Determinants of fertility

It is assumed that all women had their first child by age 40 in Kenya.This chapter takes a closer look at the proximate determinants that contributed to this decline by using the Bongaarts framework to quantify the effects on fertility of marriage patterns, contraception, postpartum infecundability, primary sterility, and abortion.

Determinants of fertility: Results from a 1989 rural

The reduction in this index is reflected almost exactly in the total fertility rate decline for Kenya as a whole, very closely for Central and Rift Valley provinces, and with reasonable agreement for Nairobi and Nyanza.Because of later entry into union for urban and well-educated women, C m had the strongest effect in inhibiting fertility among these two groups.A similar examination at the district level is precluded by the small sample sizes of the KFS.

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The national changes in these measures between the two surveys were small.Figure 5-2 Proximate determinants by education and residence, KFS and KDHS.It should be noted that the formula for C c (see the appendix at end of chapter for the exact calculation) has not been modified for each age group.

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As in many other regions of the world, contraceptive use was higher for women who were living in urban areas and were well educated.TABLE 5-9 Proximate Determinant and Fertility Indices from the KDHS as Percentages of KFS Measures.Of course, no one knows what TF really is, but Bongaarts and Potter (1983) estimated that it ranges from 13 to 17, with an average of approximately 15, Below is a description of each of the proximate determinants used in this analysis and how they affect fertility.Tables 5-7 and 5-8 present the results of the proximate determinants analysis for the KFS and the KDHS.

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FERTILITY DETERMINANTS - what-when-how

FERTILITY AT HIGH ALTITUDE 127 ographically clustered, and similar ethnic groups have been found to have widely vary-ing fertility.The doubts about the fertility measure for Siaya are discussed in Chapter 4.

In this report, the terms marriage and union are used interchangeably.At both times, Western and Rift Valley provinces had the lowest C i.However, because Bongaarts et al. (1984) used this approach in their analysis of the proximate determinants of sub-Saharan Africa, we have done the same for consistency.

A similar pattern occurs across most of the subgroups: Changing marriage patterns have resulted in slightly lower fertility.We have used the province-level I p as a proxy for each district.Case histories of primarily low-income urban women gathered in a study by Baker and Khasiani (1992) implied that abortion is fairly common, particularly among single and unemployed women.Because of the unreliability of these estimates due to very small sample sizes, we have not calculated I p for individual districts.This decline in C c is due to increasing contraceptive prevalence, since the method use-effectiveness mix has changed very little.Complete: Journals that are no longer published or that have been.

Because C i is calculated from the mean length of postpartum amenorrhea or abstinence, whichever is longer (see Appendix 5-1), the possible errors in the reported length of breastfeeding do not directly affect the estimate of C 1.Each issue includes a lively collection of book reviews and an archives section that brings to light historical writings with a resonance for contemporary population debate.This report tries to explain observed changes in fertility rates across OECD countries, with an emphasis on socio-economic considerations.Primary sterility was higher among those women with no education than among those with some education.TABLE 5-10 Reductions in Fertility and Proximate Determinants, KFS to KDHS (percent).As a measure, fertility rate is the number of offspring born per mating pair, individual or population.

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In sub-Saharan Africa, entry into union 1 has generally occurred at an early age, and although union dissolution is frequent in many regions, remarriage occurs rapidly (Cochrane and Farid, 1989).The discrepancy is not large, however, and seems to arise from the I p measure, which is particularly vulnerable.

Fertility transitions in sub -Saharan Africa: Establishing

The computational procedures used to estimate each index are described in the appendix to this chapter.

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