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Urbanization And Fertility Rates In Ethiopia
1. URBANIZATION AND FERTILITY
RATES IN ETHIOPIA
Fanaye Tadesse and Derek Headey
IFPRI ESSP-II
CSAE conference
March 20, 2012
Oxford
1
2. Outline
1. Introduction
2. Economic Theories of Fertility
3. Data and Estimation
4. Results
- Basic descriptive statistics
- Regression
5. Conclusion
3. 1. Introduction
• Ethiopia has a long history of Malthusian population
dynamics (Pankhurst 1985).
• The population-dense highlands face shrinking farm sizes,
deforestation and soil degradation
• Most of these problems are related to high fertility rates
• Also widely accepted that reducing fertility can produce a
demographic dividend via reduced age dependency
• Hence Ethiopian government has long sought to reduce
fertility
• Good news is that fertility rates are falling
7.1 (1990) 5.4 (2005) 4.8 (2011)
4. 1. Introduction – cont’d
• But Ethiopia still has the largest rural-urban fertility
differential in the world:
– 6 children in rural areas; 2.4 children in urban areas (2005)
– 5.5 children in rural areas; 2.6 children in urban areas (2011)
• So why is the rural-urban fertility gap so large in Ethiopia?
• Previous work does not satisfactorily explore this question.
• Some papers look at proximate determinants of fertility in the
Bongaarts framework or explain fertility for certain
proportion of the population
• A large World Bank study used DHS and other datasets to
look at fertility rates, including by rural and urban areas
• But not systematic tests for rural-urban differences
5. Specific objectives of the paper
1) What causes fertility in Ethiopia?
2) How do these causes differ by rural and urban areas?
6. 2. Economic theories of fertility
• Economic theories tend to emphasize demand-side
determinants of fertility rates, following Gary Becker’s
seminal work.
• Emphasis is on choice, rather than biological factors
• Children possess both consumption good and investment
good characteristics
• Quality and quantity tradeoffs, which posits a likely
substitution from quantity to quality as family income
increases (Becker and Lewis 1973).
• Opportunity costs matter – children come with both
explicit costs and implicit costs – e.g. women’s time use
7. 2. Economic theories of fertility
• Child mortality rates influence expectations; risk averse
parents may have more children than actually desired
• Women’s education tends to reduce fertility, but the
level of education that affect fertility is still not clear.
• Urbanization seems to affect fertility (Kuznets 1974),
sometimes even when other controls are introduced
• But not clear why – many rural-urban differences in
poverty, education, infrastructure, and unobservables
8. 3. Data and Estimation
• The data used for this study is EDHS (Ethiopian
Demographic and Health Survey) of 2005.
• The ERHS is a nationally representative survey of 14,070
women between the ages of 15 and 49 and 6,033 men
with ages between 15 and 59.
• Topics include family planning, fertility, child mortality,
child health, nutrition and knowledge of HIV/AIDS.
• Geographic Information Systems (GIS) estimates of travel
times to nearest facilities were merged with the DHS data
• GIS data adds info on isolation, and may also be
important since rural-urban divide can be arbitrary
9. 3. Data and Estimation
• Dependent variables are number of children born and
desired number of children
• Former is more like revealed reference, latter is stated
preference
• Desired number has two phrasing depending on age:
“If you could go back to the time you did not have any
children and could choose exactly the number of children
to have in your whole life, how many would that be?”
“If you could choose exactly the number of children to
have in your whole life, how many would that be?”
10. 3. Data and Estimation
• Some potential problems . . .
2. DHS does not have information on consumption or
income variables, but measures a wealth index
constructed from the information on asset holdings
3. Possible endogeneity of Child Mortality
– Rather than including the Child mortality of the
household, we took the average of the child mortality
in the cluster.
– So it is a locally formed expectation rather than actual
for the household.
11. k n
yi = α + ∑ β k X ik + ∑ β n ( X in *ui ) + ε i
k =1 n =1
βs
• where the are the parameters to be estimated,
• the are the explanatory variables and
Xs
X in * ui
• the expression ( ) are interactions of the explanatory
variables with the urban dummy.
• The interaction variable obviously tests whether the effects of the
explanatory variables on fertility differ by location.
• We also separately estimate urban and rural equations and conduct
Chow tests to check for parameter differences among these groups.
