Evidence (3492 claims)
Adoption
7395 claims
Productivity
6507 claims
Governance
5877 claims
Human-AI Collaboration
5157 claims
Innovation
3492 claims
Org Design
3470 claims
Labor Markets
3224 claims
Skills & Training
2608 claims
Inequality
1835 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 609 | 159 | 77 | 736 | 1615 |
| Governance & Regulation | 664 | 329 | 160 | 99 | 1273 |
| Organizational Efficiency | 624 | 143 | 105 | 70 | 949 |
| Technology Adoption Rate | 502 | 176 | 98 | 78 | 861 |
| Research Productivity | 348 | 109 | 48 | 322 | 836 |
| Output Quality | 391 | 120 | 44 | 40 | 595 |
| Firm Productivity | 385 | 46 | 85 | 17 | 539 |
| Decision Quality | 275 | 143 | 62 | 34 | 521 |
| AI Safety & Ethics | 183 | 241 | 59 | 30 | 517 |
| Market Structure | 152 | 154 | 109 | 20 | 440 |
| Task Allocation | 158 | 50 | 56 | 26 | 295 |
| Innovation Output | 178 | 23 | 38 | 17 | 257 |
| Skill Acquisition | 137 | 52 | 50 | 13 | 252 |
| Fiscal & Macroeconomic | 120 | 64 | 38 | 23 | 252 |
| Employment Level | 93 | 46 | 96 | 12 | 249 |
| Firm Revenue | 130 | 43 | 26 | 3 | 202 |
| Consumer Welfare | 99 | 51 | 40 | 11 | 201 |
| Inequality Measures | 36 | 105 | 40 | 6 | 187 |
| Task Completion Time | 134 | 18 | 6 | 5 | 163 |
| Worker Satisfaction | 79 | 54 | 16 | 11 | 160 |
| Error Rate | 64 | 78 | 8 | 1 | 151 |
| Regulatory Compliance | 69 | 64 | 14 | 3 | 150 |
| Training Effectiveness | 81 | 15 | 13 | 18 | 129 |
| Wages & Compensation | 70 | 25 | 22 | 6 | 123 |
| Team Performance | 74 | 16 | 21 | 9 | 121 |
| Automation Exposure | 41 | 48 | 19 | 9 | 120 |
| Job Displacement | 11 | 71 | 16 | 1 | 99 |
| Developer Productivity | 71 | 14 | 9 | 3 | 98 |
| Hiring & Recruitment | 49 | 7 | 8 | 3 | 67 |
| Social Protection | 26 | 14 | 8 | 2 | 50 |
| Creative Output | 26 | 14 | 6 | 2 | 49 |
| Skill Obsolescence | 5 | 37 | 5 | 1 | 48 |
| Labor Share of Income | 12 | 13 | 12 | — | 37 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Innovation
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Greater transparency and audit trails improve regulators’ ability to monitor concentration risks, model commonality and systemic vulnerabilities arising from algorithmic homogenization.
Policy analysis and regulatory design argument in the compendium, drawing on macroprudential principles and comparisons with European regulatory approaches; not empirically tested within the paper.
Regulatory certainty around rights‑based standards may reorient investment toward explainable AI, compliance tooling, audit services and governance technologies — creating a potential new sector of AI‑economics activity.
Projection based on market response theory and industry trends noted in the compendium; supported by comparative regulatory cases but not by quantified investment data in the paper.
Localized datasets and mandated disclosure could create public datasets and benchmarks that improve model fairness and enable new entrants.
Policy design proposal and comparative precedent examples in the corpus; normative expectation rather than demonstrated outcome.
Transparency standards can reduce information asymmetries between firms, borrowers and regulators, potentially lowering adverse‑selection problems in lending markets.
Theoretical economic argument grounded in market microstructure and information economics; supported by comparative regulatory literature in the corpus (no new empirical estimation reported).
Non‑discrimination and fairness requirements (procedural standards and substantive tests) must be mandated to prevent biased exclusion in automated credit and financial services.
Doctrinal analysis of jurisprudence and regulatory materials, comparative law review (Mexico ↔ Europe), and review of technical literature on algorithmic fairness in the ~4,200‑text forensic audit.
A 'White Box' regulatory model — mandatory transparency, explainability, and forensic auditability — should be required for algorithms used in banking/fintech, particularly credit scoring.
