Urbanization And Fertility Rates In Ethiopia
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Transcript of Urbanization And Fertility Rates In Ethiopia
URBANIZATION AND FERTILITY RATES IN ETHIOPIA
Fanaye Tadesse and Derek HeadeyIFPRI ESSP-II
CSAE conferenceMarch 20, 2012Oxford
1
Outline
1. Introduction2. Economic Theories of Fertility3. Data and Estimation4. Results
- Basic descriptive statistics - Regression5. Conclusion
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)
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
Specific objectives of the paper
1) What causes fertility in Ethiopia?2) How do these causes differ by rural and urban areas?
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
• 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
2. Economic theories of fertility
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
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?”
3. Data and Estimation• Some potential problems . . . 1. DHS does not have information on consumption or
income variables, but measures a wealth index constructed from the information on asset holdings
2. 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.
1 1
( * )k n
i k ik n in i ik n
y X X u
• where the are the parameters to be estimated, • the are the explanatory variables and • 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
ssX
iu*inX
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***
Explanatory variables: All differences significant at 5% level Rural Urban Difference Mother - Age (years) 28.4 26.8 -1.6Mother - No education 75% 25% -0.5Mother - 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 -2Mother - 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
• 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
4. Results – basic descriptive statistics
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
Regression Results (Poisson marginal effects) number of children born
Urban Ruralparameter difference
age 0.51*** 0.98*** *** age2 -0.01*** -0.01*** ***Secondary education -0.39** -0.74*** * Christian -0.4** -0.04 *** Other religion -1.39*** -0.08 * Child mortality 1.8*** 0.58*** ** 2nd wealth quintile -0.04 0.13**3rd wealth quintile 0 0.034th wealth quintile 0.25 0.045th wealth quintile 0.19 0.73Clerical/sales occupation -0.32** -0.21**Agricultural occupation -0.06*** -0.3*** ** Other occupation 0.04 -0.3**Husband’s primary educ. 0.35 0.14**Husband’s Secondary educ. -0.07** 0.11
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
Regression Results - Desired Children
urban Rural
significance of parameter 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 *
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
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
Thank You!