Evidence (185 claims)
Search and filter individual claims pulled from the papers. Looking for a specific finding ("what's the effect on wages?"), you're in the right place. Want to compare whole outcome categories against each other instead? Use the Evidence Explorer.
The board below groups claims two ways: by broad theme (nine paper-level topics) and by outcome category (the 34 claim-level outcomes that the Explorer and Syntheses also use).
Browse by theme
Nine broad, paper-level topics. Click one to filter the claims below.
Adoption
9875 claims
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Productivity
8807 claims
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Governance
7870 claims
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Human-AI Collaboration
7560 claims
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Org Design
4892 claims
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Innovation
4781 claims
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Labor Markets
4004 claims
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Skills & Training
3308 claims
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Inequality
2332 claims
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Claims by outcome category
Counts by direction of finding. These are the same 34 outcome categories the Explorer compares and the Syntheses are written for. A linked row has a published synthesis.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 870 | 233 | 116 | 1066 | 2363 |
| Governance & Regulation | 976 | 451 | 218 | 133 | 1809 |
| Organizational Efficiency | 949 | 224 | 144 | 88 | 1416 |
| Technology Adoption Rate | 764 | 287 | 141 | 122 | 1325 |
| Research Productivity | 501 | 152 | 74 | 362 | 1101 |
| Output Quality | 542 | 216 | 69 | 69 | 896 |
| Decision Quality | 387 | 198 | 94 | 54 | 740 |
| Firm Productivity | 513 | 67 | 101 | 27 | 714 |
| AI Safety & Ethics | 249 | 303 | 73 | 36 | 667 |
| Market Structure | 190 | 192 | 134 | 27 | 548 |
| Task Allocation | 243 | 77 | 91 | 36 | 452 |
| Innovation Output | 291 | 33 | 55 | 20 | 401 |
| Skill Acquisition | 206 | 72 | 65 | 21 | 364 |
| Employment Level | 133 | 63 | 115 | 22 | 335 |
| Fiscal & Macroeconomic | 153 | 79 | 52 | 32 | 323 |
| Task Completion Time | 206 | 37 | 12 | 15 | 272 |
| Firm Revenue | 179 | 52 | 29 | 5 | 266 |
| Consumer Welfare | 130 | 76 | 47 | 13 | 266 |
| Inequality Measures | 48 | 137 | 51 | 6 | 242 |
| Worker Satisfaction | 101 | 81 | 25 | 13 | 220 |
| Error Rate | 84 | 110 | 11 | 5 | 210 |
| Wages & Compensation | 98 | 47 | 30 | 10 | 185 |
| Regulatory Compliance | 88 | 73 | 17 | 7 | 185 |
| Automation Exposure | 66 | 64 | 33 | 16 | 182 |
| Team Performance | 105 | 29 | 30 | 11 | 176 |
| Training Effectiveness | 109 | 22 | 14 | 21 | 168 |
| Developer Productivity | 114 | 21 | 14 | 8 | 158 |
| Job Displacement | 12 | 90 | 24 | 1 | 127 |
| Hiring & Recruitment | 57 | 9 | 9 | 5 | 80 |
| Skill Obsolescence | 6 | 56 | 9 | 1 | 72 |
| Social Protection | 43 | 17 | 8 | 2 | 70 |
| Creative Output | 35 | 21 | 9 | 4 | 70 |
| Labor Share of Income | 18 | 21 | 17 | 1 | 57 |
| Worker Turnover | 15 | 16 | — | 4 | 35 |
| Industry | — | — | — | 1 | 1 |
We measure the change in the skill premium using a difference-in-differences design on freelance websites worldwide.
Statement of empirical method: difference-in-differences design applied to data from freelance platforms with global coverage; no sample size provided in the abstract.
Instrumental-variable estimates using lagged AI diffusion produce similar patterns (attenuation of overeducation penalty and slight lowering of undereducation premium), although results should be interpreted with caution.
IV estimation using lagged AI diffusion as an instrument in models applied to CLDS data; IV results reported to be qualitatively similar to OLS/fixed-effects estimates but noted as requiring cautious interpretation.
Other strategic factors (differentiation of work, digital reputation, adaptability) continue to influence illustrators' financial sustainability despite AI's effect.
