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Journal of Population and Social Studies, Volume 26 Number 1 January 2018: 13 - 31
DOI: 10.25133/JPSSv26n1.002
13
Spousal and Household Characteristics Associated with
Women’s Fertility in Sub-Saharan Africa
Olufunmilayo Olufunmilola Banjo
i
and Joshua O. Akinyemi
ii
Abstract
This paper is based on a study that examined the influence of spousal and household characteristics on
fertility of married women in sub-Saharan Africa (SSA). It utilized data from Demographic and Health
Survey (DHS) from four countries between 2010 and 2015. Fertility was measured by the number of
Children Ever Born (CEB). Descriptive and Poisson regression techniques were used for analysis. Results
showed variation in the mean number of CEB across categories of spousal and household characteristics
and across the countries. The Poisson regression analysis showed that while spousal age and age at marriage
influenced fertility similarly across the countries, spousal educational attainment and household
characteristics influenced fertility differently across the countries. The study concludes that, although some
disparities exist in the way spousal and household characteristics influence fertility across sub-Saharan
Africa, these characteristics cannot be overlooked in driving sustainable fertility transition in the region.
Keywords:
Spousal characteristics; household characteristics; women’s fertility; sub-Saharan Africa
Introduction
Fertility levels in sub-Saharan Africa remain the highest compared with other developing regions.
Although Kenya, Ghana, Zimbabwe and Botswana have experienced considerable decline in
fertility (Blacker, Opiyo, Jasseh, Sloggett, & Ssekamatte-Ssebuliba, 2005; Garenne, 2008; Tabutin
& Schoumaker, 2004), recent studies have shown that some of the countries are experiencing
‘stalls’ in recent times (Bongaarts & Casterline, 2013; Cleland, Ndugwa, & Zulu, 2011). Several
arguments have been put forward to explain the persistently high level of fertility in sub-Saharan
Africa. Most of these arguments revolve around gender and patriarchal family systems in the
region. These systems encourage subordination of women and socio-economic inequality
between men and women. Mason (2001) argued that these systems have been responsible for the
late onset and slow pace of fertility transition in the region. Isiugo-Abanihe (1994) have also
argued that the patrilineal and male dominant family systems in most countries in the region
have also encouraged large families, thereby inhibiting persistent fertility decline.
i
Demography & Social Statistics Department, Obafemi Awolowo University, Ile Ife, Nigeria
Email: banjoolufunmilayo@gmail.com
ii
Demography & Population Studies Programme, University of the Witwatersrand, Johannesburg, South Africa.
Department of Epidemiology and Medical Statistics, College of Medicine, University of Ibadan, Ibadan, Nigeria
Spousal and Household Characteristics Associated with Women’s Fertility
14
The countries in sub-Saharan Africa are steeped in cultural practices that empower men, thereby
leaving most of the household decision-making, including fertility in their hands (Ezeh & Dodoo,
2001; Makinwa-Adebusoye, 2007). This trend is strengthened by polygynous practices, which
encourage men to have children till their old age. It also results in wide age gaps between spouses,
as a result of which women may become victims of double subordination (Barbieri, Hertrich, &
Grieve, 2005; Bloom, Wypij, & Gupta, 2001; Frost & Dodoo, 2009; Goldman & Pebley, 1989;
Ratcliffe, Hill, & Walraven, 2000). Yet, most research on fertility is focused on women, particularly
married women, leaving out their spouses and the household environment they come from, even
though it is evident that fertility transition in sub-Saharan Africa (SSA) is male driven (Derose,
Wu, & Dodoo, 2010). There are few studies relating men’s characteristics to fertility in the region
(Dodoo & Frost, 2008; Ezeh, 1993). However, attempts to explore the link between the spousal
characteristics and fertility have been limited to spousal communication and contraceptive use
(Bawah, 2002; Ezeh, 1993; Feyisetan, 2000; Oyediran, 2002; Salway, 1994). Studies have shown
that spousal communication improves contraceptive use among couples (Irani, Speizer, & Fotso,
2014; Oyediran, 2002; Tawiah, 1997); and that men desire a larger family size than women
(Bankole & Singh, 1998; Isiugo-Abanihe, 1994). Despite the strong influence of spousal education
and communication on women’s contraceptive use and fertility, the effects of spousal age, age at
marriage and other household characteristics on women’s fertility in SSA are not clearly known.
This study seeks to provide answers to the following questions – (i) do spousal characteristics
such as age, age at marriage and education, influence fertility in SSA? What influence do
household characteristics have on fertility in SSA?
Literature Review and Theoretical Perspective
In the traditional African culture men enjoy greater benefit than women by having a large family
such as, economic and social benefits as well as old age security (Caldwell & Caldwell, 1987;
1990). Thus, they have many wives who bear as many children as possible, even till their old age.
These cultural practices therefore have implication for women’s fertility by the main decision
maker and the household composition. The old age security model as explained by the Caldwell’s
theory of upward intergenerational wealth flow is such that wealth flows upward from the
younger to the older generation in a traditional society. This is contrary to the downward
intergenerational wealth flow in the westernized and developed regions of the world, whereby
wealth flow from the parents to the younger ones (Caldwell, 1976; Caldwell & Caldwell, 1987).
Recent decade has shown a slight movement away from the traditional kinship family system
into a nucleated one where an increased number of women are becoming educated and are more
involved in household decision-making such as fertility but despite this, fertility remains high in
the region.
The culture of lineage perpetuation strengthened by the male dominance in fertility issues is
another barrier to the acceptance and use of family planning (FP) by most men in the region.
According to Frost and Dodoo (2009), most African men reject of family planning methods by
their spouses due to their fear of losing control over their spouse’s reproduction. This may explain
the stalls in fertility transition in countries that had earlier experienced significant fertility decline.
