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Area of Science:

  • Economics
  • Econometrics
  • Technological Innovation

Background:

  • Total factor productivity (TFP) is key to China's economic interpretation, driven by technological innovation and efficient resource allocation.
  • Complex factors cause resource misallocation, hindering economic growth and requiring effective resolution mechanisms.

Purpose of the Study:

  • To develop an artificial neural network (ANN) model to evaluate the impact of resource misallocation on TFP.
  • To simulate counterfactual scenarios to understand the relationship between resource misallocation and TFP growth potential.

Main Methods:

  • Construction of an artificial neural network (ANN) based evaluation model.
  • Conducting a counterfactual experiment (simulation) to analyze resource misallocation's effect on TFP.
  • Comparing 8 industries across different regions in China.

Main Results:

  • Capital and labor distortions significantly impact TFP, with the highest contributions in China's eastern region (-0.036 for capital, 0.065 for labor).
  • Resource misallocation negatively affects TFP across all studied regions.
  • China's TFP growth potential could reach 1.1296 in a scenario without resource misallocation.

Conclusions:

  • Resource misallocation poses a significant constraint on China's TFP growth.
  • Improving resource allocation efficiency is crucial for enhancing TFP.
  • The study provides insights into optimizing resource allocation strategies for economic development.