Evidence (7278 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
9047 claims
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Productivity
8066 claims
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Governance
7278 claims
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Human-AI Collaboration
6912 claims
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Org Design
4439 claims
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Innovation
4359 claims
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Labor Markets
3652 claims
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Skills & Training
3018 claims
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Inequality
2160 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 | 795 | 210 | 105 | 955 | 2131 |
| Governance & Regulation | 886 | 414 | 197 | 126 | 1654 |
| Organizational Efficiency | 826 | 204 | 129 | 87 | 1257 |
| Technology Adoption Rate | 681 | 259 | 128 | 110 | 1189 |
| Research Productivity | 464 | 138 | 65 | 349 | 1028 |
| Output Quality | 503 | 196 | 61 | 53 | 813 |
| Decision Quality | 351 | 180 | 84 | 51 | 673 |
| AI Safety & Ethics | 238 | 288 | 71 | 34 | 637 |
| Firm Productivity | 455 | 58 | 92 | 20 | 631 |
| Market Structure | 186 | 172 | 123 | 25 | 511 |
| Task Allocation | 222 | 70 | 76 | 34 | 407 |
| Innovation Output | 238 | 28 | 48 | 18 | 334 |
| Skill Acquisition | 177 | 62 | 62 | 17 | 318 |
| Employment Level | 107 | 57 | 108 | 13 | 287 |
| Fiscal & Macroeconomic | 135 | 72 | 44 | 26 | 284 |
| Firm Revenue | 172 | 50 | 28 | 5 | 256 |
| Consumer Welfare | 121 | 68 | 45 | 12 | 246 |
| Task Completion Time | 183 | 33 | 10 | 13 | 240 |
| Inequality Measures | 45 | 126 | 50 | 6 | 227 |
| Worker Satisfaction | 95 | 74 | 23 | 12 | 204 |
| Error Rate | 77 | 98 | 11 | 4 | 190 |
| Regulatory Compliance | 84 | 73 | 17 | 7 | 181 |
| Automation Exposure | 61 | 61 | 27 | 14 | 166 |
| Training Effectiveness | 98 | 21 | 14 | 19 | 154 |
| Wages & Compensation | 78 | 37 | 25 | 6 | 146 |
| Developer Productivity | 105 | 18 | 14 | 6 | 144 |
| Team Performance | 87 | 17 | 28 | 10 | 143 |
| Job Displacement | 12 | 83 | 23 | 1 | 119 |
| Hiring & Recruitment | 53 | 8 | 8 | 3 | 72 |
| Social Protection | 39 | 17 | 8 | 2 | 66 |
| Creative Output | 32 | 20 | 8 | 3 | 64 |
| Skill Obsolescence | 5 | 50 | 6 | 1 | 62 |
| Labor Share of Income | 17 | 20 | 17 | — | 54 |
| Worker Turnover | 15 | 15 | — | 3 | 33 |
| Industry | — | — | — | 1 | 1 |
Governance
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The paper recommends a research agenda for AI economists: causal microeconometric studies (DiD, IVs, RCTs), structural models with hybrid human–AI agents, measurement work on GenAI use, distributional analysis and policy evaluation.
Explicit recommendations listed in the implications and research agenda sections; logical follow‑on from bibliometric findings about gaps in causal and measurement evidence.
Bibliometric mapping profiles the intellectual structure and evolution of the field but does not establish causal effects of GenAI on organisational outcomes.
Methodological limitation explicitly stated in the paper; bibliometric approach (co‑word, citation, thematic mapping) is descriptive and historical in scope.
Co‑word and thematic analyses reveal six coherent conceptual clusters that bridge technical AI topics (e.g., LLMs, GANs) with managerial themes (e.g., autonomy, coordination, decision‑making).
Thematic mapping and co‑word network analysis performed on the 212‑paper corpus; identification of six clusters reported in results.
Bibliometric and conceptual tools (VOSviewer, Bibliometrix) were used to identify performance trends, co‑word structures, thematic maps, and conceptual evolution in the GenAI–organisation literature.
Methods section: use of VOSviewer for network visualization and Bibliometrix for bibliometric statistics, co‑word analysis, thematic mapping and Sankey thematic evolution.
