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African Population Studies
Union for African Population Studies
ISSN: 0850-5780
Vol. 12, Num. 1, 1997
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African Population Studies/Etude de la Population Africaine, Vol. 12, No. 1, March/mars 1997
Trends and
Differentials in Desired Family Size in Kenya
Bolaji
M. FAPOHUNDA and Prosper V. POUKOUTA
African Population
Policy Research Centre,
A programme of the Population Council, Nairobi, Kenya
Code Number: ep97002
Abstract
The
objectives of this study are to examine the trends, assess socio-economic differentials,
and to determine whether declines in desired family size are associated with
declines in fertility in Kenya. Using the synthetic cohort analytical method,
the study found a consistent and monotonic decline in desired family size over
the periods studied. These declines are highly correlated with declines in total
fertility rate (TFR) over the same period, thereby suggesting that observed
declines in TFR are probably driven by changes in desired family size. The analysis
of the differentials reveal that women's education, women's work and income
status, ownership of durable goods, husband's desired family size, knowledge
of modern contraception and ethnicity account for significant variations in
desired family size.
Les
objectifs de cette étude consistent à examiner les tendances,
à évaluer les différentiels socio-économiques, et
à déterminer si les baisses de la taille désirée
de la famille sont associées à celles de la fécondité
au Kenya. Utilisant la méthode analytique de la cohorte synthétique,
l'étude a trouvé une baisse constante et uniforme de la taille
désirée de la famille au cours des périodes étudiées.
Ces baisses sont en étroite corrélation avec les baisses du taux
global de fécondité au cours de la même période,
au point de faire croire que les baisses observées au niveau du TGF sont
probablement provoquées par des changements au niveau de la taille désirée
de la famille. L'analyse des différentiels révèle que l'éducation
des femmes, le travail et le niveau du revenu des femmes, la possession de biens
de consommation durable, la taille de la famille désirée par le
mari, la connaissance de la contraception moderne et l'ethnicité expliquent
les fortes variations enregistrées au niveau de la taille désirée
de la famille.
Introduction
Recent
data show that fertility is declining in Kenya amidst speculations that such
a decline would be impossible, given the constellation of cultural forms and
norms that provide support for high fertility (Caldwell and Caldwell, 1987;
Van de Walle, 1990). Such norms are said to be relatively impervious to social,
economic and political changes and in countries, such as Kenya, where the majority
of the population live in rural areas, such norms should cause transitory increases
in fertility (Ascadi et al., 1990). However, recent declines in fertility in
Kenya provide no support for this speculation. Therefore, to explain the current
declines in fertility, one could look into the structure of demand for children
and examine how this has changed over time. By explaining trends and differentials
in desired family size (DSF), one may gain insight into the causes of the fertility
transition that is currently underway in Kenya.
Demand
for children is a major component of completed fertility (Bongaarts, 1995).
A change in demand for children should lead to a change in supply of children,
ceteris paribus. Therefore, an explanation of changes in demand should
yield an understanding of changes in the supply of children. This assumption
forms the basis of this study. Moreover, an understanding of changes in demand
for children could also provide insight into women's attitude toward future
child-bearing, desired completed family size and the structure of demand for
contraception. When desired fertility is high, it provides the motivation to
raise large numbers of children; when low, it should motivate women to apply
fertility control measures. Thus, an analysis of desired family size (DFS) could
enhance our understanding of women's attitude towards fertility control and
of their intention to use contraception because expressed preferences regarding
desired family size should be related to behaviour. The objectives of this study,
therefore, are to: (1) examine trends in desired family size; (2) assess the
socio-economic and demographic differentials in desired family size; and (3)
determine factors which influence desired family size.
The
paper is divided into three sections. Following the introduction, Section Two
presents the literary review and then discusses the conceptual framework. The
results are presented in the third and final section.
Literature
review
The
following questions are often used in Demographic and Health Surveys (DHS) to
gather information on DFS: women with children are asked: "if you could go back
to the time you did not have any children and could choose exactly the number
of children to have in your whole life, how many would that be?" Childless women
were asked: "If you could choose exactly the number of children to have in your
whole life, how many would that be?" The reliability of these questions and
the validity of measures based on them have received tremendous attention in
the literature (Bushan and Hill, 1995; Ascadi and Ascadi, 1990; Van de Walle,
1992; Bongaarts 1992; Ware, 1974). Two main criticisms have been levelled against
the conventional measurement of DFS. One relates to questions of post-facto-rationalization
bias and the other to the ability of respondents to give numeric responses to
questions related to DFS, especially in settings where fertility decision-making
is beyond the control of the individual women. Ascadi et al. (1990) said that
the ability to respond to such queries is questionable in societies where fertility
is controlled by lineage, ancestors and gods--agents who do not recognize individual
desires in fertility decision-making, and where fertility control is not widespread.
Mhloy
(1994) argued that the conventional approach measures DFS at a fixed point in
time, which assumes that a woman adopts the same fertility behaviour, and has
the same views about the social and economic value of children throughout her
reproductive life, whereas fertility preferences are constantly reviewed over
the life course. Similarly, Rasul (1993) argued that the conventional measure
of DFS is econocentric in that individuals, as decision-makers, are assumed
to carefully weigh costs and benefits of making choices to satisfy personally-defined
objectives. He argued that overlapping cultural, socio-economic and physical
realities define the relative power of women and men in decision-making and,
therefore, that changes in the circumstances affecting women could cause them
to revise their fertility preferences over the life course irrespective of market
considerations. Kent and Larson (1982) argued that the existence of ex-post-rationalization
complicates the relationship between actual and desired family size and that
it is not clear whether the close relationship often observed between stated
and actual family size is fortuitous or systematic. Bushan and Hill (1995) argued
that responses to questions on DFS are characterized by a great deal of ambivalence
because individuals are not sure of what might happen in the future and that
is why individuals say that the number of children to have is up to God. They
argued that this uncertainty accounts for the high incidence of non-numeric
answers to questions on DFS. It logically follows that the lower the proportion
giving non-numeric answers as in the case of Kenya, the higher the reliability
of the answers, and the less the role played by ancestors and Gods in fertility
desires. On the basis of this logic, the measure of ideal family size is likely
to be highly reliable in Kenya because the proportion giving non-numeric answers
is one of the lowest in sub-Saharan Africa.
