Scenario Analysis: Modeling Three Futures for Ukraine’s Shadow Economy (2025–2030)

Scenario Analysis: Modeling Three Futures for Ukraine’s Shadow Economy (2025–2030) #

1. Statement of the Problem #

In Paper 1 of this series (Ivchenko, Ivchenko & Grybeniuk, 2026a^[2]), we established that Ukraine’s shadow economy has remained persistently high — between 30% and 45% of official GDP over the decade 2015–2025. We identified two competing feedback loops: a reinforcing cycle where high tax burdens push economic actors into informality, and a balancing mechanism where digitalization increases transparency and raises the cost of evasion.

A critical limitation of descriptive analysis is its inability to answer forward-looking questions. Policymakers need projections: if Ukraine reduces its tax-to-GDP ratio by five percentage points while simultaneously mandating electronic invoicing, what is the expected impact on the shadow economy by 2030? If digitalization stalls, does the shadow sector expand even with moderate tax reform?

Scenario modeling addresses this gap by constructing internally consistent futures based on different policy parameter combinations. Combined with a game-theoretic framework — where the government and the informal sector are treated as strategic actors with opposing incentives — we can model the conditions under which compliance becomes the rational choice for economic agents.

This paper develops three scenarios with quantitative projections, sensitivity analysis, and a comparative framework drawing on international reform experiences in Georgia and Estonia.

2. Analysis of Recent Studies and Publications #

Game-theoretic approaches to tax compliance have a well-established lineage. Allingham and Sandmo (1972)^[3] formulated the foundational model where a taxpayer chooses between full declaration and evasion based on audit probability and penalty severity. Schneider and Enste (2000) extended this logic to the macro level, arguing that the aggregate shadow economy reflects millions of individual compliance decisions shaped by institutional quality, tax morale, and enforcement capacity.

Loayza (1996)^[4] modeled the informal sector as a rational response to regulatory burden, demonstrating that excessive regulation produces larger shadow economies regardless of enforcement intensity. Elgin and Oztunali (2012) incorporated ICT adoption into cross-country panel regressions and found a significant negative relationship between digital infrastructure and shadow economy size — a result corroborated by more recent work (Gaspar et al., 2016^[5]; Bilan et al., 2020^[6]).

Medina and Schneider (2018) provided the most comprehensive cross-country estimates using the MIMIC approach, covering 158 countries over 1991–2015. Their methodology has been updated by the IMF (2024) to incorporate pandemic-era disruptions and wartime economies, with Ukraine’s revised estimate at 32.5% of GDP for 2024.

The Georgian tax reform of 2005–2015 is widely cited as a successful case study: a flat income tax of 20%, simplified business registration, and aggressive anti-corruption measures reduced the shadow economy from an estimated 67% to 24% of GDP within a decade (World Bank, 2017^[7]; Schneider, 2016^[8]). Estonia’s e-Residency program and comprehensive digital governance ecosystem have been associated with one of the lowest shadow economies in the post-Soviet space — approximately 16% of GDP by 2024 (e-Estonia, 2025; Eurostat, 2025).

However, a significant gap persists in the literature: most models treat tax burden and digitalization as independent variables. No published framework systematically models their interaction as a combined policy instrument. Furthermore, scenario-based projections for Ukraine’s shadow economy that incorporate wartime conditions and EU accession dynamics are virtually absent. This paper addresses both gaps.

3. Formulation of Research Objectives #

The objectives of this paper are:

1. To construct a game-theoretic framework modeling the strategic interaction between government policy (taxation, enforcement, digitalization) and the informal sector’s compliance decisions.
2. To develop three internally consistent scenarios (Status Quo, Tax Reform, Digital Acceleration) with quantitative projections for Ukraine’s shadow economy through 2030.
3. To perform sensitivity analysis across key parameters — tax rate, digitalization adoption, and enforcement intensity — identifying tipping points and early warning indicators.
4. To compare projected outcomes with empirical results from Georgian tax reform and Estonian digital governance.

4. Main Material and Results #

4.1 Game-Theoretic Framework #

We model the interaction as a two-player strategic game. Player 1 (Government) chooses a policy mix along three dimensions: tax rate (t), enforcement intensity (e), and digitalization level (d). Player 2 (Informal Sector) — representing the aggregate of economic agents — chooses between compliance (C) and evasion (E). The payoff for evasion depends on the probability of detection, which is a function of both enforcement and digitalization: P(detection) = f(e, d).

From a cybernetic perspective, this constitutes a control system with the government as the controller, the shadow economy share as the controlled variable, and tax/enforcement/digitalization as control inputs. The informal sector’s response function acts as the plant dynamics, with feedback arriving through tax revenue data and economic indicators (Ivchenko, O., systems dynamics angle).

Table 1 presents the baseline 2×2 payoff matrix for the simplified game.

Table 1. Baseline 2×2 payoff matrix. R = tax revenue, Y = income, t = tax rate, C_e = enforcement cost, P = detection probability, F = penalty, ΔR = lost revenue from evasion.

