Evidence (14055 claims)
Adoption
8570 claims
Productivity
7631 claims
Governance
6869 claims
Human-AI Collaboration
6491 claims
Org Design
4175 claims
Innovation
4114 claims
Labor Markets
3566 claims
Skills & Training
2966 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 758 | 199 | 100 | 900 | 2007 |
| Governance & Regulation | 826 | 400 | 191 | 122 | 1563 |
| Organizational Efficiency | 777 | 193 | 124 | 84 | 1189 |
| Technology Adoption Rate | 635 | 233 | 124 | 97 | 1098 |
| Research Productivity | 422 | 128 | 57 | 336 | 954 |
| Output Quality | 476 | 179 | 59 | 47 | 761 |
| Decision Quality | 328 | 177 | 81 | 47 | 640 |
| Firm Productivity | 435 | 57 | 88 | 20 | 606 |
| AI Safety & Ethics | 218 | 277 | 65 | 33 | 599 |
| Market Structure | 180 | 170 | 123 | 24 | 502 |
| Task Allocation | 213 | 64 | 72 | 33 | 387 |
| Skill Acquisition | 170 | 61 | 61 | 17 | 309 |
| Innovation Output | 203 | 27 | 43 | 18 | 292 |
| Employment Level | 105 | 54 | 107 | 13 | 281 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 117 | 63 | 42 | 11 | 233 |
| Firm Revenue | 153 | 48 | 26 | 3 | 230 |
| Task Completion Time | 173 | 31 | 8 | 12 | 225 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Worker Satisfaction | 89 | 65 | 22 | 12 | 188 |
| Error Rate | 69 | 92 | 10 | 2 | 173 |
| Regulatory Compliance | 77 | 69 | 14 | 5 | 165 |
| Automation Exposure | 56 | 56 | 26 | 13 | 154 |
| Training Effectiveness | 94 | 21 | 13 | 19 | 149 |
| Wages & Compensation | 77 | 36 | 25 | 6 | 144 |
| Team Performance | 86 | 17 | 27 | 10 | 141 |
| Developer Productivity | 95 | 17 | 14 | 6 | 133 |
| Job Displacement | 12 | 80 | 20 | 1 | 113 |
| Hiring & Recruitment | 52 | 7 | 8 | 3 | 70 |
| Creative Output | 31 | 18 | 8 | 3 | 61 |
| Skill Obsolescence | 5 | 46 | 6 | 1 | 58 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Censorship and legal risks constrain reporting and distribution for these fact-checking platforms.
Consistent reports from interview subjects and corroborating document analysis indicating legal/censorship-related limitations on publishing and distribution.
Political instability, legal pressure, and censorship strongly shape what platforms can investigate, publish, and access in the region.
Thematic findings from semi-structured interviews with platform staff and document analysis of public reports and policy statements across the three country cases.
Investments in alignment interventions (pluralistic evaluation, transparency) produce public‑good benefits that private firms may underinvest in absent regulation, standards, or procurement incentives.
Economic reasoning about public goods and incentives, supported by conceptual synthesis of firm behavior literature, not by original empirical investment data.
Misalignment generates negative externalities (misinformation, biased decisions, harms to vulnerable groups) that markets may underprovide solutions for, motivating public‑interest interventions.
Economic argumentation and literature synthesis on externalities and public goods; supported by referenced examples in prior work though not quantified here.
AI can augment measurement (e.g., collaboration patterns, output tracking) but if poorly designed may reinforce visibility biases that disadvantage remote workers.
Theoretical reasoning and literature citations about algorithmic bias and monitoring; illustrated with secondary examples rather than primary empirical tests.
Hybrid arrangements can exacerbate inequities in access to informal networks and career advancement, often privileging co-located or better-networked employees.
Theoretical integration of sociological and management studies with comparative case illustrations; secondary data examples referenced but no new causal empirical tests reported.
Hybrid and remote work create risks of professional invisibility, fragmented social networks, and unequal access to workplace social capital.
Literature synthesis and illustrative case studies drawn from secondary sources; qualitative/comparative case evidence rather than primary quantitative data.
Micro and small firms exhibited weak or limited responses to CAFTA spillovers because of financing constraints, lower innovation capacity, and limited international market information.
Firm‑level heterogeneity and subgroup analyses indicating attenuated effects for micro/small firms; authors attribute weaker responses to observed constraints (financing, innovation, information) in the industrial enterprise database.
