Evidence (4004 claims)
Search and filter individual claims pulled from the papers. Looking for a specific finding ("what's the effect on wages?"), you're in the right place. Want to compare whole outcome categories against each other instead? Use the Evidence Explorer.
The board below groups claims two ways: by broad theme (nine paper-level topics) and by outcome category (the 34 claim-level outcomes that the Explorer and Syntheses also use).
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Nine broad, paper-level topics. Click one to filter the claims below.
Adoption
9875 claims
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Productivity
8807 claims
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Governance
7870 claims
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Human-AI Collaboration
7560 claims
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Org Design
4892 claims
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Innovation
4781 claims
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Labor Markets
4004 claims
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Skills & Training
3308 claims
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Inequality
2332 claims
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Claims by outcome category
Counts by direction of finding. These are the same 34 outcome categories the Explorer compares and the Syntheses are written for. A linked row has a published synthesis.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 870 | 233 | 116 | 1066 | 2363 |
| Governance & Regulation | 976 | 451 | 218 | 133 | 1809 |
| Organizational Efficiency | 949 | 224 | 144 | 88 | 1416 |
| Technology Adoption Rate | 764 | 287 | 141 | 122 | 1325 |
| Research Productivity | 501 | 152 | 74 | 362 | 1101 |
| Output Quality | 542 | 216 | 69 | 69 | 896 |
| Decision Quality | 387 | 198 | 94 | 54 | 740 |
| Firm Productivity | 513 | 67 | 101 | 27 | 714 |
| AI Safety & Ethics | 249 | 303 | 73 | 36 | 667 |
| Market Structure | 190 | 192 | 134 | 27 | 548 |
| Task Allocation | 243 | 77 | 91 | 36 | 452 |
| Innovation Output | 291 | 33 | 55 | 20 | 401 |
| Skill Acquisition | 206 | 72 | 65 | 21 | 364 |
| Employment Level | 133 | 63 | 115 | 22 | 335 |
| Fiscal & Macroeconomic | 153 | 79 | 52 | 32 | 323 |
| Task Completion Time | 206 | 37 | 12 | 15 | 272 |
| Firm Revenue | 179 | 52 | 29 | 5 | 266 |
| Consumer Welfare | 130 | 76 | 47 | 13 | 266 |
| Inequality Measures | 48 | 137 | 51 | 6 | 242 |
| Worker Satisfaction | 101 | 81 | 25 | 13 | 220 |
| Error Rate | 84 | 110 | 11 | 5 | 210 |
| Wages & Compensation | 98 | 47 | 30 | 10 | 185 |
| Regulatory Compliance | 88 | 73 | 17 | 7 | 185 |
| Automation Exposure | 66 | 64 | 33 | 16 | 182 |
| Team Performance | 105 | 29 | 30 | 11 | 176 |
| Training Effectiveness | 109 | 22 | 14 | 21 | 168 |
| Developer Productivity | 114 | 21 | 14 | 8 | 158 |
| Job Displacement | 12 | 90 | 24 | 1 | 127 |
| Hiring & Recruitment | 57 | 9 | 9 | 5 | 80 |
| Skill Obsolescence | 6 | 56 | 9 | 1 | 72 |
| Social Protection | 43 | 17 | 8 | 2 | 70 |
| Creative Output | 35 | 21 | 9 | 4 | 70 |
| Labor Share of Income | 18 | 21 | 17 | 1 | 57 |
| Worker Turnover | 15 | 16 | — | 4 | 35 |
| Industry | — | — | — | 1 | 1 |
Labor Markets
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The LEAFE algorithmic procedure: summarize environment feedback into compact experience items; backtrack to earlier decision points causally linked to failures and re-explore corrective action branches; distill corrected trajectories into the policy via supervised fine-tuning.
Method section / algorithm description in paper specifying the reflective/backtracking and distillation pipeline as the core of LEAFE.
Evaluation used seven benchmarks spanning online computer-use, offline computer-use, and multimodal tool-use reasoning tasks.
Benchmarks section in the summary states seven benchmarks covering those categories; no benchmark names or dataset sizes provided in the summary.
Objectives combine trajectory-level rewards (for global consistency) with stepwise grounded rewards derived from execution outcomes.
Method summary explicitly lists these objectives as part of the TraceR1 training procedure.
