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
1
Outline
• Introduction
• Overview of the SAGE microsimulation
model
• Challenges and lessons
• The Future
2
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
3
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
4
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
5
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
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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)
7
Donor Imputation:
eg duration of partnership
Matching variables
A
recipient
SARs
B
C
Duration of
partnership
donor
BHPS
8
SAGE Model Transition Probabilities
•
•
•
•
•
•
•
•
Mortality
Fertility & Partnership
Health
Disability
Employment
Earnings
Pension scheme membership
DLA and AA
ONS LS, GAD
BHPS, GHS
QLFS
BHPS
QLFS
BHPS
FRS
BHPS
9
SAGE Model programming structure
1991
SIMULATION
INPUT (BASE)
DATA
POPULATION
EVENT LIST
1993
1995
1997
1999
OUTPUT DATA
CONSOL
E
LOG FILE
SCRIPT FILE
10
Challenges
• Technical
– Validation
– Alignment (fig 1a, 1b)
• Operational
– Timeliness
– Maintenance
– Sustainability
11
Fig 1a: Proportion in employment by birth cohort
Men, 1995- 2050
1
0.9
1930-40
0.8
1940-50
0.7
1950-60
0.6
1960-70
0.5
1970-80
0.4
1980-90
1990-00
0.3
2000-10
0.2
2010-20
0.1
2020-30
17
19
21
23
25
27
29
31
33
35
37
39
41
43
45
47
49
51
53
55
57
59
61
63
65
67
69
71
73
75
0
Source: SAGEMOD
Age
12
Fig 1b: Proportion in employment by birth cohort
Men, 1995- 2050 (aligned to HM treasury forecasts)
1
0.9
1930-40
0.8
1940-50
0.7
1950-60
0.6
1960-70
0.5
1970-80
0.4
1980-90
0.3
1990-00
2000-10
0.2
2010-20
0.1
2020-30
17
19
21
23
25
27
29
31
33
35
37
39
41
43
45
47
49
51
53
55
57
59
61
63
65
67
69
71
73
75
0
Source: SAGEMOD
Age
13
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
14
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
15
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
16
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.
17