Evidence (329 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
10085 claims
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Productivity
8974 claims
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Governance
8062 claims
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Human-AI Collaboration
7749 claims
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Org Design
5057 claims
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Innovation
4896 claims
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Labor Markets
4088 claims
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Skills & Training
3372 claims
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Inequality
2377 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 | 882 | 244 | 117 | 1097 | 2424 |
| Governance & Regulation | 1010 | 469 | 229 | 135 | 1875 |
| Organizational Efficiency | 977 | 235 | 149 | 90 | 1462 |
| Technology Adoption Rate | 781 | 299 | 143 | 128 | 1362 |
| Research Productivity | 506 | 155 | 74 | 363 | 1110 |
| Output Quality | 555 | 219 | 71 | 70 | 915 |
| Decision Quality | 395 | 200 | 95 | 54 | 751 |
| Firm Productivity | 523 | 67 | 101 | 27 | 724 |
| AI Safety & Ethics | 262 | 309 | 75 | 36 | 688 |
| Market Structure | 195 | 201 | 135 | 30 | 566 |
| Task Allocation | 248 | 77 | 96 | 38 | 464 |
| Innovation Output | 300 | 34 | 55 | 20 | 411 |
| Skill Acquisition | 207 | 75 | 65 | 21 | 368 |
| Employment Level | 138 | 67 | 119 | 24 | 350 |
| Fiscal & Macroeconomic | 156 | 80 | 53 | 33 | 329 |
| Task Completion Time | 211 | 38 | 13 | 16 | 280 |
| Firm Revenue | 183 | 52 | 29 | 5 | 270 |
| Consumer Welfare | 131 | 77 | 48 | 13 | 269 |
| Inequality Measures | 50 | 141 | 54 | 9 | 254 |
| Worker Satisfaction | 104 | 85 | 25 | 13 | 227 |
| Error Rate | 87 | 112 | 11 | 5 | 215 |
| Automation Exposure | 69 | 69 | 37 | 20 | 198 |
| Wages & Compensation | 102 | 49 | 31 | 11 | 193 |
| Team Performance | 115 | 30 | 30 | 11 | 187 |
| Regulatory Compliance | 88 | 74 | 17 | 7 | 186 |
| Training Effectiveness | 109 | 22 | 14 | 21 | 168 |
| Developer Productivity | 116 | 21 | 15 | 8 | 161 |
| Job Displacement | 12 | 92 | 26 | 1 | 131 |
| Hiring & Recruitment | 57 | 12 | 9 | 5 | 83 |
| Skill Obsolescence | 6 | 59 | 10 | 2 | 77 |
| Social Protection | 43 | 17 | 8 | 2 | 70 |
| Creative Output | 35 | 21 | 9 | 4 | 70 |
| Labor Share of Income | 18 | 23 | 17 | 1 | 59 |
| Worker Turnover | 15 | 16 | — | 4 | 35 |
| Industry | — | — | — | 1 | 1 |
Trade openness (TO) positively influences sustainable economic growth (SEG) in China but has a weaker effect on SEG in the United States.
Country-specific ARDL results using quarterly data (2015Q1–2025Q4, 44 observations) showing a positive and meaningful TO coefficient for China and a smaller (weaker) coefficient for the United States.
Long-run asymmetric response to renewable energy shocks is statistically confirmed (Wald χ² = 5.42, p = 0.020).
Long-run Wald test for asymmetry from CS-PMG-NARDL on the 18-country panel (2000–2023); reported χ² and p-value.
Short-run asymmetric response to renewable energy shocks is statistically confirmed (Wald χ² = 4.102, p = 0.043).
Short-run Wald test for asymmetry from CS-PMG-NARDL on the 18-country panel (2000–2023); reported χ² and p-value.
These positive results are not supported in all contexts (i.e., the positive effects are not universally found across all specifications/contexts).