• We use poisson regressions since these are count variables
12. 4. Results – basic descriptive statistics
• Differences in dependent variables
Number of children born Desired number of children
Rural Urban Difference Rural Urban Difference
Age
15-19 0.21 0.07 0.14*** 3.46 2.79 0.67***
20-24 1.35 0.53 0.82*** 4.41 3.18 1.23***
25-29 3.15 1.4 1.75*** 4.91 3.57 1.34***
30-34 4.82 2.49 2.33*** 5.4 3.84 1.56***
35-39 6.19 3.45 2.74*** 5.41 4.38 1.03***
40-44 6.99 4.59 2.4*** 5.7 4.32 1.38***
45-49 7.54 5.64 1.9*** 5.99 4.39 1.6***
13. Explanatory variables: All differences significant at 5% level
Rural Urban Difference
Mother - Age (years) 28.4 26.8 -1.6
Mother - No education 75% 25% -0.5
Mother - Primary education 22% 25% 3%
Mother - Secondary education 3% 44% 41%
Mother - Higher education 0% 7% 7%
Christian 66% 86% 21%
Other religion 3% 0% -2%
Child mortality 56% 18% -38%
Mother - Listens to radio 34% 80% 45%
Land owned (hectares) 2.2 0.2 -2
Mother - Not working 67% 56% -11%
Mother - Professional occupation 0% 6% 6%
Mother - Clerical/sales occupation 8% 27% 19%
Mother - Agriculture occupation 3% 1% -2%
Mother - Other occupations 3% 10% 7%
Husband - no education 70% 23% -47%
Husband - primary education 2% 35% 33%
Husband - secondary education 0% 29% 28%
Husband - higher education 1% 11% -10%
Travel time to health center (hrs) 1.3 0.5 -0.8
14. 4. Results – basic descriptive statistics
• In terms of some more proximate determinants,
contraceptive use among women was
– 18% (37% urban, 15% rural) in 2005;
– 29% (53% urban, 23% rural) in 2011
• Reasons for not using contraceptives vary across rural and
urban areas
• Rural women are more ignorant of contraceptives, desire
more children, and face slightly more opposition from
husbands
• High unmet need for family planning especially in rural
areas
15. 4. Results – Regression
• We begin with national regressions that interact explanatory
variables with an urban dummy
• Most of the interaction terms are significant, suggesting not
only differences in levels, but differences in impacts too
• The urban dummy becomes insignificant once interaction
terms are introduced.
• Wealth index not very significant in either rural or urban
areas
• Much larger effect of female education (only secondary and
above) and work status
• Child mortality has big effects, especially in urban areas
17. 4. Results – Regression
• For desired number of children, most results are quite similar,
but rural-urban differences appear to be less significant
• Some evidence that village level contraceptive knowledge,
access to radio matters, suggesting a role for fertility policies
18. Regression Results - Desired Children
significance
of parameter
urban Rural difference
age 0.21*** 0.17***
age2 -0.002** -0.002***
Primary education -0.39 -0.35***
Secondary education -0.25 -0.69***
Higher education -0.72** -0.58
Christian -1.49*** -0.58***
Other religion -1.99*** -0.17 ***
Child mortality 0.93 0.77***
Listens to radio 0.32 -0.17*
2nd wealth quintile -0.63** 0.05
Agricultural occupation 2.84 -0.42*** **
Contraceptive knowledge (village
average) 0.08 -1.43***
Travel time to health center (hrs)
0.13 0.05 *
19. 5. Conclusions
• Rural-urban fertility gap is explained by both difference
in levels of explanatory variables and differences in
impacts.
• Wealth, by itself, does not seem to matter much
• Most policy-relevant findings related to female
secondary education, and raising awareness of family
planning goals and technologies
• Female secondary education likely to have high returns
because in addition to reducing fertility, it can increase
incomes and improve nutrition outcomes
• Currently female education is so low in rural areas (3.2%)
that there is huge scope for expansion
20. 5. Conclusion
• In terms of future research we plan to more formally
decompose rural-urban differences into level effects,
parameter effects and unexplained effects (e.g. Oaxaca
decomposition).
• We will also update with forthcoming 2011 DHS
• We can explore regional effects more as there are fertility
differences across regions, even within rural and urban areas
