The Proximate Determinants of Exceptionally High Fertility
Author(s): John Bongaarts
Source: Population and Development Review, Vol. 13, No. 1 (Mar., 1987), pp. 133-139
Published by: Population Council
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Data and Perspectives
The Proximate
Determinants
of Exceptionally
Fertility
High
John Bongaarts
A not uncommon, but simplistic, view of changing reproductive behaviorduringthe transitionfrom high to low fertilityholds thata rise
in the practiceof contraceptionis virtuallythe only proximatecause of fertility
decline. In pretransitionalsocieties the prevalence of contraception(and inducedabortion)is typically negligible, so thatfertilitycan be considerednatural
(i.e., couples do not practice deliberateparity-specificbirthcontrol to achieve
family size goals). As a society develops, desired family size declines, causing
a rise in deliberate birth control and a reduction in fertility. Supporting
evidence for this view of the transitionin reproductivebehavior includes the
high degree of correlation (r = 0.92) between the total fertility rate and
the contraceptive prevalence level in contemporarypopulations (Bongaarts,
1984).1A simple regressionof the totalfertilityrateon contraceptiveprevalence
in 74 populations (ca. 1980) yielded the regression line given in Figure 1.
According to this regression, the total fertility rate equals, on average, 6.83
birthsper woman in the absence of contraception,and fertilitydeclines at a rate
of 0.62 birthsper woman for each 10 percentincrementin contraceptiveprevalence. Replacement fertility requires a prevalence level around 75 percent.
Although these results are derived from cross-sectionaldata, they are entirely
consistent with longitudinalobservationsin a numberof populations. For example, plots of trends in fertility and prevalence in Thailand (1969-83) and
Taiwan (1965-85) fall close to the regression line in Figure 1. Moreover, the
total fertility rate of the large majorityof populationsfalls within one birth of
the level predictedby this regression. No otherindicatorof reproductivebehavior predictsa population'sfertility betterthan contraceptiveprevalence.
There are a numberof developing countries, however, that have fertility
ratesfar in excess of what one would expect on the basis of the level of contraceptive use. (Interestinglyonly one country, Cambodia,had much lower than
POPULATION
AND
DEVELOPMENT
REVIEW
13, NO. 1 (MARCH
1987)
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133
Exceptionally
134
High Fertility
total fertility rate and
FIGURE 1 Relationship
between
line fitted to data
prevalence:
Regression
contraceptive
time series for Thailand and Taiwan;
from 74 countries;
for Yemen, Kenya, Syria, Jordan,
and single observations
and Zimbabwe
9
X Yemen
X Kenya
8
X Syria
X Jordan
7
X Zimbabwe
5 -
Regression line
(74 countries
ca.4
Thailand (1969-83)
\
198080)
4-4
Taiwan (1965-85)
3 -
2
0
10
20
30
40
50
60
70
80
Contraceptive prevalence (percent)
SOURCES: Chang et al. (1987); Knodel et al. (forthcoming);Bongaarts(1984)
and Table 1.
expected fertility in the late 1970s.) Table 1 comparesobserved total fertility
rates (TFRs) with rates estimatedfrom the regressionequationTFR = 6.83 0.062 x u (where u is contraceptiveprevalence as a percentageof currently
marriedwomen) in five countries with unexpectedly high fertility. "Excess"
fertility ranges from 1.7 birthsper woman in the Yemen Arab Republic (hereafter Yemen) and Kenya to 2.0 births per woman in Jordanand Zimbabwe.
While Yemen is the currentworld recordholderwith a total fertilityrateof 8.5,
it is worthnoting thatthe fertilityof the Hutteritesin the 1920s was still higher.
A demographer'sfirst reaction to the fertility estimates in Table 1 might well
be to blame measurementerror, but it is unlikely that errorsaccount for more
than a small proportionof this excess fertility because these data are derived
from surveys that are consideredto be of high quality. Moreover, in two of the
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135
John Bongaarts
total fertility rate, contraceptive
TABLE 1 Observed
prevalence
from prevalence
rate, total fertility estimated
level, and estimated
with exceptionally
excess fertility for five countries
high fertility
Country and
year of survey
Observed
TFR
Contraceptive
prevalence
(percent)
TFR estimated
from prevalence'
Excess fertility
(births per woman)b
Yemen (1979)
Kenya (1977)
Syria (1978)
Jordan(1976)
Zimbabwe(1984)
8.5
8.1
7.5
7.3
6.5
1
7
20
25
38
6.8
6.4
5.6
5.3
4.5
1.7
1.7
1.9
2.0
2.0
a
See text for estimation equation.
Equals difference between observed and estimated TFR.
