Конвергенция неравенства доходов в российских регионах

Abstract

In this paper we test convergence of income inequality in Russia’s regions for the period 1995–2020. To do this, conditional and unconditional beta convergence models for the regional Gini index are evaluated on cross-sectional and panel data using time and spatial effects. Estimates of the models show that both conditional and unconditional convergence of income inequality takes place in Russia’s regions. It is shown that the rate of convergence varies significantly within the considered period: the levels of income inequality in the regions converged most strongly at the beginning of the period with a gradual slowdown in the rate of convergence in subsequent periods. This result may be related to the recovery growth and redistribution policy in the 2000s, as well as the consequences of the 2014 crisis. The use of the same initial characteristics, such as GRP per capita, level of education and population, accelerates convergence. Spatial effects are statistically significant for models of unconditional, but not conditional convergence, but do not affect the estimates obtained. When considering a panel data structure with the inclusion of fixed time effects, convergence estimates increase for both unconditional and conditional convergence