The study analysed a corpus of 212 Scopus‑indexed publications covering 2018–2025 to map emergent literature on Generative AI and organisational change.
Bibliometric dataset constructed from Scopus; sample size = 212 peer‑reviewed articles; time window 2018–2025; analyses performed with Bibliometrix and VOSviewer.
Research agenda: causal studies (panel data, quasi-experiments) are needed to estimate effects of AI exposure on employment outcomes and to evaluate retraining/income-support interventions for pre-retirement populations.
Authors’ stated recommendation based on limits of cross-sectional regression results from the n=889 survey and the identified need to move from association to causation.
Study limitations: cross-sectional design, self-reported intentions, potential unobserved confounders, and limited generalizability to only three cities (Beijing, Guangzhou, Lanzhou).
Explicit methodological statements in the paper describing data and design: cross-sectional survey of 889 respondents from three cities and reliance on self-reported employment intentions.
The paper identifies future research directions, including empirical causal studies on how DPP+AI interventions change recycling rates, second‑hand market prices, and firm investment in circular processes; and modeling firm strategy around proprietary vs shared DPP data.
Stated research agenda and gaps in the paper informed by the study's findings and limitations; these are recommendations rather than empirical claims.
The study used a mixed-methods design focused on the Italian fashion and cosmetics industries, employing two online surveys, k‑means clustering (consumer segmentation), principal component analysis (to identify underlying dimensions of DPP functionalities and sustainability practices), and logistic regression (to identify adoption drivers).
Methods section summary provided in the paper; explicit statement of methods and industry context. Note: sample sizes and survey instrument details are not provided in the summary.
Two consumer segments were identified: 'aware' consumers (environmentally attuned and receptive to digital innovation and sustainability information) and 'unaware' consumers (prioritize immediate, tangible benefits like price and convenience over sustainability information).
K‑means cluster analysis applied to consumer responses from one of the online surveys in the Italian fashion and cosmetics context; summary identifies two clusters; sample sizes not reported.
This work is a conceptual/policy analysis rather than an original empirical study.
Explicit statement in the paper's Data & Methods section.
Study limitations include single-country (China) listed‑firm sample and reliance on secondary/administrative proxies for digitalization and innovation, which may miss internal qualitative aspects and introduce measurement error.
Authors’ stated limitations: sample restricted to Chinese A-share listed firms (2012–2022) and measures of digitalization/innovation derived from administrative/secondary data rather than direct observation/survey of internal practices.
No new primary empirical tests were performed in this paper; conclusions are based on secondary analysis and are broad and diagnostic rather than demonstrating causal mechanisms.
Explicit methodological statement in the Data & Methods and Limitations sections of the paper describing it as a qualitative literature review and synthesis.
Research should prioritize causal identification (IV, difference‑in‑differences, regression discontinuity) to disentangle whether ESG causes better financial outcomes or instead proxies for unobserved firm quality.
Methodological recommendation based on limitations in the reviewed literature (many observational/correlational studies); the paper argues for stronger causal designs going forward.
The authors propose research priorities for economists: quantify productivity gains from closing the actionability gap; estimate firm-level heterogeneity in evaluation capability and its effect on adoption; and model investment trade-offs between building evaluation-to-action pipelines versus accepting reduced LLM performance.
Paper's concluding recommendations for future research directions (explicitly listed by the authors).
The paper produces as primary outcomes a taxonomy of ten evaluation practices, the articulation of the results-actionability gap, and recommended strategies observed among successful teams.
Authors report these as the main outcomes of their thematic analysis and syntheses from the 19 interviews.
The study method consisted of semi-structured qualitative interviews with 19 practitioners across multiple industries and roles, analyzed via thematic coding.
Explicit methods section of the paper stating sample size (n=19), participant diversity, interview approach, and coding/analysis procedure.
AI-economics research should treat quantum capability as a distinct, gradually diffusing factor of production with sectoral specificity and model complementarities and policy counterfactuals endogenously.
Modeling recommendations grounded in sensitivity of macro outcomes to diffusion patterns, complementarities, and policy choices observed in the scenario and counterfactual analyses.
