Forecasting the Impact of Economic Globalization on Income Distribution in Iran’s Rural Community by Applying Artificial Neural System

Significant increase in trade, international exchange and integration of markets at a global scale is general feature of globalization process, which has been ongoing intentionally or not. The present study examines the way this phenomenon influences economic variables and provides a guide for efficient decision making by policy makers. The study compares the efficiency of vector auto-regressive (VAR) model with vector error correction (VEC) and artificial neural system (ANS) for forecasting, and then applies Iran’s economy time series data and a designed neural system to 1971-2007 period to forecast the Gini coefficient for rural areas for the years 2008 and 2009 in three scenarios. Consequently, by an out of sample forecasting the impact of globalization on income distribution in Iran’s rural community is assessed in a fourth scenario for the period 2008 to 2016. The results indicates the ANS model has a better performance in forecasting the future inequality of income in rural community, also rural income inequality tends to decline with the expansion of globalization.