Two
alternative measures of DFS have been proposed in order to remove the ex-post-rationalization
and the uncertainty biases. These are: the wanted fertility (WF) method developed
by Bongaarts (1992) and the prospective desired total fertility rate (PDTFR)
developed by Bushan and Hill (1995). But none of these alternatives is a better
measure of DFS than the conventional approach. Both alternative measures are
based on the wantedness of children which may also be rationalized for the same
reason that children are given by God and that their wantedness is beyond the
scope of the individual's of choice. Moreover, ideal/desired family size is
affected by the ceteris paribus assumption and/or ex-post-rationalization
bias no more than questions eliciting information about the future, including
but not limited to questions on contraceptive intention, the preferred timing
of the next birth, or the wantedness of children, etc. Responses to these questions
are widely used in demographic analyses. Besides, unless there is sound argument
to the contrary, biases introduced by the ceteris paribus assumption and ex-post-rationalization
bias can be assumed to be randomly distributed in the population and, as such,
should not lead to any systematic error in the data. Even if we assume that
it is not, only a small segment of the population--mostly older women who have
completed fertility--will be affected (see also McCarthy et al., 1987; Ahmed,
1990).
DFS
has several advantages over the alternative measures. First, it is a measure
of fertility desires; there is no measure that provides an equally effective
index of the potential for change in family size in the developing countries
(Ware, 1974). Secondly, it reflects the norms and culture of a place (Bankole
and Westoff, 1995), particularly, those that are related to the value of children
(Kent and Larson, 1982). Thirdly, the literature reports significant correlation
between DFS and fertility behaviour in different contexts. For example, Farooq
et al. (1987) found considerable correspondence between DFS and contraceptive
use in Egypt. The study indicated that 66% of the low-income rural women who
said they wanted no more children were using modern contraception compared with
2% among those who said they wanted more. DFS was found to have a stronger effect
on contraception than education and place of residence. Similarly, an analysis
of DHS data from 18 developing countries revealed considerable agreement between
stated preferences and demographic behaviour among women in these countries
(Bongaarts, 1991). The study found that 85% of the respondents whose actual
fertility exceeded their DFS said they wanted no more children. The inconsistences
observed among the remaining 15% were attributed to unachieved sex composition
preferences and to real life constraints which inhibited the realization of
stated preferences.
Using
the analysis of a longitudinal data set from Thailand, Knodel and Prachnabmoh
(1977), argued that the contention that respondents rationalize their DFS on
the basis of their actual family size is not valid, and that if post-facto-rationalization
bias existed at all, only a small segment of the population would be affected.
They argued that the fact that women with a given number of children state that
number as their DFS indicates that DFS is related to actual fertility. It does
not indicate that DFS is determined by actual family size or vice versa.
Similarly,
McCarthy et al. (1987) demonstrated with data from Nigeria that the high incidence
of non-numeric responses in DFS data is not due to uncertainty about DFS nor
to the misunderstanding of the questions but, rather, to a feeling of lack of
control over the number of children to have. Therefore, no-numeric answers are
valid answers according to these authors. For instance, a multivariate analysis
of data from Bangladesh revealed significant effects of completed family size,
employment status, age and education on the odds of giving non-numeric responses.
The
argument of ex-post-rationalization bias could also be an artifact of the demographers'
mind-set. This is because whenever the stated DFS is lower than actual fertility,
responses are assumed to be valid but whenever the stated DFS is greater than
actual fertility, especially for women at the tail-end of their reproductive
career, the response is said to have been affected by ex-post-rationalization
bias. Campbell (1994) pointed out that findings from research conducted among
3,006 men and 539 women in Sierra Leone indicated that men and women whose desired
family size exceeded their actual family size actually stopped child-bearing
before they reached their DFS and not because they rationalized DFS on the basis
of actual fertility. In earlier studies, Palmore et al. (1981) and Pullum (1981)
argued that respondents made different assumptions about their existential reality
in answering questions regarding DFS. For instance, in answering questions on
desire for additional children, respondents stated their preferences taking
into consideration real world constraints, such as their age, financial implications
of additional children, marital problems, etc. Such constraints are not considered
in answering questions on the total number of children desired, according to
these authors. Given this explanation, it is conceivable that there are situations
in which DFS exceeds completed fertility without necessarily resulting in post-facto-rationalization
bias.
Moreover,
recent data from around the world are increasingly showing that questions on
DFS are meaningful to respondents (Zick and Xiang, 1995; Van de Walle et al,
1992; Cleland, 1992; Hardee-Cleveland, 1982; Cochrane et al., 1990; McCarthy
et al., 1987; Zehri et al., 1988). These studies found significant variation
in DFS by age, sex, education, place of residence, mass media exposure, age
at first marriage, expected education for children, extended family ties and
number of surviving children. Other factors that have been found to be highly
correlated with DFS are gender composition of children, knowledge of modern
contraception, infant mortality and old age security (Smith, 1993; Pust et al.,
1985; Mousa, 1987).
Using
data on three West African nations, Benefo (1990) found that differentials in
women's socio-economic status account for much of the variation in DFS. The
effect of contextual factors such as religion and kinship ties were significant.
In Nigeria, McCarthy and Oni (1987) found that the number of surviving children,
women's education and sex preferences significantly affect DFS. Also the analysis
of data on young people in Kenya revealed significant negative effects of age,
education, mass media exposure and modern orientation on DFS. Of these variables,
education and age have the strongest effects (Musyoki, 1983).
However,
studies exploring the correlates of DFS and the linkages between DFS and actual
fertility in Kenya are limited. Yet such studies are necessary to understand
the on-going fertility transition, identify the determinants of DFS and to provide
insight that could inform policies and programmes. This study is intended to
fill this gap.
Conceptual
framework
The
theory of fertility decision-making propounded by Easterlin is applied as an
organizational framework in this research. This application borrows significantly
from the work of Zick and Xiang (1994) who applied this theory in explaining
the correlates of desired family size, using Chinese data. In its original formulation
by Easterlin, the theory postulates that demand for children is affected by
three proximate factors; price, income and tastes. The price of children is
composed of the direct expenditure on children and the opportunity cost of time
spent bearing and rearing children. The opportunity cost refers to both economic
and psychological costs of alternatives forgone. The underlying assumption is
that the higher the price, the lower the demand for children, given the household
income and tastes. Household income is also hypothesized to affect demand for
children. An increase in income is expected to lead to an increase in demand
for children. The third component of Easterlin's theory is taste for children.