Model parameters are calibrated using historical diffusion of enabling technologies (cloud computing, GPUs, AI toolchains), industry case studies, and expert elicitation where hard data are lacking.
Empirical grounding section describing calibration sources: historical diffusion, case studies (materials discovery, optimization), and expert elicitation.
Uncertainty quantification is performed by running Monte Carlo or scenario ensembles and conducting sensitivity and robustness checks.
Methodological claim in the uncertainty quantification section describing Monte Carlo/scenario ensemble approach.
Sectoral TFP shocks are integrated into computational general equilibrium (CGE) or multi-sector growth models (and optionally DSGE variants) to simulate GDP, sector output, trade impacts, and labor reallocation.
Method section stating integration of sectoral TFP shocks into CGE/multi-sector growth models with optional DSGE short-run dynamics.
Sectoral adoption is translated into total factor productivity (TFP) shocks or sector-specific Hicks-neutral productivity improvements based on micro evidence of quantum advantages.
Methodological description of productivity mapping linking adoption to TFP shocks using micro evidence and case studies.
The paper uses empirical diffusion functions (logistic/S-curve, Bass model) calibrated to analogous technologies to project uptake over time.
Methodological description: diffusion modeling section explicitly states use of logistic/S-curve and Bass models and calibration to past technologies (cloud, GPUs).
The analysis used sentence‑transformer models to produce dense vector representations of article text and UMAP to project those embeddings into a low‑dimensional thematic map for cluster identification and gap detection.
Methods section specifying use of sentence‑transformer embeddings and UMAP for dimensionality reduction/visualization of article text.
The study followed a PRISMA protocol for literature selection and included peer‑reviewed journal articles published between 2014 and 2024, with a final sample size of n = 109.
Explicit methodological statement in the paper describing the literature search, inclusion/exclusion criteria, and final sample.
Twenty‑seven papers study marketing in banking without using NLP methods.
PRISMA systematic review; categorization of the 109 selected articles into the three coverage groups (8, 74, 27).
Seventy‑four papers study NLP in marketing more broadly (not specifically banking).
Same PRISMA‑based systematic review and manual categorization of the final sample n = 109 into topical buckets (NLP in marketing vs. NLP in bank marketing vs. marketing in banking without NLP).
Only 8 peer‑reviewed papers directly examine NLP in bank marketing (out of a final sample of 109 articles published 2014–2024).
Systematic review following PRISMA protocol; final sample n = 109 peer‑reviewed journal articles published 2014–2024; manual screening and categorization yielding counts by topic.
The study's findings are qualitative and case-driven (Xiaomi and Deloitte); generalizability is limited by case selection and the absence of standardized quantitative metrics.
Methods section explicitly states case analysis and literature review as primary methods and notes lack of large-scale quantitative measurement.
The methodology is normative-philosophical argumentation supplemented by interdisciplinary synthesis (phenomenology, deconstruction, OOO, STS/material turn); this is not an empirical causal study and contains no quantitative datasets.
Author-declared methods and limits: statement that the intervention is theory-driven and qualitative; absence of quantitative analysis reported.
The paper’s empirical grounding consists of illustrative case studies and vignettes from healthcare robotics, autonomous vehicles, and algorithmic governance used to demonstrate distributed agency and responsibility.
Author-stated methodology: qualitative vignettes/case illustrations across three domains; no reported sample sizes or systematic data collection.
The analysis in the paper is primarily qualitative and descriptive; it does not empirically quantify AI’s effects on trade flows or welfare.
Explicit statement in the methods/data description noting a mixed qualitative approach (theoretical analysis, comparative legal analysis, case studies, scenario reasoning) and absence of empirical quantification.
The study is qualitative and law-focused and uses Vietnam as a focused case study without collecting primary quantitative field data.
Explicit Data & Methods statement in the paper indicating doctrinal legal analysis, comparative institutional analysis, and normative framework development; no primary quantitative sample.
The study recommends empirical metrics for future evaluation of reforms, including processing time per case, reversal rates on appeal, administrative litigation frequency, compliance and procurement costs, investment flows into public-sector AI, and changes in labor composition and wages in administrative agencies.
