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OCR for page 16
~6
measured is in fact endogenous. In addition to these problems of com-
parability and causal placement, the WFS measure of desired family size
has questionable reliability (Westoff, 1980:2) and an unknown correspon-
dence to underlying preferences (Szykman, 1982~.
Our model also omits the costs of fertility control. Easterlin
(1978) defines three types of cost: fixed costs, which might be tapped
by knowledge of contraception; variable costs, which refer to the
recurring expense of purchasing contraceptive supplies; and psychic
costs, which involve attitudes toward and norms governing contraceptive
use. m e higher these costs, the less likely contraceptive use will be.
Although the WFS standard recode tapes include indicators of contracep-
tive knowledge, they lack information about variable and psychic costs.
It is doubtful that adding either desired family size or cost factors
would much improve the predictive power of the model. If appropriate and
reliable indicators were available, we would place these variables in the
causal structure so that they mediated the influence of socioeconomic
variables on later fertility and contraceptive use. Weir effects are,
in our model, implicit in those of the socioeconomic variables. The model
could easily be modified to accommodate these variables should better
information become available.
Finally, given that the WFS countries are located along the
traditional/transitional continuum, it is plausible to view desired
family size and fertility regulation costs as relatively invariant either
within countries or for well-defined social groups within countries. If
so, it would be conceptually appropriate to account for these two vari-
ables at the macro level. As noted earlier, we plan to construct aggre-
gated or global macro variables that can be used to analyze varability
across settings in the micro-level coefficients of the model.
1.3 THE STRUCTURAL EQUATIONS
This section explains the sixteen structural equations of the processual
model, as well as our expectations about the nature and direction of the
relationships involved. Because these expectations depend on societal
context, we continue to invoke the ideal-typic traditional/transitional
distinction described above. Table 1.2 summarizes these cross-setting
expectations about the signs of all coefficients in the structural
equations. The following discussion also provides relatively precise
definitions of the variables comprising the model, all of which depend on
data available from WFS standard recode tapes. For simplicity, each
structural equation is treated as if it were estimable as a multiple
regression. Although actual estimation might be considerably more
complex, allowance here for details specific to discrete and ordinal
response variables would detract from explication of the substantive
hypotheses underlying the model.3
Onset
Taken by themselves, Blocks I and II of Figure 1.2 depict a model of
fertility onset, defined as age at first birth (AFB). His provides an
OCR for page 17
17
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OCR for page 18
18
unambiguous starting point for the fertility process that is comparable
across cultures. Since contraceptive use between entry into a union and
the birth of the first child is not expected in WFS countries, AFB is a
simple function of age at first union and a delay influenced by fecund-
ability and chance factors. Consequently, hypotheses about the socio-
economic determinants of age at first union can be applied directly to
AFB. Age at first union corresponds to age at marriage in many, although
not all, WFS countries.
The onset model contains a second endogenous variable: respondent
work experience prior to marriage (WBM)e The focus here is on whether
respondents work away from home in a nonfamily enterprise in order to
measure participation in the modern sector and exposure to modern norms
and ideas. WBM is dichotomous and distinguishes between experience in
the modern sector and all other work possibilities. m is reflects a
constraint of the WFS data. Although it would be helpful to know how long
respondents were employed before marriage and how modern that employment
was, this information is unavailable in the WFS data, as well as in most
other fertility surveys.
Figure 1.2 displays AFB and WBM as contemporaneously correlated
variables in Block II. This is a deliberate departure from the seemingly
obvious causal ordering in which AFB depends on WBM. Formal marriage can
occur after the first birth, thus disrupting the implied temporal
sequence of work, marriage, and first birth. More importantly, the
discretional component of AFB is the decision to enter a union, and WBM
is not predetermined with respect to that decision. Not only does work
before marriage imply later entry into a union, but delayed entry may
also necessitate or provide the opportunity for work. This simultaneity
underlies the contemporaneous placement of WBM and AFB. However, we will
not estimate a simultaneous equations model because instrumental variables
are not available (i.e., we would be unable to identify the coefficients
for these two equations). Moreover, our purpose is to study the role of
onset in mediating the effects of socioeconomic variables on family size,
child mortality, and contraceptive use, rather than to address the simul-
taneities within the onset model per se. Thus there would be no real
loss in omitting this particular simultaneity even if it were estimable.
Block I contains the only socioeconomic and sociodemographic vari-
ables available for all WFS data sets which are unambiguously predetero
mined with respect to AFB and WBM: respondent education (WED) and
respondent childhood residence (RESC). Together, Blocks ~ and II imply
the following equations:
(1.1) AFB = 60,1 + 51,tRE~ + 82,1WED-
(1.2) WBM = 60,2 + 61,2RESC + 02~2
For each coefficient' ~ i, ·. the i-subscript denotes the variable
number and the -subscript the equation number. For most WFS data sets,
we will treat respondent childhood residence as a rural-urban dichotomy
(urban = 1; rural = 0), with additional detail incorporated when available
(e.g., a trichotomy distinguishing countryside, town, and urban childhood
residence). For ease of exposition, we will assume that respondent
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19
education is scaled in years of schooling, although the model does not
require this treatment, and our empirical work will explore alternatives.
