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African Population Studies
Union for African Population Studies
ISSN: 0850-5780
Vol. 10, Num. 1, 1995
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African Population Studies/Etude de la Population Africaine, Vol. 10,
November/novembre
1995
Explaining
Contraceptive Use Differences:
Do Men Play a Role?
F. Nii-Amoo Dodoo*
Code Number: ep95002
Abstract
Data from the Kenya and Ghana Demographic and Health Surveys (DHS) are used
to assess the observed difference in modern contraceptive use between the two
countries. The findings indicate that although female fertility preferences
and education remain important, differences in male fertility goals also appear
to be a crucial determinant of the contraceptive gap between Kenya and Ghana.
Some implications of these findings are discussed.
Résumé
Les données
provenant des Enquêtes démographiques et sanitaires menées
au Kenya et au Ghana sont utilisées dans l'évaluation des
différences relevées dans l'utilisation des méthodes
contraceptives modernes entre les deux pays. Les résultats des
recherches indiquent que, bien que les choix en matière de fécondité féminine
et d'éducation restent importants, les différences dans
les objectifs en matière de fécondité masculine
apparaissent également comme un déterminant crucial du
fossé qui existe entre le Kenya et le Ghana en matière
de contraception. Certaines implications des résultats des recherches
ont fait l'objet d'une discussion.
INTRODUCTION
Fertility declines in sub-Saharan Africa are long overdue
to the extent that recent observed changes in a handful of countries have
served to curb some of the pessimism regarding the region's future. The recent
declines have been welcome, also because they offer excellent opportunities
to examine sources of fertility change in an African context which, until
recently, were unavailable. Comparisons between countries at different stages
of fertility transition (i.e., declining, rising, or stagnant) can illuminate
fruitful avenues for inducing change in areas yet to experience fertility
decline, areas which by far represent the bulk of the continent.
Although numerous explanations have been advanced
for fertility transition, it is increasingly being acknowledged that a major
explanation for the resistance of sub-Saharan Africa's fertility to change
involves the general neglect of men's real dominance in reproductive, and specifically,
family planning decision making (Mbizvo and Adamchak 1991, 1992). To date,
there has been little empirical assessment of the degree to which men influence
contraceptive adoption. This phenomenon is reflective of family planning programmes'
unequivocal focus on women, and the failure to consider the dynamics of household
and family decision making in the fertility arena, in societies where male
dominance in this realm has been extensively documented.
Further, although a handful of studies have measured
the effect of men's roles at the local level, none exists at the national level.
This study seeks to address this omission by assessing the extent to which
both men's and women's reproductive preferences are associated with contraceptive
use in two sub-Saharan African countries, Ghana and Kenya. These two countries
provide an interesting comparison; although they were the first two in sub-Saharan
Africa to institute national family planning programmes (and at approximately
the same time) they are currently experiencing vastly different fertility and
contraceptive use trends (National Council for Population and Development and
the Institute for Resource Development [IRD] 1989; Ghana Statistical Service
and IRD 1989).
Fertility
and Family Planning in Ghana and Kenya
Kenya and Ghana, two countries on the east and
west coasts of Africa, respectively, have historically exhibited some of the
highest fertility levels in the world. However, recent estimates peg Kenya's
total fertility rate (TFR) at 5.4 children per woman, and Ghana's at 6.2 (Population
Reference Bureau [PRB] 1993a, 1993b), figures reflecting the recent fertility
trends in the two countries. Kenya's fertility has fallen rapidly from the
8.1 children per woman reported in the 1977-78 Kenya World Fertility Survey
(WFS). On the other hand, the 1979 Ghana WFS recorded a TFR of 6.3 children
per woman.
The trends are also reflective of contraceptive
use patterns in the two countries. In Ghana, modern contraceptive use has been
stagnant at around 5 per cent over the last 15 years, whereas Kenya has seen
a remarkable increase from 4.3 per cent in 1977-78 to 27.3 per cent in 1993.
The corresponding 20 percent decline in Kenyan fertility from 1989 to 1993
is, consequently, the fastest national fertility decline ever recorded (PRB
1993a).