TraceR1 focuses on short-horizon trajectory forecasting to keep predictions tractable while capturing near-term consequences of actions.
Framework description in summary that emphasizes 'short-horizon trajectory forecasting' as a design choice.
During grounded fine-tuning, tools are treated as frozen agents and only the policy is adjusted using execution feedback (tools are not modified).
Explicit statement in Data & Methods section of the summary describing tool handling during grounded fine-tuning.
Stage 2 of TraceR1 is a grounded fine-tuning phase that refines step-level accuracy and executability using execution feedback from frozen tool agents.
Method description in summary: Stage 2 — Grounded fine-tuning using execution feedback; tools are not retrained (treated as frozen agents) and feedback is used to adjust the policy.
TraceR1 uses a two-stage training procedure: Stage 1 trains trajectory-level RL on predicted short-horizon trajectories with rewards that enforce global consistency.
Method description in summary: Stage 1 — Trajectory-level RL with trajectory-level rewards to encourage global consistency across predicted action-state sequences.
Pilot randomized or quasi-experimental implementations of reduced workweeks (across firms, industries, or regions) are needed to measure effects on employment, productivity, wages, and consumption.
Research-design recommendation motivated by lack of contemporary causal evidence; not an empirical finding but a stated priority for rigorous testing.
There is limited direct causal identification separating technology-driven layoffs from incentive-driven layoffs in current firm-level data, creating a need for new firm-panel datasets linking AI adoption, executive pay/ownership, layoff decisions, and local demand outcomes.
Stated limitation of the paper and research-priority recommendation; assessment based on literature gaps noted in the synthesis rather than empirical gap quantification.
Observed layoffs should be treated in empirical research as outcomes of firm governance and incentive structures; econometric studies estimating displacement from AI must control for managerial incentives and financial pressures.
Methodological recommendation based on the conceptual argument and literature linking governance/incentives to firm behavior; no new empirical demonstration provided.
Research priorities include empirical testing and simulation of ISB-based control systems, cost–benefit analysis of proactive versus reactive AI governance, and distributional impact assessments.
Explicit research agenda proposed by the author (conceptual recommendation), not empirical results.
Key empirical metrics introduced and used are: AI adoption rates (sector-level intensity), Skill shift index, Hybrid job share, and employment levels/net changes by sector.
Methods description listing the constructed metrics used in the simulated dataset and subsequent analyses (definitions and calculation procedures provided in the paper).
The study's main limitations include reliance on a simulated dataset rather than exhaustive administrative microdata, literature limited to selected publishers/years, and correlational (not causal) identification of some effects.
Authors' explicitly stated limitations in the paper's methods and discussion sections describing data choices (simulated dataset, selected publishers 2020–2024) and the observational/correlational nature of several analyses.
Further research is needed—randomized controlled trials, long-term impact measurement (earnings, employment stability, skill accumulation), distributional analysis, and model audits for bias.
Authors' stated research agenda and recommendations; not an empirical finding but a methodological recommendation following the pilot.
The paper recommends an empirical research agenda including field experiments comparing teams with and without AI mediation, structural models of labor supply and wages under reduced language frictions, microdata analysis of adopters, and measurement studies for coordination costs and mediated-action reliability.
Explicit recommendations and research agenda stated in the paper; this is a descriptive claim about the paper's content rather than an empirical finding.
The paper's primary approach is conceptual/theoretical development and agenda-setting; it does not report large-scale empirical or experimental data.
Explicit methods statement in the paper: synthesis, illustrative examples, framework development; absence of reported empirical sample or experiments.
Descriptive statistics, reliability tests, regression analysis, and structural equation modelling (SEM) were employed to analyse the relationships between AI adoption and entrepreneurial outcomes.
Methods section reporting use of descriptive statistics, reliability tests, regression analysis, and SEM to evaluate relationships between AI adoption and measured outcomes.
The study used a quantitative research design and collected data from 350 entrepreneurs and managers of small and medium-sized enterprises (SMEs) who had adopted AI in their business operations.
Methods section of the paper specifying a quantitative design and a sample size of 350 AI-adopting SME entrepreneurs/managers.
This research conducts a critical analysis of the ethical implications of artificial intelligence in terms of job displacement during the fifth industrial revolution.
