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QMSS2 Immigration and Population Dynamics Leeds, 2 – 9 July 2009

MicMac Combining micro and macro approaches in demographic forecasting A study commissioned by the European Commission 6 th Framework Programme Call for tenders: FP6-2003-SSP-3 (May 2005 – April 2009) Introduction to the MicMac project. QMSS2 Immigration and Population Dynamics

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QMSS2 Immigration and Population Dynamics Leeds, 2 – 9 July 2009

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  1. MicMacCombining micro and macro approaches in demographic forecastingA study commissioned by the European Commission6th Framework ProgrammeCall for tenders: FP6-2003-SSP-3(May 2005 – April 2009)Introduction to the MicMac project QMSS2 Immigration and Population Dynamics Leeds, 2 – 9 July 2009

  2. The project

  3. Aim of MicMac To develop a methodology that complements conventional population projections by age and sex (aggregate projections of cohorts, Mac) with projections of the way people live their lives (projections of individual cohort members, Mic)

  4. Expected outcome of MicMac A model and software program to generate detailed demographic projections that can be used in the context of the development of sustainable (elderly) health care and pension systems

  5. Participating institutes • Consortium: NIDI - Netherlands Interdisciplinary Demographic Institute VID - Vienna Institute of Demography INED - Institut National d’Études Démographiques BU- Bocconi University EMC - Erasmus Medical Centre MPIDR - Max Planck Institute for Demographic Research IIASA - International Institute for Applied Systems Analysis UROS - University of Rostock • Period:May 1, 2005 – April 30, 2009

  6. The WorkPackages WP 0 Coordination NIDI Expert Meeting on Assumptions EMC/UROS WP 4 Health NIDI IIASA WP 1 Multi-State Methods Education WP 2 Micro Simulation WP 3 Uncertainty NIDI/MPIDR VID WP 5 Fertility and living arrangements NIDI/MPIDR BU/VID/INED WP 6 Dissemination of results

  7. The model

  8. MicMacBiographic forecasting • A macro-model (MAC) • Extends the cohort-component model to multistate populations • Cohort biographies • A micro-model (MIC) that models demographic events at the individual level • a dynamic micro-simulation model that predicts life transitions at the individual level • Individual biographies • Point of departure:LifePaths (Statistics Canada

  9. The micro-macro link in demographic projection The dual approach adopted in the workplan Inspired by Coleman (1991) Foundations of social theory. Belknap Press of Harvard

  10. The projection model is a multistate probability model • States (attributes) • At the individual level: • State probability: probability that an individual has a given attribute at a given age (is in a given state at a given age) (state probability) • At the aggregate (population) level: counts • State occupancy: expected value of the number of people of a given age with a given attribute • Transitions between states • Transition probability: transitions / risk set • Transition rate: transitions / exposure time

  11. State variables and covariates age sex level of educational attainment living arrangement health MicMac is a generic model

  12. Olivia Household trajectory Formal workplace trajectory Olivia Epros_Lux

  13. State space and transitionsTransition rates 12(t,Z) state 1 state 2 23(t,Z) 13(t,Z) state 3 11 = 12 + 13 and 22 = 21+ 23

  14. State space and transitionsTransition rates 12(x,t) State 1 Healthy State 2 Disabled 21(x,t) 13(x,t) 23(x,t) State 3 Dead where 11 = 12 + 13 and 22 = 21+ 23

  15. 23(x,t) State 1 Healthy 12(x,t) State 2 Disabled State 3 Reactivated 32(x,t) 24(x,t) 14(x,t) 34(x,t) State 4 Dead

  16. Pathways to first child • States • Transitions • Transition rates

  17. Living arrangements of women Netherlands, Retrospective observations, OG98

  18. Synthetic cohort biography State occupancies, women, NL

  19. Free of CVD (2998) hCVD Death Free of CVD hCVD- hCHD- hAMI Death The dynamics of cardiovascular disease Based on the Framingham Heart Study (1948 - ) 2843 1447 2382 • hCVD = History of (other) CVD • hCHD = History of coronary heart disease • hAMI = history of acute myocardial infarction A. Peeters, A.A. Mamun, F.J. Willekens and L. Bonneux (2002) A cardiovascular life course. A life course analysis of the original Framingham Heart Study cohort. European Heart Journal, 23, pp. 458- 466

