Income inequality (INE), the uneven distribution of income, is a persistent global challenge. In 2021, the top 10% of the global population held 52% of global income, while the bottom 50% held only 8%. Rising economic hardship and INE fuel polarization, hindering economic development and causing social unrest. The COVID-19 pandemic exacerbated these issues, exposing weaknesses in global systems and widening the gap between rich and poor. Simultaneously, while rapid advancements in digital technologies offer opportunities, the digital divide – the unequal access to these technologies – creates information asymmetry and knowledge gaps, further complicating socioeconomic settings. This study aims to explore the determinants of INE and their interaction with technology to inform policies that promote inclusion.

The literature on the relationship between technological innovation and INE presents contrasting views. Some studies suggest technological innovation reduces INE by promoting economic growth, increasing labor share of income, and fostering equitable distribution of wealth through creative destruction. Other studies highlight the potential of technological innovation to exacerbate INE through biased technical change, favoring skilled labor and increasing the wage gap between skilled and unskilled workers. The role of mediating factors like economic growth, globalization, and export trade is also debated, with some research suggesting a moderating effect on the relationship between technological innovation and INE. This study addresses existing limitations by exploring potential feedback mechanisms and the moderating effects of economic growth, globalization, and export trade on the relationship between technological innovation and INE.

This study uses cross-country panel data from 59 nations (31 developed, 28 developing) spanning 1995-2020. The primary estimator used is the Common Correlated Effect Mean Group (CCEMG) estimator, which accounts for slope heterogeneity and cross-sectional dependence. The Augmented Mean Group (AMG) estimator is used for robustness checks. The Dumitrescu and Hurlin panel causality test is employed to identify potential feedback loops between variables. The study uses the top 10% income share as a proxy for INE and the number of patents as a proxy for technological innovation. Economic growth is measured by GDP per capita, globalization by the KOF Index, and export trade by exports of goods and services as a percentage of GDP. Human capital is included as a control variable. Before regression analysis, the study performs several tests, including cross-sectional dependence (CD) tests (Friedman, Frees, Pesaran), panel unit root tests (CADF, CIPS), and slope heterogeneity tests (SCH). The study uses two main models: Model I examines the direct impact of technological innovation on INE, while Models II-IV investigate the interaction effects of economic growth, globalization, and export trade on the relationship between technological innovation and INE. The CCEMG and AMG estimators are used to estimate the models.

The study finds that technological innovation significantly exacerbates income inequality in both developed and developing countries, but the effect is more pronounced in developed economies. A 1% increase in technological innovation leads to a 0.041% increase in INE in developed countries and a 0.017% increase in developing countries (CCEMG estimator). Robustness checks using the AMG estimator confirm these findings. Analysis of interaction effects reveals the moderating roles of economic growth, globalization, and export trade. In developed countries, economic growth strengthens the positive relationship between technological innovation and INE, while globalization and export trade weaken it. In developing countries, economic growth and globalization weaken the positive relationship, whereas export trade strengthens it. Heterogeneity analysis by sub-period (1995-2007 and 2008-2020) shows varying effects across time, with some periods showing a negative relationship between technological innovation and INE in developing countries. Causality tests reveal a bidirectional causal relationship between INE and technological innovation, suggesting a feedback loop. Similar bidirectional relationships are found between INE and economic growth, globalization, and export trade in developed countries, with the exception of globalization in developing countries.

The findings highlight the complex and multifaceted nature of the relationship between technological innovation and income inequality. The results suggest that while technological innovation can drive economic growth, its benefits are not always evenly distributed, leading to increased inequality. The moderating roles of economic growth, globalization, and export trade further emphasize the contextual nature of this relationship, with the impact of technological innovation varying significantly across countries and over time. The feedback loop between technological innovation and INE underscores the need for proactive policy interventions to ensure that technological advancements contribute to inclusive growth.

This study demonstrates that technological innovation exacerbates income inequality, particularly in developed economies. The moderating effects of economic growth, globalization, and export trade highlight the importance of contextual factors. Policy recommendations include investments in education and training to bridge the skills gap, labor protection measures to ensure fair distribution of benefits, and policies promoting diversified technological innovation and inclusive digitalization to mitigate the negative consequences of technological advancement. Future research should explore the feedback mechanisms between inequality and innovation, investigate mediating channels, and assess the role of specific policy interventions.

The study's limitations include the specific sample of countries, the use of patents as a proxy for technological innovation, and the potential for omitted variable bias. Further research should consider a more comprehensive global sample, explore alternative measures of technological innovation and inequality, and investigate additional mediating and moderating variables to enhance the generalizability and robustness of the findings.