Category: Shadow Economy Dynamics

Tax burden, informatization, and shadow economy dynamics in Ukraine. Scenario analysis and game-theoretic approaches.

Labor Market Informality Dynamics: AI Forecasting of Shadow Employment Under Economic Shocks

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Economic Research
Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk  ·  DOI: 10.5281/zenodo.22135534  75stabilfr·wdophcgmx
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Score = Ref Trust (80 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)

This article investigates the application of machine l[REDACTED]g (ML) forecasting models to estimate informal employment levels under economic stress scenarios. We calibrate our models using COVID-19 pandemic data and apply them to predict informality responses to AI-driven structural unemployment. Our research addresses three key questions: (RQ1) How accurately can ML models forecast informal...

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk DOI: 10.5281/zenodo.22135534 75stabilfr·wdophcgmx
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[s]Reviewed Sources53%○≥80% from editorially reviewed sources
[t]Trusted88%✓≥80% from verified, high-quality sources
[a]DOI76%○≥80% have a Digital Object Identifier
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[w]Words [REQ]4,417✓Minimum 2,000 words for a full research article. Current: 4,417
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22135534
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (80 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)
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AI-Assisted Wealth Register Verification: Cross-Border Asset Disclosure and Hidden Wealth Detection

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Economic Research
Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk  ·  DOI: 10.5281/zenodo.22033833  65stabilfr·wdophcgmx
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (74 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Artificial intelligence (AI) is rapidly transforming financial compliance, particularly in the verification of wealth register declarations and cross-border asset disclosure. Tax authorities worldwide are deploying machine l[REDACTED]g models to parse public corporate registries, property transaction logs, and offshore entity disclosures in order to detect hidden wealth and inconsistencies. Thi...

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk DOI: 10.5281/zenodo.22033833 65stabilfr·wdophcgmx
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[w]Words [REQ]1,870✗Minimum 2,000 words for a full research article. Current: 1,870
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Score = Ref Trust (74 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Informal Economy Measurement with Satellite Data: Night-Light and Activity Indicators

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk  ·  DOI: 10.5281/zenodo.21985901  57stabilfr·wdophcgmx
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (70 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

Satellite‑derived indicators have become a pivotal method for estimating informal economic activity in emerging markets [1] [2]. This article investigates the comparative effectiveness of three remote‑sensing proxies—night‑light intensity, parking‑lot occupancy, and shipping‑container movement—in measuring informal economic output across a set of pilot regions [3]. We formulate three research q...

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk DOI: 10.5281/zenodo.21985901 57stabilfr·wdophcgmx
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[f]Free Access100%✓≥80% are freely accessible
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[w]Words [REQ]1,058✗Minimum 2,000 words for a full research article. Current: 1,058
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21985901
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (70 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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Real-Time Fraud Detection in Mobile Payments: Behavioral Biometrics and Transaction Anomaly Fusion

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk  ·  DOI: 10.5281/zenodo.21844580  43stabilfr·wdophcgmx
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[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (48 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

Mobile payment ecosystems generate rich behavioral signals during user interaction, including typing patterns, device motion, and transaction graph topology. This article investigates the efficacy of fusing these behavioral biometrics with graph-based anomaly detection to achieve real-time fraud detection in mobile payment platforms. We address three core research questions: (RQ1) How do behavi...

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk DOI: 10.5281/zenodo.21844580 43stabilfr·wdophcgmx
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (48 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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AI in Customs Fraud Detection: Benchmarking Neural Approaches to Invoice Manipulation

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk  ·  DOI: 10.5281/zenodo.21461071  58stabilfr·wdophcgmx
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Score = Ref Trust (63 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The digitization of global trade has intensified the need for automated fraud detection mechanisms within customs environments. While neural network architectures have shown promise for identifying manipulative invoice patterns, systematic benchmarks comparing their performance across distinct fraud typologies are lacking. This article presents a comparative evaluation of four state‑of‑the‑art ...

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk DOI: 10.5281/zenodo.21461071 58stabilfr·wdophcgmx
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[w]Words [REQ]1,579✗Minimum 2,000 words for a full research article. Current: 1,579
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21461071
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]69%✓≥60% of references from 2025–2026. Current: 69%
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (63 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Gig Economy Tax Gaps: AI-Assisted Matching of Platform Income to Tax Declarations

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk  ·  DOI: 10.5281/zenodo.21389918  64stabilfr·wdophcgmx
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[l]Academic95%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References39 refs✓Minimum 10 references required
[w]Words [REQ]1,726✗Minimum 2,000 words for a full research article. Current: 1,726
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21389918
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]72%✓≥60% of references from 2025–2026. Current: 72%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (72 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The rapid expansion of digital platforms has transformed labor markets, but tax compliance remains uneven due to fragmented reporting of gig worker income. This article quantifies the tax gap created by under-reporting of platform-generated [REDACTED]gs in the European Union and the United States, and evaluates emerging artificial intelligence–driven data‑matching techniques that reconcile plat...