It is widely accepted that education is a measure of socioeconomic
status (SES). In the present circumstances, it is plausible to treat
place of residence similarly. The WFS data, which are notable for their
lack of exogenous socioeconomic variables, do not contain information on
parental SES. Urban-rural variability in childhood residence can approxi
mate between-fam~ly differences in childhood SES to a degree. Moreover,
in transitional settings, this variability in childhood residence is
likely to reflect differential experiences In educational quality, expo-
sure to relatively industrialized labor markets, and, in general, degree
of exposure to relatively wealthier life styles. These and other points
will be raised below in the discussion of the potential effects of child-
hood residence on AFB and WBM. Both childhood residence and education
should affect AFB, the nature of the hypothesized effects depending on
context.
In -traditional contexts, relatively advantaged women (urban child-
hood residence, more education) should have a lower mean age at marriage
and at first birth. Education and childhood residence reflect wealth and
knowledge, which in turn affect nutritional levels, health-related
behavior, and exposure to disease. Frisch (1975) and others have argued
that nutrition and health are positively related to fecundability and
inversely related to conception delay (although there is debate on this
point; see Bongaarts, 1980~. We hypothesize that in traditional contexts,
the health consequences of education and childhood residence are the
critical factors in the interval between union and first birth. Education
and childhood residence cannot reflect participation in modern labor
markets since these are largely absent in the ideal-typic traditional
setting.
The hypotheses about the effects of childhood residence and education
on AFB in transitional contexts are quite different. Relatively advan-
taged women may still have higher monthly conception probabilities than
their less advantaged counterparts. The effects of health and nutrition
may, however, be overshadowed by education and childhood residence differ-
entials in age at marriage. Durch (1980:18-19) shows that education and
urban childhood residence vary positively with mean age at marriage in
almost all of the 15 WFS countries under consideration. Childhood
residence and education co-vary with the distribution of norms governing
marriage patterns. According to Caldwell (1976), education represents a
major conduit for the transmission of Western ideals and norms (including
those that affect the timing of entry into unions) to women in less-
developed countries. Childhood residence would also affect exposure to
such norms and ideals. The length of time needed to find an appropriate
husband may also vary positively with education. Bowever, since it is
unlikely that education competes directly with marriage or motherhood in
WFS countries, education is justifiably a predetermined variable with
respect to AFB. In sum, in transitional settings, we expect positive
relationships between WED and AFB and between RESC and AFB.
Finally, we hypothesize that in transitional societies, childhood
residence and education will have positive effects on the probability of
working in the modern sector before marriage. Modern employment requires
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20
that several conditions be met. The first is awareness. Respondents may
learn of modern employment opportunities in school, or, depending on
childhood residence, may have observed the participation of other com-
munity members. Second, respondents must qualify for modern employment,
a condition in which education plays a major role. Third, respondents
must be willing to accept such jobs when offered. Norm" regarding the
desirability of extrafamilial employment may be communicated in the
classroom. Norms prevailing during childhood may also affect the accept-
ability of such work. In addition, education and childhood residence may
affect "tastes. for modern employment, both directly and indirectly, by
increasing exposure and susceptibility to such inf luence as mass media
advertising. For these reasons, WED and RESC should be positively related
to WBM in transitional societies. In traditional settings these relation-
ships should be nil due to a presumed scarcity of modern employment
opportunities.
Early Outcomes
Block III of Figure 1.2, corresponding to the second stage of the fer-
tility process, contains four early outcomes that have relevance to later
events. First, early fertility (EF) refers to the number of children
born before respondents reach age 30. Although we anticipate that EF
represents relatively uncontrolled reproductive behavior in the countries
to be studied empirically, it affects all decisions about later fertility
and contraceptive use. Second, survivorship of children during the early
phase of the fertility process is also important. ECM denotes child
deaths before respondents reach age 30. Finally, the sufficiency of sons
and daughters when respondents reach age 30 may influence subsequent
reproductive decisions, especially in cultures with strong son preference.
SCB is a dummy variable taking the value 1 if the respondent has two or
more living boys, and O otherwise. Similarly, SCG takes the value 1 if
the respondent has one or more living girls, and is O otherwise.
All variables in Blocks I and II are predetermined with respect to
both early fertility {EF) and early child mortality (ECM):
60,3 + 61,3RESC + 02,3WED + 83,3AFB + 64 3WBM.
(1.4) EF = 00,4 + 61,4RESC + 82,4WED + 63,4AFB + 54,4WBM.