Author conclusion/interpretation in the discussion, inferred from the relatively low R² and domain knowledge; these moderators/alternative determinants are asserted rather than estimated in the reported regression.
AI explains a relatively small share of income variation among illustrators (model R² = 7.4%), so its contribution to income variation is limited.
Reported model fit statistic from the above simple linear regression (R² = 7.4%) on the sample of 385 illustrators.
Robustness checks across the capital share, shock persistence, and the utility specification show that only an empirically implausible labor–AI elasticity reverses the wage and fertility signs.
Sensitivity/robustness analysis of model results by varying parameters (capital share, shock persistence, utility functional form) and the labor–AI elasticity, reporting conditions under which sign flips occur.
Industry-wise, sectors with higher levels of digitalization (e.g., mining, finance, energy) show stronger income effects, while traditional sectors (e.g., agriculture, public services) show limited impact.
Industry-level heterogeneity analysis in the two-way fixed effects panel using provincial data (2011–2021), reporting larger estimated effects for high-digital sectors and small or null effects for traditional sectors.
Decomposition analysis reveals that wage benefits are concentrated among employees aged 45 and above, managers, and white-collar workers; other worker categories experience stagnant wages, and no group shows a negative wage effect.
Decomposition of wage effects by worker groups (age, occupation/type) using the integrated dataset and the DiD/other regression analyses.
Wage increases at small firms primarily explain the positive adoption effect, while wages at medium and large firms remain stagnant after adoption.
Heterogeneity analysis by firm size within the DiD framework showing differential post-adoption wage trajectories for small versus medium/large firms.
Non-routine employment and wages exhibit a crossing pattern: initially higher under fast adoption, then lower — so faster adoption can simultaneously raise long-run wages for survivors while permanently reducing participation.
Comparative dynamic trajectories in the model showing time paths for non-routine employment and wages under fast vs. slow adoption scenarios (analytical and/or simulated model paths).
The study establishes statistically significant relationships between organizational AI adoption and compensation dynamics.
Econometric estimates (difference-in-differences and propensity score matched comparisons) using the combined datasets listed in the paper and controlling for industry, firm size, geography, occupation characteristics, and macroeconomic variables.
Residual within-task group dynamics dominate the magnitude of the gender wage gap, though task-based employment and wage channels are important for timing and direction of changes in gender inequality in the formal sector.
Decomposition analysis partitioning the gender wage gap into within-task residuals and task-based employment and wage components, with residuals accounting for the largest share of the gap but task channels explaining temporal shifts.
The proportion of consumers who adopt AI-induced services influences the pricing of those services and through price adjustments will further impact wages across traditional and non-traditional services.
Theoretical development and analysis in the paper via a demand-switching model and a Finite Change General Equilibrium framework introducing AI as a technological shock modeled through price adjustments.
Wages of labor that is substituted for by AI decrease in both absolute and relative terms.
Analytical economic model / comparative statics predicting wage declines for labor substituted by AI. No empirical sample reported.
This convergence has the potential to lower wages on entry-level thinking jobs.
Theoretical/empirical implication drawn from observed reduction in productivity differences; presented as a potential consequence rather than an established empirical result in the abstract.
In manual jobs, AI compresses the returns to undereducation as tasks become more skill-intensive.
Occupation-specific heterogeneity analysis using CLDS and city AI diffusion showing reductions in the undereducation wage premium within manual-occupation subsamples under higher AI diffusion.
AI diffusion slightly lowers the wage premium for undereducated workers.
Interaction effects from fixed-effects models using CLDS and city AI diffusion indicators showing a small reduction in undereducation-related wage premium with higher AI diffusion.
Overeducation leads to a significant wage penalty.
Microdata from the China Labor-force Dynamics Survey (CLDS) 2014–2018; cohort-based measure of educational mismatch; estimated using extensive fixed-effects models comparing wages by educational mismatch status.
AI serves as a financial risk factor for platform-based illustrators by increasing price pressures, enhancing market transparency, and increasing exposure to revenue volatility.
Author interpretation based on the statistical finding of a significant association between AI and income plus theoretical/accounting discussion; no additional quantified causal mechanism presented in the reported results.
