Abstract
In this study, we explore the pattern of efficiency among enterprises in China’s 29 provinces across different ownership types in heavy and light industries and across different regions (coastal, central and western). We do so by performing a bootstrap-based analysis of group efficiencies (weighted and non-weighted), estimating and comparing densities of efficiency distributions, and conducting a bootstrapped truncated regression analysis. We find evidence of interesting differences in efficiency levels among various ownership groups, especially for foreign and local ownership, which have different patterns for light and heavy industries.
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Notes
- 1.
The DEA was originally designed for firm-level analysis, but it has frequently been applied to more aggregated data; see, for example, Färe et al. (1994) and the more recent studies of Kumar and Russell (2002), Henderson and Russell (2005), Henderson and Zelenyuk (2006), and Badunenko et al. (2008).
- 2.
We assume that the standard regularity conditions of the neo-classical production theory hold (see Färe and Primont (1995) for details).
- 3.
Alternatively, if we add ∑ k = 1 n z k ≤ 1 and \({\sum \nolimits }_{k=1}^{n}{z}^{k} = 1\) to equation (2.3), then we can model the non-increasing returns to scale (NIRS) or the variable returns to scale (VRS), respectively.
- 4.
- 5.
- 6.
Some examples of the first category of light industries are food and beverage manufacturing, tobacco processing, and textiles and clothing, and some examples of the second category are the manufacturing of chemicals, synthetic fibers, chemical products, and glass products.
- 7.
Heavy industry consists of three branches distinguished according to the purpose of production or how the products are used. They include (1) the mining, quarrying and logging industry that involves the extraction of natural resources; (2) the raw materials industry, which provides raw materials, fuel and power to various sectors of the economy; and (3) the manufacturing industry, which processes raw materials.
- 8.
E.g., the RD-statistic for comparing the weighted average efficiency scores for groups 1 and 2 was estimated as \(\overline{\widehat{T{E}^{1}}}/\overline{\widehat{T{E}^{2}}} = 1.30\), meaning that group 1 is less} efficient than group 2, and this difference is significant, since 95% confidence interval is [1.23, 1.62], not overlapping with 1.
- 9.
We follow the categorization used by the State Planning Commission of China: (1) the Coastal region, which includes Beijing, Tianjin, Heibei, Liaoning, Shandong, Shanghai, Zhejiang, Jiangsu, Fujian, Guangdong, Hainan, and Guangxi; (2) the Central region, which includes Shanxi, Inner Mongolia, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hunan, and Hubei; and (3) the Western region, which includes Sichuan, Yunnan, Guizhou, Shaanxi, Gansu, Qinghai, Ningxia, Tibet, and Xinjiang.
- 10.
The significance tests are based on bootstrapped confidence intervals using Algorithm 2 of Simar and Wilson (2007), with 1,000 replications for the bootstrap bias correction of the DEA estimates and 2,000 replications for the bootstrapping of the regression coefficients.
- 11.
However, Zelenyuk (2009) reports Monte Carlo evidence suggesting that the power of the test of the significance of coefficients on dummy variables in the Simar-Wilson (2006) model is very low, even when the true difference is quite substantial from an economic standpoint. It is therefore likely that in some cases, we are simply unable to reject the null hypothesis of equality of efficiencies due to a relatively small sample size, which is clearly not the same as accepting the null hypothesis.
- 12.
We thank Paul Wilson for this remark.
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Shiu, A., Zelenyuk, V. (2011). Production Efficiency versus Ownership: The Case of China. In: Van Keilegom, I., Wilson, P. (eds) Exploring Research Frontiers in Contemporary Statistics and Econometrics. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-2349-3_2
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