Although Kenya and Ghana are similar in terms
of the inception of their family planning programmes in the late 1960's, the
development and implementation of family planning policies in the two countries
have diverged. After a shaky start, notable improvements were made in the Kenyan
family planning programme by the mid-1980s. In Ghana, however, process was
slow and small, and it was only in the late 1980's that evaluations of the
national policy even began (Center for Development Research [CDR] 1990; Frank
and McNicoll 1987; Lapham and Mauldin 1985; Ministry of Finance and Economic
Planning [MFEP] 1992).
How much of the observed difference in contraceptive
use between the two countries can be explained by differences in the quality
of the national programmes? Although we can not directly answer this question
with these data, we argue that extra-programmatic factors must also be significant
because the improvements in the Kenyan programme, dated around the mid-1980's,
could hardly have had enough time to be exclusively responsible for the sharp
use differences observed by 1988-1989, when the data were gathered.
Changes in fertility and contraceptive use in
Kenya have been attributed to increases in female education, and the proportion
of women who want to cease childbearing (Njogu 1991). Yet, Dodoo (1992) has
argued that a sizeable proportion of Ghanaian women also state a desire to
cease childbearing, begging the question as to why similar fertility change
has not occurred in Ghana. Statistics also show that a larger proportion of
Ghanaian (than Kenyan) women attain higher, and especially tertiary, levels
of education, although more Kenyan women have ever-attended school (World Bank
1988). This study should help ascertain the extent to which fertility differences
between Ghana and Kenya can be explained by differences in the relative situations
of women in the two countries.
The
Role of Men in Reproductive Decision Making
Even the broadest of explanatory frameworks regarding
fertility (for example, Davis and Blake 1956; Easterlin 1975) have focused
primarily on female characteristics at the point of empirical assessment (DeGraff
1991). Njogu (1991), for instance, attributed demographic change to female
education and reproductive desires without considering male desires.
As indicated earlier, researchers are becoming
increasingly convinced that a major shortcoming of the analysis and policy
pertinent to fertility-related behaviour in Africa is the limitation of these
realms to women (Dodoo 1993a, 1993b; Dodoo and Seal 1994; Ezeh 1991, 1992).
These researchers argue that such a restriction is odd, given what we know
about gender roles and fertility decision making in the African context. First,
fertility-related decisions involve at least two partners (Beckman 1983; Blumberg
1988; Oppong 1987). Further, although macro-level indicators point to negligible
gender differences in reproductive goals (Mason and Taj 1987), there is rationale
for, and evidence of, substantial variation among spouses (Caldwell 1983; Dodoo
and Seal 1994; Fapohunda and Todaro 1988; Lesthaeghe 1989; Mott and Mott 1985).
Finally, significant evidence attests to the fact that gender power relations
in the region skew decision making power in men's favour, and against women
(Beckman 1983; Frank and McNicoll 1987; Hollerbach 1980; Khalifa 1988; Mustafa
and Mumford 1984).
DATA AND METHODS
The data for the study come from the 1988 Ghana
and 1989 Kenya DHS which, in addition to general background information, provide
data on contraceptive use, and the reproductive intentions of spouses, both
male and female. The standardized format of these surveys permits reasonable
comparison of variables across countries.
The analysis is restricted to married women in
the two samples whose husbands were also interviewed, a condition that permits
the matching of 1,189 Kenyan and 1,010 Ghanaian spousal pairs, because polygamous
men are matched with each of their interviewed spouses. A further restriction
excludes women who are known to be infecund, leaving 1,145 Kenyan and 971 Ghanaian
dyads. Unfortunately, neither survey includes a representative sample of men
in the country, because male selection involved husbands of subsamples of interviewed
women. Although, this procedure provides room for bias in the comparison, not
much can be done to rectify the problem.
The data also offer little opportunity to undertake
a comprehensive examination of the exhaustive list of factors that condition
the observed differences in contraceptive use. Most notably, there is no apparent
means to examine differences in supply-side explanatory factors, (for example,
the programmatic and policy differences regarding how the two programmes actually
differ in coverage and application). With these constraints in mind, the focus
of this article remains an attempt to examine the extent to which differences
in certain measured demand-related characteristics - especially male and female
preferences, and education - explain the gap in contraceptive use between Kenya
and Ghana.
