol.3 : Analyse, Tome 6: Etat et structure de la population*.

Becker, G. S. (1960). *An economic analysis of fertility* (pp. 209–240).
National Bureau of Economic Research, Demographi.

Becker, G. S. (1985). Human capital, effort, and the sexual division of labor. *Journal of Labor Economics*, 3(1), S33–S58. doi: 10.1086/298075.

Blake, J. (1989). Family size and achievement. *Population and Development Review*, 15(3), 561–567.

Bougma, M. (2014). Fécondité, réseaux familiaux et scolarisation des enfants en milieu urbain au Burkina Faso. Canada: Université de Montréal.

Chabi, M. O., & Attanasso, M. O. (2015). Déterminants de la scolarisation et du niveau scolaire en milieu rural: Une étude empirique au Bénin en Afrique de l'Ouest. *International Journal of Innovation and Applied Studies*, 10(1), 73–84.

East African Community. (2016). EAC Vision 2050: Regional vision for socio-economic transformation and development.

Giordano, Y., & Jolibert, A. (2016). Pourquoi je préfère la recherche quantitative/Pourquoi je préfère la recherche qualitative. *Revue Internationale P.M.E.: Économie et Gestion de la Petite et Moyenne Entreprise*, 29(2), 7. doi: 10.7202/1037919ar.

Guengant, J.-P., Kamara, Y., & collab. (2011). Comment bénéficier du dividende démographique ? La démographie au centre des trajectoires de développement: Synthèse des études réalisées dans les pays de l'UEMOA, ainsi qu'au Ghana, en Guinée, en Mauritanie et au Nigéria. Paris, France: IRD-Institut de Recherche pour le Développement.

Gumucio, S. (2011). Collecte de données. Méthodes quantitatives: L'exemple des enquêtes CAP (Connaissances, Attitudes & Pratiques). *World Wide Web Internet And Web Information Systems*, 88(12), 2309–2320.

ISTEEBU & ICF. (2017). *Burundi Demographic and Health Survey 2016–2017: Summary Report*.

ISTEEBU & ICF. (2017). *Burundi Third Demographic and Health Survey 2016–2017. Final Report*.

Kakuba, C. (2014). *Evolution of inequalities in access to secondary schooling in Uganda*.

Kakuba, C., & Pilon, M. (2023). Access to boarding secondary schools in Uganda: The extent of the exacerbation of social inequalities.

Cahiers de la recherche sur l'éducation et les savoirs, 22, 171-194.
doi: 10.4000/cres.6550.

Kakuba, C., Nzabona, A., Asiimwe, J. B., Tuyiragize, R., & Mushomi, J. (2021). Who accesses secondary schooling in Uganda? Was the universal secondary education policy ubiquitously effective? *International Journal of Educational Development, 83*, 8. doi: 10.1016/j.ijedudev.2021.102370.

Kamuragiye, A., & Buzingo, D. (2019). *Maitriser la croissance de la population pour profiter du dividende démographique en Afrique subsaharienne: La cas du Burundi*. Les Editions l'Empreinte du Passant. https://lempreintedupassant.com/index.php/product/maitriser-la-croissance-de-la-population-pour-profiter-du-dividendé-demographique-en-afrique-subsaharienne-le-cas-du-burundi/.

Kobiane, J. F. (2002). *Ménages et scolarisation des enfants au Burkina Faso: À la recherche des déterminants de la demande scolaire*. Louvain-La-Neuve, Belgium: Academia Bruylant.

Kravdal, Ø., Kodzi, I., & Sigle-Rushton, W. (2013). Education in sub-Saharan Africa: A new look at the effects of number of siblings. *Studies in Family Planning*, 44(3), 275-297.

Lachaud, J. (2015). *Changements démographiques et inégalités éducatives à Ouagadougou*. PhD UDEM. Université de Montréal.

Livenais, P., & Vaugelade, J. (1993). Education, Changements démographiques et développement. *Editions de l'ORSTOM. Collection « Colloques et Séminaires »*, 239.

Manirakiza, R. (2008). *Population et développement au Burundi*. Harmattan. https://www.editions-harmattan.fr/livre-population_et_developpement_au_burundi_rene_manirakiza-9782296059443-26664.html.