Author-declared methodology: a literature-based critical analysis drawing on novel studies and the existing body of literature; no further methodological details (e.g., inclusion criteria, databases searched) provided in the excerpt.
This study analyzes comments and statements from party members in OECD countries from 2016 to 2025 through content analysis, examining media interviews, speeches, and debates.
Description of the study's data and method: content analysis of party member comments and statements drawn from media interviews, speeches, and debates across OECD countries over the 2016–2025 period (sample size and selection details not reported in the excerpt).
The study contributes to the literature by integrating evidence across higher education, vocational training, and lifelong learning to emphasize the need for balanced policy approaches to skill formation.
Stated contribution in the paper: cross-pathway synthesis of existing empirical evidence and secondary data (methods described as comparative synthesis; no primary empirical contribution reported in the summary).
The study uses secondary data and comparative evidence from prior empirical studies to analyze relationships between higher education, vocational education, and lifelong learning.
Stated methodology in the paper: analysis of secondary data and synthesis of prior empirical/comparative studies (no primary data collection; no sample sizes reported).
This study analyzed survey data from 466 Chinese food delivery riders using structural equation modeling and bootstrapping procedures, modeling work pressure as a mediator and perceived autonomy as a moderator.
Statement in abstract describing sample size (466 Chinese food delivery riders) and analytic approach (SEM and bootstrapping) and modeled variables (work pressure mediator, perceived autonomy moderator).
Drawing on leadership theory, emotional intelligence research and AI ethics informs the proposed framework.
Methodological/design statement in the paper describing its intellectual grounding; indicates literature-based synthesis rather than primary data collection.
The study uses topic modeling on a corpus of over 4,600 academic papers to identify the dominant themes in the economics of AI literature.
Unsupervised topic modeling applied to a compiled corpus of >4,600 papers (authors' described methodology and sample size).
The study's conclusions draw on three complementary evidence bases: (a) task-level evidence on what generative AI can already do in practice; (b) occupational exposure and complementarity analysis using Philippine labor force data; and (c) firm- and worker-level evidence on AI adoption.
Description of methods and data sources in the paper: task-level capability testing/assessment, analysis of national labor force/occupation data for exposure/complementarity, and firm/worker surveys or qualitative adoption evidence.
A “macro approach” that (1) directly models equilibrium behavior of large employers, (2) combines macro data with empirical estimates of employers’ responses (from the micro approach) to estimate the model, and (3) uses the model to compute aggregate costs of monopsony and optimal policies, is the appropriate methodological response.
Methodological proposal set out by the paper; this is a description of the authors' recommended empirical/theoretical strategy rather than an empirical finding. The excerpt contains no implementation details, datasets, or estimation results.
The traditional theoretical and empirical “micro approach” to studying labor market power requires that firms are small and atomistic.
Conceptual/theoretical characterization of the micro approach stated by the paper; no empirical sample, dataset, or formal model provided in the excerpt.
The review focuses on AI applications within small‑scale business environments, with a special focus on women‑owned micro firms in Jaipur, India.
Scope and aim articulated in the paper; geographic and demographic focus explicitly stated by the authors.
The systematic review follows PRISMA 2020 guidelines.
Methodological statement in the paper indicating adherence to PRISMA 2020 for the review process.
After screening and eligibility filtering, 55 open‑access journal articles were included for in‑depth analysis.
PRISMA‑guided screening and eligibility process reported in the review; final included sample explicitly stated as 55 open‑access journal articles.
A Scopus search identified 265 records using keywords related to women’s entrepreneurship and AI.
Systematic literature search reported in the paper following PRISMA 2020; search executed in Scopus with specified keywords; initial yield stated as 265 records.
This study draws on a critical AI media literacy framework to analyze user-generated discussions in the two largest higher education subreddits on Reddit.com.
Author-reported study design: application of a critical AI media literacy theoretical framework to a qualitative dataset consisting of user-generated discussions from the two largest higher-education subreddits. (Sample size/number of posts/threads not specified in the provided excerpt.)
The study used a mixed-methods design incorporating surveys from 150 LEP immigrants, interviews with 50 employers, and interviews with 20 translation service providers in various linguistically diverse U.S. cities, with quantitative analysis performed in SPSS Version 28 and qualitative thematic coding in NVivo 14.