Most studies on spousal communication and women’s contraceptive use found that the decision
to use contraceptives still lie very much with the men than with the women. Studies have shown
that men generally desire larger families compared with their wives and which influences the
latter. Isiugo-Abanihe (1994) studied the motivation for a large family size among men in Nigeria
and found that men preferred a bigger family which subsequently shaped their spouses
O. Banjo & J. Akinyemi
15
decisions. Bankole and Singh (1998) in a study of 18 developing countries based on DHS data
found that though the desire for large family sizes were reported by most husbands and wives,
the husbands’ desire for them was stronger. Similarly, Tilahun, Coene, Temmerman, and
Degomme (2014) who studied spousal communication and agreement on contraceptive use in
Jimma zone, Ethiopia found the husbands’ approval of FP was a strong determinant of
contraceptive use among couples. Despite the strong spousal influence on women’s contraceptive
use, it is yet to be ascertained, the impact of spousal characteristics such as age, age at marriage
and level of education on women’s fertility.
A few studies that investigated the link between household characteristics and contraceptive use
have shown women in urban areas were likely to use contraceptives and thus have lower fertility
(Whyte et al., 2008). Other studies on the type of marriage and fertility have shown that fertility
issues play out differently in monogamous and polygamous marriages. For example, Dodoo
(1998) in a comparative study between Ghana and Kenya, found a stronger female fertility
influence in polygamous marriages than monogamous marriages, despite women in the latter
being more educated. Hayase and Liaw (1997) in a comparative study of 4 SSA countries on
factors influencing polygamy found that, the prevalence of polygamy in each of these countries
was influenced by their type of education. Hence, they concluded that populations with greater
access to western education tended to prefer monogamous marriages where women’s level of
education influences fertility, while in a more traditional population, polygamy is predominant
as women had lower levels of education. Additionally, women from rich households use
contraceptives more than those from poorer households (Adebowale, Adedini, Ibisomi, &
Palamuleni, 2014). Yihunie Lakew, Tamene, Benedict, and Deribe (2011) in a study of
geographical variation in contraceptive use among women in Ethiopia found that wealth status
and residence (urban or rural) among other factors, were predictors of women’s use of modern
contraceptives. Thus, if household characteristics influence contraceptive use among women, it
is logical to say that these characteristics are also likely to influence women’s actual fertility
performance. Hence, with a tilt from traditional to westernization and modernization,
particularly the proliferation of monogamous marriages; nuclear family settings as against the
usual polygamous and kinship/extended family settings; increased educational attainment by
women and their improved involvement in fertility decisions among other critical household
decision-making; it is important to examine the influence of spousal and household
characteristics, such as age, age at marriage, educational attainment, household wealth status,
type of marriage and type of place of residence on fertility levels in the region.
Studies have shown that women’s attitude to contraceptive use depend on their husbands’ level
of education and not on theirs alone (Berhane, 2015; Ezeh, 1993; Mesfin, 2002; Oheneba-Sakyi &
Takyi, 1997). Ezeh (1992) examined the attitude of partners in terms of contraceptive use in Ghana
and found that husbands’ educational attainment influenced their wives’ level of contraceptive
use, but not otherwise. This is consistent with the findings of another study by DeRose and Ezeh
(2005), which showed a strong influence of husbands’ education on their wives fertility intention,
irrespective of the latter’s level of education. Another study conducted among different ethnic
groups in Nigeria by Kritz (1999) found despite the importance of women’s education in
predicting contraceptive use, a positive and consistently significant relationships were found
between husbands’ education and women’s contraceptive use. It then follows logically that
women’s actual fertility may not be dependent only on their own or their spouse’s education
attainment alone, but on spousal characteristics such as age, age at marriage, as well as well as
household characteristics.
Spousal and Household Characteristics Associated with Women’s Fertility
16
A number of studies have examined the influence of women’s demographic and socio-economic
characteristics on fertility behavior in sub-Saharan Africa. Studies relating women’s educational
attainment or years of schooling with fertility in SSA have shown that an inverse relationship
exists between the two (Martin, 1995). Women’s education also interacts with other proximate
determinants such as age at marriage and age at first birth to influence fertility. Women who stay
longer in schools have been found to marry later, thereby postponing the timing of first birth and
reduce the number of years of their exposure to the risks of conception (Bongaarts, 2003, 2010).
Ikamari (2005) in a study of the influence of education on marriage timing in Kenya found that
women who are more educated married later and this further increased their age at first birth.
Other studies on education-fertility relationship found that education above the primary level
influenced fertility more (Gupta & Mahy, 2003; Kravdal, 2001; Shapiro, 2012) while some found
the community level education as an important predictor of fertility in addition to individual
woman’s education (Benefo, 2006; Kravdal, 2002, 2012; Moursund & Kravdal, 2003). If women’s
age and age at marriage and their level of education attainment have been found to influence
their fertility, it is pertinent to also examine the influence of spousal age, age at marriage and their
educational attainment on women’s fertility as well. Other studies relating household
characteristics to women’s contraceptive use, also suggest that a relationship is likely to exist
between household characteristics and women’s fertility. Hence, there is a need to examine
spousal and household characteristics and their influence on the number of Children Ever Born
across the four regions of SSA.
Settings of countries under study and development indicators
The choice of the countries under study is based on their history of high fertility levels with the
aim of having a full representation for the four regions in SSA. Available evidence shows that
fertility levels have remained high in 3 out of the 4 countries for decades. The inclusion of the
fourth country was due to data availability for the period covered by this study and the necessity
for a representation from the Southern region of SSA.
Nigeria, otherwise known as the giant of Africa, is the largest country in SSA, situated in the
western region of Africa. With an annual growth rate of 2.2%, the population is presently
estimated at about 180 million people (based on its 2006 population and housing Census which
put the figure at 140 million). The Total Fertility Rate (TFR) from the 2013 DHS is 5.5, which is a
slight decline from the 2003 and 2008 figure of 5.7 (see Table 1). About 46% of the Nigerian
population consists of children under 15 years of age, with one in five households headed by
women. Nigeria is a developing economy with geographical diversity characterized by lowlands
and highlands, and an uneven population distribution across six geo-political zones. The
population is mostly rural with more males having some level of education compared with the
females. This has resulted to women being not empowered with only about 31% of currently
married women involved in household decision making; early or child marriages and long period
of exposure to childbearing. Despite the widespread knowledge of contraceptive its use remains
very low, where 10% and 15% of Nigerian men and women respectively use contraceptives. The
current rate of infant and childhood mortality in Nigeria implies that one in every 15 infants and
one in eight children die before their first and fifth birthdays respectively (National Population
Commission (NPC) [Nigeria] and ICF International, 2009; 2014).