Because it is presumed that fertility patterns in the early reproductive
years are not responsive to the presence or absence of conscious decision
making, major differences in the determinants of EF across societal
context are not expected, nor are major differences in the determinants
of ECM. m ere is one exception: work experience in the modern sector
before marriage (WBM) does not apply in traditional contexts and thus
will not affect either ECM or EF in those settings.
Childhood residence (RESC), respondent education (WED) , and work
experience {WBM) all influence knowledge of hygiene, health practices,
breastfeeding patterns, nutrition, exposure to disease, and access to
medical care. These variables are expected to have negative relation-
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21
ships with ECM (01,3, 82,3, 64,3 < 0) and positive relationships with EF
(5l'4' 62 4r 84~4 > 0) ~ except where breastfeeding differentials
counterbalance the expected health effects. Expectations about the effect
of age at first birth (AFB) are based mainly on exposure. The number of
years between onset and age 30 limits births and child deaths t leading to
the hypothesized negative relationships between AFB and both EF and ECM
(83 3, 03,4 < 0). To the extent that AFB is sensitive to health-related
influences over EF and ECM not measured by WED, RESC, and WBM, these
expected negative relationships may be dampened somewhat by the negative
association between health and AEB.
The two sex Buff iciency var tables depend only on EF and ECM:
(1.5) SCB = 60,5 + 65,5ECM + 66,5EF
(1.6) SCG = 60,6 + 65,6ECM + 66,6EF.
Of necessity, the number of children surviving when respondents reach age
30 (EF - ECM) affects the likelihood of having two boys (SCB) or one girl
(SCG). Equations (1.5) and (1.6) decompose the effect of survivorship
into EF and ECM components, but exclude socioeconomic variables. Sex
sufficiency may interact with socioeconomic characteristics in the deter-
mination of fertility and contraceptive behavior; however, the probability
of a particular birth being male or female does not itself depend on
socioeconomic status. Although SES may affect sex preference, which in
turn may lead to differential care and nurturing, ECM will mediate its
effect on SCB and 5CG. Emus, even as a proxy for sex preference, there
is no reason to include SES in equations (1.5) and (1.6~.
Endogenous Socioeconomic Variables
While education and childhood residence are unambiguously predetermined
with respect to onset, early fertility, and later fertility, other socio-
economic variables come into play at varying points in the reproductive
process as we model it. Work before marriage (WBM? is predetermined with
respect to both early and later fertility. Another set of socioeconomic
variables is predetermined with respect to later fertility only. Four
such variables appear in Block III of Figure 1~2: current residence
(RES), respondent postmarital work experience (WSM) f education of current
husband (HED), and occupation of current husband (HOCC). The measurement
of RES, WSM, and HED parallels that of similarly defined variables in
Blocks I and II. Several alternatives can be tried for the measurement
of HOCC, including self-employed vs. other, agricultural vs. other r and
ordinal scales tapping access to ~modern" work organization.
m e treatment of these variables as endogenous is perhaps unconven-
tional, but nevertheless justified. Unless the time ordering of a pair
of variables is clear and unquestionable, the assignment of causal order-
ing is inherently procrustean. The difficulty in assigning endogenous
socioeconomic variables to positions in the structural equations is that
the time referents of these variables are ambiguous in one way or another.
WFS respondents are asked about their current residence and the character-
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22
istics of their current husband, where "current" refers to the survey
date. There is no way to know how long they have lived in their current
residence (not even by matching it with childhood residence), or whether
there have been changes in their husband's education and occupation. It
can be known whether the current husband is the first one, but not how
long he has had his current education and occupation. The time reference
for the work since marriage q~est~an is also unclear. The same code is
assigned to women who have just started back to work after all their
children have reached a certain age and to those who worked only from
marriage to the birth of their first child.
It might be supposed that if there were continuous work, education,
and residence histories available for respondents and their spouses, the
correct causal ordering of all the socioeconomic variables could be
determined. m is would not, in fact, be the case since this kind of
detailed information would generate numerous causal orderings--one for
each distinct pattern of transitions. Given an underlying multiplicity
of patterns of status transitions, the necessity of working with summary
variables, and the ambiguous time referents for those variables, there
are two strategies for addressing the question of causal ordering in the
model. One is to treat all of the endogenous socioeconomic variables as
merely intercorrelated with the variables in Blocks I and II. Although
there are instances in which this is appropriate, in the present case we
prefer the second strategy, which is to assume a certain amount of causal
ordering. This is justified if those causal assignments are consistent
or compatible with noteworthy aspects of the broader problem.
The placement of RES, HED, and HOCC is problematic with respect to
AFB and WBM. The choice of placing these variables in Block TII can be
justified on the grounds that it is probably the correct decision for
respondents who have been married more than once, and is compatible with
the nature of the questions asked, all of which refer to "current" status.
mat is, this placement is consistent with an analytic allowance for
change. That this placement is erroneous for those whose lives have been
quite stable is granted. To the extent that this stability is present,
only the direct effects of AFB and WBM, as well as those of RES, HED, and
HOCC, have relatively clear interpretations.