Mba Oyono, P. R. (2009). *Les déterminants familiaux de la scolarisation des enfants de 6 à 14 ans au Gabon*.

MENRS. (2020). *Annuaire Statistique Scolaire 2019-2020. Tome 1: Statistiques de l'enseignement préscolaire et fondamental*.

Mincer, J. (1975). *Education, experience and distribution of earnings and employment: An overview* (pp. 71–94). National Bureau of Economic Research, Education (NBER). doi: 10.2307/1057807.

Nations Unies. (1948). *Déclaration universelle des droits de l'homme*.

Nganawara, D. (2016). *Famille et scolarisation des enfants en âge obligatoire scolaire au Cameroun: Une analyse à partir du recensement de 2005, Québec: Observatoire démographique et statistique de l'espace francophone/Université Laval*.

Pilon, M. (1995). Les déterminants de la scolarisation des enfants de 6 à 14 ans au Togo en 1981: Apports et limites des données censitaires. *Cahiers des Sciences Humaines*, 31(3), 697-718.

Pilon, M. (1996). Genre et scolarisation en Afrique Sub-Saharienne. *Genre et développement, des pistes à suivre* (pp. 25-47). Paris, France: Textes d'une rencontre à Paris.

Pilon, M. (2006). *Défis du développement en Afrique subsaharienne. L'éducation en jeu*. Les Collections du CEPED (Centre Population et Développement). Groupement d'intérêt Scientifique INED-IRD-Paris 1-Paris 5-Paris X.

Pilon, M., & Yaro, Y. (2001). *La Demande d'Education en Afrique. Etat des connaissances et perspectives de recherche*. France: UAPS Thematic Research Networks. Network on Family and Schooling in Africa.

PNUD. (2019). *République du Burundi: Rapport National sur le Développement Humain 2019: Cohésion sociale, dividende démographique et développement humain durable* (p. 153). https://www.bi.undp.org/content/burundi/fr/home/library/mdg/RNDH.html.

Rizzi, E. (2013). La régression logistique. In G. Masuy-Stroobant & R. Costa (Eds.), *Analyser les données en Sciences sociales: De la préparation des*
données à l'analyse multivariée (Editions scientifiques internationales, Vol. 5, pp. 253-278). P.I.E. Peter Lang. https://www.peterlang.com/document/1053763.

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Sindayihebura, J. F. R., & Nkunzimana, A. (2020). Changement climatique et anémie chez les femmes en âge de procréer au Burundi: Approche par la région de résidence. *Revue de l'Université du Burundi : Séries Sciences Humaines et Sociales, 18*(1), 160-173. http://revue.ub.edu.bi/JUB/article/view/84.

Sindayihebura, J. F. R., Manirakiza, R., & Nganawara, D. (2022). Profil des Femmes à Haut Risque d'Anémie : Influence des Mutations Rurales [Profile of Women at High Risk of Anemia in Burundi: Influence of Rural Changes]. *Les annales de l'IFORD, 22*(1), 109-126.

Tabutin, D., & Schoumaker, B. (2020). La démographie de l'Afrique sub-saharienne au XXIe siècle. Bilan des changements de 2000 à 2020, perspectives et défis d'ici 2050. *Population-F*, 75(2-3), 167-296. doi: 10.3917/popu.2002.0167.

UNDP. (2018). *Indices et indicateurs de développement humain 2018. Mise à jour statistique*. 123.

UNESCO. (2017). Réduire la pauvreté dans le monde à travers l'enseignement primaire et secondaire universel.

UNESCO. (2021). *Le système éducatif burundais: Enjeux et défis pour accélérer la production du capital humain et soutenir la croissance économique*.

UNFPA. (2016). La révolution contraceptive au Burundi. Perspectives pour bénéficier du dividende démographique. In *Fonds des Nations Unies pour la Population*. doi: 10.2307/j.ctv18pgxrk.8.

United Nations. (2003). *Population, education and development*. Concise Report.

Vogl, T. (2022). *Fertility and the education of African parents and children* (p. 30474). USA: National Bureau of Economic Research. doi: 10.2139/ssrn.4222678.
données à l'analyse multivariée (Editions scientifiques internationales, Vol. 5, pp. 253-278). P.I.E. Peter Lang. https://www.peterlang.com/document/1053763.