Located in the central African region, the situation in Democratic Republic of Congo (DRC) is
similar to Nigeria. Available evidence shows that TFR in DRC has consistently remained high
O. Banjo & J. Akinyemi
17
between 6.0 and 7.0 till date (see Table 1). The United Nations estimates the TFR at 6.7 in 1999,
which differs from the national estimate of 7.3 at the same period. Further estimates for the
periods between 1980-84 and 1990-95 put the rates at 6.7 for the two periods (Shapiro &
Tambashe, 2001). However, the most recent Demographic and Health Survey conducted in
2013/2014 gave a value of 6.6 for the TFR in the DRC, with a variation of 5.5 to 7.3 between urban
and rural areas respectively. Although more males are educated than the females, 4 in 5
Congolese women were employed in the 12 months preceding the survey with about 1 in 4
married women reporting that they were not involved in household decision making. Adolescent
fertility is about 3 times higher in poorer households and despite the widespread of contraceptive
knowledge in the country, only 20% of currently married women use contraceptives. Under-5
mortality decreased from 148 to 104 per thousand children between 2007 and 2014 (Ministère du
Plan et Macro International, 2008; Ministère du Plan et Suivi de la Mise en œuvre de la Révolution
de la Modernité (MPSMRM), Ministère de la Santé Publique (MSP) and ICF International, 2014)
More than 50% of the Ugandan (in East Africa) population is aged 15 or younger, with about 25%
of adolescents aged 15-19 years carrying their first child or pregnant. There was a slight decline
in the TFR from 6.9 in 2001 to 6.7 in 2006 and further to 6.2 in 2011. Infant mortality also declined
from 88 in 2001 to 54 per thousand live births in 2011. The economy of Uganda is predominantly
agricultural, most of the population largely dependent on subsistence farming and light agro-
based industries. Educational attainment is generally low in the country as 36% and 28% of men
and women have some secondary or higher level of education respectively. Statistics also show
that about 2 in 5 women currently married were involved in major household decision making,
including their health. Despite knowledge of contraception, less than 30% of currently married
women use contraceptives (Uganda Bureau of Statistics (UBOS) and ICF International Inc, 2012).
Zimbabwe is a landlocked southern African nation which lies between Limpopo and Zambezi
rivers. Its economy is well diversified but agriculture and mining remain its major sources of
foreign exchange. Zimbabwe’s level of fertility has witnessed a significant reduction from a TFR
of 5.4 in 1988 to 4.0 in 2015. This may be due to knowledge of modern contraceptives and
increased contraceptive prevalence rate among currently married women from 59% in 2011 to
67% in 2015. About 77% and 73% of men and women respectively have either attended or
completed some secondary or higher education. Almost 70% of currently married women are
involved in making decisions about major household issues including their own health. The
infant mortality reduced slightly from 60deaths per thousand live births in 2006 to 57 per
thousand live births in 2011. On the other hand, child mortality increased slightly from 24 to 29
deaths per thousand live births over the same period (Zimbabwe National Statistics Agency and
ICF International, 2012; 2016).
Spousal and Household Characteristics Associated with Women’s Fertility
18
Table 1: Fertility trends and patterns in the countries under study
Source: Measure DHS Statscompiler at https://www.statcompiler.com/en/
Data and Methods
Data Source
Data from Demographic and Health Survey (DHS) Demographic and Health Survey (DHS) a
project funded by the United States Agency for International Development and implemented by
ICF Macro was the basis of this study. The DHS is cross-sectional and nationally representative
household sample surveys. The similarities in the method of data collection and the survey
designs have also made comparison across countries possible. Couples’ data for the SSA countries
was also analyzed. These countries were selected because of their high fertility rates and
availability of data between 2010 and 2015. Although Zimbabwe has a relatively low fertility rates
as indicated by the TFR, it was included in the study as there was readily available data for the
time frame considered, as well as the necessity to examine the pattern of influence across the four
regions of SSA. The DHS data for Nigeria (2013); DRC (2013/2014); Uganda (2011) and Zimbabwe
(2010/2011) were further analyzed. In order to correct for over-sampling and under-sampling,
appropriate weights were constructed, which brought the sample size to – 9,021for Nigeria; 4,344
for the DRC; 1,076 for Uganda and 3,132 for Zimbabwe.
Variable measurements
The outcome variable:
The outcome variable for this study is women’s fertility measured by the number of Children
Ever Born (CEB). The outcome variable was treated as a count variable, which necessitated the
use of Poisson regression models at the multivariate level of analysis.
Country
Survey Year
Total Fertility Rate (TFR)
Nigeria
1990
6.0
1999
4.7
2003
5.7
2008
5.7
2013
5.5
Democratic Republic of Congo
2007
6.3
2013-14
6.6
Uganda
1988-89
7.4
1995
6.9
2000-01
6.9
2006
6.7
2011
6.2
Zimbabwe
1988
5.4
1994
4.3
1999
4.0
2005-06
3.8
2010-11
4.1
2015
4.0
O. Banjo & J. Akinyemi
19
Explanatory variable:
The main explanatory variables for this study are spousal and household characteristics, which
include spousal age, age at marriage and level of education, household wealth status, place of
residence and type of marriage. Other control variables include women’s level of education,
occupational status and ever use of contraceptives. These were mostly treated as categorical
variables. Data analysis was done in three stages, using STATA 12.
The first stage was the frequency distribution of the explanatory variables, women’s
characteristics and the outcome variable. The second stage involved the use of Analysis of
Variance (ANOVA) to examine the differences in the mean number of CEB across different
categories of spousal and household characteristics. The third stage examined the relationship
between the main explanatory variables, the control variables and the outcome variable using
Poisson regression. Three models were fitted for the main explanatory variables and the outcome
variable across the countries under study. The first model examined the influence of spousal
characteristics on fertility (model 1a); the second model examined the influence of household
characteristics on fertility (model 2a); and the third model examined the influence of both spousal
and household characteristics on fertility (model 3a). The part ‘b’ of the models included the
control variables (women characteristics) for each of the models.
