Tax Burden, Digitalization, and Shadow Economy in Ukraine: A Problem Landscape (2015–2025)

Tax Burden, Digitalization, and Shadow Economy in Ukraine: A Problem Landscape (2015–2025)

1. Statement of the Problem

Ukraine’s shadow economy remains one of the largest in Europe, consistently estimated at 30–45% of official GDP over the past decade. This phenomenon directly undermines fiscal stability, distorts market competition, and complicates the country’s path toward EU accession. The interaction between tax policy, digital governance, and informal economic activity forms a complex adaptive system where policy interventions often produce non-linear and counterintuitive results.

Two concurrent trends define the 2015–2025 period. First, Ukraine undertook significant tax reforms — simplifying the tax code, adjusting VAT administration, and introducing electronic reporting. Second, the launch of the Diia platform in 2020 brought rapid digitalization of government services, with over 20 million users by 2025 (Ministry of Digital Transformation of Ukraine, 2025). Despite these efforts, the shadow economy has proven resistant to sustained reduction, particularly during the economic shocks of the COVID-19 pandemic and the full-scale Russian invasion beginning in February 2022.

From the perspective of economic cybernetics, the shadow economy can be modeled as a feedback system: higher tax burdens incentivize informal activity, which erodes the tax base, prompting further rate increases — a reinforcing loop. Simultaneously, digitalization acts as a balancing mechanism by increasing transaction transparency and reducing opportunities for tax evasion. Understanding the relative strength of these competing feedback loops is essential for effective policy design.

This paper establishes the empirical foundation for a three-part research series examining how tax burden and informatization jointly influence the shadow economy in Ukraine. We present descriptive statistics, correlation analysis, and introduce a scenario analysis framework that will be developed quantitatively in subsequent papers.

2. Analysis of Recent Studies and Publications

The measurement and analysis of shadow economies has a substantial scholarly tradition. Schneider and Enste (2000) provided foundational estimates using the MIMIC (Multiple Indicators Multiple Causes) approach, later refined in Medina and Schneider (2018), whose IMF Working Paper estimated shadow economies for 158 countries over 1991–2015. Their estimates place Ukraine’s shadow economy at 42.9% of GDP in 2015, among the highest in Europe.

More recent IMF estimates (Kelmanson et al., 2019) confirmed the persistence of large informal sectors in post-Soviet economies, attributing this to institutional weakness, regulatory complexity, and low trust in government. The World Bank’s Doing Business indicators and Governance Indicators have been widely used as explanatory variables in cross-country shadow economy regressions (Torgler & Schneider, 2009).

Ukrainian scholars have contributed significantly to this field. Varnalii (2014) examined structural causes of shadow activity in Ukraine, emphasizing corruption and regulatory burden. Mazur (2020) analyzed the relationship between tax policy changes and shadow economy dynamics during 2014–2019, finding that administrative simplification had stronger effects than rate reductions alone. Bilan et al. (2020) studied the impact of e-governance on economic well-being in Central and Eastern European countries, finding a statistically significant relationship between institutional quality and shadow economy size.

The role of digitalization has gained attention in recent literature. Elgin and Oztunali (2012) demonstrated that countries with higher ICT adoption tend to have smaller shadow economies, a finding corroborated by Gaspar et al. (2016) for developing economies. In the Ukrainian context, the Diia platform’s impact on tax compliance has been discussed by Fedorov (2023), who noted a correlation between digital service adoption and reduced VAT gaps in pilot regions.

However, several gaps remain unresolved. First, most cross-country studies treat tax burden and digitalization as independent predictors, ignoring their interaction effects. Second, the unique conditions of Ukraine — wartime economy, rapid forced digitalization, EU accession pressures — have not been systematically incorporated into shadow economy models. Third, scenario-based approaches that project future trajectories under different policy combinations are virtually absent from the Ukrainian literature.

Transparency International’s Corruption Perceptions Index (CPI) for Ukraine improved from 27 (2015) to 33 (2023), reflecting institutional progress that may moderate the tax-shadow economy relationship (Transparency International, 2024). The National Bank of Ukraine (2025) reports increasing electronic payment penetration, from 35% of transactions in 2015 to over 72% in 2024, creating a data trail that constrains shadow operations.

