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
Remove filter
Research should prioritize dynamic, task-based models that include transitional frictions, heterogeneous agents, and sectoral structure to better measure AI exposure and impacts.
Methodological recommendation grounded in the paper's theoretical critique of static occupation-level automation metrics and noted empirical gaps.
Timing uncertainty and measurement challenges make forecasting the pace and scale of AI-induced employment change inherently uncertain.
Methodological limitations section noting uncertainty in AI adoption speed and difficulties mapping capabilities to tasks and predicting new occupation emergence.
Personal data are nonrivalrous and highly replicable, so selling data does not follow ordinary scarcity logic.
Analytic/property claim about the economic characteristics of digital information; supported by conceptual definitions and common technical facts about data replication; no empirical sampling needed.
Empirical approach measured and compared expectation formation, innovation responses, and pipeline outcomes across local exposure to closures and across distinct entrepreneurial identity groups.
Methodological description: survey-based, cross-country quantitative approach using measures of local exposure (nearby closures), identity classification (family/purpose-driven vs. wealth-driven), and outcomes (expectations, perceived impediments, self-reported innovation, pipeline transitions) in a sample >27,000.
The study analyzes a cross-country sample of more than 27,000 entrepreneurs across 43 countries (survey-based, comparative).
Descriptive claim about the dataset used in the paper: survey-based sample size >27,000 spanning 43 countries as reported in Data & Methods.
The empirical strategy uses baseline panel regressions with standard controls (e.g., firm size, performance, leverage) and fixed effects to estimate the AI → pay relationship.
Methods section describing regression specifications including firm controls and fixed effects applied to the A-share firm panel.
Data consist of a panel of Chinese A-share listed companies covering 2007–2023.
Data description in the paper specifying the sample period and population (A-share listed firms, 2007–2023).
The firm-level AI application indicator is constructed via textual analysis of corporate disclosures (e.g., filings/annual reports) to capture AI application intensity.
Methodological description in the paper describing text-based construction of an AI application indicator from corporate disclosures for listed firms in the 2007–2023 sample.
Calibration via Method of Simulated Moments (MSM) matches six empirical moments to discipline mechanism magnitudes.
Model calibration procedure reported in the paper: MSM matching six chosen empirical moments that summarize key pre/post-AI patterns (paper states six moments were used).
Empirical validation of the integrated Kondratieff–Schumpeter–Mandel framework requires firm-level adoption and profitability data, sectoral investment series, and cross-country comparisons using panel methods and identification strategies (e.g., diff-in-diff, IV).
Methods/limitations section recommendation (explicitly states no single micro-econometric identification strategy was reported and outlines required data/methods).
The three frameworks (Kondratieff, Schumpeter, Mandel) are complementary: Kondratieff frames periodicity, Schumpeter provides micro-mechanisms of innovation-driven change, and Mandel foregrounds socio-political constraints and distributional outcomes.
Conceptual integration and comparative theoretical analysis (qualitative synthesis).
Kondratieff's framework is useful for identifying broad periodicities (recurring phases of expansion and stagnation) in capitalist development but is less specific about microeconomic mechanisms.
Theoretical review of Kondratieff literature and conceptual assessment (qualitative).
Non-probability sampling and self-reported measures limit claims about prevalence and causality; cross-sectional design cannot capture dynamics of skill acquisition over time.
Study limitations explicitly reported by authors: non-probability sampling, self-reported measures, and cross-sectional design.
The study is primarily diagnostic and prescriptive rather than empirical: no explicit empirical dataset, causal identification strategy, or statistical estimation is reported.
Methods section of the paper explicitly characterizes the work as conceptual, systems-oriented, and not reporting empirical evaluation data.
The urban AI index is constructed via text-mining techniques to capture city-level AI capability/intensity.
Methodological description: authors report using text-mining to build a city-level AI capability/intensity index (details of sources and text-mining procedure not provided in the summary).
The digital trade index is constructed using the entropy-TOPSIS method (multi-indicator aggregation).
Methodological description: digital trade index aggregation via entropy-TOPSIS reported by authors.
Research recommendation: invest in longer-run, rigorous impact evaluations (RCTs, panel studies) and system-level assessments to capture spillovers and sustainability outcomes.
