Evidence (7156 claims)

Evidence Matrix

Claim counts by outcome category and direction of finding.

Outcome	Positive	Negative	Mixed	Null	Total
Other	369	105	58	432	972
Governance & Regulation	365	171	113	54	713
Research Productivity	229	95	33	294	655
Organizational Efficiency	354	82	58	34	531
Technology Adoption Rate	277	115	63	27	486
Firm Productivity	273	33	68	10	389
AI Safety & Ethics	112	177	43	24	358
Output Quality	228	61	23	25	337
Market Structure	105	118	81	14	323
Decision Quality	154	68	33	17	275
Employment Level	68	32	74	8	184
Fiscal & Macroeconomic	74	52	32	21	183
Skill Acquisition	85	31	38	9	163
Firm Revenue	96	30	22	—	148
Innovation Output	100	11	20	11	143
Consumer Welfare	66	29	35	7	137
Regulatory Compliance	51	61	13	3	128
Inequality Measures	24	66	31	4	125
Task Allocation	64	6	28	6	104
Error Rate	42	47	6	—	95
Training Effectiveness	55	12	10	16	93
Worker Satisfaction	42	32	11	6	91
Task Completion Time	71	5	3	1	80
Wages & Compensation	38	13	19	4	74
Team Performance	41	8	15	7	72
Hiring & Recruitment	39	4	6	3	52
Automation Exposure	17	15	9	5	46
Job Displacement	5	28	12	—	45
Social Protection	18	8	6	1	33
Developer Productivity	25	1	2	1	29
Worker Turnover	10	12	—	3	25
Creative Output	15	5	3	1	24
Skill Obsolescence	3	18	2	—	23
Labor Share of Income	7	4	9	—	20

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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