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 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.

2

Presentation outlineIntroduction

Kenya

Objectives of the Study

Data & Methods (Proximate Determinants Model)

Confirming the transition

Effects of the Proximate Determinants

Summary and Conclusion

3

Fertility TransitionThe 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

Kenya: Socio-economic setting

0

10

20

30

40

50

60

70

80

90

100

1989 1993 1995 1997 1999 2003

HumanPovertyIndex

AbsolutePoverty

PrimarySchoolenrolment

SecondarySchoolenrolment

5

Kenya Mortality and Life expectancy

0

20

40

60

80

100

120

140

160

1962 1969 1979 1989 1999

Year of Census

Lif

e e

xp

ec

tan

cy

an

d In

fan

t m

ort

alit

y r

ate

s

InfantMortality

LifeExpectancy

6

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?

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 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.

8

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

9

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

10

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 Insusceptibility

Mo 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.

11

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 20.6

Baringo/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

12

Pattern and trend of fertility transition in Kenya 1989-2003

2

3

4

5

6

7

8

9

1979 1989 1993 1998 2003

Year

TF

R

KENYA

NAIROBI

MURANGA

NYERI

KILIFI

MOMBASA

MACHAKOS

MERU

KISII

SOUTH NYANZA

KERICHO

NAROK

BARINGO

UASIN-GISHU

BUNGOMA

KAKAMEGA

13

Pattern of fertility decline in Kenya 1989-2003

14

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

15

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

16

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

17

Effect of each of the Proximate Determinants 1989

0

1

2

3

4

5

6

7

8

9

10

CM CC CI CP

Proximate Determinants

Inh

ibit

ion

in

Bir

ths

per

Wo

man

KENYA

NAIROBI

MURANGA

NYERI

KILIFI

MOMBASA

MACHAKOS

MERU

KISII

SOUTH NYANZA

KERICHO

NAROK

BARINGO

UASIN-GISHU

BUNGOMA

KAKAMEGA

18

Effect of each of the Proximate Determinants 1993

0

1

2

3

4

5

6

7

8

9

10

CM CC CI CP

Proximate Determinants

Inhi

bitio

n in

Bir

ths

per

wom

an

KENYA

NAIROBI

MURANGA

NYERI

KILIFI

MOMBASA

MACHAKOS

MERU

KISII

SOUTH NYANZA

KERICHO

NAROK

BARINGO

UASIN-GISHU

BUNGOMA

KAKAMEGA

19

Effect of each of the proximate Determinants 1998

0

1

2

3

4

5

6

7

8

9

CM CC CI CP

Proximate Determinants

Inh

ibit

ion

in

Bir

ths

per

wo

man

KENYA

NAIROBI

MURANGA

NYERI

KILIFI

MOMBASA

MACHAKOS

MERU

KISII

SOUTH NYANZA

KERICHO

NAROK

BARINGO

UASIN-GISHU

BUNGOMA

KAKAMEGA

20

Effect of each of the proximate Determinants 2003

0

1

2

3

4

5

6

7

8

9

CM CC CI CP

Proximate Determinants

Inh

ibit

ion

in B

irth

s p

er

wo

ma

n KENYA

NAIROBI

MURANGA

NYERI

KILIFI

MOMBASA

MACHAKOS

MERU

KISII

SOUTH NYANZA

KERICHO

NAROK

BARINGO

UASIN-GISHU

BUNGOMA

KAKAMEGA

21

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

Pro

xim

ate

Det

erm

inan

ts I

nd

ex

Linear (1-Cc)

Linear (1-cm*)

Linear (1-Ci)

22

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 9

TFR

Pro

xim

ate

Det

erm

inan

ts I

nd

ex

Linear (1-Cm*)

Linear (1-Ci)

Linear (1-Cc)

Linear (1-Cs)

23

Summary & ConclusionKenya’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.