Estimating and Decomposing the TFP Growth of Service-Oriented Manufacturing in China: A Translogarithmic Stochastic Frontier Approach

Abstract

By constructing a translogarithmic stochastic frontier production model, this study explores the total factor productivity (TFP) of service-oriented manufacturing in 30 provinces in China during 2004–2020. We carried out decomposition analysis to understand in greater depth the potential drivers of TFP growth. The results show that the overall TFP of service-oriented manufacturing continuously improved during the sample period; however, the overall growth rate showed a significant slowing trend, and the contribution of TFP growth to output growth is still low. The industrial growth of service-oriented manufacturing is mainly driven by capital input, and the transformation of its growth mode from extensive to intensive has not yet been realized. Furthermore, there exists significant regional and sub-sectoral heterogeneity in the TFP growth of the industry. The decomposition of TFP growth shows that technological progress and technical efficiency are the main sources of TFP growth, but the growth rate of technological progress is declining gradually, and its driving effect on TFP is weakening. The deterioration of both scale and allocation efficiency hinders the improvement of TFP in service-oriented manufacturing, and there is still room for the industry to improve its TFP level by improving scale efficiency and allocation efficiency.

The impact of energy-saving R&D on urban carbon emission performance: evidence from 218 prefecture-level cities in China

“…Compared with the traditional data envelopment method (DEA), the Super-SBM method has obvious advantages in solving the problem of input and output slackness by introducing undesirable outputs and modified relaxation variables (Iftikhar et al, 2018;Lin and Zhu, 2021). Furthermore, it also can effectively evaluate the factor efficiency in the presence of unexpected outputs (Li and Shi, 2014;Abudureheman et al, 2023a). Therefore, this study employs the Super-SBM method to measure the carbon emission performance of each city.…”