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
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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 6.4 5.5 3.7 2.7
North Africa 4.7 3.6 2.8 1.9
Asia (East +SE+South)
4.1 3.5 2.7 1.4
West Asia 6.4 4.6 3.5 2.9
Latin A.+ Caribbean
5.8 4.5 2.6 3.2
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 15-60 assumed to be in labor force- Does it refer to rectangular age distribution? Based on
enrolment rates versus absolute numbers enrolled
0.0 50.0 100.0 150.0 200.0
2000
1995
1990
1985
1980
1975
1970
1965
1960
1955
1950
1945
1940
1935
1930
1925
1920
1915
1910
1905
1900
No education Primary Secondary Higher
females
0.050.0100.0150.0200.0
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
males
Singapore 2000
Population (thousands)
Age Period of Birth
filename:D:\IIASA2002\Sergei\Current\Run\ 2002\Educ\Final_ 031002\WithPyr\closed\ [makePyr .xls]data
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 2020
- 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 1974-84, Nathan Keyfitz 1984-94, Wolfgang Lutz 1994- )
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 Population by Age and Sex2000 2005
MalesFemales Females
Migration
Mortality
Migration
Fertility
Migration
Males Females Males Females
Principles of Population Projection by age, sex, and education
Population by Age, Sex, and Education Population by Age, Sex, and Education 2000 2005
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 20-64 years (in millions) by levels of education, according to the “ICPD” scenario, 2000-30
Western and Eastern Europe, and Northe America
No education Primary Secondary Tertiary
0
200
400
600
800
1,000
1,200
2000 2015 2030
1,200
800
400
0
2000 2015 2030
Estimated population aged 20-64 years (in millions) by levels of education, according to the “ICPD” scenario, 2000-30
China and Centrally Planned Asia
No education Primary Secondary Tertiary
0
200
400
600
800
1,000
1,200
2000 2015 2030
1,200
800
400
0
2000 2015 2030
Estimated population aged 20-64 years (in millions) by levels of education, according to the “ICPD” scenario, 2000-30
South Asia
No education Primary Secondary Tertiary
0
200
400
600
800
1,000
1,200
2000 2015 2030
1,200
800
400
0
2000 2015 2030
0
200
400
600
800
1,000
1,200
2000 2015 2030
0
200
400
600
800
1,000
1,200
2000 2015 2030
0
200
400
600
800
1,000
1,200
2000 2015 2030
1,200
800
400
0
2000 2015 2030 2000 2015 2030 2000 2015 2030
Estimated population aged 20-64 years (in millions) by levels of education, according to the “ICPD” scenario,
2000-30, in three economic mega-regions
Western and Eastern Europe and North
America
China and Centrally Planned Asia South Asia
No education Primary Secondary Tertiary
Population in age group 15-24 (in millions) with some secondary or tertiary education according to
Constant, ICPD and American scenario in three regions, 2000-2030.
0
50
100
150
200
250
300
2000 2005 2010 2015 2020 2025 2030
Scenario Constant
Scenario ICPD
ScenarioAmerican
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 exist3. 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.
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