USING A DIGITAL TWIN ARCHITECTURE FOR MINIMUM WAGE DETERMINATION: THE MWDT FRAMEWORK FOR TÜRKİYE
DOI:
https://doi.org/10.64734/bjss.1-1-03Keywords:
Minimum Wage, Digital Twin, Microsimulation, Multi-criteria decision makingAbstract
This study treats minimum wage setting in Türkiye not as choosing a single raise rate but as a multi-criteria decision problem with trade-offs across worker welfare, poverty risk, wage distribution, employer costs, informality, prices and public finances. Fragmented data, the gross-to-net translation shaped by tax–contribution–transfer rules, and uncertainty about employment and price pass-through weaken the evidence chain. Building on ISO/IEC 30173, the paper proposes a Minimum Wage Digital Twin (MWDT) as a modular decision-support architecture rather than an automated decision engine. A digital-thread data layer harmonizes wage/employment indicators, cost-of-living measures and policy rules, producing versioned snapshots for each decision cycle. In the model layer, M1 computes gross-to-net and budget impacts via microsimulation; M2 runs sector/region vulnerability and employment stress tests; M3 maps cost shocks to prices and living-cost baskets; and M4 generates distributional and adequacy KPIs. The scenario layer versions alternative wage levels, indexation rules and coverage assumptions under a common snapshot and rule set, enabling consistent KPI-basket comparisons. The objective is to enhance transparency and strengthen the institutional learning cycle within the Turkish context by initially piloting at the digital shadow level (characterized by one-way data flow from physical to digital), and then, through bidirectional feedback of post-decision inflation, compliance, and employment signals updating the rule sets and parameter bands, converging toward a digital twin. A governance/reporting layer records audit trails and model/parameter versions and packages outputs for stakeholders and experts. Developed through a design-science lens, MWDT supports phased piloting at a “digital shadow” level and calibration using post-decision outcomes, strengthening transparency, reproducibility and institutional learning.
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