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Multi-states Projections: A Window on the Dynamics of Heterogeneous Populations

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

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Multi-states Projections: A Window on the Dynamics of Heterogeneous Populations

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

  2. Outline • Multi-educational 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 • Lebanon’s six administrative regions (Goujon & Saxena 1999, unpublished) • Yucatan (Goujon et al. 2000).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 Projectionby Age and Sex Mortality Males Females Males Females Migration Migration Migration Fertility Population by Age and Sex Population by Age and Sex 2005 2010 Goujon, Vienna University, 8/01/2008

  5. Principles of Population Projectionby 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 2005 2010 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 ActivitiesActivity of Daily Living scores by educationSoutheast 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 (1970-2050) Goujon, Vienna University, 8/01/2008

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

  36. 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 1900-2001 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. TFR Share in total population of woman 15-49 1981 2001 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 1.70 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% Fertility Differences 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 18 Scenarios from 2001 to 2051: Secularization 3 transition/secularization scenarios: Constant secularization trend (= 2001-05) High secularization trend (*2 2001-05) 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

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