By the same token, we do not allow ECM and EF to depend on RES, WSM,
HED, and HOCC. Again, the dominance of stability in the lives of the
respondents, and hence an unambiguous causal ordering, is uncertain. Of
course, it is plausible to think of ECM and EF as standing in reciprocally
causal relationships with RES, WSM, HED, and HOCC. However, we have not
specified the model in this way both because the parameters of such simul-
taneous equations would not be identified, and because the particular
simultaneities seem relatively uninteresting--even were they estimable.
Finally, there seems to be no serious conceptual problem in treating
RES, WSM, HED, and HOCC as predetermined with respect to the variables in
Block IV, which in the vast majority of instances refer to events taking
place after respondent's 30th birthday. The relative clarity of this
causal ordering between Blocks III and IV is in fact one of the assets of
the processual approach we have adopted. The usual strategy of analyzing
cumulative fertility and child mortality would require a simultaneous
specification for RES, WSM, HED, and HOCC and the variables of Block IV.
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23
m e burden of resolving the identification problem would in this case be
considerable; the processual approach involves fewer tenuous assumptions.
The equations for the endogenous socioeconomic variables are as
follows:
(1.7) RES 80,7 + 81,7RESC + 82,7WED + B3,7AFB + IBM.
80,8 + 81,8RESC + B2,~WED + B3 SAFE + B4 IBM.
ED Do,g + 81,gRESC + 82,gWED + 63,gAFB + 64 gWBM.
(1.10) HOCC = 60,10 + Bl,lORESC + 62,10WED + 63,10AFB + 84,10WBM.
m e coefficients of childhood residence, respondent education, and work
experience before marriage should be positive in all four equations.
Women who grow up in urban areas, who have more than average education,
and who work in the modern sector before marriage are likely to find
similarly situated husbands, to work after marriage, and to live in an
urban area. Except for the inapplicability of modern employment in
traditional settings, these expectations hold regardless of context.
Age at first birth potentially influences the endogenous socioeco-
nomic variables. AFB may capture aspects of respondent background missed
by the other socioeconomic variables. In a traditional agrarian setting,
childhood residence and education imperfectly discriminate between women
of differing socioeconomic status. More revealing indicators might
include ownership of land and access to water. Relatively well-off
families in these contexts may be able to arrange early marriages for
their daughters. AFB may therefore reflect aspects of childhood socio-
economic status not measured by RESC or WED. If so, then AFB will be
negatively related to the endogenous socioeconomic variables (83,7,
63,9, B3,10 < 0; 83,8 = 0). AFB may also reflect unmeasured aspects of
socioeconomic status in transitional societies, although RESC and WED
should be improved proxies in these contexts too. The relationships
between AFB and the endogenous socioeconomic variables will be positive,
however, if our hypothesis that advantaged women delay marriage is
correct.
The Adjustment Variables
Block III of Figure 1.2 contains several adjustment variables that affect
later fertility and contraceptive use patterns. Two can be considered
together: number of marriages (NMAR) and number of years spent in a
union between the 30th birthday and the survey date (DUR). NMAR controls
for disruption, whereas DUR accounts for later exposure. The two are
modeled as functions of variables in Blocks I and II:
{1.11) NMAR = Bo,l1 + B1,11RESC + 62,11wED + 83,11AFB + 64,11WBM-
(1.12) DUR = 80,12 + 81,12RESC + 62,12wED + 8 3,12AFB + B4,12WBM.
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24
Childhood residence, education, and work before marriage may affect NMAR-
and DUR in a number of ways. They reflect norms governing marriage,
divorce, and remarriage, as well as differential availability of re-
sources. Wealthy families may be better able to afford dissolution and
remarriage. On the other hand, poor families are at greater risk of
dissolution due to death. mese relationships depend upon the particular
society studied. Hypotheses about the effects of age at first birth are
also society-specific, however, there is no reason to suppose that for
either the NMAR or the DUR equation, coefficient variability between
societies is related systematically to the traditional/transitional
continuum.
A third adjustment variable is self-reported fecundability (FEC), a
dummy variable where the value 1 indicates no known problems. Perceptions
of fecundability (at the time of the survey) are undoubtedly connected
with proven fertility and survivorship. This variable is considered
contemporaneous with early outcomes and assumed predetermined with
respect to later outcomes. It is likely to depend on luck, which is
random, and health, which is tapped by variables in Blocks ~ and II:
(1.13) FEC = 60,13 + 81,13RESC + 82,13WED + 63, 13AFB + 64,13WBM.
