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

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Ukrainian cityscape with digital infrastructure overlay

Economic Research · Stabilarity Research Hub

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
Paper	Focus Area	Key Topics
1	Problem Landscape	Problem statement, literature review (Schneider, Medina), descriptive statistics on tax-to-GDP ratio and shadow economy estimates 2015–2025, Diia platform adoption metrics
2	Scenario Modelling	Three-scenario analysis (high tax/low digital, balanced, low tax/high digital), game-theoretic payoff matrices for government vs. informal sector, Nash equilibrium identification
3	Policy and Comparison	Comparative 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

Status

Ongoing. All 3 papers published. Series complete.

Published Articles

Applied Research · 20 published

Authors: Oleh Ivchenko, Iryna Ivchenko, Dmytro Grybeniuk

Analysis based on publicly available Ukrainian fiscal and governance data.

All Articles

Tax Burden, Digitalization, and Shadow Economy in Ukraine: A Problem Landscape (2015–2025) DOI 9/10 54stabilfr·wuedophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	0%	○	≥80% from editorially reviewed sources
[t]	Trusted	42%	○	≥80% from verified, high-quality sources
[a]	DOI	32%	○	≥80% have a Digital Object Identifier
[b]	CrossRef	11%	○	≥80% indexed in CrossRef
[i]	Indexed	21%	○	≥80% have metadata indexed
[l]	Academic	32%	○	≥80% from journals/conferences/preprints
[f]	Free Access	74%	○	≥80% are freely accessible
[r]	References	19 refs	✓	Minimum 10 references required
[w]	Words [REQ]	2,138	✓	Minimum 1,000 words of article body. Current: 2,138
[u]	Original [REQ]	100%	✓	≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]	Evidence [REQ]	14	✓	Cites at least 1 external source(s): links, DOIs or a reference list. Current: 14
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19008827
[o]	ORCID	✓	✓	Author ORCID verified for academic identity
[p]	Peer Reviewed	—	○	Peer reviewed by an assigned reviewer
[h]	Freshness	5%	○	≥60% of references from 2025–2026. Current: 5%
[c]	Data Charts	0	○	Original data charts from reproducible analysis (min 2). Current: 0
[g]	Code	—	○	Source code available on GitHub
[m]	Diagrams	3	✓	Mermaid architecture/flow diagrams. Current: 3
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (35 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

Applied Research · Mar 13, 2026 · 10 min read

Scenario Analysis: Modeling Three Futures for Ukraine's Shadow Economy (2025–2030) DOI 7/10 62stabilfr·wuedophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	0%	○	≥80% from editorially reviewed sources
[t]	Trusted	55%	○	≥80% from verified, high-quality sources
[a]	DOI	55%	○	≥80% have a Digital Object Identifier
[b]	CrossRef	9%	○	≥80% indexed in CrossRef
[i]	Indexed	27%	○	≥80% have metadata indexed
[l]	Academic	45%	○	≥80% from journals/conferences/preprints
[f]	Free Access	82%	✓	≥80% are freely accessible
[r]	References	11 refs	✓	Minimum 10 references required
[w]	Words [REQ]	2,485	✓	Minimum 1,000 words of article body. Current: 2,485
[u]	Original [REQ]	100%	✓	≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]	Evidence [REQ]	7	✓	Cites at least 1 external source(s): links, DOIs or a reference list. Current: 7
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19016584
[o]	ORCID	✓	✓	Author ORCID verified for academic identity
[p]	Peer Reviewed	—	○	Peer reviewed by an assigned reviewer
[h]	Freshness	17%	○	≥60% of references from 2025–2026. Current: 17%
[c]	Data Charts	0	○	Original data charts from reproducible analysis (min 2). Current: 0
[g]	Code	—	○	Source code available on GitHub
[m]	Diagrams	3	✓	Mermaid architecture/flow diagrams. Current: 3
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (48 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

