Effects of Reproductive Health Outcomes on Primary School Attendance

NiCE Working Paper 09-116

December 2009

Effects of Reproductive Health Outcomes on Primary School

Attendance: A Sub-Saharan Africa Perspective

Abiba Longwe

Jeroen Smits

Nijmegen Center for Economics (NiCE)

Institute for Management Research

Radboud University Nijmegen

P.O. Box 9108, 6500 HK Nijmegen, The Netherlands

http://www.ru.nl/nice/workingpapers

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Abstract

To what extent and in which ways does poor RH at the household level negatively

influence educational attendance of young children in Sub-Saharan Africa? This paper

sets out to answer this question by analyzing household and district level data on school

attendance of 103,000 primary-school aged children living in 287 districts of 30 Sub-

Saharan African countries. Our multilevel analyses reveal substantially decreased

attendance rates of boys and girls with short preceding and succeeding birth intervals,

with more siblings, with a young sibling present and with a pregnant mother. These

findings remain intact when controlling for socioeconomic and demographic household

characteristics and economic and health-related context factors. Interaction analysis

shows that many effects of RH outcomes depend on the context in which the household is

living. Thus highlighting the importance of a situation-specific approach.

This research is part of the WOTRO-Hewlett PopDev programme “Impact of

reproductive health services on socio-economic development in sub-Saharan Africa:

Connecting evidence at macro, meso and micro-level” which is funded by a grant from

The William and Flora Hewlett Foundation through the Netherlands Organisation for

Scientific Research (NWO), WOTRO Science for Global Development.

Correspondence address:

Abiba Longwe & Jeroen Smits, Nijmegen Center for Economics (NiCE), Institute for

Management Research, Radboud University Nijmegen, P.O. Box 9108, 6500 HK

Nijmegen, The Netherlands, [email protected]; [email protected]

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Introduction

Educating children is one of the main forms of human capital formation and is an

important instrument for sustainable development and poverty reduction. Education is

generally regarded as a powerful means for reducing poverty and achieving economic

growth. It empowers people, improves individuals’ earning potential, promotes a healthy

population, and builds a competitive economy (Hanushek and Wossmann 2007;

UNESCO 2007; Word Bank 2006). Available evidence shows that there are several

channels through which such effects may arise. For instance education raises labor

productivity (Welch 1970), increases technological innovation and adaptation (Rodriguez

and Wilson 2000), contributes to better health (World Bank 1993) and gives greater

ability to deal with shocks (World Bank 2001).

Universal free primary education has been at the center of most governments’

policies in the developing world. However, in spite of substantial progress made as part

of the Education for All campaign, millions of young children in Sub-Saharan Africa are

still not in primary education. Worldwide, primary school net enrollment exceeds 90% in

most of the worlds’ sub regions except for sub-Saharan Africa, where it was around 70%

in 2007 (Glewwe and Miguel 2008; UNESCO-UIS 2009). To improve the current

situation in this region, it is of fundamental importance to gain a better understanding of

the factors that influence school attendance of children there.

Most studies on school enrollment focus on the influence of socio-economic and

demographic factors and availability of education facilities (Bainbridge et al. 2003; Song

et al. 2006; Toor and Parveen 2004; Huisman and Smits 2009). Some focus has also been

on understanding the effects of education on family planning (Schultz 1981, 1997;

Cleland and Rodriguez 1988; Drèze and Murthi 2001; Basu 2002; Campbell et al. 2006).

There are few studies which have evaluated long-run consequences of family planning

programs on labor supply, savings or investment in children’s human capital. However,

short-run association of such programs with adoption of contraception or age-specific

fertility has been assessed (Schultz 2008).

This topic has been highly researched in developing countries as general, Asian

regions, mixing other regions and sub-Sahara African countries and/or as specific

countries from specific world regions. Very little has been done on sub-Saharan Africa as

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a region. This paper concentrates on the effects of SRH on primary school attendance in

sub-Saharan Africa. Using a database with individual and household characteristics on

103,000 children of 73,000 mothers in 30 sub-Saharan African countries, we aim to

answer the following research question: “To what extent does poor RH at the household

level influence primary school attendance of young children?”

The RH factors on which we focus are number of siblings, preceding and

succeeding birth interval, presence of young children in the household, and pregnancy of

the mother. To study the role of these factors in children’s schooling, advanced multilevel

logistic regression models were used that allowed us to estimate the effects of factors at

the household, district and national-level simultaneously.

In the next sections, we first develop hypotheses regarding the effects of the RH

factors on educational attendance and regarding the way in which their effects may

depend on the context. After that the datasets and the operationalisation of the variables is

described. In the results section, we first present the bivariate effects of the explanatory

variables on the likelihood of going to school, followed by the outcomes of the

multivariate analyses. In the final section, conclusions are drawn and some policy

recommendations are suggested.

Theoretical background

Sexual and Reproductive Health (SRH) is a human right, essential for human

development and achievement of the Millennium Development Goals (MDG). Promotion

of family planning in countries with high birth rates has the potential to reduce poverty

and hunger, avert over 30% of all maternal deaths and nearly 10% of childhood deaths,

and contributes substantially to women’s empowerment, achievement of universal

primary schooling, and long-term environmental sustainability (Cleland et al. 2006).

