Population Projections Back and Forward by Age, Sex and Educational Attainment Presented by Samir KC 1 Contributors: Bilal Barakat 1,2, Anne Goujon 1,2, Wolfgang Lutz 1,2, Warren Sanderson 1,3, Vegard Skirbekk 1 1 IIASA World Population Program, Laxenburg, Austria; 2 Vienna Institute for Demography, Vienna, Austria ; 3 Stony Brook University, Stony Brook, New York, USA

Presentation Outline Need for Population Projections by education Our Approach Applications

Need for population projections by age, sex and educational attainment Changing definition of education categories in national time series – Standardization needed – Categories based on ISCED Absence of time series data by age and sex Education being an important explanatory variable in many processes – fertility, mortality, migration, vulnerability analysis, conflict, …. Future planning, target setting (MDG, EFA) etc….

Reconstruction of past education distribution by age and sex Start with 2000 distribution as base year – 5 yearly age groups – Males/Females – 4 education categories Less than one year of Primary education More than one year of Primary education Completed lower secondary education Completed first level of tertiary education

Reconstruction Move backwards – 5 year step – With differential mortality and migration Positive Correlation between life expectancy and educational attainment At age 15: a difference of 5 years – Demographic Multi-state Cohort Component Method used Four states of education with backward education transitions from higher level to lower level Moving backward along cohort line – Matching Population Distribution with the UNPD’s estimates

Singapore

Nepal

Projections Same starting distribution 2005 – 2050 Future Demographic Trajectories – UNPD – World Population Prospects 2006 – Eurostat – Own Estimates for few non European low fertility countries Future Education Trajectories – Baseline – Global Education Trend Scenario – Other scenarios ranges from Most rapid educational expansion – Fast Track Scenario Assumptions of constant enrollment ratios and numbers

Projections Multi-state – Transition between different levels of education Cohort Component method – Projections along cohort lines 123 countries of the world

Global Education Trend Scenario Baseline Trend, Business as Usual etc. Based on the past education trend – All countries pooled together – Fitted using cubic spline General trend of improvement Plausible medium-term scenario

Fertility Differential

Mortality Differentials Life expectancy at age 15 – 5 years difference between the tertiary educated and those with no formal education Migration – Net Migration (WPP 2006) – Own calculation – age-sex distribution – By education Negative Net migration – sending country’s distribution Positive Net migration – pool of all sending countries’ distribution

Projection Result

Projection Results

Using the data Economic analysis: Human Capital and Economic Growth – (Lutz et al, 2008 in Science; Sanderson and Striessnig, 2009) Analysis of Youth Bulges and Conflict (Barakat & Urdal, 2008) OECD: Projected tertiary educated population in 2030 in a selected number of non-OECD countries. Eberstadt, Nicholas: Economic Outlook for Central Asian states, China, Russia, Iran, Turkey.

Using the data Estimating effects of educational attainment on economic growth Improving economic growth forecasts by assessing the interactions between education and demographic trends Studying the effect of human capital on health indicators Assessing the effects of education on democratization processes and political institutions

Possible Uses Check feasibility of international education targets Analyze the role of education in: – Adaptive capacity to environmental disaster – Vulnerability analysis

Future plans Country or Region specific differentials Country or Region specific Education Scenarios Adding more countries Link education and disability (health) status for projections Link education and place of residence (urban/rural) status for projections