Applied Research · Mar 14, 2026 · 11 min read

Policy Implications and a Decision Framework for Shadow Economy Reduction in Ukraine DOI 6/10 68stabilfr·wuedophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	4%	○	≥80% from editorially reviewed sources
[t]	Trusted	80%	✓	≥80% from verified, high-quality sources
[a]	DOI	36%	○	≥80% have a Digital Object Identifier
[b]	CrossRef	8%	○	≥80% indexed in CrossRef
[i]	Indexed	76%	○	≥80% have metadata indexed
[l]	Academic	36%	○	≥80% from journals/conferences/preprints
[f]	Free Access	76%	○	≥80% are freely accessible
[r]	References	25 refs	✓	Minimum 10 references required
[w]	Words [REQ]	2,293	✓	Minimum 1,000 words of article body. Current: 2,293
[u]	Original [REQ]	100%	✓	≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]	Evidence [REQ]	16	✓	Cites at least 1 external source(s): links, DOIs or a reference list. Current: 16
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19016590
[o]	ORCID	✓	✓	Author ORCID verified for academic identity
[p]	Peer Reviewed	—	○	Peer reviewed by an assigned reviewer
[h]	Freshness	13%	○	≥60% of references from 2025–2026. Current: 13%
[c]	Data Charts	0	○	Original data charts from reproducible analysis (min 2). Current: 0
[g]	Code	—	○	Source code available on GitHub
[m]	Diagrams	2	✓	Mermaid architecture/flow diagrams. Current: 2
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (59 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

Applied Research · Mar 14, 2026 · 10 min read

Tax Evasion Mechanisms in Ukraine: A Typology of Shadow Economy Channels DOI 8/10 66stabilfr·wuedophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	35%	○	≥80% from editorially reviewed sources
[t]	Trusted	48%	○	≥80% from verified, high-quality sources
[a]	DOI	52%	○	≥80% have a Digital Object Identifier
[b]	CrossRef	35%	○	≥80% indexed in CrossRef
[i]	Indexed	48%	○	≥80% have metadata indexed
[l]	Academic	52%	○	≥80% from journals/conferences/preprints
[f]	Free Access	91%	✓	≥80% are freely accessible
[r]	References	23 refs	✓	Minimum 10 references required
[w]	Words [REQ]	2,619	✓	Minimum 1,000 words of article body. Current: 2,619
[u]	Original [REQ]	100%	✓	≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]	Evidence [REQ]	18	✓	Cites at least 1 external source(s): links, DOIs or a reference list. Current: 18
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19229248
[o]	ORCID	✓	✓	Author ORCID verified for academic identity
[p]	Peer Reviewed	—	○	Peer reviewed by an assigned reviewer
[h]	Freshness	50%	○	≥60% of references from 2025–2026. Current: 50%
[c]	Data Charts	4	✓	Original data charts from reproducible analysis (min 2). Current: 4
[g]	Code	—	○	Source code available on GitHub
[m]	Diagrams	3	✓	Mermaid architecture/flow diagrams. Current: 3
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (53 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)

Applied Research · Mar 26, 2026 · 13 min read

Digital Payment Adoption and Shadow Economy Reduction: Evidence from Ukraine's Diia Platform DOI 8/10 62stabilfr·wuedophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	24%	○	≥80% from editorially reviewed sources
[t]	Trusted	52%	○	≥80% from verified, high-quality sources
[a]	DOI	33%	○	≥80% have a Digital Object Identifier
[b]	CrossRef	24%	○	≥80% indexed in CrossRef
[i]	Indexed	38%	○	≥80% have metadata indexed
[l]	Academic	52%	○	≥80% from journals/conferences/preprints
[f]	Free Access	76%	○	≥80% are freely accessible
[r]	References	21 refs	✓	Minimum 10 references required
[w]	Words [REQ]	2,472	✓	Minimum 1,000 words of article body. Current: 2,472
[u]	Original [REQ]	100%	✓	≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]	Evidence [REQ]	16	✓	Cites at least 1 external source(s): links, DOIs or a reference list. Current: 16
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19242241
[o]	ORCID	✓	✓	Author ORCID verified for academic identity
[p]	Peer Reviewed	—	○	Peer reviewed by an assigned reviewer
[h]	Freshness	40%	○	≥60% of references from 2025–2026. Current: 40%
[c]	Data Charts	4	✓	Original data charts from reproducible analysis (min 2). Current: 4
[g]	Code	—	○	Source code available on GitHub
[m]	Diagrams	3	✓	Mermaid architecture/flow diagrams. Current: 3
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (47 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)

