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Evidence (6507 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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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...
Policy implication (inference from results): prioritizing digital infrastructure investment to pass critical thresholds will unlock stronger productivity and environmental gains than focusing solely on advanced digital services.
Inference drawn from panel threshold findings (infrastructure threshold) and observed complementarities; this is a policy recommendation rather than a direct empirical test.
speculative positive Digital rural development and agricultural green total facto... AGTFP (policy-oriented inference)
The positive AGTFP gains from digital rural development are geographically heterogeneous and are concentrated in eastern provinces.
Regional heterogeneity analysis / sub-sample regressions across provinces showing larger estimated digitalization effects in eastern provinces compared with other regions.
medium-high positive Digital rural development and agricultural green total facto... AGTFP (regional subsample effects)
Digital infrastructure exhibits a threshold effect: its positive impact on AGTFP becomes stronger once digital infrastructure passes a critical level.
Panel threshold model applied to the provincial panel (2012–2022) that identifies a statistically significant threshold in the infrastructure sub-index where marginal effects increase above that value.
medium-high positive Digital rural development and agricultural green total facto... AGTFP (effect conditional on digital infrastructure level)
Authors recommend promoting a shift from single-link outsourcing (PAPM) toward whole-process integrated service provision (WAPM) as a policy implication of the findings.
Discussion/policy-implication section of the paper drawing on empirical results (TWFE and robustness checks) from the CLDS 2014–2018 analysis.
speculative positive Whole-Process Agricultural Production Chain Management and L... policy recommendation (expected productivity gains)
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...
To make AI a driver of structural change, policy interventions must link AI investment to comprehensive energy subsidy reform and accelerated development of the new and renewable energy sector.
Policy recommendation based on integrated analysis showing that subsidy burdens and import dependence limit AI's macro impact; proposed linkage is derived from the study's scenario/logic assessment.
speculative positive (conditional) AI-Based Technological Transformation as a Driver for Develo... potential for AI to drive structural change conditional on subsidy reform and re...