Evidence (14156 claims)

Evidence Matrix

Claim counts by outcome category and direction of finding.

Outcome	Positive	Negative	Mixed	Null	Total
Other	761	200	101	904	2020
Governance & Regulation	829	400	191	122	1566
Organizational Efficiency	784	193	125	84	1197
Technology Adoption Rate	637	236	124	97	1103
Research Productivity	431	131	58	340	972
Output Quality	481	183	59	47	770
Decision Quality	332	177	82	49	647
Firm Productivity	439	57	88	20	610
AI Safety & Ethics	218	279	66	33	602
Market Structure	181	170	123	24	503
Task Allocation	214	64	72	33	388
Skill Acquisition	174	62	62	17	315
Innovation Output	204	27	45	18	295
Employment Level	105	54	108	13	282
Fiscal & Macroeconomic	132	69	43	26	277
Consumer Welfare	117	63	42	11	233
Firm Revenue	154	48	26	3	231
Task Completion Time	173	31	8	12	225
Inequality Measures	44	123	50	6	223
Worker Satisfaction	89	65	22	12	188
Error Rate	71	92	10	2	175
Regulatory Compliance	77	69	14	5	165
Automation Exposure	58	56	26	13	156
Training Effectiveness	96	21	14	19	152
Wages & Compensation	77	37	25	6	145
Team Performance	86	17	27	10	141
Developer Productivity	95	17	14	6	133
Job Displacement	12	81	21	1	115
Hiring & Recruitment	52	7	8	3	70
Creative Output	32	20	8	3	64
Skill Obsolescence	5	47	6	1	59
Social Protection	28	16	8	2	54
Labor Share of Income	17	19	17	—	53
Worker Turnover	11	12	—	3	26
Industry	—	—	—	1	1

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...

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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