multi-states projections: a window on the dynamics of heterogeneous populations anne goujon...

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


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unpublished)Yucatan (Goujon et al. 2000).13 world regions (Lutz & Goujon, 2001) India’s 15 administrative states (Goujon & McNay, on-2003Egypt and Egyptian governorates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)


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


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


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


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.


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.


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.


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


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).


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


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


Why Education??? Feasibility to consider the dimension explicitly

Multi-state population projection tools existFor instance:

PopEd (Sergei Scherbov, VID)PDE Population Projection Software (IIASA)


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.


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.


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.


Example: India (1970-2050)


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


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+


No Education

Primary

Secondary

Tertiary

India in 1975



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+


No Education

Primary

Secondary

Tertiary

India in 1980



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+


No Education

Primary

Secondary

Tertiary

India in 1985



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+


No Education

Primary

Secondary

Tertiary

India in 1990



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+


No Education

Primary

Secondary

Tertiary

India in 1995



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+


No Education

Primary

Secondary

Tertiary

India in 2000


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+


No Education

Primary

Secondary

Tertiary

India in 2005



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+


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+


No Education

Primary

Secondary

Tertiary

Goal Scenario

Constant Enrolment Scenario



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+


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+


No Education

Primary

Secondary

Tertiary

Goal Scenario


India in 2015



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+


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+


No Education

Primary

Secondary

Tertiary

Goal Scenario


India in 2020



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+


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+


No Education

Primary

Secondary

Tertiary

Goal Scenario


India in 2025



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+


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+


No Education

Primary

Secondary

Tertiary

Goal Scenario


India in 2030



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+


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+


No Education

Primary

Secondary

Tertiary

Goal Scenario


India in 2035



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+


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+


No Education

Primary

Secondary

Tertiary

Goal Scenario


India in 2040



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+


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+


No Education

Primary

Secondary

Tertiary

Goal Scenario


India in 2045



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+


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+


No Education

Primary

Secondary

Tertiary

Goal Scenario


India in 2050



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+


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+


No Education

Primary

Secondary

Tertiary

Goal Scenario


Total Population = 1,658,270,000

Total Population = 1,807,725,000

India in 2050


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


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


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


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


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


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:


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


18 Scenarios from 2001 to 2051: Secularization

3 transition/secularization scenarios:

12 Scenarios from 2001 to 2051: Secularization

Migration Fertility


(Mm) High (Mh)


Stable (Fs)




Constant secularization trend (= 2001-05)

High secularization trend (*2 2001-05)

Low secularization trend (=0)


Migration Fertility


(Mm) High (Mh)


Stable (Fs)




2 migration scenarios:

12 Scenarios from 2001 to 2051: Migration


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)


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


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


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)



Results: Proportion Protestants in Total Population, 2001-2051


Results: Proportion Muslims in Total Population, 2001-2051


Results: Proportion Other Religions in Total Population, 2001-2051


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)



Results: Proportion Without religion in Total Population, 2001-2051


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


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


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.

multi-states projections: a window on the dynamics of heterogeneous populations anne goujon...

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