Variable
Selection
The dependent variable, the prevalence of modern
contraception, is scored '1' when respondents report current use, and '0' otherwise.
We focus on modern methods because they play a more effective role in fertility
decline, and also because they remain the focus of family planning programmes.
The explanatory variables in the model reflect
comparable demographic and socioeconomic variables that affect contraceptive
use. Age, measured as a continuous variable, is expected to manifest itself
most on contraceptive use when fecundity is highest, suggesting that use may
be lowest in the middle reproductive years when exposure to pregnancy is highest
(Njogu 1991). Experience with previous child mortality should be negatively
associated with contraceptive use, as parents attempt to replace deceased children
(Tuladhar 1985). This variable is dichotomous, coded '1' if a woman has experienced
child mortality in the past, and '0' if otherwise. Marital status is a three-category
dummy variable reflecting monogamous (coded '0') versus polygamous marriage;
in the latter, a distinction is made between senior and junior wives. The hypothesis
is that women in polygamous marriages are less likely to use modern forms of
contraception. Urban/rural residence is scored '1' for urban dwellers, and
'0' for their rural counterparts. Urban areas typically offer residents more
access to contraceptive and other services.
Covariates of interest in this study include
female education which has been widely noted for its fertility depressing effect
(see, for example, Caldwell 1980). The variable has three categories: no schooling;
at least some primary schooling; and, at least some secondary schooling. Men's
education is coded similarly; although, the education of men is typically neglected
in such analyses, we anticipate that more educated men have greater awareness
of the costs and benefits of contraception. Also, their higher ambition for
their children is expected to manifest itself in a desire to lower fertility.
The fertility preferences of women are also of
interest here. As stated earlier, Njogu (1991) attributed contraceptive adoption
in Kenya to an increase in the number of women who want no more children, thus,
preferences are coded to reflect respondents' potential contraceptive needs.
Women have 'no need' for contraception if they indicate that they want to have
a or indeed another child within two years. If women report indecision about
preferences, or state that they do want another child, albeit after a two-year
wait, they are classified as having a 'spacing need'. Finally, women who want
to cease childbearing altogether are categorized as having a 'stopping need'.
Male preferences are coded similarly. The rationale for including men's reproductive
preferences derives from the dominant role of men in reproductive decision
making in sub-Saharan Africa (Dodoo 1993b; Ezeh 1992).
Obviously, other variables contribute to the
observed national differences in contraceptive use. However, these are not
accounted for here for two reasons. First, as indicated earlier, the nature
of the data prescribes the limits of variable selection. Second, the variables
were carefully chosen to retain comparability to other recent studies. There
is no pretence to specify a model that fully explains the differences in contraceptive
use. What we do investigate is the extent to which differences in the levels
of these measured variables contribute to the observed variance in contraceptive
use between the two countries.
The
Analytical Model
The dichotomous nature of the dependent variable
makes it amenable to a logistic regression model. The log odds of modern contraceptive
use are expressed as a summation of the products of covariate means (X), and
the regression coefficients (b). Thus, for a given country c, the log odds
are estimated as:
Logitc =
Ln(pi/[1-pi]) =SbiXi
In the ensuing decomposition analysis, a method
described in Althauser and Wigler (1972), Iams and Thornton (1975), and Jones
and Kelley (1984) is used to partition the observed difference in contraceptive
use between Kenya and Ghana. The analytical formula is:
LK -
LG = (aK - aG) +S biG(XiK -
XiG) +S XiG(biK - biG)
+S (XiK -
XiG)(biK - biG)
where,
L = logit for a given country
K = Kenya, the reference category
G = Ghana
a = intercept
Xi = mean of ith attribute
bi = partial slope of ith attribute
The first component, measuring the difference
in intercepts, corresponds to the portion of the variance in contraceptive
use between the two countries not explained by variables in the model. The
second component reflects the portion of the contraceptive difference explained
by compositional variation in the regressors, and recognizes that the contraceptive
difference can, in part, be attributed to variations in the characteristics
that influence contraceptive use. In addition, it is plausible to argue that
the contraceptive impact of possessing these attributes differs between the
countries, and the relative contribution of this factor is represented by the
third component which measures slope (propensity), or "rate of return" differences.
