Demographic Change in Sub-Saharan Africa (1993) / Chapter Skim
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3 The Approximate Determinants of Fertility
3 The Approximate Determinants of Fertility
Pages 68-116
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From page 68... ...
and World Fertility Surveys (WFS) , this chapter examines the relative effects of four proximate determinants on fertility: marriage patterns, contraceptive use, postpartum infecundability, and primary sterility.
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From page 69... ...
(1984) enumerate nine major proximate determinants of fertility at the societal level: marriage or union patterns, contraception, lactational amenorrhea, 4.
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From page 70... ...
70 in · - ~ a)
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From page 71... ...
For example, delayed entry into marriage, use of family planning methods, and prolonged breastfeeding or postpartum abstinence are factors that reduce fertility to levels lower than those that would occur in the absence of these proximate determinants. Below is a description of the proximate determinants used in this analysis, the way these factors influence fertility through inhibiting TF, and the computational procedure used to estimate the indices.
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From page 72... ...
C'm, a modified version of Cm, captures the effect on total fertility of the specific observed union pattern, under the assumption that no births occur outside unions. The product of Mo and C'm is Cm, the usual definition of the effects of marriage patterns on fertility used in the Bongaarts model.
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From page 73... ...
. C'm can be thought of as the effect of reported union patterns on fertility if births occur only in unions, and Cm is the combined result of the fertility-inhibiting effect of union pattern and the fertility-promoting effect of sexual relations outside union.
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From page 75... ...
It is difficult to interpret such a result in the context of a proximate determinants analysis because it suggests that low levels of primary infecundity increase fertility. When calculating Ip with the data used here, most of the indices were greater than 1.
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From page 76... ...
The column, which represents TF, is divided into five segments. The solid base at the bottom indicates the observed total fertility rate based on the reported number of births occurring in the four years prior to the survey.
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From page 77... ...
. NOTE: PPI: Postpartum infecundability; DHS data for Sudan refer to only northern Sudan.
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(As explained in the description of the framework, the lower the index, the greater inhibiting effect it has on fertility.) The value of C'm for Botswana suggests that union patterns (relatively late age at marriage and substantial proportions of women not in union)
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From page 80... ...
Burundi, Mali, Ondo State, Senegal, and Uganda have relatively high Cc values, all greater than 0.95, reflecting very low contraceptive prevalence rates and ineffective method mixes. For example, Cc in Mali is 0.98, based on a contraceptive prevalence of 3.3 percent, with most women using traditional methods.
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From page 81... ...
There, the index was 0.97, indicating that primary sterility reduced fertility by an average of only 0.40 birth per woman. Interpreting the Results Using the proximate determinants and data from the WFS and DHS yields national-level TF estimates ranging from 12.1 to 16.5 (see Tables 3-4 and 3-74.
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From page 82... ...
82 ~D O E~ ._ ca _ Ct ~ 4o o ~ o o o bC ¢ 4 .~ L o ·_4 4 a' Ct ·_4 o ¢ E~ a' 4 Ct ~^ o a~ ._ ·_.
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From page 84... ...
84 sit au 5)
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From page 85... ...
For example, parity progression ratios indicate a stopping pattern in Kenya (DHS; see Chapter 2~; in some of the countries, particularly those with low contraceptive prevalence rates, contraceptive use is concentrated in the oldest age group; and in some countries, dates of first union are relatively late. A third factor not accounted for in our application of the model is the incidence of induced abortion.
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From page 86... ...
At a minimum, observed fertility would increase by 4.8 births in Botswana and, at a maximum, by 8.2 births in Ondo State in the absence of breastfeeding and postpartum abstinence. The practice of spacing children for the health of the child and mother still continues to exhibit a powerful fertility-reducing effect.
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From page 87... ...
In all three age groups, the index of postpartum infecundability has the strongest effect of the indices on reducing TF. Thus, the variation in fertility across age groups in Ondo State is attributable principally to different marriage patterns.
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From page 88... ...
NOTE: PPI: Postpartum infecundability. patterns in Zimbabwe reduce fertility more in each of the age groups than they do in most of the other DHS populations.
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From page 89... ...
Cc has a greater effect in inhibiting fertility among urban women due to higher contraceptive prevalence and use of more effective methods. The index of postpartum infecundability Ci is lower among women in rural areas, indicating that either breastfeeding or postpartum abstinence has a stronger fertility-inhibiting effect there.
