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).
Browse by theme
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 study examines the impact of AI technologies on Uzbekistan's labor market transformation in the context of implementing the national strategy 'Digital Uzbekistan - 2030' and the Strategy for the Development of AI Technologies until 2030.
Framing and scope statement in the paper; analysis based on national strategy documents, statistical data, industry reviews, and regulatory legal documents.
Identification of effects uses within-firm variation with firm and city-by-year fixed effects.
Identification strategy reported in abstract: within-firm variation under firm and city-by-year fixed effects.
The study measures four skill-category demand shares and their within-category importance from job-description text.
Methodological statement in abstract: measurement of four skill-category demand shares and within-category importance via job-description text.
AI exposure is decomposed into displacement and augmentation components based on task routineness.
Methodological claim in abstract: decomposition of exposure into displacement and augmentation using a routineness criterion for tasks.
The authors construct firm-by-year potential AI exposure via semantic matching between AI patent texts and detailed occupation task descriptions.
Method description in abstract: semantic matching of AI patent texts to occupation task descriptions to build firm-by-year exposure.
The study uses approximately 67 million online job postings from two major Chinese recruitment platforms (2019–2024).
Statement in paper abstract describing dataset size and source (job postings from two major Chinese recruitment platforms over 2019–2024).
The paper draws on empirical studies from 2024–2026.
Methodological statement in the paper specifying the time window of empirical studies used in the analysis.
Skills can be mapped into three categories: those AI is absorbing, those needed to work alongside AI today, and those that make humans irreplaceable tomorrow.
Conceptual taxonomy offered in the chapter, based on labour market data and workplace evidence; presented as an analytical framework rather than a quantified finding.
Fear and hype about technological transitions are temporary.
One of five lessons drawn from historical analogy and labour market history as presented in the chapter.
Virtually every job is being touched by AI.
Stated in chapter summary; claimed on the basis of labour market data and emerging workplace evidence (no numeric sample given in excerpt).
Only 9% of jobs are fully automatable.
Reported directly in chapter; based on labour market data (specific data source and sample size not stated in the excerpt).
AI automates tasks, not jobs.
Conceptual argument in chapter drawing on labour market data and historical analogy; presented as a framing claim rather than a specific empirical estimate.
Higher sectoral digitalization potential (telework feasibility and digital intensity) does not significantly affect aggregate employment levels.
Difference-in-differences (DiD) analysis using the COVID-19 shock as a quasi-natural experiment on a quarterly panel for 27 EU Member States (2018–2024), N = 36,685; reported DiD coefficient = 0.06, p ≈ 0.98.
Including the 2020-2021 COVID-19 lockdowns allows leveraging the pandemic to isolate structural inequalities from transient market shocks.
Design choice: use of data spanning 2016–2021, including pandemic lockdown period, to separate persistent structural disparities from short-term shock effects.
The findings are consolidated via the AI Engineering Integration Framework and the Skills Transition Risk Matrix, which provide guidelines for strategically harnessing AI while safeguarding the Engineering profession.
Paper reports development of two conceptual/practical tools (framework and matrix) as outputs of the study; no validation details provided in abstract.
Case studies were performed covering five major industries.
Paper's reported methodology (number of case studies stated in abstract).
A Delphi study was conducted with 40 global experts.
Paper's reported methodology (Delphi sample explicitly stated in abstract).
A comprehensive mixed-methods study was conducted, incorporating a survey of 320 organizations.
Paper's reported methodology (survey sample explicitly stated in abstract).
Persistent data gaps—especially concerning worker-level outcomes, informal labor, and non-Anglophone markets—warrant urgent research investment.
Authors' assessment based on scope of included studies and acknowledged limitations in observation windows and geographic/labor-form coverage.
Following PRISMA 2020 guidelines, we systematically searched six academic databases (Scopus, Web of Science, EconLit, SSRN, IEEE Xplore, Google Scholar) for empirical studies documenting observed—not predicted—labor market changes since 2020; from 1,847 initial records, 94 studies meeting inclusion criteria were retained for qualitative synthesis and 42 for quantitative data extraction.