The fourth (interaction) component reflects the covariation between endowments
and slopes in the two countries.
RESULTS
Descriptive
Analysis
The data in table 1 document interesting variations
between the Kenyan and Ghanaian samples. Compared to Ghanaian women, Kenyan
women are slightly older, less likely to have dead children, or be in polygamous
marriages. These factors should incline Kenyan women more towards contraception.
On the other hand, women in the Ghanaian sample are considerably more urban.
Further comparison of the two samples reveals
higher educational levels for both women and men in Kenya. The fertility preferences
(and contraceptive needs) of Kenyan women are also more reconcilable with lower
fertility than those of their Ghanaian counterparts. Kenyan women are more
likely to want to cease childbearing, while Ghanaian women appear to be most
interested in spacing their births. Dodoo (1995) has indicated that in Ghana,
women are more likely to lean towards traditional methods to meet their spacing
needs, but to modern methods when they want to stop childbearing. With modern
contraceptive use levels at 17 per cent in Kenya in 1989, and 5 per cent in
Ghana in 1988, it would be easy to surmise that the observed reproductive preference
differences reflect the primary reasons for the observed contraceptive gap.
However, the differences in male contraceptive needs are at least as glaring
as those noted for females. More than 48 per cent of Kenyan men want no more
children, while only 18 per cent of Ghanaian men have this desire. Interestingly,
although a higher proportion of Ghanaian men, than women, have no need for
contraception (that is, they want more children within two years), the reverse
holds true for Kenya.
Although these variations probably contribute
to the observed contraceptive prevalence differences, the propensities associated
with these variables could also differ between the two countries. Table 2 examines
bivariate relationships between the selected variables and contraceptive use.
With very few exceptions, contraceptive use has the expected relationships
with the explanatory variables in both countries. However, the selected characteristics
have larger impacts on contraceptive use in Kenya. How much of the contraceptive
gap is attributable to: a) the different distributions of men and women across
the selected characteristics, between the two countries; and b) differences
in the impacts of these variables on contraceptive use? These questions will
be addressed in the decomposition analysis.
Multivariate
Analysis
In order to explain the factors responsible for
the contraceptive prevalence difference between Ghana and Kenya, it is useful
to identify the determinants of contraceptive use in the two countries. A multivariate
model examining the effects of the independent variables on contraceptive use
allows us to disentangle the potentially confounding effects of the various
regressors on the dependent variable. The logit estimates from the model are
presented in table 3. For each country, two sets of estimates are provided;
the first reflects the traditional model limited to female variables, while
the second incorporates male characteristics.
The estimates presented in columns one and two
indicate that primary schooling of females, and urban residence, are positively
associated with contraceptive use across the board. The effects of urbanization
and primary schooling appear larger in Ghana than in Kenya. Perhaps, because
family planning is more diffuse in Kenya there is less urban and educational
variability. Similarly, the considerably larger proportion of Ghanaian (than
Kenyan) women who have never attended school, manifests itself in a sharper
impact of primary schooling. Secondary schooling is significant only in the
Kenyan case. As well, with regard to fertility preferences, there is a significant
impact on modern contraceptive use in Ghana only when women feel the need to
cease childbearing (Dodoo 1995), whereas the spacing coefficient is also significant
in Kenya.
Columns three and four include male schooling
and preferences. Male education is not a significant determinant of contraceptive
use. Men's reproductive preferences, however, are clearly important. In Ghana,
male preferences are associated with use for spacing, whereas in Kenya both
spacing and stopping preferences raise contraceptive use significantly.
