1. Anne Goujon (goujon@iiasa.ac.at)
Multi-states Projections: A Window on the Dynamics of Heterogeneous Populations
Anne Goujon
International Institute for Applied Systems Analysis (IIASA), Austria & Vienna Institute of Demography (VID), Austrian Academy of Sciences, Austria

2. Outline Multi-educational states Multi-religious states Principles
Why? (3 criteria)
Example: India
Multi-religious states
Projections of Austria’s main religions
Goujon, Vienna University, 8/01/2008

3. 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Lebanon’s six administrative regions (Goujon & Saxena 1999, unpublished)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-2003
Egypt 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

4. Principles of Population Projection by Age and Sex
Mortality
Males
Females
Males
Females
Migration
Migration
Migration
Fertility
Population by Age and Sex Population by Age and Sex
Goujon, Vienna University, 8/01/2008

5. Principles of Population Projection by Age, Sex, and Education
Mortality
Males
Females
Males
Females
Migration
Migration
Migration
Fertility
Population by Age, Sex, and Education Population by Age, Sex, and Education
Goujon, Vienna University, 8/01/2008

6. 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

7. 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

8. 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

9. 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

10. Fertility (TFR) differentials by women’s education in 2001-2006
Source: Demographic and Health Surveys
Goujon, Vienna University, 8/01/2008

11. 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

12. Infant Mortality by Mother’s Education
Factor by which IMR is higher for uneducated women than for women with secondary or higher education
Source: Macro-International, Demographic and Health Surveys, 2007
Goujon, Vienna University, 8/01/2008

13. Ability to Perform Daily Activities Activity of Daily Living scores by education Southeast Asian countries
Source: Lutz and K.C. 2007
Goujon, Vienna University, 8/01/2008

14. Why Education??? Feasibility to consider the dimension explicitly
Multi-state population projection tools exist
For instance:
PDE Population Projection Software (IIASA)
PopEd (Sergei Scherbov, VID)
Goujon, Vienna University, 8/01/2008

15. 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

16. 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

17. 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

18. Example: India ( )
Goujon, Vienna University, 8/01/2008

19. India in 1970 Goujon, Vienna University, 8/01/2008
Source: Lutz, Goujon, K.C. and Sanderson 2007
Goujon, Vienna University, 8/01/2008

20. India in 1975 Goujon, Vienna University, 8/01/2008
Source: Lutz, Goujon, K.C. and Sanderson 2007
Goujon, Vienna University, 8/01/2008

21. India in 1980 Goujon, Vienna University, 8/01/2008
Source: Lutz, Goujon, K.C. and Sanderson 2007
Goujon, Vienna University, 8/01/2008

22. India in 1985 Goujon, Vienna University, 8/01/2008
Source: Lutz, Goujon, K.C. and Sanderson 2007
Goujon, Vienna University, 8/01/2008

23. India in 1990 Goujon, Vienna University, 8/01/2008
Source: Lutz, Goujon, K.C. and Sanderson 2007
Goujon, Vienna University, 8/01/2008

24. India in 1995 Goujon, Vienna University, 8/01/2008
Source: Lutz, Goujon, K.C. and Sanderson 2007
Goujon, Vienna University, 8/01/2008

25. India in 2000
Goujon, Vienna University, 8/01/2008

26. India in 2005 Goujon, Vienna University, 8/01/2008
Source: Lutz, Goujon, K.C. and Sanderson 2007
Goujon, Vienna University, 8/01/2008

27. Constant Enrolment Scenario
India in 2010
Goal Scenario
Constant Enrolment Scenario
Source: Lutz, Goujon, K.C. and Sanderson 2007
Goujon, Vienna University, 8/01/2008

28. Constant Enrolment Scenario
India in 2015
Goal Scenario
Constant Enrolment Scenario
Source: Lutz, Goujon, K.C. and Sanderson 2007
Goujon, Vienna University, 8/01/2008

29. Constant Enrolment Scenario
India in 2020
Goal Scenario
Constant Enrolment Scenario
Source: Lutz, Goujon, K.C. and Sanderson 2007
Goujon, Vienna University, 8/01/2008