Results
The result of the frequency distribution of spousal, household and selected women’s
characteristics is presented in Table 2 below. Most spouses were aged between 25 and 34 years in
DRC, Uganda and Zimbabwe, while it was between 35 and 44 years in Nigeria. In all the
countries, most spousal age at marriage was between 20 and 35 years. Across the countries, most
spouses had secondary or higher educational attainment, except for Uganda where about 63% of
spouses had only primary education. Household characteristics showed that more than 40% of
households in three countries were in the poor wealth quintile, except for Zimbabwe where about
43% households fell within the rich wealth quintile. The results also showed that most households
were from the rural areas, with monogamous marriage prevalent across the countries.
Almost half of the Nigerian women had no formal education (48.5%); 43% in DRC and 64% in
Uganda had primary education; while in Zimbabwe about 65% of the women had
secondary/higher education. Most of the women were employed except in Zimbabwe, where
about 56% were unemployed. About 43% of the Nigerian women were employed in the sales and
services sector, while most from DRC and Uganda were employed in the agricultural sector.
About 46% and 65% of women from Nigeria and Zimbabwe respectively have ever had between
1 and 3 children, about 54% and 59% of women from DRC and Uganda respectively have had up
to 4 children. Information on ever use of contraceptive among women is quite low in Nigeria
(23.2%) and DRC (37.5%) compared with Uganda and Zimbabwe where 56% and 85% reported
ever use of contraceptives respectively.
Table 3 presents the result of the bivariate analysis showing the differences in the mean number
of Children ever Born (CEB) across the categories of spousal and household characteristics. The
table showed that women’s mean number of CEB significantly increased with spousal age;
decreased significantly with increased age at marriage except in Uganda, where mean number of
CEB increased significantly with spousal age at marriage above 35 years. The relationship
between spousal level of education and women’s mean number of CEB showed a mixed result
Spousal and Household Characteristics Associated with Women’s Fertility
20
across the countries. While mean number of CEB significantly decreased with a rise in spousal
level of education in two countries (Uganda and Zimbabwe), reduction in the mean number of
CEB was only observed in Nigeria and DRC with spouses having at least primary level of
education. Also, a significant reduction in the mean number of CEB was observed with increase
in the household wealth status in Nigeria and Zimbabwe, while in the DRC and Uganda the mean
is highest in the middle household wealth group compared with the poor and the rich
households. A significantly higher mean number of CEB was observed for rural residents while
a significantly higher mean number of CEB was observed among women from polygamous
households.
Tables 4a and 4b present the result of the Poisson regression which further examines the influence
of spousal and household characteristics on the number of CEB by women. Model 1a shows the
result of the influence of spousal characteristics on women’s fertility in Nigeria. There is a direct
and significant increase in the number of CEB as spousal age increases (p<0.001). An inverse but
significant relationship exists between spousal age at marriage and the number of CEB (p<0.001).
While a direct relationship exists between fertility and spousal educational attainment at the
primary level, a negative relationship exists between fertility and spousal educational attainment
above the primary level. Model 2a shows a negative relationship between households in the rich
wealth quintile and the number of CEB. Women from polygamous households have significantly
lower fertility (β=0.244; p<0.001). Model 3a shows the combined effects of spousal and household
characteristics on women’s fertility. While spousal characteristics showed a slight difference, but
the same pattern of relationship with fertility as observed in model 1a, household characteristics
showed significant and slightly different pattern of relationship. For example, a negative
relationship is observed between the middle household wealth group and women’s fertility,
while the relationship between the rich households and the number of CEB is significant (β=-
0.165; p<0.001). Also, fertility in the polygamous households showed decreased levels than what
is observed in model 2a (β=-0.062; p<0.01).
For the Democratic Republic of Congo (DRC), model 1a shows similar pattern with that of
Nigeria. The result showed significant increase in the number of CEB as spousal age increases
(p<0.001). Additionally, an inverse but significant reduction in the number of CEB is observed as
spousal age at marriage increases (p<0.001). The number of CEB reduced with increased spousal
educational attainment. Model 2a shows a direct relationship between household wealth status,
type of residence, type of marriage and fertility. Model 3a presents the combined effects of
spousal and household characteristics on the number of CEB. The results show a slight difference,
but the same pattern of relationship between spousal age, age at marriage, place of residence and
fertility as observed in models 1a and 2a. However, an inverse relationship was observed between
rich households and fertility; and also between type of marriage and fertility.
For Uganda, model 1a shows a significant increase in the number of CEB as spousal age increases
(p<0.001), similar to the findings in previous countries. A negative, but significant relationship
was also observed between spousal age at marriage and the number of CEB. A negative
relationship was found between fertility and spousal educational attainment above the primary
level. In model 2a, a positive relationship exists between household wealth status and fertility.
Also, the relationship between place of residence, type of marriage and fertility were both positive
and significant. The result of the influence of both spousal and household characteristics on
fertility as presented in model 3a shows similar pattern to models 1a and 2a. However, a non-
significant relationship is observed between the type of marriage and fertility; while an inverse
relationship exists between the rich wealth group and fertility.
O. Banjo & J. Akinyemi
21
For Zimbabwe, the same pattern of relationship between spousal age, age at marriage and fertility
observed in the other countries was also observed. A significant positive relationship exists
between spousal age and fertility (p<0.001). An inverse but significant relationship also exists
between spousal age at marriage and the number of CEB (p<0.001). An inverse relationship also
exists between spousal level of education attained and fertility, both at the primary and higher
levels of education. Model 2a presents the result of the influence of household characteristics on
fertility. While an inverse relationship exists between household wealth status and fertility, direct
and significant relationships exists between type of residence, type of marriage and fertility. The
combined effects of both spousal and household characteristics on fertility as presented in model
3a showed similar pattern to models 1a and 2a.
Controlling for the female characteristics in each of the model presented for each country, a slight
reduction in the effects, but similar pattern was observed across the countries. The results are
presented in Tables 5a and 5b below.
Table 2: Percentage distribution of spousal and household characteristics from DHS data for
Nigeria (2013); DRC (2013/2014); Uganda (2011) and Zimbabwe (2010/2011).
