Multi-states Projections: A Window on the Dynamics of Heterogeneous Populations
Anne Goujon ([email protected])
International Institute for Applied Systems Analysis (IIASA), Austria & Vienna Institute of Demography (VID), Austrian Academy of
Sciences, Austria
Goujon, Vienna University, 8/01/2008
Outline
Multi-educational statesPrinciplesWhy? (3 criteria)Example: India
Multi-religious statesProjections of Austria’s main religions
Goujon, Vienna University, 8/01/2008
PART 1: Population Projections by Level of Education
Already several case studies: Pioneer work in Mauritius (Lutz et al. 1994) and Cape Verde (Wils
1995) North Africa (Yousif & Goujon & Lutz 1996): Algeria, Egypt, Libya,
Morocco, Sudan, Tunisia. Middle Eastern Countries (Goujon 1997 & 2002): Jordan, Lebanon,
Syria, West Bank and Gaza Strip.Lebanon’s six administrative regions (Goujon & Saxena 1999,
unpublished)Yucatan (Goujon et al. 2000).13 world regions (Lutz & Goujon, 2001) India’s 15 administrative states (Goujon & McNay, on-2003Egypt and Egyptian governorates (Goujon et al. 2007)Southeast Asia (Goujon & K.C., 2007)120 countries (Lutz et al. on-going)
Goujon, Vienna University, 8/01/2008
Principles of Population Projectionby Age and Sex
Migration
Mortality
Migration
Fertility
Migration
Males Females Males Females
Population by Age and Sex Population by Age and Sex2005 2010
Goujon, Vienna University, 8/01/2008
Migration
Mortality
Migration
Fertility
Migration
Males Females Males Females
Principles of Population Projectionby Age, Sex, and Education
Population by Age, Sex, and Education Population by Age, Sex, and Education 2005 2010
Goujon, Vienna University, 8/01/2008
Why Education???
Education answers the three main criteria of why to explicitly consider a particular dimension in
population projections
It is interesting as such and is a desirable explicit output parameter;
It is a source of demographic heterogeneity and has an impact on the dynamic of the system;
It is feasible to consider the dimension explicitly
Goujon, Vienna University, 8/01/2008
Why Education??? Interesting as such & a desirable explicit output parameter
Output of the projection: the level of educational attainment of the population by age and by sex for a defined period:
Picture of human capital composition (age-group 20-64) in absolute values.
Show long term effects of education policies: The momentum of population and education change in development planning Assess according to present pace of improvements the likelihood
of the realization of certain education/development goals
Education is a good proxy for quality of life, autonomy of women, level of economic development.
Goujon, Vienna University, 8/01/2008
Education and Economic Growth(Lutz & Crespo-Cuaresma, 2007)
The educational attainment of younger adults is key to explaining differences in income across all countries.
For the poor countries, it turns out that not only universal primary education, but also secondary education of broad segments of the population boosts economic growth.
Goujon, Vienna University, 8/01/2008
Why Education??? A source of demographic heterogeneity with an impact on
the dynamic of the system
No other socioeconomic variable shows a similar degree of association with fertility (result shown from WFS and DHS).
Female education is also related to infant and maternal mortality; mortality differentials exist at almost all ages and for both sexes
The education-fertility relationship is very relevant because the education level of a society can be directly influenced by government policy. This brings the State to be the key variable in the demographic transition.