Smaller families and wider birth intervals resulting from use of contraceptives allow

families to invest more of their resources in each child’s nutrition, health, education, and

can reduce poverty and hunger for all household members (Singh et al. 2004).

According to ICPD Programme of Action (UNFPA 1995, p 40), “RH is a state of

complete physical, mental and social well-being and not merely the absence of disease or

infirmity, in all matters relating to the reproductive system and to its functions and

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processes. RH implies that people are able to have a satisfying and safe sex life and that

they have the capability to reproduce and the freedom to decide if, when and how often to

do so…”.

In developing countries, complications of pregnancy and childbirth (resulting

from poor RH conditions) are the principal cause of female mortality in the reproductive

ages (Maine et al. 1994) and have important long and short-term implications for

women’s health and their productivity and investments in children. Investments in

general and RH have been found to contribute to economic growth by reducing

production losses, increasing school enrollment and children's ability to learn, and freeing

up resources that would otherwise have been spent on treating illnesses (Seligman et. al.

1997; USAID 2005). For example, Sinha (2005) and Joshi and Schultz (2007) found for

Bangladesh that family planning may reduced fertility by one child and that there are

substantial gains associated with family planning in child survival and male child

schooling. Improvements in family planning - and health in general - are linked to

economic and social development and must be addressed to achieve sustainable

reductions in poverty. Moreover, the current status of a woman’s RH has a profound

impact on the human capital development of her children and, by consequence, on future

social and economic development outcomes (Seligman et al. 1997).

The direct and opportunity costs of investing in children’s education and the value

parents attach to education are influenced by many factors, both at the level of the

household and of the context in which the household lives. Because these different

factors are not independent of each other and may exert their influence at the same time,

insight into their relative importance can only be obtained if they are studied

simultaneously. This is what is done in this study. Figure 1 shows the different groups of

factors that are included in our analytical model and their expected direction of influence.

Starting point for this model is a model presented by Huisman and Smits (2009) that, for

this paper, is extended with the RH variables on which we focus: the number of children,

their spacing, and the presence of a young sibling and pregnancy of the mother. In the

next sections we expound on these factors.

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Preceding and succeeding birth interval

Child spacing benefits the mother, the child, the father and other family members. It

allows for time between births so that the mother can rest between pregnancies and

maintain good health, be less tired and have more energy. She therefore can give more

attention to her children and help ensure good health for the family. A well spaced child

grows up stronger and healthier and will be more likely to attend school when s/he is of

school going age (Nigerian government 2003).

The effect of short birth intervals is expected to have negative impact on

education participation of children. A Swedish study established that there is negative

association between very close child spacing (less than 2 years) and long-term outcomes

of children as measured by educational attainment (Pettersson-Lidbom and Thoursie

2009). According to Hill and Upchurch (1995), girls are more affected by short birth

intervals because of the practice of gender preference, whereby less priority is given to a

girl, particularly if resources are scarce. Hayes et al (2006) found that birth interval is a

significant predictor of school readiness in South Carolina, even after controlling for

various socio-demographic factors. Children born with inadequate birth intervals (less

than 24 months) are more likely to fail the Cognitive Skills Assessment Battery compared

to those with adequate birth intervals (pp 426-27).

Longer birth intervals allow parents to devote more time to each child in the early

years, ease pressures on the family’s finances and give parents more time for activities

other than child rearing (USAID 2006). In Nepal, focus group discussions revealed that

people in rural communities were in strong support of long child spacing because it

would help ease family problems, such as health and economic difficulties resulting from

too-frequent births. “Babies are forced from the breast too early,” said one. “Mothers and

newborns are weak and parents cannot send their children to school when there are too

many or they come too soon” (Shrestha and Manandhar 2003).

Number of children

Family size and composition may influence learning achievement. The lower the number

of children, the more education each individual child is likely to receive, because

available resources have to be shared among fewer children. However, not all linkages

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between family size and children’s education are as clear-cut as this dilution effect.

Theory suggests a trade-off between child quantity and quality. Family size might

adversely affect the production of child quality within a family (Booth and Kee 2005).

Controlling for parental education, parental age at birth and family level attributes, Booth

and Kee found that in Britain children from larger families had lower levels of education

and that this family size effect did not vanish when they controlled for birth order.

Similarly, Li, Zhang and Zhu (2008) confirmed the existence of a negative family size

effects on children’s education for China using instrumental variable techniques.

Echevarria and Merlo (1999) restricted their focus to the role played by fertility

and discovered that both in the steady state and along the transition path, investments in

education of both males and females decreased with the number of siblings. In the same

light, DeGraff et al. (1993) examined how the number of siblings affected children’s time

use in Bicol Province, Philippines. They found that the greater the number of younger

siblings, the lower the likelihood that the child would be enrolled in school and the more

time the child would devote to domestic work. However, Kirdar et al. (2007) found no

causal impact of sibship size on school enrollment in Turkey.

