Evidence (2066 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 |
Inequality
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Rigorous research priorities include randomized controlled trials with long-run follow-ups, cost-effectiveness studies, structural adoption models, and validated metrics for feedback quality and learning durability.
Actionable research recommendations produced by the 50-scholar interdisciplinary meeting; prescriptive synthesis rather than empirical results.
Historical transitions in standard work hours (e.g., six-day to five-day week) show that phased implementation, collective bargaining, and complementary policies can make work-time reductions feasible and economically beneficial.
Historical analyses and case studies of past industrialized-country workweek transitions cited in the synthesis; evidence drawn from historical institutional records and prior economic histories rather than a unified econometric analysis.
Economists and researchers should measure organizational mediators (governance, mentoring practices, learning processes) alongside AI adoption and use empirical designs such as difference-in-differences with phased rollouts, randomized mentoring/training interventions, matched employer–employee panels, and IV exploiting exogenous shocks to innovation backing to identify causal effects.
Methodological recommendations and proposed empirical designs contained in the paper; no implementation or empirical results reported.
The integrated framework links multi-level outcomes: micro (individual skills, task performance), meso (team coordination, workflows), and macro (organizational strategy, innovation, productivity) effects to adaptive structuration processes and affordance actualization.
Framework specification and theoretical mapping across levels in the conceptual paper; no empirical validation or sample.
The paper develops a conceptual framework that integrates Adaptive Structuration Theory (AST) and Affordance Actualization Theory (AAT) to explain how effective human–AI collaboration can be structured within organizations.
Conceptual/theoretical synthesis and literature integration combining AST and AAT streams; no original empirical data or sample reported (theoretical development).
Returns to advanced digital skills vary by firm size/type: the wage return in large Chaebol conglomerates is approximately 18.7%, significantly higher than the ~9.5% return in Small and Medium-sized Enterprises (SMEs), indicating a 'skills–scale' complementarity effect.
Heterogeneity analysis within the extended Mincerian wage regression framework using KLIPS micro-data, comparing estimated returns across firm types (Chaebol vs SMEs). (Sample size and exact model specification not provided in the excerpt.)
Workers with only general digital literacy receive a wage premium of approximately 5.8% (after controlling for education, experience, and demographics).
Same empirical framework: extended Mincerian wage equation on KLIPS micro-data with controls for education, experience, and demographic characteristics. (Sample size not specified in the provided excerpt.)
Workers possessing specialized digital skills (e.g., data analysis, programming, automation control) enjoy a significant wage premium of approximately 14.2% after controlling for years of education, work experience, and demographic characteristics.
Empirical estimation using an extended Mincerian wage equation on micro-data from the Korean Labor and Income Panel Study (KLIPS); models control for years of education, work experience, and demographic covariates. (Sample size not specified in the provided excerpt.)
DARE posits that responsible AI deployment requires the simultaneous and integrated development of Digital readiness, Administrative governance, Resilience & ethics, and Economic equity.
Descriptive claim about the framework's components as reported in the abstract (conceptual proposition).
This paper introduces the DARE Framework, a holistic, four-dimensional model for national AI strategy and international cooperation.
Factual description of paper content in abstract — the framework is introduced by the authors (conceptual/model contribution).
The Indian government believes that artificial intelligence (AI) will play an important role in India’s continued economic growth, both through its contribution to productivity in the private sector and through smarter and more data-led government.
Reported position in the paper based on review of government statements and policy documents (policy analysis/legal review). No empirical sample size applies; claim is descriptive of government belief.
Global efforts toward establishing shared norms and multilateral cooperation are underway through initiatives led by the United Nations, OECD, UNESCO, and G7.
Qualitative document review identifying initiatives and normative efforts by multilateral organizations (organizations named; specific initiatives referenced qualitatively but not enumerated as a dataset).
Mainstreaming shared input and embracing climate-resilient management approaches are fundamental action items for building institutional resilience.
Paper conclusion lists these recommended action items based on its analysis of governance and sustainability linkages grounded in SDG and global governance literature; the summary does not indicate empirical testing of these recommendations.
Medicaid, as the largest public purchaser of healthcare services in the United States, occupies a strategic position to drive systemic change through its supply chain.
Descriptive evidence from publicly available statistics and literature on Medicaid's scale and purchasing role (cited policy/literature sources within the paper); conceptual argument linking purchasing scale to leverage in supply chains.
The rapid growth of geospatial data and advances in artificial intelligence (AI) have driven GeoAI’s rise as a key paradigm in urban analytics.
Synthesis from the paper's literature review highlighting trends in data availability and AI capability; evidence likely based on counts of recent publications, reported applications, and domain examples (specific sample size or bibliometric measures not provided in the excerpt).
The model was prompted to suggest jobs to 24 simulated candidate profiles balanced in terms of gender, age, experience and professional field.