Applied Research · Mar 26, 2026 · 12 min read

Regional Disparities in Ukraine's Shadow Economy: An Oblasts-Level Analysis 2015–2025 DOI 8/10 80stabilfr·wuedophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	47%	○	≥80% from editorially reviewed sources
[t]	Trusted	73%	○	≥80% from verified, high-quality sources
[a]	DOI	73%	○	≥80% have a Digital Object Identifier
[b]	CrossRef	53%	○	≥80% indexed in CrossRef
[i]	Indexed	80%	✓	≥80% have metadata indexed
[l]	Academic	73%	○	≥80% from journals/conferences/preprints
[f]	Free Access	100%	✓	≥80% are freely accessible
[r]	References	15 refs	✓	Minimum 10 references required
[w]	Words [REQ]	2,114	✓	Minimum 1,000 words of article body. Current: 2,114
[u]	Original [REQ]	100%	✓	≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]	Evidence [REQ]	9	✓	Cites at least 1 external source(s): links, DOIs or a reference list. Current: 9
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19258692
[o]	ORCID	✓	✓	Author ORCID verified for academic identity
[p]	Peer Reviewed	—	○	Peer reviewed by an assigned reviewer
[h]	Freshness	8%	○	≥60% of references from 2025–2026. Current: 8%
[c]	Data Charts	4	✓	Original data charts from reproducible analysis (min 2). Current: 4
[g]	Code	—	○	Source code available on GitHub
[m]	Diagrams	3	✓	Mermaid architecture/flow diagrams. Current: 3
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (77 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)

Applied Research · Mar 27, 2026 · 10 min read

VAT Gap Estimation for Ukraine: Methodology and Cross-Country Comparison DOI 9/10 59stabilfr·wuedophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	11%	○	≥80% from editorially reviewed sources
[t]	Trusted	37%	○	≥80% from verified, high-quality sources
[a]	DOI	32%	○	≥80% have a Digital Object Identifier
[b]	CrossRef	16%	○	≥80% indexed in CrossRef
[i]	Indexed	37%	○	≥80% have metadata indexed
[l]	Academic	32%	○	≥80% from journals/conferences/preprints
[f]	Free Access	100%	✓	≥80% are freely accessible
[r]	References	19 refs	✓	Minimum 10 references required
[w]	Words [REQ]	2,599	✓	Minimum 1,000 words of article body. Current: 2,599
[u]	Original [REQ]	100%	✓	≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]	Evidence [REQ]	12	✓	Cites at least 1 external source(s): links, DOIs or a reference list. Current: 12
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19281632
[o]	ORCID	✓	✓	Author ORCID verified for academic identity
[p]	Peer Reviewed	—	○	Peer reviewed by an assigned reviewer
[h]	Freshness	47%	○	≥60% of references from 2025–2026. Current: 47%
[c]	Data Charts	4	✓	Original data charts from reproducible analysis (min 2). Current: 4
[g]	Code	✓	✓	Source code available on GitHub
[m]	Diagrams	3	✓	Mermaid architecture/flow diagrams. Current: 3
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (38 × 60%) + Required (4/4 × 30%) + Optional (4/7 × 10%)

Applied Research · Mar 28, 2026 · 12 min read

—

Post-War Tax Reform Blueprint — Designing Ukraine's Next-Generation Fiscal System (Draft — in preparation)

—

Defense Procurement Transparency — Audit Frameworks for Wartime Spending (Draft — in preparation)

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Humanitarian Aid Diversion — Modeling Leakage Channels and Mitigation Strategies (Draft — in preparation)

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Reconstruction Economics — Preventing Shadow Economy Capture of Rebuilding Funds (Draft — in preparation)

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Real-Time Shadow Economy Indicators — Building a Dashboard from Open Data (Draft — in preparation)

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Blockchain-Based Tax Compliance — Smart Contracts for Automated VAT Collection (Draft — in preparation)

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Neural Network Estimation of Shadow Economy Size — Improving on MIMIC Models (Draft — in preparation)

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Labor Market Informality — Wage Underreporting and Social Insurance Evasion (Draft — in preparation)

VAT Gap Estimation for Ukraine \u2014 Methodology and Cross-Country Comparison DOI 5/10 55stabilfr·wuedophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	6%	○	≥80% from editorially reviewed sources
[t]	Trusted	50%	○	≥80% from verified, high-quality sources
[a]	DOI	13%	○	≥80% have a Digital Object Identifier
[b]	CrossRef	6%	○	≥80% indexed in CrossRef
[i]	Indexed	13%	○	≥80% have metadata indexed
[l]	Academic	19%	○	≥80% from journals/conferences/preprints
[f]	Free Access	94%	✓	≥80% are freely accessible
[r]	References	16 refs	✓	Minimum 10 references required
[w]	Words [REQ]	2,032	✓	Minimum 1,000 words of article body. Current: 2,032
[u]	Original [REQ]	100%	✓	≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]	Evidence [REQ]	7	✓	Cites at least 1 external source(s): links, DOIs or a reference list. Current: 7
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19478897
[o]	ORCID	✓	✓	Author ORCID verified for academic identity
[p]	Peer Reviewed	—	○	Peer reviewed by an assigned reviewer
[h]	Freshness	60%	✓	≥60% of references from 2025–2026. Current: 60%
[c]	Data Charts	5	✓	Original data charts from reproducible analysis (min 2). Current: 5
[g]	Code	✓	✓	Source code available on GitHub
[m]	Diagrams	3	✓	Mermaid architecture/flow diagrams. Current: 3
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (29 × 60%) + Required (4/4 × 30%) + Optional (5/7 × 10%)

