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.
1
Presentation outline
Introduction
Kenya
Objectives of the Study
Data & Methods (Proximate Determinants Model)
Confirming the transition
Effects of the Proximate Determinants
Summary and Conclusion
2
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.
3
Kenya: Socio-economic setting
100
90
Human
Poverty
Index
80
70
Absolute
Poverty
60
50
40
Primary
School
enrolment
30
20
Secondary
School
enrolment
10
0
1989
1993
1995
1997
1999
2003
4
Life expectancy and Infant mortality rates
Kenya
Mortality and Life expectancy
160
140
120
Infant
Mortality
100
80
Life
Expectancy
60
40
20
0
1962
1969
1979
1989
1999
Year of Census
5
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?
6
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.
7
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,
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
8
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
9
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:
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 Insusceptibility
Mo 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.
10
Trends in Kenya's Fertility decline, 1989-2003
Total fertility Rates by year of Survey
Absolute Realtive
Region
KFS
KDHS
difference Decline
198919891978 1989
1993
1998
2003
2003
2003
KENYA
7.9
6.6
5.6
4.7
5.0
-1.6
24.9
Nairobi
4.5
3.4
2.6
2.7
-1.8
40.4
Muranga
5.8
4.4
4.4
3.7
-2.1
36.0
Nyeri/Nyandarua/…
5.7
3.7
3.3
3.6
-2.1
37.1
Kilifi/Kwale
6.4
5.8
6.0
6.4
0.0
0.5
Mombasa
4.3
3.5
3.2
3.2
-1.2
26.9
Machakos/Kitui
7.7
6.2
4.8
5.8
-1.9
24.9
Meru/Embu
5.9
5.6
3.9
3.6
-2.3
39.5
Kisii
6.9
5.9
4.2
4.5
-2.5
35.3
Siaya
6.3
5.9
5.1
5.6
-0.7
11.7
South Nyanza
6.8
6.8
6.4
5.7
-1.0
15.4
Kericho
8.2
6.6
5.5
6.6
-1.6
19.3
Uasin-Gishu
6.8
5.5
5.4
4.7
-2.2
31.7
Narok/Kajiado
6.4
6.8
6.5
8.2
1.4
20.6
Baringo/Laikipia/…
5.3
6.1
5.7
6.3
1.0
17.8
Bungoma/Busia/…
8.2
7.2
6.6
6.3
-1.9
23.011
Kakamega
7.3
6.1
5.2
5.2
-2.0
28.2
Pattern and trend of fertility transition in Kenya 1989-2003
9
KENYA
NAIROBI
8
MURANGA
NYERI
KILIFI
MOMBASA
MACHAKOS
7
TFR
6
MERU
KISII
SOUTH NYANZA
KERICHO
NAROK
5
4
BARINGO
UASIN-GISHU
BUNGOMA
KAKAMEGA
3
2
1979
1989
1993
1998
2003
Year
12
Pattern of fertility decline in Kenya 19892003
Turkana
Mandera
Legend
Moyale
Marsabit
Decline of over 30%
Stagnation from 1998
Wajir
West Pokot
Samburu
Trans Nzoia
High steadily declining fertility
Marakwet
Isiolo
Mt Elgon
Baringo
Teso
Lugari/Marava-Lugari
Bungoma
Uasin Gishu
Keiyo
Kakamega
Busia
Butere/Mumias
Siaya
Bondo
Laikipia
Sharp rise from 1998
Meru North
Nandi
Koibatek
Vihiga
Meru Central
Kisumu
Tharaka
Nyando
Kericho
Nyandarua
Rachuonyo
Buret
Suba
Nyamira/Kisii North
Homa Kisii
Bay
Slight rise from 1998
Embu
Kirinyaga
Nakuru
Muranga
Gucha/Kisii South Bomet
Migori
Kuria
Garissa
Meru South
Nyeri
Mbeere
Mwingi
Maragua
Thika
Kiambu
Trans Mara
Narok
Nairobi
Ijara
Machakos
Least decline of 1%
Tana River
Kitui
Lamu
Kajiado
Makueni
Malindi
Fertility gain from 1993
Area not covered in the study
Taita Taveta
Kilifi
Kwale
Mombasa
13
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
14
Effects of the Proximate Determinants on Fertility 1989
Indexes of the Original Bongaarts Model
Cm
Cc
Ci
Cp
Region
MeanD of
Origina. BreastF
Pathol.
