中国全要素生产率重估——ACF模型中弹性估计改进和实证

索洛余额法、随机前沿生产函数法以及数据包络分析方法都没有解决函数的内生性和模型参数的时变性问题，ACF方法克服了这些局限性，对全要素生产率的测度更加准确。本文推导了参数的内生性和时变性问题，基于ACF模型提出了我国的时变参数估计方法，并对我国1990－2017年28个省份的全要素生产率进行重估。研究结果表明，ACF方法对全要素生产率的测度更加准确。从全国来看，资本投入增长对经济增长的贡献度最大，TFP增长对我国经济增长的贡献度正在逐渐下降，劳动投入对经济增长的贡献度相对较弱且波动大。分地区来看，各地区的TFP平均水平有所回落，近年来东北地区的TFP增长率水平最低，2012－2017年均为负值。同时，东北地区的劳动力流失情况较为严重。

Re-estimating the Total Factor Productivity in China—Improvement and Examples of Elasticity Estimation in ACF Model

Solow Model, Stochastic Frontier Production Function Model and Data Envelopment Analysis Model are the three types of classical models in measuring Total Factor Productivity (TFP). All of these models ignore the endogenous problem of production function and the time varying output elasticity of the model parameters, but ACF model improves and provides a more reliable way to estimate TFP. We deduct the endogenous problem and time varying output elasticity of the parameters, and propose a new method to estimate time varying output elasticity based on the ACF model. Using ACF model with our new method we re estimate the TFP in China with the data of 28 provinces of China from 1990 to 2017. The results show that the ACF model is more accurate in measuring TFP. From a national perspective, capital investment growth has the largest contribution to economic growth, and the contribution of TFP growth to China’s economic growth is decreasing. The contribution of labor input to economic growth is relatively weak and showing great volatility. From a regional perspective, the TFP level of all regions shows a downward trend. The growth rate of TFP in the northeast region of China was the lowest in recent years, and the TFP growth rate from 2012 to 2017 was negative. Meanwhile, the loss of labor in Northeast China is very serious.