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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Findings have implications for policymakers, platform companies, and civil society organizations designing equitable AI governance frameworks for the gig economy in India and the Global South.
Authors' stated implications and recommendations drawn from the qualitative study (16 workers, 21 stakeholders) and normative interpretation.
The study advocates a pragmatic hybrid governance model—an 'Algorithmic Human Manager'—where technological efficiency and human accountability operate together.
Author recommendation based on analysis of interview data and normative argumentation; proposed governance framework introduced by the paper.
AI-powered systems generate operational efficiencies for platform operations.
Same mixed-methods interviews (16 gig workers and 21 key stakeholders) reporting perceived efficiency gains from automated allocation/monitoring systems.
AI-powered systems expand access to work in location-based gig services (ride sharing and delivery).
Mixed-methods study using interviews (16 gig workers and 21 key stakeholders; total N=37) and qualitative analysis reporting worker and stakeholder accounts that platforms enable access to gigs.
A 'favourable transmission path' exists in which AI-induced productivity strengthens purchasing power and effective demand.
Conceptual framework presented in the review (the paper characterises possible transmission paths).
Returnees are more likely than comparable stayers to receive unemployment benefits, and among recipients they receive higher daily benefit levels.
Chapter 4: linked Belgian administrative registers with benefit receipt and benefit-level information; causal comparisons between returnees and comparable stayers.
Augmentation AI stimulates program openings (new Bachelor programs) in exposed fields.
Chapter 3: supply-side analysis of program openings using U.S. higher-education program data 2010–2022; IV using lagged CS research intensity.
Augmentation AI attracts more and higher-ability students into exposed Bachelor programs.
Chapter 3: student demand and student-ability margins analyzed using U.S. enrollment/graduation data 2010–2022 and IV identification (lagged CS research intensity).
Augmentation AI increases Bachelor-degree graduations in AI-exposed fields in the U.S. (2010–2022).
Chapter 3: analysis of U.S. Bachelor program graduations 2010–2022; IV strategy using lagged computer-science research intensity to instrument AI exposure; margins include aggregate graduations and program supply.
AI-exposed occupations expand (grow) in employment/demand in the European sample.
Chapter 2: job-posting counts and occupational-level analyses from 75 million postings (2018–2023) across four countries, IV using lagged CS research intensity.
AI-exposed (AI, Data, Prediction) skills pair more frequently with complementary skill bundles (Judgment, Decision-Making, Leadership), i.e., increased co-occurrence of AI-exposed and complementary skills.
Chapter 2: co-occurrence analysis of extracted skills from 75 million job postings across four European countries (2018–2023), identified using multilingual skill classifiers and IV strategy.
In four European countries (2018–2023), there is significant growth in demand for AI, Data, and Prediction skills within AI-exposed occupations.
Chapter 2: analysis of 75 million online job postings across four European countries (2018–2023); multilingual skill extraction and classification using data-science methods; IV strategy instrumenting AI exposure with lagged computer-science research intensity.
Augmentation AI generates new work primarily for high-skilled occupations.
Chapter 1: occupational-level analysis using novel exposure measures and IV strategy (lagged CS research intensity) on U.S. data 2015–2022, heterogeneous by skill.
Augmentation AI raises wages primarily for high-skilled occupations.
Chapter 1: heterogeneous IV estimates by skill group using occupational exposure measures (Stack Overflow mapping and O*NET) for 2015–2022 U.S. data.
Automation AI increases employment in the U.S. (2015–2022).
Chapter 1: occupational exposure measures from Stack Overflow and O*NET; instrumental-variables strategy with lagged computer-science research intensity; U.S. labor-market data 2015–2022.
Augmentation AI stimulates the creation of new work in the U.S. (2015–2022).
Chapter 1: novel longitudinal measures of occupational exposure to augmentation AI constructed by mapping developer activity on Stack Overflow to occupational descriptions and an emerging-work measure from O*NET; instrumental-variables strategy using lagged computer-science research intensity; U.S. labor-market data covering 2015–2022.
AI development enhances firms' technological innovation capability.
Mediation analysis reported in the paper showing that AI development increases a firm-level measure of technological innovation capability (mediator) based on the same sample of Chinese A-share listed firms (2014–2024). The paper identifies technological innovation capability as a channel.
