Evidence (13870 claims)
Adoption
8467 claims
Productivity
7558 claims
Governance
6805 claims
Human-AI Collaboration
6363 claims
Org Design
4132 claims
Innovation
4065 claims
Labor Markets
3526 claims
Skills & Training
2945 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 749 | 196 | 98 | 892 | 1984 |
| Governance & Regulation | 817 | 394 | 188 | 121 | 1544 |
| Organizational Efficiency | 771 | 189 | 124 | 83 | 1177 |
| Technology Adoption Rate | 627 | 233 | 123 | 96 | 1088 |
| Research Productivity | 411 | 123 | 56 | 332 | 933 |
| Output Quality | 467 | 178 | 59 | 47 | 751 |
| Decision Quality | 320 | 174 | 75 | 42 | 618 |
| Firm Productivity | 435 | 55 | 88 | 20 | 604 |
| AI Safety & Ethics | 214 | 276 | 65 | 33 | 593 |
| Market Structure | 178 | 167 | 122 | 24 | 496 |
| Task Allocation | 207 | 64 | 71 | 32 | 379 |
| Skill Acquisition | 165 | 59 | 60 | 17 | 301 |
| Innovation Output | 203 | 27 | 43 | 18 | 292 |
| Employment Level | 105 | 52 | 107 | 13 | 279 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 116 | 63 | 42 | 11 | 232 |
| Firm Revenue | 150 | 48 | 26 | 3 | 227 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Task Completion Time | 169 | 29 | 8 | 12 | 219 |
| Worker Satisfaction | 89 | 63 | 20 | 12 | 184 |
| Error Rate | 69 | 92 | 10 | 2 | 173 |
| Regulatory Compliance | 76 | 68 | 14 | 5 | 163 |
| Training Effectiveness | 93 | 21 | 13 | 19 | 148 |
| Wages & Compensation | 77 | 36 | 25 | 6 | 144 |
| Automation Exposure | 51 | 54 | 22 | 12 | 142 |
| Team Performance | 86 | 17 | 27 | 9 | 140 |
| Developer Productivity | 94 | 17 | 14 | 6 | 132 |
| Job Displacement | 12 | 80 | 20 | 1 | 113 |
| Hiring & Recruitment | 51 | 7 | 8 | 3 | 69 |
| Creative Output | 31 | 17 | 7 | 3 | 59 |
| Skill Obsolescence | 5 | 46 | 6 | 1 | 58 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 17 | 17 | — | 51 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Analyses were conducted as intent-to-treat comparisons across arms, with hypothesis tests reported (including p-values) and principal stratification used for mechanism decomposition.
Methods statement: intent-to-treat comparisons, reported p-values for score differences, and use of principal stratification for separating total effect into adoption and effectiveness channels in the randomized trial (n = 164).
The primary outcomes analyzed were LLM adoption (use), exam score (grade points), and answer length.
Study’s stated primary outcomes in methods: adoption indicator, exam score on an issue-spotting exam, and answer length (measured). Sample size n = 164.
The study used a randomized controlled design with three arms: no LLM access, optional LLM access, and optional LLM access plus brief training.
Study methods description: randomized assignment of 164 law students to three experimental conditions as listed.
The intervention consisted of roughly a ten-minute training focused on how to use the LLM effectively.
Study description of the intervention in the randomized experiment (three-arm design with one arm receiving ~10-minute targeted training).
Findings are estimated for Chinese cities and require replication in other institutional contexts to assess external validity.
Scope statement in the paper — primary empirical sample limited to 274 Chinese cities; authors note generalizability limits and call for replication elsewhere.
The paper’s AI exposure index — capturing automation and service-sector transformation — is important for robust measurement in empirical work on AI’s macro and environmental effects.
Methodological claim justified by the paper's construction of the index and its use in the main and robustness regressions; robustness checks reported using alternative index specifications.
The paper constructs an AI exposure index that captures both industrial automation (robots) and AI-enabled transformation of service-sector jobs/tasks.