The demand premium enjoyed by workers with strong human capital declines in more AI-exposed categories.
Heterogeneity analysis within the Upwork dataset: workers characterized by stronger human-capital signals (via profile embeddings) show a reduced demand premium in job categories more exposed to AI following ChatGPT; identified using difference-in-differences around ChatGPT release. (Sample size not reported in abstract.)
Algorithmic systems are not structured to reward additional labour with proportionate pay.
Worker and stakeholder interviews (N=37) reporting that increased labour/intensity does not yield proportionate compensation under platform algorithms.
Automation AI depresses wages in the U.S.
Chapter 1: same occupational exposure measures and IV strategy (lagged computer-science research intensity) applied to U.S. wage data, 2015–2022.
A "free-for-all" model based on fair use fails because it does not compensate creators for their contributions.
Analytical / conceptual argument presented in the paper (no empirical sample); model-based reasoning showing creators receive no compensation under a free-for-all regime.
Wage gaps are present in AI‑mediated platform work and contribute to unequal outcomes for women.
Reviewed literature synthesized in the paper repeatedly cites wage gaps as one mechanism producing gendered disadvantage; reported in Findings.
Workers with a higher share of standardized routine tasks face more pronounced downward wage pressure.
Subgroup analysis by share of standardized routine tasks in workers' duties showing larger negative wage effects for those with higher routine-task shares.
Wage growth for occupational groups with high exposure to automation lags markedly behind that of low-exposure groups.
Heterogeneity analysis across occupational exposure groups using CFPS panel data comparing wage growth trajectories for high- vs low-exposure occupations.
Total compensation declines persistently in the short and medium run following AI adoption.
Panel local projections indicating persistent declines in total compensation associated with higher establishment-level shares of AI-skill job postings (13 industries, 2017-2025).
AI-driven efficiency pressures in IT services may compress billable work and alter hiring and wage structures, raising transition risks even for technical workers.
Abstract cites high-reliability sector evidence (Reuters 2026a; Nasscom) to support this sector-specific claim; no sample size provided in abstract.
Since 2023, high-exposure neighborhoods have experienced wage stagnation even as they continue to attract high-skilled workers (a 'high-skill trap').
Temporal analysis of job-posting wage signals in Beijing neighborhoods (2018--2024) using the GenAI Exposure Index to compare wage trajectories before and after 2023 between high- and low-exposure neighborhoods.
The potential widening of the gender wage gap would operate through existing patterns of gender-based occupational sorting (i.e., because women are concentrated in occupations more exposed to generative AI).
Mechanistic interpretation supported by the combination of descriptive occupational sorting evidence from Swedish administrative data and results from the partial-equilibrium simulations incorporating predicted AI exposure and task complementarity.
Mechanical partial-equilibrium simulations indicate that generative AI may widen the gender wage gap.
Counterfactual simulations (mechanical partial-equilibrium) based on hypothesized deviations from the 2021 occupational and wage distribution, incorporating predicted AI exposure and task complementarity; applied to Swedish context.
Low-wage workers on platforms perform supporting tasks—such as data annotation and content moderation—that underpin technological infrastructures.
Empirical grounding drawn from cited ethnographic, sociological and anthropological studies and mapping exercises discussed in the paper documenting the kinds of work performed on microtask platforms.
Yapay zekâ gelişmekte olan ekonomiler için hem fırsatlar hem de tehditler yaratmaktadır: AI işgücü maliyeti avantajını törpüleyebilir.
Kavramsal değerlendirme; mekanizma temelli argüman (otomasyon işgücü maliyeti avantajını azaltır); ampirik veri ya da örneklem belirtilmemiştir.
The price-setter for cognitive labor is no longer the labor market.
Central normative/conceptual claim of the paper supported by the analytical model and the CAW bound: authors argue the compute capital market (through rental price of compute) sets the effective price for cognitive labor. Stated as the paper's concise position; based on theoretical derivation and argumentation.
Compute-Anchored Wage (CAW) bound: on tasks where human and agent cognitive labor are substitutes, the competitive human wage is bounded above by λ · k · r_c (where r_c is the rental rate of compute capital, k is the compute intensity of one effective agent-labor unit, and λ is the relative human-to-agent productivity).