3. Formulation of Research Objectives

The objectives of this paper are:

1. To compile and analyze descriptive statistics on Ukraine’s shadow economy, tax burden, and digitalization indicators for 2015–2025.
2. To calculate correlation coefficients between tax-to-GDP ratio and shadow economy size, and between informatization indicators and shadow economy reduction.
3. To identify feedback loop structures connecting tax policy, digital governance, and informal economic behavior using a systems dynamics perspective.
4. To introduce a three-scenario analytical framework for projecting shadow economy trajectories under different policy combinations (to be developed quantitatively in Paper 2).
5. To establish the empirical and conceptual foundation for subsequent papers in this series.

4. Main Material and Results

4.1 Descriptive Statistics: Shadow Economy, Tax Burden, and Digitalization

Table 1 presents key indicators compiled from IMF estimates (Medina & Schneider, 2018; IMF, 2024), World Bank data, Ukrainian State Statistics Service (Derzhstat, 2025), and Ministry of Digital Transformation reports.

Table 1. Shadow economy and related indicators for Ukraine (2015–2025). Sources: IMF WP/18/17; Derzhstat (2025); UN E-Government Survey; Ministry of Digital Transformation of Ukraine; Transparency International.

Several observations emerge. The shadow economy declined from 42.9% in 2015 to 35.5% in 2019, reversed during COVID-19 and the 2022 invasion (peaking at 39.5%), and resumed its downward trend, reaching an estimated 34.5% in 2025. The tax-to-GDP ratio decreased from 35.5% to approximately 33.0%, reflecting both rate adjustments and wartime fiscal disruptions. The E-Government Development Index made a substantial jump in 2019 with the launch of Diia, and Diia adoption grew exponentially from 3.2 million users in 2020 to 21.5 million in 2025.

4.2 Correlation Analysis

We calculate Pearson correlation coefficients for the key variable pairs using annual data from Table 1.

Table 2. Pearson correlation coefficients for key variable pairs. Authors’ calculations based on Table 1 data.

The correlation between tax burden and shadow economy size (r = +0.72) supports the theoretical prediction that excessive taxation pushes economic agents toward informal activity. This aligns with Schneider’s (2015) finding that tax and social security contribution burdens are the primary drivers of shadow economies in OECD and transition countries.

The strong negative correlation between digitalization indicators and shadow economy size (r = -0.81 for E-Government Index; r = -0.89 for Diia users) suggests that informatization operates as a significant constraining force on informal activity. This is consistent with the economic cybernetics interpretation: digital platforms increase system observability, making it harder for agents to operate outside the formal economy without detection.

It is important to note that correlation does not establish causation. The observed relationships may be influenced by confounding variables, including GDP growth, institutional reforms, EU accession pressures, and the effects of armed conflict. Establishing causal mechanisms requires the controlled scenario analysis proposed for Paper 2.

4.3 Systems Dynamics Perspective

From an economic cybernetics standpoint, the shadow economy can be represented as a system with two primary feedback loops.

graph TD
    A[Tax Burden Increase] -->|incentivizes| B[Shadow Economy Growth]
    B -->|erodes| C[Tax Base Reduction]
    C -->|pressures| A
    D[Digital Platform Adoption] -->|increases| E[Transaction Transparency]
    E -->|constrains| F[Shadow Economy Reduction]
    F -->|expands| G[Tax Base Expansion]
    G -->|enables| H[Tax Rate Reduction]
    H -->|reduces incentive for| B
    D -->|facilitates| I[Simplified Compliance]
    I -->|reduces cost of| F

Figure 1. Feedback loop structure of the tax-digitalization-shadow economy system.

The reinforcing loop (A to B to C to A) represents the “tax trap”: higher burdens push activity underground, shrinking the base and creating pressure for further increases. The balancing loop (D to E to F to G to H) represents the digitalization pathway: transparency and simplified compliance expand the formal economy, potentially enabling lower rates.

The key policy question is whether the balancing loop can dominate the reinforcing loop. The correlation data suggest that digitalization has stronger explanatory power (|r| = 0.81-0.89) than tax burden alone (r = 0.72), indicating that the balancing mechanism may be the more effective policy lever.