Goujon, Vienna University, 8/01/2008
Fertility (TFR)
differentials by women’s education in 2001-2006
No education
(A) Primary
Secondary or higher
(B)Difference
(A) - (B)
Ethiopia 2005 6.10 5.10 2.00 4.10 Burkina Faso 2003 6.30 4.50 2.50 3.80 Tanzania 2004 6.90 5.60 3.30 3.60 Zambia 2001/02 7.40 6.50 3.90 3.50 Kenya 2003 6.70 5.50 3.20 3.50 Mozambique 2003 6.30 5.30 2.90 3.40 Uganda 2006 7.70 7.20 4.40 3.30
Morocco 2003-2004 3.00 2.30 1.80 1.20 Egypt 2005 3.80 3.40 2.90 0.90
Philippines 2003 5.30 5.00 3.10 2.20 Cambodia 2005 4.30 3.50 2.60 1.70 Nepal 2006 3.90 2.80 2.20 1.70 Bangladesh 2004 3.60 3.10 2.50 1.10
Bolivia 2003 6.80 4.90 2.50 4.30 Haiti 2005 5.90 4.30 2.40 3.50 Colombia 2005 4.50 3.40 2.10 2.40 Dominican Republic 2002 4.50 3.60 2.50 2.00
South & Southeast Asia
Latin America & Caribbean
Highest educational level
Sub-Saharan Africa
North Africa
Source: Demographic and Health Surveys
Goujon, Vienna University, 8/01/2008
Heterogeneity in the Level of Heterogeneity
Fertility differentials between upper and lower education groups tend to cluster regionally, with linkages to the level of socioeconomic development, the stage of the demographic transition, the stage in the level of mass education in the country and the cultural setting (Jejeebhoy 1995, Cochrane 1979, UN 1987)
Narrowest fertility gap: countries quite advanced in the process of development and demographic transition
Largest differentials: Countries in settings of medium development and “halfway” through the process of demographic transition.
Developed world: narrow gap with a diminishing negative effect of education and in some countries a high education even turns into a stimulating factor (Kravdal, 2001).
Goujon, Vienna University, 8/01/2008
Infant Mortality by Mother’s Education
Source: Macro-International, Demographic and Health Surveys, 2007
IMR No education
0
0.5
1
1.5
2
2.5
3
Phi
lippi
nes
Vie
tnam
Bol
ivia
S
eneg
al
Mal
i
Nic
arag
ua
Col
ombi
a In
done
sia
Moz
ambi
que
Eth
iopi
a M
adag
asca
r
Mor
occo
Nep
al
Hon
dura
s
Ben
in
Leso
tho
Rw
anda
E
gypt
Tan
zani
a
Ken
ya
Nig
eria
Cha
d
Gui
nea
Mal
awi
Cam
eroo
n
Erit
rea
Ban
glad
esh
Zam
bia
Jord
an
Gha
na
Factor by which IMR is higher for uneducated women than for women with secondary or higher education
Goujon, Vienna University, 8/01/2008
Ability to Perform Daily Activities Activity of Daily Living scores by education
Southeast Asian countries
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
40-49 50-59 60-69 70-79
Age Group
AD
L S
core Primary
Secondary
Tertiary
Source: Lutz and K.C. 2007
Goujon, Vienna University, 8/01/2008
Why Education??? Feasibility to consider the dimension explicitly
Multi-state population projection tools existFor instance:
PopEd (Sergei Scherbov, VID)PDE Population Projection Software (IIASA)
Goujon, Vienna University, 8/01/2008
Multi-State Cohort Component Method & the Extended Leslie
Matrix The multi-state population projection method allows division of
the population to be projected into any number of “states”: originally geographic regions (Rogers 1975) and for our purpose educational categories
Combination of the discrete time cohort component projection used for single-state populations (Leslie 1945), and an adapted form of the multi-state population projection method first compiled in complete form by Rogers (1975) and Wilson and Rogers (1980).
The demographic method of cohort-component projection is most appropriate to educational projections because education is typically acquired in childhood and youth and then changes the educational composition of the population along cohort lines.
Goujon, Vienna University, 8/01/2008
The Extended Leslie Matrix Multi-state projection method: the age- and sex-specific population is further divided
into states and the transitions between these states are included in the projection. Transitions are specific to each age and gender group, and are represented by age-
and sex-specific transition matrices. These transition matrices can replace the age- and sex-specific birth, death, and net
migration scalars in the Leslie matrix. The multi-state population projection is then represented as an extended Leslie
matrix. The population vector is also extended to include the population by states. The matrix is arranged as the original one-state Leslie matrix, but now, each scalar in
the matrix has been replaced by a small transition matrix and each scalar in the population vector is a small vector of the population states.
Transitions refer to movements from one state to another and are distinct from mortality or its inverse, survivorship. Each transition can be called Tij (a) which means the transition rate into state i out of state j in age group a. In every period, each person is exposed to a certain probability of making a socio‑economic transition and to dying. Thus, in the matrix of transitions, survivorship S(a) and the transitions Tij (a) are included.
