Machines, Markets, and the Disappearing Middle: AI and Wage Inequality Across OECD Economies (2000–2024)

Abstract

Artificial intelligence and automation technologies are increasingly reshaping labor markets across advanced economies. This paper examines how AI adoption has influenced wage inequality and middle-skill employment across sixteen OECD countries between 2000 and 2024. The study investigates whether technological change has contributed to labor market polarization, and why some countries experienced significantly different inequality outcomes despite comparable levels of technological advancement. The research combines data from the OECD Income Distribution Database, the International Federation of Robotics, Oxford’s AI Readiness Index, and labor market datasets from the ILO, Eurostat, and the U.S. Bureau of Labor Statistics. To evaluate these relationships, the paper applies multiple econometric approaches, including pooled ordinary least squares, country fixed effects, instrumental variable estimation, and Arellano-Bond generalized method of moments models. Institutional variables such as union density, labor market policy spending, employment protections, and trade openness are also incorporated into the analysis. The findings reveal a strong and consistent relationship between higher AI adoption and rising income inequality. Countries with stronger AI readiness generally experienced larger increases in market-income Gini coefficients and sharper declines in middle-skill employment. However, the results also show substantial cross-country variation. Economies such as Sweden, Denmark, and Germany maintained relatively lower inequality growth despite widespread AI adoption, suggesting that labor institutions, coordinated wage bargaining systems, and workforce retraining policies significantly influence how technological gains are distributed. The paper argues that the economic effects of AI are shaped not only by technological progress itself, but also by the institutional structures surrounding labor markets. It concludes by discussing policy implications related to worker retraining, collective bargaining, labor protections, and long-term economic inclusion in increasingly automated economies.

Keywords

Convergence Science - Social Science

Citation

Publication Details

ISSN: Online ISSN: 3143-5653

License: Author-retained; open access display by NSRI unless a separate article license states otherwise.

Peer review status: NSRI uses editorial and scholarly review. When appropriate, manuscripts may undergo blinded review by reviewers with relevant subject knowledge.

AI disclosure: No AI disclosure is attached to this public record unless stated in the manuscript.

Conflict of interest statement: No conflict of interest statement is attached to this public record unless stated in the manuscript.

References

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