Evidence (57 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 |
The urban digital economy exerts a stronger effect than the rural digital economy in promoting servicization and inhibiting industrialization.
Heterogeneity analysis in the provincial panel (2013–2024) comparing urban versus rural digital-economy measures and their associations with changes in employment shares.
After 2017, industrial digitalization continued to strengthen servicization while suppressing industrialization.
Post-2017 analysis of provincial panel data (2013–2024) showing continued positive association of industrial digitalization with service employment and negative association with industrial employment after 2017.
After 2017, digital industrialization shifted toward promoting industrialization and restraining servicization.
Post-2017 subset analysis of provincial panel data (2013–2024) comparing the direction and magnitude of digital industrialization's association with industry and service employment shares before and after 2017.
The elevation of the 'digital economy' to a national strategy in 2017 constituted a critical turning point in the relationship between digital-economy development and labor-structure change.
Before-and-after (pre/post-2017) analysis using China's provincial panel data (2013–2024) showing a structural change in estimated effects around 2017.
The development of the digital economy generally promotes the servicization and deindustrialization of the labor structure.
Panel analysis using China's provincial data from 2013 to 2024 examining relationships between digital economy development and labor-structure indicators (servicization and industrial employment shares).
There are factor-share consequences from agent adoption (i.e., implications for the shares of income accruing to factors such as labor and capital).
Model-based discussion and comparative-static analysis in the paper deriving implications for factor shares as agents/compute capital alter production technology. The excerpt indicates qualitative/theoretical analysis rather than empirical measurement.
The central analytic object is the derivative of household consumption demand and the collective wage bill with respect to automation.
Paper's stated modeling focus: comparative-static derivatives linking automation to household consumption demand and aggregate wages; used to characterize incidence and welfare effects.
India exhibits a distinctive polarisation pattern: a shrinking middle-skill workforce alongside a persistently large low-skill labour segment.
Descriptive analysis of secondary data and official reports from 2020–2024 comparing occupational and skill distributions in India.
Firms of different ownership structures and industries exhibit different responses to the income distribution changes brought by AI (heterogeneous effects).
Paper reports performing grouped regressions by ownership type and industry to identify heterogeneous responses.
Financing constraints are a key factor that hinder firms' choice of technology level, which alters the corresponding income distribution effect of AI.
Paper posits financing constraint as a moderator and states it is considered in empirical analysis (interaction/moderation tests).
The development of AI may trigger new changes in the interest pattern between corporate profits and labor compensation.
Framed as the central research question/hypothesis; paper conducts empirical tests on firm panel data to evaluate this.
AI has caused a decrease in the labor share of income.
Estimated impacts reported in paper indicate a decline in labor share associated with higher AI exposure; stated as a result of the analysis.
In the distribution phase, behavioral data unconsciously generated by workers drives algorithmic iteration yet remains excluded from the distribution system, resulting in hidden data exploitation.
Theoretical argument that worker-generated behavioral data fuels algorithmic development but is not accounted for in value distribution; no empirical data or sample reported.
In that same limiting case, surplus value tends to zero.
Limit-case implication of the model under the value-transfer assumption (theoretical derivation; no empirical backing).
Deeper AGI adoption compresses the source of surplus value.
Theoretical implication derived in the model under the value-transfer assumption: as living labor falls, the base generating surplus value narrows (model argument; no empirical data).
Translators have functioned as 'invisible teachers' of AI—through the construction of translation memories, post-editing, and quality assessment—without recognition as teachers of models.
Conceptual framing and synthesis of workflow practices (TM construction, post-editing, QA) and their role as supervision for ML; qualitative argument and illustrative examples in the paper. No quantitative sample reported.
Translators' renditions have been bought as deliverables under contract, segmented as technical objects, and processed as 'information analysis' data under copyright law—resulting in the loss of moral, creative, and economic attribution to the translators who produced them.
Comparative reading of contract practices and copyright treatment (legal/contractual analysis across jurisdictions), descriptive examples of how translations are delivered, segmented, and processed; qualitative argumentation in the paper. No quantitative sample reported.