Knodel et al (1990) showed that the decline in fertility and cohort sizes in

Thailand allowed existing primary school facilities to be adapted to accommodate

growing enrollment at the secondary level. These studies are important because of the

cross-generational effects they illuminate. Recent research confirms that children with

fewer siblings tend to outperform those with more siblings (Dronkers and Robert 2008;

Park, 2008). Eloundou-Enyegue (2000) found that African children with more siblings

tend to enroll later, repeat grades more often, and drop out of school earlier than children

with fewer siblings.

A number of studies suggest that siblings are unlikely to receive equal shares of

the resources devoted by parents to their children’s education. Booth and Kee (2005), for

example, found that in Britain the shares in the family educational resources decreased

with birth order and Jejeebhoy (1992) found for India that in families in which girls have

fewer siblings, they are disadvantaged because they are more likely to assume tasks

traditionally assigned to boys so that their brothers can pursue additional education. Such

sex differences disadvantaging girls have also been found for Turkey, where Dayioglu et

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al. (2009) found sex composition of siblings to matter for girls’ (but not for boys) school

enrollment. The amount of education that girls receive is inversely related to the number

of children parents have, hence, although it is still true that boys receive more education

than girls, the gap increases with higher fertility (Echevarria and Merlo 1999). Adding to

this, Filmer et al. (2009) noted that as long as quantity-quality trade-offs exist that result

in fewer material and emotional resources allocated to children in larger families, son-

preference in fertility decisions can have important indirect implications for investments

and for the well being of girls relative to boys.

Mother’s pregnancy and presence of young children

Women in developing countries are more than 45 times more likely to die from

pregnancy related complications than women in the developed world. As a result, girls in

developing countries are often pulled out of school to care for their siblings. Access to --

as well as correct and consistent use of -- contraceptives can therefore significantly

increase girl’s schooling (UNFPA 2005). There are also indications that the presence of a

baby in the household has a negative effect on other children’s schooling time because of

increasing care needs of the household (Chernichovsky 1995). Using data from Ghana,

Lloyd and Gage-Brandon (1993) showed that teenage girls are relatively more likely to

be withdrawn from school as new siblings are added to the family.

It is possible that adverse effects are stronger if the pregnancy is unwanted.

Unintended pregnancy is an important public health concern, because of its association

with negative social and health outcomes for mothers, children and society as a whole.

Women with mistimed pregnancies tend to delay prenatal care, are associated with

maternally risk behavior, including cigarette smoking, alcohol and illicit drug use and

with postpartum depression, and are more likely to consume inadequate folic acid

(Weller et al. 1987; Altfeld et al. 1997; Than et al. 2005; Cheng et al. 2009).

Additionally, there is the potential for deleterious health outcomes of unintended

pregnancies for the index children, their siblings, and their parents (Gipson et al. 2008).

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Control factors

Besides the RH variables discussed so far, our model contains a number of other factors

that are known or expected to influence educational enrollment. Most of them are

discussed in detail by Huisman and Smits (2009). They are included in our model as

control factors, so as to be able to estimate the independent effects of the RH outcomes

on educational enrollment. At the household level, parental education, father’s

occupation, and household wealth have been known for long to be important

determinants of educational participation in both the developed and developing world

countries (Coleman et al., 1966; Jencks, 1972; Shavit & Blossfeld, 1993; Tansel, 2002;

Glewwe & Jacoby, 2004; Mingat, 2007; Evangelista de Carvalho Filho, 2008).

Demographic characteristics of the child -- age, gender and birth order (Chiswick and

DebBurman 2004; Ejrnaes and Portner 2004; Kirdar et al. 2007; Dayioglu et al. 2009) --

as well as of the household they live in – presence of the parents, extended or nuclear

family, and age at which the mother got her first child (Bradbury 2007; Wichman et al.

2006) -- are also known to influence education enrolment

Besides these household-level factors, characteristics of the context the household

lives in may affect the ability of children to enroll in education. Previous research has

shown that, for example, the relationship between family size and educational attainment

is likely related to a society’s level of development, modes of production, and access to

schooling, which in turn shapes the relative influence of the family on the schooling of

children (Desai, 1995; King, 1987; Lloyd, 1994). Depending on the context or level of

development, having more siblings to share household and labor market work may

provide children with more resources for schooling. The macro-socioeconomic

mechanisms relating family size and children’s schooling include availability of schools,

transportation and communication infrastructure, and participation in labor-intensive

production like agriculture.

In less developed regions of a country and in rural areas, schooling may entail

higher production costs due to more limited availability and accessibility of schools. For

instance, in rural areas of Turkey, especially in the eastern part of the country students

have to be bussed to schools that are sometimes at significant distance to their village or

they have to walk long distances which may be a challenging task in winter (Kirdar 2007;

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Hazarika 2001; Smits and Gunduz-Hosgor 2006). Higher schooling costs would imply a

lower marginal rate of return and, therefore, a lower demand for schooling. Even when

schools are available in less developed or rural regions, they may be of lower quality

because of poor facilities and teachers who are less motivated as these are less popular

places for them to live and to work.