Methods reported in the paper: experimental prompting of GPT-5 with N=24 simulated profiles, balanced across specified attributes.
This study evaluates how a state-of-the-art generative model (GPT-5) suggests occupations based on gender and work experience background for under-35-year-old Italian graduates.
Study design described in the paper: targeted population (under-35 Italian graduates), model used (GPT-5) and evaluation focus (occupation suggestions).
Entrepreneurs' expectations about future opportunities were significantly shaped by interpersonal influence (peer effects).
Quantitative analysis linking measures of interpersonal/peer exposure among entrepreneurs to reported expectations about future opportunities; analysis conducted within the >27,000 respondent sample across 43 countries.
Crisis adaptation among small business owners during COVID-19 was driven less by macroeconomic structure and more by social embedding (social networks, peer influence, and collective identities).
Comparative quantitative analysis of a survey sample of over 27,000 individual entrepreneurs in 43 countries using a novel socially embedded framework (networks, collective identities, normative motivations); empirical tests comparing explanatory power of social mechanisms versus macro-structural factors for adaptation outcomes.
The positive AI → executive pay relationship is robust to endogeneity controls, including instrumental variable approaches, and to multiple robustness checks.
Instrumental variable analyses and a battery of robustness checks reported in the paper applied to the same A-share firm panel and baseline specifications; IV strategy and robustness test details provided in the methods section.
Firm-level AI adoption raises executive compensation in Chinese A-share listed companies (2007–2023).
Baseline panel regressions on a panel of Chinese A-share listed firms (2007–2023) linking a firm-level AI application indicator to executive compensation, controlling for standard firm controls and fixed effects.
Two regimes emerge: an inequality-increasing regime when AI is proprietary (concentrated control), rents concentrate because firms capture most gains (low ξ), and complementary assets are concentrated.
Model regime characterization and calibrated simulations showing rising firm profits and aggregate inequality under proprietary-AI assumptions and low rent-sharing elasticity.
Generative AI shifts economic value toward concentrated complementary assets (firm-level capital, proprietary data/algorithms), increasing firm profits and rents captured by asset owners.
Model results from a task-based framework with heterogeneous firms and complementary assets; calibration via MSM to six empirical moments; counterfactuals show increased profit shares when AI confers advantages to firms owning complementary assets.
The paper identifies gaps and recommends that economists conduct randomized evaluations and quasi-experimental studies to estimate causal effects of interventions (hands-on labs, instructor training, compute subsidies) on competencies and earnings.
Policy and research agenda section of the paper arguing for randomized/quasi-experimental methods; no such causal interventions were implemented in this study.
The study conducted a cross-sectional online survey of more than 600 higher-education students and educators from multiple world regions.
Cross-sectional online survey; sample size reported as >600 participants; recruitment targeted a mix of disciplines and institution types; survey mapped to UNESCO 2024 AI competency frameworks.
Digital trade development raises city-level house prices in China in a robust, linear manner.
City-level panel regressions using a constructed digital trade index (entropy-TOPSIS aggregation of multiple indicators). Authors report tests for nonlinearity (none found) and multiple robustness checks. Sample: Chinese cities (years and exact sample size not specified in the summary).
Canada emphasizes teacher-led assessment, cautious regulation, and a focus on equity and professional development in responding to AI-related assessment issues.
Country case study based on Canadian policy documents and secondary sources highlighting teacher-led approaches and regulatory caution; illustrative description.
The task frontier expands: new tasks become profitable and are created endogenously as coordination costs decline.
Analytical derivation in the model (proposition about task frontier) and simulation exercises that permit endogenous task entry.
Aggregate output increases when coordination costs fall because reduced frictions and endogenous task creation raise productive capacity.
Analytical result (one of the five propositions) showing comparative statics of output with respect to coordination compression; supported by calibrated numerical simulations.
Lower coordination costs expand managers’ spans of control (managers can supervise more subordinates).
Analytical comparative statics derived in the model (one of the five propositions) and corroborating numerical simulations with heterogeneous agents.
A one standard-deviation increase in AI adoption causally increases employment in occupations requiring complex problem-solving and interpersonal skills by 1.8%.
Same panel (38 OECD countries, 2019–2025) and AI Adoption Index; IV estimation with occupational employment classified by task type (complex problem-solving & interpersonal); fixed effects and robustness checks reported.
Overinvestment increases inequality (greater tail concentration of income).
Model computations showing that exponential returns amplify income at the top; comparative statics indicate inequality measures rise with greater investment/technology under lognormal wage assumption.
Overinvestment increases measured GDP (output).
Comparative statics in the theoretical model linking higher private investment/technology adoption to higher aggregate output; model shows positive effect on measured GDP despite welfare loss possibilities.