CAFTA reduced procurement costs for firms importing agricultural goods, lowering marginal procurement costs.
Mediator tests in the paper linking CAFTA to reduced procurement costs using firm‑level cost/price/procurement indicators from the industrial enterprise database and customs data within DID design.
HACCA proliferation increases negative externalities and public-good failure risks, meaning private markets will underinvest in mitigation absent public intervention.
Public-goods and externality economic theory applied to cybersecurity; policy analysis (qualitative).
Widespread HACCA availability compresses the capability gap between resource-rich and resource-poor actors, empowering criminal groups and smaller states and concentrating harms in less-protected sectors and geographies.
Diffusion and strategic externalities analysis; scenario reasoning about capability democratization (qualitative).
Firms will shift investment toward cybersecurity and away from other productive uses; small and medium enterprises (SMEs) will be disproportionately affected due to limited defenses.
Investment-allocation reasoning and distributional analysis of firm capabilities (qualitative; no firm-level panel data).
Cyber insurance markets will face increased premium pressure and uncertainty; insurers may raise prices, restrict coverage, or withdraw from some lines.
Economic analysis of risk pricing under higher uncertainty and tail risks; analogy to prior insurance market reactions to emerging risks (qualitative).
Automation lowers fixed and marginal costs of conducting high-skill cyber operations, changing the supply-side economics and enabling a rapid expansion in the number of attackers.
Cost-structure reasoning about automation effects on labor and tool costs; conceptual economic analysis (no empirical cost data provided).
Widespread diffusion of HACCAs will raise the baseline cyber threat and reduce the monopoly of advanced states and groups on high-end offensive capabilities.
Capability diffusion assessment and historical analogies to proliferation of technologies (qualitative; no large-scale empirical diffusion model).
HACCAs would intensify interstate cyber competition by increasing operational tempo and reducing attribution certainty, complicating deterrence and crisis management.
Strategic scenario analysis and expert judgment linking automation features (speed, scale, opacity) to deterrence and attribution challenges (qualitative).
Automation via HACCAs lowers the barrier to entry for conducting sophisticated cyber operations, enabling criminal groups, non-state actors, and less-resourced states to perform high-tier attacks.
Economic reasoning about fixed and marginal cost reductions, capability-diffusion analysis, and analogy to automation in other domains (qualitative; no empirical cost-study sample).
HACCAs would sustain operations using five core operational tactics: autonomous infrastructure setup; credential and access harvesting; advanced detection evasion; adaptive shutdown-avoidance; and operational persistence and scaling.
Attack-lifecycle mapping, review of APT case studies, and red-team threat-modeling to extrapolate automated equivalents of human-led tactics (qualitative categorization).
HACCAs would materially change the threat environment by enabling top-tier offensive cyber operations to be automated and widely proliferable, creating large strategic, economic, and systemic security risks.
Scenario-based forecasting, capability-trajectory assessment, review of APT case studies, and threat-modeling/red-team reasoning (qualitative synthesis; no large-n empirical quantification).
Counterfactual simulations show that modest salary increases have a smaller effect on predicted attrition than eliminating overtime (in this dataset and model).
Comparative counterfactual experiments run on the calibrated logistic model: simulations altering salary vs. altering overtime feature; reported that overtime elimination outperforms modest pay increases in retained headcount and probability reductions (exact salary-change amounts and comparative numbers not given in the summary).
In the dataset used, eliminating overtime could potentially retain about 31 employees — a larger effect than modest salary increases.
Aggregated counterfactual simulation on the IBM HR Analytics dataset: after setting overtime to zero for applicable records, the model-predicted net retained headcount ≈ 31; compared to simulations of modest salary increases which yielded smaller retained headcount (exact salary-change magnitude and headcount numbers not provided).
Eliminating overtime could lower predicted attrition probability by 17.35% for affected employees (per the model's counterfactual simulation).
Counterfactual policy simulation using the calibrated logistic model on the IBM HR Analytics dataset: set overtime feature to zero for affected employees and compute change in each employee's calibrated attrition probability; reported average reduction = 17.35%.
AI adoption is skill-biased and spatially uneven, increasing risks of labor-market exclusion among low-educated, middle-aged workers in high-AI regions.
Inference from observed negative associations between AI-rich regions and employment intention for low-educated respondents in the survey of 889; supported by region-level AI adoption proxies used in regressions.