AI development significantly increases the share of high-educated labor: for each one-unit increase in AI development, the share of high-educated labor increases by 0.006 units.
Empirical analysis using firm-level AI development indicators constructed via text analysis and machine learning on Chinese A-share listed firms in Shanghai and Shenzhen from 2014–2024; reported regression coefficient of +0.006 for high-educated labor share per one-unit AI increase.
Adaptive measures from workers, employers, and governments are crucial to enable the new labor force to thrive under the future of AI.
Concluding synthesis/recommendation from the SLR that emphasizes multi-actor adaptation; no empirical effect estimates or intervention studies described in the excerpt.
Training centers, training programs, the education system, and governments have to adapt to help close the skills gap.
Policy/recommendation claim in the paper based on synthesis of reviewed studies that emphasize the need for institutional adaptation; specific policy evaluations or measured impacts not provided in the excerpt.
The review identifies new and emerging skills needed for jobs, such as data skills, machine learning skills, and digital communication skills, which the next generation of the workforce should have.
Synthesis from the SLR indicating recurring skills highlighted across empirical studies (specific studies, measures, and counts not provided).
To address these dilemmas, coordinated reconstruction of production relations is needed across three levels: macro-level institutional constraints, meso-level organizational transformation, and micro-level rights protection (e.g., recognition of data labor rights, anti-monopoly regulation, and algorithmic transparency).
Prescriptive policy recommendations based on the paper's theoretical analysis; no empirical evaluation of these measures is provided.
Aggregate employment gains from robot exposure accrue through firm expansion and new worker entry, rather than through intensive-margin expansion of incumbent workers.
Combination of district-level employment growth results and worker-level cohort evidence showing reductions in incumbent worker intensive margins, implying expansion occurs via firm growth and new hires (administrative employer-employee data and industry robot stocks, 2014-2021).
The positive district-level employment effects of robot exposure are concentrated in manufacturing and are driven by the automotive industry.
Heterogeneity/subsample analyses of the shift-share IV estimates showing larger effects within manufacturing sectors and particularly in the automotive sector, using the same administrative employer-employee dataset and industry robot stock measures for 2014-2021.
Robot exposure has positive effects on district-level employment growth in Turkey (2014-2021).
Shift-share specifications estimating the relationship between industry-level robot adoption and district-level employment growth in Turkey for 2014-2021; industry-level robot adoption instrumented with the same indicator from eight leading European countries; combined administrative employer-employee data and industry-level robot stocks.
The paper serves as a resource for policymakers and researchers addressing the economic and social impacts of robotics, artificial intelligence, and automation.
Stated in the paper's implications; reflects intended audience and utility rather than an empirical finding.
The study contributes to a limited body of research on robot taxation and offers guidance on adapting tax systems to technological change.
Claim about the paper's original contribution and scope, stated in the implications/originality/value section; based on the authors' review and synthesis of existing literature.
Implementing a robot tax approach supports responsible automation, reduces inequality, and fosters sustainable economic growth.
Conclusion/implication in paper based on synthesis of reviewed literature and normative argument; not presented as an empirically tested result within the study.
A robot tax would address tax policy biases that favour capital over labour.
Paper argues this normative point based on literature synthesis; presented as a rationale for the tax rather than proven empirically within the paper.
A robot tax could fund workforce retraining.
Policy recommendation in the paper deriving from the scoping review; framed as intended use of tax proceeds (no empirical trial or evaluation reported).
A robot tax is proposed to offset lost income tax revenue.
Paper proposes robot taxation as a policy response based on review of literature; presented as a policy recommendation rather than reporting new empirical estimation.
By employing ANT, the research underscores the strategic potential of digital technologies in addressing systemic challenges within the construction sector and offers practical strategies for firms to improve retention by focusing on respect, support, and perceived value.
Interpretation and recommendations derived from the socio-technical framework and thematic interview findings (23 interviews); policy/practice suggestions are proposed in the paper.
Technologies such as Building Information Modelling (BIM), Artificial Intelligence (AI), and online mentoring platforms do more than enhance operational efficiency; they actively reshape workplace dynamics to promote inclusivity and improve women's perceptions of respect, support, and value.
Qualitative evidence from 23 interviews and the paper's socio-technical framework linking technology functions to RSV concepts; participants reported examples where digital tools affected interactions and perceptions.
