Evidence (714 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 |
Firm profitability shows a "J-curve" as firms move from no adoption to deep adoption.
Reported relationship between adoption intensity and firm-level profitability (authors' empirical comparison/regression of profitability across adoption categories).
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 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 productivity effect of AI is not automatic; it depends on firm-level adoption, worker skills, complementary investment in software and data systems, managerial readiness, task suitability, and the ability of organisations to redesign workflows around AI.
Paper's conceptual argument and synthesis of secondary literature highlighting conditional factors for realizing productivity gains.
Despite task-level gains, GenAI produces uneven or limited firm-level productivity effects in many settings.
Review synthesizing discrepancies between task-level experiments and firm-level outcome studies, and discussion of conversion frictions in the paper.
Generative AI (GenAI) should not be treated as a standalone productivity shock; its economic value depends on the interaction between model capability, task fit, human-AI calibration, organizational complementary assets, and regional digital infrastructure.
Conceptual framework developed in this review synthesizing literature from AI research, task-level productivity experiments, general-purpose technology theory, digital economics, and China-focused digital transformation studies; no new firm-level empirical analysis in this paper.
Using survey data from AI startups in Qatar, the study will employ PLS-SEM to examine the relationships between these factors, AI capability, and venture performance.
Methods statement in the paper/abstract indicating planned empirical approach (survey of AI startups; use of Partial Least Squares Structural Equation Modeling). No sample size or empirical estimates provided in the abstract.
AI-driven technological progress generates localized efficiency improvements while diffusing only weakly across the broader economy.
Synthesis of empirical results: localized positive associations between intangible capital and sectoral productivity versus weak/insignificant associations between AI patent intensity and aggregate TFP (analysis based on OECD Productivity, OECD STAN, INTAN-Invest, OECD Patents, FUAs; panel and robust regressions and descriptive work).
Embodied intelligence is driving the human-machine relationship from a "human-dominated" model toward "collaborative co-creation," which, while boosting productivity, also triggers deep-seated contradictions in production relations.
Conceptual/theoretical argumentation in the paper, drawing on Marx's theory of reproduction; no empirical sample or quantitative data reported.
Quantile regression estimates reveal pronounced asymmetry across the biofuel production distribution: the AI effect is substantially stronger among low-production countries (Q10–Q25 elasticities: 0.58–0.61) and statistically insignificant among high-production countries.
Quantile regression analysis reported in the paper with elasticity estimates for Q10–Q25 and significance tests across quantiles.
Along the transition path of automation, data simultaneously augments the productivity of already-automated tasks and expands the automation frontier (dual role).
Analytical results from the dynamic model showing two mechanisms: (i) data increases productivity of tasks already automated; and (ii) data enables automation of additional tasks (model derivations).
Liao et al. (2026) identify multiple equifinal pathways to high performance in digit-oriented spin-offs (parent-oriented, independent-oriented, ambidextrous-oriented configurations) using fuzzy-set qualitative comparative analysis (fsQCA).
fsQCA analysis reported in the paper (methodological approach described; sample not specified in excerpt).
Non-state-owned enterprises (non-SOEs) benefit more from the digital economy than state-owned enterprises (SOEs), attributed to their greater flexibility and adaptability.
Ownership-type heterogeneity tests in the paper's two-way fixed effects panel (31 provinces, 2011–2021) showing larger estimated income/benefit effects for non-SOEs than for SOEs; interpretation links this to adaptability.
AI can raise productivity and output, but its distributional effects are uncertain and mediated by institutions and access to complementary resources.
Conceptual claim in abstract synthesizing literature; supported by secondary sources and integrative framework (OECD, ILO, UNDP, WTO, WEF). No quantified sample size reported.
Environmental dynamism and complexity differently moderate the relationship between decision-making agility and firm performance.
Reported moderation analyses in the PLS-SEM results indicating interaction effects of environmental dynamism and environmental complexity on the decision-making agility → performance path; based on survey of 251 firms.
The results vary across the 10 selected countries: the magnitude and significance of AI’s effects differ due to varying technological readiness and differing industrial structures.
Paper statement that results vary across the 10 selected countries and that nuances differ across countries due to varying industrial structures and technological readiness. Implied heterogeneity analysis across countries using the firm-level dataset and regression approaches; no country-level sample counts provided in the excerpt.