Schoumaker, B. (2013). La régression linéaire multiple. In G. Masuy-Stroobant & R. Costa (Eds.), *Analyser les données en Sciences sociales: De la préparation des données à l'analyse multivariée* (Editions Scientifiques Internationales, Vol. 5, pp. 227-252). P.I.E. Peter Lang. https://www.peterlang.com/document/1053763.

Sindayihebura, J. F. R., & Nkunzimana, A. (2020). Changement climatique et anémie chez les femmes en âge de procréer au Burundi: Approche par la région de résidence. *Revue de l'Université du Burundi : Séries Sciences Humaines et Sociales, 18*(1), 160-173. http://revue.ub.edu.bi/JUB/article/view/84.

Sindayihebura, J. F. R., Manirakiza, R., & Nganawara, D. (2022). Profil des Femmes à Haut Risque d'Anémie : Influence des Mutations Rurales [Profile of Women at High Risk of Anemia in Burundi: Influence of Rural Changes]. *Les annales de l'IFORD, 22*(1), 109-126.

Tabutin, D., & Schoumaker, B. (2020). La démographie de l'Afrique subsaharienne au XXIe siècle. Bilan des changements de 2000 à 2020, perspectives et défis d'ici 2050. *Population-F*, 75(2-3), 167-296. doi: 10.3917/popu.2002.0167.

UNDP. (2018). *Indices et indicateurs de développement humain 2018. Mise à jour statistique*. 123.

UNESCO. (2017). Réduire la pauvreté dans le monde à travers l'enseignement primaire et secondaire universel.

UNESCO. (2021). *Le système éducatif burundais: Enjeux et défis pour accélérer la production du capital humain et soutenir la croissance économique*.

UNFPA. (2016). La révolution contraceptive au Burundi. Perspectives pour bénéficier du dividende démographique. In *Fonds des Nations Unies pour la Population*. doi: 10.2307/j.ctv18pgxrk.8.

United Nations. (2003). *Population, education and development*. Concise Report.

Vogl, T. (2022). *Fertility and the education of African parents and children* (p. 30474). USA: National Bureau of Economic Research. doi: 10.2139/ssrn.4222678.
# Appendix

Selection of sample sub-hills is shown in Table A1.