Age at first birth is also predetermined with respect to FEC, even though
its effect can be positive or negative. On the one hand, late onset may
indicate subfecundity, producing a negative relationship between AFB and
FEC (03,13 < 0~. On the other hand, because late onset leaves little
time for women to judge their fecundability, their self-report may be
optimistic {83,13 > 0~. As with the NMAR and DUR equations, intersociety
coefficient variation for the FEC equation should not be related to the
traditional/transitional continuum.
Later Child Mortality
Block IV of Figure 1.2 contains three endogenous variables, one of which
is later child mortality {LCM). This refers to child deaths occurring
between the 30th birthday and the survey date, although the children
could have been born before respondents reached age 30. All of the
variables in Blocks I through III can determine LCM:
(1.14) LCM = 60,14 + 01,14RESC + 02,14WED + 63, 14AFB + 64,14WSM
65914ECM ~ 56,14EF + 8 7,14SCB + B8,14SCG
+ 69,14=S + 610,l4WSM + 611,14=D + 612,14H°CC
+ ~13 :4NMAR + 514,l4DUR + 61S,14FEC
Major differences across context in the estimated effect parameters seem
unlikely (although average number of child deaths will differ).
Justifications for the expected effects of the predetermined vari-
ables in equation (1.14) fall into two broad categories. The first
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25
concerns the size and age-sex composition of the early family. Older
children enjoy lower mortality risks than younger ones. Controlling for
EF and ECM, an early age at first birth implies older children and lower
later child mortality (83~14 > 0)e There should be a positive
relationship between EF and LCM (06r14 > 0) because larger families
will have more children at risk and place a greater strain on family
resources than smaller families, other things being equal. It is also
possible that, apart from family size, sex composition influences the
distribution of family resources among children in some cultures. We
include SCB and SCO so that this hypothesis can be examined; their
hypothesized effects are culture specifics
m e rationale for the second category of expected effects involves
health conditions. Residence, education, work experience, and occupation
influence exposure to disease, health-related behavior, and access to
professional care. The health implications of the socioeconomic vari-
ables will in all likelihood influence later child mortality (01,14,
82,14, 64,14, Bg,l4, 010,14, 611,14, B12~14 < 0). Women believing that
they are no longer fecund may invest more time and care in children than
fecund women, since the latter can replace lost children (615,14 > 0)
For genetic and family-specific reasons, there should be a positive
relationship between early and later childhood mortality (85,14 > 0).
Number of marriages and time spent in a union may affect resources for
child care and the equity of distribution across children, suggesting
(613,14 > 0; 614,14 < 0~. It is also true, however, that OUR, measured
as time in union since age 30, will be associated with the number of
children born after age 30, and will therefore reflect the number of
infants exposed to the risk of dying (814,14 > 0). m us, the effect of
marital duration (614,14) may be either positive or negative. We assume
it to be negative for the purposes of the present discussion, but have
determined that the sign of this coefficient has only minor consequences
for the implications of the model (derived in Section 1.4~.
Contraceptive Use Patterns
Other things being equal, women who use contraception will end up with
smaller families than those who do not. It is probably fortunate that
this proposition is generally thought to be self-evident, since it would
be difficult to demonstrate it empirically using the WFS and other
similar survey data. m e problem is that the proposition contains an
implicit temporal ordering not typically measured in sample surveys of
fertility behavior. If it is unclear when contraception began and how
consistently it was used, it is possible to obtain a positive correlation
between current or ever use of contraception and fertility. This will
occur in transitional settings because the decision to use contraception
is based in part on the number of living children at the time. Thus, not
only is the association in the wrong direction, but also the flow of
causality is in both directions (between fertility and contraception)
rather than unidirectional (from contraception to fertility).
What, then, is the point of including contraceptive use as typically
(incompletely) recorded in sample surveys such as the WFS? One answer,
OCR for page 26
26
the answer that motivates our strategy, is that one must avoid the attempt
to show that contraception works and instead concentrate on the forces
that lead to differentials in contraceptive uses. A positive correlation
between, say, current use of contraception and fertility is irrelevant.
We know why it occurs, and we know also that there are efficient methods
of contraception, which is to say that it works. The pressing task is to
increase our understanding of the processes that lead to use and to
estimate the relative magnitude of their effects.
In this connection, our partitioning of children ever born into
early and later fertility again proves useful. Because contraception in
the early years of childbearing is relatively uncommon in transitional
societies, becoming more common In the later years, it is plausible to
treat (incompletely} measured contraceptive use as endogenous with
respect to early fertility. As noted above, this makes the model much
more precise and substantively meaningful than if the basis were children
ever born (the commonly used measure). The essential point is that
contraceptive use as typically measured (e.g., current or ever use} is
simultaneous not only with children ever born, but also with later
fertility. In our view, the requirements for identifying this simul-
taneity cannot be satisfied reasonably,--given the kind of information
available in the WFS and other similar surveys. -The decomposition of
children ever born resolves this problem. Similarly, there is a simul-
taneity between child mortality and contraceptive use as typically
measured. me division of child mortality into its early and later
components also resolves this simultaneity. It is meaningful to treat
early child mortality as predetermined with respect to contraceptive use
in the data to be used for our empirical studies.