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk DOI: 10.5281/zenodo.21389918 64stabilfr·wdophcgmx
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[t]Trusted95%✓≥80% from verified, high-quality sources
[a]DOI92%✓≥80% have a Digital Object Identifier
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[l]Academic95%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References39 refs✓Minimum 10 references required
[w]Words [REQ]1,726✗Minimum 2,000 words for a full research article. Current: 1,726
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21389918
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (72 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Cryptocurrency-Enabled Shadow Economies: On-Chain Analytics for Illicit Flow Detection

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk  ·  DOI: 10.5281/zenodo.21299476  59stabilfr·wdophcgmx
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[t]Trusted100%✓≥80% from verified, high-quality sources
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[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References31 refs✓Minimum 10 references required
[w]Words [REQ]1,358✗Minimum 2,000 words for a full research article. Current: 1,358
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21299476
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
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[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (74 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

The increasing adoption of cryptocurrency has facilitated the emergence of shadow economies that operate beyond traditional regulatory frameworks. Detecting illicit flow patterns within blockchain transactions is critical for financial compliance and risk assessment. This article addresses the following research questions: (RQ1) How can on-chain analytics be leveraged to identify shadow economy...

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk DOI: 10.5281/zenodo.21299476 59stabilfr·wdophcgmx
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[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI97%✓≥80% have a Digital Object Identifier
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[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References31 refs✓Minimum 10 references required
[w]Words [REQ]1,358✗Minimum 2,000 words for a full research article. Current: 1,358
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21299476
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]3%✗≥60% of references from 2025–2026. Current: 3%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (74 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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EU AI Act Compliance for Ukrainian Tech: How Explanation Requirements Affect AI Exports

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk  ·  DOI: 10.5281/zenodo.20372892  39stabilfr·wdophcgmx
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[t]Trusted50%○≥80% from verified, high-quality sources
[a]DOI25%○≥80% have a Digital Object Identifier
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[l]Academic50%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References4 refs○Minimum 10 references required
[w]Words [REQ]2,109✓Minimum 2,000 words for a full research article. Current: 2,109
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20372892
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]33%✗≥60% of references from 2025–2026. Current: 33%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (31 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

This paper investigates ... placeholder text ...

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk DOI: 10.5281/zenodo.20372892 39stabilfr·wdophcgmx
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[t]Trusted50%○≥80% from verified, high-quality sources
[a]DOI25%○≥80% have a Digital Object Identifier
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[i]Indexed0%○≥80% have metadata indexed
[l]Academic50%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References4 refs○Minimum 10 references required
[w]Words [REQ]2,109✓Minimum 2,000 words for a full research article. Current: 2,109
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20372892
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
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[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (31 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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The Transformation of Shadow Labor Markets: How AI Platforms Reshape Informal Work

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk  ·  DOI: 10.5281/zenodo.20358396  42stabilfr·wdophcgmx
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[t]Trusted75%○≥80% from verified, high-quality sources
[a]DOI38%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed13%○≥80% have metadata indexed
[l]Academic50%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References8 refs○Minimum 10 references required
[w]Words [REQ]875✗Minimum 2,000 words for a full research article. Current: 875
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20358396
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
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[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (46 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

The rise of AI-driven gig platforms has dramatically altered informal labor ecosystems, creating new shadow market dynamics that traditional economic models fail to capture. This article investigates how platform design choices directly reshape worker vulnerability, income stability, and social protection gaps in emerging economies. We demonstrate that platform-mediated work arrangements are no...

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk DOI: 10.5281/zenodo.20358396 42stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted75%○≥80% from verified, high-quality sources
[a]DOI38%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed13%○≥80% have metadata indexed
[l]Academic50%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References8 refs○Minimum 10 references required
[w]Words [REQ]875✗Minimum 2,000 words for a full research article. Current: 875
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20358396
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]20%✗≥60% of references from 2025–2026. Current: 20%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (46 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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Public Procurement AI: Detecting Corruption Patterns with Explainable Machine Learning

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk  ·  DOI: 10.5281/zenodo.20351356  64stabilfr·wdophcgmx
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[s]Reviewed Sources44%○≥80% from editorially reviewed sources
[t]Trusted78%○≥80% from verified, high-quality sources
[a]DOI67%○≥80% have a Digital Object Identifier
[b]CrossRef56%○≥80% indexed in CrossRef
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[l]Academic78%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References9 refs○Minimum 10 references required
[w]Words [REQ]1,198✗Minimum 2,000 words for a full research article. Current: 1,198
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20351356
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]63%✓≥60% of references from 2025–2026. Current: 63%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
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[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (73 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Government procurement processes are vulnerable to corruption, inefficiency, and opaque decision‑making. This article presents an explainable artificial intelligence (XAI) framework for detecting corruption patterns in public procurement datasets, focusing on invoice analysis and contract anomaly detection. Using a combination of statistical feature extraction, graph‑based anomaly scoring, and ...

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk DOI: 10.5281/zenodo.20351356 64stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources44%○≥80% from editorially reviewed sources
[t]Trusted78%○≥80% from verified, high-quality sources
[a]DOI67%○≥80% have a Digital Object Identifier
[b]CrossRef56%○≥80% indexed in CrossRef
[i]Indexed56%○≥80% have metadata indexed
[l]Academic78%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References9 refs○Minimum 10 references required
[w]Words [REQ]1,198✗Minimum 2,000 words for a full research article. Current: 1,198
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20351356
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]63%✓≥60% of references from 2025–2026. Current: 63%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (73 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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