Applied Research · Apr 9, 2026 · 6 min read

Labor Market Informality — Wage Underreporting and Social Insurance Evasion DOI 8/10 67stabilfr·wuedophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	12%	○	≥80% from editorially reviewed sources
[t]	Trusted	81%	✓	≥80% from verified, high-quality sources
[a]	DOI	31%	○	≥80% have a Digital Object Identifier
[b]	CrossRef	15%	○	≥80% indexed in CrossRef
[i]	Indexed	15%	○	≥80% have metadata indexed
[l]	Academic	50%	○	≥80% from journals/conferences/preprints
[f]	Free Access	85%	✓	≥80% are freely accessible
[r]	References	26 refs	✓	Minimum 10 references required
[w]	Words [REQ]	3,587	✓	Minimum 1,000 words of article body. Current: 3,587
[u]	Original [REQ]	100%	✓	≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]	Evidence [REQ]	17	✓	Cites at least 1 external source(s): links, DOIs or a reference list. Current: 17
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19482013
[o]	ORCID	✓	✓	Author ORCID verified for academic identity
[p]	Peer Reviewed	—	○	Peer reviewed by an assigned reviewer
[h]	Freshness	75%	✓	≥60% of references from 2025–2026. Current: 75%
[c]	Data Charts	5	✓	Original data charts from reproducible analysis (min 2). Current: 5
[g]	Code	✓	✓	Source code available on GitHub
[m]	Diagrams	2	✓	Mermaid architecture/flow diagrams. Current: 2
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (49 × 60%) + Required (4/4 × 30%) + Optional (5/7 × 10%)

Applied Research · Apr 9, 2026 · 17 min read

Estonia's Digital Transformation — Lessons for Ukraine's Shadow Economy Reduction DOI 6/10 59stabilfr·wuedophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	4%	○	≥80% from editorially reviewed sources
[t]	Trusted	83%	✓	≥80% from verified, high-quality sources
[a]	DOI	9%	○	≥80% have a Digital Object Identifier
[b]	CrossRef	4%	○	≥80% indexed in CrossRef
[i]	Indexed	4%	○	≥80% have metadata indexed
[l]	Academic	22%	○	≥80% from journals/conferences/preprints
[f]	Free Access	100%	✓	≥80% are freely accessible
[r]	References	23 refs	✓	Minimum 10 references required
[w]	Words [REQ]	1,970	✓	Minimum 1,000 words of article body. Current: 1,970
[u]	Original [REQ]	100%	✓	≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]	Evidence [REQ]	13	✓	Cites at least 1 external source(s): links, DOIs or a reference list. Current: 13
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19484648
[o]	ORCID	✓	✓	Author ORCID verified for academic identity
[p]	Peer Reviewed	—	○	Peer reviewed by an assigned reviewer
[h]	Freshness	75%	✓	≥60% of references from 2025–2026. Current: 75%
[c]	Data Charts	3	✓	Original data charts from reproducible analysis (min 2). Current: 3
[g]	Code	✓	✓	Source code available on GitHub
[m]	Diagrams	3	✓	Mermaid architecture/flow diagrams. Current: 3
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (37 × 60%) + Required (4/4 × 30%) + Optional (5/7 × 10%)