The rational agent evades when the expected cost of evasion (P·F) is less than the tax burden (t·Y). Therefore, the government’s objective is to set parameters such that P(e,d) · F > t · Y, making compliance the dominant strategy. This condition structures all three scenarios below.

graph TD
G["Government"] -->|"High tax + Low digital"| A["Scenario A: Status Quo"]
G -->|"Low tax + Moderate digital"| B["Scenario B: Tax Reform"]
G -->|"Moderate tax + High digital"| C["Scenario C: Digital Acceleration"]
A --> A1["IS: Evade (dominant)"]
A --> A2["Shadow ≈ 30% by 2030"]
B --> B1["IS: Mixed strategy"]
B --> B2["Shadow ≈ 25% by 2030"]
C --> C1["IS: Comply (dominant)"]
C --> C2["Shadow ≈ 20% by 2030"]

4.2 Scenario A: Status Quo #

Under Scenario A, the tax-to-GDP ratio remains at approximately 42% — the 2024 baseline from Paper 1 (Ivchenko et al., 2026a^[2]). Digitalization progresses at its current organic rate (Diia adoption growing ~8% annually), and enforcement intensity remains constant. The payoff matrix favors evasion for a significant proportion of economic agents because the high tax burden exceeds the expected penalty from detection at current enforcement levels.

Using the linear projection from Paper 1’s correlation analysis (r = 0.87 between tax burden and shadow economy share), and adjusting for moderate digitalization gains, Scenario A projects the shadow economy at approximately 30% of GDP by 2030 — a marginal improvement from the 2024 estimate of 32.5% (IMF, 2024). This trajectory is consistent with Schneider’s (2016) observation that high-tax regimes rarely achieve shadow economy reductions exceeding 0.5 percentage points per year without structural reform.

4.3 Scenario B: Tax Reform #

Scenario B models a comprehensive tax reform reducing the effective tax-to-GDP ratio to 28%, combined with a broadened tax base and moderate digitalization. This mirrors the Georgian reform model (World Bank, 2017^[7]), where simplified taxation and reduced rates produced rapid formalization.

In the extended 2×3 payoff matrix (Table 2), the government adds a third strategy: Reform (lower rates, broader base). At a 28% effective rate, the compliance payoff Y−0.28Y significantly exceeds Y−P·F for most agents when detection probability remains moderate (P ≈ 0.3), shifting the equilibrium toward compliance.

Table 2. Extended 2×3 payoff matrix (Government strategies × Informal Sector responses). F = fine for detected evasion. Detection probability P increases from 0.3 to 0.6 under Digital Acceleration due to mandatory e-invoicing and Diia integration.

Based on the Georgian precedent (shadow economy reduction from 67% to 24% over ten years), and adjusting for Ukraine’s wartime constraints and institutional baseline, Scenario B projects the shadow economy at approximately 25% of GDP by 2030. The key risk indicator (Grybeniuk, D., anticipatory angle) is reform reversal: if political instability interrupts implementation, the trajectory reverts to Scenario A within 2–3 years.

4.4 Scenario C: Digital Acceleration #

Scenario C represents the most ambitious policy combination: a moderate tax reduction (to 35% of GDP), coupled with aggressive digital infrastructure expansion — mandatory e-invoicing for all businesses, full Diia integration with tax administration, and real-time transaction monitoring. This scenario draws on Estonia’s digital governance model, where 99% of government services are online and the shadow economy stands at approximately 16% of GDP (e-Estonia, 2025).

The critical mechanism is the detection probability: under full digitalization, P(detection) rises from approximately 0.3 (current) to 0.6 or higher, as every transaction generates a digital trace. When P · F > t · Y, evasion becomes irrational. At t = 0.35 and P = 0.6 with a penalty multiplier of 1.5x evaded amount, the compliance condition is satisfied for the majority of economic agents.

From a systems dynamics perspective (Ivchenko, O.), Scenario C creates a virtuous cycle: higher compliance increases revenue, enabling further investment in digital infrastructure, which increases detection probability, further discouraging evasion. This positive feedback loop is the key structural difference from Scenarios A and B.

Scenario C projects the shadow economy at approximately 20% of GDP by 2030, conditional on sustained investment in digital infrastructure and political commitment to implementation. The tipping point (Grybeniuk, D.) occurs when Diia adoption exceeds 75% of the working-age population and e-invoicing coverage reaches 90% of registered businesses — estimated at approximately 2027–2028 under optimistic assumptions.

4.5 Quantitative Projections #

Table 3 summarizes the projected trajectories across all three scenarios, using 2024 as the baseline year (Ivchenko, I., quantitative projections).

Table 3. Shadow economy as % of GDP: projected trajectories 2024–2030. Baseline from IMF (2024); projections by authors using linear-adjusted model with digitalization correction factor. See Paper 1 (Ivchenko et al., 2026a^[2]) for baseline methodology.

