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 longevity shock compresses asset returns and lowers the real interest rate, and generates hump-shaped, persistent dynamics.
Numerical impulse-response dynamics from the overlapping-generations model following a longevity shock; reported time paths for returns and the real interest rate.
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.)
Algorithmic management introduces significant challenges related to fairness, transparency, and worker dignity.
Synthesis of qualitative interview findings (16 gig workers and 21 stakeholders) interpreted through a social justice framework.
Algorithmic systems are not structured to reward additional labour with proportionate pay.
Worker and stakeholder interviews (N=37) reporting that increased labour/intensity does not yield proportionate compensation under platform algorithms.
Algorithmic systems produce inequitable outcomes for gig workers.
Interview data (16 workers, 21 stakeholders) reporting examples and perceptions of unequal treatment and distributional harms arising from algorithmic rules.
Algorithmic systems are opaque by design (lack transparency in allocation, monitoring, and evaluation).
Qualitative evidence from interviews with 16 gig workers and 21 stakeholders describing opaque/black-box practices of algorithmic management.
A 'critical transmission path' can occur in which AI-induced productivity gains are weakly transmitted to households and may generate absorption tension.
Conceptual framework / theoretical argument in the review (no empirical sample reported).
Productivity gains from AI do not automatically translate into broadly distributed welfare or into output fully absorbed by market demand.
Conceptual review / theoretical argument and literature synthesis presented in the paper (no empirical sample reported).
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.
AI development significantly reduces the share of low-educated labor: for each one-unit increase in AI development, the share of low-educated labor decreases by 0.007 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 −0.007 for low-educated labor share per one-unit AI increase.
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).
The root causes of these problems include the disruption of labor relations boundaries by the transformation of the means of production, the exclusion of implicit data labor from distribution rules, the concentration of capital driven by high industry barriers, and social structural constraints on technological dissemination.
Synthesis and causal argumentation grounded in Marx's theory of reproduction; conceptual reasoning rather than empirical testing.
In the consumption phase, high costs lead to service stratification, making it difficult for technological dividends to benefit the general public.
Theoretical/qualitative argument about cost barriers and unequal access to AI-enabled services; no empirical evidence or sample sizes reported.
In the exchange phase, high barriers to entry for technology and capital foster market monopolies.
Analytical claim based on structural characteristics of AI/embodied intelligence industries; no empirical sample or quantitative measures provided in the paper.
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 the production stage, workers are alienated into becoming data producers.
Conceptual claim based on Marxian analysis of labor and data extraction; no empirical sample or quantitative evidence presented.
In the production stage, workers are disciplined by algorithms.
Theoretical/qualitative argument in the paper describing algorithmic management and control; no empirical measures or sample provided.
In the production stage, workers lose decision-making power.
Theoretical analysis of production relations using Marxist reproduction framework; qualitative claim without reported empirical data.
Incumbent workers in more robot-exposed industries are unlikely to transition outside manufacturing over 2014-2021.
Longitudinal worker-level analysis of the 2014 manufacturing cohort through 2021 showing low rates of transition out of manufacturing for workers in higher-exposure industries (administrative employer-employee data).
Incumbent manufacturing workers in more robot-exposed industries experience a reduction in cumulative workdays at their original plants between 2014 and 2021.
Worker-level (intensive-margin) panel analysis tracking a 2014 manufacturing-worker cohort through 2021 using administrative employer-employee data; comparison of cumulative workdays at original plants across workers in industries with different robot exposure.
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.
The study identifies specific retention issues including rigid work practices, a predominantly masculine culture, and occurrences of bullying and harassment.
Findings from thematic analysis of 23 interviews using NVivo 13; participants' accounts raised these specific themes as retention-related issues.
Women in UK construction continue to face major retention challenges driven by structural biases that lead to feelings of disrespect, insufficient support, and being undervalued.
Thematic analysis of 23 qualitative interviews with women involved in digitally enabled projects; participants reported experiences and perceptions related to retention and workplace culture.
Women make up less than 15% of the UK construction workforce.
Statement in the paper likely citing national labour/industry statistics or prior literature (not primary data from this study).
Interactive effects and dynamic vicious cycles exist among the three mechanisms: temporal loss of control amplifies the physiological effects of temporal predation, while temporal acceleration intensifies the psychological effects of temporal loss of control.