Taste refers to the subjective preferences for children compared to general
goods. The greater this preference, the greater the demand for children (see
also Caldwell and Caldwell, 1987).
All
three factors are measured in relation to other goods. In other words, children
are assumed to be affected by market forces just as other goods are. But one
major advantage of the Easterlin model over other economic theories of fertility
decision-making (see Becker, 1981; 1991) is that he integrates the economic
and sociological factors in explaining fertility behaviour (see also Zick and
Xiang, 1994). Although the model has been criticized for not specifying factors
which are important in different contexts (see Hardee-Cleveland, 1982), the
integration of economic and sociological factors makes the theory appropriate
in settings (such as Kenya) where market forces are conditioned by socio-cultural
factors (see also Blake, 1986).
In
measuring these three components, we relied on proxies as is the practice in
most studies that have applied this theory, because most data sets do not contain
direct measures of the different dimensions of the theory. For example, parental
aspiration for sons/daughters' education could have been an appropriate measure
of expenditure on children in the context of Kenya, but this variable is not
included in data analyzed in this paper. In spite of these limitations, the
analysis indicates that the theory is applicable in the context of Kenya and
most of the hypothesized relationships are supported by data.
Opportunity
cost is measured using information on whether women work and earn income (see
also Zick and Xiang, 1995). The assumption is that those who work and earn income
are likely to have a higher opportunity cost than those who do not work or those
who work but earn no income and, as such, will be more likely to desire fewer
children. Another dimension of the opportunity cost measured in this study is
the psychological dimension. Zick and Xiang measured this dimension by using
data on the type of households in which women live. They hypothesized that the
pressure to raise larger numbers of children is likely to be greater for women
who live in extended family environments than for women who live in nucleated
family environments. This logic is applied in this study. However, we used place
of residence as a proxy for the psychological cost dimension. We hypothesized
that women residing in rural areas are more likely to live with other family
members than those located in the urban areas and, as such, the desire for large
numbers of children is likely to be higher in the rural than in the urban contexts.
In addition, the influence of normative values supporting high fertility are
likely to be higher in the rural than in the urban areas because of the constellation
of factors that help support high fertility. Therefore, the psychological cost
of living in extended family environmentsin the rural areas is likely to be
much higher than the psychological cost of living in extended family environments
in the urban areas Those cultural norms and rules of behaviour are likely to
have been altered by the forces of modernization in the urban place. Therefore,
we expect women living in urban areas to have smaller desired family size than
those in the rural areas.
Taste
for children is measured using indicators of traditional pronatalist values.
These are type of marriage and religion. Persons who are polygynously married
are likely to be more pronatalist and, as such, to have higher desired family
size than persons who are monogamously married. Religion also measures attachment
to cultural practices that are supportive of large family sizes. Therefore,
we expect Catholics and Protestants to desire smaller family size than women
affiliated with traditional religion.
Two
indicators of income are used in this study. These are women's educational attainment
and household wealth. The latter measures physical wealth (see also Zick and
Xiang, 1994). Education is used as a measure of human capital. It is also highly
correlated with income. Household wealth is measured using ownership of durable
goods. Six of such goods are measured: electricity, refrigerator, television,
radio, bicycles, and type of toilet facility; flush or pit. Similar measures
were used as indicators of household wealth by Zick et al. (1994) in China and
by Speizer (1995) in a study conducted in three francophone African countries:
Burkina Faso, Cameroon, and Niger. Zick and Xiang (1994) explained that the
relationship between income and demand for children is not necessarily linear.
An increase in income may not necessarily lead to an increase in demand for
children because individuals may choose to invest in the quality of surviving
children rather than have another child. Thus, it is important that researchers
bear this alternative income effects in mind in applying this theory.
In
addition to these dimensions, other socio-demographic controls are included
in the analysis in order to understand the impact of the context and women's
experiences on their fertility desires. These variables include ethnic affiliation,
husband's desired family size, women's age, number of surviving children, infant/child
mortality and knowledge of modern contraception. These variables have also been
demonstrated by previous research to have significant effects on desired family
size. For instance, women's perceptions of their husbands' fertility desires
are likely to shape their own reproductive choices (Muvandi, 1995). In addition,
it is important to include ethnic affiliation in a study such as this because
it is a measure of the context of women's lives (Kritz, et al, 1997, Caldwell
and Caldwell, 1887). It captures norms, beliefs, and perceptions which are difficult
to measure in standard surveys, such as the ones analyzed in this study, but
which, nonetheless, influence the individual's reproductive attitude and behaviour
(Njogu, 1991). Furthermore, because ethnic groups in Kenya speak the same ethnic
language, have similar cultural practices and live within the same or contiguous
districts, the flow of ideas about small family sizes are likely to be faster
within than across ethnic groups. Also, ethnic boundaries are co-terminus with
political/administrative boundaries to a large extent in Kenya and, as such,
access to education, health and infrastructural development is likely to vary
by ethnicity. For example, the Kikuyu, who coincidentally live in the central
province, are likely to have access to better infrastructure because they had
an early access to colonial education and political power than did many other
ethnic groups in the country. Therefore, ethnic affiliation also reflects, albeit
indirectly, differentials in access to socio-economic development across the
country.
Data
This
paper uses data from the 1989 and 1993 KDHS standard recode files. Data are
also obtained from published reports on the Kenya World Fertility Surveys (KWFS)
and the Kenya Contraceptive Prevalence Survey (KCPS). These data sets are used
in order to examine trends in the desired family size in the country. The 1993
standard recode file is used to explain the differentials and the key factors
in desired family size because it is the most recent nationally representative
data set on Kenya. The sample consists of 4,629 currently married women who
are within the age of 15-49. The KDHS data set is weighted to correct the unequal
probability of sampling among strata (National Council for Population and Development
et al, 1994). That weight is applied to the estimates presented in this work.
Measurement
of variables
The
dependent variable analyzed in this paper is the desired family size. It is
an interval variable measuring the number of children desired by individual
women. Another dependent variable also used in this analysis is preference for
no more children. Preference for no more children is constructed from responses
to the following questions: those who were not pregnant or unsure were asked:
"...Would you prefer to have a/another child or would you prefer not to have
any more children?" The pregnant women were asked: "After the child you are
expecting, would you like to have another child?" Desire for additional fertility
is a dummy variable coded "1" if women said they wanted no more children and
"0" otherwise. Although this is not a measure of desired family size, it indicates
what women are willing to do in the immediate future, given their current fertility.
