Presentation on theme: "1 Benefits from accelerating the demographic transition – evidence from Ethiopia Luc Christiaensen, Hans Lofgren, Rahimaisa Abdula, Presentation at the."— Presentation transcript:
1 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 nexus Rapid population growth (declining mortality) Results in higher dependency ratios/lower savings Induces 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 for A decline in dependency ratios, an increase in the share of the working age population, increased savings and increased private investment A 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 market Total factor productivity MAMS a dynamic economy-wide model of Ethiopia run from 2005-2030 earlier used to analyze scenarios to reach the MDGs and to develop poverty reduction strategies.
The powers of MAMS – Public spending Detailed modeling of government activities Ethiopia 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 education Family planning explicitly accounted for Other 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 on labor composition of each of the production activities rate 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 exogenously First 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 changes Mortality 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)
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 effects Income effect: 2x household income associated with 1-1.5 fewer children on average FP Knowledge among women in communities Empowerment: Women in communities where women earn cash income have fewer children Family 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 planning Literature: lifetime exposures to fp reduces TFR by 0.5 to 1.5 child per women with most studies in the 0.5 to 1 range Ethiopia 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 200020152030 Scenario 1 % women in union63.5959.1653.09 Annual CPR increase 1%point/year188.8.131.52 Simulated TFR5.94.613.63 Scenario 2 % women in union63.5959.1653.09 Annual CPR increase 1.32%/year8.127.947.7 Simulated TFR184.108.40.206
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 4 th 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 scenarios CMR 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 projections Excludes 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 2030 high 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
Dependency ratio declines from 0.94 to (1) 0.7 and (2) 0.61
Setting Ethiopia on a different pop path Population growth 2000 is 2.5 %. By 2030: (1) 1.85% and (2) 1.64%
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 indicators Access 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 1 Lower pop growth, high fp, scenario 2 Gap low-high School enrollment4.43.6-0.8 Labor force0.930.75-0.18 Government consumption5.895.43-0.46 Government investment5.825.2-0.62 GDP at factor cost5.15-0.1 Private investment4.594.850.26 Private capital stock220.127.116.11 Private consumption5.055.070.02 Priv. consumpt. per cap18.104.22.168
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
From 2010 onwards there are 1.5 to 3 million more people in poverty 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 remarks There 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.