1. In physics, there is no one process and related equation, which can explain real object behavior in sub-critical and super-critical state. As an example, the crash of a concrete cube under compression. Elastic law is working before the cube starts to crack and the relationship between stress and deformation fails and the exact number and size distribution of cracks do no follow from or described by the same elastic process parameters. Instructively, the size distribution of cracks is close to a power-law, i.e. similar to the Pareto distribution of high incomes. Therefore, I doubt that the distribution in the low-income range and in the Pareto zone can be described by one process/equation. However, we can estimate the critical pressure and describe the evolution of each crack as well as their interaction when knowing their positions from measurements.

2. Some potential value for the description of the Pareto law has the process of two-exponential growth (size and local measurement/stopping time - Huges ... ). The exponential growth in the labor force (US population has been growing at a rate of ~1% per year in the previous half-century) and GDP per capita (however, this growth is rather linear) might result in a power-law distribution in two asymptotic cases. Has to be modeled and evaluated.

3. The sudden drop in employment/labor force and GDP per capita, GDPpc, presents the best opportunity for the study of the evolution law of income distribution in developed countries. As observed in the US income (CPS) data and described in our model (based on the evolution of linear and nonlinear stresses in solid under deformation with various rates), the overall income distribution and income distributions in various age/gender/race groups evolve according to their dependence on real GDP per capita (taken as an external parameter). Our model can predict the most striking features of the income distribution like the change in the age when the population of a given country with known GDPpc reaches the peak average income. Also, we can accurately predict the decay of growth rate in the incomes of the younger population with increasing GDPpc and the share of the population in the Pareto zone (highest income) depending on age and GDPpc.

4. The 2008 crisis was endogeneous and the income distribution adjusted to the decrease in GDPpc (or even drove the economic crisis), i.e. we observed smooth change in all age/race/gender groups fully synchronized with the GDPpc fall. In other words, the income distribution system is internally reversible, i.e. its behavior is driven by the same equations (our model) in both directions of the GDPpc growth - positive and negative.

5. The coronavirus pandemic presents an external force, which affects the GDPpc and labor force beyond any endogenous economic links. The overall and rage/race/gender income distributions are also changed dramatically during so short time intervals (weeks and tens of percent of change) that the income distribution system is not able to adjust itself to the new state along with its internal (links) processes, as was observed in 2008. Currently, the income distribution is in some metastable state, and thus, will return to its normal (or to some different and likely unpredictable) state by processes not observed in the past. It is possible to suggest that a large portion of people in the lower-income zone are getting even poorer due to job loss and other negative socio-economic processes. At the same time, the richest people are getting richer - the billionaires in the USA got an additional ~500 bn and the number of billionaires increased. The income distribution is highly disturbed in all income-age-race-gender groups and one can trace the evolution of these new distributions to their final state, which can also be the same as observed now.

6. Therefore, the evolution of income distribution in various groups of population (income-age-race-gender) is of principal importance and interest for empirical and theoretical study. The community studying income distribution is waiting for fresh data on income distribution.