Western Africa
Central Africa
Eastern Africa
Southern Africa
Spousal Characteristics
Nigeria
n=9,021 (100%)
DRC
n=4,344 (100%)
Uganda
n=1,076 (100%)
Zimbabwe
n=3,132 (100%)
Age
<25
406 (4.5)
323 (7.5)
84 (7.8)
298 (9.5)
25-34
2,896 (32.1)
1,531 (35.2)
440 (40.9)
1,320 (42.1)
35-44
3,859 (42.8)
1,407 (32.4)
365 (33.9)
1,026 (32.8)
45+
1,860 (20.6)
1,083 (24.9)
187 (17.4)
488 (15.6)
Age at marriage
<20
1,511 (16.8)
971 (22.3)
311 (28.9)
469 (15.0)
20-35
7,163 (79.4)
3,240 (74.6)
751 (69.8)
2,604 (83.1)
36+
347 (3.8)
133 (3.1)
14 (1.3)
60 (1.9)
Education level
No Education
3,102 (34.4)
225 (5.2)
70 (6.5)
35 (1.1)
Primary education
1,937 (21.5)
1,066 (24.5)
680 (63.2)
778 (24.9)
Secondary/Higher
3,982 (44.1)
3,052 (70.3)
325 (30.3)
2,319 (74.0)
Household Characteristics
Wealth status
Poor
4,136 (45.9)
1,863 (42.9)
472 (43.9)
1,212 (38.7)
Middle
1,544 (17.1)
953 (22.0)
205 (19.1)
564 (18.0)
Rich
3,341 (37.0)
1,528 (35.1)
398 (37.0)
1,357 (43.3)
Type of Residence
Urban
3,090 (34.3)
1,252 (28.8)
153 (14.2)
1,064 (34.0)
Rural
5,931 (65.7)
3,092 (71.2)
923 (85.8)
2,068 (66.0)
Type of marriage
Monogamy
6,438 (71.4)
3,618 (83.3)
876 (81.5)
2,941 (93.9)
Polygamy
2,582 (28.6)
725 (16.7)
199 (18.5)
192 (6.1)
Women’s characteristics
Education level
No Education
4,370 (48.5)
883 (20.3)
165 (15.4)
72 (2.3)
Primary education
1,714 (19.0)
1,872 (43.1)
691 (64.2)
1,023 (32.7)
Secondary/Higher
2,937 (32.5)
1,589 (36.6)
219 (20.4)
2,038 (65.0)
Occupational Status
Not working
2,876 (32.0)
820 (19.1)
237 (22.1)
1,720 (55.5)
Spousal and Household Characteristics Associated with Women’s Fertility
22
Western Africa
Central Africa
Eastern Africa
Southern Africa
Spousal Characteristics
Nigeria
n=9,021 (100%)
DRC
n=4,344 (100%)
Uganda
n=1,076 (100%)
Zimbabwe
n=3,132 (100%)
Prof.,Manag.,Techn.,clerical
428 (4.8)
122 (2.8)
48 (4.4)
143 (4.6)
Sales and services
3,822 (42.5)
977 (22.7)
198 (18.4)
493 (15.9)
Agric employee
928 (10.3)
2,372 (55.1)
593 (55.1)
403 (13.0)
Services/skilled/unskilled
936 (10.4)
11 (0.26)
-
342 (11.0)
Number of CEB
None
937 (10.4)
273 (6.3)
54 (5.0)
237 (7.6)
1-3
4,161 (46.1)
1,748 (40.2)
391 (36.4)
2,048 (65.4)
4+
3,923 (43.5)
2,322 (53.5)
631 (58.6)
847 (27.1)
Ever Use Contraceptives
Never Used Contraceptives
6,930 (76.8)
2,715 (62.5)
473 (44.0)
469 (15.0)
Ever Used Contraceptives
2,090 (23.2)
1,629 (37.5)
603 (56.0)
2,663 (85.0)
“-” Data not available
Table 3: Bivariate analysis showing the differences in women’s mean number of Children Ever
Born (CEB) across different categories of spousal and household characteristics
Spousal Characteristics
Western Africa
Central Africa
Eastern Africa
Southern Africa
Nigeria
n=9,021(SE)
DRC
n=4,344 (SE)
Uganda
n=1,076 (SE)
Zimbabwe
n=3,132 (SE)
Age
<25
0.76 (0.050)
1.03 (0.046)
1.16 (0.120)
0.89 (0.049)
25-34
2.06 (0.030)
2.65 (0.042)
3.06 (0.086)
1.87 (0.031)
35-44
3.86 (0.038)
4.91 (0.065)
5.74 (0.132)
3.24 (0.049)
45+
5.34 (0.064)
6.52 (0.087)
7.24 (0.221)
4.58 (0.100)
F=1,079.21; p<0.05
F=909.87; p<0.05
F=249.13; p<0.05
F=591.03; p<0.05
Age at marriage
<20
4.10 (0.073)
4.50 (0.092)
4.90 (0.165)
2.87 (0.048)
20-35
3.36 (0.030)
4.16 (0.049)
4.38 (0.108)
2.62 (0.036)
36+
2.42 (0.115)
3.83 (0.207)
5.52 (0.865)
2.27 (0.227)
F=81.04; p<0.05
F=6.84; p<0.05
F=4.18; p<0.05
F=5.04; p<0.05
Education level
No Education
3.68 (0.054)
3.94 (0.183)
5.10 (0.309)
4.05 (0.377)
Primary education
3.87 (0.057)
4.47 (0.081)
4.83 (0.120)
3.36 (0.075)
Secondary/Higher
3.07 (0.035)
4.16 (0.052)
3.85 (0.145)
2.39 (0.035)
F=82.46; p<0.05
F=6.15; p<0.05
F=13.64; p<0.05
F=94.43; p<0.05
Wealth status
Poor
3.72 (0.045)
4.19 (0.062)
4.71 (0.140)
3.04 (0.056)
Middle
3.65 (0.065)
4.45 (0.089)
4.85 (0.222)
2.67 (0.084)
Rich
3.02 (0.037)
4.13 (0.079)
4.19 (0.138)
2.29 (0.045)
F=75.01; p<0.05
F=4.18; p<0.05
F=4.65; p>0.05
F=53.47; p<0.05
Type of Residence
Urban
3.16 (0.041)
3.94 (0.080)
3.16 (0.155)
2.16 (0.049)
Rural
3.60 (0.035)
4.34 (0.050)
4.78 (0.103)
2.90 (0.043)
F=59.14; p<0.05
F=18.27; p<0.05
F=40.58; p<0.05
F=113.99; p<0.05
Type of marriage
Monogamy
3.16 (0.030)
4.13 (0.047)
4.34 (0.099)
2.60 (0.034)
Polygamy
4.18 (0.056)
4.70 (0.103)
5.44 (0.210)
3.31 (0.167)
F=294.04; p<0.05
F=25.05; p<0.05
F=22.96; p<0.05
F=25.88; p<0.05
Table 2 (continued)
O. Banjo & J. Akinyemi
23
Table 4A: Poisson Regression showing the relationship between spousal characteristics (model 1a); household characteristics (model 2a); Spousal and
household characteristics (model 3a) and fertility behavior in Nigeria and the Democratic Republic of Congo (DRC).
