1 benefits from accelerating the demographic transition – evidence from ethiopia luc...
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Benefits from accelerating the demographic transition – evidence from Ethiopia
Luc Christiaensen, Hans Lofgren, Rahimaisa Abdula, Presentation at the World Bank Economists’ Forum: April 19, 2007.
Study Questions
How do population growth and economic development interact: would Ethiopia stand to gain from a more rapid decline in fertility?
What is the relative role of broad development policies/investment versus population specific interventions in addressing fertility behavior
Central Theses
There are substantial gains from accelerating the demographic transition in poor countries that relate to an earlier capture of the demographic bonus.
This can be done by complementing overall gender equitable development interventions with population specific programs such as family planning
Outline Background - Ethiopia at the brink of its fertility
transition Conceptual framework - the demographic bonus Modeling interactions between population and growth
- MAMS Micro-determinants of population growth (mortality
and fertility) – female education and empowerment are key
Simulations results – private consumption per capita about 10 percent higher under lower population growth scenario
Concluding remarks – development is the best contraceptive, but contraceptives are also good for development
Ethiopia - a demographic giant at the brink of its fertility transition
Size: about 78 million people today, 2nd largest population in SSA, after Nigeria
Speed: current population growth 2.5% (or 2 million people) per year; the demographic transition started in the 1950s when mortality rates started to decline in 1950s; the fertility transition has also started with TFR declining from 6.4 in 1990 to 5.7 in 2005, but still high
Structure: high dependency ratios (83+3)/100) and a youth bulge (50% between 15-29 yrs old)
Space: a young population largely concentrated in the rural Highlands (15% urban) – land pressure/ environmental degradation/resettlement
The population-growth nexusRapid population growth (declining mortality)
Results in higher dependency ratios/lower savingsInduces a trade-off between increasing demand for public investment in social (health, education) vs productive goods and services (infrastructure)Affects productivity by changing the land/labor and capital/labor ratios
==> slower economic growth inducing Malthusian and Boserupian responses, and both are observed in Ethiopia
The Demographic Bonus When followed by a decline in fertility, rapid
population growth also sets the stage forA decline in dependency ratios, an increase in the share of the working age population, increased savings and increased private investmentA decline in public spending on social sectors freeing up resources for public investment in economic sectors
Foster growth, yielding a demographic bonus As Ethiopia is at the brink of its fertility
transition, it is poised to capture its much needed demographic bonus.
The Demographic Bonus(2)Can be Large
Is Not automatic
Is larger, the faster the fertility transition.
How much is the gain in Ethiopia and how to accelerate the fertility transition?
Maquette for MDG Simulations (MAMS)- Introduction
GDP growth depends on factor accumulation (labor, capital, land) and TFP growth.
Lower population growth (e.g due to more spending on family planning) may influence growth and poverty reduction through:
Composition of public expenditures (skills/capital)Labor marketTotal factor productivity
MAMSa dynamic economy-wide model of Ethiopia run from 2005-2030earlier used to analyze scenarios to reach the MDGs and to develop poverty reduction strategies.
The powers of MAMS – Public spending
Detailed modeling of government activitiesEthiopia specific information for production and cost functions for the provision of social services (education, health, water and sanitation)
• increasing marginal costs as a function of coverage rates• cross-sectoral synergies• derived demand for skilled labor (teachers, nurses, doctors)
key b/c fertility decline largely driven by female educationFamily planning explicitly accounted forOther public infrastructure (roads and energy) and other government
Government and country operate under budget constraints trade-offs are explicit
The powers of MAMS (2)- Labor market
Three types of labor (unskilled, semi-skilled (completed secondary school), skilled (completed tertiary cycle)
Demand for different labor categories depends onlabor composition of each of the production activitiesrate at which output changes over time as a result of profit-maximizing producer decisions;rate of government consumption, largely driven by rapid expansion of education and health services and associated demand for skilled labor
Labor markets clear through wage adaptation for each labor category; unemployment is implicit—only a share of those entering the labor market are employed, this ratio is fixed over time
The powers of MAMS (3)TFP growth is assumed independent of population
growth Population growth enters exogenouslyFirst application of such a macro model to
population policy – it provides a tool to compare welfare effects of different population growth scenarios and a cost-benefit analysis provided the cost difference related to different population scenarios can be identified
IMR and CMR are main drivers of CDR changesMortality rates by age in Ethiopia, 2000
Further reduction in child mortality will substantially reduce the CDR and foster population growth (maternal mortality and HIV/AIDS not considered to affect CDR substantially)
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Socio-economic Drivers of Fertility
Age specific regressions of # of children born (DHS, 2000)Female education: the key socio-economic variable both directly and indirectly through community effectsIncome effect: 2x household income associated with 1-1.5 fewer children on averageFP Knowledge among women in communitiesEmpowerment: Women in communities where women earn cash income have fewer childrenFamily planning per se is not controlled for implicitly loaded on other variables closely associated with contracepitve use (education, urbanization, income)
Pathfinder Survey analysis – access to family planningLiterature: lifetime exposures to fp reduces TFR by 0.5 to 1.5 child per women with most studies in the 0.5 to 1 rangeEthiopia specific evidence suggests a decline of 1 to 2 children among the older women with longer FP exposure
Drivers of Fertility Changes: Bongaarts The Bongaarts model
TFR = Cm * Cc *Ci * Ca * Cs* Fn
marriage indicator (exposure to sexual union - % women of reproductive age who are married, Cm) and contraceptive index (Cc) are the major driving factors;
others include post partum infecundability index (Ci), abortion index (Ca), sterility index (Cs) and natural fecundity (Fn)
Education and urbanization key determinants in determining age
at at marriage and thus the marriage indicator
Dramatic expansion of family planning services over past 5 years growth in CPR of 1.32 %point per year; in most African countries annual increase has been less than 1; internationally, annual increases of 2%points been rarely sustained; 1%point increase used as alternative
Simulated evolution of TFR 2000 2015 2030
Scenario 1
% women in union 63.59 59.16 53.09
Annual CPR increase 1%point/year 8.1 20.1 32.1
Simulated TFR 5.9 4.61 3.63
Scenario 2
% women in union 63.59 59.16 53.09
Annual CPR increase 1.32%/year 8.1 27.9 47.7
Simulated TFR 5.9 4.25 2.87
Ethiopia’s Population Tomorrow
Assume Envisioned Progress under PASDEP & MDGs Attained
Progress in female enrollment and educational achievement• By 2015 all girls have completed primary schooling
(grade 4) all women entering child bearing age (15-19) have at least 4th grade by 2020
• By 2030, about 2/3 of 15-19 yr olds have completed grade 8; 1/3 of 20-25 yr olds have some sec schooling.