If the result of the analysis of preference for additional fertility is consistent
with findings on desired family size, it will enhance the reliability of the
result. This is one major advantage of using multiple dependent variables enhanced
reliability and validity of findings. However, this variable, desire for additional
fertility, is not used in the multivariate section since the goal of that section
is to examine differentials in desired family size.
The
independent variable measured in this analysis include women's education, ownership
of durable goods (used as a proxy for household wealth), women's work and income
status, type of union, religion, husband's desired family size, place of residence,
knowledge of contraception, ethnic affiliation, and women's age. Women's education
was measured in years. Ownership of durable goods was constructed from the following
questions: (1) "What kind of toilet facilities does your household have?", (2)
"Does your household have: electricity, a radio, a television, a refrigerator",
and (3) "Does any member of your household own a bicycle." Respondents received
a score of "1" for any of these durable goods they have in their household and
"0" otherwise. The final measure consists of four dummy variables: the first
is coded "1" if a woman has none of these commodities; the second is coded "1"
if a woman has one out of six; the third is coded "1" if a woman has two out
of six and the fourth is coded "1" if a woman has 3 or more of the six items
in her household and "0" otherwise. In the multivariate analysis, the first
group is used as the comparison group. These six items are measured because
they are luxury goods. Those who can afford them must have satisfied the first
order needs: food, clothing and shelter. This measure, ownership of durable
goods, in our opinion, is a reflection of household income.
Women's
work and income status is a dummy variable coded "1" if a woman is currently
working and earning income and "0" otherwise. Type of union is a dummy variable
coded "1" if a woman is married to a polygynous husband and "0" otherwise. Religion
is measured by two dummy variables: the first is coded "1" if the woman is Catholic;
the second is coded "1" if the woman is Protestant and "0" if otherwise. The
last group, the other category, is made up of persons who belong to other religions.
This group is used as the reference category in the multivariate analysis. Knowledge
of contraception is indicated by three dummy variables: "knows no method", "knows
a traditional method" and "knows a modern method". A woman received code "1"
for each of these variables if any of these conditions applied and "0" if otherwise.
Place
of residence is coded "1" if urban and "0" if rural. Age is measured in years.
Ethnic affiliation is measured using five dummy variables. The first is coded
"1" if the woman is Kalenjin; the second is coded "1" if the woman is Kamba;
the third is coded "1" if the woman is Kikuyu; the fourth is coded "1" if the
woman is Luhya and the fifth is coded "1" if the woman is Luo. All others who
do not belong to these groups are pooled into the "other category" because members
in other groups are too few to constitute separate dummy categories.
Husband's
desired family size indexes differed between the number of children women said
their husbands desired and the number they themselves desired although it has
been suggested that wives' responses to questions on husband's fertility, attitude
and behaviour are likely to be less reliable than husbands' responses to questions
about their own desired family size. Hence, husbands' report about their own
desired family size should be used when those data are available. However, in
this study, wives responses are used because our aim is to measure women's perception
of their husbands' preferences and the effect of that perception on their own
reproductive choices. These perceptions are likely to have significant effects
on wives' behaviour since husbands' actual preferences may be unknown if spousal
communication is rare. Another reason for using the wives' responses relates
to data. The KDHS collected data on only 25% of the husbands. Therefore, the
use of data on a couple could lead to a significant loss of information due
to sample attrition. Moreover, a comparison of spousal responses to questions
on fertility preferences indicates a great degree of agreement between wives'
and husbands' responses. The final measures consist of three dummy variables:
the first is coded "1" if the number of children desired by the husband is greater
than the number desired by the wife; the second is coded "1" if the husband's
desired family size is smaller than the wife's and the third is coded "1" if
both husband and wife desire the same number of children. This last category
is used as a reference category in the multivariate analysis. Some of the variables
described above are included as controls in the multivariate context. Tables
1 and 2 present a descriptive analysis of these variables by desired family
size.
The
variables described above are used in the bivariate and multivariate analyses.
Other variables included for the purpose of describing bivariate differentials
in desired family size are age at first marriage, number of times married, literacy
(used as an additional measure of education), number and composition of living
children, number of children who have died and contraceptive use. Age at first
marriage is measured in years. Number of times married is coded "1" if the woman
has been married once and "0" if she has been married twice or more. Literacy
is coded "1" if the woman says she can read and write easily and "0" otherwise.
Number and composition of children are the actual number of sons and daughters
alive at the time of the 1993 KDHS survey. Number of children who have died
measures the actual mortality experience of women.
Two
dimensions of contraceptive behaviour are measured: "ever" and "current" use.
"Ever use" is indicated by three dummy variables. The first is coded "1" if
the woman has never used any method; the second is coded "1" if she ever used
a traditional method and the third is coded "1" if the woman ever used a modern
method. Similarly, three dummy variables measure current contraceptive practices:
currently using no method, currently using a traditional method and currently
using a modern method. A woman receives code "1" if any of these conditions
tended to be applicable. The categories are mutually exclusive. In other words,
a woman who says she is currently using no method cannot say that she is also
currently using a modern method. Although a woman may be using traditional and
modern methods concurrently, such women are automatically coded as currently
using a modern method, thus avoiding multiple scoring for the same respondent.
Method
of analysis
Two
goals are pursued in this paper. The first is to explain trends in desired family
size and the second is to explain the differentials. The former is achieved
by using a synthetic cohort analytical technique. This procedure permits a quantitative
description of temporal variations in the experiences of synthetic cohorts (see
also Halli and Rao, 1992). Using data from KWFS (1978), KCPS (1984), and KDHS
(1989, 1993) surveys, we constructed synthetic cohort data sets for women aged
between 15-19 and 30-34 in 1978.
To
explain the differentials in desired family size, we used both bivariate and
mutivariate procedures. The bivariate procedure consists of summary statistics
such as percentages, means and standard deviations. Ordinary least square regression
was used to verify differentials observed in the bivariate context. The results
of these analyses are presented in Table 3.
Results
Trends in desired family size
Figure
1 below traces changes in desired family size from 1978 to 1993 for cohorts
15-34. This Figure demonstrates a consistent and sharp decline in desired family
size over the periods studied. For cohort 15-19 in 1978, the average desired
family size declined from 6.5 in 1978 to 5.6 in 1984, with a difference of about
1 child. By 1989, the desired family size was 4.6 and it reached 4.0 by 1993.
