Digital Economy & Innovations

Abstract: Problem. Russian public companies are required to prepare financial statements under both International Fi-nancing Reporting Standards (IFRS) and Russian Accounting Standards (RAS) simultaneously. Differences in the account-ing rules for leases, consolidation, and provisions cause the debt burden of the same company to differ substantially across the two reporting frameworks, creating measurable information opacity. At the same time, the dependence of the impact of this divergence on the market valuation of a company upon its level of leverage remains unstudied. Aim. To establish whether the divergence between debt under IFRS and RAS affects the EV/EBITDA market multiplier of public companies listed on the Moscow Exchange beyond the effect of absolute debt burden, and whether the strength of this effect depends on the company’s own level of leverage. Methods. Using an unbalanced panel of 189 firm-observations across 17 Mos-cow Exchange sectors for 2015–2024, three nested OLS regressions with sector and year fixed effects were estimated; standard errors were clustered by company. An author-developed indicator of the standards divergence, MASK = (IFRS Debt − RAS Debt) / IFRS Assets, was proposed. Robustness was verified on a quarterly sample (169 observations). Results. For companies with low debt burden, the divergence in standards significantly reduces the multiplier (coefficient −49.20; p = 0.055). As debt grows, the penalty weakens: the coefficient of interaction between MASK and leverage is posi-tive (+33.77; p = 0.005). The net discount reaches zero at Debt/EBITDA ≈ 1.46. On quarterly data, the results are repro-duced with increased significance. Conclusions. The market discounts valuations for the reporting divergence primarily among issuers with a simple debt structure, which is consistent with information asymmetry theory. Voluntary disclosure of a reconciliation of the standards, or the convergence of RAS with IFRS, serves as a tool for managing capitalisation.

Abstract: 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.