Faster adoption causes a sustained compression of the labor share throughout the transition window.
Model result showing time-path of labor's income share under varying adoption speeds in the theoretical framework.
Automation reduces paid human labor.
Model comparative statics in the same equilibrium framework showing substitution away from paid human labor as firms choose automation; result reported in the paper's static benchmark and general-equilibrium analysis.
Human expertise is viewed by the industry as an extractable resource whose value can be judged relative to AI expertise.
The paper's thematic analysis of public-facing statements from five annotation firms/CEOs showing language that frames human expertise as a resource to be extracted and monetized for AI training.
AI development may reduce firms' labor income share.
Further analysis reported in the paper linking firm-level AI development to reductions in the labor income share within firms.
Our baseline model finds evidence that AI is input saving.
Outcome reported from the baseline empirical specification indicating reductions in inputs associated with AI (authors' baseline model results).
Restricting AI productivity gains to the labor-generated portion of each sector's gross value added reduces the naive addressable base by approximately 72 percent.
Bottom-up sectoral model described in the paper that applies labor share to gross value added across 21 NAICS industries; the paper explicitly states the labor-generated restriction reduces the naive addressable base by ~72%.
Some of this reduced price is related to reduced input cost contributions, in particular labor and materials costs.
Decomposition/mediation analysis reported in the paper attributing part of the observed price reductions to declines in input cost contributions (labor and materials); exact methods, sample size, and statistical estimates not provided in the excerpt.
The convergence of geopolitical fragmentation and AI-driven economic concentration could produce a structural transformation that stabilizes into a neo-feudal equilibrium, in which a vanishingly small class of infrastructure owners wields power comparable to pre-Enlightenment monarchs while the vast majority loses labor value and political leverage.
Theoretical/modeling exercise and historical analogy presented in the paper; argumentative prediction rather than reported empirical measurement (no sample size or quantified projection in the abstract).
New mechanisms of surplus value distribution operate in platform-based business models and through AI-mediated processes.
Analytical/theoretical argumentation and literature synthesis in the conceptual paper (no empirical validation reported).
AI can promote enterprises to adopt different income distribution modes by improving the marginal output of capital and substituting low-skilled labor (technology bias).
Theoretical mechanism articulated in the paper based on capital-labor substitution principle and factor reward theory; implied empirical testing using firm-level data.
Rising technological capital (K_T) — proxied by robot/automation density, software and intangible capital accumulation, AI adoption surveys, and AI-related patenting — leads to a decline in labor’s share of output.
Firm- and industry-level panel regressions linking constructed K_T intensity measures to labor shares, supported by macro growth-accounting decompositions; robustness checks include difference-in-differences and instrumenting adoption with plausibly exogenous shocks (e.g., cross-border technology diffusion, trade shocks); validated with cross-country comparisons and case studies.
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 digital sector contributes twice as much labor input to output growth (relative to the physical sector).
Empirical statement based on BEA–BLS data covering 63 U.S. industries (1997–2023) as reported in the paper.
AI is not a simple labor replacement but a powerful enabler, pushing the overall labor structure toward higher skills and added value.
Author interpretation based on the paper's empirical findings (DiD results) that show upward movement of labor-skill composition; specific empirical measures not provided in excerpt.
The findings provide strong empirical support for the 'skill-biased technological change' theory, revealing a significant complementary synergy between technological progress and high-skilled labor in the AI era.
Empirical analysis reported in the paper using a Difference-in-Differences design showing complementarity between AI-related technological progress and high-skilled labor; details on coefficients, confidence intervals, or sample not provided in excerpt.
AI innovation exerts a significant positive impact on the labor structure, optimizing the proportion of high-skilled and low-skilled labor.
Paper's empirical result using a Difference-in-Differences (DiD) empirical strategy; specific sample size or data source not reported in provided excerpt.
The impact of household-side digital economy applications on labor-structure change is significantly greater than that of government- and enterprise-side applications.