Authors' stated research agenda based on identified methodological gaps (limited long-term and system-level evidence) in the review.
There is variation in study design and quality in the evidence base (RCTs, quasi-experimental studies, observational case studies, pilots).
Methodological caveats noted by the authors summarizing the diversity of designs reported across reviewed studies.
The review used a structured literature review with thematic synthesis and a comparative effect-size analysis to quantify ranges for yield, cost, and efficiency outcomes.
Authors' description of review approach and analytical methods in the Data & Methods section.
The evidence base reviewed comprises more than 60 peer-reviewed articles and institutional reports from 2020–2025, primarily focusing on Sub-Saharan Africa.
Statement in the paper's Data & Methods section describing the scope and composition of the review sample.
Effect sizes and impacts vary substantially across contexts—by crop, farm size, and institutional setting.
Comparative synthesis across studies showing heterogeneity in reported outcomes and authors' methodological caveats highlighting context dependence.
Technologies assessed in the review include predictive analytics, digital advisory systems, smart irrigation, pest/disease detection, and precision fertilization.
Descriptive synthesis of the types of AI and digital technologies evaluated across the >60 reviewed articles and reports (2020–2025).
The study has potential selection and ecological-validity constraints because it was conducted at two institutions across six courses, limiting generalizability.
Authors note limitations regarding sample scope (two institutions, six courses) and the ecological validity of the experimental tasks/settings.
The study employed a multi-method approach combining experimental quantitative analysis (descriptives, GLM, non-parametric robustness checks) with qualitative topic-based coding of open-ended survey responses.
Methods description: randomized/experimental assignment; quantitative analyses using GLM and non-parametric tests; qualitative topic-based coding of student responses; sample N = 254 across six courses at two institutions.
The study did not directly measure accessibility or impacts on students with disabilities, though qualitative results suggest possible intersections with inclusive and multimodal learning design.
Limitation stated by authors: no direct measurement of accessibility outcomes; qualitative responses hinted at potential relevance to inclusive design but no empirical measurement of disability-related impacts.
The study focused on short-term, knowledge-based tasks and did not measure long-term learning or retention.
Authors explicitly note as a limitation that the experimental tasks were short-term and knowledge-based and that long-term retention was not measured.
Empirical generalization across all climate-AI systems is constrained by heterogeneous data availability and proprietary models, limiting the ability to produce universal quantitative claims.
Stated methodological limitation in the paper, noting heterogeneous data and the proprietary nature of some models restrict broad generalization.
The paper does not provide granular quantitative estimates of the economic cost of infrastructural asymmetries in climate-AI.
Explicit limitation stated by the authors in the Methods/Limitations section.
Falsifiability condition for intermediation-collapse: If intermediary margins remain stable despite measurable declines in information frictions, the intermediation-collapse mechanism is falsified.
Stated empirical test in the paper that compares measured intermediary markups/margins to proxies for information frictions and AI-driven automation across affected sectors.
Falsifiability condition for Ghost GDP: If monetary velocity does not decline (or instead rises) as the labor share falls, the Ghost GDP channel is unsupported by the data.
Explicit falsification condition provided in the paper based on the model link labor share -> velocity -> consumption; suggested empirical test using monetary-velocity proxies and labor-share series from FRED.
Empirically, top-quintile households account for roughly 47–65% of U.S. consumption.
Calibration and reported quantitative scenarios in the paper using U.S. consumption concentration data (constructed from U.S. consumption/income micro- and macro-data sources referenced in the methods section).
Instrumental-variable (IV) estimation is used to address endogeneity of AI adoption and to identify causal effects on employment and wages.
Paper states IV identification strategy applied to the 38-country panel; robustness checks and alternative specifications reported (paper refers to instrument details in full text).
The AI Adoption Index is constructed as a composite measure combining enterprise investment in AI, AI-related patent filings, and workforce/firm surveys on AI use across 38 OECD countries (2019–2025).
Paper's methodological description of the index construction; data sources enumerated as investment, patenting, and survey measures over the panel period.
The paper is entirely theoretical/analytical and does not report an empirical dataset.