Abstract statement noting heterogeneity/robustness: preferred results hold but are 'not supported in all contexts.' Implies some specifications or subsets do not show the effects.
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.
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).
A forecast-error variance decomposition attributes most aggregate volatility to the longevity shock, while the AI shock dominates the variance of the return to AI capital.
Model-based forecast-error variance decomposition implemented on the simulated stochastic model to apportion variance of aggregate variables and the return to AI capital across shocks.
The macroeconomic significance of AI-induced productivity depends not only on technological efficiency, but also on the distributive transmission of productivity gains through labour income, disposable income, prices, investment, public expenditure, transfers and external demand.
Theoretical argument and synthesis of literature in the conceptual review (no new empirical estimation reported).
With endogenous capital accumulation, data-driven automation generates explosive growth but stagnant long-run wages.
Extended model incorporating endogenous capital accumulation: analytical solution/characterization showing unbounded (explosive) growth in aggregate variables while real wages remain stagnant in the long run (model derivation).
Aggregate AI metrics (the composite AI Vibrancy Score) obscure heterogeneous pillar-level effects on tourism’s economic contribution.
Comparison of null result for the aggregate AI Vibrancy Score with significant positive effects for specific pillars (R&D, Policy and Governance, lagged Talent) in the same fixed-effects analyses on 33 countries (2017–2023).
There is a long-run equilibrium (cointegrating) relationship among AI adoption, skill-disaggregated unemployment, and sustainable development in South Africa.
Empirical ARDL results reported in the paper indicating a long-run equilibrium relationship based on annual 2003–2024 time-series data.
The paper examines the macroeconomic impact of AI (drawing on the cited institutional projections) to understand sectoral and aggregate economic implications for Georgia.
Method: macroeconomic synthesis of external projections (Goldman Sachs, McKinsey, Penn Wharton, IMF) and application to Georgia; no reported experimental sample size.
Resource (digital talent) agglomeration should remain at a moderate level and achieve coordinated development, because excessive concentration can reduce the growth benefits (implied by the inverted-U finding).
Policy implication drawn from the paper’s finding of an inverted-U relationship between talent agglomeration, industrial digitalization, and regional economic growth (normative recommendation based on empirical nonlinear result).
The relation among digital talent agglomeration, industrial digitalization, and regional economic growth follows an inverted-U shape (consistent with the Williamson hypothesis).
Systematic empirical examination of China's provincial regions using regional-level empirical analysis (paper reports an econometric test of nonlinear/quadratic relationships between digital talent agglomeration, industrial digitalization, and regional economic growth). Sample size (number of provinces/observations) not stated in the excerpt.
The negative quadratic term confirms a concave (inverted-U) relationship between AI and economic growth (diminishing marginal returns of AI).
Panel data for 19 G20 countries (2005–2023) estimated with a quadratic specification in GMM; reported negative and statistically significant coefficient on the AI-squared term.
Even when two economies share the same long-run automation level, adoption speed alone determines transition welfare.
Comparative-welfare analysis in the dynamic theoretical model holding long-run automation level fixed while varying adoption speed (analytical comparative statics).
Modeling fiscal policy as a government problem (instead of an abstract planner) implies a tax changes the firm's automation first-order condition, raises revenue only on the remaining automation base, and requires specifying rebates and administrative losses.
Explicit governmental optimization and budget-accounting setup in the model: taxes enter firms' automation first-order conditions; revenue is computed on post-tax automation activity and rebates/administration are modeled.
The magnitude of AI’s effect on potential GDP varied across industries and depended on the level of digital maturity, human resources, and institutional conditions.
Decompositional analysis across aggregated industry data and scenario-based modeling drawing on sectoral sources and reviews.
If employment losses are relatively small and productivity gains are realised, AI adoption could boost Exchequer revenues. But if job displacement is sizeable, tax receipts fall while welfare spending rises, resulting in potentially large pressures on the public finances.
