A fiscal and statistical governance loop for decent work in digital employment: a methodological approach for Uzbekistan

Problem. The expansion of platform employment, self-employment, and other forms of digital employment increases the accessibility of work activities but weakens the connection between the registration of economic activity, social protection, fiscal incentives, and statistical assessment of job quality. As a result, there is no reproducible procedure for converting identified risk into targeted management decisions. Aim. To develop a methodological fiscal and statistical governance loop for decent work that connects the diagnosis of digital employment risks with the selection of support in-struments, verification of results, and policy adjustment. Methods. The research applies institutional-functional analysis of the literature, structural-dynamic interpretation of aggregated employment indicators for 2019–2024, logical modeling, scenario verification of internal consistency, and sensitivity analysis of parameters. Results. It is shown that the growth in the share of formal employment to 52.4 % in 2024 was combined with the persistence of informal employment at 37.1 % and an increase in self-employment to 7.7 %. A six-stage loop of “data – risk – instrument – implementation – verification – ad-justment” was proposed, along with the principle of conditional support across four result groups and a panel of seven op-erational indicators. Sensitivity analysis demonstrated that thresholds for income volatility, platform dependence, and em-ployment retention duration should be considered adjustable parameters rather than universal norms. Scenario verification established the internal traceability of the “risk – instrument – result” link without claiming to assess the actual causal ef-fect. Conclusions. For piloting targeted support for digital and non-standard employment in Uzbekistan, it is recommend-ed to apply a six-stage loop with a multidimensional risk passport, while calibrating indicator thresholds on panel and ad-ministrative data before scaling up measures.

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