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From page 90... ...
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From page 92... ...
92 Cal U
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From page 95... ...
However, when C'm is examined, all but Botswana, Mali, and Ondo State show a monotonically negative effect of union patterns on fertility as education increases. The inhibitive effect of postpartum infecundability on fertility decreases with education, except in Botswana, Mali, and Zimbabwe.
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From page 96... ...
The effect of postpartum infecundability decreases monotonically with education, although Ci is the most important proximate determinant in reducing fertility across all education groups.
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From page 97... ...
The most important determinant of the change in TFR of 1.5 births was a drop in the index of contraception, from Births per woman (log scale) 10 8 6 3 2 WFS DHS WFS DHS WFS DHS WFS DHS Ghana Kenya Senegal Sudan _ Birthe ~ Union Patterns 1~ Contraceptive Use ~ PPI ~0 Intecundity FIGURE 3-5 Comparison of WFS and DHS data of the relationship between the fertility-inhibiting effects of the proximate determinants and various measures of fertility.
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From page 98... ...
98 50 Pa ~ lo, O EM .
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From page 99... ...
At the time of the DHS (1988-1989) , contraceptive prevalence in Kenya was estimated at 26.8 percent, with 5.2 percent using pills, 3.7 percent using IUDs, 4.7 percent using sterilization, and 13.2 percent using other, mainly traditional, methods.
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From page 100... ...
The proportion of never-married women had risen by 12 percentage points in the 10 years since the Sudan Fertility Survey. Postpartum infecundability had a large effect in reducing fertility at both times, but it did not exhibit as much of a decrease between the two periods as Cm.
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From page 101... ...
Although prolonged breastfeeding and postpartum abstinence are not universal in sub-Saharan Africa, they generally play an important role in spacing births and reducing total fertility. Marriage patterns also reduce fertility substantially in many populations.
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From page 102... ...
Although some of the indices changed in importance over time, postpartum infecundability continues to be the greatest fertility-inhibiting proximate determinant for all four populations. TECHNICAL NOTES Data Sources · DHS Ondo State, Nigeria A question on whether the survey respondent was currently amenorrheic was not asked, so the question (V215)
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From page 103... ...
Because the weights developed for the denominators of the TFRs were not included in the WFS Sudan standard recode file, the TFRs used in this analysis are taken from Volume 1 of The Sudan Fertility Survey 1979, Principal Report, Vol. 1 (Sudan, 1982)
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From page 104... ...
This assumption leads to an underestimation of TUFR, resulting in an overestimation of Mo (or the effect of childbearing outside of union on fertility) , as well as an overestimation of the effect of union patterns on fertility (i.e., an underestimation of C'm)
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From page 105... ...
Pill IUD Sterilization Other modern methods Traditional methods Index of Postpartum Infecundability 0.90 0.95 1.00 0.70 0.30 The index of postpartum infecundability is calculated as Ci = 20/~18.5 + i) , where i is the mean number of months of postpartum infecundability (estimated as the mean number of months of postpartum amenorrhea or abstinence, whichever is longer)
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From page 106... ...
For example, by using the formulas outlined above, the effects of the proximate determinants for Botswana would be as follows (see Table 3-2 and Figure 3-1) in terms of number of births: Proximate determinant Marriage patterns Contraception Postpartum infecundability Primary sterility Number of births 0.74 2.45 4.84 o If the order in which each variable is calculated is reversed, the results would be as follows (number of births)
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From page 107... ...
APPENDIX The tables that follow present the proximate determinants of fertility by age (Table 3-A.1) , by urban and rural residence (Table 3-A.2)
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From page 108... ...
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From page 112... ...
112 au ;, ~ PI O EN V, Ct o .~ Ct o > · _4 o Ct 4 Ct .~ o so ¢ 1 ¢ EN ~ ^ o X ·V' _4 ;^ ~ .
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From page 113... ...
113 ~ ~ oo ~ ~ .
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From page 114... ...
Page, and R Lesthaeghe 1977 Breast-feeding and postpartum abstinence in metropolitan Lagos.
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From page 115... ...
International Union for the Scientific Study of Population and World Fertility Survey, London. Mhloyi, M.M.
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From page 116... ...
Paper presented at the International Union for the Scientific Study of Population Seminar on the Biomedical and Demographic Determinants of Human Reproduction. Johns Hopkins University, Baltimore, Md.
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