1. 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

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

3. Introduction
ESRC Research Group ‘Simulating social policy in an Ageing Society’ (SAGE) funded ; 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

4. 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

5. 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

6. 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

7. 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)

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

9. 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

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

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

14. 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

15. 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

16. 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

17. Selected publications
M. Evandrou and J. Falkingham (2007) ‘Demographic Change, Health and Health-Risk Behaviour across cohorts in Britain: Implications for Policy Modelling’ pp 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 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 in Zaidi, A. and Marin, B. (eds) New Frontiers in Microsimulation Modelling Aldershot: Ashgate.