Reported study design and sample: survey n=150 LEP immigrants; employer interviews n=50; translation provider interviews n=20; analytic software specified as SPSS v28 (quantitative) and NVivo 14 (qualitative).
Viable transition pathways are operationally defined in this study as sharing at least 3 skills and achieving at least 50% skill transfer.
Methodological definition stated in the paper used to determine whether a job-to-job transition is considered viable.
We identified 4,534 feasible transitions between jobs in the dataset.
Count of feasible job-to-job transition pairs found in the knowledge graph analysis (4,534 transitions reported).
We constructed and validated a knowledge graph of 9,978 Egyptian job postings, 19,766 skill activities, and 84,346 job-skill relationships with a 0.74% error rate.
Empirical construction and validation of a knowledge graph using a dataset of 9,978 job postings, 19,766 distinct skill/activity nodes, and 84,346 job–skill edges; reported overall error rate 0.74% (validation method not detailed in the excerpt).
The study examines 268 Chinese cities from 2010 to 2023 and integrates theoretical analysis with empirical testing to study AI innovation's employment effects.
Study description specifying sample size (268 cities), period (2010–2023), and combined theoretical and empirical approach.
The study employs an input–output (I–O) modeling framework using IMPLAN 2022 data to estimate direct, indirect, and induced impacts of investments in greenhouse and robotics sectors for Northwest Indiana as part of Project TRAVERSE.
Explicit methodological statement in the paper: use of IMPLAN 2022 I–O model; geographic scope NWI; linkage to EDA Project TRAVERSE.
We extract the Big 5 personality traits from facial images of 96,000 MBA graduates using advances in AI and LinkedIn microdata.
Methodological claim reported in the paper: AI-based model applied to facial images linked to LinkedIn microdata for a sample of 96,000 MBA graduates; extraction yields 'Photo Big 5' trait scores.
The essay reviews seven books from the past dozen years by social scientists examining the economic impact of artificial intelligence (AI).
Qualitative book-review performed by the author; sample size explicitly stated as seven books published within the last ~12 years; method = synthesis/assessment of those seven books.
The study is limited by the scope of available industry data and the generalisability of case study findings.
Explicit limitation reported in the paper summary stating constraints related to industry data availability and generalisability of case studies.
The research adopts a mixed-method approach, combining theoretical analysis with empirical insights, and uses data gathered from the 'AI-driven transformation' Scopus database.
Explicit methodological statement in the paper summary: mixed-method design and Scopus database as the data source. (No further methodological details or sample counts provided in the summary.)
The paper presents relevant tradeoffs and design choices across human-LLM archetypes, including decision control, social hierarchies, cognitive forcing strategies, and information requirements.
Qualitative analysis and discussion in the paper synthesizing insights from the literature review and empirical evaluation. Method: thematic synthesis and design analysis. Sample size: based on the review of 113 papers and the clinical-case evaluation (details in full text).
We describe 17 human-LLM archetypes derived from a scoping literature review and thematic analysis of 113 LLM-supported decision-making papers.
Scoping literature review and thematic analysis method; corpus size = 113 LLM-supported decision-making papers (as reported in the paper).
The paper introduces the concept of human-LLM archetypes, defined as re-occurring socio-technical interaction patterns that structure the roles of humans and LLMs in collaborative decision-making.
Conceptual contribution presented in the paper (definition and framing). Method: theoretical/conceptual description in the manuscript. Sample size: not applicable.
Battery and motor performance were evaluated (in laboratory tests).
Laboratory tests assessing battery and motor performance are reported in the methods/results; no quantitative battery/motor metrics provided in the summary.
A composite index capturing concerns about mental health, privacy, climate impact, and labor market disruption was constructed to measure societal risk perceptions of AI.
Author-constructed composite index derived from survey items on mental health, privacy, climate, and labor market disruption concerns in the 2023–2024 UK survey.
The analysis is framed through the integrated lens of the Technology-Organization-Environment (TOE) framework and Institutional Theory to provide a multi-faceted understanding of adoption dynamics.
Stated theoretical framing and analytical approach in the study (methodological claim).
The research synthesizes evidence from a wide array of sources, including recent academic literature by Nigerian scholars, NPA official performance reports, policy documents, and international trade facilitation reports (e.g., UNCTAD).
Explicit description of data sources in the study methodology; method: secondary data synthesis (no sample size applicable).