A much higher proportion of men in Kenya (than
Ghana) want to cease childbearing, and their preferences have a significant
effect on their spouses' contraceptive use. A plausible explanation may lie
in lineage differences between the two countries; while close to one-half of
Ghana is matrilineal, Kenyan ethnic groups are primarily patrilineal, making
it conceivable that male preferences play a larger role in Kenya than in Ghana
(Caldwell and Caldwell 1993; Vellenga 1986). Explanations centred around the
traditional family and lineage systems surrounding marriage are viable because
these forms of social organization manifest themselves in potentially different
reproductive goals for spouses (Frank and McNicoll 1987; Lesthaeghe 1989).
The extent to which these differences can be negotiated and resolved among
reproductive partners differs between patrilineal and matrilineal societies.
In the latter, the traditional authority of men in decisions regarding sex
and reproduction is considerably attenuated (Caldwell and Caldwell 1993; Vellenga
1986). Unfortunately, the data at hand, and most of those currently available,
do not permit direct assessment of these features of African society, so that
it may be useful for future data-gathering efforts to be more sensitive to
the peculiar cultural contexts.
The significance of including male variables
can also be imputed from their impact on female variable effects. For one thing,
their inclusion attenuates the effects of urbanization and female education,
suggesting that some of these effects should really be credited to male preferences.
Similarly, the effects of female preferences are reduced in Kenya, and completely
lose significance in Ghana.
The importance of male and female preferences
leads us to explore the formulation of a joint measure of contraceptive need.
The three preference categories are cross-classified for men and women. Table
4 presents the resulting nine categories with their associated levels of contraceptive
use. The modal category in Kenya is one in which both spouses want to cease
childbearing, or have a (stopping) need for contraception. More than 40 per
cent of Kenyan dyads are in this category which, logically, also has the highest
level of contraceptive use (31 per cent). Similar Ghanaian pairs do not represent
the modal category, although they remain the most prolific contraceptors (approximately
12 per cent). In Ghana, the modal category is one in which both spouses have
a spacing need (41 per cent), a category characterized by lower contraceptive
use (6 per cent).
A close inspection of table 4 reveals that, in
Kenya contraceptive use levels are at their highest (31 per cent and 23 per
cent) when men want to cease childbearing, and their wives also either want
to stop or space their fertility. The next highest use levels occur when men
want to space (18 per cent and 17 per cent), and their wives want to stop or
space. On the other hand, when women want to cease childbearing, the proportions
who use contraception equal 31 per cent and 18 per cent when their husbands
want to stop or space, respectively. When women want to space their childbearing,
the relevant proportions are 23 per cent and 17 per cent, depending on whether
their husbands want to stop or space. Use is much higher when men want to stop
and their wives want to space (23 per cent), than it is when women want to
stop and their husbands want to space (18 per cent).
A similar examination of the ranking of use levels
in Ghana suggests that Ghanaian men apparently have less influence than Kenyan
men, on women's contraceptive use. The highest use levels (12, 11, and 8 per
cent) are associated with female preferences to cease childbearing. When men
in Ghana want to cease childbearing, use is at 4 per cent when their spouses
want to space, and nil when their wives want children within two years, implying
that contraceptive use is more reflective of female (than male) preferences
in Ghana.
Decomposition
Analysis
In this section we attempt to separate the influences
of composition or endowment differences between the two countries, from propensity
(slope) differences. The results of this decomposition analysis, presented
in table 5, indicate that the primary component of the difference (47 per cent)
is attributable to propensity, which comprises intercept and slope effects.
By far, the largest contribution to this is from the intercept, which signals
the importance of unmeasured variables such as differences in the density of,
and access to, family planning services.
Differences in the "rate of return" to men's
preferences (slopes) also contribute to the observed gap. When men in Kenya
want to cease childbearing, contraceptive use returns are higher than in Ghana.
Perhaps, Kenyan men have greater access to family planning, such that even
when men in both countries want to cease childbearing the return for Kenyan
men is higher. Of course, this point is speculative and will need further empirical
validation. Alternatively, men in patrilineal societies may have more of an
inclination to ensure that their preferences are translated into decision making;
matrilineal men may be less likely to do so because of the relatively higher
ability of women (compared to patrilineal women) to govern their own behaviour.
The returns to female preferences are also higher in Kenya, whereas the reverse
is true for primary schooling for both men and women.