Methodological construction described in the paper combining measures of industrial robot adoption (sectoral push) and AI-driven changes in service-sector job/task content.
The study uses a panel of 274 Chinese cities from 2007–2021 as the primary empirical sample.
Descriptive dataset information reported in the paper — city-level panel covering 274 cities and the years 2007 through 2021.
The paper's empirical approach is primarily qualitative and interpretive: a systematic literature review plus comparative qualitative case studies, using policy documents, public diplomacy examples, development initiatives, technology export and standards behaviour, and secondary empirical studies as evidence.
Methods section of the paper explicitly states the approach and evidence types; sample of four comparative cases (US, China, EU, Russia) is specified.
The paper demonstrates different mixes and institutional practices of smart power in practice by applying the framework to the United States, China, the European Union, and Russia.
Explicit comparative qualitative case studies of four major international actors (sample size: four cases) using policy documents, public diplomacy examples, and development/technology initiatives as illustrative evidence.
Empirical validation of the book’s proposals would require complementary case studies, model documentation, and outcome measurements.
Author/reviewer recommendation in the blurb about methodological limitations and next steps; not an empirical finding.
The book is predominantly conceptual and policy-analytic and uses illustrative case vignettes rather than presenting a single empirical study.
Explicit methodological description in the Data & Methods blurb: synthesis of technical ideas, governance requirements, and illustrative vignettes; no empirical sample or experimental protocol described.
The research program is grounded in 12 years of forensic legal research spanning 2014–2026.
Author-stated research timeline and methodology (2014–2026 forensic legal research).
The protocol is underpinned by a forensic audit of approximately 4,200 specialized texts (legal doctrine, regulation, standards, technical literature).
Stated corpus and audit in the Methods section: ~4,200 texts reviewed as part of the forensic audit.
The protocol systematizes arguments for 16 projected rulings at Mexico’s Supreme Court (SCJN) to anchor the proposed rights and rules in constitutional practice.
Doctrinal projection and constitutional strategy section of the compendium describing 16 projected SCJN rulings (method: legal projection/modeling).
The compendium’s findings and recommendations are based on a forensic audit of approximately 4,200 specialized texts covering doctrine, jurisprudence, regulation and technical literature.
Stated methodological claim in the compendium: forensic corpus audit of ~4,200 texts (sample size reported).
The evidence base is qualitative: the study uses conceptual framework synthesis, comparative analysis of multi-sector implementations, and case examples rather than randomized or large-sample empirical evaluation.
Methods and limitations section of the paper explicitly describing the evidence base and methods (qualitative synthesis, pattern extraction, cross-case lessons).
The paper presents a deployment pattern intended to be adapted by sector and regulatory context rather than a one-size-fits-all blueprint.
Explicit statement in the paper and the described pattern design; based on qualitative pattern extraction and prescriptive guidance.
Partial least squares structural equation modeling (PLS-SEM) was used to test hypothesized direct, mediated, and moderated paths.
Methods/analysis section states PLS-SEM was the statistical approach to estimate paths, mediation, and moderation effects.
The study employed a 2 × 2 between-subjects experimental design manipulating (1) identity disclosure (transparent vs. nondisclosed) and (2) conversational tone (empathetic/personalized vs. generic).
Explicit description of experimental factors and design in the methods (2 × 2 between-subjects).
Stimuli (chatbot dialogues) were standardized and pretested using a large-language-model (LLM) workflow to ensure consistent experimental stimuli across conditions.
Methods section describing stimuli creation: LLM-generated dialogues were produced and pretested to standardize messages across the 2 × 2 conditions.
Methodological claim: combining fixed-effects panel estimation, mediation analysis, and panel threshold models is an effective multi-method approach to (a) estimate average effects, (b) unpack causal channels, and (c) detect nonlinear stage-dependent impacts.
The paper's applied methodology: fixed-effects panel regressions, mediation framework, and panel threshold modeling on the 2012–2022 provincial panel.
