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 review proposes possible indicators for future empirical research, including the productivity–real labour income gap and an absorption tension indicator.
Paper's methodological/propositional content (explicitly proposes indicators for empirical work).
The paper defines the 'Distributional Absorption Threshold of AI-Induced Productivity' as the point beyond which productivity gains associated with AI are no longer accompanied by proportionate increases in broadly distributed real purchasing power and household consumption.
Textual/definitional content of the conceptual review (the paper introduces and defines this concept).
There is no evidence of a sizeable effect on wages following return from cross-border employment.
Chapter 4: wage outcomes examined in linked Belgian administrative registers comparing returnees and stayers; reported null/insignificant effects on wages.
Automation AI has no significant effect on aggregate Bachelor graduations.
Chapter 3: aggregate graduation margin analyzed for automation AI exposure using IV (lagged CS research intensity) on U.S. data 2010–2022; reported null/insignificant estimates.
This systematic literature review (SLR) synthesizes empirical studies concerning AI and the implications of these changes on labor skills across all sectors between 2017 and 2025.
Methodological claim in the paper describing its scope and timeframe (SLR of empirical studies, 2017–2025). No numeric count of included studies provided in the excerpt.
The study uses a pragmatic research philosophy and conducts a qualitative scoping review following the framework of Arksey and O’Malley.
Methodological statement in the paper (explicit).
The framework can be operationalized in future empirical research (the article outlines directions for operationalizing the framework).
Methodological/research agenda claim stated in the article's conclusion; describes future empirical operationalization rather than presenting results.
The paper derives tight conditions that determine whether the economy is partially versus fully automated in the long run.
Analytical characterization in the model: derivation of necessary and sufficient (tight) conditions separating long-run partial automation from full automation (mathematical proofs within the paper).
Data accumulates endogenously as a byproduct of economic activity.
Model assumption and mechanism in the theoretical dynamic model: data generation is modeled as an endogenous outcome of agents' economic activity (analytical model specification).
Data is heterogeneous and task-specific.
Model assumption stated in the paper's setup: the model is built with data that varies across tasks and is task-specific (analytical model specification).
The analysis covers 83,000 employed graduates.
Stated sample size in the paper (subset of PLFS 2025: employed graduates = 83,000).
We map three occupational AI-exposure indices to India's redesigned Periodic Labour Force Survey (2025).
Author description of methods: mapping three occupational AI-exposure indices onto the (redesigned) Periodic Labour Force Survey (PLFS) 2025.
The six middle macros form a low-contrast band between the poles; equivalence testing (TOST at d = 0.2) admits only 1 out of 15 macro-pair comparisons as equivalent.
Authors' analysis of pairwise macro comparisons using Two One-Sided Tests (TOST) for equivalence at Cohen's d = 0.2.
We decomposed 1,961 O*NET Detailed Work Activities (DWAs) into 15,817 micro-actions using a multi-agent LLM pipeline with 31-expert human-in-the-loop (HITL) calibration.
Empirical method reported by the authors: automated multi-agent LLM pipeline plus 31-expert HITL calibration producing the stated counts (1,961 DWAs -> 15,817 micro-actions).
Empirical research since Frey and Osborne (2017) has converged on a continuous-gradient representation in which each occupation is assigned a real-valued exposure score on [0,1] obtained by linear aggregation across capability dimensions.
Literature synthesis / statement in the paper referencing Frey and Osborne (2017) and subsequent empirical work using continuous exposure scores.
The findings provide empirical insights for managing employee wellbeing and refining human resource strategies during organizational digital transformation.
Authors' stated implications in the discussion, based on the reported empirical associations and moderation results from the survey of 411 employees.
The study draws on the Conservation of Resources Theory and the Cognitive Appraisal Theory of Stress to explain how AI application influences employees' job insecurity via resource gain and resource threat mechanisms.
Theoretical framing stated in the introduction and discussion explaining the mechanisms (resource gain vs. resource threat) underlying the observed U-shaped association.
