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Evidence (2608 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
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Skills Training Remove filter
Policy and managerial implication suggested: investing in short, targeted onboarding/training for GenAI tools (rather than only providing access) may deliver measurable performance gains and increase voluntary adoption.
Authors derive this implication from the randomized trial results showing increased adoption and improved scores with brief training (n = 164); this is an extrapolation from the trial findings.
speculative positive Training for Technology: Adoption and Productive Use of Gene... Organizational adoption and productivity (extrapolated from student trial outcom...
Vacancies explicitly requiring AI skills carry wage premia.
Wage regressions using an AI-skill flag (vacancies explicitly requesting AI competencies identified via text analysis) showing positive wage differentials for AI-skill vacancies.
medium-high positive Bridging Skill Gaps for the Future Wages / wage premia for AI-skill vacancies
Low-skilled workers can benefit indirectly through increased demand for services supplied to high-skilled earners.
Observed indirect (secondary) employment/wage gains in service occupations typically employing lower-skilled workers, consistent with a demand-side channel from higher incomes of high-skilled workers; based on occupation-level correlations in the panel/cross-sectional analyses.
low-medium positive Bridging Skill Gaps for the Future Employment and wages in low-skilled service occupations (indirect demand effects...
Vacancies demanding new skills (including AI) offer higher wages on average (wage premia).
Vacancy-level regressions estimating wage premia associated with new-skill requirements, controlling for occupation, firm, and other observables; new-skill and AI-skill flags identified by text analysis.
medium-high positive Bridging Skill Gaps for the Future Wages / estimated wage premia for vacancies requiring new skills
Research gaps include the need for causal evaluations (RCTs or quasi-experiments) of bundled interventions (training + placement + income support), cross-country comparisons of informality's moderating role, and better data on platform employment dynamics.
Identified research agenda and priorities summarized from the literature review and gap analysis in the paper; recommendation rather than empirical finding.
speculative positive Who Loses to Automation? AI-Driven Labour Displacement and t... evidence on effectiveness of bundled interventions and cross-country moderation ...
Empirical work on automation should distinguish task vs job displacement, measure platform algorithmic effects on labour demand, and quantify fallback employment options available to displaced informal workers.
Methodological recommendation based on gaps identified in the reviewed literature and limitations of existing studies; no new data collection presented.
speculative positive Who Loses to Automation? AI-Driven Labour Displacement and t... quality of empirical measurement (ability to isolate task vs job displacement an...
Policy responses should go beyond reskilling to include mechanisms addressing informality and job quality (e.g., portable benefits, minimum standards for platforms, guaranteed work or public employment schemes, wage floors, and training linked to placement).
Policy recommendation synthesized from literature on platform labour, social protection, and training program design; normative prescription rather than empirically validated intervention within this paper.
speculative positive Who Loses to Automation? AI-Driven Labour Displacement and t... worker welfare and employment security under combined policy interventions
Unchecked shifts toward K_T-dominated production can amplify political risks (rising inequality, fiscal strain) that may fuel populism, protectionism, and demands for renegotiated social contracts.
Theoretical political‑economy discussion supported by historical analogies and model scenarios linking fiscal stress and distributional change to political-instability risks; qualitative case evidence.
speculative positive The Macroeconomic Transition of Technological Capital in the... political risk indicators (populist support, policy volatility) — discussed qual...