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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Policy implications derived from the literature include interventions spanning labor transition (reskilling/transition support), competition regulation, and digital governance.
Narrative synthesis of policy recommendations across the 78 studies and institutional reports included in the SLR.
Firm-level productivity gains from AI are contingent on complementary organizational investment.
Synthesis finding from the SLR: multiple studies report that complementary investments (e.g., organizational change, worker training, data infrastructure) are necessary for realizing productivity benefits.
Employment effects follow the same timing (i.e., emerge in 2021) but diverge by exposure type.
Paper reports employment effects with temporal alignment to output effects (emerging in 2021) and heterogeneity by type of AI exposure.
The Twin Transition is macro-feasible, but its adjustment costs fall unevenly on the manufacturing workforce.
Distributional outcomes and sectoral labor adjustment results from the CGE model (S4) showing heterogeneous effects across manufacturing sectors and implied labor reallocation costs.
Under S3 alone the Electricity sector expands only +0.10% by 2030 despite a 14.87% per year IT investment surge, indicating binding generation capacity that the Green AI productivity shock relaxes in S4.
CGE simulation of S3 (exogenous IT investment surge at 14.87% per year) and comparison with S4 results in the 23-sector model calibrated to 2019 I-O table.
Green AI’s export surge causes real exchange rate appreciation that displaces output in Textiles by 5.5% and in Leather and Footwear by 16.1%, while Heavy Manufacturing expands by 12.9% and IT Hardware by 10.9%.
Sectoral output changes reported from the CGE model under scenario S2 (Green AI) calibrated to Vietnam 2019 I-O table.
Brown AI (S3) is macro-neutral in GDP terms but imposes a consumption cost of 0.42% by 2030 as infrastructure investment crowds out household expenditure.
Model simulation of scenario S3 (Brown AI) in the 23-sector recursive dynamic CGE calibrated to Vietnam 2019 I-O table; S3 modelled as an exogenous IT hardware and services investment surge.
Traditional jobs based on manual work are transforming into collaborative management and exception-handling roles that demand new cognitive and ethical skills from employees.
Secondary data literature review of peer-reviewed research and industry evidence published 2022–2026 (method: secondary data review / synthesis). No specific sample size reported.
The model yields propositions on threshold effects, productivity J-curve dynamics, distributional stress, and policy sequencing.
Model-derived propositions and theoretical implications presented in the paper (analytical derivations and theory-building).
The DIAC model identifies three regimes of AI adoption and absorption: adoption without absorption, constrained complementarity, and adaptive complementarity.
Taxonomy and regime definitions derived in the paper's theoretical model (analytical/theory-building).
The same AI shock can produce divergent outcomes in small open economies.
Core theoretical claim derived from the Dynamic Institutional Absorptive Capacity (DIAC) model developed in the paper (analytical/theory-building).
Artificial intelligence is widely expected to raise productivity, yet its macroeconomic gains remain uncertain, uneven, and institutionally mediated.
Statement and literature-motivated framing in the paper's introduction; supported by analytical theory-building (DIAC model) rather than empirical data.
The dominant paradigm has shifted from 'substitution' (machines replacing workers) to 'augmentation' (AI augmenting human work).
Interpretive conclusion in the paper drawn from secondary literature (WEF, ILO, McKinsey, PwC) and observed policy/industry trends.
A backdating exercise on the synthetic difference-in-differences yields larger absolute estimates than the actual treatment date across most age bands.
Robustness/check: synthetic DiD backdating experiment reported in the paper produced larger absolute estimates when using earlier (backdated) treatment dates.
Optimal tax and regulatory policies that achieve Pareto-improvements differ depending on whether there is competition in AI production.
Policy analysis within the theoretical model deriving optimal tax/regulatory prescriptions under different market structures (competitive vs monopolistic). No empirical sample reported.
The impact of productivity gains differs depending on whether AI production is competitive or monopolistic.
Comparative theoretical analysis in the model contrasting competitive vs monopolistic AI production. No empirical sample reported.
Improvements in AI productivity trigger labor reallocation and changes in absolute and relative wages for different types of labor.
Analytical economic model / comparative statics in the paper (theoretical result). No empirical sample reported.
The net effect of AI on work is better described as displacement than wholesale elimination.
Author's conceptual argument and synthesis of literature/reports (qualitative argumentation in the paper).