Methodological recommendation arising from the paper's normative and comparative analysis.
Analysis compared responses across 16 predefined dimension pairs (ethical dimensions or response axes) and used repeated measures and qualitative coding to characterize system behavior.
Methods and Analysis sections reporting use of 16 dimension-pair comparisons, repeated-measures tests for delta between blind and declared administrations, and qualitative coding to derive D3 failure taxonomy.
Probe administration included operational controls: runs were administered by two human raters across three machines to ensure operational consistency.
Methods statement describing administration by two human raters on three machines.
The ceiling discrimination probe used Gemini Pro (Google) and Copilot Pro (Microsoft) as independent judges.
Methods: reported use of Gemini Pro and Copilot Pro as independent judges for the ceiling probe.
Primary blind scoring was performed by Claude (Anthropic) used as an LLM judge.
Methods: primary blind scoring explicitly performed by Claude.
Re-administration under declared conditions produced zero delta across all 16 dimension-pair comparisons (no measurable change when declaration status changed).
Reported repeated-measures comparisons across 16 predefined dimension pairs between blind and declared administrations, with reported zero delta.
Series 2 consisted of local and API open-source systems (n = 6) administered blind and declared, with four systems re-administered under declared conditions.
Methods description detailing Series 2 composition, modes (blind and declared), and that four systems were re-tested under declared conditions.
Series 1 consisted of frontier commercial systems administered blind (n = 7).
Methods description specifying Series 1 composition and blind administration.
The study employed 24 experimental conditions spanning 13 distinct LLM systems across two series.
Study design reported in Methods: Series 1 (frontier commercial, blind, n=7), Series 2 (local/API open-source, blind and declared, n=6), plus re-administered declared runs and ceiling-probe runs summing to 24 conditions.
The experiment used NYSE TAQ transaction and quote data for SPY covering 2015–2024 and tested six pre-specified hypotheses about market-quality trends.
Data and methods section specifying dataset (NYSE TAQ SPY, 2015–2024), the number of pre-specified hypotheses (six), and experimental protocol with 150 autonomous agents.
Agents' methodological choices and resulting effect estimates were systematically recorded and used to quantify dispersion and measure switching across stages.
Study design description: recorded agents' methodological choices (measure selection, estimation procedures), resulting estimates, and tracked switching and dispersion metrics (IQR) across the three-stage protocol applied to SPY TAQ data (2015–2024) with 150 agents.
AI peer review (agents exchanging written critiques) produced minimal reduction in dispersion of estimates.
Three-stage protocol: after stage 1 (independent analyses) and stage 2 (AI peer review), measured dispersion (e.g., IQR) across agents showed little change following the peer-review stage across the six hypotheses and agent pool (n=150).
The work is qualitative and exploratory — presenting naturalistic phenomena rather than causal empirical estimates, and is intended to be hypothesis-generating rather than definitive.
Methodology explicitly stated: naturalistic, qualitative daily observations over one month across multiple platforms; comparative observational documentation without experimental manipulation or causal identification.
Future empirical work should measure calibration (user trust vs. model accuracy), hallucination rate, user comprehension of capability limits, and behavioral dependence on system recommendations.
Explicit methodological recommendations and suggested metrics in the paper; these are proposed future measurements rather than reported findings.
Conversational AI differs from interpersonal conversation: it has no true beliefs/intentions or accountability and produces probabilistic, sometimes inconsistent outputs with opaque training/data provenance.
Analytical/distinctive claim based on properties of LLMs and machine learning models discussed in the paper; conceptual analysis, no empirical testing.
Research agenda items for economists include: quantifying willingness-to-pay for verifiable reasoning, studying labor-market impacts for validators, designing contracts/mechanisms to incentivize truthful argument provision, and evaluating regulatory interventions.
Paper's stated research and policy agenda; prescriptive rather than empirical.
Evaluation currently lacks metrics and benchmarks for argument quality, fidelity, contestability, and human trust; developing these is necessary.
Paper notes the gap and proposes evaluation metrics and experimental designs; no new benchmarks introduced.