The urban child has for long been considered to be more likely to realize the

dream of fully participating in school than his/her rural counterpart. This ‘‘urban

advantage’’ is associated with increased access to facilities such as schools in urban areas

(Mugisha 2006). Returns to education are higher in cities because there are better chances

to find a white collar job (Hazarika 2001; Huisman and Smits 2009). In more modern and

urban areas, there is also more impact of globalization, including the diffusion of value

patterns that stress the importance of education and equality among sexes. However,

children living in urban or better to do regions of a country also have better chances of

finding market work (piece work), which increases the opportunity cost of schooling for

them (Kirdar 2007). This relationship between the RH factors and children’s school

attendance can differ within the same country and change over time as contextual factors

evolve with socioeconomic development. This is evidenced by a study in Indonesia

(Maralani, 2008), which showed that in urban areas, the association between family size

and children’s schooling was positive for older cohorts but negative for more recent

cohorts while there was no significant association for any cohort in rural areas.

Potential relevant context factors that have not yet been studied very much are

characteristics of the available health facilities in the area where a household lives. The

presence of good and accessible health facilities can be expected to lead to a better

general health status in the area of mothers and children, which may translate into a

higher educational enrollment. RH facilities are also important for educational

participation. However, as they will have their major effect through the household-level

RH variables, we do not include them in our model.

Interactions with the context

The effects of RH factors on educational participation of children need not be everywhere

the same. For example, Heyneman and Loxley (1983) found that in less developed areas

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the importance of school and teacher quality is higher and of resources at the household

level is lower than in more developed areas in explaining educational achievement (see

also Fuller and Clarke 1994). If this is also true for effects of RH related resources, we

would expect the relationships between the RH factors studied in this paper and

educational participation to be stronger in the more developed SSA regions.

On the other hand, it seems not unreasonable to suppose that the benefits children

can reap from a favorable situation at the household level – small family size, long birth

intervals, no pregnant mother or young siblings – are higher under more difficult

circumstances. From this perspective we would expect to find stronger effects of the RH

factors in less developed areas, in rural areas, and in areas where the quality of the

educational facilities is lower and the general health situation is worse.

Data and methods

To answer the research question, we have combined Demographic and Health Surveys

(DHS) for 30 Sub-Saharan African countries. Our dataset contains information on all

children aged 8-11 born from the women interviewed in the women’s surveys. In total,

102,638 children (52,520 boys and 50,118 girls) born from 73,337 women were included.

The household-level data on the children, their mothers and households was

supplemented with context information at the district and nation-level. District-level

information for 287 districts was derived by aggregating from the household surveys. The

aggregation was possible because the surveys involved large samples and had a variable

indicating the district.

Methods and variables

The data are analyzed with multilevel logistic regression models, including explanatory

variables at the household, district and national level. With multilevel analysis it is

possible to include explanatory variables at different levels simultaneously and to study

interactions among levels (Hox 2002; Snijders and Bosker 1999). The analyses are

performed separately for boys and girls. In all analyses robust standard errors (sandwich

estimators) are used.

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The dependent variable is a dummy variable indicating whether (1) or not (0)

children aged 8-11 were attending school at the time of the interview. The upper age limit

of 11 was chosen to restrict the analysis to primary education. The lower age limit was

put at 8, because compulsory entry ages differ per country and not all children start

schooling at the compulsory age (compare Huisman and Smits 2009).

Independent variables include RH variables, other household-level factors, district

characteristics and national characteristics. Regarding the RH variables, we measure child

spacing by two dummy variables indicating whether the preceding and succeeding birth

intervals were less (1) or more (0) than two years. Number of siblings is indicated by an

interval variable. The presence of a young child in family is measured by a dummy

variable indicating whether (1) or not (0) a child aged less than 3 years was present in the

household. Pregnancy of the mother is measured by a dummy with categories (0) not

pregnant and (1) pregnant.

Of the household characteristics, father’s occupation is measured as (1) farm, (2)

lower non-farm and (3) upper non-farm. Employment of the mother is measured by two

categories indicating whether (1) or not (0) the mother is gainfully employed. Father’s

education is measured by three categories: (1) none, (2) at least some primary, and (3) at

least some secondary. Due to low levels of education for women in sub-Saharan Africa,

mother’s education is measured by a dummy indicating whether (1) or not (0) she has at

least some primary education. Household wealth is used as a proxy for income and is

measured by an index constructed on the basis of household assets. Using a method

developed by Filmer and Pritchett (1999), all households within the country were ranked

on the basis of the available characteristics and divided into wealth index deciles, with 1

representing the poorest 10% and 10 representing the richest 10%. Whether the

household is an extended family is measured with two categories: (0) nuclear family, and

(1) extended family (more than two adults in the household). Age of the child and age of

the mother at first birth are measured in years. For the mother’s age at first birth also a

quadratic term is included in the model, because both starting getting children at a very

young age and starting at a relatively old age point to an exceptional position of the

mother. Birth order of the child is measured as an interval variable.