So over the 15-year period studied, we observed a decline of about 2.5 children
in average desired family sizes, from a high of 6.5 in 1978 to a low of 4.0
in 1993 for this cohort of women.
For
the cohort 20-24 in 1978, their desired family size declined from a high of
6.3 in 1978, 5.9 in 1984, 4.9 in 1989 to 4.1 in 1993. As in the cohort 15-19,
we observed a decline of over 2 children in average desired family size for
this cohort. The pattern of change was slightly different for older cohorts.
For instance, the cohort 25-29 in 1978 experienced no decline in desired family
size between 1978 and 1984. However, by 1989, the desired family size for this
cohort had declined by 1.6 children and by 1993, the number of desired family
size stood at 4.1., with a decline of about 2.2 children from the 1978 levels.
The last cohort, 30-34 years old in 1978, also behaved like the previous one.
The desired family size for this cohort was 7.2 in 1978. By 1984, it was 6.9,
thus representing a very slight difference from the 1978 level. However, by
1989, the desired family size had declined to 5.5 children on average, with
a decline of about 2 children over this interval. The next five-year period
saw an additional decline of 1 child in desired family size so that by 1993,
the desired family size had declined to a low of 4.4 children.
We
also analyzed synthetic cohort differences in the percentage of women wanting
no more children. The results are presented in Figure 2. Figure 2 presents data
for cohorts 15-19, 20-24, 25-29, 30-34, and 35-39 years old in 1984. As shown
in Figure 2, the percentage of women wanting no more children rose significantly
from 3.8 in 1984 to 18.8 in 1989 and to 46.1 in 1993 for the cohort 15-19. By
1993, the percentage wanting no more children among this cohort had increased
more than 12 times over the 1984 level.
Figure
2 also revealed very significant changes in demand for no more children for
the cohort 20-24. The percentage wanting no more children increased from a low
of 10.7 in 1984, 39.8 in 1989 to a high of 60.8 in 1993. Between 1984 and 1993,
the percentage tripled and the 1989 figures doubled by 1993. Among women aged
25-29 in 1984, the percentage wanting no more children rose from 23.4 to 58.2
in 1989 and to 74.7% in 1993. Similar increases were recorded among women between
30-34 and 35-39 years old in 1984. Among this cohort, the percentage increased
from 45 in 1984 to 70.9 in 1989 and to 80.6 in 1993. Among the 35-39 age group,
the percentage of women wanting no more children increased from 53.7 percent
to 84.7 in 1989. The percentage declined by about 8 points between 1989 and
1993, from 84.7 to 76.7. Except for this cohort, the percentage wanting no more
children increased monotonically during the periods studied. These increases
were
consistent
with the changes observed in desired family size, thus indicating a great deal
of consistency in women's fertility attitude during the period.
It
is plausible that a sequence of attitudial changes beginning with a transition
from high DFS to low DFS is a precursor to fertility transition in Kenya. This
seems to be borne out by data from different time periods presented in Figure
3. The observed declines in DFS over the past 15 years, and especially between
1989 and 1993, are closely correlated with declines in actual fertility for
every synthetic cohort. This is a clear indication that changes in desired family
size can and do affect changes
in
fertility. Both the TFR and desired family size started from a very high level
in 1978 and declined monotonically toward low levels in 1993, thus indicating
that observed declines in fertility are driven by the changes in desired family
size.
Differentials
in desired family size
Tables
1 and 2 present differentials in desired family size in 1993 by selected background
characteristics of the respondents. The dependent variable is divided into three
categories: (1) those who gave non-numeric responses, (2) those who said they
wanted three children or fewer, and (3) those who said they wanted four to six
children or more. According to Table 1 (row 1), only 6.1% of the respondents
gave non-numeric responses to the question or desired family size. This finding
indicates that questions on desired family size are not only meaningful to women
but also that the idea of numeracy about children has evolved, an attribute
which Van de Walle (1992) said is a precursor to fertility transition. 36% of
the sample opted for three or fewer children and 58%, for 4-6 or more children.
Given the high percentage of women wanting 4-6 children or more, Kenya is still
a pronatalist country. Compared to other countries in the region, however, they
are far ahead in terms of becoming numerate about their desired family size.
Column
2 of Table 1 describes women who gave non-numeric answers by selected background
characteristics. According to Table 1, women who own no durable goods, live
in rural areas, or who are not literate are more likely to give non-numeric
responses than women who own one or more durable goods, can read and write,
or who are urban. The percentage of non-numeric responses is also higher among
women in polygynous unions, who knew no method of family planning and never
used any contraception than it is among women who knew a method of family planning,
ever used one, or who are monogamously married. The percentage is also higher
among women who did not know the husbands' desired family size than among those
who knew it. Women who gave non-numeric responses were also older (their mean
age is 34 years whereas the mean age in the sample is 31 and had larger families
than those who gave numeric answers (Table 2). These findings indicate that
women who gave non-numeric answers were more traditional and older than an average
woman in the sample. Therefore, this percentage can be expected to decrease
as the population in place ages out and women became more numerate as they become
younger and better informed about population issues.
Table
1: Desired Family Size by Selected Background Characteristics, Currently
Married Women* (Percentage), 1993
|
Desired
Family Size
|
Background
Characteristics
|
Non-Numberic
Values
|
0-3
|
4+6+
|
No
of Cases
|
Total
Literacy
Level
Cannot
read/write
Read/write
Owns
Durable Goods
Owns
0 of 6
Owns
1 of 6
Owns
2 of 6
Owns
3+ of 6
Work
and Earning Income
Yes
No
Spousal
Educ. Gap
Same
educ. level
Women
more educated
Husb.
more educated
Husb.
