Socioeconomic Determinants of Fertility in Ethiopia

Fanaye TadesseESSP II

January 26, 2011

BackgroundEthiopia:• One of most populous countries in Africa• Characterized by high population growth rate of 2.6%• High population growth rate sustained by high

fertility rate• Total fertility rate stands at 5.4 which is high by any

standard • In 15 years the TFR showed only a one child drop• Ranked among African countries that have high

fertility rate • Not considered to be among the countries at or near

the start of the transition to low level of fertility (United Nations Population Fund, 2007)

Objective

To identify the socioeconomic determinants of Fertility in urban and rural Ethiopia using the DHS data (2005)

Conceptual framework

TFR

Age at first marriage

Postpartum infecundity

Contraceptive use

Induced abortion

Socio-economic characteristics

Proximate determinants

EDHS Data• Sample size of women – 14,070– 4,423 - urban– 9,647 - rural

• Women with in the age range of 15 and 49• Stark difference in the TFR of women in Urban

and rural areas– Rural women on average have two and a half

times more than urban women– TFR of 6.0 Vs 2.4

Measuring fertility at household level:• Dependent variable of interest is the number of children ever

born by a woman - a non-negative integer or count. Result compared with other models with dependent

variables given by:• Completed fertility – for women between ages 40 and 49• Ideal number of children women desire to have–gives

insight for younger women in the data who have not completed their fertility (though it could be very much hypothetical )

• The variable is discrete and the distribution places probability mass at non negative integer values only

• a Poisson model which is starting point of estimation of count data models

Variables • Explanatory variables included in the model that are expected

to have an effect on the fertility decision of women are : • Education• Child mortality - number of children that were born alive

to the woman but died before the age of five; • The participation of the woman in income generating

activity; • Access to information measured by access to radio

(whether a woman listens to a radio at least once a week)

• A variable to indicate the woman’s economic status (whether poor or rich)

• control variables - Age, Age2 , marital status, religion, region

Residence Percentage

Urban 17.8

Rural 82.2

Place of residence

Education PercentageNo education 65.9Primary 22.2Secondary 10.5Higher 1.4

Education

Description

Descriptive – variables of interestVariable Urban Rural

Number of children born 3.43 1.53Desired number of children 5.35 3.68Number of children for women between ages 40-49 7.29 4.79Age 27 28

Child mortality 0.56 0.16

Variable Percentage

Religion

Christian 65.76

Muslim 32.14

other 2.07Labor force participation 39.83

Listens to the radio at least once a week 47.5

- DHS doesn’t have an income data- Uses nationally constructed wealth index with each asset

assigned to a weight (factor score) generated through principal components

- However, the index is criticized of being urban biased- Separate indexes were constructed for rural and urban

areas using separate asset composition (following Vyas and Kumaranayaka, 2006)

- The separately constructed indexes are then mapped to the national index to adjust for distributions and a composite national index is constructed (methodology suggested by Rutstein, 2008)

Measuring economic status

Endogeneity

• Wealth is likely to be endogenous in the model• Predicted value of wealth index• Instrument used – access to financial institution• Original wealth index and wealth quintiles used for

regression on desired number of children – no possible endogeneity expected

Results – Whole sample

Children ever born

Completed (or near completed)

FertilityDesired children

age 0.2628*** 0.035 0.045***age squared -0.003*** primary educ 0.030 -0.543 -0.429***secondary educ -0.194*** -0.673 -0.306**higher educ -0.290*** -2.937 -0.375**Labor force participation -0.093*** -0.774*** -0.161**christian -0.012 -0.032 -0.669***other religion -0.037 -0.356 0.149child mortality 0.113*** 0.627*** listens to radio 0.077 0.737*** -0.126*urban -0.148*** -0.562*** -0.644***poor -0.101*** -0.717** -0.060*middle -0.167*** -1.368*** -0.073rich -0.197*** -1.518*** -0.085richer -0.256** -1.708* -0.237ever married 2.403*** 0.848***N 13769 1663 12411

Results – Urban

Children ever born

Completed Fertility

Desired children

age 0.083*** 0.103** 0.051***

age squared -0.001***

primary educ -0.005 -0.162 -0.242**

secondary educ -0.051** 0.256 -0.336***

higher educ -0.087*** -1.592 -0.467

Labor force participation -0.052*** -0.554*** -0.074

christian -0.080*** -1.452*** -1.332***

other religion -0.104 -1.353 -2.380***

child mortality 0.068*** 0.714***

listens to radio 0.005 -0.027 0.068

poor -0.009 0.586 -0.318***

middle -0.030 0.685 -0.161

rich -0.025 0.268 -0.172

richer -0.026 0.119 -0.039

ever married 1.326*** 0.282***

N 4253 464 4017

Children ever

bornCompleted

FertilityDesired children

age 0.364*** 0.040 0.058***age squared -0.004***primary educ 0.078 -0.265 -0.404***secondary educ -0.172** -2.647 -0.735***higher educ -0.915*** -1.094**Labor force participation -0.119** -0.694*** -0.166**christian 0.036 0.197 -0.723***other religion -0.011 -0.264 0.505*child mortality 0.151*** 0.619***

listens to radio0.127***

0.686** -0.233***poor -0.145*** -0.463* -0.051middle -0.234*** -0.879** -0.083rich -0.305*** -1.545*** -0.345***richer -0.364*** -1.575*** -0.241***

ever married 2.634*** 0.884***N 9516 1199 8405

Results – Rural

Discussion of Results• Education of women affects fertility negatively

• Formal schooling for women is the single most consistent variable correlated with their low fertility (Bledsoe and Cohen, 1993) • Awareness about family planning• Empowers women in making their own decision –

raises their status• The effect of education on fertility is not significant

for women with completed fertility since the percentage of women with education is very small• However, Primary education is not significant in

affecting the number of children ever born

• Countries with lower levels of development and modernization and highly gender-stratified cultural settings, are likely to find that a higher level of education is required (Akmam, 2002)

• Child mortality affects fertility positively - hoarding or replacement - with the death of an infant, duration of breast feeding and

post-partum abstinence is curtailed, which promotes fertility• Participation of women in income generating activity affects

fertility negatively- Higher opportunity cost of childbearing and childrearing

Discussion of Results

• Economic status (wealth)- Found to be negatively related to fertility for the whole sample and rural areas - Quantity - quality tradeoff

- Higher status households would have lesser children not to sacrifice consumption of status goods

- Women in higher quintiles may have better information and access to contraceptives

- Higher fertility of the poor may or may not necessarily be a rational decision

• The effect of economic status on fertility in urban areas is found to be insignificant– Innovation diffusion approach – Fertility decline coming

from spread of ideas values and tech to population regardless of economic status ( Casterline, 2001)

Discussion of Results

• Urban dwellers are likely to have lower number of children than those living in rural areas– Early stage of fertility transition accompanied by a widening gap in

urban- rural fertility– Fertility decline advances jointly with increasing rates of urbanization

• Married woman have more children than single women, other things remaining constant– Delay in marriage would possibly result in lower number of children

• Positive effect of access to information on actual fertility is a bit puzzling – However, the negative and significant effect on desired number of

children is as expected.

Discussion of Results

Conclusion• Urbanization plays a key role in reducing fertility• Improving economic status of women leads to lower fertility• Education of women beyond primary level has a strong effect

in reducing fertility • Lowering child mortality through better access to health

services could reduces fertility

Further investigation

• Possibility of feedback effect from fertility to– Child mortality– Labor force participation of women