Evidence (7953 claims)

Evidence Matrix

Claim counts by outcome category and direction of finding.

Outcome	Positive	Negative	Mixed	Null	Total
Other	402	112	67	480	1076
Governance & Regulation	402	192	122	62	790
Research Productivity	249	98	34	311	697
Organizational Efficiency	395	95	70	40	603
Technology Adoption Rate	321	126	73	39	564
Firm Productivity	306	39	70	12	432
Output Quality	256	66	25	28	375
AI Safety & Ethics	116	177	44	24	363
Market Structure	107	128	85	14	339
Decision Quality	177	76	38	20	315
Fiscal & Macroeconomic	89	58	33	22	209
Employment Level	77	34	80	9	202
Skill Acquisition	92	33	40	9	174
Innovation Output	120	12	23	12	168
Firm Revenue	98	34	22	—	154
Consumer Welfare	73	31	37	7	148
Task Allocation	84	16	33	7	140
Inequality Measures	25	77	32	5	139
Regulatory Compliance	54	63	13	3	133
Error Rate	44	51	6	—	101
Task Completion Time	88	5	4	3	100
Training Effectiveness	58	12	12	16	99
Worker Satisfaction	47	32	11	7	97
Wages & Compensation	53	15	20	5	93
Team Performance	47	12	15	7	82
Automation Exposure	24	22	9	6	62
Job Displacement	6	38	13	—	57
Hiring & Recruitment	41	4	6	3	54
Developer Productivity	34	4	3	1	42
Social Protection	22	10	6	2	40
Creative Output	16	7	5	1	29
Labor Share of Income	12	5	9	—	26
Skill Obsolescence	3	20	2	—	25
Worker Turnover	10	12	—	3	25

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...

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...

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