Europe’s Demographic Future: 4-Dimensional Scenarios for

assessing the Impacts of Migration

Wolfgang LutzEUI Demography Forum, 4-5 Feb 2016

What is the impact of migration on the

future population structure of Europe?

What kind of migration is good or bad for future economic well-being in Europe?

• Conventional Demography: Populations by age and sex

• 3-D Demography: Populations by age, sex and level of

education – productivity & speed of learning/integration.

• 4-D Demography: Populations by age, sex, education and labor force participation.

• Multi-Dimensional: also by migrant status, religion and other stable sources of observable population

heterogeneity.

Probabilistic Population Pyramid

0.0 1.0 2.0 3.0 4.0

2050204520402035203020252020201520102005200019951990198519801975197019651960195519501945

females

0.01.02.03.04.0

05

101520253035404550556065707580859095

100105

European Union, 2050

Population (millions)

males

Age Period of Birth

Sergei's DELL PIII, file: C:\Sergei\Current\Run\2002\EU\[make_pyramid_to_file2_EU2.xls],21-May-02 14:36

95 percent

80 percent

4

European Union, Support ratio

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.5020

00

2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

Year

Su

pp

ort

rat

io

0.8

0.40.2

0.025

0.975

Median0.6

Fractiles

Sergei's DELL PIII, file: C:\Sergei\Share\EU\[presentation02.xls],21-May-02 13:56

Rethinking Population Ageing

Age is not what it used to be. Science tells us that the meaning of age is changing:

• “50 is the new 40” or “70 is the new 60”.

• People all over the world do not only live longer, they are also longer in good health.

• Cognitive decline with age is shifting to higher ages, in particular for better educated people that stay mentally and physically active.

• In countries without mandatory retirement ages (e.g. the USA) the better educated voluntarily tend to work longer and longer. We derive identity from meaningful work even at higher ages.

Redefining old age dependency

Integrating the cognitive dimension

into demographic analysis9

Strengthening the Human Resource

Base for Sustainable Development

Adding education to age and sex

Education is the most important source of observable population heterogeneity after age and sex.

This matters because:

• Almost universally during demographic transition more educated women have fewer children, have lower child mortality, and more educated adults live longer. Changing education composition changes population forecasts.

• Education is a crucial determinant of individual empowerment and human capital, is a key driver of socio-economic development (public health, economic growth, quality of institutions and democracy, and adaptive capacity to climate change).

Planned Sustainable Development Goal 2015:

“By 2030 ensure universal, free, equitable access to and

completion of quality primary and secondary education for all girls

and boys leading to effective learning outcomes”.

Explicitly including education in

demographic models

makes demography more relevant

Oxford University Press

20141056 pages,

26 lead authors,

46 contributing authors,

550 expert assessments,

191 country tables

IIASA Medium

Scenarium

0

1000000

2000000

3000000

4000000

5000000

6000000

7000000

8000000

9000000

10000000

11000000

12000000

13000000

1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

Post Secondary

Upper Secondary

Lower Secondary

Primary

Incomp. Primary

No Education

Pop < 15 yrs

World Population Projections

Future MonitorAustria‘s population structured by age, sex,

education and labor force participation ….

For more information see http://www.futuremonitor.at/

• Age (5-year age groups)

• Sex

• Education (4 categories)

– B1 (compulsory education only)

– B2 (Apprenticeship, vocational)

– B3 (senior secondary)

– B4 (University, other post-secondary)

• Labor Force status (two categories)

• Country of Birth (born inside or outside EU-27)

18*2*4*2*2 = 576 cells

Austria 2011

Finnish Scenario

Finnish Scenario

Finnish Scenario

Finnish Scenario

Constant Scenario

Vergleich Bildungsszenarien

Anteil mit Matura oder höher

31

Wien Österreich

Active and incative population, Austria, 2006

Frauenerwerbsquote nach Bildung, 2008

Quelle: European Labor Force Survey, 2008.

Männererwerbsquote nach Bildung, 2008

Quelle: European Labor Force Survey, 2008.

Number of 20-64 year olds in Austria who have higher education

by Migration Scenario(red = qualified migration, blue = constant, green = zero net migration)

Proportion of 20-64 year olds in Austria who have higher

education by Migration Scenario

(red = qualified migration, blue = constant, green = zero net migration)

Loichinger

2015

Migration and Human Capital in the EU