There is no one best way to operationalize contraceptive use in the
WFS data. Indeed, LOU is more difficult to measure than any other vari-
able in the model, because of both the limitations of the WAS question-
naire and the inherent complexity of contraceptive behavior. The WFS
data on contraceptive use are rather less than a complete contraceptive
history. At most they include use of efficient and inefficient methods
currently, in the open birth interval, in the last closed birth interval,
and ever. Our goal is to use this information somehow to tap the
"modernity" of contraceptive behavior. To this end, we are experimenting
with an ordinal scale for contraceptive use that attempts to distinguish
long periods of effective use from short or ineffective periods of use,
based on lengths of the open and closed intervals among current users.
mere are several challenges to the successful use of this or other
contraceptive use scales based on WFS data. First is the distinction
between efficient and inefficient methods. The latter are apparently
being used with a high degree of success in the Philippines (Williamson,
1982), and perhaps elsewhere. For this reason, it may prove worthwhile
to consider use of such methods (e.g., rhythm) in addition to efficient
methods such as the pill and IUD. Second is sterilization, which poses a
different kind of problem. We propose grouping contraceptive steriliza-
tion with efficient contraceptive methods, and reserve for empirical
study the problem of what to do with the very small number of
noncontraceptive sterilizations. Our treatment of this problem may vary
across countries.
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There remains the question of what to do about abortion in the
context of a contraceptive use scale. Although some WFS countries
included an induced abortion question, many did not. It appears that
abortion frequencies are not low in countries where the abortion question
was not asked (Tietze, 1981~. Even where the question was included, the
data are likely to severely underestimate the frequency of abortion
(Srikantan, 19821. An ~ndividual's past use of abortion is not highly
predictive of future use of abortion. Consequently, Current or potential
use of abortion cannot be ascertained. For all of these reasons we plan
to exclude abortion from individual-level measures of contraceptive use,
although we may be able to incorporate some of its effects through the
inclusion of contextual variables.
The distinction between traditional and transitional settings is
based on the concept of discretionary fertility behavior, which is
implemented primarily through contraception. m us expectations about the
effects of the predetermined variables on LCU apply only to transitional
settings. m e following equation summarizes relationships involving the
contraceptive use patterns depicted in Figure 1.2:
(1.15) LCM = 80,15 ~ 5l,15RESC + 62,1sWED + 63, 15AFB + 64, 15WBM
+ ss,1~CM + IF + c7,1~CB + 68,15SCG
+ 69,15RES + 610,15WSM + 611,15HED + 312,15H°CC
+ 613 15NMAR + 614,lsDUR + B15,15FEC ~ 616,15(AFB)
617,l5EF WED + 818,lsEF*HoCC + 819,15SCB*WED
+ 820, 1sSCB*HOCC.
As can be seen, the prediction of LOU is considerably more complicated
than that of other endogenous variables considered thus far. Onset,
early fertility and mortality outcomes, socioeconomic characteristics,
and the adjustment variables may all influence contraceptive use patterns.
In addition, two sets of interaction terms appear in equation (1.15~.
We hypothesize that onset plays a dual role in the determination of
LOU. On the one hand, a young AFB may signal compliance with traditional
norms and ideals. To the extent that it does, it will imply a similar
compliance in contraceptive practice. On the other hand, late AFB could
also be linked with low contraceptive usage because it might indicate
subfecundity, lessening the need to contracept. Although FEC appears in
equation (1.~5) , it may not capture this possibility completely. In
addition, women who are fecund may be less inclined to control their
fertility after a late AFB. Fey may be aware that the probability of
secondary sterility will increase as they age, and may wish to insure
against this possibility. mese arguments suggest a curvilinear
relationship between AFB and LOU (53,15 > 0; 816,15 < 0)
Contraceptive use patterns should be sensitive to boy and girl
sufficiency (SOB and SCG) and the size of the early family. Early
fertility and mortality outcomes, judged against ~ family-size target,
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will affect the subsequent use of contraception. EF should be positively
related to LCU: respondents who are close to their targets when they
reach 30 are likely to adopt effective birth control methods thereafter,
and will do so earlier than their less prolific counterparts (06,15 > 0)
Of course, early family size depends on child deaths as well as births.
Early experience with child mortality (ECM) should diminish the proba-
bility of subsequent contraceptive use (B5~5 < 0~. LCU may respond to
sex composition as well (as reflected in the "sufficiency variables).
Where son preference is strong, repondents may not even consider family-
size limitation until they have achieved a ~sufficiency" of boys, here
operationalized as two living sons. Respondents may also have a strong
preference for at least one daughter (Arnold et al., 1975~. If so, the
presence of two living sons (SCB) and one living daughter (SCG) should
increase the likelihood of subsequent contraceptive use {87,15, 68 15
> 0), although the magnitude of this impact will undoubtedly vary from
one country to another.