This study applies Actor-Network Theory (ANT) to investigate how digital technologies (as non-human actors) influence the retention of women (as human actors) in the construction industry — a perspective overlooked in previous research.
Methodological claim based on the paper's literature review and stated theoretical approach; empirical work comprised 23 qualitative interviews framed through ANT.
The article recommends incorporating 'algorithmic time politics' into occupational health risk assessments and promoting 'health-friendly algorithmic design.'
Normative policy recommendations drawn from the theoretical framework and literature synthesis; no policy implementation or evaluation data presented in the abstract.
Moderating variables—including social security, algorithmic transparency, and alternative employment opportunities—can attenuate or shape the health effects of algorithmic time politics.
Policy/moderation discussion within the theoretical framework and literature review; presented as hypothesized moderators rather than empirically validated moderators in this article.
Data exhibits spillovers such that data generated by one task can augment the productivity of another task.
Model assumption and formalization of cross-task data spillovers in the analytical framework (theoretical derivation and model structure).
Exposure to generative AI commands a wage premium of up to 20 per cent.
Wage comparisons/regressions relating occupational AI-exposure indices to wages in PLFS 2025 (reported maximum estimated premium = up to 20%).
Dengan strategi yang terarah, terukur, dan berkelanjutan, tenaga kerja Indonesia tidak hanya mampu bertahan, tetapi juga berperan aktif dalam mendorong pertumbuhan ekonomi digital yang kompetitif, inklusif, dan berkeadilan.
Kesimpulan dan rekomendasi yang diambil dari tinjauan literatur sistematis (n=33).
Diperlukan kerangka regulasi adaptif, budaya pembelajaran sepanjang hayat, dan perlindungan sosial (perluasan jaminan sosial dan program transisi karir) untuk melindungi pekerja terdampak transisi AI.
Rekomendasi kebijakan yang disimpulkan dari studi literatur sistematis (33 sumber).
Kolaborasi erat antara pemerintah, institusi pendidikan, dan industri melalui skema link and match diperlukan untuk mendukung transformasi SDM.
Rekomendasi berbasis studi literatur sistematis yang menelaah 33 sumber (n=33).
Penguatan soft skills seperti komunikasi dan adaptabilitas penting untuk menjaga daya saing tenaga kerja di era AI.
Rekomendasi berbasis studi literatur sistematis yang menelaah 33 sumber (n=33).
Diperlukan program upskilling dan reskilling yang inklusif untuk menghadapi transformasi akibat AI.
Rekomendasi berbasis studi literatur sistematis yang menelaah 33 sumber (n=33).
Strategi transformasi SDM harus dijalankan melalui peningkatan kualitas pendidikan.
Rekomendasi berbasis studi literatur sistematis yang menelaah 33 sumber (n=33).
Penerapan AI mendorong pergeseran kebutuhan kompetensi dari keterampilan teknis konvensional menuju literasi digital, analisis data, kreativitas, dan kemampuan berpikir kritis.
Sistematis studi literatur yang menelaah 33 sumber ilmiah, laporan lembaga internasional, dan kebijakan terkait (n=33).
The paper proposes an evolutionary framework of AI-Economy transformation and calls for further research on governance, sustainability, and inclusive growth.
Abstract states the paper suggests an evolutionary framework and points to future research directions (governance, sustainability, inclusive growth); this is a conceptual recommendation rather than an empirical result.
Generative AI can transform value generation by enriching cognitive work instead of automating habitual processes.
Abstract claim synthesizing reviewed literature that generative models augment cognitive work; no empirical effect sizes or study counts given in abstract.
Deep Learning (DL) hastens automation and capital deepening in high-skill industries.
Synthesis claim in abstract from reviewed literature; no specific empirical estimates or sample sizes provided in abstract.
Machine Learning (ML) mainly boosts productivity by increasing predictive efficiency.
Synthesis claim in abstract based on the systematic review of peer-reviewed literature (Scopus and SCI); no specific empirical studies or sample sizes cited in abstract.
The review estimates sectoral, macroeconomic, and labor market effects of ML, DL, and Generative AI.
Stated scope in abstract: review used to estimate sectoral, macroeconomic, and labor market effects; no quantitative details provided in abstract.