Income growth/adult equivalent = 1.5 %Urbanization: 14.9% in 2000 to 28% in 2030 (UN medium variant)
Under different fertility and mortality scenarios, what would be the demographic outlook
Fertility scenarios TFR: from 5.9 in 2000 to 2.94 in 2030
- Similar to UN projections which are 3.65, 3.15, 2.65 for high, medium, low variant respectively, but this estimate is grounded in micro-behavior and development plans)
- Given reduced form, implicitly assumed supply of FP services keeps up with contraceptive demand
Assume TFR 0.7 children higher if slower FP expansion- Bongaarts – 1%point CPR expansion (vs 1.32%
currently) per year 3.65- Lifelong exposure to FP reduction between 0.5 and
1 children- 0.7 children more puts us at high population growth
scenario
Mortality scenariosCMR declines from 166 in 2000 to 76
given projected increase in female education
well above MDG goal (reduction by 2/3 by 2015), but similar to UN projectionsExcludes effects through improved sanitation and expansion of FP
Alternative scenario: CMR declines to 50 (a scenario similar to applying effect of education only to surprisingly low CMR noted in 2005 of 123
Two scenarios(1) Reaching PASDEP (high UN variant)
high fertility: TFR from 5.9 to 3.65 in 2030high mortality (U5CMR from 166 to 76 in 2030)low family planning
(2) Reaching PASDEP with FP (medium UN variant)
low fertility (TFR from 5.9 to 2.94)low mortality (U5CMR from 166 to 50)high family planning
Both scenarios have assumptions regarding mortality and fertility effects of HIV/AIDS
Population grows to (1) 135.3 vs (2) 124.2 or 11.1 million people less in 2030
Population in Ethiopia (in millions) 2000-2030
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80
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120
140
2000 2005 2010 2015 2020 2025 2030
High Fertility, High Mortality, Low F.P
Low Fertility, Low Mortality, High F.P
Dependency ratio declines from 0.94 to (1) 0.7 and (2) 0.61
Dependency Ratio 2000-2030
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2000 2005 2010 2015 2020 2025 2030
High Fertility, High Mortality, Low F.P
Low Fertility, Low Mortality, High F.P
Setting Ethiopia on a different pop pathPopulation growth 2000 is 2.5 %. By 2030: (1) 1.85% and (2) 1.64%
CDR and CBR (per 1,000) 2000-2030
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1990 1995 2000 2005 2010 2015 2020 2025 2030
Crude Birth Rate
Crude Death Rate
High Fertility, High Mortality, Low F.P.
Low Fertility, Low Mortality, High F.P.
Combining micro with macro (MAMS) - simulation assumptions
Scenarios simulated with MAMS for the period 2005-2030:(1) Higher pop growth with low spending on FP; government budget balanced through changes in direct taxes or domestic govt borrowing(2) Lower pop growth with higher FP spending (52 million US$ in 2030)
Population projections: Exogenous paths for total population and cohorts entering the first year of primary school and the labor force; High population growth scenario has a higher dependency ratio and a smaller population share in working age.
The different scenarios are identical in terms of: Educational quality (resources per student)Health indicatorsAccess to safe water and sanitation (MDGs 7a and 7b)Government per-capita spending in other areas.
Aid and other inflows from the rest of the world.
MAMS simulation results
% growth per year, 2005-2030
Higher pop growth, direct taxation, low fp,
scenario 1Lower pop growth, high fp, scenario 2
Gap low-high
School enrollment 4.4 3.6 -0.8
Labor force 0.93 0.75 -0.18
Government consumption 5.89 5.43 -0.46
Government investment 5.82 5.2 -0.62
GDP at factor cost 5.1 5 -0.1
Private investment 4.59 4.85 0.26
Private capital stock 3.6 3.8 0.2
Private consumption 5.05 5.07 0.02
Priv. consumpt. per cap 2.8 3.1 0.3
GDP and private consumption per capita
Under low pop growth, pvt cons per capita is about 10% higher in 2030; in NPV terms (5% discount rate) ~ 110$ similar to a person’s current annual cons person
Real GDP and per capita Household consumption index
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GDP-Low population
GDP-High population
Consumption-Low population
Consumption-High population
From 2010 onwards there are 1.5 to 3 million more people in poverty
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low population growth high population growth
Under low pop growth scenario poverty incidence declines to 15% in 2015 and 0.9%points in 2030, a difference of 2 and 0.9 % respectively
Concluding remarksThere are substantial welfare benefits
from a more rapid fertility transition, also in Ethiopia; this follows from lower government spending on social services which crowds out other public and private investment
Gender equitable development is the best contraceptive, contraceptives are also good for development, with huge payoffs at the margin.
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Thank you !