Evidence (8653 claims)

Evidence Matrix

Claim counts by outcome category and direction of finding.

Outcome	Positive	Negative	Mixed	Null	Total
Other	452	119	70	526	1183
Governance & Regulation	463	217	126	68	891
Research Productivity	277	103	36	304	726
Organizational Efficiency	451	107	78	43	683
Technology Adoption Rate	350	132	77	51	615
Firm Productivity	325	39	75	13	457
Output Quality	275	78	28	30	411
AI Safety & Ethics	125	191	47	27	392
Market Structure	119	134	89	14	361
Decision Quality	184	82	44	21	335
Fiscal & Macroeconomic	98	58	34	22	219
Employment Level	79	37	81	9	208
Skill Acquisition	105	37	42	9	193
Innovation Output	131	12	31	14	189
Firm Revenue	103	38	24	—	165
Task Allocation	97	18	37	9	163
Consumer Welfare	77	38	37	7	159
Inequality Measures	29	81	33	6	149
Regulatory Compliance	54	61	13	3	131
Task Completion Time	92	8	4	4	108
Worker Satisfaction	49	36	14	8	107
Error Rate	45	55	6	—	106
Training Effectiveness	60	13	12	16	102
Wages & Compensation	56	16	20	5	97
Team Performance	51	13	15	8	88
Automation Exposure	29	29	12	7	80
Job Displacement	7	46	13	—	66
Hiring & Recruitment	42	4	7	3	56
Developer Productivity	39	5	4	3	51
Social Protection	22	12	7	2	43
Creative Output	17	8	6	1	32
Skill Obsolescence	3	26	2	—	31
Labor Share of Income	12	8	10	—	30
Worker Turnover	10	12	—	3	25
Industry	—	—	—	1	1

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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