Heterogeneity analysis using provincial panel data (2013–2024) comparing household-, government-, and enterprise-side measures of digital-economy application and their associations with servicization/industrialization.
The driving effect of industrial digitalization on changes in the labor structure is stronger than that of digital industrialization.
Comparative effect estimates from the same provincial panel (2013–2024) separating two dimensions of the digital economy: 'digital industrialization' and 'industrial digitalization'.
Living labor remains the sole source of new value; the core insights of the labor theory of value remain essential for critiquing contemporary digital capitalism.
Argumentative/theoretical development grounded in Marxist political economy and literature synthesis (conceptual paper, no empirical testing reported).
AI should be classified as constant capital rather than as labor.
Theoretical analysis and critical literature synthesis in a conceptual study (no empirical sample reported).
Under economy-wide deployment, the share of computer-vision-exposed labor compensation that is cost-effectively automatable rises sharply (relative to the firm-level 11% estimate).
Model counterfactuals or calibration scenarios comparing firm-level deployment vs economy-wide deployment; qualitative statement that share increases substantially.
At the firm level, cost-effective automation captures approximately 11% of computer-vision-exposed labor compensation.
Calibration and implementation in computer vision; reported firm-level estimate from the framework.
AI-enabled projects reallocate resources toward human capital (i.e., shift budget allocations toward labor / human capital).
Analysis of detailed budget allocations in the proposal dataset, comparing projects identified as AI-enabled versus non-AI projects using keyword extraction and LLM classification to identify AI presence and role.
Platform work accounts for 12.8% of labor income for participants in the studied sample.
Earnings and income calculations using platform transaction records combined with labor force survey and administrative income data for the 24-country sample (2015–2025).
Platform-mediated gig work has grown to represent 4.2% of total employment across 24 OECD countries (2015–2025).
Aggregate analysis of administrative data, national labor force surveys, and platform transaction records covering 24 OECD countries over the 2015–2025 period.
VIS produces interrelated metrics that explicitly include indirect labor embodied throughout the supply chain rather than only direct labor employed in a reported sector.
Computation of vertically integrated sector vectors from input–output matrices and allocation of upstream labor inputs to final-sector output; reported construction of VIS-based labor input metrics.
The VIS approach captures both direct and indirect (upstream) labor effects by attributing upstream labor requirements to final-sector outputs using Leontief-type inverses / vertically integrated sector vectors.
Methodology constructs annual input–output matrices (BEA + IMPLAN mapping) and computes Leontief-type inverses/vertically integrated sector vectors to allocate direct and indirect requirements; upstream labor is attributed to final output using BLS employment/hours data.
Automation of routine administrative tasks may reduce demand for certain clerical roles while increasing demand for oversight, auditing, and legal-technical expertise, altering public-sector labor composition and retraining needs.
Qualitative labor-market reasoning based on task-based automation literature and the administrative context; no field labor-data or sample provided.
AI functions like a capital-augmenting technology that substitutes routine tasks while complementing creative and coordination tasks, altering the capital–labor mix and returns to different human capital types.
Conceptual framing and synthesis of literature and survey impressions; not directly tested empirically in the paper.
Managing factor market rivalry (competition for labor, land, and capital amid informality) is an OSCM-relevant phenomenon that African contexts can illuminate.
Synthesis of labor and land market literature within the paper's conceptual framework.
Consolidation creates platform monopolies extracting value from professional labour while eliminating the expertise that creates it.
Synthesis of market concentration data and theoretical frameworks (platform capitalism) presented in the paper.
AI implementation serves vendor interests in labour cost reduction rather than improving information access.
Analytic argument supported by synthesis of vendor consolidation data, documented implementations, and theoretical analysis of vendor incentives.
Broader cognitive automation potential across administrative, financial, and professional services amounts to 11.7% (~$1.2 trillion).
Iceberg Index computation summing the wage-value contributions of skills that current AI capabilities can perform; based on mapping of thousands of AI tools to ~32,000 skills and the simulated 151M-agent workforce across ~3,000 counties.