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Of the context factors is level of urbanization measured by a dummy indicating

whether (1) or not (0) the household lives in an urban area. District-level of development

is measured by an index constructed on the basis of six variables aggregated from our

household datasets: the percentages of households in the district with a fridge, car,

telephone, television, electricity, or running water. Of these characteristics the mean was

taken of the standardized values. Availability of health facilities in the district is indicated

by the percentage of women in the district who delivered their last baby in a hospital and

by the percentage of the last-born children of the women in our dataset who received a

DTP vaccination. Public expenditure on education and national GDP per capita in

Purchasing Power Parity (constant 2000 international dollar) are derived from World

Bank (2007).

Children with a missing father were given the mean score of the other children in

the database on fathers’ education and occupation. Because there are dummies for

missing father in the model, this procedure leads to unbiased estimates of these variables

(Allison, 2001, p. 87). To address the fact that the effects of the RH factors may differ

depending on the situation of the household, also models with interactions between

control factors and the RH outcomes are estimated. To compute these interaction terms,

centered versions of the involved variables are used. Given the large number of possible

interactions, only significant interaction effects are included in these models.

Results

Bivariate analysis

The coefficients of the bivariate logistic regression models are shown in Table1. Both the

logistic and multiplicative versions (within brackets) of the coefficients are presented.

The multiplicative versions can be interpreted more easily. For example, the value of

0.852 for the effect of preceding birth interval on boys school attendance means that the

odds of attending school is 0.852 times (or 14.8%) lower for boys with a preceding birth

interval of less than 2 years compared to boys with a long preceding birth interval. The

value of 1.278 for girls whose mothers are gainfully employed indicates that these girls

have 1.278 times (or 27.8%) higher odds of attending school than girls whose mothers are

not employed.

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Table1 shows that the effects of most of the RH variables are in line with our

expectations. The likelihood to attend school for both boys and girls is significantly

reduced by short preceding and succeeding birth intervals. Having more siblings shows

the expected negative effect on school attendance. If a mother is currently pregnant, her

children have less chance of attending school. The presence of a child below age three in

a family has a negative influence on older children’s ability to participate in school.

Most of the effects of the household-level control factors are in line with

expectations. Age of child, household wealth, parental education and occupation have

significant positive effects on school attendance of both boys and girls. There is a

significant negative relationship between missing father, living in rural area and attending

school. Belonging to an extended family has a significant positive influence on school

attendance for girls while it has a negative non-significant effect on boys. Birth order is

negatively associated with boys’ school attendance while it positively affects girls’

likelihood to attend school. Mothers’ age at first birth has a parabolic effect. Both

children born to relatively young and to relatively old mothers are less likely to attend

primary school.

All the context factors show the expected positive effects on children’s

educational attendance. Attendance levels for boys and girls are significantly higher in

more developed districts and countries, in countries with higher public expenditure on

education, and in districts where more women give birth in a hospital and where more

children received a DTP vaccination.

The coefficients of the bivariate analyses show how school attendance of boys

and girls varies among households and districts with different RH characteristics. These

coefficients thus represent the observable reality in the countries and districts under

study. However, they give no insight into the relative importance of the various

characteristics explaining school attendance, because these characteristics may be related

to each other (e.g. mothers with many children tend to have them within short birth

intervals; being the fifth child means having at least four siblings). To gain insight into

the relative importance of the RH and other explanatory variables, we now turn to the

multivariate results.

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Multivariate analysis

The results of the multivariate logistic regression analyses are presented in Tables 2 to 4.

Table 2 presents results for boys and Table 3 for girls. Models 1 in these tables contains

only direct effects of the explanatory variables. Models 2 contain beside direct effects

also interaction effects between RH factors and other factors. To keep the tables readable,

the interaction effects for both boys and girls are presented separately in table 4.

Comparing the coefficients in Tables 2 and 3 with the bivariate coefficients, we

see that the effects of socio-economic as well as the RH factors do not differ much. The

effect of number of siblings on school attendance is strong in both the bivariate and

multivariate models. Boys’ as well as girls’ school attendance is negatively affected by

number of siblings present in the household. School attendance is reduced for the

children with short preceding birth interval in all models. The effect of the succeeding

birth interval becomes insignificant in multivariate Model 1, but retains its significance

for girls with the addition of interactions in Model 2. Overall, this effect is weaker than

that of the preceding birth interval. The presence of a young child of less than 3 years in a

household reduces school attendance for both boys and girls. Pregnancy of the mother

has a substantial negative effect in the bivariate analysis, but loses its significance at first

in the multivariate models. However, after adding the significant interaction effects in

Models 2, it becomes significant again for both boys and girls. This indicates that

mother’s pregnancy is important under specific conditions.

The effects of the control factors at the household level differ little between the

bivariate and multivariate analyses. The effect of birth order loses its significance in the

multivariate models for girls but not for boys. Belonging to an extended family, which

was not significant for girls and positive for boys bivariately, shows significant negative

effects in the multivariate models. All other household-level control factors keep their

sign and significance.

