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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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**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**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)**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**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**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**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**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**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**State variables and covariates**age sex level of educational attainment living arrangement health MicMac is a generic model**Olivia**Household trajectory Formal workplace trajectory Olivia Epros_Lux**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**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**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**Pathways to first child**• States • Transitions • Transition rates**Living arrangements of women Netherlands, Retrospective**observations, OG98**Synthetic cohort biography**State occupancies, women, NL**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**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**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**State space and transitionsWork Package 5 (D22)**Table 1. Marital status. State space and transitions**State space and transitionsWork Package 5 (D22)**Table 2. Living arrangement. State space and transitions**State space and transitionsWork Package 5 (D22)**Table 3. Fertility (own children ever born). State space and transitions**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)**Allowed covariates for each transition*** “Own children ever born” is always coded in only two categories: “childless/with children”.**State space and transitionsWork Package 5 (D22)Episodes and**dates required for each transition**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**Proportional effects of education**on the transition TR1, Italy Baseline = grand mean for whole same (deviation coding); report p. 24**Proportional effects of education**on the transition TR1, Italy Smoothed curves**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**Age specific fertility, 2005**Italy and average of Europe TFR (Europe2005): 1.46 TFR (IT2005): 1.32**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**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**Age specific fertility, 2005**TOPALS fit TFR (Europe2005): 1.46 TFR (IT2005): 1.32**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**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**Thank**youwww.micmac-projections.orgwww.demogr.mpg.de/go/micmac

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