Normative protocol design and synthesis of legal, regulatory and technical literature in the forensic audit; policy operationalization component of the compendium (method: doctrinal analysis and normative design).
Digital Sovereignty should be recognized as a fundamental human right protecting citizens’ control over algorithmic decisions affecting economic life.
Normative/doctrinal legal argumentation and comparative law synthesis across the compendium; grounded in rights‑based reasoning and alignment with international human‑rights norms (no experimental/empirical test).
Verifiable compliance (privacy budgets, provenance, auditability) becomes a key economic input; demand for standards, attestation services, and transparent governance frameworks will grow.
Policy/economic argumentation and proposed governance layer including audit logs and policy controllers. No empirical adoption or demand measurements provided.
Prototype simulations indicate that decentralized training with coordination protocols can approach centralized personalization performance under realistic constraints (communication budgets, DP noise, heterogeneity).
Prototype/simulation-based evaluation described qualitatively in the paper. The paper emphasizes illustrative experiments; specific simulation parameters, dataset sizes, and numeric performance comparisons are not reported in detail.
Re-conceptualizing federated learning as a socio-technical infrastructure (not merely a distributed optimizer) enables cross-platform personalized advertising that substantially reduces centralized data custody risks while retaining effective personalization, provided system design integrates secure aggregation, differential privacy, solutions for heterogeneous and delayed feedback, adversarial defenses, and explicit governance mechanisms.
High-level systems and conceptual design with a proposed multi-layer architecture; analytical discussion of privacy/accuracy trade-offs; prototype/simulation-based evaluation described qualitatively. No large-scale field deployment reported; simulations described without detailed sample sizes or numeric benchmarks.
Complementarities matter: digitalization increases AGTFP more when combined with complementary investments and institutions (mechanization, R&D, cooperative organization).
Findings from mediation analysis and interaction/heterogeneity checks indicating larger effects where complementary inputs/institutions are present.
Non-grain-producing provinces experience larger AGTFP gains from digital rural development than major grain-producing provinces.
Comparative sub-sample analysis (non-grain vs. major grain-producing regions) showing larger estimated effects in non-grain-producing areas.
Digital service capacity shows diminishing marginal returns: the marginal positive effect of digital services on AGTFP weakens at more advanced stages of digital-service development.
Panel threshold/modeling of nonlinearity indicating a decreasing marginal effect of the digital service sub-index on AGTFP at higher development levels.
Digitalization accelerates agricultural mechanization and the diffusion of agricultural R&D, which act as channels raising AGTFP.
Mediation analysis including mechanization rate and agricultural R&D input/technology diffusion indicators as mediators; reported significant indirect effects.
Digital rural development strengthens cooperative organizational forms (farmer cooperatives), and this organizational upgrading contributes to higher AGTFP.
Mediation tests showing digitalization is associated with greater cooperative organization indicators, which in turn are associated with higher AGTFP.
Digital rural development encourages larger-scale agricultural operations (land consolidation/scale expansion), which contributes to increases in AGTFP.
Mediation models that include farm scale/land transfer indicators as mediators and find significant indirect effects; analysis notes institutional constraints limit full realization.
Digital rural development raises AGTFP in part by promoting labor mobility and reallocating labor toward higher-productivity uses.
Mediation analysis using the same provincial panel (2012–2022) showing significant indirect effects through labor reallocation/factor allocation variables.
Robustness checks and sensitivity analyses (alternative mappings, sector aggregation, price/base-year choices) are performed or at least implied to assess the stability of VIS results.
Paper notes cross-checks with alternative mappings and sensitivity tests to examine stability; specifics depend on paper details.
VIS provides a framework to quantify cross-sectoral labor spillovers and dependencies.
Input–output based VIS construction attributes upstream labor requirements to final sectors, enabling accounting of cross-sector labor embodied in outputs (demonstrated in the electricity case study).
VIS enables robust estimation of productivity trends over time that can inform policy, planning, and comparative analysis across sectors.
VIS produces annual time-series productivity measures using 2014–2023 data; authors argue these trend estimates are suitable for policy and comparative use.
VIS captures interactions among generation, distribution, storage, and consumption consistent with Integrated Energy Systems concepts.