<table><caption>Table A1: Selection of sample sub-hills</caption><thead><tr><td>No</td><td>Sub-hills</td><td>Households/sub-hill</td><td>Aggregate households</td><td>Sampling interval</td><td>Sample sub-hills</td></tr></thead><tbody><tr><td>1</td><td>Gaheza</td><td>215</td><td>215</td><td>1</td><td>Gaheza</td></tr><tr><td>2</td><td>Gasenyi</td><td>215</td><td>430</td><td></td><td></td></tr><tr><td>3</td><td>Gatare (Kivoga)</td><td>221</td><td>651</td><td></td><td></td></tr><tr><td>4</td><td>Gatare (Nzove)</td><td>241</td><td>892</td><td></td><td></td></tr><tr><td>5</td><td>Gatongati</td><td>490</td><td>1,382</td><td></td><td></td></tr><tr><td>6</td><td>Gatunga</td><td>215</td><td>1,597</td><td>1,845</td><td></td></tr><tr><td>7</td><td>Gerangabo</td><td>377</td><td>1,974</td><td></td><td>Gerangabo</td></tr><tr><td>8</td><td>Giharahata</td><td>328</td><td>2,302</td><td></td><td></td></tr><tr><td>9</td><td>Gitaba</td><td>554</td><td>2,856</td><td></td><td></td></tr><tr><td>10</td><td>Gitongo</td><td>345</td><td>3,201</td><td>3,690</td><td></td></tr><tr><td>11</td><td>Hayiro</td><td>500</td><td>3,701</td><td></td><td>Hayiro</td></tr><tr><td>12</td><td>Kabungere</td><td>288</td><td>3,989</td><td></td><td></td></tr><tr><td>13</td><td>Kagwa</td><td>376</td><td>4,365</td><td></td><td></td></tr><tr><td>14</td><td>Karehe</td><td>288</td><td>4,653</td><td></td><td></td></tr><tr><td>15</td><td>Kavumu</td><td>500</td><td>5,153</td><td></td><td></td></tr><tr><td>16</td><td>Kayaga</td><td>288</td><td>55441</td><td>5,535</td><td></td></tr><tr><td>17</td><td>Kibungere</td><td>241</td><td>5,682</td><td></td><td>Kibungere</td></tr><tr><td>18</td><td>Kidasha</td><td>204</td><td>5,886</td><td></td><td></td></tr><tr><td>19</td><td>Kirehe</td><td>500</td><td>6,386</td><td></td><td></td></tr><tr><td>20</td><td>Kivoga</td><td>220</td><td>6,606</td><td></td><td></td></tr><tr><td>21</td><td>Kiziba</td><td>214</td><td>6,820</td><td></td><td></td></tr><tr><td>22</td><td>Mariza</td><td>323</td><td>7,143</td><td></td><td></td></tr><tr><td>23</td><td>Massango</td><td>214</td><td>7,357</td><td>7,380</td><td></td></tr><tr><td>24</td><td>Mibazi</td><td>530</td><td>7,887</td><td></td><td>Mibazi</td></tr><tr><td>25</td><td>Mirama</td><td>260</td><td>8,147</td><td></td><td></td></tr><tr><td>26</td><td>Mishehe</td><td>327</td><td>8,474</td><td></td><td></td></tr><tr><td>27</td><td>Muhororo</td><td>288</td><td>8,762</td><td>9,225</td><td></td></tr><tr><td>28</td><td>Muririmbo</td><td>606</td><td>9,368</td><td></td><td>Muririmbo</td></tr><tr><td>29</td><td>Mushikanwa</td><td>500</td><td>9,868</td><td></td><td></td></tr><tr><td>30</td><td>Muzenga</td><td>323</td><td>10,191</td><td></td><td></td></tr><tr><td>31</td><td>Mwumba</td><td>554</td><td>10,745</td><td>11,070</td><td></td></tr><tr><td>32</td><td>Ncaramba</td><td>334</td><td>11,079</td><td></td><td>Ncaramba</td></tr><tr><td>33</td><td>Ngoma</td><td>158</td><td>11,237</td><td></td><td></td></tr><tr><td>34</td><td>Ngoro (Nord-Sud)</td><td>204</td><td>11,441</td><td></td><td></td></tr><tr><td>35</td><td>Nkongwe</td><td>260</td><td>11,701</td><td></td><td></td></tr><tr><td>36</td><td>Nyabikenke_Bigera</td><td>334</td><td>12,035</td><td></td><td></td></tr><tr><td>37</td><td>Nyabikenke_Mutaho</td><td>500</td><td>12,535</td><td></td><td></td></tr><tr><td>38</td><td>Nyabisaka</td><td>273</td><td>12,808</td><td>12,915</td><td></td></tr><tr><td>39</td><td>Nyakabungo</td><td>158</td><td>12,966</td><td></td><td>Nyakabungo</td></tr><tr><td>40</td><td>Nyakeru</td><td>204</td><td>13,170</td><td></td><td></td></tr><tr><td>41</td><td>Nyakumba</td><td>220</td><td>13,390</td><td></td><td></td></tr><tr><td>42</td><td>Nyamugari</td><td>345</td><td>13,735</td><td></td><td></td></tr><tr><td>43</td><td>Nyamugosi</td><td>220</td><td>13,955</td><td></td><td></td></tr><tr><td>44</td><td>Nyangungu</td><td>288</td><td>14,243</td><td></td><td></td></tr><tr><td>45</td><td>Nyangwe</td><td>288</td><td>14,531</td><td>14,760</td><td></td></tr><tr><td>46</td><td>Nyarubuye</td><td>334</td><td>14,865</td><td></td><td>Nyarubuye</td></tr><tr><td>47</td><td>Nyarure</td><td>605</td><td>15,470</td><td></td><td></td></tr><tr><td>48</td><td>Nzove</td><td>240</td><td>15,710</td><td></td><td></td></tr><tr><td>49</td><td>Rubagabaga</td><td>500</td><td>16,210</td><td></td><td></td></tr></tbody></table>

(Continued)
# Appendix

Selection of sample sub-hills is shown in Table A1.

