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The Impact of Tax Burden and Informatization on the Shadow Economy of Ukraine

API Access for Researchers — All data and models from this series are available via the API Gateway. Get your API key →
Ukrainian cityscape with digital infrastructure overlay
Economic Research · Stabilarity Research Hub
The Impact of Tax Burden and Informatization on the Shadow Economy of Ukraine

Oleh Ivchenko1, Iryna Ivchenko1 & Dmytro Grybeniuk1

1 Odesa National Polytechnic University (ONPU)

Type
Applied Research
Status
Ongoing · Paper 3/3 · 2026–ongoing
Tool
GRI Shadow Economy Tab  →  API  →  GitHub
3 Articles  ·  3 Papers Planned  ·  2026–ongoing  ·  Ongoing
Abstract

This research series investigates the joint effects of fiscal policy (tax burden) and state digitalization (informatization) on the dynamics of Ukraine’s shadow economy. Using scenario analysis and game-theoretic frameworks accessible to second-year economic cybernetics students, we model three plausible futures for Ukraine’s informal sector under varying tax and digitalization regimes. The series synthesizes World Bank governance indicators, IMF shadow economy estimates, and Ukrainian State Statistics Service data with the Diia digital governance platform metrics, producing actionable policy recommendations for shadow economy reduction during wartime reconstruction and EU accession preparation.


Idea and Motivation

Ukraine’s shadow economy remains among the largest in Europe, estimated at 30–45% of GDP. Two forces push in opposite directions: tax burden drives economic activity underground, while digitalization of state services — the Diia platform, e-invoicing, cashless payments — increases transparency and raises the cost of informality. No existing study models their joint interaction using accessible quantitative methods suitable for economic cybernetics curricula.

This gap matters for two reasons. First, Ukraine’s EU accession process requires fiscal convergence, including measurable reduction of the informal sector. Second, wartime reconstruction will depend on tax revenue mobilization — understanding how digitalization can substitute for punitive taxation in reducing shadow activity is directly policy-relevant.


Goal

Produce a 3-paper series that (1) maps the problem landscape with descriptive statistics covering 2015–2025, (2) models three scenarios using game theory and scenario analysis with payoff matrices for government vs. informal-sector actors, and (3) derives policy recommendations with a decision framework integrating comparative analysis of Estonia, Georgia, and Poland — three post-socialist economies that have achieved significant shadow economy reductions through different policy mixes.


Scope

The series covers 3 papers across three complementary phases:

Table 1. Paper structure and thematic coverage
PaperFocus AreaKey Topics
1Problem LandscapeProblem statement, literature review (Schneider, Medina), descriptive statistics on tax-to-GDP ratio and shadow economy estimates 2015–2025, Diia platform adoption metrics
2Scenario ModellingThree-scenario analysis (high tax/low digital, balanced, low tax/high digital), game-theoretic payoff matrices for government vs. informal sector, Nash equilibrium identification
3Policy and ComparisonComparative analysis (Estonia, Georgia, Poland), policy recommendations, integration with geopolitical risk monitoring, EU accession fiscal requirements, projections to 2030

Focus

The primary analytical focus spans six areas: tax-to-GDP ratio dynamics in Ukraine and peer economies; shadow economy estimation methods following the Schneider–Medina MIMIC approach; Ukraine’s Diia digital governance platform as a transparency mechanism; game theory applied to government–informal sector interaction; scenario analysis methodology for policy evaluation; and EU accession fiscal requirements as external constraints on Ukrainian fiscal policy.

The methodological approach is deliberately accessible — designed for second-year economic cybernetics students while remaining analytically rigorous. Game-theoretic models use simplified 2×2 and 3×3 payoff matrices rather than continuous strategy spaces, making the framework reproducible in educational settings.


Limitations

Estimation uncertaintyShadow economy estimates are inherently uncertain, with margins of ±5–10% of GDP across methodologies. Results should be interpreted as directional, not precise.
Wartime data gapsReliable economic statistics for 2022–2024 are limited due to wartime disruption. The series acknowledges and documents these gaps explicitly.
Model simplificationGame-theoretic models use simplified payoff structures. Real-world actors face richer strategy spaces and information asymmetries than captured here.
Public data onlyNo proprietary fiscal data is used. All sources are publicly available (World Bank, IMF, State Statistics Service, Diia reports).

Scientific Value

The series makes four contributions. First, it provides the first joint analysis of tax burden and informatization effects on the Ukrainian shadow economy — existing studies treat these factors in isolation. Second, it develops an accessible game-theoretic framework suitable for economic cybernetics education, bridging the gap between advanced research methods and undergraduate curricula. Third, it produces scenario projections to 2030 under varying policy regimes, providing a structured basis for policy evaluation. Fourth, the work is directly policy-relevant during Ukraine’s EU accession preparation, where fiscal transparency metrics are a core assessment criterion.


Resources

  • Geopolitical Risk Intelligence — Shadow Economy Tab→
  • Stabilarity API Gateway (/v1/geo-risk/)→
  • GitHub Repository→

Status

Ongoing. All 3 papers published. Series complete.


Published Articles

Applied Research · 3 published
Authors: Oleh Ivchenko, Iryna Ivchenko, Dmytro Grybeniuk
Analysis based on publicly available Ukrainian fiscal and governance data.
All Articles
1
Tax Burden, Digitalization, and Shadow Economy in Ukraine: A Problem Landscape (2015–2025)  DOI  10/10
Applied Research · Mar 13, 2026 · 11 min read
2
Scenario Analysis: Modeling Three Futures for Ukraine's Shadow Economy (2025–2030)  DOI  8/10
Applied Research · Mar 14, 2026 · 13 min read
3
Policy Implications and a Decision Framework for Shadow Economy Reduction in Ukraine  DOI  9/10
Applied Research · Mar 14, 2026 · 13 min read
3 published63 total views37 min total readingMar 2026 – Mar 2026 published

Contribution Opportunities

Researchers and students wishing to contribute are encouraged to engage in the following directions:

  • Data collection: Compile and validate time-series data on Ukrainian tax-to-GDP ratios, Diia adoption metrics, and shadow economy estimates from multiple methodological sources.
  • Comparative cases: Extend the comparative analysis beyond Estonia, Georgia, and Poland to other post-socialist economies with documented digitalization-driven fiscal reforms.
  • Game-theoretic extensions: Develop richer strategy spaces (continuous or multi-stage games) building on the simplified payoff matrices presented in Paper 2.
  • Policy engagement: Connect findings to ongoing EU accession working groups and Ukrainian fiscal reform initiatives.
  • Tool development: Contribute to the Shadow Economy tab in the Geopolitical Risk Intelligence tool on the Stabilarity Research Hub.

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