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Productivity change in the privatized water sector in China (1999–2006)

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Abstract

We estimate the effects of the Chinese water sector’s privatization reforms based on firm-level panel data from 1999 to 2006. By applying a translog stochastic frontier production function and a Parametric Generalized Malmquist Productivity Index, we find that the privatization reforms and the participation of multinational water firms contributed to the performance improvement of the Chinese water sector. The foreign-owned water firms stood at the sector’s productivity frontier and the domestic privately-owned water firms were close to the frontier. However, state-owned water firms had the lowest technical efficiency scores, though they improved modestly over time.

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Notes

  1. The two possible limitations of estimating a stochastic frontier production function is that first, inputs are very likely to be endogenous, and second, as water utilities are not free to set water prices, water production is likely to be exogenous and water utilities are likely to select inputs conditional on outputs.

  2. In the literature, the cost frontier function and the input distance function are the two main approaches employed to examine a water sector’s efficiency. The cost frontier function requires the cost information of all inputs, which is not available in our dataset. The input distance function has been proposed as a solution to the multi-input and multi-output production technologies, such as water industry. When single output considered, the input distance function is not different from the frontier production function. In this study, because only one output data is available, we adopt the translog stochastic frontier production function. In light of this choice, this study might fail to fully capture the network features of the Chinese urban water industry. We hope this limitation can be overcome when more data become available.

  3. The Annual Survey of Industrial Enterprise is an annual census of all non-state-owned firms with more than RMB five million in revenue and all state-owned firms in China in the mining, manufacturing and utility industries, covering the period from 1999 to 2006. The dataset contains more than 50 firm-level statistical indicators, including input, output, R&D expenditure, capital composition, employment, geographical location, the industry (at four-digit industrial classification level) in which a firm operates, ownership status, and assets and liabilities.

  4. Based on the industrial identification, firms with fewer than 20 employees are identified as self-employed businesses, which are not included in our study.

  5. Two inefficiency variables adopted in this study, i.e. water population density and proportion of water abstracted from surface water, can be viewed as drivers of technology rather than inefficiency determinants in urban water production. Hence we test a model with them as regressors as well. The results are not materially different from those with them as inefficiency determinants and available upon request.

  6. We also include the quadratic terms of water population density and proportion of water abstracted from surface water in the function as inefficiency determinants. However, the maximum likelihood estimation of the models with both two quadratic terms and the model with quadratic water population density only do not converge. We only obtain the result of the model with quadratic proportion of water abstracted from surface water. The coefficient of quadratic proportion of water abstracted from surface water is positive but statistically insignificant. The result is thus not materially different from those of the models without these two quadratic terms and available upon request.

  7. We divide the sample by the 33th and 66th percentiles of employee number into three groups of firms with different sizes. We then compute and compare the returns to scale for these three groups of firms. In general, the three groups of water firms showed diseconomies of scale, which is consistent with the main finding. The result is available upon request.

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Acknowledgements

Jinjin Zhao is grateful for the financial support of the National Social Science Foundation of China Grant No. 16CTJ003 and the National Natural Science Foundation of China Grant No. 71503229.

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  1. China Institute of Regulation Research, Zhejiang University of Finance and Economics, Hangzhou, Zhejiang, PR China

    Jinjin Zhao

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Zhao, J. Productivity change in the privatized water sector in China (1999–2006). J Prod Anal 53, 227–241 (2020). https://doi.org/10.1007/s11123-019-00572-7

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