Applied Research · Apr 9, 2026 · 9 min read

Georgia's Tax Reform Miracle: Flat Tax Impact on Shadow Economy DOI 10/10 58stabilfr·wuedophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	0%	○	≥80% from editorially reviewed sources
[t]	Trusted	67%	○	≥80% from verified, high-quality sources
[a]	DOI	28%	○	≥80% have a Digital Object Identifier
[b]	CrossRef	0%	○	≥80% indexed in CrossRef
[i]	Indexed	0%	○	≥80% have metadata indexed
[l]	Academic	33%	○	≥80% from journals/conferences/preprints
[f]	Free Access	94%	✓	≥80% are freely accessible
[r]	References	18 refs	✓	Minimum 10 references required
[w]	Words [REQ]	1,420	✓	Minimum 1,000 words of article body. Current: 1,420
[u]	Original [REQ]	100%	✓	≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]	Evidence [REQ]	10	✓	Cites at least 1 external source(s): links, DOIs or a reference list. Current: 10
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19484870
[o]	ORCID	✓	✓	Author ORCID verified for academic identity
[p]	Peer Reviewed	—	○	Peer reviewed by an assigned reviewer
[h]	Freshness	31%	○	≥60% of references from 2025–2026. Current: 31%
[c]	Data Charts	4	✓	Original data charts from reproducible analysis (min 2). Current: 4
[g]	Code	✓	✓	Source code available on GitHub
[m]	Diagrams	3	✓	Mermaid architecture/flow diagrams. Current: 3
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (37 × 60%) + Required (4/4 × 30%) + Optional (4/7 × 10%)

Applied Research · Apr 9, 2026 · 7 min read

Poland's Digitalization Strategy in Tax Administration: Impact Assessment of JPK and Split Payment Mechanisms DOI 6/10 54stabilfr·wuedophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	0%	○	≥80% from editorially reviewed sources
[t]	Trusted	63%	○	≥80% from verified, high-quality sources
[a]	DOI	6%	○	≥80% have a Digital Object Identifier
[b]	CrossRef	0%	○	≥80% indexed in CrossRef
[i]	Indexed	0%	○	≥80% have metadata indexed
[l]	Academic	19%	○	≥80% from journals/conferences/preprints
[f]	Free Access	81%	✓	≥80% are freely accessible
[r]	References	16 refs	✓	Minimum 10 references required
[w]	Words [REQ]	1,955	✓	Minimum 1,000 words of article body. Current: 1,955
[u]	Original [REQ]	100%	✓	≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]	Evidence [REQ]	9	✓	Cites at least 1 external source(s): links, DOIs or a reference list. Current: 9
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19486374
[o]	ORCID	✓	✓	Author ORCID verified for academic identity
[p]	Peer Reviewed	—	○	Peer reviewed by an assigned reviewer
[h]	Freshness	67%	✓	≥60% of references from 2025–2026. Current: 67%
[c]	Data Charts	4	✓	Original data charts from reproducible analysis (min 2). Current: 4
[g]	Code	✓	✓	Source code available on GitHub
[m]	Diagrams	2	✓	Mermaid architecture/flow diagrams. Current: 2
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (28 × 60%) + Required (4/4 × 30%) + Optional (5/7 × 10%)

Applied Research · Apr 9, 2026 · 10 min read

Post-Conflict Shadow Economies — Evidence from Bosnia, Croatia, and Kosovo DOI 6/10 57stabilfr·wuedophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	0%	○	≥80% from editorially reviewed sources
[t]	Trusted	67%	○	≥80% from verified, high-quality sources
[a]	DOI	13%	○	≥80% have a Digital Object Identifier
[b]	CrossRef	0%	○	≥80% indexed in CrossRef
[i]	Indexed	0%	○	≥80% have metadata indexed
[l]	Academic	33%	○	≥80% from journals/conferences/preprints
[f]	Free Access	93%	✓	≥80% are freely accessible
[r]	References	15 refs	✓	Minimum 10 references required
[w]	Words [REQ]	1,921	✓	Minimum 1,000 words of article body. Current: 1,921
[u]	Original [REQ]	100%	✓	≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]	Evidence [REQ]	6	✓	Cites at least 1 external source(s): links, DOIs or a reference list. Current: 6
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19487663
[o]	ORCID	✓	✓	Author ORCID verified for academic identity
[p]	Peer Reviewed	—	○	Peer reviewed by an assigned reviewer
[h]	Freshness	67%	✓	≥60% of references from 2025–2026. Current: 67%
[c]	Data Charts	3	✓	Original data charts from reproducible analysis (min 2). Current: 3
[g]	Code	✓	✓	Source code available on GitHub
[m]	Diagrams	3	✓	Mermaid architecture/flow diagrams. Current: 3
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (33 × 60%) + Required (4/4 × 30%) + Optional (5/7 × 10%)