30. Constant Enrolment Scenario
India in 2025
Goal Scenario
Constant Enrolment Scenario
Source: Lutz, Goujon, K.C. and Sanderson 2007
Goujon, Vienna University, 8/01/2008

31. Constant Enrolment Scenario
India in 2030
Goal Scenario
Constant Enrolment Scenario
Source: Lutz, Goujon, K.C. and Sanderson 2007
Goujon, Vienna University, 8/01/2008

32. Constant Enrolment Scenario
India in 2035
Goal Scenario
Constant Enrolment Scenario
Source: Lutz, Goujon, K.C. and Sanderson 2007
Goujon, Vienna University, 8/01/2008

33. Constant Enrolment Scenario
India in 2040
Goal Scenario
Constant Enrolment Scenario
Source: Lutz, Goujon, K.C. and Sanderson 2007
Goujon, Vienna University, 8/01/2008

34. Constant Enrolment Scenario
India in 2045
Goal Scenario
Constant Enrolment Scenario
Source: Lutz, Goujon, K.C. and Sanderson 2007
Goujon, Vienna University, 8/01/2008

35. Constant Enrolment Scenario
India in 2050
Goal Scenario
Constant Enrolment Scenario
Source: Lutz, Goujon, K.C. and Sanderson 2007
Goujon, Vienna University, 8/01/2008

36. Constant Enrolment Scenario
Total Population = 1,658,270,000
India in 2050
Goal Scenario
Constant Enrolment Scenario
Total Population = 1,807,725,000
Source: Lutz, Goujon, K.C. and Sanderson 2007
Goujon, Vienna University, 8/01/2008

37. PART 2: Population Projections by Religion
New Times, Old Beliefs:
Predicting the future of religions in Austria
Anne Goujon, Vegard Skirbekk, Katrin Fliegenschnee, Pawel Strzelecki

38. Austrian Population by Religion
Source: Statistic Austria, Census 1900 to 2001
Goujon, Vienna University, 8/01/2008

39. Religious Influences on Demographic Events
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.
Goujon, Vienna University, 8/01/2008

40. Fertility Differences
TFR
Share in total population of woman 15-49
1981
1991
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
1.61
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
1.33
100.0%
Goujon, Vienna University, 8/01/2008

41. Different Fertility Patterns
Goujon, Vienna University, 8/01/2008

42. Main 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?
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?
Goujon, Vienna University, 8/01/2008

43. 12 Scenarios from 2001 to 2051: Fertility
2 fertility scenarios:
Constant Fertility by religion
Converging Fertility by religion
Goujon, Vienna University, 8/01/2008

44. 12 Scenarios from 2001 to 2051: Secularization
3 transition/secularization scenarios:
Constant secularization trend (= )
High secularization trend (* )
Low secularization trend (=0)
Goujon, Vienna University, 8/01/2008

45. 12 Scenarios from 2001 to 2051: Migration
2 migration scenarios:
Goujon, Vienna University, 8/01/2008

46. Results: Total Population of Austria, 2001-2051
Goujon, Vienna University, 8/01/2008

47. Results: TFR of Austria, 2001-2051
Results: Total Fertility Rate
Goujon, Vienna University, 8/01/2008

48. Results: Proportion Roman Catholics in Total Population, 2001-2051
Goujon, Vienna University, 8/01/2008

49. Results: Proportion Protestants in Total Population, 2001-2051
Goujon, Vienna University, 8/01/2008

50. Results: Proportion Muslims in Total Population, 2001-2051
Goujon, Vienna University, 8/01/2008

51. Results: Proportion Other Religions in Total Population, 2001-2051
Goujon, Vienna University, 8/01/2008

52. Results: Proportion Without religion in Total Population, 2001-2051
Goujon, Vienna University, 8/01/2008

53. Age and Religion: A Clash of Generations
Goujon, Vienna University, 8/01/2008

54. 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

55. Thank you Questions & comments Conclusion 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.
Questions & comments
Goujon, Vienna University, 8/01/2008