1. 1 Fertility Transition in Kenya: A Regional Analysis of the Proximate Determinants By Ekisa L Anyara Dr Andrew Hinde School of Social Sciences and Southampton Statistical Sciences Research Institute University of Southampton Southampton SO17 1BJ United Kingdom Paper prepared for the British Society for Population Studies Annual Conference, 12-14 September 2005, University of Kent at Canterbury.

2. 2 Presentation outline Introduction Kenya Objectives of the Study Data & Methods (Proximate Determinants Model) Confirming the transition Effects of the Proximate Determinants Summary and Conclusion

3. 3 Fertility Transition The study of Human fertility is important. Drastic change in fertility may trigger undesirable changes in other processes of human life Fertility transition has taken place in all continents except in most of Africa. The transition is currently underway in some African countries: Botswana and Kenya. This paper focuses on fertility transition in Kenya.

4. 4 Kenya: Socio-economic setting

5. 5 Kenya Mortality and Life expectancy

6. 6 Study Objective To demonstrate the extent of regional variation in fertility decline in Kenya. To determine the potential role of the proximate determinants in explaining regional patterns of fertility in Kenya since the 1980s. The study question is: What is the contribution of each of the proximate determinants in the regional differentials in fertility in Kenya?

7. 7 Data and methods Data The current study uses Kenya DHS data collected in 1989, 1993, 1998 and 2003. Analysis is based on original districts which are treated as regions Some districts within provinces have been combined into one region Twenty regions have been studied Findings for fifteen regions are presented Computation of fertility rates is based on exact exposure to risk within a four year window We use the proximate determinants model to compute the indexes.

8. 8 Data and Methods The Proximate Determinants Model Bongaarts (1982) distinguished four variables that are mainly responsible for fertility variation among populations. These are: The proportion of women married Contraceptive use Induced abortion and Postpartum infecundity These four variables were quantified using four coefficients namely, C m is the index of marriage, C c the index of contraception, C a the index of Induced abortion and C i the index of lactational infecundity. The total fertility rate TFR is partitioned into the effects of the above four variables using the equation TFR = C m. C c.C a.C i.TF. Induced abortion is not included in the current study

9. 9 Data and methods The Proximate Determinants model The indexes measure the fertility reducing effect of the respective proximate determinants Each index takes only values from 0 to 1. A value of 0 means that the determinant completely inhibits fertility while a value of 1 means that it has no effect on fertility. We have reversed the strength of the values for ease of interpretation in some parts of the presentation

10. 10 Data and Methods Modified versions of Bongaarts’ Indexes We present the fertility inhibiting effects of the modified versions of the original Indexes of Bongaarts model. This are: C m* the index of marriage- no births outside union, C c* the index of contraception- no Infecundability consideration C s the index of sterility due to all causes and C i* the index of Postpartum Insusceptibility M o a measure of the proportion of births outside marriage The differences are highlighted The fertility inhibiting effects of the modified indexes in births per woman is not presented.

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13. 13 Pattern of fertility decline in Kenya 1989- 2003

14. 14 Explanation to Kenya’s fertility Decline Kenya’s fertility decline may have resulted from: A rise in living standards and declines in child mortality (Brass et al. 1993). Massive external pressures (Dow et al. 1994). Increased use of contraceptive methods (Cross et al. 1991, Blacker 2002). These explanations are neither clear nor conclusive. They do not account for the regional fertility differential in Kenya. Fertility decline in areas with low contraceptive use is not explained. The effect of the proximate determinants is little known

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23. 23 Summary & Conclusion Kenya’s fertility has declined by 37 per cent since 1978 Pastoral regions show gains in fertility Low fertility in the urban regions of Nairobi and Mombasa appear to be partly a function of marital patterns Low fertility in some rural regions which according to literature have high human development Index tends to be explained by contraception. The effect of sterility due to all causes is increasing considerably especially in regions with low fertility The effect of Postpartum Non-susceptibility is highest in regions other than the urban ones Kenya’s fertility decline appears to have been driven by other factors and also by contraception as far as the current analysis of the proximate determinants is concerned.