DESIGNING ALTERNATIVE INEQUALITY MEASURE USING EUROPEAN ASSOCIATION OF TAX LAW PROFESSORS (EATLP) 2024 INEQUALITY CONGRESS COUNTRY REPORTS AND AI
DOI:
https://doi.org/10.18623/rvd.v22.n6.3935Palavras-chave:
AI-Driven Inequality Metrics, Taxation and Socioeconomic Disparities, Institutional Effectiveness, Global Governance Assessment, LLMs in Public PolicyResumo
This study presents a novel method for approximating inequality by converting expert qualitative data from 32 EATLP country reports into quantitative indicators with the help of large language models (LLMs). Nine sub-indicators—covering aspects such as economic rights, judicial enforcement, tax policy, and anti-discrimination—are aggregated to build an overall indicator. Validation against the World Inequality Database's Gini coefficients shows that the composite index explains more than half of the variation in post-tax Gini, validating the role of legal and institutional factors in shaping inequality outcomes. A sub-index based solely on tax-specific metrics, the Tax Equality Score, also displays a positive association with Gini differentials before and after taxation. While it suffers from some limitations, such as potential model biases and no temporal dimension, this approach shows the potential of LLMs to supplement traditional socioeconomic data. It offers a scalable and cost-effective way to capture complex policy and institutional dimensions of inequality.
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