If our hypothesis about the effect of EF on LCU is correct, the
strength of the relationship between the two will depend on family-size
targets. These, in turn, may depend on socioeconomic characteristics.
mere is thus a possibile interaction between EF and socioeconomic
variables; in this case, respondent's education (WED) and husband's
occupation (HOCC) are selected as likely candidates. One way to inter-
pret these interactions is in terms of innovative behavior. Innovators
will be even more likely to use contraception than would be expected
based on their early family size and socioeconomic characteristics
(017,15' 818,15 > 0, assuming innovation among relatively advantaged
groups). m e magnitudes of the interaction effects may reflect how much
diffusion of new behavior patterns to less innovative groups has
occurred. There is also a possibility of culture-specific interactions
between socioeconomic characteristics and SCB.
Socioeconomic characteristics should also be related to knowledge of
modern birth control methods, willingness to use them, and efficiency of
use. Current residence (RES) has implications for contraceptive
knowledge, attitudes, and practice. In particular, mass media campaigns
designed to promote the use of birth control are often concentrated in
urban areas. Urban residents are also likely to have access to media
channels (e.g., radios). Specific information may be disseminated not
only via the mass media, but also by classroom instruction or even
special programs in the workplace (e.g., sterilization campaigns in
India). Education, respondent's work experience, and occupation can
influence LCU in this way. Moreover, socioeconomic characteristics
define reference groups and role models: educated urban dwellers in
modern occupations are more likely than their traditional counterparts to
have friends, neighbors, and peers who practice effective contraception.
m ese expectations are summarized in the hypothesis that contraceptive
use patterns vary positively with socioeconomic status (01,15, 62,15,
84,15' 69,15' 610,15' 611,15, 612,15 > 0)
We include the adjustment variables in equation (1.15~. Women who
think they are subfecund have no reason to adopt contraception (B15,l5 > 0~.
Respondents still in their first marriage or union at the time of inter-
view are more likely to be losing birth control than those in higher-ord--
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29
unions ( ~ 13,15 ~ 0 ) . Quite of ten , children symbolize the existent e
of a marriage and the joining of two families across future generations.
Family-size targets may apply within marriages, with women who partici-
pate in several having less incentive to contracept than otherwise.
Moreover, some exposure time is lost as one marriage dissolves and
another comes into being, further weakening the incentive to contracept
(014,15 > 0~. In addition, since contraceptive practice has increased
over time in many WFS countries (Johnson-Aceadi and Weinberger, 1982),
respondents with low recent exposure are less likely to be users of
efficient means of contraception. The inclusion of DUR and NMAR helps
adjust for this, so that the effect of other variables is less distorted.
Later Fertility
Later fertility (LF) refers to children born between the 30th birthday
and the survey date. Variability in LF will occur for women at least 35
years old when interviewed, and will increase with respondent's age. For
this reason, Block IV requires older cohorts of women. m e following
equation summarizes the effects of predetermined variables on LF,
depicted in Figure 1.2:
( 1 . 16 ~ LF ~ 0, 16 + ~ 1, 16RESC + ~ 2, 16WED + ~ 3, 16AFB + ~ 4, 16WBM
+ 65,1~CM + 66,1~F + 67,16=B + 68,16=G + 69,16~S
+ ~ 10, 16WSM + ~ 11, 16HED + ~ 12, 1tHOCC + 613, 16NMAR
14, 16DUR + ~ 15, 16FEC + ~ 16, 16 (AFB) 2 + ~ 17 16EF*WE D
+ ~ 18, 16E:F*HOCC + ~ 19, 16SCB*WED + ~ 20, 16SCB*HoCC ~
Equation {1.16) includes the same predictors as equation (1.15~. As will
be seen, however, contextual differences in our hypotheses about constitu-
ent relationships are much more pronounced for LF than for LOU. More
individual discretion is possible with LF than with any earlier components
of the fert' 1` ty process, In transitional settings, LF should be a func-
tion of family-size targets. Factors affecting the attainment of these
targets should also have an influence. In traditional settings, fam~ly-
size targets are less germane. Indeed, there is some question whether
well-defined numerical targets exist at all (Caldwell, 1977~. To the
extent that socioeconomic characteristics and early outcomes affect LF in
traditional contexts, they should do so via consequences for exposure,
fecundability, and health. Our hypotheses about the signs of almost all
of the coefficients in equation (1.16) depend on societal context. as
discussed more fully below.
Tr ens i t iona l Borate - at ,;
In transitional populations, there should be an inverse relationship
between socioeconomic status and LF, other things being equal. We view
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30
socioeconomic characteristics as critical determinants of implicit
family-size targets. Respondents who grew up in urban areas, live in
urban areas at the time of the survey, received some schooling, worked in
the modern sector before and after marriage, and are married to educated
and well-off husbands are likely to want small families and have low later
fertility (~31~16' 632~16, 64~16, 639~16r 6310~36, Allele, `312~16 ~ 0~.