Methods: systematic literature search following PRISMA 2020 across six named databases; initial records = 1,847; retained = 94 for qualitative synthesis, 42 for quantitative extraction.
We thematically analysed twelve semi-structured interviews with SME owners and managers conducted in early 2025 using Atlas.ti, yielding 19 codes grouped into six categories.
Methods statement in the paper describing qualitative sample and analysis procedures.
We examine the interplay between AI adoption, social capital formation, workforce dynamics, and sustainable development in Eastern Macedonia and Thrace (EMT), one of the EU's least developed regions.
Study context and scope as stated in the paper; empirical work conducted in EMT.
Research has concentrated on advanced urban economies, leaving the implications of AI for peripheral small and medium-sized enterprises (SMEs) operating under weak human capital, thin digital infrastructure, and constrained social capital — underexplored.
Statement in the paper contrasting existing research focus (advanced urban economies) with a lack of attention to peripheral SMEs; no empirical sample size for this bibliographic claim reported in the excerpt.
The model is not designed to forecast labour market outcomes or to conduct counterfactual tests.
Explicit methodological limitation stated in the abstract regarding scope of the simulation/model.
Using data from the Occupational Information Network (O*NET), integrated with two exposure measures—routine task automation and AI-driven cognitive automation—we simulate how the removal of 332 tasks alters skill requirements across 736 occupations.
Simulation study using O*NET data combined with two task-exposure measures (routine task automation and AI-driven cognitive automation); simulated removal of 332 tasks affecting 736 occupations (method described in abstract).
The paper constructs a firm-level measure of AI development using AI-related patent data from Chinese listed firms.
Descriptive/method section: AI-related patent data from Chinese listed firms used to construct a firm-level AI development measure.
The analysis uses over 23 million WIOA participation records from 2017–2023.
Statement in the paper about the data coverage: administrative records of WIOA participants totaling >23 million records across 2017–2023.
The paper introduces the 'Retrainability Index' to measure program outcomes using post-intervention wage recovery and shifts in Routine Task Intensity (RTI).
Methodological contribution described in the paper: formulation of a composite index (Retrainability Index) combining wage recovery and occupation RTI change to evaluate WIOA outcomes.
There is little empirical exploration of how professionals making high-stakes decisions perceive their agency and level of control when working with genAI systems.
Statement about a gap in the existing literature made by the authors (literature review / framing); no sample size (gap claim).
AI adoption has no detectable effects on overall employment.
Difference-in-differences estimates using administrative employment totals linked to survey-reported adoption show no statistically significant change in total employment.
As of 2024, AI adoption remains limited: about 10 per cent of firms report current use.
Newly collected firm-level survey data linked to administrative balance sheet and employer–employee records; prevalence reported in 2024 survey.
Methodological basis: the study used analysis of aggregated industry data and a scenario approach; information sources were Russian-language materials including the Ministry of Digital Development, HSE, the Autonomous Non-Profit Organization 'Digital Economy', and analytical reviews.
Explicit methodological and data-source statements in the paper.
Fears of AI automation do not primarily increase support for traditional interventions such as unemployment benefits and training programs.
Comparative analysis of policy preference responses in the 2024 OECD 'Risks that Matter' survey as reported in the paper.
At this stage, AI adoption in Israel does not result in widespread layoffs; its primary impact lies in restructuring the labor market through a slowdown in recruitment, changes in job composition, and the emergence of new AI-related roles.
Empirical claim reported in the paper; the excerpt does not specify datasets, time periods, or sample sizes supporting this observation.
The analysis employs rigorous econometric methods including difference-in-differences estimation and propensity score matching to control for confounding variables across industry (NAICS 2-digit), firm size, geographic location, occupation-level characteristics, and macroeconomic conditions.
