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Has the unbundling reform improved service efficiency of Chinas power grid firms? Bai-Chen Xie College of Management and Economics, Tianjin University Montreal May 29 -June 1, 2019 42nd IAEE International Conference Outline Introduction Literature Review Methodology Empirical Study Results and Conclusions 42nd IAEE, Montreal, June 1 Introduction Since the 1970s, the electricity sector has witnessed considerable liberalization activities in many countries around the world. The main principles of power sector liberalization are restructuring, regulation and privatization. power generation sector and retail supply sector Promote: internal efficiency in the firms. Ignore the improvement of customer service quality Source: Agency I E. Electricity market reform: An IEA handbook[J]. 1999. 42nd IAEE, Montreal, June 1 Introduction The unbundling reform in China: 2002, 2003. To break the vertical monopoly and increase the

efficiency and competition on the transmission and distribution division. The State Power Corporation (SPC) -- , was dismantled into two power grid firms, five generation groups and four auxiliary firms. Source: The No. 5 document: The plan of power system reform issued by the State Council in 2002 . Electricity in China: the latest 42nd IAEE, Montreal, June 1 Introduction The transmission and distribution industry, natural monopoly and cost sub additivity over-construction and lower efficiency. A high social service nature. One of the main objectives of the 2003 reforms is to improve the service quality of electricity industry. If an enterprise wants to obtain better quality of service, it must sacrifice a certain amount of cost. Source: Yeoh B S, Rajaraman R. Electricity in China: The Latest Reforms[J]. Electricity Journal, 2017, 17(3):60-69. Hang G, Johannes V B. Effects of Deregulation and Vertical Unbundling on the Performance of China's Electricity Generation Sector [J]. Journal of Industrial Economics, 2014, 62(1):41-76. 42nd IAEE, Montreal, June 1 Introduction This paper aim to estimate whether the unbundling reform of 2003 played a key role in promoting the service efficiency of regional power grid firms by applying a panel data Stochastic Frontier Analysis (SFA) model.

Introduced a dummy variable How service efficiency change facing the inclusion or non-inclusion of cost? To analyze the service efficiency at the national level, regional level and provincial levels. 42nd IAEE, Montreal, June 1 Literature Review The majority of the studies have found that increased competition and unbundling may work to improve the efciency of power grid firms. Three levels: national, regional, firm ---national level:mainly European countries, OECD countries, developing countries, developed countries ( Hattori 2003, Pollitt 2008, Growitsch et al. 2009 , Cengiz et al. 2018) ---firm level: most researches focus on the power industry in Europe , U.S. Japan et al. (Jamasb& Pollitt. 2003; See,Coelli. 2013; Taniguchi. 2014) Hattori T., 2003. Relative performance of U.S. and Japanese electricity distribution: An application of stochastic frontier analysis. Journal of Productivity Analysis. 19, 115-119. Pollitt M., 2008. Electricity reform in Argentina: Lessons for developing countries. Energy Economics. 30, 1536-1567. Taniguchi M., 2014. The impact of liberalization on the production of electricity in Japan: Stochastic frontier analysis. Open Journal of Applied Sciences. 4, 155-167. 42nd IAEE, Montreal, June 1 Literature Review ----regional level: mainly focus on comparison between Chinese provincial firms. Li et al.(2016) : State Grid Corporation of China using data from 2005 to 2009 in 23 provinces and found that per capita GDP of the province is negatively related to the costs of the electric grid firm. Deng et al.(2018) found that economic development has a comparatively greater influence on technical efficiency. Li H Z, Kopsakangas-Savolainen M, Xiao X Z, et al. Cost efficiency of electric grid utilities in China: A comparison of

estimates from SFAMLE, SFABayes and StoNEDCNLS[J]. Energy Economics, 2016, 55:272-283. Deng N Q, Liu L Q, Deng Y Z. Estimating the effects of restructuring on the technical and service-quality efficiency of electricity companies in China[J]. Utilities Policy, 2018, 50.:91-100 42nd IAEE, Montreal, June 1 Literature Review What else do researchers tend to ignore? The importance they attached to the quality of service in the power industry during the reform process. There is only a limited body of literature has addressed the issue of quality of service. Service quality often plays a key role in the reform of power industry. Only a few researchers have taken quality of service into account when study power industry. 42nd IAEE, Montreal, June 1 Literature Review Pollitt et al. (2005) was the first to introduce the concept of quality of service. The quality of service plays an important role in the evaluation of the power industry. A combination of input distance function and panel data SFA model. Introducing quality dimension into analysis affect the estimated efficiency significantly. This is one of the pioneering attempts to incorporate the cost and unbundling reform into the estimate of service efficiency. Directly introduce the service quality dimension into the estimate of the technical efficiency to obtain the service efficiency. Pollitt M., 2008. Electricity reform in Argentina: Lessons for developing countries. Energy Economics. 30, 1536-1567 42nd IAEE, Montreal, June 1