Goujon, Vienna University, 8/01/2008
Data Availability:Population, Fertility, Mortality, Migration,
Transitions
Population by age, sex and education can be extracted directly from censuses, but also from UNESCO publications, and others.
Fertility data by education can be extracted from DHS, and other surveys.
Mortality data are more difficult to obtain for all age groups but exists for some countries.
Migration data by education can sometimes be extracted from censuses or surveys.
Transitions probabilities have most of the time to be calculated, e.g. based on two surveys or along cohort lines.
Goujon, Vienna University, 8/01/2008
Example: India (1970-2050)
Goujon, Vienna University, 8/01/2008
80000 60000 40000 20000 0 20000 40000 60000 80000
0-45-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
Males Population in Thousands Females
No Education
Primary
Secondary
Tertiary
India in 1970
Source: Lutz, Goujon, K.C. and Sanderson 2007
Goujon, Vienna University, 8/01/2008
80000 60000 40000 20000 0 20000 40000 60000 80000
0-45-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
Males Population in Thousands Females
No Education
Primary
Secondary
Tertiary
India in 1975
Source: Lutz, Goujon, K.C. and Sanderson 2007
Goujon, Vienna University, 8/01/2008
80000 60000 40000 20000 0 20000 40000 60000 80000
0-45-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
Males Population in Thousands Females
No Education
Primary
Secondary
Tertiary
India in 1980
Source: Lutz, Goujon, K.C. and Sanderson 2007
Goujon, Vienna University, 8/01/2008
80000 60000 40000 20000 0 20000 40000 60000 80000
0-45-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
Males Population in Thousands Females
No Education
Primary
Secondary
Tertiary
India in 1985
Source: Lutz, Goujon, K.C. and Sanderson 2007
Goujon, Vienna University, 8/01/2008
80000 60000 40000 20000 0 20000 40000 60000 80000
0-45-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
Males Population in Thousands Females
No Education
Primary
Secondary
Tertiary
India in 1990
Source: Lutz, Goujon, K.C. and Sanderson 2007
Goujon, Vienna University, 8/01/2008
80000 60000 40000 20000 0 20000 40000 60000 80000
0-45-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
Males Population in Thousands Females
No Education
Primary
Secondary
Tertiary
India in 1995
Source: Lutz, Goujon, K.C. and Sanderson 2007
Goujon, Vienna University, 8/01/2008
80000 60000 40000 20000 0 20000 40000 60000 80000
0-45-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
Males Population in Thousands Females
No Education
Primary
Secondary
Tertiary
India in 2000
Goujon, Vienna University, 8/01/2008
80000 60000 40000 20000 0 20000 40000 60000 80000
0-45-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
Males Population in Thousands Females
No Education
Primary
Secondary
Tertiary
India in 2005
Source: Lutz, Goujon, K.C. and Sanderson 2007
Goujon, Vienna University, 8/01/2008
India in 2010
80000 60000 40000 20000 0 20000 40000 60000 80000
0-45-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
Males Population in Thousands Females
No Education
Primary
Secondary
Tertiary
80000 60000 40000 20000 0 20000 40000 60000 80000
0-45-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
Males Population in Thousands Females
No Education
Primary
Secondary
Tertiary
Goal Scenario
Constant Enrolment Scenario
Source: Lutz, Goujon, K.C. and Sanderson 2007
Goujon, Vienna University, 8/01/2008
80000 60000 40000 20000 0 20000 40000 60000 80000
0-45-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
Males Population in Thousands Females
No Education
Primary
Secondary
Tertiary
80000 60000 40000 20000 0 20000 40000 60000 80000
0-45-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
Males Population in Thousands Females
No Education
Primary
Secondary
Tertiary
Goal Scenario
Constant Enrolment Scenario
India in 2015
Source: Lutz, Goujon, K.C. and Sanderson 2007
Goujon, Vienna University, 8/01/2008
80000 60000 40000 20000 0 20000 40000 60000 80000
0-45-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
Males Population in Thousands Females
No Education
Primary
Secondary
Tertiary
80000 60000 40000 20000 0 20000 40000 60000 80000
0-45-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
Males Population in Thousands Females
No Education
Primary
Secondary
Tertiary
Goal Scenario
