Evidence (3308 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 |
Skills Training
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Overeducation leads to a significant wage penalty.
Microdata from the China Labor-force Dynamics Survey (CLDS) 2014–2018; cohort-based measure of educational mismatch; estimated using extensive fixed-effects models comparing wages by educational mismatch status.
Lagged AI-related R&D activity is negatively associated with subsequent participation in education and training (one-year lag).
Lagged (one-year) structural panel models (two-way fixed effects) on 18 European countries, 2017–2024. One-year lag coefficients reported as −1.2310 (ages 18-74), −0.9392 (ages 45-54), and −0.8911 (ages 50-74).
Average lifelong-learning participation declines with age: 20.09% among adults aged 18-74, 14.82% among those aged 45-54, and 9.34% among those aged 50-74.
Descriptive statistics computed from the study panel of 18 European countries (2017–2024).
The essay introduces the concept of a 'vouching gap' to describe a growing divide between students who graduate with credible advocates willing to stake their reputations on their behalf and those who do not.
Conceptual contribution defined in the essay and motivated by social capital theory and mentoring research; no empirical quantification or sample provided.
Automation of student work and candidate screening will widen existing inequalities between students.
Theoretical claim in the essay linking AI-driven automation to differential outcomes across students, motivated by social capital and mentoring literature; no empirical data or sample reported.
This automation threatens to hollow out the value of a university degree.
Argument presented in the essay, grounded in social capital theory and mentoring research; no empirical test or sample size reported.
The demand premium enjoyed by workers with strong human capital declines in more AI-exposed categories.
Heterogeneity analysis within the Upwork dataset: workers characterized by stronger human-capital signals (via profile embeddings) show a reduced demand premium in job categories more exposed to AI following ChatGPT; identified using difference-in-differences around ChatGPT release. (Sample size not reported in abstract.)
In more AI-exposed job categories, the importance of human capital information in predicting labor demand declines.
Empirical analysis of Upwork platform data using high-dimensional text embeddings to represent worker profiles; the paper computes the predictive importance of human-capital-related profile information and uses a difference-in-differences design around the release of ChatGPT to estimate changes by AI exposure of job categories. (Sample size not reported in abstract.)
Automation AI raises program closures and reduces new program openings.
Chapter 3: program-supply analysis (program closures and openings) using U.S. higher-education program data 2010–2022 with IV identification (lagged CS research intensity); reported associations for automation AI exposure.
Automation AI is associated with a greater likelihood of not pursuing postgraduate studies and with higher rates of field-switching after graduation.
Chapter 3: individual-level analyses of post-graduation decisions (postgraduate enrollment and field-switching) using U.S. data 2010–2022 and IV with lagged CS research intensity.
In those European countries, demand for Social skills declines in AI-exposed occupations.
Chapter 2: same 75 million job postings dataset, multilingual skill extraction, and IV approach with lagged CS research intensity to identify effects on skill demand between 2018–2023.
Automation AI harms low-skilled workers.
Chapter 1: heterogeneous effects across skill groups estimated using occupational exposure measures and IV approach (lagged CS research intensity); results reported by skill group (low-skilled vs high-skilled).
Automation AI depresses wages in the U.S.
Chapter 1: same occupational exposure measures and IV strategy (lagged computer-science research intensity) applied to U.S. wage data, 2015–2022.
At the macro level, values-driven withdrawal from AI use has the potential to narrow the diversity of visible applications, amplifying risk-focused narratives and reinforcing perceptions of harm in public discourse.
Theoretical extension of the guarded engagement loop to societal/public discourse dynamics; based on synthesis of social amplification of risk literature rather than empirical measurement in the abstract.
These constrained (guarded) interactions can lower output quality and increase the likelihood of visible errors, which may further erode trust and reinforce cautious engagement.
Theoretical causal chain posited by the authors within their conceptual framework; supported by literature-based argumentation rather than reported empirical results in the abstract.
At the micro level, elevated risk salience related to privacy, safety, or ethical concerns may lead users to adopt guarded interaction strategies characterized by reduced contextual disclosure and limited iteration.