VIS mapping and analysis applied to electricity subsystem sectors (generation, distribution, storage, consumption) showing interconnections via input–output relationships.
Because DPP benefits accrue systemically (e.g., improved circularity), private incentives to adopt may be insufficient and thus policy interventions, subsidies, or consortium governance are needed to correct underinvestment and coordination failures.
Inference from stakeholder survey responses and theoretical public‑good/coordination failure reasoning presented in the paper; not directly established by causal empirical tests in the study.
Convergence in the literature and concentration of influential authors suggest rapid standard‑setting; analogous real‑world concentration of model/platform providers could affect competitive dynamics and access to algorithmic capabilities.
Observation of lexical convergence and author concentration in bibliometric analyses; extrapolated implication to market structure based on comparative reasoning.
Adoption of GenAI may deliver productivity gains for adopters but also generate 'winner‑take‑most' dynamics (first‑mover advantages, network effects), with implications for wage dispersion and market concentration.
Argument based on literature convergence, theoretical reasoning about platform/model concentration and potential network effects; not directly measured in the bibliometric study.
Decentralised decision‑making mediated by GenAI may lower some internal transaction costs (faster local decisions) but raise coordination costs absent new governance mechanisms.
Theoretical implication drawn in the discussion/implications section based on conceptual mapping of literature; no direct causal empirical test in the bibliometric data.
Autonomous agents in industries like mobility and manufacturing will affect labor demand; the speed and distribution of displacement or augmentation depends on interoperability and upgrade cycles.
Labor‑economics reasoning and scenario analysis; conceptual and conditional statement without empirical labor market modeling or data.
FederatedFactory's synthesized datasets allow organizations with data scarcity to obtain balanced training sets without sharing raw data, but training generative modules may incur nontrivial compute costs and require certification/trust frameworks.
Paper discussion weighing practical costs and adoption incentives: acknowledges compute cost to train generative modules and the need for certification to ensure modules are safe/non-leaking. This is a reasoned assessment, not an empirical measurement.
Marginal returns to generating additional early-stage candidates may diminish unless AI also reduces attrition rates later in development.
Economic reasoning based on portfolio theory and observed persistence of late-stage attrition; presented as implication/recommendation rather than empirically tested claim.
Firms may expand preclinical candidate generation and run larger early portfolios enabled by AI, potentially shifting value and risk earlier in the pipeline.
Theory-driven implication from observed reductions in time-per-hit and candidate generation capacity reported in case examples; no firm-level portfolio empirical analysis provided.
AI-driven natural language processing and cross-cultural modeling can lower translation frictions across markets but also risk homogenizing offerings and reducing product differentiation and consumer surplus.
Theoretical argument combining NLP capabilities and economic implications for product differentiation; supported by conceptual examples; no empirical tests or cross-market analyses reported.
Digital tools and legal and economic legislation tended to act against each other, though both have potential to facilitate and achieve sustainability-related goals.
Reported interaction/contradiction between technological measures and policy measures observed in the empirical analysis; specifics of the antagonistic mechanisms, effect magnitudes, and statistical tests are not provided in the summary.
Digital transformation reconfigures investment strategies.
Stated in the abstract as one of the impacted domains; no methodological details or empirical evidence (e.g., investor surveys, portfolio analyses) are provided in the abstract.
New patterns are emerging as a result of digital transformation, including regionalization, sustainability-driven growth, and decentralized economic systems.
Descriptive finding reported in the paper; the abstract does not indicate empirical tests, time series, geographic scope, or sample for these patterns.
Additional testing of economic significance clarifies the economic importance of factors influencing BT adoption.
Authors report additional analyses (marginal effects / economic significance tests) applied to the primary models on the 27,400 firm-year dataset to quantify economic magnitudes of the influences on BT adoption.
Class and labor responses (bargaining, regulation, strikes, political backlash) can shape AI adoption patterns, increase the costs of labor substitution, and affect the redistribution of AI rents.
Political-economy reasoning based on Mandelian perspective and historical labor responses to technological change; qualitative, no event-study or microdata provided.
The taxonomy predicts compositional shifts in health labor markets: reduced demand for some routine roles and increased demand/returns for clinical judgment, coordination, and data-literacy skills.