Regarding the context factors, we see that district level of development, which

was positively significant in the bivariate model, becomes significantly negative in the

multivariate models. This difference seems surprising, but additional analyses (not

presented) showed that it is due to the presence of wealth at the household level in the

multivariate models; hence the positive effect in the bivariate analyses was due to the fact

15

that wealthier households are living in more developed districts. All other context factors

keep their significant positive effects on educational attendance in the multivariate

models.

Interaction effects

Tables 2 and 3 show that, apart from some small changes in the magnitude of the

coefficients, there are no substantial differences between Models 1 and 2 for boys and

girls. All household-level and context control factors with significant effects in model 1

remain significant and keep their sign in Model 2. The only substantial changes were the

ones discussed above, regarding the effects of succeeding birth interval and mothers’

pregnancy.

Table 4 shows that all RH variables interact significantly with some of the

household and context factors for both boys and girls. We are therefore led to the

conclusion that their effects depend on the situation of the household and the context in

which the household lives.

With regard to the number of siblings, we see negative interaction effects with

mother’s education and employment and with public expenditure on education. Hence,

children with more siblings seem to be less able to fully enjoy the positive benefits of

having an educated and working mother, or to profit from educational investments. On

the other hand, there is a positive interaction effect with district level of development,

which suggests that better infrastructure or the influence of more modern values

compensates to a certain extent the family size effect. For girls, there is also a positive

interaction effect of number of siblings with father’s education (secondary), which might

mean that educated fathers discriminate less against girls when sending their children to

school. However, at the same time we see that girls with a short preceding birth interval

can profit less than other children from having a high educated father. So when there is

direct competition with a closely spaced earlier born sibling, even having a highly

educated father is less profitable.

We also see that a negative effect of a short succeeding birth interval is enhanced

for boys if the father is missing from the family. This might mean that when the husband

is missing, mothers are less able to cope with short spacing and older sons might have to

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take over some of the missing father’s tasks. This is also suggested by the negative

interaction effect of mother’s pregnancy with missing father on boys’ school attendance.

Both boys and girls are less able to enjoy the benefits of good school facilities provided

by government if they are succeeded by a sibling in less than two years. Boys are also

less able to profit from a higher household wealth. However, when they have a mother

with some education having a short succeeding birth interval is less problematic.

If there is a young sibling in the family, boys are less able to profit from their

father’s education. The negative effect of a young sibling is stronger in urban and more

developed areas, thus indicating that under more traditional circumstances it is easier to

arrange care (or children don’t go to school anyway, so that the presence of a young

sibling cannot make much of a difference anyway). Girls with a pregnant mother are less

able to benefit from the positive impact of household wealth on their school attendance.

Conclusions

We have studied the effects of RH outcomes (number of siblings, birth interval, mother’s

pregnancy, and presence of a young sibling) alongside other household- and district-level

factors on primary school attendance of over 100,000 children aged 8-11 in 30 Sub-

Saharan African countries. We used multilevel logistic regression analysis in order to

include explanatory variables at different levels simultaneously and to study interactions

among levels. Poor RH outcomes were expected to reduce investment in human capital of

children, because with less children there are more resources for each individual child,

longer birth intervals are better for women’s health and allow mothers to give children

more attention in their vulnerable young years, and pregnancy of the mother or presence

of a young sibling might pull children, particularly daughters, out of school to help at

home.

The findings of our study are largely in line with these expectations. Our analyses

revealed that, in these countries, children living in larger families have a significantly

lower probability of being in school than children living in smaller families. This is also

true for children who were born shortly after their preceding sibling, for girls who were

succeeded shortly by a younger sibling, for children with a pregnant mother or a young

sibling when they reach at school-going age.

17

For each additional sibling, the odds of being in school decreases by 4% for boys

and 5% for girls. A preceding birth interval of less than two years decreases these odds

for boys and girls by about 15%. Having a pregnant mother decreases it by about 9% and

presence of a sibling below age three in the family by 13% for boys and by 18% for girls.

These effects are independent of each other; which means they add up. A girl with five

siblings, one of which is below age three, which has a pregnant mother and a short

preceding birth interval has a 50% lower odds of being in school than a girl with no

siblings and whose mother is not pregnant. A boy in this situation would have a 45%

reduced odds of being in school.

Regarding the effects of the control factors, our results are mostly in line with

earlier findings that socio-economic and demographic characteristics of a household

contribute significantly to a child’s chance of attending primary school (e.g. Huisman &

Smits 2009; Tansel 2002; Mingat 2007; Evangelista de Carvalho Filho 2008). If the

parents have a higher educational level, if the household is wealthier, and if the father is

working in a non-farm job the chances that children are in school are substantially

increased. Having an employed mother also has a positive effect on children’s schooling.

Effects of demographic factors are less pronounced, but still important. Living in an

extended family reduces the chance of boys attending school while it does not affect

girls. For girls, birth order increases the likelihood of later-born girls to be in school

while the older girls stay at home. If a father is missing in a household, both boys’ and

girls’ odds of attending school is reduced. Primary school attendance is also influenced

by characteristics of the context. Living in a rural area reduces the odds of attending

school by almost 30% for both boys and girls. National level of development and public

investment in education enhances primary school participation. As could be expected,

also a good health infrastructure (measured by availability of health facilities and access

to vaccination) tends to increase children’s ability to attend school.

