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This research investigates the relationship between artificial intelligence and income distribution in OECD countries from 2015 to 2019.

July 16, 2026Open Access

The role of artificial intelligence in income distribution: a dynamic fsQCA study based on 20 OECD countries from 2015 to 2019

YQYu QianHong Kong Polytechnic University ZXZeshui XuSichuan University MSMarinko SkareJuraj Dobrila University of Pula

Key Points

This research investigates the relationship between artificial intelligence and income distribution in OECD countries from 2015 to 2019.
Utilized fuzzy-set qualitative comparative analysis (fsQCA) on panel data (N=20, T=5).
Examined three AI-related and four socioeconomic condition variables affecting income inequality outcomes.
Identified 14 conditional configurations impacting income distribution among the countries studied.
Found no single essential condition that independently affects income distribution outcomes.
Demonstrated that AI's impact on income inequality varies based on its compatibility with socioeconomic conditions.
Recognized both similarities and differences in AI's influence on different income levels.

Abstract

The fast progress of artificial intelligence (AI) technology transforms industrial efficiency levels and work abilities, significantly affecting worldwide income distribution patterns. This research uses fuzzy-set qualitative comparative analysis (fsQCA) to study the complex mechanisms by which AI affects income inequality across 20 OECD nations from 2015 through 2019. The analysis uses panel data ( N = 20, T = 5) to examine three AI-related condition variables and four socioeconomic condition variables that interact with the three outcome variables, i.e., the Gini coefficient, the top 1% income share, and the bottom 50% income share. Our research sequentially evaluates the necessity of individual conditions and the sufficiency of conditional configurations. Specifically, we identify 14 conditional configurations that are classified into seven types of AI-centered pathways affecting changes in income distribution. The study demonstrates that (1) no specific condition operates independently as essential for achieving particular income distribution results, (2) AI produces different effects on income inequality depending on its compatibility with socioeconomic conditions, and (3) there are both similarities and differences in AI’s influence patterns on income distribution at different income levels. For the OECD countries involved and other countries with similar contexts in recent years, the research reveals that policymakers need to implement balanced strategies to use AI to create sustainable social development that remains equitable. Implementing long-term strategies between technological progress and socioeconomic equilibrium should be the top priority for policymakers in these countries to reduce risks of inequality. Despite the data cut-off resulting from keeping the panel data balanced, the methodological strengths of this study ensure its relevance for understanding ongoing trends in AI-related inequality.

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