Although not as statistically prominent as propensity,
composition differences still explain 28 per cent of the contraceptive gap
between Ghana and Kenya. Differences in the proportion of men and women in
the two countries who want to stop or cease childbearing each comprise 17 per
cent of the variance, respectively.
The bottom line of table 5 provides the composition,
propensity, and interaction effects of the model that excludes male variables.
Including men's characteristics reduces the propensity component from 55 per
cent to 47 per cent, indicating an increase in explanatory power. The composition
effect increases more significantly (from 21 to 28 per cent), however, affirming
the importance of male characteristics. The implication is that, what is really
a result of male attributes may be construed as an urbanization, or female
variable effect, when male characteristics are excluded from the model; models
that exclude the male perspective bias the relationship between contraceptive
use and the traditional (female) explanatory variables.
DISCUSSION
A goal of this study was to consider the significance
of including the habitual omission of male perspective in the analysis of reproductive
behaviour in Ghana and Kenya. Another purpose was to examine the extent to
which the sizeable contraceptive gap between Kenya and Ghana could be attributed
to differences in male and female education, and fertility preferences.
The results of the analysis point to the importance
of the male perspective. Although men's influence is not yet significant in
Ghana, their preferences are clearly important predictors of contraceptive
use in Kenya. It has been suggested elsewhere, that fertility change in countries
like Ghana awaits a transition in men's desires (Dodoo 1993a; Ezeh 1991, 1992;
Mbizvo and Adamchak 1991), an argument that has some merit given that 75 per
cent of the women in the Ghanaian sample state a need for stopping or spacing
their childbearing.
There is also, clearly, a role played by women's
education and preferences. In this vein, the five-fold increase to almost universal
education between 1963 and 1985 in Kenya, while Ghanaian enrolment rates stagnated
in the 1970's and 1980's (Njogu 1991; World Bank 1988), has probably been instrumental
in raising the level of contraceptive prevalence in Kenya.
The role of family planning programmes, although
not directly measured here, apparently should not be understated. Half of the
difference between the two countries derives from the propensity component,
and a sizeable portion of this, from the intercept which reflects unmeasured
factors such as differences in availability of, and access to, family planning.
Although the Kenyan programme was rated 'weak', and the Ghanaian 'very weak'
in 1982 (Lapham and Mauldin 1985), there apparently were qualitative differences
that favoured Kenya. By 1989, the two programmes rated higher (moderate) and
the gap between them had narrowed (Mauldin and Ross 1991).
The findings have some bearing for policy. Revitalizing
the education system, particularly in countries like Ghana, where economic
hardships have reversed post-independence gains appears crucial. The link between
female education and reproductive preferences is well documented (see, for
instance, Dodoo 1992), and our results echo recommendations for improving the
status of women. Ironically, the particularly weak economies of these sub-Saharan
African countries is what compels a need to search for alternate routes to
fertility decline. Although, improving the status of women remains imperative,
and should continue to be high on national and population agendas, the irony
of the situation is that many of the countries that require this impulse are
the very ones that can not, realistically, afford the levels of investment
needed to attain meaningful changes in, for example, levels of female education,
at least not in the near future. While attempts to elevate the status of women
continue, efforts to identify other means of inducing lower fertility must
also be intensified. Particularly in societies where men exercise such control
over reproduction, reducing male fertility preferences seems to be a reasonable
choice. Will it suffice to educate men about the benefits of contraception?
Are men already cognizant of these, but, perhaps, need a nudge (for instance,
in the form of taxation)? What are the most effective ways to reach men with
the message of urgency? These constitute some new challenges for the future.
Table
1: Kenya-Ghana Differences in Selected Characteristics.