Constant Enrolment Scenario
India in 2020
Source: Lutz, Goujon, K.C. and Sanderson 2007
Goujon, Vienna University, 8/01/2008
80000 60000 40000 20000 0 20000 40000 60000 80000
0-45-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
Males Population in Thousands Females
No Education
Primary
Secondary
Tertiary
80000 60000 40000 20000 0 20000 40000 60000 80000
0-45-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
Males Population in Thousands Females
No Education
Primary
Secondary
Tertiary
Goal Scenario
Constant Enrolment Scenario
India in 2025
Source: Lutz, Goujon, K.C. and Sanderson 2007
Goujon, Vienna University, 8/01/2008
80000 60000 40000 20000 0 20000 40000 60000 80000
0-45-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
Males Population in Thousands Females
No Education
Primary
Secondary
Tertiary
80000 60000 40000 20000 0 20000 40000 60000 80000
0-45-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
Males Population in Thousands Females
No Education
Primary
Secondary
Tertiary
Goal Scenario
Constant Enrolment Scenario
India in 2030
Source: Lutz, Goujon, K.C. and Sanderson 2007
Goujon, Vienna University, 8/01/2008
80000 60000 40000 20000 0 20000 40000 60000 80000
0-45-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
Males Population in Thousands Females
No Education
Primary
Secondary
Tertiary
80000 60000 40000 20000 0 20000 40000 60000 80000
0-45-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
Males Population in Thousands Females
No Education
Primary
Secondary
Tertiary
Goal Scenario
Constant Enrolment Scenario
India in 2035
Source: Lutz, Goujon, K.C. and Sanderson 2007
Goujon, Vienna University, 8/01/2008
80000 60000 40000 20000 0 20000 40000 60000 80000
0-45-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
Males Population in Thousands Females
No Education
Primary
Secondary
Tertiary
80000 60000 40000 20000 0 20000 40000 60000 80000
0-45-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
Males Population in Thousands Females
No Education
Primary
Secondary
Tertiary
Goal Scenario
Constant Enrolment Scenario
India in 2040
Source: Lutz, Goujon, K.C. and Sanderson 2007
Goujon, Vienna University, 8/01/2008
80000 60000 40000 20000 0 20000 40000 60000 80000
0-45-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
Males Population in Thousands Females
No Education
Primary
Secondary
Tertiary
80000 60000 40000 20000 0 20000 40000 60000 80000
0-45-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
Males Population in Thousands Females
No Education
Primary
Secondary
Tertiary
Goal Scenario
Constant Enrolment Scenario
India in 2045
Source: Lutz, Goujon, K.C. and Sanderson 2007
Goujon, Vienna University, 8/01/2008
80000 60000 40000 20000 0 20000 40000 60000 80000
0-45-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
Males Population in Thousands Females
No Education
Primary
Secondary
Tertiary
80000 60000 40000 20000 0 20000 40000 60000 80000
0-45-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
Males Population in Thousands Females
No Education
Primary
Secondary
Tertiary
Goal Scenario
Constant Enrolment Scenario
India in 2050
Source: Lutz, Goujon, K.C. and Sanderson 2007
Goujon, Vienna University, 8/01/2008
80000 60000 40000 20000 0 20000 40000 60000 80000
0-45-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
Males Population in Thousands Females
No Education
Primary
Secondary
Tertiary
80000 60000 40000 20000 0 20000 40000 60000 80000
0-45-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
Males Population in Thousands Females
No Education
Primary
Secondary
Tertiary
Goal Scenario
Constant Enrolment Scenario
Total Population = 1,658,270,000
Total Population = 1,807,725,000
India in 2050
Source: Lutz, Goujon, K.C. and Sanderson 2007
New Times, Old Beliefs:
Predicting the future of religions in Austria
Anne Goujon, Vegard Skirbekk, Katrin Fliegenschnee, Pawel Strzelecki
PART 2: Population Projections by Religion
Goujon, Vienna University, 8/01/2008
Austrian Population by Religion1900-2001
Source: Statistic Austria, Census 1900 to 2001
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1900 1911 1921 1931 1941 1951 1961 1971 1981 1991 2001
Roman Catholic
Protestant
Muslim
Without religion
Others
Goujon, Vienna University, 8/01/2008
Most major religions contain texts and commands to increase their number of followers.
