Evidence (3224 claims)
Adoption
7395 claims
Productivity
6507 claims
Governance
5877 claims
Human-AI Collaboration
5157 claims
Innovation
3492 claims
Org Design
3470 claims
Labor Markets
3224 claims
Skills & Training
2608 claims
Inequality
1835 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 609 | 159 | 77 | 736 | 1615 |
| Governance & Regulation | 664 | 329 | 160 | 99 | 1273 |
| Organizational Efficiency | 624 | 143 | 105 | 70 | 949 |
| Technology Adoption Rate | 502 | 176 | 98 | 78 | 861 |
| Research Productivity | 348 | 109 | 48 | 322 | 836 |
| Output Quality | 391 | 120 | 44 | 40 | 595 |
| Firm Productivity | 385 | 46 | 85 | 17 | 539 |
| Decision Quality | 275 | 143 | 62 | 34 | 521 |
| AI Safety & Ethics | 183 | 241 | 59 | 30 | 517 |
| Market Structure | 152 | 154 | 109 | 20 | 440 |
| Task Allocation | 158 | 50 | 56 | 26 | 295 |
| Innovation Output | 178 | 23 | 38 | 17 | 257 |
| Skill Acquisition | 137 | 52 | 50 | 13 | 252 |
| Fiscal & Macroeconomic | 120 | 64 | 38 | 23 | 252 |
| Employment Level | 93 | 46 | 96 | 12 | 249 |
| Firm Revenue | 130 | 43 | 26 | 3 | 202 |
| Consumer Welfare | 99 | 51 | 40 | 11 | 201 |
| Inequality Measures | 36 | 105 | 40 | 6 | 187 |
| Task Completion Time | 134 | 18 | 6 | 5 | 163 |
| Worker Satisfaction | 79 | 54 | 16 | 11 | 160 |
| Error Rate | 64 | 78 | 8 | 1 | 151 |
| Regulatory Compliance | 69 | 64 | 14 | 3 | 150 |
| Training Effectiveness | 81 | 15 | 13 | 18 | 129 |
| Wages & Compensation | 70 | 25 | 22 | 6 | 123 |
| Team Performance | 74 | 16 | 21 | 9 | 121 |
| Automation Exposure | 41 | 48 | 19 | 9 | 120 |
| Job Displacement | 11 | 71 | 16 | 1 | 99 |
| Developer Productivity | 71 | 14 | 9 | 3 | 98 |
| Hiring & Recruitment | 49 | 7 | 8 | 3 | 67 |
| Social Protection | 26 | 14 | 8 | 2 | 50 |
| Creative Output | 26 | 14 | 6 | 2 | 49 |
| Skill Obsolescence | 5 | 37 | 5 | 1 | 48 |
| Labor Share of Income | 12 | 13 | 12 | — | 37 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Labor Markets
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A variance decomposition indicates that most expert disagreement about long-run macroeconomic outcomes is driven by differing beliefs about the economic effects of highly capable AI, rather than disagreement about the pace of AI capability progress.
Authors' variance-decomposition analysis of survey responses separating components due to beliefs about AI capabilities vs. beliefs about economic effects given capabilities (methodological details referenced but not provided in excerpt).
The paper addresses three institutional audiences: enterprise finance and operations teams; government and regulatory bodies developing AI labor displacement frameworks; and financial markets requiring a machine labor index as a long-duration economic signal.
Stated intended audiences in the paper (descriptive statement).
The framework is calibrated with O*NET task data, a survey of 3,778 domain experts, and GPT-4o-derived task decompositions, and implemented in computer vision.
Calibration and empirical implementation using O*NET, a domain expert survey (n=3,778), and GPT-4o task decompositions; applied to computer vision tasks.
We introduce an entropy-based measure of task complexity that maps model accuracy into a labor substitution ratio, quantifying human labor displacement at each accuracy level.
New metric proposed in the paper (entropy-based task complexity) and mapping procedure from accuracy to substitution ratio; implemented in the framework.
Costinot and Werning (2023) develop a sufficient-statistic approach and find optimal technology taxes of 1–3.7% on robots.
Citation reported in the paper summarizing Costinot and Werning (2023)'s quantitative sufficient-statistic estimate.
Guerreiro et al. (2022) characterize optimal Mirrleesian tax system with automation and find that robot taxes should be transitional—high when incumbent workers cannot retrain, converging to zero as new cohorts adjust skill investments.
Citation reported in the paper summarizing Guerreiro et al. (2022)'s theoretical result on transitional robot taxes.
If labor becomes economically redundant, the policy focus shifts from steering innovation to redesigning public finance and redistribution (e.g., new tax instruments, redistribution mechanisms).
Theoretical scenario analysis in the paper with references to related works (Korinek and Juelfs 2024; Korinek and Lockwood 2026).
We critically compare LLM-generated rulings against 10,000 real-world court judgments from China Judgments Online (CJOL).
Dataset statement: the paper compares model outputs to a corpus of 10,000 CJOL labor dispute judgments.
We introduce a novel stress test that evaluates LLM-generated labor dispute outcomes by injecting social media sentiment as an external pressure.
Methodological description in the paper: a designed stress test where social media sentiment is used to perturb LLM outputs for labor dispute cases.
Economic evaluations of GLAI should account for end-to-end risk externalities (error propagation, institutional trust, rights impacts), not only short-term productivity gains.
Methodological recommendation grounded in conceptual synthesis of technical, behavioral, and legal risks; normative argument rather than empirical result.
Generative Legal AI (GLAI) systems are built on token-prediction (LLM) architectures rather than formal legal-reasoning architectures.
Conceptual and technical analysis in the paper distinguishing GLAI from other legal-tech; literature synthesis on common LLM architectures. No original empirical dataset or sample size—qualitative/technical review.
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.
This project studies how much labor is required to manage capital across those waves by tracking a simple productivity measure: assets under management per employee.
Stated research design: longitudinal tracking of assets under management (AUM) per employee as the primary productivity measure; described in the paper's methods/summary. No numeric sample size provided in the excerpt.
Financial firms have gone through three major technological waves: computerization in the 1980s and 1990s, the rise of indexing and passive investing in the 2000s and 2010s, and the AI and automation wave from roughly 2015 to the present.
Author's historical categorization stated in the paper's introduction/summary (time periods specified). No sample or empirical test reported in the excerpt.