Predicting Economic Efficiency of MENA Countries Based on Quality of Formal Social Institutions Using Neural Network Approach

Abstract

Nowadays, efficacy of formal and informal Institutions is considered an essential component of economic growth and productivity. The main objective of the present study is to predict economic efficiency of countries in Middle East and North Africa (MENA) region based on quality of formal social Institutions by modeling a neural network and using The World Economic Forum’s Global Competitiveness Report 2014-2015. In this regard, 21 indicators of social institutions quality as well as total economic efficiency of countries were derived from Global Competitiveness Report, and analyzed using multilayer perceptron artificial neural network (MPANN) in MATLAB software. Obtained results indicate that variables of social institution quality have the ability to predict economic efficiency of countries accurately, and neural network technique is an accurate and efficient tool in estimating prediction. We concluded that countries in the region should promote the quality of social institutions to achieve maximum economic efficiency.