Socioeconomic determinants of fertility in ethiopia

Socioeconomic Determinants of Fertility in Ethiopia Fanaye Tadesse ESSP II January 26, 2011

Background
Ethiopia:
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, Age 2 , marital status, religion, region

Place of residence Education Description Residence Percentage Urban 17.8 Rural 82.2 Education Percentage No education 65.9 Primary 22.2 Secondary 10.5 Higher 1.4

Descriptive – variables of interest Variable Urban Rural Number of children born 3.43 1.53 Desired number of children 5.35 3.68 Number of children for women between ages 40-49 7.29 4.79 Age 27 28 Child mortality 0.56 0.16 Variable Percentage Religion Christian 65.76 Muslim 32.14 other 2.07 Labor 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) Fertility Desired 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 0.149 child 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*** -0.073 rich -0.197*** -1.518*** -0.085 richer -0.256** -1.708* -0.237 ever 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

Results – Rural Children ever born Completed Fertility Desired 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 radio 0.127*** 0.686** -0.233*** poor -0.145*** -0.463* -0.051 middle -0.234*** -0.879** -0.083 rich -0.305*** -1.545*** -0.345*** richer -0.364*** -1.575*** -0.241*** ever married 2.634*** 0.884*** N 9516 1199 8405

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

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)

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.

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

Socioeconomic determinants of fertility in ethiopia

by essp2 on Apr 07, 2011

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