The paper constructs a multidimensional digitalization index composed of digital infrastructure, digital service capacity, and the digital development environment.
Index construction described in data/methods: composite indicator combining measures of connectivity/broadband (infrastructure), e-commerce/digital finance (service capacity), and policy/institutional/human capital indicators (development environment).
The study is observational (panel) and subject to limitations: residual confounding is possible; two-way fixed-effects estimators can be biased with heterogeneous treatment timing or dynamics; external validity beyond China and non-grain crops is not established.
Authors' stated limitations and caveats in the paper regarding identification and generalizability of results from the CLDS 2014–2018 observational panel.
The study uses two-way fixed-effects (household and year) models as the primary identification strategy and employs propensity score matching (PSM) as a robustness check.
Methods section of the paper describing estimation strategy applied to the CLDS 2014–2018 panel of grain-producing households.
The regional average minimum cost of salaried labor (MCSL) was 43.1% of GDP per worker in 2023.
Computed for the same 19-country sample (baseline 2023) using country statutory employer obligations and reporting MCSL relative to GDP per worker following the updated IDB approach.
The regional average non-wage cost of salaried labor (NWC) in Latin America and the Caribbean was 51.1% of formal wages in 2023.
Calculated for a sample of 19 Latin American and Caribbean countries for baseline year 2023 by compiling country-specific statutory employer obligations (payroll taxes, social contributions, mandated benefits, severance, etc.) and expressing employer non-wage costs relative to formal wages using the updated IDB methodology.
Attributing productivity changes specifically to AI requires causal identification beyond VIS accounting (e.g., experiments, instrumental variables, difference-in-differences).
Paper notes that VIS is an accounting framework and that causal attribution to AI requires econometric/experimental methods beyond input–output accounting.
The method uses BEA for industry output and industry-by-industry transactions, BLS for employment and hours worked, and IMPLAN for detailed input–output structure and sector mapping; coverage period is 2014–2023.
Explicit data sources and time coverage stated: public BEA, BLS, and IMPLAN annual data 2014–2023 used to construct input–output matrices and labor measures.
Limitations of the review include the small sample of studies, uneven geographic coverage, heterogeneity in methods across studies, and limited long‑run evidence (especially on generative AI), which complicate causal aggregation.
Author-reported limitations based on the meta-assessment of the 17 included studies (variation in methods, contexts, and time horizons).
Design of this work: a systematic literature review and meta‑synthesis of empirical findings from peer‑reviewed journals (2020–2025), based on 17 publications.
Stated methods and inclusion criteria of the paper: systematic review of peer‑reviewed literature (sample = 17).
Long-term evidence on generative AI’s structural labor‑market effects is scarce; few longitudinal studies exist.
Assessment of study horizons and methods among the 17 papers indicates limited long-run and longitudinal analyses specifically on generative AI impacts.
Empirical coverage is limited for low‑income countries; evidence from such settings is scarce.
Geographic distribution of the 17 reviewed studies shows concentration in advanced economies with few or no studies focused on low-income countries.
The literature shows a surge in research activity on AI and labor markets in 2023–2025 and a concentration of studies in advanced economies.
Meta-analytic summary of the publication years and geographic focus among the 17 selected publications (temporal and geographic count of included studies).
Results depend on accurate skill extraction from vacancy texts and valid measures of occupational exposure/complementarity; causal interpretation of diffusion effects may be limited by endogeneity (e.g., technology adoption responding to labor-market conditions).
Authors' stated methodological limitations: reliance on text-analysis identification of skills and on constructed measures of exposure/complementarity; acknowledgement of endogeneity concerns limiting causal claims.
The paper proposes two conceptual models (AI/ML‑Driven Labor Market Transformation Model and Sectoral Impact and Resilience Model) to organize heterogeneous findings and generate testable hypotheses about how AI reshapes labor across sectors and skill levels.
Conceptual synthesis integrating Technological Determinism, Socio‑Technical Systems Theory (STS), and Skill‑Biased Technological Change (SBTC); the models are theoretical outputs of the review used to map mechanisms and heterogeneity rather than empirical findings.