4.4 Data Flow and Analytical Framework

The analytical pipeline for this research series follows a structured approach integrating multiple data sources.

graph LR
    A[IMF Shadow Economy Estimates] --> D[Integrated Dataset]
    B[Derzhstat Tax and GDP Data] --> D
    C[Diia Platform Metrics] --> D
    D --> E[Descriptive Statistics - Paper 1]
    D --> F[Scenario Modeling - Paper 2]
    D --> G[Policy Framework - Paper 3]
    E --> H[Correlation Matrices]
    E --> I[Trend Decomposition]
    F --> J[Three-Scenario Projections]
    G --> K[Decision Readiness Assessment]

Figure 2. Data flow across the three-paper research series.

4.5 Scenario Analysis Framework

Building on the descriptive findings, we propose three scenarios for Ukraine’s shadow economy trajectory through 2030, to be modeled quantitatively in Paper 2.

graph TD
    S[Current State: Shadow Economy 34.5% GDP] --> SA[Scenario A: Status Quo]
    S --> SB[Scenario B: Accelerated Digitalization]
    S --> SC[Scenario C: Comprehensive Reform]
    SA -->|Tax burden stable at 33%
Digitalization incremental| OA[Projected: 30-32% by 2030]
    SB -->|Tax burden stable
Aggressive digital expansion| OB[Projected: 25-28% by 2030]
    SC -->|Tax reduction plus full digitalization
plus EU compliance measures| OC[Projected: 20-24% by 2030]

Figure 3. Three-scenario framework for shadow economy projections.

Scenario A (Status Quo) assumes continuation of current tax policy and incremental digitalization. Based on the 2019–2025 trend (excluding the 2022 war shock), this yields a projected shadow economy of 30–32% by 2030.

Scenario B (Accelerated Digitalization) assumes stable tax rates but aggressive expansion of digital governance — mandatory e-invoicing, expanded Diia functionality, blockchain-based registries. Given the strong negative correlation between digitalization and shadow economy (r = -0.89), this scenario projects 25–28% by 2030.

Scenario C (Comprehensive Reform) combines tax burden reduction with full digitalization and EU accession-driven fiscal transparency requirements. This scenario, drawing on the Estonian and Georgian reform experiences (Schneider, 2016; World Bank, 2023), projects the most ambitious reduction to 20–24% by 2030.

Each scenario carries distinct risk profiles. Scenario A risks stagnation and EU accession delays. Scenario B depends on sustained investment in digital infrastructure during wartime reconstruction. Scenario C requires political will for simultaneous tax and digital reform — historically difficult in post-Soviet states.

4.6 Trend Visualization

The following chart displays the relationship between shadow economy size and tax-to-GDP ratio over the study period:

Figure 4. Shadow economy and tax-to-GDP ratio trends in Ukraine (2015–2025).

The chart reveals a notable pattern: both indicators generally decline over the period, but with divergent responses to external shocks. The 2022 invasion caused the shadow economy to spike (from 36.2% to 39.5%) while simultaneously depressing the tax-to-GDP ratio (from 33.1% to 29.4%), illustrating the vulnerability of fiscal systems during conflict.

5. Conclusions and Prospects for Further Research

This paper has established the empirical foundation for analyzing the joint influence of tax burden and digitalization on Ukraine’s shadow economy. The key findings are:

1. Ukraine’s shadow economy declined from 42.9% of GDP in 2015 to an estimated 34.5% in 2025, but remains among the highest in Europe and exhibits vulnerability to external shocks (pandemic, war).

1. Correlation analysis reveals a strong positive association between tax burden and shadow economy size (r = +0.72) and a very strong negative association between digitalization indicators and shadow economy size (r = -0.81 to -0.89).

1. The systems dynamics analysis identifies two competing feedback loops — a reinforcing “tax trap” and a balancing “digitalization pathway” — whose relative strength determines shadow economy trajectory.

1. The three-scenario framework (Status Quo, Accelerated Digitalization, Comprehensive Reform) provides a structured basis for quantitative projection in Paper 2.

1. Early warning signals from the anticipatory framing suggest that the 2022 war shock demonstrated the fragility of progress — any policy framework must account for exogenous disruption risk.

These results have direct practical implications for Ukraine’s EU accession process, which requires demonstrated progress in fiscal transparency and shadow economy reduction (European Commission, 2024). The scenario framework introduced here provides policymakers with a tool for evaluating alternative reform pathways.

Prospects for further research. Paper 2 will develop quantitative projections for each scenario using sensitivity analysis and simple payoff matrices. Paper 3 will synthesize results into a policy decision framework, incorporating comparative analysis of Estonia, Georgia, and Poland’s reform experiences and aligning recommendations with EU accession requirements.

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