Applied Research · Apr 9, 2026 · 9 min read

EU Accession Conditionality and Shadow Economy Reform — A Systematic Review DOI 4/10 57stabilfr·wuedophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	6%	○	≥80% from editorially reviewed sources
[t]	Trusted	59%	○	≥80% from verified, high-quality sources
[a]	DOI	12%	○	≥80% have a Digital Object Identifier
[b]	CrossRef	6%	○	≥80% indexed in CrossRef
[i]	Indexed	6%	○	≥80% have metadata indexed
[l]	Academic	35%	○	≥80% from journals/conferences/preprints
[f]	Free Access	82%	✓	≥80% are freely accessible
[r]	References	17 refs	✓	Minimum 10 references required
[w]	Words [REQ]	2,075	✓	Minimum 1,000 words of article body. Current: 2,075
[u]	Original [REQ]	100%	✓	≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]	Evidence [REQ]	10	✓	Cites at least 1 external source(s): links, DOIs or a reference list. Current: 10
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19500718
[o]	ORCID	✓	✓	Author ORCID verified for academic identity
[p]	Peer Reviewed	—	○	Peer reviewed by an assigned reviewer
[h]	Freshness	62%	✓	≥60% of references from 2025–2026. Current: 62%
[c]	Data Charts	3	✓	Original data charts from reproducible analysis (min 2). Current: 3
[g]	Code	✓	✓	Source code available on GitHub
[m]	Diagrams	3	✓	Mermaid architecture/flow diagrams. Current: 3
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (33 × 60%) + Required (4/4 × 30%) + Optional (5/7 × 10%)

Applied Research · Apr 10, 2026 · 7 min read

Agent-Based Modeling of Tax Compliance — Simulating Government-Citizen Interactions DOI 8/10 62stabilfr·wuedophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	0%	○	≥80% from editorially reviewed sources
[t]	Trusted	75%	○	≥80% from verified, high-quality sources
[a]	DOI	19%	○	≥80% have a Digital Object Identifier
[b]	CrossRef	0%	○	≥80% indexed in CrossRef
[i]	Indexed	0%	○	≥80% have metadata indexed
[l]	Academic	56%	○	≥80% from journals/conferences/preprints
[f]	Free Access	94%	✓	≥80% are freely accessible
[r]	References	16 refs	✓	Minimum 10 references required
[w]	Words [REQ]	2,017	✓	Minimum 1,000 words of article body. Current: 2,017
[u]	Original [REQ]	100%	✓	≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]	Evidence [REQ]	10	✓	Cites at least 1 external source(s): links, DOIs or a reference list. Current: 10
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19534434
[o]	ORCID	✓	✓	Author ORCID verified for academic identity
[p]	Peer Reviewed	—	○	Peer reviewed by an assigned reviewer
[h]	Freshness	62%	✓	≥60% of references from 2025–2026. Current: 62%
[c]	Data Charts	3	✓	Original data charts from reproducible analysis (min 2). Current: 3
[g]	Code	✓	✓	Source code available on GitHub
[m]	Diagrams	2	✓	Mermaid architecture/flow diagrams. Current: 2
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (41 × 60%) + Required (4/4 × 30%) + Optional (5/7 × 10%)

Applied Research · Apr 12, 2026 · 10 min read

Neural Network Estimation of Shadow Economy Size — Improving on MIMIC Models DOI 6/10 59stabilfr·wuedophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	0%	○	≥80% from editorially reviewed sources
[t]	Trusted	73%	○	≥80% from verified, high-quality sources
[a]	DOI	13%	○	≥80% have a Digital Object Identifier
[b]	CrossRef	0%	○	≥80% indexed in CrossRef
[i]	Indexed	0%	○	≥80% have metadata indexed
[l]	Academic	53%	○	≥80% from journals/conferences/preprints
[f]	Free Access	67%	○	≥80% are freely accessible
[r]	References	15 refs	✓	Minimum 10 references required
[w]	Words [REQ]	2,031	✓	Minimum 1,000 words of article body. Current: 2,031
[u]	Original [REQ]	100%	✓	≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]	Evidence [REQ]	7	✓	Cites at least 1 external source(s): links, DOIs or a reference list. Current: 7
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19545656
[o]	ORCID	✓	✓	Author ORCID verified for academic identity
[p]	Peer Reviewed	—	○	Peer reviewed by an assigned reviewer
[h]	Freshness	30%	○	≥60% of references from 2025–2026. Current: 30%
[c]	Data Charts	3	✓	Original data charts from reproducible analysis (min 2). Current: 3
[g]	Code	✓	✓	Source code available on GitHub
[m]	Diagrams	3	✓	Mermaid architecture/flow diagrams. Current: 3
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (38 × 60%) + Required (4/4 × 30%) + Optional (4/7 × 10%)