<table><caption>Table A1: Selection of sample sub-hills</caption><thead><tr><td>No</td><td>Sub-hills</td><td>Households/sub-hill</td><td>Aggregate households</td><td>Sampling interval</td><td>Sample sub-hills</td></tr></thead><tbody><tr><td>1</td><td>Gaheza</td><td>215</td><td>215</td><td>1</td><td>Gaheza</td></tr><tr><td>2</td><td>Gasenyi</td><td>215</td><td>430</td><td></td><td></td></tr><tr><td>3</td><td>Gatare (Kivoga)</td><td>221</td><td>651</td><td></td><td></td></tr><tr><td>4</td><td>Gatare (Nzove)</td><td>241</td><td>892</td><td></td><td></td></tr><tr><td>5</td><td>Gatongati</td><td>490</td><td>1,382</td><td></td><td></td></tr><tr><td>6</td><td>Gatunga</td><td>215</td><td>1,597</td><td>1,845</td><td></td></tr><tr><td>7</td><td>Gerangabo</td><td>377</td><td>1,974</td><td></td><td>Gerangabo</td></tr><tr><td>8</td><td>Giharahata</td><td>328</td><td>2,302</td><td></td><td></td></tr><tr><td>9</td><td>Gitaba</td><td>554</td><td>2,856</td><td></td><td></td></tr><tr><td>10</td><td>Gitongo</td><td>345</td><td>3,201</td><td>3,690</td><td></td></tr><tr><td>11</td><td>Hayiro</td><td>500</td><td>3,701</td><td></td><td>Hayiro</td></tr><tr><td>12</td><td>Kabungere</td><td>288</td><td>3,989</td><td></td><td></td></tr><tr><td>13</td><td>Kagwa</td><td>376</td><td>4,365</td><td></td><td></td></tr><tr><td>14</td><td>Karehe</td><td>288</td><td>4,653</td><td></td><td></td></tr><tr><td>15</td><td>Kavumu</td><td>500</td><td>5,153</td><td></td><td></td></tr><tr><td>16</td><td>Kayaga</td><td>288</td><td>55441</td><td>5,535</td><td></td></tr><tr><td>17</td><td>Kibungere</td><td>241</td><td>5,682</td><td></td><td>Kibungere</td></tr><tr><td>18</td><td>Kidasha</td><td>204</td><td>5,886</td><td></td><td></td></tr><tr><td>19</td><td>Kirehe</td><td>500</td><td>6,386</td><td></td><td></td></tr><tr><td>20</td><td>Kivoga</td><td>220</td><td>6,606</td><td></td><td></td></tr><tr><td>21</td><td>Kiziba</td><td>214</td><td>6,820</td><td></td><td></td></tr><tr><td>22</td><td>Mariza</td><td>323</td><td>7,143</td><td></td><td></td></tr><tr><td>23</td><td>Massango</td><td>214</td><td>7,357</td><td>7,380</td><td></td></tr><tr><td>24</td><td>Mibazi</td><td>530</td><td>7,887</td><td></td><td>Mibazi</td></tr><tr><td>25</td><td>Mirama</td><td>260</td><td>8,147</td><td></td><td></td></tr><tr><td>26</td><td>Mishehe</td><td>327</td><td>8,474</td><td></td><td></td></tr><tr><td>27</td><td>Muhororo</td><td>288</td><td>8,762</td><td>9,225</td><td></td></tr><tr><td>28</td><td>Muririmbo</td><td>606</td><td>9,368</td><td></td><td>Muririmbo</td></tr><tr><td>29</td><td>Mushikanwa</td><td>500</td><td>9,868</td><td></td><td></td></tr><tr><td>30</td><td>Muzenga</td><td>323</td><td>10,191</td><td></td><td></td></tr><tr><td>31</td><td>Mwumba</td><td>554</td><td>10,745</td><td>11,070</td><td></td></tr><tr><td>32</td><td>Ncaramba</td><td>334</td><td>11,079</td><td></td><td>Ncaramba</td></tr><tr><td>33</td><td>Ngoma</td><td>158</td><td>11,237</td><td></td><td></td></tr><tr><td>34</td><td>Ngoro (Nord-Sud)</td><td>204</td><td>11,441</td><td></td><td></td></tr><tr><td>35</td><td>Nkongwe</td><td>260</td><td>11,701</td><td></td><td></td></tr><tr><td>36</td><td>Nyabikenke_Bigera</td><td>334</td><td>12,035</td><td></td><td></td></tr><tr><td>37</td><td>Nyabikenke_Mutaho</td><td>500</td><td>12,535</td><td></td><td></td></tr><tr><td>38</td><td>Nyabisaka</td><td>273</td><td>12,808</td><td>12,915</td><td></td></tr><tr><td>39</td><td>Nyakabungo</td><td>158</td><td>12,966</td><td></td><td>Nyakabungo</td></tr><tr><td>40</td><td>Nyakeru</td><td>204</td><td>13,170</td><td></td><td></td></tr><tr><td>41</td><td>Nyakumba</td><td>220</td><td>13,390</td><td></td><td></td></tr><tr><td>42</td><td>Nyamugari</td><td>345</td><td>13,735</td><td></td><td></td></tr><tr><td>43</td><td>Nyamugosi</td><td>220</td><td>13,955</td><td></td><td></td></tr><tr><td>44</td><td>Nyangungu</td><td>288</td><td>14,243</td><td></td><td></td></tr><tr><td>45</td><td>Nyangwe</td><td>288</td><td>14,531</td><td>14,760</td><td></td></tr><tr><td>46</td><td>Nyarubuye</td><td>334</td><td>14,865</td><td></td><td>Nyarubuye</td></tr><tr><td>47</td><td>Nyarure</td><td>605</td><td>15,470</td><td></td><td></td></tr><tr><td>48</td><td>Nzove</td><td>240</td><td>15,710</td><td></td><td></td></tr><tr><td>49</td><td>Rubagabaga</td><td>500</td><td>16,210</td><td></td><td></td></tr></tbody></table>