Paper methodology section and abstract state primary tool is an analytical economic model; no empirical data or sample sizes are reported.
The same formal framework can be interpreted as a firm-level model where human skill investment maps onto AI/chatbot investment decisions.
Paper provides an alternative interpretation and formally maps agent skill-investment choices into an analogous firm R&D/AI-capital decision problem within the same mathematical framework.
The systematic review followed PRISMA protocol and analyzed a corpus of 103 items (peer‑reviewed articles and institutional reports) published 2010–2024.
Explicit methodological statement in the paper describing PRISMA use and corpus size/timeframe.
The study is limited by being a single‑country case; contextual factors (regulatory regime, infrastructure capacity, procurement practices) may limit generalizability and the study emphasizes institutional and ethical analysis rather than quantitative measurement of economic impacts.
Explicit limitations reported in the paper summarizing scope and emphasis.
Methods used include qualitative interviews with researchers and administrators, observation/documentation of tool use, mapping of data flows and third‑party dependencies, and normative/legal analysis contrasting local practices with GDPR principles.
Methods section of the paper as reported in the provided summary.
The study's empirical basis is a qualitative case study centered on environmental science research in Chile that adopts the GDPR as an organizing normative framework.
Paper description of study scope and normative framing (methods and focus described in Data & Methods).
There is a need for validated administrative and firm-level data on AI adoption, workplace monitoring, and worker outcomes, and for evaluation of policy interventions (mandated impact assessments, transparency requirements, worker representation rules) using randomized or quasi-experimental designs where feasible.
Research and measurement priorities set out in the commentary based on identified gaps; prescriptive recommendation rather than evidence-based finding.
The paper is a policy and legal commentary/synthesis and not an empirical causal study; it does not provide microdata on employment or wage effects but identifies plausible channels and institutional dynamics.
Author-stated methodology and limitations section describing type of study and data sources; explicitly reports lack of primary empirical data.
The federal U.S. approach to AI governance combines export controls for key AI hardware/software with a relatively permissive domestic regulatory stance that relies on executive guidance, voluntary standards, and sector-specific measures rather than comprehensive federal worker protections.
Comparative policy and legal review of federal-level instruments (export control lists, executive orders, agency guidance, proposed/final rules) described in the commentary; no primary empirical data or sample size.
The report has limited primary quantitative impact evaluation and relies on policy texts and secondary sources rather than large-scale empirical measurement of AI’s economic effects.
Explicit limitations section in the report describing methods and data constraints.
Methodological needs for AI-era labor models include dynamic skill taxonomies, high-frequency labor data (job postings, firm-level automation measures), and uncertainty quantification.
Paper's Research & policy recommendations and Methodological needs section (explicit recommendations).
The scenario analysis framework varies economic growth, automation rates, policy interventions, and investment to produce probabilistic demand–supply gaps.
Methods description of scenario analysis components and the variables varied in scenario experiments (explicit in Data & Methods).
Intended users of the Hub include organizations, educational institutions, and policymakers to inform reskilling/education strategies, regional economic policy, and labor-market interventions.
Explicit statement of target users and use cases in the Key Points / Implications sections.
The system produces interpretable outputs for stakeholders: demand–supply trend analysis, geospatial hotspot maps, skill-gap radar charts, and policy simulation dashboards.
Paper's description of outputs and interactive visual analytics (listed output modalities).
The core modeling approach uses probabilistic growth modeling combined with intelligent skill synthesis to estimate future workforce requirements under alternative economic and policy scenarios.
Methods section describing the modeling components: probabilistic growth modeling and intelligent skill synthesis (architectural description).
The platform integrates multiple indicators such as regional economic growth projections, automation velocity, policy intervention strength, investment intensity, and market volatility (macro- and micro-level indicators).
List of input indicators given in the Data & Methods section of the paper (explicit enumeration of macro and micro variables).
The paper's conclusions are limited by reliance on secondary sources, heterogeneous cross‑study comparisons, limited causal identification of long‑run macro effects, and measurement challenges for AI‑driven intangible capital.
Authors' stated limitations section summarizing the nature of evidence used (qualitative literature review, secondary macro indicators, sectoral examples); this is an explicit self‑reported methodological limitation rather than an external empirical finding.