SOURCES: Jordan, Departmentof Statistics (1979); Kenya, CentralBureau of Statistics (1980); Syria,
CentralBureauof Statistics (1982) and United Nations (1984); Yemen A. R., CentralPlanningOrganization
(1983); Zimbabwe National Family Planning Council (1985).
b
countries,Kenya and Jordan,othersurveys were conducteda few years earlier,
and the results of the two successive surveys were similar. A full explanation
for the high fertility of these populationsmust be found in reproductivebehaviors otherthan the practiceof contraception.
Clearly, contraceptiveprevalence is not the only proximatedeterminant
of fertility rates. Previous researchhas demonstratedthat the principalproximate determinantsof naturalfertility are the marriagepatternand the duration
and intensity of breastfeeding(Bongaartsand Potter, 1983). Since these two
factorsvary among societies, so does naturalfertility. In fact, a review of total
fertilityratesin societies with naturalfertilityfoundthatthe highestnaturalTFR
was more than twice the lowest (Leridon, 1977). Not only is naturalfertility
not the same in all populations, it also can vary over time if marriageor breastfeeding behaviorchanges. In societies where contraceptiveprevalenceis high,
the effects of variationsin the marriagepatternor in the durationof breastfeeding are modest because the large majorityof couples have access to effective
birthcontrolmethodsto adjustfertilityto desiredlevels. But in the early phases
of the transition,large proportionsof the populationdo not controlfertilitywith
contraceptionor inducedabortion,andin these cases variationsin the proximate
determinantsof naturalfertilitycan have a substantialimpacton actualfertility.
A plausiblehypothesis to explain the high fertilityof the five populations
in Table 1 is that marriageand breastfeedingbehaviorsin these societies exert
less restrainton fertility than is the case in other societies with the same level
of contraceptiveprevalence. Fortunately,it is possible to test this hypothesis
because the marriagepatternand the durationsof breastfeedingand associated
postpartuminfecundabilitycan be estimated from fertility survey data in four
of the five countries (data on postpartuminfecundabilityare not available for
Zimbabwe). The degree to which late marriageand short breastfeedingare
responsible for the excess fertility can then be determinedby comparingthe
observed and expected fertility-inhibitingeffects of these proximatevariables
at given prevalencelevels. The resultsof this exercise are summarizedin Figure
2, which presentsthe estimatedexcess fertilityrates (from Table 1) before and
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136
Exceptionally
High Fertility
FIGURE 2 Observed
excess
total fertility rates and
for duration of postpartum
effects of standardization
and marriage pattern
infecundability
- Observedexcess fertility
- Standardized
Yemen
Kenya
Syria
Jordan
excess fertility
2-
T,
T:
E
I
I
infecundabilityl
0
Ii
.;
0
Effect of standardizationfor:
durationof postpartum
t
j
marriage pattern
I
-1
afterstandardizationfor the effects of the marriageandbreastfeedingvariables.
This standardizationis carriedout separatelyfor each of these two proximate
determinants.2First, for each of the four populationsan estimateis made of the
fertilitychange that would occur if the durationof lactationalinfecundabilityis
adjustedto the level expected from the contraceptiveprevalencerate(the higher
the prevalence, the shorterthe expected durationof lactationalinfecundability).
The solid part of each arrow in Figure 2 indicates the adjustmentachieved in
this way. Next, the process is repeated for the marriagepattern;that is, the
fertilitychange resultingfrom a standardizationof the marriagepatternis estimated. The part of excess fertility attributableto the marriagepatternis indicated by the dashed partof each arrowin Figure2.
The results presentedin Figure 2 producea clearcutconclusion. In each
of these four countries both breastfeeding and the marriagepattern exerted
smallerfertility-inhibitingeffects than in other societies with the same level of
contraceptiveprevalence, and these differences account for a large partof the
excess fertility. Standardizingthe patternsof breastfeedingand marriagereduced excess fertility by more than half in Kenya, Jordan,and Syria, while in
Yemen the large excess turnedinto a small deficit. Shortdurationsof lactational
infecundabilitywere responsible for largerproportionsof excess fertility than
was early marriagein all four countries. It is also evident, however, that this
exercise has not resultedin a complete explanationof excess fertilityin Kenya,
Jordan,and Syria. Several factors, including measurementerrorsand atypical
levels of frequency of intercourseor other biological proximatedeterminants,
may accountfor this; but, withoutthe necessarydata, it is not possible to quantify the effects of these factors.
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137
John Bongaarts
The preceding discussion has focused on the proximatedeterminantsof
very high fertility at one point in time (i.e., the time of a recent fertility
survey). A brief comment on possible past trendsin fertility and contraceptive
prevalencewill conclude this note. It is plausible to assume that contraceptive
prevalencehas risen in each of the populationsexaminedhereexcept in Yemen,
where naturalfertility still prevailedin 1979. In the absence of a reliable series
of fertility surveys, it is much more difficult to evaluate trends in fertility.