Projected implications from the cross-case qualitative analysis and theoretical reasoning about task substitution/complementarity; not estimated empirically in the paper.
More effective social robots could substitute for some human-provided social or care services, shifting labor demand; alternatively, they may complement human workers by augmenting productivity.
Theoretical labor-market implications and scenarios; no empirical labor-market studies included.
Effects of DE on carbon outcomes differ by city agglomeration type: in 'optimization and upgrading' agglomerations DE reduces carbon emissions (PCE), though the effect is timed/later; in 'growth and expansion' agglomerations DE’s impact is concentrated on improving CEE.
Heterogeneity / subgroup analyses across city agglomeration classifications within the 278-city panel (2011–2022). Separate fixed-effects (and/or threshold) estimations by agglomeration type show statistically different DE effects on PCE and CEE across the two groups.
Improved access to timely finance can accelerate adoption of capital‑intensive and AI‑augmented technologies within MSMEs, amplifying productivity gains and creating positive spillovers while widening gaps between digitally enabled firms and laggards.
Theoretical linkage and suggested channel evidence; the paper calls for causal measurement of these effects and notes this claim is a projected implication rather than demonstrated with causal data in the study.
Two business models are likely to coexist: open/academic models that democratize access and proprietary platforms offering higher‑performance, integrated pipelines (SaaS/APIs).
Paper posits this dichotomy in the 'Market structure and value capture' section as a probable business outcome; it is a forecast rather than an empirically supported claim in the text.
Fragmented enforcement may permit harmful algorithmic behaviors to persist in some jurisdictions while strict measures in others alter global externalities (e.g., misinformation diffusion, discrimination).
Scenario and impact reasoning with qualitative examples of algorithmic harms; no cross-jurisdictional empirical harm incidence data included.
Delegation models (allowing agents to act on users’ behalf) change control and liability, with implications for insurance, liability allocation, and market structure.
Conceptual claim from interdisciplinary workshop discussions on delegation and legal/policy implications; not supported by empirical studies in the summary.
Adoption of these surrogate methods can shift organizational capital from purchasing raw compute (HPC/GPU cycles) toward investment in software, data pipelines, and domain-expert modelization capabilities.
Economic implication argued in the discussion section of the paper; based on the premise of reduced compute requirements from the empirical savings.
FDI effects on domestic firms and employment can be either crowding‑in (via linkages) or crowding‑out (via competition), depending on the strength of market linkages.
Mechanism mapping and mixed empirical findings synthesized in the review; underlying studies report both crowding‑in and crowding‑out conditional on linkages and absorptive capacity.
AI adoption can lead to capital reallocation and affect comparative advantage and global value chains, with implications for trade and investment patterns.
Analytical discussion based on secondary literature and economic theory summarized in the paper; empirical evidence cited is heterogeneous and not synthesized into a single estimate.
AI and automation may displace routine agricultural tasks, requiring measurement of net labor effects, reallocation to higher‑value tasks, and retraining policies.
Conceptual discussion and policy implications drawn from technology adoption literature; limited empirical evidence on net labor effects for AI specifically noted as a research priority.
Faster workflows and lower transaction costs due to AI may increase publication rates, change authorship practices, and affect incentives for replication and robustness.
Raised in Incentives and Research Behavior as a predicted effect. This is a theoretical prediction grounded in observed workflow changes; the abstract does not supply longitudinal or causal evidence documenting these behavioral changes.
Policy implication: policymakers seeking to balance openness and security should consider layered, adaptive instruments that can be tuned by sector or actor; economic analysis can help identify where centralized coordination yields scale economies versus where decentralized rights‑based approaches preserve competition and trust.
Normative policy recommendation extrapolated from the paper's comparative findings and theoretical framing; not tested empirically in the paper.
Increased liability risk and compliance costs could raise barriers to entry for startups and niche vendors and potentially consolidate market power among larger firms better able to absorb compliance overhead; alternatively, new markets could emerge for compliant, certified providers.
Economic reasoning about compliance costs and market structure (theoretical predictions), not supported by empirical industry data in the Article.
Smart power strategies that promote domestic AI champions (via procurement, subsidies, industrial policy) affect labour markets, inequality, and international labour arbitrage.
Conceptual claim grounded in literature on industrial policy and labour economics with policy examples referenced; no primary microdata analysis in the paper.