The Bible promotes childbearing: (Gen 1:28) “And God blessed them, and God said unto them, Be fruitful, and multiply, and replenish the earth”.
While Mohammed says “Marry women who are loving and very prolific for I shall outnumber the peoples by you” (al-Masabih 1963, p 659)
Marriages are endorsed in all religions and divorced are largely forbidden in Catholicism and Islam. Protestants permit divorce. Interreligious marriages are allowed in Islam only if the husband is Muslim.
All major religions promote transmission of religions to their children. Conversion or secularization is strongly discouraged in all religious, although the degree of punishment differ according to religion and society.
Religious Influences on Demographic Events
Goujon, Vienna University, 8/01/2008
Fertility Differences
ROMAN CATHOLIC
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
15-19 20-24 25-29 30-34 35-39 40-44 45-49
1981
1991
2001
TFR Share in total population of woman 15-49
1981 1991 2001 1981 2001 ROMAN CATHOLICS 1.70 1.52 1.32 85.7% 74.5% PROTESTANT 1.51 1.37 1.21 5.8% 4.5% OTHER 1.70 1.61 1.44 3.4% 6.2% ISLAM 3.09 2.77 2.34 0.9% 4.6% WITHOUT 1.12 1.04 0.86 4.2% 10.2% TOTAL 1.67 1.51 1.33 100.0% 100.0%
TOTAL POPULATION
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
15-19 20-24 25-29 30-34 35-39 40-44 45-49
1981
1991
2001
Goujon, Vienna University, 8/01/2008
PROTESTANT
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
15-19 20-24 25-29 30-34 35-39 40-44 45-49
1981
1991
2001
OTHER
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
15-19 20-24 25-29 30-34 35-39 40-44 45-49
1981
1991
2001
MUSLIM
0
0,2
0,4
0,6
0,8
1
1,2
1,4
15-19 20-24 25-29 30-34 35-39 40-44 45-49
1981
1991
2001
WITHOUT
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
15-19 20-24 25-29 30-34 35-39 40-44 45-49
1981
1991
2001
Different Fertility Patterns
Goujon, Vienna University, 8/01/2008
Question 1: If secularization and the increase of other religions in the population continue, when will Roman Catholics make up less than 50% of the total population?
Question 2: Will the Muslims or those without religion become the dominant group in Austria?
Question 3: What is the influence of migration on the religion structure of the country?
Question 4: Could a change in the religious composition lead to increased fertility in Austria?
Main Questions for the Projections:
Goujon, Vienna University, 8/01/2008
12 Scenarios from 2001 to 2051: Fertility
Constant Fertility by religion
Converging Fertility by religion
2 fertility scenarios:
Migration Fertility
Transition/ Secularisation Medium
(Mm) High (Mh)
Constant (Tc) Fs Mm Tc Fs Mh Tc High (Th) Fs Mm Th Fs Mh Th
Stable (Fs)
Low (Tl) Fs Mm Tl Fs Mh Tl Constant (Tc) Fc Mm Tc Fc Mh Tc
High (Th) Fc Mm Th Fc Mh Th Converging
(Fc) Low (Tl) Fc Mm Tl Fc Mh Tl
Goujon, Vienna University, 8/01/2008
18 Scenarios from 2001 to 2051: Secularization
3 transition/secularization scenarios:
12 Scenarios from 2001 to 2051: Secularization
Migration Fertility
Transition/ Secularisation Medium
(Mm) High (Mh)
Constant (Tc) Fs Mm Tc Fs Mh Tc High (Th) Fs Mm Th Fs Mh Th
Stable (Fs)
Low (Tl) Fs Mm Tl Fs Mh Tl Constant (Tc) Fc Mm Tc Fc Mh Tc
High (Th) Fc Mm Th Fc Mh Th Converging
(Fc) Low (Tl) Fc Mm Tl Fc Mh Tl
Constant secularization trend (= 2001-05)
High secularization trend (*2 2001-05)
Low secularization trend (=0)
Goujon, Vienna University, 8/01/2008
Migration Fertility
Transition/ Secularisation Medium