Theoretical proposition within the paper's guarded engagement loop framework, drawing on prior research in privacy calculus and algorithm aversion; no specific empirical data reported in the abstract.
Generative AI adoption is often framed primarily as a question of learning technical skills, and this perspective overlooks a defining feature of large language models (LLMs): their output quality depends heavily on how users engage with them.
Conceptual argument presented in the paper's introduction/abstract; literature synthesis framing adoption debates (no empirical sample or experimental method reported in the abstract).
Expertise moderated the effect of LLM guidance: novices exhibited passive AI reliance.
Stratified analyses by participant expertise level using behavioral and eye-tracking measures indicating novices shifted attention to the AI/chat and exhibited more passive acceptance of guidance.
Investment is being directed toward AI deployment when achieving productivity gains requires prior development of convergence capacity (C), leading to a misallocation of investment.
Theoretical reasoning within the paper: conceptual argument that deployment-focused spending misses prerequisite cognitive capacity (C).
Prevailing production-function frameworks encounter a structural boundary because they treat AI as a separable factor of production without modeling the cognitive mediation through which AI generates productive value.
Theoretical / conceptual argument presented in the paper (derivation and critique of existing production-function approaches).
Massive AI investment has failed to generate commensurate productivity gains (the "AI productivity paradox").
Stated as the motivating empirical paradox in the paper; presented as an observed phenomenon motivating the theoretical argument (no specific dataset or numeric evidence provided in the abstract).
There are barriers and challenges that the labor force faces in meeting new skill requirements.
Review conclusion noting barriers and challenges reported in the empirical literature (types of barriers not enumerated in the excerpt; no measures or prevalence reported).
Automation reduces employment-based tax revenue and increases public financial pressure.
Explicit finding reported in paper; derived from the scoping review of existing literature (method: qualitative scoping review following Arksey & O'Malley). No quantitative sample or meta-analysis size reported in the abstract.
Automation often displaces workers without adequate retraining, leading to unemployment and reduced income tax contributions, which worsens income inequality.
Statement in paper's purpose/intro; synthesized from the literature via a qualitative scoping review (framework of Arksey & O'Malley). No primary empirical sample size reported in the abstract.
AI adoption is significantly hampered by a lack of workforce skills and supporting infrastructure in these accounting organizations.
Qualitative interview findings and questionnaire responses synthesized via thematic analysis and inferential/statistical analysis (sample size not reported).
Accounting organizations in the study are still in the early stages of AI adoption.
Synthesis of questionnaire and interview findings with thematic analysis indicating limited breadth/depth of AI use (sample size not reported).
AI is used mainly for repetitive and routine accounting tasks, with very little use for higher-level work.
Questionnaire responses and interview data summarized with descriptive statistics and thematic analysis (sample size not reported).
Unequal access to GenAI tools in higher education may exacerbate employability gaps and inequities among students.
Concern identified and discussed in the literature as summarized in the review article (conceptual/literature-based; no new empirical evidence reported).
GenAI raises concerns including passive dependence, weakened critical thinking, uncertain authorship, academic integrity breaches, algorithmic bias, unequal access, and employability gaps.
Synthesis of concerns reported in prior studies and discussions in the literature (review article); no new empirical data provided.
Penerapan AI menimbulkan isu etika dan keamanan data yang memerlukan tata kelola AI yang bertanggung jawab.
Sistematis studi literatur yang menelaah 33 sumber ilmiah, laporan lembaga internasional, dan kebijakan terkait (n=33).
AI meningkatkan risiko pengangguran pada sektor yang pekerjaannya bersifat rutin.
Sistematis studi literatur yang menelaah 33 sumber ilmiah, laporan lembaga internasional, dan kebijakan terkait (n=33).
Penerapan AI menyebabkan kesenjangan keterampilan (skill gap) antara kebutuhan pasar dan kemampuan tenaga kerja.
Sistematis studi literatur yang menelaah 33 sumber ilmiah, laporan lembaga internasional, dan kebijakan terkait (n=33).