4.6 Sensitivity Analysis #

To assess the robustness of these projections, we perform sensitivity analysis by varying three key parameters: tax rate (±5 percentage points), digitalization adoption (±20%), and enforcement intensity (±30%). Table 4 presents the resulting ranges for 2030 shadow economy estimates (Ivchenko, I.).

Table 4. Parameter sweep: 2030 shadow economy projections under parameter variations. Each row varies one parameter while holding others at scenario defaults.

The sensitivity analysis reveals that digitalization has the highest marginal impact across all three scenarios. A 20% increase in digital adoption reduces the shadow economy by 1.5–2.0 percentage points, compared to 1.0–1.5 pp for equivalent tax reduction and 1.5–2.0 pp for enforcement increases. This finding aligns with Gaspar et al. (2016)^[5] and supports the prioritization of digital infrastructure in policy design.

From a risk perspective (Grybeniuk, D.), the key early warning indicators are: (1) Diia adoption rate falling below 60% of working-age population, (2) e-invoicing implementation delays exceeding 18 months, and (3) enforcement budget cuts exceeding 15%. Any two of these conditions simultaneously would shift the trajectory from Scenario C toward Scenario B, while all three would revert to Scenario A.

graph LR
subgraph "Sensitivity Impact on Shadow Economy 2030"
T["Tax Rate ±5pp"] -->|"±2.5pp effect"| SE["Shadow Economy %"]
D["Digitalization ±20%"] -->|"±1.5-2.0pp effect"| SE
E["Enforcement ±30%"] -->|"±2.0pp effect"| SE
end
SE -->|"Best case"| BC["18.0% - Scenario C all favorable"]
SE -->|"Worst case"| WC["33.0% - Scenario A all adverse"]

4.7 International Comparison #

To validate the projected ranges, we compare with documented reform outcomes. Georgia achieved a 43-percentage-point reduction in shadow economy share over ten years (67% to 24%) through aggressive tax simplification (World Bank, 2017^[7]). Estonia maintained a low shadow economy (~16%) through sustained digital governance investment (e-Estonia, 2025). Ukraine’s Scenario C target of 20% by 2030 — a 12.5 pp reduction over six years — is ambitious but falls within the range of observed international outcomes.

4.8 Feedback Loop Structure #

The systems dynamics analysis (Ivchenko, O.) reveals two distinct loop structures that differentiate the scenarios. Scenarios A and B are dominated by a balancing loop: reforms reduce the shadow economy, but the effect attenuates over time as the easiest-to-formalize sectors transition first, leaving harder-to-reach informal activity. Scenario C introduces a reinforcing loop: digitalization increases detection, which increases compliance, which increases tax revenue, which funds further digitalization. This structural difference explains the non-linear acceleration visible in Scenario C’s trajectory after 2027.

graph TD
subgraph "Scenario C: Reinforcing Loop"
DIG["Digital Infrastructure Investment"] --> DET["Higher Detection Probability"]
DET --> COMP["Increased Compliance"]
COMP --> REV["Higher Tax Revenue"]
REV --> DIG
end
subgraph "Scenario A: Reinforcing Trap"
HT["High Tax Burden"] --> EVA["Evasion Incentive"]
EVA --> LR["Lower Revenue"]
LR --> HT2["Pressure to Raise Rates"]
HT2 --> EVA
end

5. Conclusions and Further Prospects #

This paper developed three scenario models for Ukraine’s shadow economy trajectory through 2030, framed by a game-theoretic interaction between government policy and informal sector behavior. The analysis yields several conclusions.

First, the Status Quo scenario (A) produces only marginal improvement — from 32.5% to 30.0% of GDP — confirming that incremental adjustments without structural reform are insufficient. Second, tax reform alone (Scenario B) can achieve meaningful reduction to 25.0%, but requires sustained political commitment and is vulnerable to implementation reversals. Third, the combination of moderate tax reform with aggressive digitalization (Scenario C) offers the most substantial reduction to 20.0%, driven by a virtuous feedback loop between digital infrastructure, detection probability, and compliance incentives.

Sensitivity analysis demonstrates that digitalization has the highest marginal impact on shadow economy reduction, exceeding the effects of equivalent tax cuts or enforcement increases. The most realistic trajectory for Ukraine lies between Scenarios B and C: some tax reform is politically feasible, and Diia platform expansion is already underway, but achieving full e-invoicing coverage by 2028 requires additional institutional capacity.

Early warning indicators for trajectory monitoring include Diia adoption rates, e-invoicing coverage percentages, and enforcement budget trends. If two or more of these indicators deteriorate simultaneously, policymakers should expect a reversion toward the Status Quo trajectory.

Paper 3 in this series will synthesize these scenario results into a policy decision framework, applying a lightweight Decision Readiness Index (DRI) to rank reform options by feasibility and impact. The comparative analysis will expand to include Poland’s digitalization experience and the implications of EU accession conditionality for Ukraine’s fiscal governance.