The exponential returns to skill and technology create strong private incentives for agents to escalate skill (education) investment toward the high tail of the distribution (an educational arms race).
Equilibrium analysis and comparative statics in the theoretical model showing that marginal returns to additional investment are increasing toward the distribution tail, producing higher optimal private investment at the top relative to social optimum.
When wages follow a lognormal distribution, technological progress makes wages increase exponentially in both skill and technology.
Analytical derivation in the paper's economic model that assumes a lognormal wage distribution and specifies wages as an exponential function of skill and a technology parameter; result follows from model algebra (no empirical data).
The paper proposes a research agenda prioritizing interoperable, ethical‑by‑design platforms; metrics to measure social equity impacts; and adaptation of global standards to local institutional capacities.
Explicit list of three prioritized research directions provided in the paper, derived from the systematic synthesis of the 103 items.
High‑income examples (e.g., Estonia, Singapore) demonstrate mature integration of digital/AI systems in e‑government, urban mobility, and e‑health.
Empirical case examples drawn from the reviewed literature and institutional reports cited in the review; specific country examples (Estonia, Singapore) repeatedly referenced as mature adopters.
Research priorities include developing robust measures of AI adoption and using causal methods (difference-in-differences, synthetic controls, RDD, IV) to estimate effects of AI and regulation on productivity, employment, and inequality.
Methodological recommendations in the report based on identified evidence gaps and normative evaluation of empirical priorities.
The American Artificial Intelligence Initiative emphasizes R&D and innovation leadership, standards development, workforce readiness, and fostering 'trustworthy AI' (transparency, fairness, accountability).
Primary source policy documents from the U.S. American Artificial Intelligence Initiative reviewed in the report.
The paper introduces a Predictive Skill Gap Intelligence Hub — an AI-driven platform that combines macro- and micro-level indicators with probabilistic growth models and intelligent skill-synthesis to proactively forecast regional and sectoral labor demand–supply gaps.
Description of system architecture and modeling approach in the paper (methods section). No numerical evaluation metrics or datasets provided for this descriptive claim.
Vendor support, warranties, and service-level agreements (SLAs) are important for clinical adoption and liability management.
Policy and implementation literature, industry reports, and stakeholder feedback synthesized in the paper highlighting the role of vendor contractual commitments in adoption decisions.
Proprietary systems lead on reliability, maintenance, and validated integrations with clinical systems.
Literature synthesis including vendor case studies, deployment reports, and stakeholder surveys indicating more mature productization and validated integrations for proprietary offerings.
Open-source deployment options (e.g., on-premises) reduce data-sharing exposure and improve privacy.
Aggregated evidence from deployment reports and technical papers describing on-premises and local inference architectures; industry analyses of data governance tradeoffs.
Open-source models provide greater transparency and inspectability, enabling better auditability and explainability.
Systematic literature synthesis of peer-reviewed studies, industry reports, and case studies comparing open-source and proprietary systems; comparative analysis highlights inspectability of open-source code/models. No new primary experiments reported.
Coordinated policy reform, targeted infrastructure investment, workforce training, and equity-focused implementation are strategic priorities to realize AI’s potential in Indonesian healthcare.
Consensus recommendations drawn from the narrative synthesis, thematic analysis, and Delphi consensus studies included among the 42 supplementary documents and the broader 2020–2025 literature body.
High‑frequency sensor and satellite data, processed with AI, improve precision in measuring yields, input use, and environmental externalities, enhancing the quality of economic impact evaluations and policy targeting.
Methodological and validation studies using high‑resolution satellite imagery and field sensors that show improved measurement accuracy versus traditional survey methods; referenced empirical demonstrations in the literature.
Enhanced gross‑flows estimation using longitudinal microdata can better track transitions (job-to-job, upskilling, unemployment spells) and measure occupational churn and reallocation.
Established econometric practice cited in paper; recommendation to use panel/admin microdata (CPS longitudinal supplements, LEHD/LODES, UI records); no new empirical results but aligns with standard methods.
Automated equivalency systems require algorithmic oversight features (audit trails, human-in-the-loop checks) to maintain trust and labor-market legitimacy.
Governance recommendation following best practices in algorithmic accountability; not supported by empirical testing of oversight mechanisms in this context.
AI tools (automated document parsing/NLP, translation, equivalency-prediction classifiers, anomaly detection) can scale credential processing and reduce transaction costs and processing time.
Paper cites potential AI capabilities and application areas; the claim is inferential from known AI functionalities, with no implementation benchmark or throughput numbers provided.
If companies are treated as recipients, they would be required to comply with nondiscrimination obligations (e.g., Title VI, Title IX, Section 504) in education contexts and may be subject to enforcement actions, corrective requirements, and private suits where applicable.
Interpretation of recipient obligations under existing civil‑rights statutes and enforcement mechanisms; doctrinal analysis and illustrative case law.