Evidence (97 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
20058 claims
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Productivity
17184 claims
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Governance
16099 claims
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Human-AI Collaboration
16034 claims
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Innovation
10501 claims
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Org Design
10496 claims
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Labor Markets
6444 claims
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Skills & Training
5385 claims
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Inequality
4148 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 | 1820 | 479 | 278 | 1820 | 4588 |
| Organizational Efficiency | 2711 | 616 | 401 | 173 | 3922 |
| Governance & Regulation | 2075 | 886 | 459 | 246 | 3714 |
| Technology Adoption Rate | 1467 | 530 | 258 | 206 | 2488 |
| Decision Quality | 1281 | 496 | 289 | 152 | 2228 |
| Output Quality | 1227 | 447 | 207 | 138 | 2025 |
| AI Safety & Ethics | 634 | 754 | 207 | 83 | 1688 |
| Research Productivity | 826 | 241 | 114 | 422 | 1624 |
| Firm Productivity | 1052 | 154 | 163 | 66 | 1441 |
| Task Allocation | 685 | 211 | 331 | 99 | 1335 |
| Market Structure | 433 | 423 | 242 | 46 | 1150 |
| Innovation Output | 639 | 91 | 105 | 34 | 871 |
| Task Completion Time | 476 | 113 | 43 | 36 | 672 |
| Firm Revenue | 445 | 126 | 58 | 25 | 656 |
| Skill Acquisition | 364 | 119 | 109 | 34 | 626 |
| Consumer Welfare | 288 | 167 | 104 | 31 | 592 |
| Employment Level | 214 | 140 | 174 | 50 | 582 |
| Error Rate | 230 | 251 | 35 | 16 | 535 |
| Fiscal & Macroeconomic | 268 | 136 | 71 | 50 | 532 |
| Inequality Measures | 100 | 307 | 96 | 12 | 515 |
| Worker Satisfaction | 221 | 173 | 60 | 30 | 484 |
| Automation Exposure | 155 | 138 | 65 | 36 | 398 |
| Regulatory Compliance | 171 | 120 | 30 | 13 | 335 |
| Developer Productivity | 222 | 58 | 27 | 13 | 321 |
| Team Performance | 188 | 56 | 50 | 24 | 320 |
| Wages & Compensation | 146 | 104 | 46 | 16 | 312 |
| Training Effectiveness | 207 | 41 | 21 | 26 | 298 |
| Job Displacement | 23 | 153 | 52 | 4 | 232 |
| Hiring & Recruitment | 102 | 57 | 30 | 11 | 202 |
| Skill Obsolescence | 16 | 102 | 24 | 6 | 148 |
| Creative Output | 71 | 42 | 23 | 6 | 143 |
| Social Protection | 57 | 30 | 11 | 3 | 101 |
| Labor Share of Income | 29 | 42 | 24 | 2 | 97 |
| Worker Turnover | 43 | 29 | 6 | 4 | 82 |
| Industry | — | — | — | 1 | 1 |
Data ownership and algorithmic control influence who receives economic rents from AI-driven value creation.
Qualitative case studies and literature synthesis focused on data rights, data dividends, ownership, and algorithmic governance.
The paper argues that digitalization increases the organic composition of capital and expands a reserve army of labor while contributing to the emergence of a new middle class composed mainly of medium-skilled workers.
Marxist political-economy interpretation of the empirical pattern, linking reduced low-skill employment and increased medium- and high-skill demand to capital deepening, surplus labor, and middle-class formation.
AI can raise worker productivity while increasing returns to capital or highly skilled workers unless institutions enable productivity gains to be redistributed or shared with lower-skilled workers who use AI tools.
Conceptual application of the SPEW framework to AI and automation, focusing on complementarity, gain capture, and bargaining institutions.
The reviewed evidence indicates that spatial dispersion and digital infrastructures reshape bargaining power and organizational form in platform labor markets.
Interpretive synthesis situated within labor geography, based on four empirical studies.
A Total Factor Productivity (TFP) analysis reveals an 'AI Distortion Effect' that diminishes returns to labor scale while amplifying technological leverage.
TFP analysis reported in the paper based on the multiple-case comparative data and theoretical interpretation of factor returns under generative AI adoption.
Labor share depends sensitively on the capital-labor ratio.
Sensitivity analysis / simulation results from the model showing labor share is responsive to changes in the capital-labor ratio.
The effect of the model's structural parameters on key variables such as labor shares of output is quantified.
Results from high-throughput model simulations / quantitative sensitivity analysis of structural parameters reported for labor share of output.
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's capital- and skill-biased effects may reduce the labor share of national income and widen income differences between highly skilled workers and low- to medium-skilled workers.
The paper synthesizes theoretical mechanisms and cites an IMF working paper on AI adoption and inequality; it does not report original estimates or a statistical sample.