This is due to exposure to small-family ideals and norms, and perhaps to
a decline in the influence of significant others who hold traditional
norms and ideals.
Given a specific family-size target, women having a large family by
their 30th birthday are closer to their target than are those with smaller
families. We therefore hypothesize an inverse relationship between early
and later fertility in transitional contexts (06,16 < 0~. High rates of
child survival imply closer proximity to a target (05,16 > 01. Hey may
also reassure parents about future mortality prospects, thereby reducing
the need for "insurance" births. Family-size targets can involve desired
minimum numbers of boys and girls, with the boy and girl sufficiency of
the early family expected to have the greatest impact in cultures with a
strong son preference (07,16' 68,16 < 0~. Since the strength of son
preference can vary across socioeconomic groups within cultures, we
include interactions between SCB and two socioeconomic characterist~cs--
respondent's education {WED) and husband's occupation tHOCC). We also
include interactions between these two socioeconomic variables and EF.
mese terms denote the possibility that the coincidence of small-family
norms and close proximity to a target produces a stronger fertility
response than that measured by an additive model. AS was the case in the
LOU equation, these interactions can also be interpreted in terms of
innovative behavior.
There should be a curvilinear relationship between AFB and LF, such
that early and late onset correspond to more children after age 30
{03,16 0~. The rationale for this specification is the
same here as for LCU. It is self-evident that women who are fecund have
higher fertility later in life than their less fecund counterparts, other
things being equal (015,16 ~ 0~. The adjustment variables control for
exposure (~4,16 > 0) and the fertility-enhancing effect of several
unions (313,16 ~ 0)
Traditional Contexts
The expected effects of the predetermined variables are quite different
in traditional contexts. Indeed, hypotheses about the impact of self-
reported fecundability and adjustment variables are the only ones
invariant across context. We assume that in traditional settings, early
and later fertility are equally unregulated. Consequently, respondents
with high early fertility will probably end up with more children later
in life than their less prolific counterparts (06,16 > 0~. Early child
mortality is not expected to exert much influence because any response
would imply fertility control (05,16 = 0~. For the same reason, boy and
girl sufficiency should matter little (07,16' 5~,16 = 0~. The absence of
relationships between early outcomes and LF removes the need for inter-
action effects (817,16' 018,16, 619,16, 620,16 = 0)
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31
mere should be a weak positive relationship between socioeconomic
status and later fertility in traditional settings. This hypothesis
derives from the positive health effects implied by relatively advantaged
social positions. Husband characteristics may prove vital in measuring
family well-being (B11 16, B12,16 > 0), especially nutritional levels and
overall health. Healthy women are more fecund. AFB may also reflect
social standing (63,16 < 0), as argued earlier. However, there is no
basis for positing a curvilinear relationship between AFB and LF in
traditional societies; hence in these settings, we would set 816,16 = 0.
Because of their correlations with health and fecundity, respondent char-
acteristics should also affect LF (~l,16, 82,16, 89,16, 610,16 > 0).
Each individual relationship should be weak both because there are so
many, and because they imperfectly approximate the underlying, unmeasured
variables.
1 .4 REDUCED AND SE==DUCED FOR IMPLICATIONS OF THE MODEL
His section discusses some implications of the model, first explaining
why its complexity needs to be reduced. Next, certain implications of
the model for three familiar variables--children ever born, contraceptive
use, and infant and child mortality--are traced. This leads to develop-
ment of implied and actual semireduced form equations for these
variables, followed by a discussion of inferences that can be drawn about
the coefficient signs in these equations.
The Need for Reduction
As shown above, the threefold distinction between onset, early fertility,
and later fertility permits development of an intricate and comprehensive
micro model embodying a number of untested hypotheses. This micro model
is not the total model, which would also incorporate macro variables to
explain coefficient variability across settings. We intend to continue
the theoretical development of the model begun in this paper elsewhere.
Our present focus is on testing the implications of the micro model. As
a starting point, Table 1.2 presented the predicted signs of the
structural coefficients in traditional and transitional settings implied
by the micro model.
How is the model to be tested? Its estimation for a single country
would permit one kind of test. In particular, where we have hypothesized
that a coefficient should have a positive sign in all settings, the
hypothesis could be rejected empirically if the sign were found to be in
the opposite direction. However, this would be a weak test. Among other
things, it applies only to those coefficients for which a single sign was
hypothesized. How can hypotheses be tested which relate to coefficients
for which sign variation across settings is possible? There will be
variation not only in the sign, but also in the magnitudes of the
coefficients. His variability is theoretically admissible and is
implicitly assumed to exist in the above discussion, since for a
coefficient to Travels from negative to positive, it must take on a
Representative terms from entire chapter:
child mortality
