Population and business digital twin for public-policy evaluation
A regional government needs to evaluate ex ante the effects of tax, welfare or macroeconomic-shock response measures on local households and businesses, but only has static, linear macro and micro multipliers: aggregate estimates that miss redistributive, employment and financial-risk effects at the level of individual households or firms.
Analysis
Inventory of the region’s data assets (registries, tax data, business-registry records, cyclical and demographic indicators), assessment of available computing capacity, and definition of the priority policy questions to simulate, from a multidimensional view of profitability, financial soundness and well-being.
Solution
Building a population and business digital twin through dedicated micro-simulation models that replicate the region's households and firms, to simulate ex ante the effects of tax-benefit measures and macroeconomic shocks, with a multi-year time horizon and breakdowns by geographic area and sector.
Architecture
Micro-simulation models for households and businesses, integration with regional data lakes and statistical registries, high-performance computing infrastructure for running scenarios, summary dashboards for decision-makers, and versioning/audit of simulated scenarios.
Implementation
Rollout on an initial set of regional policies, validation of results with the decision-makers involved, calibration of the models on historical data, and subsequent extension to other measures, sectors and areas of the region.
Results
- Ex-ante comparison of alternative policy scenarios before implementation
- Disaggregated view of redistributive, employment and financial-risk effects
- Greater transparency in evaluating the impact of public spending
- Shared analytical basis for technical offices and policy-makers
ROI: Value is measured in policies better calibrated to their objectives, lower risk of measures with unintended effects, and reduced time needed to evaluate alternative scenarios before deciding.
Public reference: Amartya, the population and business digital twin developed by the Emilia-Romagna region with the Universities of Bologna and Modena-Reggio Emilia to simulate the effects of tax and welfare policies. This is not a project delivered by Futura AI: we cite it as a public example of this type of approach. margherita.regione.emilia-romagna.it/it/amartya