TFR/TMFR.
model
equat.
Sterility
KENYA
NAIROBI
MURANGA
NYERI
KILIFI
MOMBASA
MACHAKOS
MERU
KISII
SOUTH NYANZA
KERICHO
NAROK
BARINGO
UASIN-GISHU
BUNGOMA
KAKAMEGA
Modified versions of the Original Indexes
Cm *
Mo
Cc *
Ci *
Cs
0.83
0.77
0.77
0.75
0.81
0.76
0.85
0.79
0.83
0.89
0.88
0.99
0.79
0.80
0.86
0.83
0.80
0.71
0.71
0.60
0.91
0.78
0.79
0.65
0.82
0.96
0.83
0.76
0.76
0.86
0.91
0.87
0.61
0.67
0.66
0.65
0.62
0.69
0.64
0.55
0.66
0.64
0.60
0.66
0.67
0.67
0.60
0.63
1.04
1.04
1.04
1.05
1.05
1.05
1.05
1.05
1.05
1.05
1.05
1.04
1.05
1.05
1.05
1.05
N
4765
519
227
499
364
147
341
220
245
290
267
56
66
235
410
335
TUFR/
TMFR
0.70
0.59
0.59
0.66
0.77
0.63
0.69
0.64
0.71
0.89
0.78
0.92
0.67
0.70
0.79
0.75
Births out Infecundit
Sterility
side
y Consid. Postpart. from all
Union
removed Insuscept. causes
1.18
1.30
1.32
1.13
1.06
1.20
1.22
1.25
1.17
1.14
1.12
1.07
1.18
1.13
1.09
1.11
0.81
0.73
0.73
0.63
0.99
0.80
0.81
0.68
0.83
0.96
0.85
0.77
0.78
0.87
0.92
0.88
0.67
0.75
0.72
0.66
0.68
0.82
0.71
0.67
0.63
0.60
0.70
0.54
0.84
0.74
0.64
0.67
0.81
0.71
0.77
0.78
0.75
0.68
0.89
0.83
0.84
0.78
0.91
0.87
0.77
0.88
0.85
0.86
15
Cc & Ci Overestimate the inhibiting effect of Contraception and Lactaional Infecundity on fertility
Cm Underestimates the inhibiting effect of marital patterns on fertility
Effects of the Proximate Determinants on Fertility 2003
Indexes of the Original Bongaarts Model
Cm
Cc
Ci
Cp
MeanD
of
TFR/ Origina. BreastF Pathol.
TMFR. model Equat. Sterility N
Modified versions of the Original Indexes
Cm *
Mo
Cc*
Ci*
Cs
Births
out
TUFR/ side
TMFR Union
Sterility
Infecundity Postpart. due to
Consid.
Insuscep all
removed
t.
