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).
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 |
Labor Markets
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AI has changed who works in jobs (i.e., workforce composition).
Stated in the paper's abstract as an asserted effect of AI on employment composition; presented as part of the paper's review rather than a specific empirical estimate.
The penetrating utilization of AI-based methods to perform tasks has drastically changed how jobs are performed.
Claim asserted in the paper (abstract) as a descriptive conclusion from the paper's review/analysis; no empirical sample or quantified effect reported in the provided text.
AI is altering nearly every aspect of human interaction—such as work and society.
Statement in the paper's abstract/intro; presented as a general observation in the paper (literature review/qualitative synthesis implied). No primary sample size or empirical estimate reported in the provided text.
Comparative analysis of Japanese, European, and United States legal frameworks shows differing treatments of translation data and points toward the need for redistributive design to remedy unequal attribution and capture.
Comparative legal analysis across jurisdictions (Japan, EU, US) and normative argument proposing redistributive design directions; no experimental or quantitative evaluation provided.
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.
The paper contributes by providing a structured synthesis that bridges efficiency-driven and labor-oriented perspectives on AI-driven manufacturing.
Authors' stated contribution in the paper: a structured thematic synthesis integrating two perspectives from the reviewed literature.
While new high-skill roles emerge from AI adoption, their limited accessibility constrains workforce transition.
Literature synthesis indicating emergence of high-skill roles alongside barriers to access (skills, education, hiring practices) reported in reviewed studies.
This study analyzes three key dimensions: labor displacement as a structural risk, the limitations of job transformation, and the emergence of human-centered AI.
Explicit methodological statement in the paper: systematic literature review and thematic synthesis focusing on three named dimensions.
Decomposition analysis reveals that wage benefits are concentrated among employees aged 45 and above, managers, and white-collar workers; other worker categories experience stagnant wages, and no group shows a negative wage effect.
Decomposition of wage effects by worker groups (age, occupation/type) using the integrated dataset and the DiD/other regression analyses.
Wage increases at small firms primarily explain the positive adoption effect, while wages at medium and large firms remain stagnant after adoption.
Heterogeneity analysis by firm size within the DiD framework showing differential post-adoption wage trajectories for small versus medium/large firms.
Key mechanisms of AI's impact on employment structure were identified: automation of routine processes, formation of new professional profiles, and changes in requirements for employees' competencies.
Qualitative analysis of statistical data, industry reviews, and regulatory legal documents described in the paper (no experimental or survey sample size reported).
The effects of digital transformation on labor demand vary substantially across types of digital technologies.
Analysis across different digital technology categories reported in the paper showing heterogeneous effects on labor demand (data: Chinese A-share manufacturing firms, 2011–2024). (Sample size not stated in provided text.)
The impact of digital transformation on labor demand differs across firms with different ownership structures, factor intensity, and asset sizes.
Heterogeneity analysis reported in the paper using subsample or interaction regressions by firm ownership, factor intensity, and asset size (Chinese A-share manufacturing firms, 2011–2024). (Sample size not stated in provided text.)
Labor-market adjustment to generative AI is a process of organizational reconfiguration, in which firms reshape both hiring demand and the task architecture of work.
Synthesis/conclusion drawn from the paper's empirical findings (decomposition results, heterogeneity analyses).
Adjustment to generative AI differs across the job ladder: senior jobs adjust earlier and mainly through reallocation, whereas junior jobs adjust through a broader mix of reallocation, redesign, and their interaction.
Heterogeneity analysis by job seniority reported in the paper (timing and margin composition of adjustment by seniority).
Generative AI exposure is dynamic rather than fixed, changing substantially over time.
Empirical application of the dynamic posting-level exposure measure to the nationwide job-postings data showing substantial temporal change (as stated in the paper's findings).
The authors construct a dynamic, posting-level measure of generative AI exposure using a two-stage large language model pipeline that identifies tasks in each posting and classifies the extent to which generative AI can perform or assist them.
Paper methodology description: two-stage LLM pipeline to identify tasks and classify generative AI perform/assist capacity at the posting level.