Desired Family Size
Husb
wants same as wife
Husb
wants more than wife
Husb
wants fewer than wife
Don't
know
Contraceptive
Knowledge
None
Knows
Traditional Method
Knows
Modern Method
Ever
Used Contraception
Never
Used
only Trad. Method
Used
Modern Method
Contraceptive
use
None
Using
Trad. Method
Using
Modern Method
No.
of Time Married
Once
Twice
or more
|
6.1(.24)
9.2(.28)
3.3(.18)
14.4(.44)
6.5(.45)
5.16(.45)
3.6(.37)
5.5(.23)
6.6(.25)
7.3(.26)
5.0(.21)
5.7(.23)
2.9(.16)
8.4(.28)
0.9(.09)
12.2(.33)
25.5(.44)
8.7(.30)
5.5(.23)
9.5(.29)
4.7(.21)
2.8(.16)
7.7(.26)
3.0(.17)
2.8(.16)
6.0(.24)
7.4(.27)
|
36.0(.47)
23.9(.43)
46.5(.50)
15.5(.21)
30.6(.42)
37.9(.48)
46.8(.48)
38.8(.48)
33.7(.47)
35.0(.48)
40.2(.49)
35.3(.48)
42.4(.49)
29.9(.46)
39.0(.49)
26.0(.44)
10.8(.31)
2.2(.16)
36.8(.48)
25.5(.44)
30.8(.46)
48.5(.50)
30.0(.46)
34.9(.48)
51.0(.50)
36.4(.48)
31.2(.46)
|
58.0(.49)
66.9(.67)
50.1(.50)
70.1(.34)
62.9(.46)
57.0(.47)
49.7(.43)
55.7(.49)
59.8(.49)
57.8(.49)
54.7(.50)
59.0(.49)
54.7(.50)
61.7(.49)
60.1(.49)
61.8(.49)
63.7(.48)
89.1(.33)
57.7(.49)
64.9(.48)
64.5(.48)
48.7(.50)
62.4(.48)
62.0(.49)
46.3(.50)
57.7(.49)
61.4(.49)
|
4629
2162
2467
513
1255
1566
1294
2569
2060
1173
792
2562
2397
661
315
1254
129
15
4483
2072
583
1972
3113
252
1263
4308
320
|
Type
of Union
Monogamous
Polygamous
Place
of Residence
Urban
Rural
Religion
Catholic
Protestant
Other
Ethnicity
Kalenjin
Kamba
Kiluyu
Luhya
Luo
Other
N
57.3(.49)
60.8(.49)
35.2(.48)
62.0(.49)
59.8(.49)
57.1(50)
57.8(.49)
70.7(.46)
56.3(.50)
43.6(.50)
59.7(.49)
64.4(.48)
58.7(.49)
2,684
|
3724
904
696
3932
1409
2761
458
577
600
842
783
566
1262
4629
|
|
|
|
Source:
Kenya DHS, 1993
*
Figures in parentheses are Standard Deviations
Table
2: Desired Family Size by Selected Background Characteristics, Currently
Married Women* (Means), 1993.
|
Desired
Family Size
|
Background
Characteristics
|
Non-Numeric
Values
|
0-3
|
4-6+
|
Total
|
No
of Cases
|
Mean
Yrs of Educ.
Mean
Yrs of Husb. Educ.
Mean
Age
Age
at 1st Marriage
Mean
No. of Sons
Mean
No. of Daughters
Mean
No. of Children Dead
Mean
No. Living Children
|
3.0
(3.56)
5.0
(4.15)
34.2
(8.54)
17.1
(4.01)
2.2
1.73)
2.5
(1.88)
0.9
(1.31)
4.7
(2.89)
|
7.0
(3.58)
8.4
(3.67)
29.6(7.72)
18.9
(3.40)
1.6
1.42)
1.6
(1.49)
0.3
(0.73)
3.3
(2.32)
|
4.7
(3.69)
6.5
(3.78)
31.4
(8.29)
17.8
(3.36)
2.1
(1.71)
2.2
(1.75)
0.5
(0.95)
4.3
(2.75)
|
5.3
(3.76)
7.1
(3.80)
30.9
(8.19)
18.2
(3.46)
1.9
(1.63)
2.0
(1.69)
0.5
(0.92)
3.9
(2.66)
|
4629
4528
4629
4629
4629
4629
4629
4629
|
Columns
3 and 4 of Table 2 compare women who wanted three or fewer children with those
who chose 4-6 or more children by selected socio-economic and demographic characteristics.
Women who wanted 3 or fewer children were younger, married later and had lower
actual fertility than those who opted for larger families. The percentage currently
using contraception is higher among women who chose 0-3 (51%) children than
among those who wanted larger numbers of children (46.3%), but the "ever use"
rates are equal among both groups of women (Table 1). Although the difference
in "current use" rates in these two samples is not large and it does indicate
that the discontinuation rate is higher among women who demand large numbers
of children and that women who wanted smaller families are likely to be more
consistent contraceptive users than those opting for a large family size. Women
who stood for three or fewer children are better educated or are married to
husbands with higher education, wealthier and more urban than other categories
of women. These results indicate that the modernizing effects of education
and
urban residence are correlated with the desire for smaller families.
We
also examined the effect of husband-desired family size on wive's reproductive
preferences. Table 1 reveals that, overall, the percentage opting for 4-6 or
more children is larger than the one for three children or fewer among the women
who did not know the husband's desired family size or who said their husbands
wanted more children they expected. In other words, women tend to accept smaller
families when their husbands want fewer or the same number of children as their
wives; they are least likely to opt for smaller families when they do not know
their husbands' fertility preference.
The
results of the multivariate analysis are presented in Table 3. The model explains
18.6% of the variance in desired family size (p<.001). All the independent
variables have significant effects on desired family size. The exceptions are
age, type of union and the child mortality indicator (as described earlier).
We observed no significant differences in desired family size among polygynous
and monogamously married women. Thus, our hypothesis that polygynous women are
more likely to display pronatalist orientations and, as such, are more likely
to have higher desired family size than their counterparts in monogamous marriages
is not supported by data. Polygynous women should be more likely to compete
with co-wives for their husbands' affection and raising large numbers of children
is perceived as one way of winning their husbands' love (see also Mason, 1993,
1987; Cain, 1993). Although the demographic literature indicates that polygynous
women tend to have lower completed fertility than monogamous women (Ascadi and
Ascadi, 1990), this is due to their relatively lower exposure to intercourse
rather than a lower desire for children. However, our finding could be due to
the fact that most polygynous marriages are contracted for economic reasons
and, as such, polygynous women may not be significantly different from their
counterparts in monogamous unions in the extent to which they possess pronatalist
values. Additionally, the monogamous status is temporary in that currently monogamous
man could take another wife as soon as he was economically capable of doing
so (see also Speizer, 1995). Similarly, age has no significant effect on desired
family size among the women, but the relationship is positive, thus indicating
that the older the women, the larger the number of children desired. Also, the
effect of child mortality experienced by women is positive but not significant.
