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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 - PowerPoint PPT Presentation
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Fertility Transition in Kenya: A Regional Analysis of the
Proximate DeterminantsBy
Ekisa L AnyaraDr Andrew Hinde
School of Social Sciences and Southampton Statistical Sciences Research InstituteUniversity of Southampton
Southampton SO17 1BJUnited Kingdom
Paper prepared for the British Society for Population Studies Annual Conference, 12-14 September 2005, University of Kent at
Canterbury.
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Presentation outlineIntroduction KenyaObjectives of the Study Data & Methods (Proximate Determinants Model)
Confirming the transitionEffects of the Proximate DeterminantsSummary and Conclusion
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Fertility TransitionThe study of Human fertility is important.Drastic change in fertility may trigger undesirable changes in other processes of human lifeFertility 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.
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Kenya: Socio-economic setting
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1989 1993 1995 1997 1999 2003
HumanPovertyIndex
AbsolutePoverty
PrimarySchoolenrolment
SecondarySchoolenrolment
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Kenya Mortality and Life expectancy
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20
40
60
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1962 1969 1979 1989 1999Year of Census
Life
exp
ecta
ncy
and
Infa
nt m
orta
lity
rate
s
InfantMortality
LifeExpectancy
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Study ObjectiveTo 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?
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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 regionsSome districts within provinces have been combined into one region Twenty regions have been studiedFindings for fifteen regions are presented Computation of fertility rates is based on exact exposure to risk within a four year windowWe use the proximate determinants model to compute the indexes.
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Data and MethodsThe 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,
Cm is the index of marriage, Cc the index of contraception, Ca the index of Induced abortion and Ci the index of lactational infecundity.
The total fertility rate TFR is partitioned into the effects of the above four variables using the equation
TFR = Cm.Cc.Ca.Ci.TF.
Induced abortion is not included in the current study
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Data and methodsThe 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
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Data and MethodsModified versions of Bongaarts’ Indexes
We present the fertility inhibiting effects of the modified versions of the original Indexes of Bongaarts model. This are:
Cm* the index of marriage- no births outside union, Cc* the index of contraception- no Infecundability consideration Cs the index of sterility due to all causes and Ci* the index of Postpartum InsusceptibilityMo a measure of the proportion of births outside marriage
The differences are highlightedThe fertility inhibiting effects of the modified indexes in births per woman is not presented.
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Region KFSAbsolute difference
Realtive Decline
1978 1989 1993 1998 20031989-2003
1989-2003
KENYA 7.9 6.6 5.6 4.7 5.0 -1.6 24.9Nairobi 4.5 3.4 2.6 2.7 -1.8 40.4Muranga 5.8 4.4 4.4 3.7 -2.1 36.0Nyeri/Nyandarua/… 5.7 3.7 3.3 3.6 -2.1 37.1Kilifi/Kwale 6.4 5.8 6.0 6.4 0.0 0.5Mombasa 4.3 3.5 3.2 3.2 -1.2 26.9Machakos/Kitui 7.7 6.2 4.8 5.8 -1.9 24.9Meru/Embu 5.9 5.6 3.9 3.6 -2.3 39.5Kisii 6.9 5.9 4.2 4.5 -2.5 35.3Siaya 6.3 5.9 5.1 5.6 -0.7 11.7South Nyanza 6.8 6.8 6.4 5.7 -1.0 15.4Kericho 8.2 6.6 5.5 6.6 -1.6 19.3Uasin-Gishu 6.8 5.5 5.4 4.7 -2.2 31.7Narok/Kajiado 6.4 6.8 6.5 8.2 1.4 20.6Baringo/Laikipia/… 5.3 6.1 5.7 6.3 1.0 17.8Bungoma/Busia/… 8.2 7.2 6.6 6.3 -1.9 23.0Kakamega 7.3 6.1 5.2 5.2 -2.0 28.2
KDHS
Total fertility Rates by year of SurveyTrends in Kenya's Fertility decline, 1989-2003
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Pattern and trend of fertility transition in Kenya 1989-2003
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1979 1989 1993 1998 2003Year
TFR
KENYANAIROBIMURANGANYERIKILIFIMOMBASAMACHAKOSMERUKISIISOUTH NYANZAKERICHONAROKBARINGOUASIN-GISHUBUNGOMAKAKAMEGA
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Pattern of fertility decline in Kenya 1989-2003
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Explanation to Kenya’s fertility DeclineKenya’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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Cm Cc Ci Cp Cm * Mo Cc * Ci * Cs
Region TFR/TMFR.Origina. model
MeanD of BreastF equat.
