Modelling with SAGE: lessons and future plans Jane Falkingham & Maria Evandrou ESRC Centre for Population Change University of Southampton BSPS Annual Conference, University of Sussex 11th September, 2009

Outline Introduction Overview of the SAGE microsimulation model Challenges and lessons The Future

Introduction ESRC Research Group ‘Simulating social policy in an Ageing Society’ (SAGE) funded 1999-2005; originally based at LSE and KCL (Falkingham, Evandrou, Rake & Johnson) Main aim: “to carry out research on the future of social policy within an ageing society that explicitly recognises the diversity of life course experience” Substantive research on the life course Development of a dynamic microsimulation model Exploration of alternative policy options

Simulating life course trajectories to 2050: the SAGE Model Project likely future socio-economic characteristics of older population Family circumstances Health & dependency Financial resources Project future demand for welfare benefits & services among older people Assesses impact of social policy reform scenarios

Overview of characteristics of the SAGE Model Base population: 0.1% of GB population = 53,985 individuals Partially closed (internal marriage market) Transitions – both deterministic and stochastic Discrete time (rather than continuous) Time based processing (rather than event based) C++ Efficiency in processing → quick run times

Contents of the SAGE Model Demographic Mortality Fertility Partnership formation Partnership dissolution Health Limiting long-term illness Disability Employment Paid work Unpaid work (informal care) Earnings Pensions Public Private Other Social security transfers Pension Credit, disability living allowance, attendance allowance

SAGE Model Base population 10% sample of 1991 Household SARs and 5% of institutional residents from 2% Individual SARs plus Additional characteristics Data matching / Donor imputation Duration of partnership (BHPS) Missing labour market characteristics Pension contribution & caring histories (FWLS) Regression imputation Aligning limiting long-term illness (QLFS)

Donor Imputation: eg duration of partnership Matching variables A B C Duration of partnership recipient donor SARs BHPS

SAGE Model Transition Probabilities Mortality ONS LS, GAD Fertility & Partnership BHPS, GHS Health QLFS Disability BHPS Employment QLFS Earnings BHPS Pension scheme membership FRS DLA and AA BHPS

SAGE Model programming structure POPULATION INPUT (BASE) DATA 1991 SIMULATION EVENT LIST 1993 1995 1997 OUTPUT DATA 1999 CONSOLE LOG FILE SCRIPT FILE

Challenges Technical Operational Validation Alignment (fig 1a, 1b) Timeliness Maintenance Sustainability

Microsimulation models are resource hungry Lessons Microsimulation models are resource hungry Data Human resources (DWP MDU c.20; SAGE 1fte programmer and 1fte analyst) Ideal team involves range of skills At a minimum need demographer, economist, statistician/ operational researcher, social policy analyst and computer scientist

Lessons Time spend in efficient programming reaped rewards in short run times Minimising ‘embedded’ parameters maximising ‘what if’ scenarios Desktop user model increases flexibility Sharing expertise across modelling groups (PENSIM, SESIM, MOSART, DYNACAN, DYNAMOD) But No quick fix, every model and every social system different

Future plans Development of dynamic multi-state population model within CPC (ESRC) Collaboration with University of Southampton colleagues in Centre for Operational Research, Management Science and Information Systems (CORMSIS) and Institute for Complex Systems Simulation (ICSS) on updating and extending SAGE model (EPSRC) Incorporation of uncertainty and expert opinion through Participative Modelling

Selected publications M. Evandrou and J. Falkingham (2007) ‘Demographic Change, Health and Health-Risk Behaviour across cohorts in Britain: Implications for Policy Modelling’ pp. 59-80 in A. Gupta and A. Harding (eds.), Modelling Our Future: Population Ageing, Health and Aged Care, International Symposia in Economic Theory and Econometrics, 16, Elsevier. M. Evandrou, J. Falkingham, P. Johnson, A. Scott and A. Zaidi (2007) ‘The SAGE Model: A Dynamic Microsimulation Population Model for Britain’ pp. 443-446 in A. Gupta and A. Harding (eds.), Modelling Our Future: Population Ageing, Health and Aged Care, International Symposia in Economic Theory and Econometrics, 16, Elsevier. A. Zaidi, M. Evandrou, J. Falkingham, P. Johnson and A. Scott (2009) ‘Employment Transitions and Earnings Dynamics in the SAGE Model’ pp. 351-379 in Zaidi, A. and Marin, B. (eds) New Frontiers in Microsimulation Modelling Aldershot: Ashgate.