Nigeria
Democratic Republic of Congo (DRC)
Model 1a
Model 2a
Model 3a
Model 1a
Model 2a
Model 3a
Coef. (SE)
Coef. (SE)
Coef. (SE)
Coef. (SE)
Coef. (SE)
Coef. (SE)
Number of CEB
Spousal Characteristics
Age
below 25
RC
-
-
-
25-34
1.104***(0.088)
1.132***(0.090)
0.995***(0.060)
1.005***(0.060)
35-44
1.761***(0.087)
1.808***(0.089)
1.633***(0.064)
1.645***(0.064)
45+
2.106***(0.088)
2.155***(0.090)
1.934***(0.066)
1.950***(0.068)
Age at Marriage
below 20
RC
-
-
-
20-35
-0.302***(0.021)
-0.292***(0.021)
-0.206***(0.027)
-0.199***(0.027)
36+
-0.922***(0.057)
-0.895***(0.058)
-0.632***(0.065)
-0.601***(0.062)
Level of Education
no education
RC
-
-
-
primary education
0.024(0.022)
0.056*(0.023)
0.048(0.057)
0.060(0.057)
secondary/higher
-0.113(0.022)
-0.025(0.025)
-0.026(0.063)
0.015(0.063)
Household characteristics
Wealth Status
poor
RC
-
-
-
middle
0.002(0.027)
-0.021(0.022)
0.059(0.037)
0.007(0.030)
rich
-0.153(0.032)
-0.165***(0.028)
0.064(0.044)
-0.022(0.044)
Type of place of Residence
Urban
RC
-
-
-
Rural
-0.021(0.030)
0.028(0.022)
0.118*(0.044)
0.106*(0.040)
Type of Marriage
monogamy
RC
-
-
-
polygamy
0.244***(0.021)
-0.062**(0.018)
0.115***(0.032)
-0.030(0.030)
* p < 0.05, ** p < 0.01, *** p < 0.001 Coef. – Coefficient SE – Standard Error
Spousal and Household Characteristics Associated with Women’s Fertility
24
Table 4B: Poisson Regression showing the relationship between spousal characteristics (model 1a); household characteristics (model 2a); Spousal and
household characteristics (model 3a) and fertility behavior in Uganda and the Zimbabwe.
* p < 0.05, ** p < 0.01, *** p < 0.001 Coef. – Coefficient SE – Standard Error
Uganda
Zimbabwe
Model 1a
Model 2a
Model 3a
Model 1a
Model 2a
Model 3a
Coef. (SE)
Coef. (SE)
Coef. (SE)
Coef. (SE)
Coef. (SE)
Coef. (SE)
Number of CEB
Spousal Characteristics
Age
below 25
RC
-
-
-
25-34
1.033***(0.123)
1.035***(0.124)
0.779***(0.059)
0.802***(0.059)
35-44
1.664***(0.121)
1.661***(0.123)
1.348***(0.058)
1.369***(0.057)
45+
1.913***(0.126)
1.912***(0.129)
1.641***(0.064)
1.681*** (0.064)
Age at Marriage
below 20
RC
-
-
-
20-35
-0.214***(0.038)
-0.202***(0.037)
-0.169***(0.028)
-0.147***(0.028)
36+
-0.447*(0.213)
-0.421(0.218)
-0.670*** (0.120)
-0.631***(0.109)
Level of Education
no education
RC
-
-
-
primary education
0.041(0.080)
0.041(0.083)
-0.060(0.077)
-0.010(0.068)
secondary/higher
-0.117(0.085)
-0.045(0.088)
-0.248**(0.077)
-0.112(0.069)
Household characteristics
Wealth Status
poor
RC
-
-
-
middle
0.026(0.062)
0.006(0.042)
-0.085*(0.042)
-0.066**(0.028)
rich
0.000(0.054)
-0.053(0.037)
-0.150**(0.047)
-0.164***(0.034)
Type of place of Residence
urban
RC
-
-
-
rural
0.393***(0.079)
0.287***(0.056)
0.194***(0.048)
0.157***(0.035)
Type of Marriage
monogamy
RC
-
-
-
polygamy
0.206***(0.052)
0.020(0.042)
0.188**(0.055)
0.096*(0.041)
O. Banjo & J. Akinyemi
25
Table 5A: Poisson Regression showing the relationship between spousal characteristics (model 1b); household characteristics (model 2b); Spousal
and household characteristics (model 3b) and fertility behavior, while controlling for women’s characteristics in Nigeria and the
Democratic Republic of Congo (DRC).