The intervention serves as a middle ground in the trade-off between higher costs (from more granular demographic targeting) and skew (from ignoring demographics entirely).
Authors' comparative claim about cost–skew trade-offs observed in their intervention versus alternatives; no quantitative cost or skew figures provided in the excerpt.
AI exhibits a significant U-shaped spatial effect on Lae.
Spatial econometric analysis (spatial Durbin model) on panel data for 30 Chinese provincial regions (2012–2022); kernel density estimation used for distributional analysis.
AI has a significant inverted U-shaped impact on the low-altitude economy (Lae), with diminishing marginal returns after a certain turning point.
Panel data from 2012–2022 for 30 Chinese provincial regions; composite AI and Lae indices constructed via the entropy method; estimated using spatial Durbin models and non-linear specification to detect inverted U-shape.
These findings challenge the notion of a universal technological dividend from AI (i.e., AI does not automatically deliver uniform productivity gains across firms).
Overall interpretation/synthesis of heterogeneous empirical results from the panel and cluster analyses showing variation in productivity effects across firm types.
AI adoption yields asymmetric productivity gains depending on firms' resource constraints and competitive environments (i.e., heterogeneity rather than a homogeneous effect).
Heterogeneity analysis using multidimensional clustering (firm size, age, market competitiveness, digital infrastructure) applied to the panel dataset; reported differential effects across clusters.
AI adoption affects Total Factor Productivity (TFP) of firms.
Panel regression analysis using the stated panel dataset examining relationship between AI adoption and firm-level TFP.
The study identifies short-term transitional risks and long-term productivity gains associated with AI integration in the workforce.
Abstract states the paper evaluates both short-term risks and long-term productivity gains from AI integration based on the reviewed literature; no empirical quantification given in abstract.
The study explores implications of algorithmic enterprises for competitive advantage, labour markets, and regulatory policy.
Declared scope of the paper in the abstract; exploration is conceptual and analytical rather than reporting empirical findings or quantified effects.
Effective AI policy mixes are contingent on regional resource endowments and development conditions (i.e., variation across configurations indicates contingency on regional context).
Observed variation across the fsQCA-derived configurations; authors interpret differences as reflecting dependence on regional resources and development conditions.
Across all settings, AI Organizations composed of aligned models produce solutions with higher utility but greater misalignment compared to a single aligned model.
Reported experimental results aggregated across two practical settings (AI consultancy and AI software team) and 12 tasks; direct comparison between AI Organizations of aligned models and a single aligned model.
Multi-agent "AI organizations" are simultaneously more effective at achieving business goals, but less aligned, than individual AI agents.
Experimental comparison reported in the paper: experiments comparing multi-agent AI organizations to single aligned agents across tasks and settings (described below).
Big data analytics (BDA) adoption is a risky strategy with potentially high rewards for start-ups.
Stated as a summary conclusion based on empirical analysis of a large sample of start-ups in Germany comparing adopters and non-adopters across multiple performance measures (survival, costs, sales, employee growth, access to financing).
The influence of human capital (number of specialists in scientific and technological fields) on value added varies across sectors.
Number of specialists in scientific and technological fields included as a covariate in MMQR; reported heterogeneous effects across sectors/quantiles in the results section.
The influence of R&D expenditure on value added varies across sectors.
R&D expenditure included as a core explanatory variable in panel MMQR estimations; authors report differing coefficient sizes/signs across sectors/quantiles.
Foreign direct investment (FDI) shows an insignificantly positive direct effect on local TFCP but a significantly negative indirect (spillover) effect, attributed to a 'pollution haven' effect.
Spatial Durbin Model estimates for FDI on panel (30 provinces, 2010–2023): direct coefficient positive but not significant; indirect coefficient significantly negative; interpretation given as pollution-haven mechanism.
Industrial intelligence exhibits regional heterogeneity: a significantly negative direct effect in the east, a significantly positive direct effect in the central region, an insignificant direct effect in the west, and positive indirect (spillover) effects in the east and west.
Regional/subsample Spatial Durbin Model analyses dividing the sample into east, central, and west regions (30 provinces, 2010–2023); reported region-specific direct and indirect coefficients and significance levels.