Conditional fiscal scenarios simulated in the report combining employment, wage and benefit changes with the public finance implications (tax receipts and welfare spending); reported as scenario-based outcomes.
Automation leads economic growth to accelerate, but the acceleration is remarkably slow because of the prominence of 'weak links' (an elasticity of substitution among tasks substantially less than one); even when most tasks are automated by rapidly-improving capital, output is constrained by the tasks performed by slowly-improving labor.
Theoretical mechanism from the task-based model (σ < 1 weak-links structure) combined with calibrated simulations that incorporate historical accounting results.
The growth effects of AI are conditional on institutional quality and organizational adaptability.
Theoretical/analytical claim in the paper's framework and supported by the stylized-facts analysis indicating heterogeneity in productivity and growth outcomes by institutional and digital capacity indicators.
The magnitude and timing of macroeconomic impact from quantum computing are highly uncertain.
Monte Carlo / scenario ensemble results showing wide (fat-tailed) outcome distributions driven by uncertainty in technical milestones, adoption rates, and complementarity strengths; use of expert elicitation to parameterize tail risks.
Artificial intelligence (AI) has a positive and statistically significant effect on growth at lower conditional quantiles (τ = 0.10–0.25) but is insignificant at higher quantiles.
MMQR estimation results reported in the paper showing significant positive AI coefficients at τ = 0.10–0.25 and insignificant coefficients at higher quantiles.
Kondratieff, Schumpeter, and Mandel each highlight different drivers of capitalist long waves: Kondratieff emphasizes regular technological-driven renewal, Schumpeter emphasizes entrepreneurship and innovation-led creative destruction, and Mandel emphasizes class relations and production structures.
Comparative theoretical analysis and literature synthesis across the three schools; conceptual summary of canonical positions (no original dataset; qualitative interpretation).
Increasing tariff protection (Trump Tariff 2.0 policy environment) exerts a negative effect on sustainable economic growth (SEG) by reducing the efficiency gains associated with international trade and technology diffusion.
ARDL model estimates over 2015Q1–2025Q4 (quarterly data, 44 observations) incorporating measures of tariff protection; reported negative coefficient and statistical inference linking higher tariffs to lower SEG via reduced trade/technology diffusion efficiency.
The interaction between renewable energy and CO2 emissions is negative and significant (RE × CO2 = −0.041, p < 0.001), implying high emissions undermine renewable energy benefits for green growth.
GMM interaction term RE × CO2 estimated on the 18-country panel (2000–2023); reported coefficient −0.041 with p < 0.001.
Negative renewable-energy shocks have a statistically significant but smaller long-run negative effect on green growth (−0.012, p = 0.015).
Long-run negative-shock coefficient from CS-PMG-NARDL on 18 G20 countries (2000–2023); reported coefficient −0.012 with p = 0.015.
Contemporary capitalism is characterised by persistent overaccumulation, declining profitability, and intensified financialisation under conditions of hegemonic instability.
Theoretical synthesis drawing on Marxian crisis theory, social structures of accumulation, and theories of hegemonic transition; no specific sample size or quantitative dataset reported in the provided text.
This structural under‑serving of SMEs by advanced BI and analytics is threatening inclusive economic growth and resiliency.
Argument presented in the review synthesizing literature (2020–2025); no quantified causal estimates or sample sizes provided in the excerpt.
The gross tax gap in the U.S is over 600 billion a year.
Statement in paper citing standard U.S. tax-gap estimates (presumably IRS estimates); presented as a factual background statistic in the literature review.
Experts in the study assign a 14% probability to 'rapid-progress' scenarios characterized by substantial GDP growth, declining labor force participation, and accelerating wealth inequality.
Result from the 2025 forecasting study of experts (69 economists + 52 AI experts), reporting a probability estimate (14%) for a named scenario with specified macroeconomic and labor-market features.
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.
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.
Expected crisis losses are convex in aggregate leverage.