Nigeria
Democratic Republic of Congo (DRC)
Model 1b
Model 2b
Model 3b
Model 1b
Model 2b
Model 3b
Coef. (SE)
Coef. (SE)
Coef. (SE)
Coef. (SE)
Coef. (SE)
Coef. (SE)
Number of CEB
Spousal Characteristics
Age
below 25
RC
-
-
-
25-34
1.087***(0.090)
1.107***(0.091)
0.985***(0.059)
0.989***(0.059)
35-44
1.715***(0.090)
1.753***(0.091)
1.598***(0.064)
1.603***(0.064)
45+
2.028***(0.090)
2.070***(0.092)
1.888***(0.066)
1.893***(0.066)
Age at Marriage
below 20
RC
-
-
-
20-35
-0.278***(0.021)
-0.278***(0.020)
-0.195***(0.026)
-0.198***(0.026)
36+
-0.821***(0.057)
-0.822***(0.057)
-0.538***(0.062)
-0.540***(0.062)
Level of Education
no education
RC
-
-
-
primary education
0.024(0.023)
0.033(0.023)
0.074(0.055)
0.075(0.055)
secondary/higher
-0.037(0.023)
-0.009(0.024)
0.075(0.061)
0.076(0.061)
Household characteristics
Wealth Status
poor
RC
-
-
-
middle
0.004(0.025)
-0.021 (0.022)
0.086*(0.036)
0.022(0.030)
rich
-0.103**(0.034)
-0.127***(0.031)
0.132***(0.037)
0.021(0.036)
Type of place of Residence
Urban
RC
-
-
-
Rural
-0.031(0.030)
0.015(0.022)
-0.021(0.041)
0.028(0.035)
Type of Marriage
monogamy
RC
-
-
-
polygamy
0.216***(0.020)
-0.061**(0.018)
0.097*(0.031)
-0.032(0.028)
Women’s characteristics
Education level
No Education
RC
-
-
-
-
-
Primary education
-0.065**(0.021)
-0.051(0.029)
-0.045*(0.022)
-0.036(0.025)
-0.085*(0.036)
-0.036(0.025)
Secondary/Higher
-0.262***(0.024)
-0.349***(0.033)
-0.213***(0.028)
-0.262***(0.036)
-0.399***(0.051)
-0.261***(0.035)
Occupational Status
Not working
RC
-
-
-
-
-
Prof.,Manag.,Techn.,clerical
0.025(0.038)
0.250***(0.043)
0.037(0.037)
-0.064(0.074)
0.156(0.087)
-0.062(0.073)
Sales and services
0.201***(0.022)
0.356***(0.025)
0.209***(0.022)
0.082(0.048)
0.209**(0.063)
0.084(0.047)
Agric employee
0.267***(0.028)
0.372***(0.039)
0.241***(0.029)
0.097*(0.038)
0.225**(0.067)
0.092*(0.040)
Services/skilled/unskilled
0.141***(0.032)
0.222***(0.038)
0.150***(0.032)
-0.303(0.250)
-0.249(0.321)
-0.301(0.251)
Ever Use Contraceptives
Never Used Contraceptives
RC
-
-
-
-
-
Ever Used Contraceptives
0.113***(0.019)
0.232***(0.024)
0.125***(0.020)
0.098***(0.021)
0.130***(0.027)
0.097***(0.022)
* p < 0.05, ** p < 0.01, *** p < 0.001 Coef. – Coefficient SE – Standard Error
Spousal and Household Characteristics Associated with Women’s Fertility
26
Table 5b: Poisson Regression showing the relationship between spousal characteristics (model 1a); household characteristics (model 2a); Spousal and
household characteristics (model 3a) and fertility behavior, while controlling for women’s characteristics in Uganda and the Zimbabwe.
Uganda
Zimbabwe
Model 1b
Model 2b
Model 3b
Model 1b
Model 2b
Model 3b
Coef. (SE)
Coef. (SE)
Coef. (SE)
Coef. (SE)
Coef. (SE)
Coef. (SE)
Number of CEB
Spousal Characteristics
Age
below 25
RC
-
-
-
25-34
0.986***(0.130)
0.978***(0.131)
0.711***(0.054)
0.725***(0.054)
35-44
1.566***(0.128)
1.558***(0.129)
1.255***(0.053)
1.268***(0.053)
45+
1.810***(0.135)
1.805***(0.137)
1.563***(0.060)
1.590***(0.060)
Age at Marriage
below 20
RC
RC
-
-
20-35
-0.197***(0.037)
-0.192***(0.037)
-0.126***(0.030)
-0.121***(0.031)
36+
-0.428(0.213)
-0.410(0.211)
-0.615***(0.104)
-0.597***(0.099)
Level of Education
no education
RC
RC
-
-
primary education
0.079(0.077)
0.069(0.078)
-0.024(0.062)
0.005(0.059)
secondary/higher
0.028(0.084)
0.042(0.084)
-0.133*(0.062)
-0.068(0.059)
Household characteristics
Wealth Status
poor
RC
-
-
middle
0.061(0.061)
0.021(0.042)
-0.024(0.037)
-0.038(0.026)
rich
0.071(0.053)
-0.019(0.038)
-0.082(0.047)
-0.127***(0.034)
Type of place of Residence
Urban
RC
-
-
Rural
0.282***(0.075)
0.236***(0.056)
0.134**(0.043)
0.124***(0.032)
Type of Marriage
monogamy
RC
-
-
polygamy
0.157**(0.050)
0.016(0.042)
0.189**(0.056)
0.109*(0.046)
Women’s characteristics
Education level
No Education
RC
RC
-
Primary education
-0.149***(0.041)
-0.358***(0.048)
-0.146**(0.042)
-0.062(0.059)
-0.315***(0.063)
-0.047(0.059)
Secondary/Higher
-0.494***(0.066)
-0.795***(0.082)
-0.445***(0.068)
-0.257***(0.061)
-0.621***(0.063)
-0.187**(0.061)
Occupational Status
Not working
RC
-
-
-
-
Prof.,Manag.,Techn.,clerical
0.060(0.135)
0.187(0.149)
0.106(0.133)
-0.278*** (0.044)
0.003(0.057)
-0.200***(0.045)
Sales and services
0.009(0.055)
0.100(0.073)
0.045(0.055)
-0.059(0.032)
0.111**(0.037)
-0.009(0.031)
Agric employee
0.058(0.046)
0.109(0.063)
0.043(0.046)
0.043(0.030)
0.101*(0.135)
0.013(0.030)
Services/skilled/unskilled
-
-0.059(0.033)
0.025(0.044)
-0.018(0.033)
Ever Use Contraceptives
Never Used
RC
-
-
-
-
-
Ever Used
0.097*(0.037)
0.228***(0.045)
0.117**(0.038)
0.405***(0.057)
0.522***(0.066)
0.402***(0.055)
* p < 0.05, ** p < 0.01, *** p < 0.001 Coef. – Coefficient SE – Standard Error
* p < 0.05, ** p < 0.01, *** p < 0.001 Coef. – Coefficient SE – Standard Error
O. Banjo & J. Akinyemi
27
Discussion
This study examined spousal and household characteristics influencing fertility in four countries
in sub-Saharan Africa. The spousal characteristics were age, age at marriage and level of
education attained while the household characteristics included household wealth status, type of
residence and type of marriage. Findings from the study clearly showed that while some
characteristics influenced fertility similarly across countries, some other characteristics
influenced fertility differently. For example, spousal age at marriage between ages 20 and 35 years
across the countries is an indication of cultural expectation of men to be married at a particular
age, which is usually above that of their wives. Also at the bivariate and multivariate levels of
analyses, it was evident that both spousal age and age at marriage influenced fertility similarly
across the countries. The number of CEB corresponded with increase in spousal age across the
countries at the bivariate and the multivariate levels of analyses. Also, a reduction in the number
of CEB was observed across the countries based on the result of the Poisson regression models.