At the maximal-growth equilibrium, the interest rate equals the growth rate; consequently, any positive human consumption rate out of wealth causes the human ownership share ε_t to decay exponentially at that rate.
The paper’s stated golden-rule decoupling theorem, derived from the maximal-growth condition r = g and ownership-share dynamics.
Directly productive market labour accounts for approximately 7.8% of total societal time in advanced post-industrial economies, compared with roughly 11% in earlier industrial periods.
Illustrative calculation attributed to Eberstadt (2016) and OECD (2024a); the paper does not report an original dataset or sample size.
Digital platforms commodify unpaid user activity by extracting revenue from nonwaged activity such as user-generated content and attention.
Theoretical argument concerning platform monetisation of unpaid activity, supported by the review’s discussion of attention, datafication, and value extraction.
The calibrated model reproduces a decline in the labor income share beginning around the mid-1990s and remaining low thereafter.
The model is calibrated to U.S. macroeconomic data and its simulated labor-share dynamics are compared with observed trends.
Under factor complementarity, accelerated obsolescence lowers the labor income share by making efficient capital relatively more valuable than skills.
The model combines efficient labor and efficient capital as complementary factors and derives the labor share during the transition and at the new equilibrium.
Firms more exposed to generative AI show a decline in labor share after adoption/exposure.
Difference-in-differences estimates on the same sample, reporting a negative coefficient for labor share: -0.007 (SE = 0.002), interpreted as consistent with a shift in rent sharing.
The field analyzes economic structures and possibilities in a future where technological progress, particularly artificial intelligence, substantially reduces or eliminates the need for human labor.
Scope statement of the review; literature review and conceptual analysis summarizing emerging research on AI-driven reductions in labor demand (no empirical sample reported).
The functional distribution of income shifts structurally from labour income toward agent and capital income as AI agents substitute for labour, creating potential risks for aggregate demand.
Theoretical allocation of factor rewards in the paper's model showing reduced labour share and increased returns to agent-capital combinations; discussion of macroeconomic demand-side implications; no empirical sample.
At the current stage, AI generally exerts a negative impact on female economic contributions.
Panel-data empirical tests across 58 countries (2000–2022) reported in the paper indicating an overall negative effect of AI on women's economic contributions.
The labor force’s gender imbalance significantly suppresses female economic contributions.
Theoretical model and empirical panel analysis covering 58 countries (2000–2022) reported in the paper.
In Afonso (2024)'s definition of a balanced growth path, the progress of automation makes the ratio of skilled labor to all labor asymptotically approach zero in the long run.
Reported description of Afonso (2024)'s theoretical model and stated balanced growth path result as summarized in the paper's abstract; based on analytical/theoretical model (no empirical sample).
Each state in the data cycle from inputs to model weights to synthetic outputs refines technical signal but strips economic equity from data generators.
Theoretical argument described in the paper; no specific empirical quantification provided in the excerpt.
Agencies with higher AI exposure exhibit declining routine employment shares.
Empirical analysis of administrative employment data for U.S. federal agencies from 2019–2024; agency-level associations between AI occupational exposure scores and employment composition over time.
Subsequent increases in machine productivity were unrelated to labor yet measured output per unit of labor increased, and firms had no reason to increase wages on the grounds that the supervisory input (labor) was not responsible for the increase.
Historical/theoretical reasoning in the paper linking mechanization-driven productivity growth to measured labor productivity and firm wage-setting incentives (qualitative argument)
In a stylized high adoption scenario with no institutional change, the combined wage plus capital income accruing to workers falls by roughly 5% points of value added.
Quantified result from a stylized simulation exercise in the paper (high AI adoption, no institutional change).
Artificial intelligence (AI) may intensify these dynamics by increasing the importance of capital and intangible assets.
Theoretical argumentation and conceptual synthesis in the paper (and motivating literature); supported by model intuition and referenced empirical/theoretical work rather than a new empirical estimate in the abstract.
Emerging evidence suggests a declining labor share alongside rising markups, profits, and rents in parts of advanced economies.
Literature synthesis cited in the paper summarizing macroeconomic empirical studies documenting trends in factor shares, markups, and profits across advanced economies; no single sample size reported in abstract.
AI-augmented labour is being commodified.
Claim derived from qualitative interviews with knowledge workers in Philippine BPO firms and the paper's analytical synthesis; sample size not reported.
For Zerynth, increases in AI patent stock produce consistently negative and statistically significant effects on labor cost shares.
Zerynth-specific fixed-effects regressions on the 2005–2024 panel showing declines in labor cost shares associated with AI patent stock.
For UniCredit, increases in AI patent stock reduce labor cost shares.
Firm-specific fixed-effects regressions reported for UniCredit in the 2005–2024 panel; AI patent stock associated with a decline in labor cost shares.
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).