Besides models with direct effect of the explanatory variables, we estimated

models with interaction effects between RH variables and household- and district-level

factors. This interaction analysis was meant to increase our understanding of how the

effects of RH factors differ among households and contexts with different characteristics

and to make the outcomes more situation-specific. Results reveal a substantial number of

18

significant interactions. The idea that the effects of RH factors differ among contexts and

that a situation-specific approach is important is thus confirmed by our data. However,

the results do not clearly lead into one direction. Parental education is a good example.

Children with more siblings profit less of the education of their mother, but at the same

time girls in that situation profit more of having a well-educated father. Similarly, we see

that girls with a short preceding birth interval profit less of the education of their father,

while boys with a short succeeding birth interval profit more of the education of their

mother. Another example is the effects of the context factors. For children with a young

sibling in the family it is negative for their educational attendance to live in a more

developed or urban area. At the same time we find that children with more siblings tend

to be more in school if they live in more developed areas. These seemingly contrasting

findings suggest that each interaction effect should be carefully considered on its own

merits. It might for example be more difficult to arrange childcare in the more developed

areas, while at the same time the negative effect of large family size might be

compensated by the shorter distances to school and more modern values there.

Other interaction effects are more straightforward. Having an employed mother is

negative for children with more siblings and having a missing father is negative for boys

with a pregnant mother; both are situations in which children may have to take over tasks

at home. Also the finding that children in more difficult situations can profit less of

household wealth or of higher public expenditure on education is not so surprising in

light of earlier findings indicating that investments in education are not enough to bring

the children in the weakest position into school (Huisman and Smits, 2009).

This research contributes evidence to the ongoing debates on linkage between

SRH investments and poverty reduction. It also contributes new knowledge to research

on factors influencing education participation by demonstrating that poor RH at

household level can be detrimental to the achievement of Millennium Development Goal

2: Universal primary education. The substantial negative effects of short birth intervals,

number of siblings, presence of young sibling and mothers’ pregnancy on school

attendance of children in sub-Saharan Africa stresses the importance of good and

accessible RH facilities. Since RH directly relates to the achievement of universal

19

primary education, countries should focus their efforts on actual usage of the RH services

by all women of child bearing age.

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26

Tables and Figures

TABLE 1 Logistic coefficients of the bivariate multilevel logistic regression analysis

BOYS GIRLS

Household level variables

Demographic factors

Age 0.237 (1.268)** 0.185 (1.203)**

Father missing -0.002 (0.998) 0.037 (1.038)

Birth order -0.017 (0.983)** 0.013 (1.013)*

Extended family -0.011 (0.989) 0.086 (1.089)**

Age of mother at first birth 0.212 (1.236)** 0.228 (1.256)**

Age of mother at first birth square -0.005 (0.995)** -0.005 (0.000)**

Socio-economic factors

Education father primary 0.506 (1.659)** 0.427 (1.533)**

Education father more than primary 1.483 (4.408)** 1.674 (5.331)**

Education mother at least some primary 1.302 (3.675)** 1.409 (4.093)**

Occupation father lower nonfarm 0.822 (2.276)** 0.761 (2.140)**

Occupation father upper nonfarm 1.255 (3.507)** 1.509 (4.523)**

Mother employed 0.122 (1.129)** 0.206 (1.229)**

Wealth index 0.281 (1.324)** 0.300 (1.350)**

Living in rural area -1.350 (0.259)** -1.400 (0.247)**

Contextual Variables

Economic factors

district level of development 0.675 (1.964)** 0.789 (2.201)**

National level of development -GDP 0.712 (2.038)** 0.837 (2.310)**

Educational facilities

Public expenditure on education 0.257 (1.293)** 0.346 (1.414)**

Health facilities

Hospital birth 0.852 (2.345)** 0.965 (2.625)**

DTP vaccination 0.759 (2.137)** 0.915 (2.496)**

RH factors

Number of siblings -0.091 (0.913)** -0.094 (0.911)**

Short preceding birth Interval -0.285 (0.752)** -0.231 (0.794)**

Short succeeding birth interval -0.194 (0.824)** -0.196 (0.822)**

Presence of young child in family -0.248 (0.781)** -0.334 (1.3970**

Mother currently pregnant -0.199 (0.819)** -0.245 (0.783)**

N 52469 50169

Children not in school 14600 15664

** P < 0.01; * P < 0.05

27

TABLE 2 Logistic and multiplicative (bracketed) coefficients of multilevel logistic

regression analysis for boys

Model 1 Model 2


Demographic variables

Age of child 0.229 (1.257)** 0.229 (1.257)**

Father missing -0.111 (0.895)** -0.126 (0.881)**

Birth order of child 0.019 (1.019) 0.019 (1.019)

Extended family -0.170 (0.843)** -0.173 (0.841)**




Education Father

None 0.000 (1.000) 0.000 (1.000)

At least some primary 0.824 (2.279)** 0.843 (2.323)**

At least some secondary 0.850 (2.339)** 0.850 (2.339)**


Occupation father

Farm 0.000 (1.000) 0.000 (1.000)

