| Issue |
E3S Web Conf.
Volume 214, 2020
2020 International Conference on Energy Big Data and Low-carbon Development Management (EBLDM 2020)
|
|
|---|---|---|
| Article Number | 02025 | |
| Number of page(s) | 4 | |
| Section | Machine Learning and Energy Industry Structure Forecast Analysis | |
| DOI | https://doi.org/10.1051/e3sconf/202021402025 | |
| Published online | 07 December 2020 | |
Empirical Study on Economies of Scale in China Manufacturing
1 School of Economics and Management, Beijing Jiaotong University, Beijing, China
2 School of Economics and Management, Beijing Jiaotong University, Beijing, China
a wanglingyao99@163.com
b ydzhou@bjtu.edu.cn
The paper is based on Chinese industrial enterprises database, applying the method of translog cost function to measure the economies of scale in manufacturing during the period between 2000 to 2013. The result shows that the mean of scale economies (SCE) is between 0.993 and 0.996, which indicates slight diseconomies of scale. From the perspective of the SCE variation trend, before 2010, there was a decreasing trend year by year, and the variation remained stable after 2010. Considering manufacturing heterogeneity, the paper divides manufacturing into nine groups to measure economies of scale. The group measurement results show that mining industry and light industry have high economies of scale, but in a decreasing state, other sub-sectors show slight diseconomies of scale and in a stable state.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.