(Mm) High (Mh)
Constant (Tc) Fs Mm Tc Fs Mh Tc High (Th) Fs Mm Th Fs Mh Th
Stable (Fs)
Low (Tl) Fs Mm Tl Fs Mh Tl Constant (Tc) Fc Mm Tc Fc Mh Tc
High (Th) Fc Mm Th Fc Mh Th Converging
(Fc) Low (Tl) Fc Mm Tl Fc Mh Tl
2 migration scenarios:
12 Scenarios from 2001 to 2051: Migration
Goujon, Vienna University, 8/01/2008
Results: Total Population of Austria, 2001-2051
7.6
7.8
8.0
8.2
8.4
8.6
8.8
2001 2006 2011 2016 2021 2026 2031 2036 2041 2046 2051
To
tal p
op
ula
tio
n (
in m
illio
n)
High migration (Mh)
Medium migration (Mm)
Goujon, Vienna University, 8/01/2008
Results: Total Fertility Rate
1,30
1,35
1,40
1,45
1,50
1,5520
01-0
5
2006
-10
2011
-15
2016
-20
2021
-25
2026
-30
2031
-35
2036
-40
2041
-45
2046
-51
Tota
l fer
tilit
y ra
te
Stable fertility (Fs) high migration (Mh) low secularisation (T l)
Converging fertility (Fc) medium migration high (Mm) high secularisation (T l)
Results: TFR of Austria, 2001-2051
Goujon, Vienna University, 8/01/2008
30
40
50
60
70
80
2001 2006 2011 2016 2021 2026 2031 2036 2041 2046 2051
Per
cen
t R
om
an C
ath
oli
cs
Low secularisation (Tl)
Constant secularisation (Tc)
High secularisation (Th)
Results: Proportion Roman Catholics in Total Population, 2001-2051
Goujon, Vienna University, 8/01/2008
3
4
5
6
2001 2006 2011 2016 2021 2026 2031 2036 2041 2046 2051
Per
cen
t P
rote
stan
ts Low secularisation (Tl)
Constant secularisation (Tc)
High secularisation (Th)
Results: Proportion Protestants in Total Population, 2001-2051
Goujon, Vienna University, 8/01/2008
Results: Proportion Muslims in Total Population, 2001-2051
Goujon, Vienna University, 8/01/2008
Results: Proportion Other Religions in Total Population, 2001-2051
Goujon, Vienna University, 8/01/2008
0
5
10
15
20
25
30
35
40
2001 2006 2011 2016 2021 2026 2031 2036 2041 2046 2051
Per
cen
t w
ith
ou
t re
ligio
n
Low secularisation (Tl)
Constant secularisation (Tc)
High secularisation (Th)
Results: Proportion Without religion in Total Population, 2001-2051
Goujon, Vienna University, 8/01/2008
Age and Religion: A Clash of Generations
0
1
2
3
4
5
6
0-14
15-6
4
65+
0-14
15-6
4
65+
0-14
15-6
4
65+
0-14
15-6
4
65+
Po
pu
lati
on
(m
illio
ns)
Catholics Protestants Muslims Others Without Religion
2051 : Converging fertilityMedium migrationLow secularization
2051 : Converging fertilityMedium migration
Constant secularization
2051 : Stable fertilityHigh migration
High secularization
2001
Goujon, Vienna University, 8/01/2008
The Answers to the Questions for the Projections:
Question 1: If secularization and the increase of other religions in the population continue, when will Roman Catholics make up less than 50% of the total population? Starting from 2031
Question 2: Will the Muslims or those without religion become the dominant group in Austria? Not before 2051
Question 3: What is the influence of migration on the religion structure of the country? Quite important
Question 4: Could a change in the religious composition lead to increased fertility in Austria? Yes, but not really
Goujon, Vienna University, 8/01/2008
ConclusionThank you
Questions & comments
Conclusion
Global aggregate figures of any kind tend to have little meaning Information content typically lies in variation Variation can be over time, space or over individuals
(sub-populations) Such variation is the source of information for studying
change as well as its determinants and consequences. To also make sense of such information we need theories,
hypotheses, models.