  The first part of this paper is a brief survey of the approaches to economic
inequality based on ideas from statistical physics and kinetic theory. These
include the Boltzmann kinetic equation, the time-reversal symmetry, the
ergodicity hypothesis, entropy maximization, and the Fokker-Planck equation.
The origins of the exponential Boltzmann-Gibbs distribution and the Pareto
power law are discussed in relation to additive and multiplicative stochastic
processes. The second part of the paper analyzes income distribution data in
the USA for the time period 1983-2018 using a two-class decomposition. We
present overwhelming evidence that the lower class (more than 90% of the
population) is described by the exponential distribution, whereas the upper
class (about 4% of the population in 2018) by the power law. We show that the
significant growth of inequality during this time period is due to the sharp
increase in the upper-class income share, whereas relative inequality within
the lower class remains constant. We speculate that the expansion of the
upper-class population and income shares may be due to increasing digitization
and non-locality of the economy in the last 40 years.
