Investigation of Productivity Growth Factors in Iran Using Artificial Neural Networks Algorithm

Today most developed and developing countries emphasize on the importance of productivity as one of the necessities of economic development and competitiveness in the world. Because todays, competition is taking various dimensions and striving for higher productivity is one of the important factor of these competitions. On this basis, identifying the factors of affecting productivity growth in the Iran economy is essential for economic growth and development. Therefore, this study intends to first identify the factors affecting productivity growth by using feature selection logic, basis on Non-Dominated Sorting Genetic Algorithm (NSGA-II) then estimate the selective model using Artificial Neural Networks (ANN) for the period (1991-2016) and finally using the Garsen index to measure the sensitivity analysis of factors affecting productivity growth. Based on the results of the feature selection among the twenty variables, foreign investment, health investment, rail lines, innovation index and exchange rate (five variables) were removed from the model. Based on the results of ANN model with Tansig activation function with 3 neurons, it has a prediction power of 0.993 and minimum error of model 0.0019. Also, according to the Garsen index, human capital (15%), government size (11%), openness, research and development and economic corruption control (8%) had the highest impact on productivity growth and monetary development (1.48%) the rule of law (2.27%) and physical capital (3.2%) had the least impact on productivity growth.