Theoretical interaction hypotheses articulated in the framework based on cross-model synthesis and literature discussion; no empirical interaction tests presented in the abstract.
Temporal loss of control is expected to contribute to depression and to heighten occupational injury risk, with learned helplessness and the depletion of cognitive resources as key mediating processes.
Theoretical claim derived from integrating Karasek’s demand-control model and job demands-resources literature; proposed mediators and outcomes come from conceptual argument and cited studies rather than new empirical tests.
Temporal acceleration and discipline are theorized to undermine mental health, giving rise to anxiety and burnout via time panic and emotional exhaustion.
Framework/theoretical argument grounded in integration of Rosa’s social acceleration and psychological job-stress models; claim supported by referenced literature but no new empirical data reported in the abstract.
Temporal predation primarily damages physiological health—manifesting as cardiovascular strain and musculoskeletal injuries—through the mediating pathway of chronic fatigue.
Theoretical proposition based on literature synthesis and mediation logic presented in the framework; no primary empirical data or sample size reported in the article text provided.
Algorithmic time politics damages occupational health through three interconnected mechanisms—temporal predation, temporal acceleration and discipline, and temporal loss of control—which form a progressive chain from 'the quantity of time' through 'the quality of time' to 'the sovereignty over time.'
Theoretical multilevel framework developed by the article combining disciplinary theory, social acceleration theory, job demand-control and job demands-resources models and literature review; no empirical testing reported.
In platform labor, algorithms reshape workers’ perception and control of time through mechanisms such as dynamic pricing, compulsory task assignment, time-limit compression, and real-time surveillance, giving rise to a novel power formation—“algorithmic time politics.”
Conceptual/theoretical claim constructed by the article via literature integration and argumentation (synthesis of Foucault, Rosa, Karasek, Bakker & Demerouti); no empirical sample or quantitative study reported.
The economy is generically inefficient (under the laissez-faire equilibrium) and a planner can optimally tilt the direction of data accumulation to improve outcomes.
Welfare analysis within the model: comparison of decentralized equilibrium and planner's problem, demonstrating inefficiency and characterizing planner's optimal policy for directing data accumulation (analytical welfare results).
In the fully automated long-run case, short-run dynamics depend on the pattern of data spillovers, but automation is always slow in the long run: the share of tasks produced by labor decays asymptotically as a power law in time.
Analytical asymptotic result from the dynamic model showing that, under full automation, the labor-produced task share follows a power-law decay; short-run behavior is shown to depend on spillover structure (model derivation and asymptotic analysis).
One in three Scheduled Tribe (ST) graduates work in farm or elementary occupations untouched by AI.
Occupational distribution from PLFS 2025 after mapping AI-exposure indices; reported share of ST graduates in farm/elementary (AI-unexposed) occupations in the 83,000-employed-graduate sample.
One in four Scheduled Caste (SC) graduates work in farm or elementary occupations untouched by AI.
Occupational distribution from PLFS 2025 after mapping AI-exposure indices; reported share of SC graduates in farm/elementary (AI-unexposed) occupations in the 83,000-employed-graduate sample.
Graduates from the Scheduled Castes and the Scheduled Tribes are 0.24--0.37 standard deviations less exposed than upper-caste graduates within the same district.
Within-district comparisons using three occupational AI-exposure indices mapped to PLFS 2025; reported standardized exposure differences for SC and ST graduates relative to upper-caste graduates in the 83,000-employed-graduate sample.
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).
Macro-level correlation between Frey-Osborne (2013) and Eloundou-era rankings is Spearman rho = -0.750, p = 0.020 (against the original Oxford Martin appendix), indicating inversion.
Reported Spearman correlation and p-value comparing macro-level rankings between the original Frey-Osborne appendix and the paper's Eloundou-era results.
Tool-Mediated Physical (macro M2) has mean OAI = 0.054.
Reported macro-level mean OAI computed after projecting DWA OAI values into the 7-macro typology.
Transformational leadership negatively moderates the relationship between AI application and employees' job insecurity, buffering employees' insecurity responses across varying levels of AI application.
Moderation analysis reported in the study using the same employee survey dataset (411 valid responses), indicating a statistically significant buffering (negative) moderating effect of transformational leadership on the AI–job insecurity relationship.