The study uses a nationwide dataset of job postings in the United States covering all sectors of the economy.
Paper statement: 'Using a nationwide dataset of job postings in the United States, covering all sectors of the economy.' (dataset description)
The urban digital economy exerts a stronger effect than the rural digital economy in promoting servicization and inhibiting industrialization.
Heterogeneity analysis in the provincial panel (2013–2024) comparing urban versus rural digital-economy measures and their associations with changes in employment shares.
After 2017, industrial digitalization continued to strengthen servicization while suppressing industrialization.
Post-2017 analysis of provincial panel data (2013–2024) showing continued positive association of industrial digitalization with service employment and negative association with industrial employment after 2017.
After 2017, digital industrialization shifted toward promoting industrialization and restraining servicization.
Post-2017 subset analysis of provincial panel data (2013–2024) comparing the direction and magnitude of digital industrialization's association with industry and service employment shares before and after 2017.
The elevation of the 'digital economy' to a national strategy in 2017 constituted a critical turning point in the relationship between digital-economy development and labor-structure change.
Before-and-after (pre/post-2017) analysis using China's provincial panel data (2013–2024) showing a structural change in estimated effects around 2017.
The development of the digital economy generally promotes the servicization and deindustrialization of the labor structure.
Panel analysis using China's provincial data from 2013 to 2024 examining relationships between digital economy development and labor-structure indicators (servicization and industrial employment shares).
AI is less prevalent in simpler channels of automation overall, but AI is more prevalent on labour-substituting margins in lower-income settings and tends to augment labour in higher-income settings.
Task-level coding for technological channel and whether AI is involved, aggregated across 124 countries (2.33M task-country labels) and compared across income groups and labour margins (substitute vs augment).
Across countries, exposed tasks are skewed towards labour-substituting automation rather than labour-augmenting automation; low-income countries are disproportionately exposed to substitution, whereas middle-income countries are more heterogeneous.
Cross-country breakdown of exposed tasks by labour margin (substitution vs augmentation) using the task-country labels across 124 countries, with comparisons by income group.
Comparative analysis reveals significant institutional differences between EU and Ukrainian legal systems that are relevant to regulatory stability, the cost of innovation, data accessibility, the balance of market power, and guarantees for consumers and employees.
Qualitative comparative examination of institutional and cultural/procedural differences between EU and Ukraine as presented in the paper (method: comparative approach; no quantitative metrics provided).
Most Ukrainian laws relevant to the digital economy are based on existing legal structures and systems, and Ukraine currently lacks a unified regulatory system specifically designed for artificial intelligence.
Comparative analysis of Ukrainian and EU legal frameworks as described in the paper (method: comparative approach; legal document review referenced qualitatively).
Digitalisation is making data and algorithmic systems increasingly important economic resources, thereby changing the way markets operate, how labour is organised, how productivity is measured and how income is distributed.
Conceptual analysis and theoretical model developed via literature synthesis and comparative approach (no empirical sample reported).
The paper formalizes the non-classical measurement error, deriving probability limits and partial-identification bounds for employment elasticities.
Theoretical/mathematical derivations presented in the paper that model the non-classical measurement error structure and derive probability limits and partial-identification bounds for elasticities.
Within-vendor consumer-versus-enterprise channels produce estimates that disagree in sign.
Within-vendor comparison of exposure measures constructed from consumer-facing versus enterprise-facing conversation channels; reported that resulting estimates (e.g., employment effects) have opposite signs.
Holding outcome, sample, controls, and estimator fixed while varying only the platform input changes the post-ChatGPT employment coefficient by a factor of 1.9.
Empirical robustness exercise where the authors keep outcome, sample, controls, and estimator constant and vary only the platform input (different conversation-log sources) and report change in estimated post-ChatGPT employment coefficient multiplicatively by 1.9.
AI platform conversation-log exposure scores partly measure the platform user base rather than the underlying workforce.
Comparative empirical analysis using AI platform conversation logs to construct occupation exposure scores; authors compare exposure measures across platforms and show variation attributable to platform user composition rather than labor-force composition.