Pathol. Sterility N
TUFR/ TMFR
Births out side Union
Infecundity Consid. removed
Postpart. Insuscept.
Sterility from all causes
KENYA 0.83 0.80 0.61 1.04 4765 0.70 1.18 0.81 0.67 0.81NAIROBI 0.77 0.71 0.67 1.04 519 0.59 1.30 0.73 0.75 0.71MURANGA 0.77 0.71 0.66 1.04 227 0.59 1.32 0.73 0.72 0.77NYERI 0.75 0.60 0.65 1.05 499 0.66 1.13 0.63 0.66 0.78KILIFI 0.81 0.91 0.62 1.05 364 0.77 1.06 0.99 0.68 0.75MOMBASA 0.76 0.78 0.69 1.05 147 0.63 1.20 0.80 0.82 0.68MACHAKOS 0.85 0.79 0.64 1.05 341 0.69 1.22 0.81 0.71 0.89MERU 0.79 0.65 0.55 1.05 220 0.64 1.25 0.68 0.67 0.83KISII 0.83 0.82 0.66 1.05 245 0.71 1.17 0.83 0.63 0.84SOUTH NYANZA 0.89 0.96 0.64 1.05 290 0.89 1.14 0.96 0.60 0.78KERICHO 0.88 0.83 0.60 1.05 267 0.78 1.12 0.85 0.70 0.91NAROK 0.99 0.76 0.66 1.04 56 0.92 1.07 0.77 0.54 0.87BARINGO 0.79 0.76 0.67 1.05 66 0.67 1.18 0.78 0.84 0.77UASIN-GISHU 0.80 0.86 0.67 1.05 235 0.70 1.13 0.87 0.74 0.88BUNGOMA 0.86 0.91 0.60 1.05 410 0.79 1.09 0.92 0.64 0.85KAKAMEGA 0.83 0.87 0.63 1.05 335 0.75 1.11 0.88 0.67 0.86
Cm Underestimates the inhibiting effect of marital patterns on fertility
Effects of the Proximate Determinants on Fertility 1989Indexes of the Original Bongaarts Model Modified versions of the Original Indexes
Cc & Ci Overestimate the inhibiting effect of Contraception and Lactaional Infecundity on fertility
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Cm Cc Ci Cp Cm * Mo Cc* Ci* Cs
RegionTFR/ TMFR.
Origina. model
MeanD of BreastF Equat.
Pathol. Sterility N
TUFR/ TMFR
Births out side Union
Infecundity Consid. removed
Postpart. Insuscept.
