Econophysics of Income and Wealth Distributions

Abstract

The distribution of wealth and income is never uniform, and philosophers and economists have tried for years to understand the reasons and formulate remedies for such inequalities. This book introduces the elegant and intriguing kinetic exchange models that physicists have developed to tackle these issues. This is the first monograph in econophysics focused on the analyses and modelling of these distributions, and is ideal for physicists and economists. It explores the origin of economic inequality. It is written in simple, lucid language, with plenty of illustrations and in-depth analyses, making it suitable for researchers new to this field as well as more specialized readers. bikas k. chakrabarti is a Senior Professor of Theoretical Condensed Matter Physics at the Saha Institute of Nuclear Physics, and a visiting Professor of Economics at the Indian Statistical Institute. He has research interests in statistical physics, condensed matter physics, computational physics and econophysics. anirban chakraborti is an Associate Professor at the Quantitative Finance Group,École Centrale Paris. He has research interests in statistical physics, econophysics and quantum physics. satya r. chakravarty is a Professor in the Economic Research Unit of the Indian Statistical Institute. His main areas of research interests are welfare economics, public economics, mathematical finance, industrial organization and game theory. arnab chatterjee is a Postdoctoral Researcher at Aalto University. He has research interests in statistical physics, and its application to condensed matter and social sciences.

Invariant features of spatial inequality in consumption: The case of India

“…However, the high end of the distribution (known as the 'tail') fits well to a power law as observed by Pareto, the exponent known as the Pareto exponent, usually ranging between 1 and 3 (see e.g. [6]; Ref. [18] contains a historical account of Pareto's data as well as some recent sources).…”

“…Economic inequality can be measured in terms of three major variables: wealth, income and consumption. For decades, if not centuries, economists (and very recently, physicists) have worked on the statistical descriptions, manifestations and precise mechanisms giving rise to such inequalities [5][6][7]. Income inequality is the most cited measure followed by wealth inequality which in turn is followed by consumption inequality.…”

“…Pareto [9] made extensive studies by the end of the 19th century, and found that wealth distribution in Europe follows a power law for the richest, later came to be known as the Pareto law. Subsequent studies revealed that the distributions of income and wealth possess a number of fairly robust features: the bulk of both the income and wealth distributions seem to reasonably fit both the log-normal and the Gamma distributions (see, e.g., [6]). Economists prefer the lognormal distribution [10,11], while statisticians [12] and physicists [13][14][15][16][17] emphasize on the Gamma distribution for the probability density or Gibbs/ exponential distribution for the corresponding cumulative distribution.…”

Inequality of Chances as a Symmetry Phase Transition

“…It is just the numerator ² ² in the second Equation (5). Let be the entropy functional and the entropy in the ² equal-chance state.…”

“…We also discussed [3,4] the relevancy for the characterisation of inequality of a notion originated in thermodynamics and statistical physics-entropy. Analogies between economics and physics have often been reported [5][6][7]. Georgescu-Roegen in particular discussed [8] the role of entropy in production, that is, material economic processes.…”