  20. The effect of covariates or treatment is incorporated in the model via the transition intensity (transition rate) COX baseline transition intensity ’s represent influence of covariates or treatment on transitions between the states

  21. Survival with and without cardiovascular disease Males hOCVD hCHD No hCVD • hCVD = History of (other) CVD • hCHD = History of coronary heart disease • hAMI = history of acute myocardial infarction

  22. State space and transitionsWork Package 5 (D22) Table 1. Marital status. State space and transitions

  23. State space and transitionsWork Package 5 (D22) Table 2. Living arrangement. State space and transitions

  24. State space and transitionsWork Package 5 (D22) Table 3. Fertility (own children ever born). State space and transitions

  25. State space and transitionsWork Package 5 (D22) • Covariates • Sex • Men • Women • Education • 1. Primary (ISCED0 pre-primary education and ISCED1 first stage of basic education) • 2. Lower secondary (ISCED2 second stage of basic education) • 3. Upper secondary (ISCED3 upper secondary education and ISCED4 post secondary non-tertiary education) • 4. Tertiary (ISCED5 first stage of tertiary education and ISCED6 second stage of tertiary education)

  26. Allowed covariates for each transition * “Own children ever born” is always coded in only two categories: “childless/with children”.

  27. State space and transitionsWork Package 5 (D22)Episodes and dates required for each transition

  28. State space and transitionsWork Package 5 (D22) Age-specific transition rates are estimated using Generalized Additive Models (GAM) Hastie and Tibshirani (1990) http://en.wikipedia.org/wiki/Generalized_additive_model http://www.statsoft.com/textbook/stgam.html Purpose of generalized additive models: maximize the quality of prediction of a dependent variable Y from various distributions of the predictor variables. Predictor variables are "connected" to the dependent variable via a link function. GAMs combine GLMs and linear models Effect of covariates for each age interval delimited by 2 knots Cubic spline

  29. Proportional effects of education on the transition TR1, Italy Baseline = grand mean for whole same (deviation coding); report p. 24

  30. Proportional effects of education on the transition TR1, Italy Smoothed curves

  31. Age-specific rates of transition TR1, Italy (smooth)

  32. Age-specific rates of transition TR2, Italy (smooth)

  33. Age-specific rates of transition TR2, Italy (smooth)

  34. Age-specific rates of transition TR11, Italy (smooth)

  35. Transitions that can be analyzed with FFS-NL

  36. Age-specific rates of transition TR1, NL (smooth)

  37. State space, several domains of life

  38. TOPALSA TOol for Projecting Age profiles using Linear Splines Joop de BeerNicole van der Gaag(NIDI) TOPALS is a relationale method: describes deviations from a standard schedule by linear splines

  39. Age specific fertility, 2005 Italy and average of Europe TFR (Europe2005): 1.46 TFR (IT2005): 1.32

  40. TOPALS relational model • Assume a standard age schedule • European average / Model schedule (Hadwiger) • Model deviations using relative risks (RR) • RRs for limited number of knots • RR is average value for age interval • Describe age pattern of RRs by linear splines • A piecewise linear curve • Calculate transition rates • Multiply standard age schedule by RRs

  41. Age groups and relative risks is the rate at age x according to the standard age schedule transition rate at age x in country i

  42. Linear spline through relative risks

  43. Age specific fertility, 2005 TOPALS fit TFR (Europe2005): 1.46 TFR (IT2005): 1.32

  44. Assumptions for MicMac scenarios Future values of transition rates General procedure: - specify model curve describing age pattern choose age schedule that captures general pattern - specify assumptions on future values of the parameters of the model curve model deviations from the general pattern using relative risks

  45. The software

  46. MicMac: Processor • Pre-processor: estimates the transition rates • Processor: • Produces population projections • Produces cohort and individual biographies • Sequence of states • Sojourn times • Postprocessor • Processes the results • Tabulations • Graphics • Analysis

  47. Thank youwww.micmac-projections.orgwww.demogr.mpg.de/go/micmac

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