Market concentration, space agglomeration and the capacity utilization of China’s manufacturing: empirical study based on microenterprise data

JIA Runsong1,2 HU Qiuyang3

(1.Post-doctoral Mobile Station of Peking University)
(2.Post-doctoral Workstation of China Banking Regulatory Commission )
(3.School of Economics, Nankai University)
【Knowledge Link】spatial Gini coefficient

【Abstract】Based on the data of manufacturing enterprises, this paper has measured the capacity utilization of Chinese enterprises at the micro level with the data envelopment analysis method, and by focusing on market concentration and space agglomeration, it has examined the two theoretical views on excess capacity: the “development stage theory” that overcapacity is caused by the wave phenomenon in investment and the influx into the market; and the “local government theory” that local governments’ racing to promote investment leads to repeated construction and overcapacity. The result has shown that the average capacity utilization of China’s manufacturing enterprises was less than 70% and it decreased year by year during the research period. Looking from the inside, we can find that there is a great difference in capacity utilization between large and small enterprises, with the former usually being 30% higher. Although there are differences in terms of industry, region and the ownership type, the difference is often within 10%. The empirical test has shown that there is a positive correlation between the market concentration and enterprise scale and the enterprise capacity utilization, as well as and between the spatial Gini coefficient and government subsidies and the enterprise capacity utilization. Test on both pairs show that the significance and coefficient of the latter two are very unstable. This means that the fragmented market and mall enterprise scale are the leading factors for overcapacity in China’s economy before the global financial crisis, and that the repetitive industrial construction by the local governments plays a role by influencing market dispersion and enterprise scale. In other words, the repetitive homogeneous industry construction scattered in different places led by local governments is one of the driving forces of the wave phenomenon in investment and the influx into the market that lead to fragmented market and small enterprise scale.

【Keywords】 capacity utilization; market concentration; spatial agglomeration;

【DOI】

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(Translated by DENG Honghong)

    Footnote

    [1]. ① OECD, Business Tendency Surveys for Manufacturing: Capacity Utilization: Rate of Capacity Utilization: European Commission and National Indicators for China. The data come from Federal Reserve Bank of St. Louis. See https://research.stlouisfed.org/fred2series/BSCURT02CNQ160S/ [^Back]

    [2]. ② Enterprise Surveys (http://www.enterprisesurveys.org), The World Bank. [^Back]

    [3]. ③ For the review of oversea literature, see Zhong, C. and Pan, L. (2014). [^Back]

    [4]. ④ The research group in Development Research Center of State Council (2015) used micro investigation data to analyze the capacity utilization of industrial enterprises. As described in the following, the cost function method used in this study has limitations in the selection of function forms and the susceptibility of information on input price. [^Back]

    [5]. ⑤ Empirical studies on capacity utilization at the macro and industry level often make empirical analysis based on the historical peak or time trend of the capacity output or the long-term cointegration relationship between capacity output and variables such as capital stock, such as the peak value method (Klein et al., 1973; Christiano, 1981), the Hodrick-Prescott filter (Kennedy, 1998), the cointegration method (Shaikh and Moudud, 2004; Cheng, 2015 and He, 2015), the production function method (IMF, 2012) as well as the structural vector autoregression method (Dergiades and Tsoulfidis, 2007). Due to the requirement for time-series data, such methods are not applicable to the analysis of capacity utilization at the microenterprise level. [^Back]

    [6]. ⑥ Both methods take the capita input as the quasi-fixed factor to reflect this point. [^Back]

    [7]. ⑦ As the analysis of capacity utilization belongs to the short-term economic phenomenon, the VRS constraint is appropriate, while for the long term, the constraint OF constant returns to scale might be more appropriate. [^Back]

    [8]. ⑧ The average here refers to the weighted average of gross output of enterprises. According to the Business Trends Surveys: A Handbook by OECD, large enterprises should be given greater weight when industry data are calculated by aggregating microenterprise data. The aggregated data followed are based on the weighted average of the total output of enterprises, which will no longer be pointed out. [^Back]

    [9]. ⑨ For the constructive thought, the readers can also refer to the empirical study on the influencing factors for the change of capacity utilization of the light industry in Thailand by Lecraw (1978) and that of the chemical industry in the US by Lieberman (1989). [^Back]

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This Article

ISSN:1002-5502

CN: 11-1235/F

Vol , No. 12, Pages 25-35

December 2016

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Abstract

  • 1 Introduction
  • 2 Calculation of the capacity utilization for Chinese manufacturing enterprises
  • 3 Empirical analysis on market concentration, space agglomeration and capacity utilization
  • 4 Conclusion
  • Footnote

    References