Title Accelerating the reduction of income inequality through ai: promoting asset-based wealth via government support
Translation of Title Pajamų nelygybės mažinimo spartinimas pasitelkiant dirbtinį intelektą: turto kaupimo skatinimas per valstybės paramą.
Authors Usevičiūtė, Vitalija
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Pages 90
Keywords [eng] Income inequality, artificial intelligence adoption, AI-driven inequality reduction, asset-based wealth creation, human capital development, skill development and upskilling, entrepreneurial education and training, government AI readiness, social expenditure and redistribution, inequality of opportunity, market income inequality, disposable income inequality, Gini coefficient analysis, cross-country panel data analysis, AI patents and innovation, digital transformation and labor markets, automation and task-based technological change, inclusive economic growth, AI governance and public policy, wealth accumulation dynamics, globalization and trade openness, econometric panel methods, CS-ARDL modeling, Granger causality analysis, mixed-methods research design.
Abstract [eng] This Master’s thesis examines income inequality in a cross-country context, focusing on the role of artificial intelligence (AI) and its interaction with asset-based wealth creation mechanisms. The study is based on the premise that sustainable reductions in income inequality require not only redistributive policies but also structural interventions that promote skill development, entrepreneurial education, asset accumulation, and institutional capacity. The empirical analysis relies on an unbalanced panel dataset covering 22 countries over the period 2018–2022. Income inequality is measured using both market and disposable income Gini coefficients, allowing a distinction between inequality generated by market processes and inequality after taxes and transfers. AI development is operationalised through AI patent counts and the Government AI Readiness Index. Additional variables include skill development, entrepreneurial education, social expenditure, adjusted net wealth per adult, GDP, trade openness, and globalization. The study applies advanced panel econometric methods, including Mundlak corrections, cross-sectional dependence tests, unit root and cointegration tests, CS-ARDL models, robustness checks using CCEMG and CCEP estimators, and Dumitrescu–Hurlin panel Granger causality tests. These techniques allow the identification of short-run and long-run relationships between AI, institutional factors, and income inequality. The quantitative analysis is complemented by a qualitative case study of Norway, based on secondary policy documents. The case study examines how AI governance, education and skill policies, entrepreneurship support, and welfare state mechanisms interact in shaping disposable income inequality outcomes. Overall, the thesis develops an integrated analytical framework for assessing the impact of AI on income inequality through channels of human capital formation, asset-based wealth creation, and public policy, providing an empirical basis for future comparative research and policy evaluation.
Dissertation Institution Vilniaus universitetas.
Type Master thesis
Language English
Publication date 2026