Applied Research · Apr 12, 2026 · 10 min read

Real-Time Shadow Economy Indicators — Building a Dashboard from Open Data DOI 5/10 62stabilfr·wuedophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	5%	○	≥80% from editorially reviewed sources
[t]	Trusted	70%	○	≥80% from verified, high-quality sources
[a]	DOI	20%	○	≥80% have a Digital Object Identifier
[b]	CrossRef	5%	○	≥80% indexed in CrossRef
[i]	Indexed	10%	○	≥80% have metadata indexed
[l]	Academic	55%	○	≥80% from journals/conferences/preprints
[f]	Free Access	90%	✓	≥80% are freely accessible
[r]	References	20 refs	✓	Minimum 10 references required
[w]	Words [REQ]	2,401	✓	Minimum 1,000 words of article body. Current: 2,401
[u]	Original [REQ]	100%	✓	≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]	Evidence [REQ]	12	✓	Cites at least 1 external source(s): links, DOIs or a reference list. Current: 12
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19582647
[o]	ORCID	✓	✓	Author ORCID verified for academic identity
[p]	Peer Reviewed	—	○	Peer reviewed by an assigned reviewer
[h]	Freshness	60%	✓	≥60% of references from 2025–2026. Current: 60%
[c]	Data Charts	4	✓	Original data charts from reproducible analysis (min 2). Current: 4
[g]	Code	✓	✓	Source code available on GitHub
[m]	Diagrams	3	✓	Mermaid architecture/flow diagrams. Current: 3
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (42 × 60%) + Required (4/4 × 30%) + Optional (5/7 × 10%)

Applied Research · Apr 14, 2026 · 1 min read

Machine Learning for Shadow Economy Detection — Classification of Suspicious Transaction Patterns DOI 5/10 59stabilfr·wuedcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	0%	○	≥80% from editorially reviewed sources
[t]	Trusted	63%	○	≥80% from verified, high-quality sources
[a]	DOI	21%	○	≥80% have a Digital Object Identifier
[b]	CrossRef	0%	○	≥80% indexed in CrossRef
[i]	Indexed	0%	○	≥80% have metadata indexed
[l]	Academic	47%	○	≥80% from journals/conferences/preprints
[f]	Free Access	89%	✓	≥80% are freely accessible
[r]	References	19 refs	✓	Minimum 10 references required
[w]	Words [REQ]	1,961	✓	Minimum 1,000 words of article body. Current: 1,961
[u]	Original [REQ]	100%	✓	≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]	Evidence [REQ]	10	✓	Cites at least 1 external source(s): links, DOIs or a reference list. Current: 10
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19513733
[c]	Data Charts	4	✓	Original data charts from reproducible analysis (min 2). Current: 4
[g]	Code	✓	✓	Source code available on GitHub
[m]	Diagrams	3	✓	Mermaid architecture/flow diagrams. Current: 3
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (36 × 60%) + Required (4/4 × 30%) + Optional (3/4 × 10%)

Applied Research · Apr 24, 2026 · 10 min read

Blockchain-Based Tax Compliance — Smart Contracts for Automated VAT Collection DOI 5/10 74stabilfr·wuedophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	0%	○	≥80% from editorially reviewed sources
[t]	Trusted	90%	✓	≥80% from verified, high-quality sources
[a]	DOI	87%	✓	≥80% have a Digital Object Identifier
[b]	CrossRef	0%	○	≥80% indexed in CrossRef
[i]	Indexed	0%	○	≥80% have metadata indexed
[l]	Academic	90%	✓	≥80% from journals/conferences/preprints
[f]	Free Access	100%	✓	≥80% are freely accessible
[r]	References	31 refs	✓	Minimum 10 references required
[w]	Words [REQ]	4,067	✓	Minimum 1,000 words of article body. Current: 4,067
[u]	Original [REQ]	100%	✓	≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]	Evidence [REQ]	25	✓	Cites at least 1 external source(s): links, DOIs or a reference list. Current: 25
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20039119
[o]	ORCID	✓	✓	Author ORCID verified for academic identity
[p]	Peer Reviewed	—	○	Peer reviewed by an assigned reviewer
[h]	Freshness	77%	✓	≥60% of references from 2025–2026. Current: 77%
[c]	Data Charts	0	○	Original data charts from reproducible analysis (min 2). Current: 0
[g]	Code	—	○	Source code available on GitHub
[m]	Diagrams	2	✓	Mermaid architecture/flow diagrams. Current: 2
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (67 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)

