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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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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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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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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 !