Proportions: |
Kenya |
Ghana |
Age: |
15-24 |
0.186 |
0.195 |
25-34 |
0.406 |
0.447 |
35-44 |
0.329 |
0.268 |
45+ |
0.080 |
0.091 |
Marital status: |
Monogamous |
0.788 |
0.693 |
Senior wives |
0.053 |
0.149 |
Junior wives |
0.159 |
0.158 |
Type of residence: |
Urban |
0.132 |
0.229 |
Rural |
0.868 |
0.771 |
Previous child loss: |
None |
0.669 |
0.568 |
1+ |
0.331 |
0.433 |
Education: |
None |
0.368 |
0.565 |
Primary |
0.480 |
0.398 |
Secondary |
0.148 |
0.030 |
More than secondary |
0.003 |
0.007 |
Education (husband): |
None |
0.168 |
0.394 |
Primary |
0.555 |
0.453 |
Secondary |
0.264 |
0.118 |
More than secondary |
0.013 |
0.034 |
Fertility preferences/contraceptive need: |
No need |
0.125 |
0.211 |
Spacing need |
0.316 |
0.539 |
Stopping need |
0.558 |
0.249 |
Preferences/contraceptive need (husband): |
No need |
0.114 |
0.228 |
Spacing need |
0.403 |
0.591 |
Stopping need |
0.483 |
0.181 |
Contraceptive use: |
0.206 |
0.059 |
Means: |
Age |
32.30 |
31.48 |
Age (husband) |
41.97 |
40.69 |
Number
of dyads |
1145 |
971 |
Source: 1988 Ghana Demographic and Health
Survey.
Table 2: Contraceptive Use by Selected Characteristics.
|
Kenya |
N |
Ghana |
N |
Age: |
15-24 |
0.147 |
213 |
0.048 |
189 |
25-34 |
0.204 |
465 |
0.051 |
434 |
35-44 |
0.249 |
376 |
0.081 |
260 |
45+ |
0.181 |
91 |
0.057 |
88 |
Marital status: |
Monogamous |
0.218 |
901 |
0.066 |
671 |
Senior wives |
0.093 |
60 |
0.049 |
144 |
Junior wives |
0.183 |
182 |
0.039 |
153 |
Type of residence: |
Urban |
0.328 |
151 |
0.113 |
222 |
Rural |
0.188 |
994 |
0.043 |
749 |
Previous child loss: |
None |
0.228 |
766 |
0.065 |
551 |
1+ |
0.163 |
379 |
0.050 |
420 |
Education: |
None |
0.120 |
421 |
0.029 |
549 |
Primary |
0.209 |
550 |
0.101 |
386 |
Secondary |
0.405 |
170 |
0.035 |
29 |
More than secondary |
0.505 |
4 |
0.143 |
7 |
Education (husband): |
None |
0.141 |
193 |
0.026 |
383 |
Primary |
0.155 |
636 |
0.080 |
440 |
Secondary |
0.348 |
302 |
0.078 |
115 |
More than secondary |
0.395 |
14 |
0.091 |
33 |
Preferences/contraceptive need: |
No need |
0.057 |
143 |
0.024 |
205 |
Spacing need |
0.158 |
362 |
0.052 |
523 |
Stopping need |
0.268 |
639 |
0.103 |
242 |
Preferences/contraceptive need (husband): |
No need |
0.060 |
131 |
0.018 |
221 |
Spacing need |
0.151 |
461 |
0.065 |
574 |
Stopping need |
0.287 |
553 |
0.091 |
176 |
Source: 1988 Ghana Demographic and Health
Survey.
Table 3: Logit Estimates of Contraceptive
Use.