Methodology Aigner et al.(1977) and Meeusen & van den Broeck (1977). It depends on model specication, distributional assumptions and temporal behavior of inefciency. Two categories: cross-sectional data, panel data. It enables the modeler to take into account some heterogeneity that may exist beyond what is possible to control using a crosssectional approach. Aigner D., Lovell C.A.K., Schmidt P., 1977. Formulation and estimation of stochastic frontier production function models. Journal of Econometrics. 6, 21-37. Meeusen W., Broeck J.V.D., 1977. Efficiency estimation from Cobb-Douglas production functions with composed error. International Economic Review. 18, 435-444. 42nd IAEE, Montreal, June 1 Methodology Panel data stochastic frontier analysis method. Time-Invariant Technical Inefficiency(Distribution-Free) Model Schmidt & Sickles (1984) Time-Varying Technical Inefciency Models Kumbhakar (1990) Battese & Coelli (1992) and Lee & Schmidt (1993) Models that Separate Firm Heterogeneity from Ineciency.ciency. Battese & Coelli (1992) Greene (2005a, b), Wang & Ho (2010) 42nd IAEE, Montreal, June 1 Methodology Models that Separate Persistent and Time-varying Ine ciency.ciency. Models that Separate Firm Eects, Persistent Ineciency and Time-varying ects, Persistent Ine ciency.ciency and Time-varying Ineciency.ciency. Four separate error components. first component: firms' latent heterogeneity second component: time-varying inefficiency

third component: time-invariant inefficiency fourth component: stochastic noise beyond control of the firm 42nd IAEE, Montreal, June 1 Methodology Our model is specified as follows: yit 0 f ( xit ; ) i - i vit -uit i denoting the ith province of the sample, i=1,2,3.... yit xit is the log of output for province i at time t; is the vector of inputs. f ( xit ; ) i is the production technology. captures firms effects which in our case is unobserved heterogeneity in ith province , uit i is inefficiency that is time-invariant while is inefficiency that is time-variant. And vit captures stochastic noise in panel data.

42nd IAEE, Montreal, June 1 Methodology The distributional assumptions on the four components are as follows: uit ~ N ( , u2 ) vit ~ N (0, v2 ) i ~ N (0, 2 ) i ~ N ( i , 2 ) Residual Technical Efficiency (RTE), and Persistent Technical Efficiency (PTE). Overall Technical Efficiency (OTE). RTE exp( uit ) PTE exp( i ) where uit is the Jondrow et al. estimator of uit And i is the Jondrow et al. estimator of i 42nd IAEE, Montreal, June 1 OTE RTE * PTE Methodology We introduce a dummy variable called unbundling. The value of unbundling is 0 before year 2003 including the year 2003. M

ln xKit k k 1 k 1 M M 1 k k * * 0 m ln ymit mn ln ymit ln ynit k ln xkit kl ln xkit ln xlit* 2 m 1 n 1 2 k 1 l 1 m 1 k 1 M m K 1 2 * * ln x ln y t tt t km ln xkit t m ln y mit t i - i vit -uit

km kit mit t 2 m 1 k 1 m 1 * xkit where: xkit xKit Some constraints on the unknown parameters are required. k k 1 k k =1 kl =0k = 1, 2, . . . . K and l 1

m km =0m 1, 2,....., M m 1 Source: China's Power Yearbook 1999-2015; Chinas Electric Power Industry Statistics Compiled 1999-2015 China's Demographic Yearbook 1999-2015; China's Land and Resources Statistical Yearbook 1999-2015 China's Meteorological Yearbook 1999-2015; China's Energy Statistics Yearbook - regional energy balance sheet Chinas State Power Grid Corporation Yearbook 1999-2015 Chinas Southern Power Grid Corporation Yearbook 1999-2015 Chinas Industry and Commerce Statistical Yearbook 1999-2015 Some Meteorological Data Website details will be added in in a more official way 42nd IAEE, Montreal, June 1 Empirical Study Data Table 1. Summary statistics of inputs and outputs for the 30 provincial power gid firms studied, 1999-2016 Variables Name Unit Mean