Data were collected via mixed online and offline questionnaires: 453 questionnaires were distributed (242 online, 211 offline); 449 were returned (242 online, 207 offline); following validity screening, 411 valid questionnaires were retained (219 online, 192 offline), yielding an effective response rate of 90.73%.
Reported survey administration and response counts provided in the methods section of the paper.
The model frames near-complete AGI substitution not merely as an efficiency transition but as a boundary case for value production under a strict political-economy theory of value.
Interpretive conclusion drawn from the theoretical model and its limiting-case implications (conceptual/theoretical claim; no empirical sample).
Under the paper's core value-theoretic assumption, AGI transfers value but does not itself create new value.
Explicit model assumption / value-theoretic premise stated in the paper (theoretical assumption, no empirical backing).
The paper distinguishes technical substitutability (the feasible replacement ceiling implied by AGI capability) from actual adoption (the realized replacement share chosen under cost, profitability, and adoption frictions).
Conceptual/theoretical definition introduced in the political-economy model (no empirical sample; definitional argument within the paper).
Future research should adopt a more intersectional approach exploring how race, class, and geography interact with gender to shape platform work experiences.
Research limitations and implications section of the paper recommends more intersectional research directions.
This paper conducted a systematic literature review and thematic synthesis of 48 peer‑reviewed studies (2010–2024) to analyze the gendered dynamics of AI‑mediated digital labor.
Methods statement in the paper: systematic literature review and thematic synthesis; explicitly reports reviewing 48 peer‑reviewed studies covering 2010–2024.
There is a need to examine the impacts of LLM on workers in jobs where the technology is prominent.
Recommendation in the paper's conclusion based on the observed concentration of LLM exposure in lower-precarity occupations.
These occupations (those with higher LLM exposure and lower precariousness) have previously been sheltered from technological change.
Statement in the paper's conclusion asserting that occupations with higher LLM exposure are ones historically sheltered from technological change (no specific empirical evidence provided in abstract).
The study used Canada's Labour Force Survey, developed a multidimensional index summarizing occupational exposure to precarity (contractual instability, earnings inadequacy, schedule unpredictability, working-time mismatch), and estimated associations using four multivariate linear regression models with cluster-robust standard errors plus a fifth model for the multidimensional index.
Methods description in abstract specifying data source (Canada's Labour Force Survey), index construction, and multivariate linear regression models with cluster-robust standard errors.
The article aims to provide systematic literature support for subsequent research and adaptive policy formulation.
Statement of the paper's stated objective; methodological and policy-intent claim from the authors.
This article is based on a systematic literature review and summarizes the four core theoretical mechanisms of substitution, complementarity, new task creation, and skill mismatch.
Methodological claim from the paper: the authors conducted a systematic literature review and identified these four theoretical mechanisms.
Experts rated 24 AI risks on harm probability and severity, sector and actor vulnerability, actor responsibility, and overall concern.
Study design described in paper: set of 24 defined AI risks rated across several dimensions by Delphi panel participants (n=272).
We conducted a three-round Delphi study conducted late 2025 with 272 international AI experts.
Methodological description in the paper: three-round Delphi study, timing reported as late 2025, sample size reported as 272 international AI experts.
Total (aggregate) unemployment is statistically insignificant in explaining sustainable development, indicating aggregate measures mask critical distributional differences across skill groups.
ARDL estimation results reported in the paper showing an insignificant coefficient for total unemployment; discussion emphasizing distributional masking.
Explicit commercial content (product placement) shows no engagement premium (−3.8%, not significant).
Analysis comparing videos labeled for explicit commercial content (product placement) to others; reported percent difference and non-significance.
We conducted a multimodal AI audit of 5,051 videos across 79 kidfluencer channels using weak supervision (LLM-based classification of titles and GPT-4 Vision analysis of thumbnails and descriptions across six literature-grounded dimensions) to assign a probabilistic exploitation score to each video.
Described dataset and methods in paper: multimodal automated pipeline combining weak supervision labeling functions (LLM classifiers on titles, GPT-4 Vision on thumbnails/descriptions) applied to 5,051 videos from 79 channels.