AI exposure is more positive for occupations performing nonroutine interactive work and more negative for occupations concentrated in analytical, scientific, and operations-control skills.
Occupation-level analysis mapping skill content (interaction-and-communication vs. analytical/scientific/operations-control) to market-implied AI premium; comparison across occupational skill categories.
The study reveals an 'AI Competency Paradox'—AI raises technical skills while increasing demand for meta-competencies that established frameworks fail to assess.
Synthesis of empirical findings reported in the paper linking measured increases in technical skills with unmet assessment needs for meta-competencies.
There are two distinct regional catch-up trajectories: Digital Leapfrogging in the Baltic States and Industrial Deepening in the Visegrad Group.
Systematic empirical documentation across the Visegrad Group and Baltic States (2022–2025) using the paper's assessment approach; patterns labeled and interpreted by the author.
Important gaps remain in the literature and warrant further research.
Paper's abstract statement that the review identifies important gaps that warrant further research (based on review of 194 articles).
The existing literature on AI and economic development remains fragmented, with limited integration across development dimensions.
Conclusion drawn in the abstract from the systematic review of 194 peer-reviewed articles noting fragmentation and limited cross-dimension integration.
AI's effects are often uneven and highly context-dependent.
Summary statement in the abstract based on the systematic review of 194 articles noting heterogeneity in AI impacts across contexts and dimensions.
Other refugee groups saw meaningful gains in job placements, but increases were concentrated among males and in low-skilled jobs, with only limited effects for females.
Subgroup difference-in-differences analyses by origin group, gender, and skill level using administrative placement data.
Overall conclusion: AI plays a dual role — fostering productivity and inclusion (through employment and some gender balance gains) while posing risks of increased within-firm inequality.
Synthesis of empirical findings from fixed-effects regressions, mediation and moderation analyses on the firm panel showing employment and wage gains alongside increased pay dispersion.
AI’s impact on university-educated labour cannot be understood through technological capability alone; it requires analysing the rentier dynamics of contemporary capitalism.
Theoretical argument and conceptual framework drawing on political economy and sociology (no empirical sample reported).
A 2025 forecasting study of experts reveals an apparent disconnect between expectations of significant AI capability improvements and modest near-term economic projections.
2025 forecasting study / expert elicitation involving 69 leading economists and 52 AI experts, plus additional expert panels; comparison of experts' expectations about AI capability progress versus their near-term economic projections.
When candidate quality is heterogeneous, prompt injection is less effective on average, but can occasionally allow lower-quality candidates to outrank higher-quality ones, raising fairness concerns.
Controlled experiments comparing homogeneous vs. heterogeneous candidate quality conditions and tracking ranking outcomes; specific experimental counts not included in the abstract.
The long-term success of AI-enabled talent acquisition depends not only on technological performance but also on the ability to ensure fairness, accountability, transparency, and ethical decision-making throughout the recruitment lifecycle.
Concluding synthesis drawn from the systematic review of 34 studies combining evidence on technical performance, bias risks, governance, and regulatory considerations.
The analysis reveals the emergence of five levels of talent acquisition maturity, ranging from traditional applicant tracking systems and data-driven workforce acquisition to predictive talent acquisition and fully autonomous recruiting models.
Qualitative synthesis and classification produced from the systematic review of 34 studies.
The study distinguishes foundational theoretical perspectives from the contemporary 2015–2025 evidence base and clarifies the relationship between task transformation and structural transformation, emphasizing institutional complementarity as the key mechanism shaping AI-driven growth outcomes.
Analytic separation of theoretical literature and empirical studies in the structured review (2015–2025); thematic mapping linking task-level changes to broader structural transformation contingent on institutional complementarities.
Rather than proposing a deterministic growth model, the study advances a conditional and ecosystem-centered interpretation of AI-led development.
Authors' interpretive conclusion based on their structured review and the integrative innovation-ecosystem framework synthesizing mechanisms and contextual dependencies in the 2015–2025 literature.
Interpreting task-based automation models alongside endogenous-growth and open-innovation frameworks clarifies why similar AI investments may lead to divergent structural outcomes.
Theoretical synthesis combining task-based automation literature with endogenous-growth and open-innovation models, illustrated by examples from the reviewed empirical literature (2015–2025).
