Forecasting Education Wolfgang Lutz Education Matters Measuring Education Stocks by Age Why is Education Forecasting Important Previous Attempts to Forecast Education Stocks Demographic Multi-state Methods for Forecasting Education First Global Forecasts of Human Capital

Education Matters For individual life (Micro) For economic development (Macro) “Whereas at the micro case... it is established beyond any reasonable doubt that there are tangible and measurable returns to investment in education, such evidence is not as consistent and forthcoming in the macro literature “ (Psacharopoulos and Patrinos 2002) Findings are dependent on education indicators chosen (age-range). For health/mortality and fertility (cross-sectoral effects) Indirect effects of education on institutions and good governance (control by the educated)

Fertility Rates by Level of Education Region (1) No Education (2) Primary (3) Secondary and higher Difference (1) – (3) SS-Africa North Africa Asia (East +SE+South) West Asia Latin A.+ Caribbean

Measuring Formal Education Education Flows – Policy variable (Gross and Net Enrolment by Age, Repetition Rates) Education Stocks - Change very slowly due to great momentum –Mean years of schooling –Distribution by highest educational attainment –Functional literacy

Measuring Human Capital Stocks Directly from census data (Barro and Lee, 25+; Lutz and Goujon, 5-year age groups) Perpetual Inventory Method (Nehru, Swanson and Dubey 1993, Ahuja and Filmer 1995) Sums of enrolment over time to get time series of average number of years of schooling - adjust for mortality (Problem: differential mortality) - back projection and lots of other assumptions involved - all people aged assumed to be in labor force - Does it refer to rectangular age distribution? Based on enrolment rates versus absolute numbers enrolled

Importance of Forecasting Education For Planning Short-term educational expansion in terms of school capacity (teachers, buildings, stundents by grade) Long-term changes in human capital and its economic and geopoloitical implications For Realistic Target Setting (e.g. target to halve adult illiteracy within 15 years impossible, except in the case of massive adult literacy campaings, but it will automatically happen without any extra efforts within 30 years) For Demonstrating the Long-Term Benefits of Near-Term Investments

Methods for Forecasting Educataion Stocks Ahuja and Filmer (1995) - Apply perpetual inventory method to UNESCO enrolment projections to Distinguish two age groups (6-24, 25+) - Superimpose this projected education structure to the exogeneously given UN population projections. Demographic Multi-State Projections Adds education to age and sex in the context as a break-down category. Multi-dimensional Leslie Matrix considering competing risks.

BRIEF HISTORY 1972 initiative of U.S. and Soviet Union Created research center as a“neutral bridge between east and west” 1994 Ministerial Conference: renewed mandate as independent, scientific institution for global change research IIASA World Population Program (Leaders: Andrei Rogers , Nathan Keyfitz , Wolfgang Lutz ) International Institute for Applied Systems Analysis (IIASA)

Principles of Population Projection by age and sex Migration Mortality Migration Fertility Migration Males Population by Age and Sex MalesFemales

Migration Mortality Migration Fertility Migration MalesFemalesMalesFemales Principles of Population Projection by age, sex, and education Population by Age, Sex, and Education

Data and Assumptions We need to know the current composition of the population by age, sex and education categories. We need to know how the birth rates differ for women with different levels of education. We need to know school enrollment at different levels and make alternative assumptions for the future. We need to make assumptions about future trends in fertility, mortality and migration by level of education.

Estimated population aged years (in millions) by levels of education, according to the “ICPD” scenario, Western and Eastern Europe, and Northe America No educationPrimarySecondaryTertiary 1,

Estimated population aged years (in millions) by levels of education, according to the “ICPD” scenario, China and Centrally Planned Asia No educationPrimarySecondaryTertiary 1,

Estimated population aged years (in millions) by levels of education, according to the “ICPD” scenario, South Asia No educationPrimarySecondaryTertiary 1,

1, Estimated population aged years (in millions) by levels of education, according to the “ICPD” scenario, , in three economic mega-regions Western and Eastern Europe and North America China and Centrally Planned Asia South Asia No educationPrimarySecondaryTertiary

Population in age group (in millions) with some secondary or tertiary education according to Constant, ICPD and American scenario in three regions, Scenario Constant Scenario ICPD Scenario American Sub-Saharan Africa South Asia China and CPA

Summary- substantive Formal education is typically acquired at young ages and then does not change over the life course (goes along cohort lines, multi-state population models). This is why the educational composition of the total population changes only very slowly. Educational efforts made today will only improve the average education of the work force many years later.

Summary - feasibility Multi-state projection by age, sex, and level of education are feasible for all countries in the world (possibly even provinces) because: 1. Empirical data exist from censuses, surveys and education statistics 2. Well-proven demographic methods exist 3. Meaningful policy scenarios can be defined Thus: Given the importance of education forecasts, there is no good reason for not doing education projections for all countries in the world systematically and on a regular basis.