However,
education and household wealth (both of which are proxies for women's income)
are negatively related to desired family size, as expected. An additional year
of education reduces desired family size by .08 (p<.01) children. Similarly,
the impact of household status is large and significant. Women who own one or
two of the durable goods wanted .27 and .31 fewer children respectively, than
those who have none of the items measured. Those who own 3 or all six goods
measured wanted .34 fewer children than those who own none of the six durable
goods in their households. These differentials are statistically significant
at .01 alpha level (see Table 3). Therefore, the more goods individuals have
in their households, the fewer the number of children desired. The classical
economic hypothesis that an increase in income should lead to an increase in
demand for children is not supported by these data. However, a variant of this
hypothesis which states that an increase in income could cause individuals to
increase investment in child quality rather than quantity is supported by the
findings of this research. Indeed, although it has not been documented, it is
plausible that the desire to educate children and increase their access to opportunities
is a major force driving the engine of the fertility transition in Kenya. Parents
want their children to have a better life than they themselves have. Land used
to be a major endowment parents passed on to their children to guarantee a better
life for them in the past (Mburugu, 1994, Mloyi, 1992). But, given the declines
in land holdings and changes in the land tenure system in Kenya, parents are
increasingly choosing to invest in their children's quality by giving them better
education (see also Mburugu, 1994, 1996). Although not directly tested in this
study, scholars have suggested a strong association between land shortage and
parental educational aspiration for children in Kenya. For instance, Mloyi (1994)
argued that in areas of considerable land shortages,"... education may have
replaced land as an inter-generational status transfer" (pp. 8). Therefore the
motivation to educate children could account for the preference for smaller
family sizes.
Table
3 Parameter Estimates of the OLS Regression of Number of Children Desired
on Indicator Variable for Currently Married Women.
Independent
Variables
|
Desires
Family
Size
|
Women's
education (in yrs)
Owns
1 of 4 durable goods measured
Owns
2 of durable goods measured
Owns
3 or more of durable goods measured
(Ref
category : Owns none of 4 durable goods
Women
work and earn income
(Ref
category : Women have no income)
Place
of residence : = 1 if Urban
Type
of Union : 1 = Monogamous
Religion
Protestant
Catholic
(Ref.
category=Other)
Husband's
desired Family Size
Does
not know husband's ideal family size
Husband's
ideal fam. size > respondent
(Ref.
Category : Husband's ideal fs is same as or fewer than wife's)
Other
Demographic Variables
Age
(in years)
Child
Mortality (= 1 if woman has lost 1 or more children and 0 otherwise)
Knows
a modern method of contraception :=1 if yes
Ethnicity
Kalenjin
Kamba
Kikuyu
Luhya
Other
(Ref.
category=Luo)
Constant
Adj
R Squared
No.
of cases
|
-.07**
-.30**
-.34**
-.37**
-.08*
-.51**
.04
-.32**
-.28**
-.20**
.14**
.00
.08
-.41**
-.07
-.50**
-.62**
-.26**
-.27**
5.3**
0.186**
4,348+
|
Source: Kenya
DHS, 1993, * Significant at .05 alpha level, **Significant at .01 alpha
level + This excludes the 281 women who gave non-numeric answers.
Furthermore,
the analysis revealed that Catholics and Protestants desire significantly fewer
children than women affiliated with other traditional religions. Catholic and
Protestant women wanted .29 and .33 (p<.01) fewer children, respectively,
than other categories of women. The magnitude of the effect is greater among
Protestants probably because Catholics tend to subscribe more to pronatalist
values than Protestants. These differentials in desired family size by religion
are consistent with our expectation that taste is significantly shaped by religion
and that persons affiliated with other more traditional religions will be more
likely to opt for larger numbers of children than those who are Catholics or
Protestants.
Place
of residence and women's work and income status are used as proxies for the
opportunity cost of time spent producing children. As expected, these variables
have significant inverse effects on DFS. Women who live in the urban areas or
who work and earn income want 0.51 and .07 fewer children, respectively, than
other categories of women.
Women's
desired family size is significantly affected by their husband's reproductive
preferences. When wives believed their husbands want more children than they
do, they demanded 0.14 (p<.05) more children than their counterparts whose
husbands wanted fewer or the same number of children as they did. The greater
effect, however, is observed among women who do not know their husbands' desired
family size. These women want .20 (p<.01) more children than wives whose
husbands want fewer or the same number of children as envisaged by them. This
result underscores the importance of spousal communication about children. Communication
increases wives' understanding of husbands' preferences regarding the number
of children to have. This analysis indicates that such an understanding could
have significant effects on wives' reproductive choices.
Table
3 also shows that knowledge of modern contraception is significantly related
to desired family size. Women who report that they know a modern method of contraception
want .41 (p<.01) fewer children than their counterparts who do not have any
such knowledge. The study revealed further that, apart from women's socio-economic
status, perceptions of husband's preferences and other demographic factors,
ethnicity has significant effects on DFS. Five ethnic dummies are included in
the analysis as shown in Table 3. These groups are compared with the Luo (used
as the reference group in this study). The study revealed that DFS is smaller
among Kamba, Kikuyu, Luhya, and women from other ethnic groups than the Luo.
The difference between the Kalenjin and the Luo is not significant. However,
compared to other ethnic groups in the country, the Luo appear to have a higher
demand for children. The strong preference for large family sizes probably accounts
for the persistently high fertility and limited contraceptive practice observed
among the Luo by other scholars (see Ndeti, 1988; Watkins, 1995; National Council
for Population and Development et al., 1994). The ethnic differential in desired
family size is probably attributable to fundamental differences in the prevalence
of cultural forms supportive of high fertility in each group and in access to
socio-economic opportunities. For instance, the early access to colonial education
could have set the Kikuyu and the Kamba ahead of other ethnic groups and, as
this study shows, of all the groups studied, the Kikuyu and the Kamba have the
least preference for large families. The Kikuyu, for instance, live in the Central
Province which has better infrastructure, education, and health facilities than
other parts of the country.
Discussion
and Conclusion
Studies
of both trends and differentials in DFS are limited in Kenya. As such, there
is neither an adequate understanding of the relationship between DFS and actual
fertility in the country nor of the determinants of DFS. It is this gap that
this study attempted to bridge.