The paper develops an integrative innovation-ecosystem framework linking three core transmission channels: (i) total factor productivity (TFP), (ii) task reallocation and labor-market restructuring, and (iii) innovation and knowledge-generation dynamics.
Conceptual framework constructed by the authors via integrative review of theoretical and empirical literature from 2015–2025; framework synthesizes mechanisms reported across studies.
Empirical evidence remains heterogeneous, and estimates of AI’s macroeconomic contribution vary across institutional and structural contexts.
Synthesis of heterogeneous empirical studies from the 2015–2025 literature identified in the structured review; comparative thematic classification highlighting variation by institutional/structural context.
AI adoption does not generate uniform or automatic growth effects.
Structured literature review / mechanism-oriented synthesis covering studies from 2015–2025; transparent search, screening and thematic classification (no formal meta-analysis).
The field is shifting from building models from existing data to actively creating data for building models (characterised as 'hyper-datafication').
Conceptual argument supported by observed trends in dataset creation and growth in the analysed dataset collection and the paper's theoretical framing.
The simulation offers a template of how firms ought to reorganize internal promotion ladders when junior positions are significantly automated.
Model-based policy/reorganization recommendation derived from the simulation results; presented as guidance for firm-level reorganization rather than an empirically tested organizational intervention in the abstract.
We simulate the elasticity of substitution between human intuition and the output of an algorithm.
Paper reports a simulation exercise modeling the elasticity of substitution between human inputs (intuition) and algorithmic outputs; no simulation parameters or sample size provided in the abstract.
We measure the change in the skill premium using a difference-in-differences design on freelance websites worldwide.
Statement of empirical method: difference-in-differences design applied to data from freelance platforms with global coverage; no sample size provided in the abstract.
The present wave of automation targets non-routine cognitive activity such as coding, technical writing, and graphic design, unlike past automation which mainly involved routine manual activity.
Framing/background statement in the paper contrasting historical automation (routine manual tasks) with current AI-driven automation of non-routine cognitive tasks; no sample size or quantitative test reported in the abstract.
Cluster analysis reveals diverse yet cohesive national profiles across the EU that reflect differences in digital readiness, human capital, and institutional factors.
Cluster analysis performed on country-level indicators (AI adoption, digital readiness, human capital measures, institutional factors) to group EU countries into profiles; summary reports heterogeneous but cohesive clusters; exact cluster counts and sample size not reported.
Participants' IAT scores were predictive of the time they spent in human-AI collaboration.
Reported predictive relationship between individual IAT scores and measured time spent interacting with/considering resumes during human-AI collaborative screening tasks (likely from regression or correlation analyses); exact statistics and sample size not provided in the excerpt.
Instrumental-variable estimates using lagged AI diffusion produce similar patterns (attenuation of overeducation penalty and slight lowering of undereducation premium), although results should be interpreted with caution.
IV estimation using lagged AI diffusion as an instrument in models applied to CLDS data; IV results reported to be qualitatively similar to OLS/fixed-effects estimates but noted as requiring cautious interpretation.
Other strategic factors (differentiation of work, digital reputation, adaptability) continue to influence illustrators' financial sustainability despite AI's effect.
Author conclusion/interpretation in the discussion, inferred from the relatively low R² and domain knowledge; these moderators/alternative determinants are asserted rather than estimated in the reported regression.
AI explains a relatively small share of income variation among illustrators (model R² = 7.4%), so its contribution to income variation is limited.
Reported model fit statistic from the above simple linear regression (R² = 7.4%) on the sample of 385 illustrators.
The relevance of Chinese experience for Russia can be assessed in contexts such as eGrocery, O2O services, ecosystem delivery and remote/northern regions, and Russian material serves as an applied block for that assessment.
Methodological claim based on the study's comparative framework combining Chinese case analysis with applied Russian regional material (Sakha Republic).
This article adopts a contextual approach to technology, considering it in conjunction with the social context in which it is situated.
Methodological statement made by the author about the approach taken in the paper (contextual rather than purely technical); not an empirical claim.
Longevity produces a short-run welfare loss that recedes as capital deepening raises wages, since households initially compress consumption and fertility to finance a longer retirement.
Model-derived welfare time path following a longevity shock showing initial welfare decline and subsequent recovery as aggregate capital deepens and wages rise; mechanism traced to household saving and fertility responses in simulations.