These findings suggest that in situations where the spousal age and age at marriage are higher
than those of the women (which in most cases is the situation in SSA), patriarchy may be at play
as spouses have the final say as regards the number of children born. These findings support
Caldwell’s theory of using children as old age security. It may also mean that men who marry
late prefer to have fewer children. In that regard, increased age at marriage among men could
complement efforts to lower fertility levels by increasing women’s age at marriage. This could be
a long-term effort to lower fertility in SSA.
The level of educational attainment among the husbands was higher across the countries. Above
the primary level education may have led to increased household wealth, with a positive impact
on contraceptive use. However, compared with other countries where most spouses attained
secondary or higher level of education, three in five married women in Uganda had only primary
level education. At the bivariate level of analyses, while a progressive reduction was observed in
the mean number of CEB as spousal level of educational attainment increased in Uganda and
Zimbabwe, reduction was only observed among those who had above the primary level in
Nigeria and the DRC. At the multivariate level of analysis, an inverse relationship was found
between women’s number of CEB and spousal level of educational attainment above the primary
level for Nigeria, DRC and Uganda; an inverse relationship was observed both at the primary
and post-primary levels for Zimbabwe. These findings suggest that the relationship between
spousal education and women’s fertility differ across the countries. Additionally, the findings are
also consistent with those of previous studies that spousal education significantly predicted
fertility intentions and contraceptive use among couples (Assefa, Berhane, Worku, & Tsui, 2012;
Berhane, 2015; DeRose & Ezeh, 2005). Further, having secondary or higher level of education
increases the likelihood of reducing fertility on the long run.
With respect to household characteristics, the relationship between household wealth status and
fertility seem to play out the same way in Nigeria and Zimbabwe and in DRC and Uganda at the
bivariate level of analysis, but differently at the multivariate level across all countries. Whereby
an inverse relationship was observed between the middle and the rich class in terms of wealth
and number of CEB in Zimbabwe and Nigeria. But for DRC and Uganda, a direct relationship
was observed. However, for the two countries at the multivariate level, a direct relationship was
noted across the classes. These findings are similar to that of Adebowale et al. (2014) and Yihunie
Lakew et al. (2011), who found contraceptive use was greater among the rich. It is important to
note that while increase in household wealth status may act to further lower fertility in Nigeria
Spousal and Household Characteristics Associated with Women’s Fertility
28
as in Zimbabwe, it may not really be so in the DRC and Uganda. This again may depend on the
spousal level of education and household structures in place among other indicators of
development.
Further findings from this study showed that at the bivariate and multivariate levels of analyses,
rural residence was found to be a significant predictor of high fertility. This again suggests low
use of contraceptive in the rural areas consistent with the findings of Yihunie Lakew et al. (2011)
in their study of geographical variation in contraceptive use in Ethiopia. This is not surprising
because most households were in the rural areas and that efforts aimed at fertility reduction often
marginalized rural dwellers in favor of urban dwellers. Additionally, urban dwellers are usually
more educated. Polygamy was found to be associated with high fertility across the countries at
the bivariate level of analysis, but a mixed result was found across the countries at the
multivariate level of analysis, especially at the third models where an indirect relationship was
observed for Nigeria and DRC, while a slight change in the relationship was observed for Uganda
and Zimbabwe. The prevalence of monogamous marriages across the countries suggests greater
tendencies towards modernization and westernization as concluded by Hayase and Liaw (1997).
When women attain higher levels of education, combined with their spousal level of educational
attainment, there is a higher likelihood of rapid fertility decline in the region.
Conclusion
The fact that spousal age and age at marriage tend to influence fertility the same way as women’s
age and age at marriage is points to the cultural expectations and practices of marriage in SSA.
Findings from this study showed that spousal educational attainment is very important in
predicting fertility levels across the countries. This also indicates that men must be fully involved
for persistent decline in fertility levels, particularly in those regions presently experiencing stalls
in fertility decline. Among the household characteristics examined, household wealth was the
most important predictor of fertility across the countries. This finding suggests that household
wealth status is likely to have worked through the medium of spousal and women’s’ educational
attainment to influence fertility. This is clearly shown in the model where women’s characteristics
were controlled for (Tables 5a & 5b). Policies aimed at further reduction of fertility in countries
experiencing stalled or early fertility decline need to pay close attention to both men and women’s
educational advancements.
Acknowledgements:
The authors express their gratitude to “Consortium for Advanced Research Training in Africa”
(CARTA) for supporting this research. CARTA is jointly led by the African Population and Health
Research Center and the University of the Witwatersrand and funded by the Wellcome Trust
(UK) (Grant No: 087547/Z/08/Z), the Carnegie Corporation of New York (Grant No--B
8606.R02), Sida (Grant No:54100029)” The statements made and views expressed are solely the
responsibility of the authors.
The authors also thank the ICF Macro International and MEASURE DHS for giving them
permission to access data.
O. Banjo & J. Akinyemi
29
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