Applied Research · May 5, 2026 · 21 min read

War and Shadow Economy — How Armed Conflict Reshapes Informal Markets in Ukraine DOI 4/10 90stabilfr·wuedophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	67%	○	≥80% from editorially reviewed sources
[t]	Trusted	100%	✓	≥80% from verified, high-quality sources
[a]	DOI	92%	✓	≥80% have a Digital Object Identifier
[b]	CrossRef	75%	○	≥80% indexed in CrossRef
[i]	Indexed	75%	○	≥80% have metadata indexed
[l]	Academic	100%	✓	≥80% from journals/conferences/preprints
[f]	Free Access	100%	✓	≥80% are freely accessible
[r]	References	12 refs	✓	Minimum 10 references required
[w]	Words [REQ]	1,567	✓	Minimum 1,000 words of article body. Current: 1,567
[u]	Original [REQ]	100%	✓	≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]	Evidence [REQ]	10	✓	Cites at least 1 external source(s): links, DOIs or a reference list. Current: 10
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.23454463
[o]	ORCID	✓	✓	Author ORCID verified for academic identity
[p]	Peer Reviewed	—	○	Peer reviewed by an assigned reviewer
[h]	Freshness	100%	✓	≥60% of references from 2025–2026. Current: 100%
[c]	Data Charts	0	○	Original data charts from reproducible analysis (min 2). Current: 0
[g]	Code	—	○	Source code available on GitHub
[m]	Diagrams	0	○	Mermaid architecture/flow diagrams. Current: 0
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (95 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

Applied Research · May 7, 2026 · 8 min read

—

Reconstruction Economics — Preventing Shadow Economy Capture of Rebuilding Funds (Draft — in preparation)

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Humanitarian Aid Diversion — Modeling Leakage Channels and Mitigation Strategies (Draft — in preparation)

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Defense Procurement Transparency — Audit Frameworks for Wartime Spending (Draft — in preparation)

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Post-War Tax Reform Blueprint — Designing Ukraine's Next-Generation Fiscal System (Draft — in preparation)

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AI-Driven Tax Compliance: How Explainable AI Transforms Shadow Economy Detection (Draft — in preparation)

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Digital Transformation Economics: When AI Adoption Reduces Informality (Draft — in preparation)

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Public Procurement AI: Detecting Corruption Patterns with Explainable Machine Learning (Draft — in preparation)

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The Transformation of Shadow Labor Markets: How AI Platforms Reshape Informal Work (Draft — in preparation)

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EU AI Act Compliance for Ukrainian Tech: How Explanation Requirements Affect AI Exports (Draft — in preparation)

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Cash Flow Anomaly Detection: AI Models for Identifying Undeclared Income in SME Tax Returns (Draft — in preparation)

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Cryptocurrency-Enabled Shadow Economies: On-Chain Analytics for Illicit Flow Detection (Draft — in preparation)

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Gig Economy Tax Gaps: AI-Assisted Matching of Platform Income to Tax Declarations (Draft — in preparation)

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AI in Customs Fraud Detection: Benchmarking Neural Approaches to Invoice Manipulation (Draft — in preparation)

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Shadow Banking Detection with Graph Neural Networks: Mapping Unofficial Lending Networks (Draft — in preparation)

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Real-Time Fraud Detection in Mobile Payments: Behavioral Biometrics and Transaction Anomaly Fusion (Draft — in preparation)

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AI for Anti-Money Laundering: From Rule-Based to Neural Transaction Monitoring in Banking (Draft — in preparation)

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Informal Economy Measurement with Satellite Data: Night-Light and Activity Indicators (Draft — in preparation)

—

AI-Assisted Wealth Register Verification: Cross-Border Asset Disclosure and Hidden Wealth Detection (Draft — in preparation)

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Labor Market Informality Dynamics: AI Forecasting of Shadow Employment Under Economic Shocks (Draft — in preparation)

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Informal Labor Platforms and AI Wage Suppression: Evidence from Gig Marketplaces in 2025-2026 (Draft — in preparation)

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Cross-Border Remittance Fraud Detection: Graph AI Models for Hawala-Adjacent Informal Transfer Networks (Draft — in preparation)

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Counterfeit Goods Detection at Scale: Computer Vision Models for Customs and Marketplace Enforcement (Draft — in preparation)

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Shadow Real Estate Markets: Using Satellite and Utility Data to Estimate Undeclared Property Value (Draft — in preparation)

—

Algorithmic Redlining in Microfinance: Auditing AI Credit Scoring for Informal-Sector Borrowers (Draft — in preparation)

20 published9,892 total views204 min total readingMar 2026 – May 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.