The economics literature uses specific quantitative arguments and methods to estimate the changes produced by automation, and there is an ongoing debate in the field about these quantification methods.
Paper presents and synthesizes economic studies and methodological approaches (task-based methods, decomposition analyses, etc.) as part of a literature review and critical discussion.
The study evaluates contemporary mitigation frameworks for algorithmic bias in HR settings.
Statement of the paper's evaluative aim; implies review/assessment of mitigation strategies but no specific methods or metrics provided in excerpt.
The paper analyses three primary vectors of AI bias in hiring: data bias, interaction bias, and evaluation bias.
Stated analytic framework in the paper (categorization of bias vectors); descriptive content rather than quantified empirical result.
This study examines the dual role of AI in the workplace: as a tool for bias reduction and as a potential vehicle for systemic discrimination.
Statement of the paper's research aim / framing; descriptive claim about the paper's scope rather than empirical finding.
The future of work will be shaped by decisions made at every level of society.
Normative/concluding statement in the chapter; presented as an implication of the prior analysis rather than an empirically tested claim.
AI affects the labour market through four channels: evolution of existing roles, creation of entirely new ones, redistribution across geographies and demographics, and selective displacement concentrated among older and lower-mobility workers.
Chapter synthesises labour market data, historical analogy, and emerging workplace evidence to propose these four channels; selective displacement claim references demographic concentration (older and lower-mobility workers).
Adaptation determines who benefits from technological (AI) change.
One of five lessons; argued using historical analogy and labour market patterns (qualitative claim in chapter).
Digital transformation reconfigures development patterns across regions and countries, altering established trajectories of regional development.
Theoretical integration of a technology–labor–space framework together with comparative regional field evidence illustrating changing development patterns (no quantified effect sizes or sample sizes reported).
Artificial intelligence (AI) is rapidly reshaping knowledge-intensive work by automating, augmenting, and reconfiguring core professional activities.
Paper asserts this as a motivating observation based on prior literature and descriptive claims; no original empirical sample or quantified data reported.
Aggregate effects are geographically uneven (geographic unevenness in AI-driven labor market impacts).
Synthesis across studies observing variation by geography and noting non-Anglophone markets and developing economies as under-studied and differentially affected.
Wage polarization characterizes the aggregate pattern of labor market change associated with recent AI advances.
Aggregate characterization from synthesized studies reporting divergent wage outcomes (higher wages for AI-augmented workers, pressures on junior/routine roles) consistent with polarization.
Sectoral effects are heterogeneous: infrastructure, security, and quality-assurance roles have expanded while developer roles have contracted.
Qualitative and quantitative results aggregated across the included studies noting role-level expansions and contractions; no single pooled effect size provided.
Non-routine employment and wages exhibit a crossing pattern: initially higher under fast adoption, then lower — so faster adoption can simultaneously raise long-run wages for survivors while permanently reducing participation.
Comparative dynamic trajectories in the model showing time paths for non-routine employment and wages under fast vs. slow adoption scenarios (analytical and/or simulated model paths).
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).
Responses [about AI's effects] vary by cohort and depending on survey framing.
Paper asserts heterogeneity in survey responses across demographic cohorts and due to framing effects (no subgroup sample sizes or framing experiment details in excerpt).
This [model divergence] may explain why public opinion is not settled about the effects of AI.
Paper's interpretive claim linking model divergence to unsettled public opinion (presented as a plausible explanation; no causal test or survey linkage provided in excerpt).
Current models about the vulnerability level of occupations and economic sectors differ widely in their forecasts.
Paper's comparative statement about existing models and their forecasts (no specific models, quantitative comparisons, or sample sizes provided in the excerpt).
The rapid emergence of agentic AI tools raises new questions that the political science discipline must address.
Epilogue of the report raises agentic AI tools as a rapidly emerging phenomenon and lists questions for the discipline; based on expert judgment and forward-looking analysis rather than empirical measurement in the introduction/epilogue.