(Continued)
<table><caption>**Table A1: Continued**</caption><thead><tr><th>No</th><th>Sub-hills</th><th>Households/sub-hill</th><th>Aggregate households</th><th>Sampling interval</th><th>Sample sub-hills</th></tr></thead><tbody><tr><td>50</td><td>Rubizi</td><td>287</td><td>16,497</td><td>16,605</td><td rowspan="6">Rukorobwa</td></tr><tr><td>51</td><td>Rukorobwa</td><td>333</td><td>16,830</td><td></td></tr><tr><td>52</td><td>Rurengera</td><td>530</td><td>17,360</td><td></td></tr><tr><td>53</td><td>Ruvumu</td><td>490</td><td>17,850</td><td></td></tr><tr><td>54</td><td>Rwisabi</td><td>327</td><td>18,177</td><td></td></tr><tr><td>55</td><td>Saga</td><td>272</td><td>18,449</td><td></td></tr><tr><td>Total</td><td>55</td><td>18,449</td><td></td><td>18,450</td><td></td></tr></tbody></table>

*Source:* Authors, based on data from the Mutaho commune civil registry, 2019.
<table><caption>**Table A1: Continued**</caption><thead><tr><th>No</th><th>Sub-hills</th><th>Households/sub-hill</th><th>Aggregate households</th><th>Sampling interval</th><th>Sample sub-hills</th></tr></thead><tbody><tr><td>50</td><td>Rubizi</td><td>287</td><td>16,497</td><td>16,605</td><td rowspan="6">Rukorobwa</td></tr><tr><td>51</td><td>Rukorobwa</td><td>333</td><td>16,830</td><td></td></tr><tr><td>52</td><td>Rurengera</td><td>530</td><td>17,360</td><td></td></tr><tr><td>53</td><td>Ruvumu</td><td>490</td><td>17,850</td><td></td></tr><tr><td>54</td><td>Rwisabi</td><td>327</td><td>18,177</td><td></td></tr><tr><td>55</td><td>Saga</td><td>272</td><td>18,449</td><td></td></tr><tr><td>Total</td><td>55</td><td>18,449</td><td></td><td>18,450</td><td></td></tr></tbody></table>

*Source:* Authors, based on data from the Mutaho commune civil registry, 2019.