|
Model |
|
Wife-only |
Both
spouses |
|
Kenya |
Ghana |
Kenya |
Ghana |
Intercept |
-4.310**
(0.568) |
-5.210**
(0.871) |
-4.644**
(0.699) |
-6.228**
(1.014) |
Age |
0.020
(0.012) |
0.029
(0.022) |
0.004
(0.014) |
0.034
(0.022) |
Previous
child loss (1+) |
-0.182
(0.179) |
-0.312
(0.318) |
-0.083)
(0.184) |
-0.318
(0.320) |
Marital
status: |
|
|
|
|
Junior
wives |
0.055
(0.221) |
-0.196
(0.461) |
0.058
(0.225) |
-0.079
(0.465) |
Senior
wives |
-0.563
(0.467) |
-0.055
(0.433) |
-0.503
(0.475) |
-0.009
(0.440) |
Residence
type (urban) |
0.561*
(0.222) |
0.707*
(0.299) |
0.486*
(0.232) |
0.702*
(0.303) |
Education: |
|
|
|
|
Primary |
0.818**
(0.198) |
1.152**
(0.330) |
0.602**
(0.210) |
1.010**
(0.358) |
Secondary
plus |
1.824**
(0.253) |
0.338
(0.807) |
1.453**
(0.287) |
0.385
(0.851) |
Education
(husband): |
|
|
|
|
Primary |
|
|
-0.171
(0.252) |
0.508
(0.415) |
Secondary
plus |
|
|
0.487
(0.286) |
0.250
(0.527) |
Preferences/contraceptive
need: |
|
|
|
|
Spacing
need |
1.083**
(0.399) |
0.785
(0.503) |
0.944*
(0.404) |
0.368
(0.526) |
Stopping
need |
1.938**
(0.392) |
1.237*
(0.518) |
1.567**
(0.404) |
0.865
(0.555) |
Preferences/contraceptive
need (husband): |
|
|
|
|
Spacing
need
|
|
|
0.954*
(0.395) |
1.150*
(0.563) |
Stopping
need
|
|
|
1.543**
(0.404) |
0.872
(0.628) |
-2LogL
Chi-square
df
N |
1035.85
130.37
9
1188 |
392.63
41.18
9
971 |
1000.61
165.62
13
1188 |
385.46
48.35
13
971 |
Note:a.
Omitted categories: previous child loss (none);
marital
status (monogamous); type of residence (rural); Education (none); Preference/contraceptive
need (no need--wants more children).
b.
Standard errors are in parentheses.
c.
* - significant at the .05 level.
d.
** - significant at the .01 level.
Source:1988
Ghana Demographic and Health Survey.
Table
4: Joint Contraceptive (Preferences) Need by Actual Use.
|
Kenya |
N |
Ghana |
N |
Both have stopping need |
0.310 |
453 |
0.119 |
118 |
Wife space; husband stop |
0.227 |
76 |
0.044 |
45 |
Wife no need; husband stop |
0.032 |
24 |
0.000 |
13 |
Wife stop; husband space |
0.178 |
150 |
0.083 |
97 |
Both spacing need |
0.165 |
225 |
0.063 |
396 |
Wife no need; husband space |
0.059 |
84 |
0.050 |
80 |
Wife stop; husband no need |
0.089 |
35 |
0.111 |
27 |
Wife space; husband no need |
0.050 |
61 |
0.000 |
82 |
Both have no need |
0.047 |
35 |
0.009 |
112 |
Number |
1143 |
|
970 |
|
Source: 1988 Ghana Demographic and Health
Survey.
Table
5: Decomposition of Contraceptive Use Difference.
|
Proportion
of Difference due to: |
Selected
covariates: |
Composition |
Propensity |
Interaction |
Age |
0.02 |
-0.61 |
-0.02 |
Previous child loss (1+) |
0.02 |
0.06 |
-0.02 |
Marital status: |
Junior wives |
-0.00 |
0.01 |
-0.00 |
Senior wives |
0.00 |
-0.05 |
0.03 |
Type of Residence (urban) |
-0.04 |
-0.03 |
0.01 |
Education: |
Primary |
0.05 |
-0.10 |
-0.02 |
Secondary plus |
0.03 |
0.03 |
0.08 |
Education (husband): |
Primary |
0.03 |
-0.20 |
-0.04 |
Secondary plus |
0.02 |
0.02 |
0.02 |
Preferences/contraceptive need: |
Spacing need |
-0.05 |
0.20 |
-0.08 |
Stopping need |
0.17 |
0.11 |
0.14 |
Preferences/contraceptive need (husband): |
Spacing need |
-0.14 |
-0.07 |
0.02 |
Stopping need |
0.17 |
0.08 |
0.13 |
Intercept |
- |
1.02 |
- |
Proportion of difference |
0.28 |
0.47 |
0.26 |
Proportion of difference for model excluding
male education and preferences |
0.21 |
0.55 |
0.21 |
Source: 1988 Ghana Demographic and Health
Survey.
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Copyright 1995 - Union for African Population Studies
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