Min Max Energy Energy Delivered TWh 959.35 33.93 5235.00 Customer Number of customers Million 12.83 1.005 35.744 Length Network Length km

37654.00 3216.00 155320.00 Capital Capital increment Trillion yuan 30.06 18.87 201.18 Labor Number of workers thousand 30.33 4.43 117.337 CHL

Customer hour lost hour 10.26 0.54 223.64 TD Maximum Temperature Difference degree 46.74 17.38 84.50 Unbundling The dummy variable \ 0.47 1.00 0.00

42nd IAEE, Montreal, June 1 Variables Table 2. The specification of models. Variables Model1 Model2 Model3 Energy Customer Capital

Labor Length CHL TD Unbundling

Inputs Outputs Environmental 42nd IAEE, Montreal, June 1 Results Service efficiency Analysis of overall service efficiency of provincial power grid firms. 0.89 0.87 0.85 0.83 0.81 0.79 0.77

0.75 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 year Model 1

Model 2 Model 3 Fig1. The overall service efficiency of power grid firms at national level during 1999- 2015 42nd IAEE, Montreal, June 1 2014 2015 2016 Results Provincial comparision of overall service efficiency. SH IM, GS Service efficiency 0.84 0.82 0.8 0.78 0.76 0.74

0.72 0.7 0.68 BJ TJ HeB SX NMG LN JL HLJ SH JS ZJ AH FJ JX SD HeN HuB HuN GD DX HaiN CQ SC GZ

Province Fig2. The comparison of service efficiency of power grid firms at provincial level. 42nd IAEE, Montreal, June 1 YN ShX GS QH NX XJ Results Regional division of China's power grid Northeast China(NE): Heilongjiang, Jilin, Liaoning, Inner Mongolia North China(North): Beijing, Tianjing, Heibei, Shanxi, Shandong Central China(Cent): Heinan, Hubei, Hunan, Jiangxi East China(East): Jiangsu, Anhui, Shanghai, Zhejiang, Fujian South China(South): Guangdong, Guangxi, Hainan, Sichuan, Guizhou, Yunnan, Chongqing Northwest China(NW): Shannxi, Gansu, Ningxia, Qinghai, Xinjiang 42nd IAEE, Montreal, June 1 Results Regional comparision of overall service efficiency. Service efficiency

0.82 0.81 0.8 0.79 0.78 0.77 0.76 0.75 East Cent North NW Region Fig3. Regional comparison of service efficiency 42nd IAEE, Montreal, June 1 South NE

Results Determinant analysis of service efficiency of power grid firms Model 1 Model 2 SE Length Electricity Customer CHL Capital Labor Unbundling TD cons Model 3 Coef. Std. Err. Coef. Std. Err. Coef. -0.066* 0.068** 0.021*** -0.017** -0.034*

-0.011 -0.008* 0.184*** 1.097*** 0.0116 0.0088 0.0043 0.0056 0.0086 0.0147 0.0123 -0.014*** 0.074*** 0.018*** -0.011** \ -0.032** -0.006** 0.0081 0.0121 0.0046 0.0051 \ 0.0151 0.0128 -0.472*** 0.042*** 0.019*** -0.024*

0.009** \ -0.045* Std. Err. 0.009 0.006 0.004 0.056 0.006 \ 0.016 0.0079 -0.167*** 0.0102 -0.131** 0.007 0.1469 0.35*** 0.1151 -0.540** 0.242

Note: *** Denotes statistical significance at the 1% level ** Denotes statistical significance at the 5% level * Denotes statistical significance at the 10% level 42nd IAEE, Montreal, June 1 Conclusion The unbundling reform taken place in 2003 has not pl ayed a key role in promoting the service efficiency of regional power grid firms. The impact of cost variables on service efficiency can not be ignored. Regions with higher economic level or larger scale of ten achieve higher service efficiency. 42nd IAEE, Montreal, June 1 Thank You [email protected] 42nd IAEE, Montreal, June 1

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