Regional heterogeneity: eastern and northern areas with greater AI penetration intensify displacement pressure on low-skilled, pre-retirement workers.
Subsample/interaction results in the regression analysis separating regions (Beijing, Guangzhou, Lanzhou and broader eastern/northern regional classification) and linking regional AI penetration proxies to employment intention outcomes among low-skilled workers.
Low-educated workers—especially in eastern and northern regions with greater AI adoption—experience increased displacement pressure and lower employment intent.
Interaction/heterogeneity analysis from multivariate regressions on the sample of 889 respondents, using region-level AI adoption intensity (proxied by region) to identify differential associations by education level; stronger negative associations for low-educated respondents in eastern and northern areas.
Higher household economic pressure is negatively associated with willingness to remain employed pre-retirement.
Regression controls included household economic pressure measured in the cross-sectional survey (n=889); coefficient on economic pressure indicated a negative association with employment intention.
Traditional STP showed a 67% performance decline after six months in unstable market conditions.
Empirical observation reported in the study—likely derived from simulation scenarios and/or longitudinal analysis of behavioral data; precise data source (simulation vs. observed field data), statistical tests, and sample framing are not specified in the summary.
Reporting is frequently criticized for imposing excessive administrative burden on companies, which can lead to low-quality disclosures and limited usefulness.
Synthesis of critiques and conceptual argumentation; implied observations about administrative cost impacts; no empirical quantification provided.
Predominant reporting regimes emphasize firm-level (financial) risk to the company rather than cumulative impacts on climate and nature, leaving systemic environmental risks underreported.
Conceptual analysis and synthesis of critiques of reporting standards and incentives; no original empirical data presented.
Current corporate sustainability reporting is insufficient for addressing cumulative environmental risks because it focuses on firm-level risks and imposes heavy administrative burdens.
Conceptual/policy analysis synthesizing critiques of existing reporting regimes; no original empirical study or sample reported.
Geopolitical risk premiums and de-risking strategies increase investment instability—making foreign capital, cloud services, and partnership networks less stable and affecting startup financing, MNC investments, and technology transfer essential to local AI ecosystems.
Observations of shifts in FDI and venture capital flows, corporate de-risking statements, and changes in partnership patterns; quantitative corroboration suggested via volatility in capital flows and investment withdrawal events. (Data sources: FDI/VC flow data, corporate announcements; sample sizes not specified.)
Dual-track regulatory regimes (US-aligned vs China-aligned) create market fragmentation: firms must adapt products, compliance, and data practices to divergent regimes, increasing fixed and variable costs.
Analysis of diverging regulatory texts and standards; firm reports on product adaptation and compliance burdens; suggested quantitative measures include firm cost estimates and market fragmentation indicators. (Data sources: regulatory texts, firm statements; sample sizes not specified.)
Relocation of assembly or lower-tier manufacturing may occur, but upstream dependencies (leading-edge chips, EDA software, design tools) remain concentrated and politically sensitive, keeping core capabilities inaccessible to many developing countries.
Supply-chain mapping showing concentration of upstream suppliers; network concentration metrics and value-chain analysis indicating where high-value inputs reside; process tracing of technology-control regimes. (Data sources: supply-chain maps, concentration metrics; sample sizes not specified.)
Export controls on semiconductors and advanced manufacturing restrict access to AI-critical hardware (chips, sensors), raising costs and slowing AI capability adoption in developing countries.
Documentation of export-control measures and their target items; trade-flow and price data showing constrained availability and increased costs; firm-level reports of supply constraints. (Data sources: export-control lists, trade/price data, firm statements; sample sizes not specified.)
Net effect: global economic integration is becoming more power-contested (politically mediated) rather than neutral and market-driven; dependence on external suppliers rises even as some production relocates.
Synthesis of process-tracing events showing political conditions attached to trade and technology links; quantitative corroboration suggested via import-dependence ratios and network concentration metrics before/after shocks. (Data sources: trade shares, network concentration metrics; sample sizes not specified.)
Competing US and Chinese regulation (export controls, standards, data rules) force developing countries to choose or juggle incompatible regimes, raising compliance costs and producing policy trade-offs.
Document analysis of export-control lists and regulatory texts; interviews and qualitative materials reporting government and firm-level compliance burdens; firm adaptation evidence from announcements. (Data sources: regulatory texts, interviews, firm statements; sample sizes not specified.)