causes
Region
KENYA
0.74
0.70
0.62
1.04 4919 0.63 1.18
0.72
0.66
NAIROBI
0.56
0.57
0.67
1.04
567 0.45 1.25
0.60
0.77
MURANGA
0.76
0.50
0.68
1.05
119 0.58 1.30
0.54
0.69
NYERI
0.68
0.45
0.66
1.05
345 0.55 1.23
0.49
0.63
KILIFI
0.89
0.89
0.57
1.04
234 0.76 1.16
0.90
0.65
MOMBASA
0.62
0.71
0.69
1.04
175 0.51 1.23
0.73
0.78
MACHAKOS
0.77
0.70
0.57
1.04
321 0.64 1.21
0.72
0.66
MERU
0.68
0.48
0.56
1.04
238 0.58 1.19
0.52
0.72
KISII
0.76
0.62
0.68
1.04
235 0.65 1.16
0.64
0.65
SOUTH NYANZA0.89
0.69
0.64
1.04
229 0.79 1.14
0.72
0.64
KERICHO
0.83
0.69
0.65
1.05
147 0.72 1.15
0.72
0.71
NAKURU
0.73
0.70
0.70
1.05
146 0.61 1.20
0.72
0.64
NAROK*
0.90
0.82
0.55
1.05
143 0.77 1.16
0.83
0.47
BARINGO
0.80
0.84
0.63
1.04
149 0.73 1.09
0.85
0.57
UASIN-GISHU 0.70
0.72
0.66
1.04
127 0.57 1.22
0.74
0.68
BUNGOMA
0.77
0.76
0.63
1.04
266 0.69 1.12
0.78
0.69
KAKAMEGA
0.79
0.72
0.63
1.05
328 0.69 1.15
0.74
0.67
The fertility inhibiting effect of C s is increasing over time surpassing contraception in some areas
The fertility inhibiting effect of C s is most felt in low fertility areas
0.75
0.67
0.69
0.63
0.80
0.66
0.77
0.66
0.66
0.76
0.81
0.69
0.85
0.91
0.72
0.86
0.81
16
Effect of each of the Proximate Determinants 1989
10
Inhibition in Births per Woman
9
8
KENYA
NAIROBI
MURANGA
NYERI
KILIFI
MOMBASA
MACHAKOS
MERU
KISII
SOUTH NYANZA
KERICHO
NAROK
BARINGO
UASIN-GISHU
BUNGOMA
KAKAMEGA
7
6
5
4
3
2
1
0
CM
CC
CI
Proximate Determinants
CP
17
Effect of each of the Proximate Determinants 1993
10
Inhibition in Births per woman
9
KENYA
NAIROBI
MURANGA
NYERI
KILIFI
MOMBASA
8
7
6
MACHAKOS
MERU
KISII
SOUTH NYANZA
KERICHO
5
4
3
NAROK
BARINGO
UASIN-GISHU
BUNGOMA
KAKAMEGA
2
1
0
CM
CC
CI
Proximate Determinants
CP
18
Effect of each of the proximate Determinants 1998
9
Inhibition in Births per woman
8
KENYA
NAIROBI
MURANGA
NYERI
KILIFI
MOMBASA
MACHAKOS
MERU
KISII
SOUTH NYANZA
KERICHO
NAROK
BARINGO
UASIN-GISHU
BUNGOMA
KAKAMEGA
7
6
5
4
3
2
1
0
CM
CC
CI
Proximate Determinants
CP
19
Effect of each of the proximate Determinants 2003
9
Inhibition in Births per woman
8
7
KENYA
NAIROBI
MURANGA
NYERI
KILIFI
MOMBASA
MACHAKOS
MERU
KISII
SOUTH NYANZA
KERICHO
NAROK
BARINGO
UASIN-GISHU
BUNGOMA
KAKAMEGA
6
5
4
3
2
1
0
CM
CC
CI
Proximate Determinants
CP
20
The relationship between fertility and the proximate determinants
0.6
Proximate Determinants Index
0.5
0.4
Linear (1-Cc)
Linear (1-cm*)
Linear (1-Ci)
0.3
0.2
0.1
0.0
2
3
4
5
6
TFR
7
8
21
The relatioship between fertility and the proximate
determinants including Cs
0.6
Proximate Determinants Index
0.5
0.4
Linear (1-Cm*)
Linear (1-Ci)
Linear (1-Cc)
Linear (1-Cs)
0.3
0.2
0.1
0.0
2
3
4
5
6
TFR
7
8
9
22
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
23
concerned.