Methodological description in the paper specifying DiD and propensity score matching and listed covariates/controls.
The study uses U.S. Census Bureau Business Trends and Outlook Survey data tracking over 1.2 million businesses.
Paper statement that it incorporates the Census Bureau Business Trends and Outlook Survey covering >1,200,000 businesses.
The analysis integrates the Anthropic Economic Index capturing approximately one million AI usage interactions.
Paper statement that the Anthropic Economic Index was used and captures ~1,000,000 AI usage interactions.
Overall, robot exposure is only weakly related to job-quality outcomes once controls and fixed effects are included.
Individual-level data from the European Working Conditions Telephone Survey (EWCTS) 2021 merged with country–industry robot exposure measures from International Federation of Robotics (IFR) statistics; weighted logistic regression models including individual and job controls and country and industry fixed effects.
There is no decrease in coding skills among new hires associated with GHC adoption.
Comparison of coding-skill indicators on LinkedIn profiles for new hires at GHC-adopting firms versus non-adopting firms; finding of no measurable decline in coding-skill measures.
The paper proposes a conceptual framework linking AI adoption to employability and role transformation, mediated by skill adaptation, continuous learning, and organizational readiness.
Author-proposed conceptual framework presented in the review paper (theoretical linkage based on literature synthesis).
This study takes food delivery riders as the research object and analyzes the dilemma of labor relations determination under AIGC.
Methodological statement in the paper specifying the chosen subject of analysis (food delivery riders); this is an explicit description of the paper's scope rather than an empirical finding.
The paper develops an interdisciplinary conceptual framework that integrates insights from economics, management theory, and digital governance to characterize algorithmic enterprises.
Methodological claim about the paper's approach; stated in abstract as the paper's contribution (conceptual framework built from interdisciplinary literature).
Future research should strengthen cross-national comparisons, longitudinal tracking, and interdisciplinary collaboration to support development of a technology governance framework that balances efficiency with equity.
Author recommendation based on identified research gaps in the literature review (prescriptive/recommendation).
Existing research has clear gaps: limited evidence from developing-country contexts, insufficient attention to within-occupation heterogeneity, incomplete accounts of psychological mechanisms underlying AI anxiety, and a shortage of rigorous evaluations of reskilling policy effectiveness.
Author's assessment based on the reviewed literature identifying thematic gaps and methodological limitations (critical literature review).
The paper synthesizes sector-specific insights across manufacturing, information technology, healthcare, and finance to examine AI's influence on task automation, job augmentation, and skill requirements.
Descriptive claim about the scope of the review (sectors named in the abstract); no breakdown of sectoral evidence or counts provided in the abstract.
There is a lack of comparative sectoral assessments and standardized risk evaluation frameworks in the literature.
Identified research gap reported by the authors from their systematic review (no counts or formal gap-analysis metrics provided in the abstract).
A structured methodology (systematic review) was adopted to identify literature on AI-driven job transformation and associated employment risks using major academic databases.
Methodological statement in the paper claiming a systematic review approach (specific databases, search terms, inclusion/exclusion criteria and number of studies are not reported in the abstract).
The staggered expansion of Turkey's national natural gas pipeline network provides plausibly exogenous variation in connectivity because pipeline routing is determined by energy distribution priorities rather than digital demand.
Identification strategy described by the authors: using pipeline expansion as an instrument/conduit for fiber-optic deployment; argument rests on institutional routing rules and timing.
The goal is not to identify causal effects, but to document stylized facts about how technology changes the scale of asset management work.
Author's stated research objective in the paper's summary/introduction (explicitly notes descriptive, not causal, intent).
Using a small panel of representative firms, we compare changes in AUM per employee, revenue per employee, and operating expense intensity over time.
Stated empirical approach: analysis of a small panel of representative firms comparing three metrics (AUM/employee, revenue/employee, operating expense intensity) over time. The excerpt notes panel is 'small' but gives no numeric sample size or firm list.