Abstract
The conversion from conventional to new driving forces and their time-varying characteristics are of great importance to China’s economic transformation. In this paper, we attempt to investigate the economic growth driving force conversion in China and the time-varying characteristics of driving forces by constructing a time-varying coefficient panel data model during the period of 1998–2015. The empirical results indicate that China’s economy has undergone driving force conversion. Specifically, China’s economic growth driving forces have been transformed from traditional ones (human capital and gross fixed capital formation) to new ones (innovation capacity and structural transformation). Furthermore, we find that the characteristics of the driving forces are time-varying and heterogeneous. Innovation capacity and structural transformation have a more crucial impact on economic growth. Finally, based on the conclusions of the quantitative analysis, some important policy implications can be pursued to foster economic growth. Chinese government ought to enact various policies that are conducive to enhancing innovation capacity and accelerating structural transformation.
Highlights
Since the seminal studies of Caves et al [13], there has been an explosion of work exploring the productivity index by employing the DEA method. e methods include Malmquist (M) productivity index [13], Luenberger (L) productivity index [14], Malmquist–Luenberger (ML) productivity index [15], global Malmquist (GM) productivity index [16], global Malmquist–Luenberger (GML) index [17], and GML index based on the slacks-based measure (SBM)-directional distance function (DDF) [10]
To more accurately measure the green total factor productivity (GTFP), combining the advantages of the above method, Liu and Xin [10] construct a GML index based on the SBM-DDF
Compared with the above methods, this method can effectively deal with radial and oriented problems and achieve global comparability in the production frontier, and take undesirable output into consideration. e GML index based on the SBM-DDF is extensively adopted to calculate the GTFP [19]. erefore, in this paper, a GML index based on the SBMDDF is employed to measure the GTFP
Summary
In the process of transforming China’s economic growth driving forces, green total factor productivity (GTFP) can effectively reflect the sustainability of economic growth [7]. To more accurately measure the GTFP, combining the advantages of the above method, Liu and Xin [10] construct a GML index based on the SBM-DDF. Erefore, in this paper, a GML index based on the SBMDDF is employed to measure the GTFP. In the past few years, China’s rapid economic growth has been through labour and investmentdriven modes, which we regarded as the main traditional economic growth driving forces This mode promotes growth but entails serious environmental, resource, ecological, and social problems, resulting in less sustainable economic growth. We consider innovation capacity and industrial structural transformation as new economic growth driving forces. Employing a sample of 30 Chinese provinces for the 1998–2015 periods, we construct a time-varying coefficient panel data model to exploit the characteristics of economic growth driving force conversion.
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