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OCR for page 1
INTRODUCTION
Breastfeeding and contraception are the two key dis-
cretionary variables affecting fertility; that is, they
are the primary ways in which women may, through their
own behavior, influence when, if ever, they have a next
birth. Breastfeeding and contraceptive practice are
themselves influenced by socioeconomic characteristics.
In addition, a child death may truncate breastfeeding or
alter contraceptive behavior. Recent work has shown the
impor tance of these intermediate variables in accounting
for differences among populations in aggregate fertility
levels (Bongsarts, 1976, 1982). Biometric models and
clinical studies give us quite precise estimates of the
contribution of an additional month of breastfeeding or
contraception to the length of the birth interval (for a
summary of some of this work, see Leridon, 1977; Sheps
and Menken, 1973; Bong Marts, 1983; or Bongaarts and
Menken, 1983). However, the biometric models have relied
on mathematical simplifications, while the clinical
studies have been restricted to small samples, usually
based on local populations for which detailed prospective
data could be gathered.
In this paper, a model of the dynamics of childbearing
for the birth histories of individual women is developed
and applied to World Fertility Survey (WFS) data from
Colombia and Costa Rica. The analysis focuses on the
determinants of breastfeeding and contraception, and on
the ways they, in turn, influence fertility. The
sophisticated and precise biometric models are extended
so that they are applicable to the gross level of mea-
surement and heterogeneous samples of the retrospective
birth histories available from surveys. To the extent
that this analysis produces empir ical results comparable
to those expected from biometric models and clinical
1
OCR for page 2
2
studies, it both lends credibility to the use of so p is-
ticated modeling techniques with survey data and extends
the validity of the biometric mcdele to broader popula-
tions. To the extent that breastfeeding and contraceptive
use can be modeled, new insights into some of the behsv-
ioral aspects and the dynamics of the fertility decision-
making process are gained. The statistical techniques
used rely on estimation procedures recently developed for
the analysis of survival data and event histories (Cox,
1972; Kalbfleish and Prentice, 1980). m ese techniques
have previously been applied to the analysis of labor
force dynamics (Flinn and Beckman, 1982, Tuma et al.,
1979), marriage dissolution (Menken et al., 1981) ,
contraceptive discontinuation (Potter and Phillips, 1980)
and child survival (Truseell and Ha_ralough , 1983), as
well ~ to a wide variety of biomedical data. -their
application to fertility is a natural one (see, for
example, Singer and Beckman, 1982, Braun and Boem, 19797.
Previous work has segmented the birth interval into a
waiting time to conception, a period of gestation, and a
period of postpartum amenorrhea. Typically, each of
these segments teas been modeled separately. The length
of ache waiting time to conception has been assumed to be
dependent upon fecundity, the monthly risk of conception.
The lengths of gestation and postpartum amenor~nea were
taken as either constant or Weir distributions were
modeled separately. The modeling strategy adopted here
calculates conception rates (which may be equal to zero)
by duration, disregarding segmentation of the birth
interval. mat is, it replaces the idea of fecundability
with a conception rate dependent on bre~tfeeding and
contraceptive use, and on a biologically determined
propensi~cy to conceive that is dependent on the time
elapsed since the last birch. We question is whether,
given the reported data on duration of breastfeeding and
duration of contraceptive use from retrospective surveys
like the WFS, we can estimate ache impact of breastfeeding
and contraceptive practice on fertility as measured
through birth interval length and parity progression.
The basic idea is that at each point in time since the
previous birth, there is some risk of conception. This
risk is influenced primarily by breastfeeding and
contraception, as well as by socioeconomic factors . I n
addition, it is well known that infant mortality can
influence the length of the birth interval, either
because breastfeeding stops when the child dies or
because contraceptive practice changes. A similar
~ ~ _ _
OCR for page 3
3
strategy is adopted for Analyzing termination rates for
breastfeeding and contraceptive use. Just as there is
some risk of conception at each point in time from the
previous birth, there is some risk of terminating breast-
feeding or contraceptive use at each point in time after
these behaviors begin. Infant mortality influences the
conception rate by changing these risks. Similarly, the
principal impact of the socioeconomic variables on fer-
tility can be expected to operate through the decision to
breastfeed or contracept and through the duration of
these practices.
In what follows the data from the World Fertility
Surveys for Colombia and Costa Rica are described along
w ith the social, economic, and demographic settings of
these two countries for the period 1960 to 1976. A
three-stage scheme for the analysis of breastfeeding,
contraception, and fertility is also described. The
stages of the model include: first, defining a set of
background covariates that predict whether or not a woman
breastfed and whether she used contraception; second,
modeling durations of brea~tfeeding and of contraceptive
use; third, development of a biometrically-based model of
the interval between births. Since the theory for the
last stage is most fully developed, the scheme in
described in reverse order in the text. Estimation
procedures for descriptive statistics and for the equa-
tions specified are given next, along with a description
of the birth intervals sampled and definitions of the
variables used. This is followed by a set of descriptive
r esults, the f inal results, and a sugary.
Representative terms from entire chapter:
biometric models
