A Study of Per Capita Income Inequality Dynamics in Iranian Provinces Using Spatial Markov Chain

Abstract

The main objective of this study is to investigate the dynamics of income distribution and to find evidence of convergence or divergence in per capita income in Iranian provinces by using newly developed methods for Exploratory Space–Time Data Analysis (ESTDA). In order to achieve this goal, per capita income data were collected for the period from 1997 to 2014. Then, using the markov chain and the spatial markov chain, the transition probability matrix is estimated at different time periods. The results show that in an 18-year period in Iranian economy, there was a very small possibility that poor provinces (in terms of per capita income) could increase their per capita income. Also, the values of asymptotic distribution show very weak tendency to divergence in per capita income of Iranian provinces during period 1997 to 2000. According to the high probability of 77% for staying at each level of per capita income, one can claim that there is no strong evidence of convergence or divergence in the distribution of per capita income between provinces of Iran. Also, the estimation results of the spatial transition probability matrix show that the moves between income classes for each province depends on the performance and status of neighboring provinces.

Keywords

• per capita income
• markov chain
• transition probability matrix
• convergence