Formal analytical result presented in the paper (mathematical derivation within the factor-pricing model). This is a theoretical bound derived from the model rather than an empirical estimate.
Once agents are recognized as a production technology, the elastic-supply margin that anchors the equilibrium wage migrates from the labor market to the compute capital market.
Analytical derivation using a textbook factor-pricing framework (citing Mankiw 2020) within the paper's theoretical model; derivation and verbal argument linking supply-elasticity margins to compute capital market. No empirical data reported in the excerpt.
The industry envisions AI expertise as cheap, meaning that it can offer a better return on investment than human expertise.
Interpretive coding of statements from five data-annotation firms and their CEOs on social media and podcasts indicating that AI-based expertise is framed as lower-cost and higher-ROI relative to human experts.
AI compresses the value of standardized middle-tier labor by making good-enough synthetic substitutes scalable at low marginal cost, hollowing out the middle of the skill distribution currently categorized by knowledge work.
Conceptual/theoretical argument presented in the paper (no reported empirical sample, statistical analysis, or quantified experiment in the excerpt).
Automation has put downward pressure on wages.
Cited empirical studies and wage analyses in the reviewed literature indicating wage suppression associated with automation adoption (literature review).
Platforms can exploit workers' uncertainty about the cost of labor to effectively suppress wages.
Interpretation / implication drawn from the theoretical model and the result that a platform can achieve coverage while paying only O(log(M)/M) fraction of total labor cost under assumptions about workers' cost estimates.
There exists a simple pricing strategy for the platform that covers all M tasks with wait time O(M) while paying only an O(log(M)/M) fraction of the total cost of labor.
Theoretical result from the paper's posted-price procurement model under stated assumptions on workers' estimated costs; formal analysis/proof showing existence of such a pricing strategy for general M (no empirical sample).
Under this emerging order, the vast majority of humanity will lose their labor value.
Claim made via theoretical argument about automation and AI replacing labor value; no quantitative empirical evidence or sample detailed in the excerpt.
Over time the equalizing channel weakened because market valuation (wage exposure) became increasingly unfavorable to female-concentrated occupations, contributing to a renewed widening of the gender wage gap in 2015–2019.
Decomposition results showing a temporal decline in the wage-exposure contribution to equality and a negative wage-exposure trend for female-concentrated occupations, coinciding with gap widening in 2015–2019.
Informal workers cannot capture augmentation rents: the estimated coefficient for H^A in informal sector is negative (beta_2 = -0.044).
Subsample or interaction estimate from the augmented Mincer regression using the same merged dataset (N = 105,517); reported coefficient beta_2 = -0.044 for informal workers.
The sentiment-induced divergences lead to unstable and often inflated compensation predictions by the models.
Analysis of model-predicted compensation amounts under sentiment perturbations showing increased variability and upward bias compared to CJOL amounts.
The cost of formalizing informal labor (CFIL) implies formalizing a worker costs on average 88% more than the informal wage in 2023.
New CFIL metric calculated for 19 countries (2023 baseline) by estimating the additional employer cost of hiring and formalizing an informal worker and reporting it relative to the informal wage, using compiled statutory obligations and informal wage benchmarks.
Using aggregate data, the study provides no evidence that AI benefits any particular group of workers — neither highly educated nor less-educated ones.
Authors' interaction analysis between AI adoption and human capital using aggregate panel data; reported null finding for differential benefits across education/skill groups (1995–2017, 35 OECD countries).
Wages of labor that is used only in final goods production and is not displaced by AI increase in line with overall GDP.
Analytical economic model result indicating proportional wage growth for final-goods-only labor relative to GDP. No empirical sample reported.
AI diffusion is not significantly associated with individual wages.
City-level indicators of AI diffusion linked to CLDS microdata; estimated main effects of AI diffusion on individual wages in fixed-effects models found no significant association.
There is no evidence of a sizeable effect on wages following return from cross-border employment.
Chapter 4: wage outcomes examined in linked Belgian administrative registers comparing returnees and stayers; reported null/insignificant effects on wages.
A randomly sampled coalition of equal size remains largely ineffective at increasing platform spending / wages.
Theoretical comparison in the model between targeted coalitions and randomly sampled coalitions of the same size; analytical results showing limited impact for random coalitions.