The
study began with the premise that changes in demand should lead to changes in
the supply of children, all things being equal. Using the Easterlin framework,
we hypothesized that attitudinal changes in desired family size should be related
to changes in income, the opportunity cost of alternatives forgone and the taste
for children. In order to understand these changes, we examined both the trend
and differentials in DFS. The trend analysis was performed with cross-sectional
data collected over a 15-year period for synthetic cohorts of women. The results
show a significant and consistent decline in the desired family size by actual
fertility across the years. In particular, over the 15-year period studied,
a decline of about 3.3 children in desired family size was observed, from a
high of 7.2 children in 1978 to a low of 3.9 children in 1993 for all women.
The analysis of synthetic cohort differences in preferences for additional fertility
also revealed significant increases in the percentage of women wanting no more
children between 1984 and 1993 for cohorts 15-19 to 35-39. Further analysis
revealed that the decline in DFS is highly correlated with declines in actual
family size over this period.
The
multivariate analysis revealed significant differentials in desired family size
influenced by women's education, household wealth, women's work and income status,
place of residence and religion. Better educated women who own one or more durable
goods, work and earn income, live in urban areas and who are either Protestants
or Catholics want fewer children than women with no income, limited or no education,
who live in rural areas, and are affiliated with other religions. The study
also found that women's perceptions of husband's ideal family size significantly
affect their own DFS. Women who do not know their husbands' desired family size
are significantly more likely to demand large numbers of children than women
who know. This finding indicates that husbands' preferences do affect wives'
reproductive preferences. As such, an understanding of the preference structure
among men could provide useful insights in developing IEC programmes to change
women's reproductive behaviour.
Knowledge
of modern contraception also accounts for significant variations in DFS: women
who know a modern method of contraception demand fewer children than those who
know no modern method of contraception. These findings indicate that the on-going
fertility transition in Kenya is due both to changes in reproductive preferences
and increases in the implementation of those "new" preferences. Similar conclusions
were drawn earlier by Bongaarts (1991) from an analysis of data on 18 developing
countries.
The
study also revealed significant ethnic differences in DFS: unlike the Luo women,
the Kamba, the Kikuyu, the Luhya and the Kalenjin, to some extent, and women
from other ethnic groups want smaller families. We argued that differences in
fertility orientation, access to education and opportunities in each group could
account for the observed ethnic variation in DFS.
Of
all the variables examined, knowledge of modern contraception and place of residence
explained most of the variance in DFS. This implies that, in addition to income,
cost-benefit constraints, access to information about family planning and modern
ideas, as provided in the urban context, lead individuals to re-evaluate their
family size values and modify them accordingly. This fact probably accounts
for why the TFR declined almost at the same rate as the DFS over the 15-year
period studied. These findings indicate that policies oriented toward changing
reproductive attitudes and behaviour among women should improve their socio-economic
position, their access to education and information about family planning and
small family size values. Such policies could significantly decrease the demand
for children. Furthermore, the fertility transition that is currently under
way in Kenya is likely to be sustained by policies which guarantee the continued
modernization of values related to children as well as the provision of family
planning services that enable women to implement their reproductive choices.
However,
the hypothesis that changes in ideas precede changes in behaviour is not directly
tested in this work. To do this, we need to model the change in DFS between
Time 1 and Time 2 on independent variables measured at Time 1 or on changes
in the independent variables between the two time periods. Such an analysis
requires either a time series or longitudinal data sets. Neither of these data
sets are available for Kenya. Future work on this topic should focus on generating
such data sets for the country.
Acknowledgements
The
authors would like to acknowledge the support of the Population Council in providing
the fellowship under which this paper was prepared. They are also grateful to
Anrudh Jain, Susan Watkins, Cynthia Lloyd, Cheikh Mbacke, John Kekovole, Naomi
Rutenberg and Evasius Bauni for helpful comments in improving this paper. They
also thank Cheikh Mbacke for assisting with the Kenya DHS data.
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NOTES
- WF
is based on assumptions regarding the wantedness of children. A child is considered
wanted if a woman/couple reported wanting another birth at the time of the survey.
On the basis of this information, "want more" age specific fertility rates
(WMASFR) are calculated.
- The
PDTFR proposed by Bushan et al. (1995) is constructed from responses to
questions on the desire for additional fertility. The DPTFR is estimated
from births which
will occur in the 12 months following the survey assuming that women have
the births they wanted. The numerator includes all births to non pregnant
women
who said they wanted a/another birth within 12 months after the survey,
all wanted current pregnancies which could result in wanted births within
the 12
months after the survey plus some adjustment for women not yet married
who would marry in the next three months after the survey. The denominator
covers all
women of reproductive age (Bushan and Hill, 1995).
- Place
of residence was used as an indicator of price by Ahmed (1984) in the application
of the Easterlin model to data from Bangladesh.
- See
page 2 for a detailed discussion of how the variable was derived.
- Women's
work and income status is constructed from two sets of questions. In the first
set of questions, women were asked whether they were engaged in any type of
work: family business/farm, small retail trade (whether within or outside the
home environment), formal employment, etc. Those who responded "yes" were then
asked if they received cash (any amount) for this work. The indicator of women's
work and income status is coded "1" if they said "yes" they work and earn cash,
and "0" otherwise.
- A
comparison of spousal responses to questions on reproductive preferences
among 25% of the women whose husbands were interviewed indicate that there
is considerable
agreement between wife's and husband's responses. With regard to desire
for additional fertility, for instance, among 30% of the spouses, both spouses
said
they wanted more children; among 28% both said they wanted no more. The
proportion of couples in which the husband wanted more children than the
wife is greater
(12%)than the proportion in which the wife wanted more and the husband
did not (7%). Among 22% of the respondents, one or both of the spouses appeared
to be
undecided about whether they wanted to have more children. This general
high degree of agreement between spouses, according to the KDHS report, is
consistent,
regardless of parity. Similarly, the analysis of spousal perception of
each other's attitude to FP also reveals a high degree of accuracy in the
husbands'
and wives' report of their spouses' attitude. For instance, in 94% of the
cases in which wives reported that their husbands approved of FP, the husbands
also
said they approved (NCPD et al., 1993:162).
- This
analysis is restricted to KCPS and DHS data sets. Data on desire for additional
fertility were not collected in the Kenya WFS.
- Non-numeric
responses are those who said that they did not know their desired family
size or that it is up to God or chance.
- Number
of surviving children was not included in the model because of high multicolinearity
problems. Sex composition of children was included at first but was later
removed
for lack of significance and, more importantly, because its inclusion reduces
the amount of variance explained.
Copyright 1997 - Union for African Population
Studies.
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