There are substantial measurement and identification gaps in the literature: heterogeneity in measuring 'AI adoption', limited long‑run causal evidence, and geographic bias toward advanced economies.
Methodological assessment within the review noting variability across studies in AI measures (patents, investment, task exposure proxies), paucity of long‑run causal designs, and concentration of empirical studies in advanced economies; this is a meta‑evidence limitation statement.
The Iceberg Index indicates where capability exists but does not indicate whether or when job losses will occur.
Explicit caution in the paper noting the distinction between technical exposure (capability overlap) and realized labor-market outcomes; methodological limitation described.
The Iceberg Index captures capability overlap but does not capture firm adoption choices, regulatory constraints, social acceptance, complementarity effects, or worker reallocation dynamics.
Limitations section in the paper explicitly listing these omitted factors; methodological boundaries of the Iceberg Index stated.
Model and simulations are implemented with the AgentTorch framework.
Implementation note in the paper indicating AgentTorch was used to build the agent-based models and run simulations.
The simulation model represents 151 million U.S. workers as autonomous agents, covers 32,000+ distinct skills, links agents to thousands of AI tools, and provides county-level resolution (~3,000 U.S. counties).
Model specification described in the paper: large-population agent-based model (AgentTorch) parameterized with occupation, skills portfolios, wages, and county locations; counts provided in the paper.
The Iceberg Index is a skills-centered metric that measures the wage value of specific skills AI systems can perform within each occupation; it quantifies technical exposure (capability overlap), not displacement, adoption timelines, or realized outcomes.
Methodological definition: mapping of ~32,000 skills to occupations with wage-value contributions, summing wages of skills that current AI capabilities cover to compute the index.
The study maps employment channels for AI-competent graduates and documents the most frequent job titles/roles and associated wage levels.
Descriptive analysis of employer channels, occupational role frequencies, and wage data compiled in the monitoring dataset covering graduates and alternative-route entrants.
Quasi-experimental designs (difference-in-differences, instrumental variables, event studies) and panel regressions are useful methods for identifying causal effects of AI adoption where plausibly exogenous variation exists.
Methodological summary in the paper listing common empirical strategies used in the literature to estimate causal impacts of technology adoption.
Current research is limited by measurement challenges in capturing AI capabilities and firm-level adoption, and by a lack of longitudinal worker-firm data and causal identification in many settings.
Explicit limitations noted by the paper: gaps in task measures, scarce longitudinal linked datasets, and methodological challenges in causal inference.
This paper's approach is qualitative and based on secondary literature synthesis; it does not collect primary survey, experimental, or administrative data.
Explicit statement in the Data & Methods section of the paper.
Key empirical gaps remain: better measurement of K_T (AI/software capital), more granular matched employer‑employee and wealth data, and improved estimates of task-substitution elasticities are required to precisely quantify incidence and policy impacts.
Authors’ stated research agenda and limitations section, including sensitivity analyses showing outcome variation with parameter choices and measurement uncertainty.
The study classifies economic activities into a binary grouping (highly digitalized vs less digitalized) based on telework feasibility and digital intensity and uses COVID-19 as a quasi-natural experiment within a DiD framework on quarterly panel data for 27 EU Member States (2018–2024, N = 36,685).
Study design and data description reported in abstract: binary classification of sectors by telework feasibility and digital intensity; DiD using COVID-19 shock; panel 2018–2024 for 27 EU Member States; sample size N = 36,685.
The study employs a multidimensional clustering approach based on firm size, age, market competitiveness, and digital infrastructure to examine heterogeneous AI effects.
Methodological description in the paper stating multidimensional clustering variables (size, age, competitiveness, digital infrastructure) used to form firm clusters for heterogeneity analysis.
The study uses panel data of 3,366 Chinese A-share listed firms from 2015 to 2023.
Direct statement of dataset and time period in the paper (panel of 3366 Chinese A-share listed firms, 2015–2023).