Lower nonfarm 0.426 (1.532)** 0.427 (1.533)**

Upper nonfarm 0.462 (1.5870** 0.450 (1.568)**

Mother employed 0.171 (1.186)** 0.175 (1.191)**

Household wealth 0.133 (1.142)** 0.133 (1.142)**

Living in a rural area -0.338 (0.713)** -0.333 (0.717)**

Contextual variables

Economic factors

District development index -0.112 (0.894)** -0.119 (0.888)**

National GDP per capita 0.387 (1.473)** 0.394 (1.483)**


National public expenditure on Education 0.097 (1.102)** 0.094 (1.099)**

Health factors

Hospital birth 0.260 (1.297)** 0.260 (1.297)**

Vaccination for DPT 0.152 (1.1640** 0.151 (1.163)**

RH factors


Short preceding birth interval -0.160 (0.852)** -0.162 (0.851)**

Short succeeding birth interval -0.019 (0.981) -0.032 (0.969)

Presence of young child in family -0.085 (0.918)** -0.142 (0.868)**

Mother currently pregnant -0.069 (0.933) -0.084 (0.920)*

** P < 0.01; * P < 0.05

28

TABLE 3 Logistic and multiplicative (bracketed) coefficients of multilevel logistic

regression analysis for girls

Model 1 Model 2


Demographic variables

Age of child 0.195 (1.215)** 0.195 (1.215)**

Father missing -0.096 (0.908)** -0.094 (0.910)**

Birth order of child 0.057 (1.058)** 0.053 (1.055)**

Extended family -0.071 (0.931)** -0.070 (0.932)**




Education Father

None 0.000 (1.000) 0.000 (1.000)

At least some primary 0.898 (2.455)** 0.901 (2.463)**

At least some secondary 1.047 (2.850)** 1.041 (2.832)**


Occupation father

Farm 0.000 (1.000) 0.000 (1.000)

Lower nonfarm 0.360 (1.433)** 0.350 (1.419)**

Upper nonfarm 0.565 (1.759)** 0.551 (1.735)**

Mother employed 0.245 (1.278)** 0.253 (1.288)**

Household wealth 0.131 (1.140)** 0.131 (1.140)**

Living in a rural area -0.329 (0.720)** -0.323 (0.7240**

Contextual variables

Economic factors

District development index -0.064 (0.938)* -0.068 (0.934)*

National GDP per capita 0.400 (1.492)** 0.408 (1.492)**


National public expenditure on

Education

0.165 (1.179)** 0.163 (1.177)**

Health factors

Hospital birth 0.201 (1.223)** 0.202 (1.223)**

Vaccination for DPT 0.242 (1.274)** 0.242 (1.274)**

RH factors


Short preceding birth interval -0.131 (0.877)** -0.155 (0.856)**

Short succeeding birth interval -0.056 (0.946) -0.071 (0.932)**

Presence of young child in family -0.167 (0.846)** -0.197 (0.821)**

Mother currently pregnant -0.071 (0.931) -0.100 (0.905)**

** P < 0.01; * P < 0.05

29

TABLE 4 Logistic and multiplicative (bracketed) interaction coefficients of

multilevel logistic regression analysis for boys and girls (Extension of Models 2 of

tables 2 and 3)

Boys Girls

Number of siblings

Education father at least secondary 0.063 (1.065)**

Education mother at least some primary -0.050 (0.951)** -0.051 (0.950)**

Mother employed -0.029 (0.971)** -0.027 (0.973)**

District level of development 0.022 (1.022)** 0.025 (1.025)**

National public expenditure on Education -0.013 (0.987)** -0.020 (0.980)**

Short preceding birth interval

Education father at least secondary -0.251 (0.7780**

Short succeeding birth interval

Father missing -0.189 (0.827)**

household wealth -0.026 (0.974)**

Education of mother (at least primary) 0.138 (1.148)*

National public expenditure on Education -0.036 (0.964)* -0.041 (0.960)*

Presence of young child in family

Education father at least some primary -0.159 (0.853)**

Living in rural area 0.210 (1.234)**

District level of development -0.083 (0.920)* -0.085 (0.918)*

Mother currently pregnant

Father missing -0.220 (0.802)*

Household wealth -0.031 (0.970)*

** P < 0.01; * P < 0.05

30

FIGURE 1 Theoretical model of school attendance

-father’s occupation (+)

-mother employment status (+/-)

-parent’s education (+)

-wealth (+)

-age of child (+)

-extended family (+)

-birth order (+) -missing father (-)

-age of mother at first birth (+/-)

Economic factors


-modernization (+)

-urbanization (+)

-public expenditure on

education (+)

Education facilities

-availability of hospitals (+)

-vaccinations (+)

Health status

Primary School

Attendance

Demographic factors

Context level

-number of siblings (-) -preceding birth interval (+)

-succeeding birth interval (+) -mother’s pregnancy (-)

-presence of young child (-)

Family planning factors

Household

level

Documents

Effects of Reproductive Health Outcomes on Primary School Attendance