Assessing the Relationship between Resource Misallocation and Total Factor Productivity Based on Artificial Neural Network.

Abstract

For interpretation of China's economy, total factor productivity is considered as one of the crucial aspects which is generally dependent on innovation in technologies especially those driven by both scientific research and efficiency of the methodology or process which is dedicated for the allocation of numerous resources available, among enterprises. It is important to note that various factors, which are either directly or indirectly involved, to cause misallocation of the resources to the enterprises, are overly complex. Therefore, an affective mechanism is needed to be realized which is capable of resolving these issues with the available resources and infrastructures. In this paper, we have focused on the construction or development of an artificial neutral network (ANN) based evaluation model to study the impact of resource misallocation on total factor productivity. Likewise, we have conducted a counterfactual experiment, i.e., simulation only, to thoroughly examine the relationship between two very important factors, that is, (i) resource misallocation and (ii) total factor productivity. To do this, we are aiming at investigating the growth potential of total factor productivity when there is no resource misallocation. After comparing 8 industries in different regions, we conclude that the contribution of capital and labor distortion to total factor productivity is the highest in the eastern region of China with −0.036 and 0.065, respectively, followed by the northeast, central, and western regions. In the experiment, China's total factor productivity growth potential could reach 1.1296, if there is no resource misallocation. The results in this paper would shed some lights on the paths to improve resource allocation efficiency and total factor productivity.