The Gini Index quantifies the degree of inequality in income distribution (or sometimes consumption) among individuals or households within an economy. It compares the actual income distribution to a perfectly equal one. This is visualized through the Lorenz curve, which charts the cumulative share of total income against the cumulative share of recipients, starting from the poorest. The Gini index is derived from the area between the Lorenz curve and the line representing absolute equality, measured as a percentage of the total area beneath that line. A Gini index of 0 indicates perfect equality, while 100 signifies complete inequality.
The United States of America displayed a stable Gini Index trajectory, consistently hovering between 35.5 and 41.5 throughout the last five decades. This pattern indicates a persistent level of income inequality, with only modest fluctuations. The Gini coefficient of the US peaked from 2014 to 2019 when the value consistently remained above 41, indicating a period of heightened economic disparity. In 2020, there was a noticeable dip to 39.7, potentially reflecting the economic disruptions caused by the COVID-19 pandemic.
However, Brazil emerged as an outlier among these top ten economies with exceptionally high-income inequality. This country’s Gini index values consistently ranged between 52 and 63.3 from the early 1980s to 2022. This profound, long-standing income inequality indicates deep-rooted socioeconomic challenges and significant wealth concentration. Brasil’s Gini coefficient peaked in 1989 at 63.3, and after that, it has gradually decreased to around 52-53 in recent years, hinting at slow but steady efforts to address economic disparities.
European countries, which ranked among the top 10 economies, demonstrated more moderate and relatively stable Gini Index values. Most of the time, these European countries maintained a consistent Gini index between 30 and 35, reflecting a more balanced economic system. Canada also followed the same pattern. However, the United Kingdom (UK) showed an exciting pattern. The Gini coefficient of the UK increased from 27-29 in the 1970s to approximately 39 in 2020, but after that, the value began to decline.
In the early 1990s, China’s Gini index ranged between 32 and 35, and since then, it always remained above 35 and peaked around 2010 at 43.7. This reflects China’s rapid economic development, market reforms, and the complex social changes accompanying its economic growth. However, it’s not possible to analyse Japan’s income inequality trajectory due to the limited availability of the Gini coefficient data.
The variation in the Gini coefficients among these top ten major economies highlights the profound differences in economic policies, social structures, and historical contexts. Some nations have more robust mechanisms for wealth redistribution, while others struggle with more pronounced economic disparities. Developed economies like the United States and European countries have shown more moderate inequality levels compared to emerging economies like Brazil. Economic transitions and rapid development can significantly impact income distribution, as evidenced by China’s trajectory. The persistence of high Gini Index values across multiple decades suggests that addressing income inequality requires comprehensive, long-term strategies targeting systemic economic and social challenges. The limited data for Japan underscores the importance of comprehensive and consistent economic reporting to facilitate meaningful global comparisons.
References
- Glossary | DataBank. (n.d.). https://databank.worldbank.org/metadataglossary/gender-statistics/series/SI.POV.GINI
- World Bank Open Data. (n.d.). World Bank Open Data. https://data.worldbank.org/indicator/SI.POV.GINI
- India, F. (2024, October 7). The top 10 largest economies in the world in 2024. Forbes India. https://www.forbesindia.com/article/explainers/top-10-largest-economies-in-the-world/86159/
- Hasell, J., & Roser, M. (2023, December 28). Measuring inequality: what is the Gini coefficient? Our World in Data. https://ourworldindata.org/what-is-the-gini-coefficient
About Author
Pankaj Chowdhury is a former Research Assistant at the International Economic Association. He holds a Master’s degree in Demography & Biostatistics from the International Institute for Population Sciences and a Bachelor’s degree in Statistics from Visva-Bharati University. His primary research interests focus on exploring new dimensions of in computational social science and digital demography.
Disclaimer: The views expressed in this article are those of the author and do not necessarily reflect the views of 360 Analytika.
Acknowledgement: The author extends his gratitude to the World Bank for providing data support.
This article is posted by Akash Dey, Assistant Editor at 360 Analytika.