New York City’s Local Law 144 mandates annual bias audits to increase transparency.
Statement of law/policy in paper (factual claim about NYC Local Law 144); legal requirement as described in the text.
The fairness of AI-enabled hiring systems remains uncertain.
Statement in paper (background/interpretive claim); no direct empirical measure provided in the excerpt.
We leverage logo design job posts before and after the launch of an early-stage platform-embedded logo-AI tool on the online labour market EPWK, using a difference-in-differences design and a new large language model-based skill extraction and embedding framework.
Paper's described empirical design and methods: dataset of logo design job posts on EPWK around the logo-AI tool launch; difference-in-differences analytic approach; LLM-based skill extraction and embedding pipeline. No sample size provided in the abstract.
Existing research mainly examines general-purpose GenAI, such as ChatGPT, and focuses on aggregate outcomes, including falling demand and compressed prices in easily automated tasks, while revealing little about the demand for work skills and the role of platform-embedded GenAI.
Paper's literature review / background statement summarizing prior empirical work on general-purpose GenAI (e.g., studies documenting falling demand and price compression in automatable tasks). No sample size reported in this statement.
The empirical strategy uses panel local projections to estimate the dynamic effects of AI adoption.
Methodological statement in the paper: application of panel local projections to panel data of industries/establishments over 2017-2025.
AI adoption is measured using the share of establishment-level job postings that explicitly require AI-related skills across 13 industries over 2017-2025.
Study design / data description: share of establishment-level job postings requiring AI skills; coverage across 13 industries for years 2017-2025.
This paper uses the Difference-in-Differences method for empirical research.
Methodological statement in the excerpt explicitly naming the DiD approach.
The paper proposes a policy framework consisting of six groups of solutions for Vietnam to both promote AI development and control risks in the digital age.
Declared in abstract: the paper presents a six-group policy framework for Vietnam; the framework itself is the paper's output (proposal), not empirically tested in the paper.
This study employs document synthesis and comparative analysis of international policies.
Methodological statement in the paper abstract describing the research approach; no sample size specified beyond document sources.
The rise of artificial intelligence (AI) is shaping a new Agent Economy (AE), in which autonomous AI agents represent humans in performing a wide range of complex tasks.
Statement in paper abstract/intro (conceptual definition); no empirical data or sample reported.
Outputs are graded by a fact-anchored chain of rubrics, averaging 35.6 binary criteria per task.
Benchmark grading methodology reported by the authors, with a reported average of 35.6 binary criteria per task (presumably calculated across the benchmark tasks).
JobBench covers 130 agentic tasks across 35 occupations.
Dataset/benchmark composition reported by the authors (explicit counts provided in the paper).
The analysis is structured across past, present, and future phases using an integrative socio-technical political economy framework and validated secondary sources (OECD, ILO, UNDP, WTO, WEF) alongside official Indian statistics and sector evidence.
Methodological claim stated in abstract describing the approach and data sources used in the paper (OECD, ILO, UNDP, WTO, WEF, MoSPI/NSO, PLFS, HCES, Reuters, Nasscom).
The study uses 5 million job postings from Beijing covering 2018--2024 as its primary data source.
Stated dataset scope and size in the paper's description of data.
We construct a neighborhood-level GenAI Exposure Index by aggregating task-level assessments from five leading large language models.
Methodological construction described in the paper: task-level GenAI suitability assessments from five LLMs applied to tasks in 5 million Beijing job postings (2018--2024), aggregated to the neighborhood level.
Identification strategy exploits import lumpiness in product categories linked to automation technologies (including robots) to disentangle adoption effects from selection into adoption.
Methodological claim: use of import 'lumpiness' in automation-related product categories as a plausibly exogenous source of adoption variation within a difference-in-differences framework.
We integrate datasets on trade activities, firm, and worker characteristics for the population of Italian importing firms from 2011 to 2019.
Data integration described in abstract; population-level administrative datasets on trade, firm, and worker characteristics for Italian importing firms covering years 2011–2019.