Sterility due to all causes
KENYA 0.74 0.70 0.62 1.04 4919 0.63 1.18 0.72 0.66 0.75NAIROBI 0.56 0.57 0.67 1.04 567 0.45 1.25 0.60 0.77 0.67MURANGA 0.76 0.50 0.68 1.05 119 0.58 1.30 0.54 0.69 0.69NYERI 0.68 0.45 0.66 1.05 345 0.55 1.23 0.49 0.63 0.63KILIFI 0.89 0.89 0.57 1.04 234 0.76 1.16 0.90 0.65 0.80MOMBASA 0.62 0.71 0.69 1.04 175 0.51 1.23 0.73 0.78 0.66MACHAKOS 0.77 0.70 0.57 1.04 321 0.64 1.21 0.72 0.66 0.77MERU 0.68 0.48 0.56 1.04 238 0.58 1.19 0.52 0.72 0.66KISII 0.76 0.62 0.68 1.04 235 0.65 1.16 0.64 0.65 0.66SOUTH NYANZA0.89 0.69 0.64 1.04 229 0.79 1.14 0.72 0.64 0.76KERICHO 0.83 0.69 0.65 1.05 147 0.72 1.15 0.72 0.71 0.81NAKURU 0.73 0.70 0.70 1.05 146 0.61 1.20 0.72 0.64 0.69NAROK* 0.90 0.82 0.55 1.05 143 0.77 1.16 0.83 0.47 0.85BARINGO 0.80 0.84 0.63 1.04 149 0.73 1.09 0.85 0.57 0.91UASIN-GISHU 0.70 0.72 0.66 1.04 127 0.57 1.22 0.74 0.68 0.72BUNGOMA 0.77 0.76 0.63 1.04 266 0.69 1.12 0.78 0.69 0.86KAKAMEGA 0.79 0.72 0.63 1.05 328 0.69 1.15 0.74 0.67 0.81The fertility inhibiting effect of Cs is increasing over time surpassing contraception in some areas
Effects of the Proximate Determinants on Fertility 2003Indexes of the Original Bongaarts Model Modified versions of the Original Indexes
The fertility inhibiting effect of Cs is most felt in low fertility areas
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Effect of each of the Proximate Determinants 1989
0
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CM CC CI CP
Proximate Determinants
Inhi
bitio
n in
Birt
hs p
er W
oman KENYA
NAIROBIMURANGANYERIKILIFIMOMBASAMACHAKOSMERUKISIISOUTH NYANZAKERICHONAROKBARINGOUASIN-GISHUBUNGOMAKAKAMEGA
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Effect of each of the Proximate Determinants 1993
0
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CM CC CI CPProximate Determinants
Inhi
bitio
n in
Birt
hs p
er w
oman
KENYANAIROBIMURANGANYERIKILIFIMOMBASAMACHAKOSMERUKISIISOUTH NYANZAKERICHONAROKBARINGOUASIN-GISHUBUNGOMAKAKAMEGA
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Effect of each of the proximate Determinants 1998
0
1
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CM CC CI CPProximate Determinants
Inhi
bitio
n in
Birt
hs p
er w
oman KENYA
NAIROBIMURANGANYERIKILIFIMOMBASAMACHAKOSMERUKISIISOUTH NYANZAKERICHONAROKBARINGOUASIN-GISHUBUNGOMAKAKAMEGA
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Effect of each of the proximate Determinants 2003
0
1
2
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4
5
6
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8
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CM CC CI CPProximate Determinants
Inhi
bitio
n in
Birt
hs p
er w
oman
KENYANAIROBIMURANGANYERIKILIFIMOMBASAMACHAKOSMERUKISIISOUTH NYANZAKERICHONAROKBARINGOUASIN-GISHUBUNGOMAKAKAMEGA
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The relationship between fertility and the proximate determinants
0.0
0.1
0.2
0.3
0.4
0.5
0.6
2 3 4 5 6 7 8
TFR
Prox
imat
e D
eter
min
ants
Inde
x
Linear (1-Cc)Linear (1-cm*)Linear (1-Ci)
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The relatioship between fertility and the proximate determinants including Cs
0.0
0.1
0.2
0.3
0.4
0.5
0.6
2 3 4 5 6 7 8 9TFR
Prox
imat
e D
eter
min
ants
Inde
x
Linear (1-Cm*)Linear (1-Ci)Linear (1-Cc)Linear (1-Cs)
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Summary & ConclusionKenya’s fertility has declined by 37 per cent since 1978Pastoral 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 fertilityThe 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.