Only in Kenya are fertility estimates available from censuses and surveys
starting in 1961. The sources suggest a substantialrise in Kenyan fertility
between the early 1960s and 1977, but the low quality of the earlier data
precludesa conclusive assessment (Kenya, CentralBureauof Statistics, 1980).
A simple, but admittedly crude, indicator of fertility trends can be obtained
from one survey by comparing currentfertility with the average number of
childrenever born to women aged 45-49, who did most of their childbearing
10 to 30 years before the survey. The results are shown in Table 2.
If taken at face value, these estimates are not directly consistent with
trendsin contraceptiveprevalencein the firstthreecountries.In Yemen a large
rise in fertility occurred in the absence of a trend in contraceptiveuse. In
Kenya a slight rise in fertility was accompaniedby a small increase in prevalence, and in Syria fertility remained virtually unchanged despite a very
substantialincrease in contraceptiveprevalence. Althoughat firstperhapspuzzling, these findings would be entirely plausible if breastfeeding,age at marriage, and/or maritaldisruptionhave declined over time. Each of these trends
in proximatedeterminantswould have pushed fertility higher, independentof
the trend in contraceptive prevalence. The absence of reliable estimates of
trends in these proximate determinantsprecludes a direct quantitativeassessment of their fertility impact. Only in Jordan is the decline in fertility approximatelyof the magnitude one would expect from the rise in prevalence.
Zimbabwe apparentlyalso experienced a fertility decline, but if prevalence
has indeed risen to 38 percent, a larger reductionwould have been expected.
Again, compensatingtrends in the marriagepatternor in the durationof postpartuminfecundabilitymay play a role, but it is also possible thatan unusually
low level of contraceptiveeffectiveness is partlyresponsiblefor the relatively
high fertility in Zimbabwe.
of time trends in fertility from a
TABLE 2 Indications
current
between
single survey based on difference
fertility and children ever born, five countries
Country
Children ever
born (average
for women 45-49)
Total fertility
rate (current)
Yemen (1979)
Kenya (1977)
Syria (1978)
Jordan(1976)
Zimbabwe (1984)
7.0
7.9
7.6
8.6
7.5
8.5
8.1
7.5
7.3
6.5
SOURCES: See Table 1.
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Estimated
fertility
change
+ 1.5
+0.2
-0.1
-1.3
- 1.0
Exceptionally
138
High Fertility
This brief review of the dynamics of fertility and its proximate determinants indicates that there is no necessary connection between trends in
fertility and contraceptiveprevalence at the onset of the fertility transition.It
is quite possible for fertility to remain constant or even rise temporarilyas
contraceptiveuse increases, because other proximate determinantscan exert
offsetting upward pressure on fertility. Similar conclusions were reached in
earlierstudies by Bongaarts(1981), Lesthaegheet al. (1981), and Nag (1980).
If contraceptive prevalence continues to rise, however, fertility inevitably
declines.
Notes
1 The total fertility rate equals the average
numberof births per woman by the end of the
reproductiveyears if fertility rates throughout
the childbearing years are equal to the rates
prevailing in the period for which the total
fertility rate is measured. Contraceptiveprevalence equals the percentage(or proportion)of
currently married women who are currently
practicing any form of contraception (male
methods included).
2 The standardizationfor the effect of each
proximate variable is accomplished in three
steps. First, the actual fertility-inhibitingeffects of the marriage pattern and lactational
infecundabilityin each of the four countries
are quantifiedwith the indexes C,mand Ci in
the proximatedeterminantsmodel describedin
detail by Bongaarts(1978) and Bongaartsand
Potter(1983). The values of Ci are calculated
from the durationof the postpartuminfecundability interval obtained by Singh and Ferry
(1984), giving estimates for Ci of 0.758,
0.694, 0.797, and 0.806 for, respectively,
Yemen, Kenya, Syria, and Jordan. Estimates
of C,mwere taken directly from Casterline et
al. (1984), giving values of 0.861, 0.775,
0.709, and 0.716 for the same set of countries.
In the second step of the standardizationprocedure the expected values of C' and Cmwere
calculated from the equations C' = (9.5 = (7.3
4.8 x u)I(15.3 - 13.7 x u)andC'
- 6.4 x u) / (9.5 - 4.8 x u), where u is
the level of contraceptive prevalence. These
equations are based on a regression analysis
discussed by Bongaartsand Potter (1983). Finally, standardizedtotal fertility rates are calculated. Fertility standardizedfor the duration
of postpartuminfecundabilityis estimated as
TFR x C' / Ci, and fertility standardizedfor
boththe durationof postpartuminfecundability
and the marriage pattern equals TFR x
C' x Cm/ (CmX C). Subtractingthese standardized total fertility rates from the actual
TFRs yields the adjustmentspresentedin Figure 2.
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John Bongaarts
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