Analytical result/proposition derived within the model showing convex relationship between expected losses and aggregate leverage. No empirical sample.
Temporary accommodation has become a major fiscal and administrative pressure for English local authorities, particularly in London, where demand and costs have risen sharply.
Statement in paper introduction/background; contextual claim based on administrative observations and cited motivation for building DOMUS (no specific sample size or numerical data reported in the provided text).
The COVID-19 pandemic reduced tourism’s GDP share by approximately 37%.
Fixed-effects panel estimation including a COVID-19 indicator on 33 countries (2017–2023); reported coefficient β = –0.455, p < 0.001 (interpreted as ~37% reduction in the dependent variable).
Unemployment among highly educated workers consistently impedes sustainable development across both short- and long-run horizons.
Skill-disaggregated unemployment coefficients from ARDL short- and long-run estimates reported in the paper showing negative effects of highly educated workers' unemployment on development.
In the short run, AI adoption negatively impacts sustainable development due to adjustment costs from routine-task substitution, labour market rigidities, and skill mismatches.
Short-run ARDL coefficient estimates reported in the paper showing a negative short-run effect of AI adoption on development; interpretive explanation attributing causes to adjustment costs, rigidities, and mismatches.
AI adoption has the potential to amplify systemic vulnerabilities in financial markets.
Comparative institutional analysis and qualitative evidence across China, the United States, and the United Kingdom (2022–2025) reported in the abstract noting potential amplification of systemic vulnerabilities linked to AI.
Power utilization is particularly important as grid power capacity is a scarce resource in the AI era.
Contextual claim in the paper linking increased AI demand to constrained grid power capacity; supported by the paper's framing rather than reported empirical measurements in the abstract.
Developing countries face macroeconomic vulnerabilities because of dependence on remittances, which are exposed by automation-driven changes in migrant labor demand.
Analytical linkage developed in the paper supported by comparative field evidence and macroeconomic reasoning; remittance dependence highlighted as a vulnerability (no quantitative estimates or sample sizes reported).
Social welfare is strictly concave in adoption speed and is maximized at an interior optimum below the market rate of adoption.
Analytical welfare optimization in the theoretical model: social-welfare function as a function of adoption speed yields strict concavity and an interior social optimum; comparison with market equilibrium adoption speed indicates market rate exceeds social optimum.
Sustained investment in large-scale chatbot infrastructures increases environmental costs.
Paper asserts environmental impacts from infrastructure investment (energy, resource use) as part of systemic critique; no quantified environmental measurements or sample size reported.
Whether it is the periodic compulsory recoinage in medieval Europe or Gesell's stamp scrip, both are essentially mechanisms for taxing money holdings.
Interpretive/historical claim presented by the authors; no empirical testing or sample reported in the excerpt.
The devaluation of money runs through almost the whole process of history, from the weight reduction and purity decrease of metallic coin to the unanchored over-issuance of paper currency.
Historical summary/claim by the authors referencing long-run monetary history; no specific empirical study or sample size given in the excerpt.
AI-adopting firms anticipate smaller increases in their own prices and lower medium- to long-term inflation than non-adopters.
Survey questions on firms' price-change expectations and macro inflation expectations, comparing responses of adopting vs non-adopting firms.
There are macroeconomic risks associated with AI-led unemployment.
Paper's macroeconomic analysis drawing on labor economics and technology adoption research; no quantitative estimates or sample sizes provided in the summary.
Premature workforce contraction in response to AI adoption foreshadows deeper structural challenges as AI systems mature.
Forward-looking claim based on synthesis of literature and theoretical projection; no empirical quantification or sample provided in the summary.
Unbalanced or poorly governed adoption of Big Data and AI contributes to increased systemic risk, cybersecurity vulnerability, regulatory fragmentation and third-party dependence on BigTech platforms.
Argument based on qualitative literature review and synthesis of international empirical studies and comparative sector analysis; no single-sample empirical study in this paper.