The Pareto Distribution of World’s GDP


The problem of wealth distribution has gathered the attention of researchers for many years. In their work, the researchers are mainly engaged in the issue of distribution of wealth between individuals by analyzing empirical results at the country level, or specific lists that particular organization form like the Forbes list. Research are also increasingly directed toward the analysis of new models such as Boltzmann Gibbs or application of Gama function that describes this distribution. An interesting issue is the analysis of the distribution of wealth among the countries themselves. In these works, the value of GDP is used as the wealth that country has. In this article, the author dealt with the analysis of the distribution of GDP between countries at the global level. Analysis were performed using the Pareto distribution model of wealth distribution and GINI coefficient based on the data of the value of GDP for countries from IMF estimation. The analysis was conducted for the period from 1980 to the present, as well as analysis of data provided by IMF estimates for the value of GDP by 2022. The goal is to determine the degree of uneven distribution between the countries themselves in the world, analyzing the dynamics of change in the degree of unevenness and an analysis of the degree of unevenness in the future based on forecasts of the IMF on the values of countries GDP. The author also wanted to test if Pareto's 80/20 rule applies when it comes to the distribution of GDP at world level.



Keywords: GDP, distribution of wealth, IMF, Pareto distribution, GINI coefficient

[1] Atkinson, A. B. (2015). Inequality: What Can Be Done? Harvard University Press.

[2] Clementi, F. and Gallegati, M. (2005). Pareto’s law of income distribution: Evidence for Germany, the United Kingdom, and the United States. Econophysics of Wealth Distribution, Springer, New York, pp. 3–14.

[3] Chakrabarti, B. K. and Chatterjee, A. (2003). Ideal gas-like distribution in economics: Effects of saving propensity. The Application of Econophysics, Springer, pp. 280–285.

[4] Dragulescu, A. and Yakovenko, V. M. (2001). Exponential and power-law probability distributions of wealth and income in the United Kingdom and the United States. Physica A, vol. 299, pp. 213–221.

[5] Dunford, R., Su, Q., Tamang, E., et al. (2014). The Pareto Principle. The Plymouth Student Scientist, vol. 7, no. 1, pp. 140–148.

[6] Klass, O. S., Biham, O., Levy, M., et al. (2006). The Forbes 400 and the Pareto wealth distribution. Economics Letters 90, pp. 290–295.

[7] Klass, O. S., Biham, O., Levy, M., et al. (2007). The Forbes 400, the Pareto power-law and efficient markets, The European Physical Journal B, no. 55, pp. 143–147.

[8] Levy, M. and Solomon, S. (1997). New evidence for the power-law distribution of wealth. Physica A, pp. 90–94.

[9] Oltean, E. and Kusmartsev, F. (2014). An econophysical approach of polynomial distribution applied to income and expenditure. American Journal of Modern Physics vol. 3, no. 2, pp. 88–92.

[10] Piketty, T. (2014). Capital in the Twenty-First Century. Belknap Press.

[11] Sinha, S. (2006). Evidence for Power-law tail of the wealth distribution in India. Physica A: Statistical Mechanics and its Applications, pp. 555–562.

[12] Stiglitz, J. E. (2013). The Price of Inequality: How Today’s Divided Society Endangers Out Future. W. W. Norton & Company.

[13] Skipper, R. (2011). Zipf ’s law and its correlation to the GDP of nations. The University of Maryland McNair Scholars Undergraduate Research Journal, vol. 3, pp. 217–226.

[14] Yakovenko, V. M. (2008). Econophysics, Statistical Mechanics Approach to Encyclopedia of Complexity and System Science, pp. 2800–2826. New York, NY: Springer.

[15] Yakovenko, V. M. (2010). Statistical mechanics of money, debt and energy consumption. Science and Culture, September–October, pp. 430–436.

[16] World Economic Outlook Database. (April 2017). Available at external/pubs/ft/weo/2017/01/weodata/index.aspx (accessed 9 January 2017).