Dynamic Systems Model
This model enables us to predict the economic and health outcomes of potential policy choices.
At SIPHER, we wish to empower evidence-based policy decision-making towards creating an inclusive economy at national, regional, and local levels.
Given the complex interactions and influences of economic factors on our health outcomes, only by constructing a comprehensive, system-wide dynamic model can we support informed decision-making.
Using a causal systems map developed by experts, in conjunction with annual data on key indicators, we have built a dynamic systems model.
Integrated with our SIPHER Decision Support Tool, this Dynamic Systems Model equips policymakers with valuable insights.
The Dynamic Systems Model enables users to:
- Forecast Future Outcomes: By understanding the outcomes of all indicators over a time period, such as the next ten years, we can analysing the impact of policy interventions.
- Identify Effective Strategies: Pinpointing the economic indicators that are most instrumental in improving a selected health or economic measure.
- Assess Uncertainty: We quantify the uncertainty inherent in our model predictions, offering policymakers a better understanding of different policy scenarios.
SIPHER Glossary For clarification of our terminology and use of acronyms.
Contact: sipher@glasgow.ac
Related Resources
- Balancing improvements in average life expectancy with inequality reductions across Scotland: An inclusive economy trade-off study. Duro, J. et al. (2024) Project Report. SIPHER Consortium.(doi: 10.36399/gla.pubs.337650).
- SIPHER Glossary For clarification of our terminology and use of acronyms
News
-
In March 2024 we held a meeting with our policy partner Scottish Government to share our latest research on decision support modelling for health and economic impacts.
-
Showcasing Dynamic Systems Modelling of the Inclusive Economy & Health Outcomes at the Prevention Research 2023! UKPRP's Community of Practice Conference, Edinburgh in November 2023.
David Veres, Rsesearcher Causal Systems Dynamics Modelling (Workstand 4) presented "Evidence Based System Maps Representations of Health Inequalities Among Local Authorities - a Data Driven Approach" Abstract - Read at Community of Practice Conference handbook - Pg 60.
Ping Li Research Associate on Causal Systems Dynamics Modelling (Workstand 4) presented "State Space Dynamic Systems Modelling of the Inclusive Economy and Health Outcomes" Poster
Techincal Information
Provides technical details of the characteristics including strengths and limitations for this model - plus variable definitions.
| Characteristic | Details |
|---|---|
| Main Perspective | Population Level (Macro) |
| Purpose | This state-space dynamic system model provides a simulation of how each variable contained in the systems map will be affected over time, given specific changes to one or more variables. All studied variables (unemployment, poverty, health, etc.) have to be represented by the input data. Model provide results at the local authority level and allow us to compare system-level effects of different (or no) policy interventions over time. |
| Strengths | The model captures an entire system, including feedback loops to allow for the modelling of dynamic behaviour. In addition, the model allows the testing of policy changes ex-ante - rather than retrospectively. The model can capture both, increases and decreases (such as increases or decreases in funding to supplement disposable household income). |
| Limitations | Any change to be modelled must be quantifiable by the model. This means that changes in variables which are not explicitly covered or for which there is no dependency will not become visible in the model. This implies that results are sensitive to pre-defined pathways which were specified in the systems map. Another limitation is posed by the assumption of known causal pathways between domains. This can be problematic in some cases and requires careful consideration and good justification. Furthermore, assumptions on the time frame for causal relationships needs strong justification and supporting information, which might not always be available. Finally, all modelled policy interventions need to be attributable to the LA level. |
| Geography | Local Authority level for Scotland/England/Wales. |
| Time Period | Based on available and imputed data for previous years (currently 2004-2021). The model provides a dynamic annual forecast for a specified period, for example 5 years, for each variable in the model. |
| Adjustments / Extensions | Factors which can be modified include: the underlying systems map (representing domains and their interactions), features of each respective intervention (including the amount of uplift or characteristics of recipients). In addition the method can be used to capture different systems (environment, housing etc.). |
| Data Requirements | Aggregate level inputs for units of the studied geographical level (e.g. unemployment rate for the LA). Sufficient longitudinal data is required for all variables to validate the model. Cross-sectional data can supplement the longitudinal data for model determination. Domain-specific definitions need to be similar across all geographical units. Please note that different indicators have been selected for England and Wales and Scotland due to data availability. |
| Applications | Typical applications include a systems behaviour as a result of policy interventions, such as interventions to improve poverty, living wage, participation in employment, skills and qualification. In addition, this set of models can help to answer questions about the potential impact of direct policy responses to the current cost-of-living crisis.It is possible to forecast the impact of an intervention for a specific local authority. |
| Modelling Assumptions | Models depend on a pre-defined systems map that describes how domains impact each other and which domains can be subject to interventions. These systems maps need to specify causal pathways between domains with pre-defined time lags. Models also depend on data to provide evidence for quantifying relationships. |
| User Options | Which variable to change and by how much, corresponding to the policy intervention (or shock/absence of intervention) which is evaluated. All changes can be applied differentially to local authorities. |
| User Type(s) | Modellers, decision makers |
| Examples / Link with Other Models and Data | Models of dynamic systems can inform individual-level approaches and help to validate results which were obtained in individual-level approaches. Works also in opposite direction: changes on individual-level which can be aggregated and expressed on LA level. |
| Software Requirement(s) | Matlab. |
| Options for Extension | Building different models for different systems. Modelling and quantifying uncertainty. |
Inclusive Economy Dynamic Systems Model Variable Definitions
| Model Variable | Scotland Model Indicator | England and Wales Model Indicator |
|---|---|---|
| Employment Rate | Employment rate – aged 16-64 | Employment rate – aged 16-64 |
| Job Security / Precarity | Percentage of 16+ in non-permanent employment amongst employed | Percentage of 16+ in non-permanent employment amongst employed |
| Skills and Qualifications | Percentage of adults aged 16-64 with a NVQ 2+ qualification | Percentage of adults aged 16-64 with a NVQ 2+ qualification |
| Labour Remuneration | Percentage of employee jobs paid below “living wage” | Percentage of employee jobs paid below “living wage” |
| Involuntary Exclusion (long term sick) | Proportion of economically inactive due to long-term ill health over working age population | Proportion of economically inactive due to long-term ill health over working age population |
| Disposable Income | Gross disposable household income per head | Gross disposable household income per head |
| Earnings Inequality | Ratio of weekly earnings between 80th and 20th percentiles | Ratio of weekly earnings between 80th and 20th percentiles |
| (Child) Poverty | Percentage of children living in low income households | Percentage of children living in low income households |
| Cost of living | Percentage of household in a LA with finance problem | % of fuel poor households in a LA |
| Health Outcome | Percentage of people in a LA who reported mental health problem | SF-12 mental health values from Survey data (Understanding Society) |
| Health Outcome | Directly age-standardized mortality rate per 100,000 (age under 75) | Directly age-standardized mortality rate per 100,000 (age under 75) |
| Health Outcome | Life expectancy at birth (male) | Healthy life expectancy at birth |