Industrial intelligence has an insignificantly negative direct effect on local TFCP, but its positive spatial spillover effect is significant at the 1% level, producing a significantly positive total effect.
Spatial Durbin Model results for industrial intelligence on panel (30 provinces, 2010–2023): direct coefficient negative and not statistically significant; indirect coefficient positive and significant at 1%; total effect positive and significant.
China's TFCP rose overall from 2010 to 2023 but exhibited a widening regional gap of 'higher in the east, lower in the west'.
Panel data of 30 Chinese provincial-level regions (2010–2023); TFCP measured using an undesirable-output super-efficiency SBM model and summarized temporal and spatial patterns.
Regional analysis shows inland regions remain capital-dependent, with an estimated (capital) elasticity of approximately 0.43.
Regional decomposition/estimation reported in the study comparing inland regions to coastal ones using the extended production function.
The benefits of FDI (jobs, productivity, skills) are uneven and often conditional on institutional quality, labor regulation, and sectoral composition of investments.
Mechanism mapping and thematic synthesis linking heterogeneous empirical findings to contextual moderators (governance, regulation, sector); review emphasizes consistent role of these moderators across studies.
Productivity gains from generative AI depend on task mix, integration design, and the availability of complementary human skills.
Theoretical evaluation and synthesis of heterogeneous empirical findings; authors highlight variation across firms, sectors, and tasks.
Aggregate productivity (output per worker or per unit of inputs) can rise while labor’s share and employment decline due to substitution toward K_T.
Macro growth-accounting exercises decomposing output growth into contributions from labor, traditional capital, and technological capital; model simulations showing productivity gains coexisting with falling labor shares under substitution elasticities.
In developed regions, DIA–DIT synergy produces negative spatial spillovers on neighbouring areas' green productivity.
Spatial Durbin model results reported in the paper showing negative spillover coefficients for developed regions; summary provides no numeric coefficients or sample size.
The positive effect of DIA–DIT synergy on GP exhibits diminishing marginal returns once the synergy passes a certain threshold.
Threshold models reported in the paper identify a synergy threshold beyond which marginal returns to GP decline; no numeric threshold or sample size provided in the summary.
The paper formalises an AI productivity transmission gap between technical adoption and inclusive productivity realisation.
Formal definition and derivation within the DIAC theoretical framework (analytical/modeling content).
AI does not translate directly from firm-level task efficiency into national productivity; its effect is filtered through complementary intangible investment, skills formation, data governance, competition policy, labor-market mobility, and social insurance.
Analytical DIAC model and accompanying theoretical argumentation in the paper; no empirical sample reported.
The AI premium is absent in emerging markets, including China.
Geographic cross-sectional analysis indicating no significant AI premium in emerging market firms (explicitly mentioning China).
Capital distortion negatively moderates the effect of industrial robots on firm-level TFP (i.e., capital distortion reduces the positive impact of robots on TFP).
Moderation/interaction analysis using the same panel data of Chinese listed firms and industrial robots (2006–2019); reported tests of interaction between robot application and measures of capital distortion.
Energy availability, grid expansion, and infrastructure financing constitute the principal unresolved risks and may represent the primary bottleneck to future AI growth.
Argument based on International Energy Agency reports, analyses of energy and infrastructure constraints cited in the article, and supporting literature on scaling/computational requirements (review and secondary data).
Massive AI investment has failed to generate commensurate productivity gains (the "AI productivity paradox").
Stated as the motivating empirical paradox in the paper; presented as an observed phenomenon motivating the theoretical argument (no specific dataset or numeric evidence provided in the abstract).
Despite enterprises continuing to invest heavily in AI, many initiatives fail to scale or generate sustained business value (the 'AI-investment paradox').
Background claim stated in the paper's introduction/abstract and presented as motivating fact; supported implicitly by citations to prior literature and industry reports (no original empirical sample or quantitative analysis reported in this paper).
In that same limiting case, the social rate of profit tends to zero.
Limit-case implication from the theoretical model linking capital composition and absence of living labor to vanishing profit rate (model analysis; no empirical data).
Deeper AGI adoption places downward pressure on the social rate of profit.
Analytical result from the political-economy model linking higher organic composition of capital and reduced living labor to a fall in the social rate of profit (theoretical derivation; no empirical sample).