Current research on AI-supported conflict techniques has focused predominantly on Devil's Advocate (DA) and has neglected Dialectical Inquiry (DI).
Literature review / gap statement in the paper pointing to relative emphasis on DA in prior research and lack of work on DI.
Interviews provide expanded analysis on existing skill gaps and lifelong learning needs among wind-energy professionals.
Qualitative interview data are reported to highlight skill gaps and lifelong learning needs; specific counts of interviewees not provided in the summary.
Reporting on ethics, transparency and governance was inconsistent.
Reported synthesis result from the scoping review noting variability in reporting practices across included studies.
Formal theory use was limited, with only a small minority of studies explicitly drawing on established frameworks.
Authors' assessment of methodological/theoretical characteristics of the included empirical studies in the scoping review.
Fewer studies evaluated individual-facing developmental support, and sustained career outcomes were rarely measured.
Reported gap identified in the scoping review findings summarised in the abstract.
Empirical evidence on applications designed to support women’s career development remains limited.
Conclusion drawn from the scoping review: authors searched seven databases + backward/forward citation searching and synthesised identified empirical studies.
The framework reframes the education–employer gap as a structural failure in the pathway and outlines implications for universities, employers, accreditors, and policymakers.
Conceptual claim and implications drawn by the author(s) in the paper (stated in the abstract).
The architecture of the undergraduate degree is structurally incapable of replacing the informal post-degree apprenticeship system through curricular revision alone.
Argument presented in the paper, supported by the systematic review of eighteen peer-reviewed studies and labor-market analyses cited in the abstract.
The informal post-degree apprenticeship system that historically completed graduate formation no longer reliably exists.
Claim based on the paper's systematic review of eighteen peer-reviewed studies and current labor-market analyses (as described in the abstract).
Higher education has misdiagnosed the resulting challenge as curriculum misalignment—a content problem assumed to be solvable through revised syllabi, AI electives, and marginal expansions of experiential learning.
Argument presented in the paper, supported by the paper's systematic review of eighteen peer-reviewed studies and labor-market analyses (as described in the abstract).
Artificial intelligence and automation are restructuring early-career knowledge-work roles by compressing the entry-level functions through which graduates historically built portfolios, developed professional judgment, and earned professional credibility.
Statement supported in the paper by a systematic review of eighteen peer-reviewed studies and current labor-market analyses (as described in the abstract).
GenAI usage significantly decreased creativity-relevant skills.
Experiment with 82 participants reported in the paper; authors report a statistically significant decrease in measures of creativity-relevant skills for participants using GenAI.
GenAI usage significantly decreased domain-relevant skills.
Experiment with 82 participants reported in the paper; authors report a statistically significant reduction in measures of domain-relevant skills for the GenAI condition.
GenAI usage significantly decreased intrinsic task motivation.
Randomized experiment reported in the paper with 82 participants; authors report a statistically significant decrease in intrinsic task motivation for participants using GenAI.
Algeria lags behind peer countries on key indicators of digital infrastructure, human capital, and institutional frameworks as evidenced by World Bank (2022) and Oxford Insights indices.
Specific comparative claim based on the paper's use of World Bank (2022) indicators and Oxford Insights Government AI Readiness Index scores; the summary does not report numeric index values or sample sizes.
Findings reveal that Algeria exhibits significant lag in digital infrastructure, human capital, and institutional frameworks compared to peers (Morocco, Egypt, Turkey).
Result reported from the paper's comparative analysis using World Bank indicators, the Oxford Insights Government AI Readiness Index, and sector-specific studies comparing Algeria to Morocco, Egypt, and Turkey; specific quantitative comparisons not provided in the summary.
Workers with a higher share of standardized routine tasks face more pronounced downward wage pressure.
Subgroup analysis by share of standardized routine tasks in workers' duties showing larger negative wage effects for those with higher routine-task shares.
The task substitution mechanism is the core channel underlying these effects of automation on wage structure.
Mediation/heterogeneity tests reported in the paper showing stronger automation effects where task substitution (standardized routine tasks) is higher; authors interpret this as the primary channel.