For developing countries, the trade war generates new, concentrated vulnerabilities—despite some short-term gains from production relocation—because trade diversion, regulatory alignment pressures, and securitization convert participation in global supply chains into a geo-strategic liability that undermines developmental autonomy.
Combined qualitative sequence analysis (process tracing) tracing tariff and control shocks to downstream effects; corroboration with trade and FDI flow data, supply-chain maps, and firm-level relocation announcements. (Quantitative indicators noted: trade shares, import-dependence ratios, network concentration metrics; sample sizes not specified.)
The US–China trade war has produced a structural shift in global economic governance: economic integration is increasingly embedded in geopolitical competition.
Process-tracing of policy events (tariff escalations, export controls, sanction announcements) and chronologies of regulatory interventions; corroborated with policy documents and qualitative materials. (Data sources indicated: chronologies of tariff changes and export-control lists; sample size/details not specified in text.)
Machine learning systems that rely on ESG signals can be misled by greenwashing or earnings management, producing overconfident or systematically biased recommendations.
Logical extension of literature on disclosure manipulation and model vulnerability; proposed as a risk for AI systems but not empirically validated within the review.
ESG disclosures that are unaudited or manipulated introduce noise and bias into datasets used by machine‑learning models (e.g., credit scoring, portfolio optimization).
Conceptual inference based on literature-documented unreliability of ESG reporting; no primary ML experiments presented in the paper—claim is drawn as an implication for AI/economic modeling.
The reliability of ESG information is often weak; external public auditors and stronger internal controls are critical to ensure trustworthy disclosure.
Aggregated findings from studies on assurance uptake and quality reported in the review; observational evidence in the literature shows low prevalence and variability of assurance services for ESG reporting.
Without reliable assurance and internal controls, ESG disclosure can undermine its credibility for stakeholders.
Literature synthesis noting limited assurance practices, heterogeneous reporting standards, and documented credibility problems in prior studies; conclusion based on secondary analysis rather than new audit data.
ESG disclosure can mask earnings management and opportunistic accounting — the paper terms this an 'ESG paradox'.
Review of empirical and theoretical studies documenting cases and statistical associations between ESG reporting and earnings management indicators (e.g., abnormal accruals, restatements). The paper synthesizes prior findings; it does not present new causal tests.
The persistence of interpretive, human-in-the-loop evaluation implies ongoing labor requirements (annotation, sense-making, governance roles), affecting forecasts of automation and labor substitution in sectors adopting LLMs.
Interview reports describing continued manual work for evaluation tasks across participants; authors draw implications for labor demand.
Environmental and informational externalities from AI (energy use, privacy harms, bias) justify regulatory and Pigouvian-style interventions to correct market failures.
Conceptual and policy literature reviewed, combined with empirical observations about environmental impacts and privacy/bias incidents reported in prior studies; the paper does not provide new causal estimates of externality magnitudes.
AI may alter firms' competitive dynamics by amplifying scale advantages and platform effects, making antitrust, data portability, and competition policy relevant to preserve contestability and innovation.
Synthesis of industrial organization theory and empirical observations of platform markets and data-driven firms cited in the literature review; no primary empirical study included in this paper.
Evaluation metrics for multi-hazard forecasting and decision tools should go beyond predictive accuracy to include calibration, sharpness, decision-relevance, fairness metrics, and economic utility loss.
Recommendations in the paper's implications section for AI economics and tool evaluation, based on stakeholder needs and decision-relevance considerations identified by MYRIAD-EU.
Open, benchmarked multi-hazard datasets with standardized metadata and labels are needed to enable method comparison and transferability.
Concrete research/practice actions recommended in the synthesis; identification of data standardization and benchmarking gaps from project experience.
Decision and valuation frameworks (e.g., cost–benefit and cost–effectiveness analyses) should be extended to multi-hazard contexts to account for cascading and correlated losses across sectors and time.
Implications for AI economics and concrete recommendations in the paper calling for extensions to existing economic evaluation frameworks to handle multi-hazard complexity.
Early Career Researchers (ECRs) should be empowered through leadership roles and capacity-building within project structures to sustain interdisciplinary innovation.
Project recommendations emphasize ECR leadership and capacity-building as a priority based on internal reflection and organizational learning from MYRIAD-EU activities.