The Theoretical Model And Emperical Research On Green Total Factor Productivity And Cost Efficiency Of China’s Logistics Industry

According to the ‘State Council on the Issuance of Mid- and Long-Term Planning for Logistics Industry Development(2014-2020)’ issued during 12 th Five-Years perod, the main tasks of China’s logistics industry would focus on the improvement of logistics efficiency, the reduction of logistics cost, and the relief of resource pressure and environment pressure. In the most recent 13 th Five-Years Plan, one of the important elements is to cut down the cost of enterprises in real economy.The measurement and understanding of logistics Green total factor productivity(TFP) and cost efficiency is crucial for developing Chinese logistics industry and enhancing logistics companies’ competitiveness. On the one hand, the Green TFP describes the surplus of output and its changes can be explained by technical efficiency change(TEC) and technical change(TC). Distinguished from traditional TFP research, the Green TFP of logistics considers both energy input and ‘unsatisfied’ output so it accurately explains the quality of logistics industry growth. On the other, efficiency reflects the distance between actual production and optimal production. Thus, cost efficiency can be seen as a mirror of not only management skills and the allocation of resources, but also the capability of profit-making. In recent years, logistics companies have been facing many challenges, like the rise of oil price, the shrink of land use policy, global financial crisis etc. Against this situation, the measurement of cost efficiency and the understanding of its change are a key helping to improve the productivity of logistics.However, existing research on the TFP of Chinese logistics industry doesn’t take both energy consumption and environmental factor into account so the measurement of traditional TFP may be biased. In addition, the research on Malmquist index is mainly based on C-D function and assumes fixed price. This research method only explains how TEC and TC affect TFP change, but cannot describe the impact of price change(PC) and allocative efficacy change(AEC). However, due to the fact that the price of inputs is extremely difficult to acquire for most researchers, the logistics cost efficiency, PC and AEC is still under researched. By considering energy consumption, ‘unsatisfied’ output and price of inputs this paper builds models based on production function and cost function respectively by stochastic frontier analysis(SFA) and data envelope analysis(DEA). This multiple research framework addresses industry level and firm level, as well as the static and dynamic analysis of logistics Green TFP and cost efficiency. Drawing upon this framework, we are able to measure the static and dynamic features, difference, convergence and impact factors. The expected research findings include accurate measurement and understanding of Chinese logistics Green TFP and cost efficiency, and therefore provide both theoretical and empirical support for improving the Green TFP and enhancing cost efficiency. The detailed research includes:(1) This paper applies translog production function based SFA and super efficiency DEA(SEDEA) approaches respectively by taking energy consumption, capital and labor as inputs to measure Chinese logistics TFP, as well as makes a comparison between the results generated by the two approaches. The results show that the measurements generated by SFA and SEDEA are highly correlated by using T test, although the growth rate of logistics TFP generated by SFA is higher than the date generated by SEDEA; the growth of TFP is driven by TC, but TFC is a barrel for logistics TFP growth. In terms of impact factor, all impact factors pass significant test except the business volume of post and telecommunications.(2) Taking energy consumption as an input and CO2 emission as a unsatisfied output, this paper builds up a framework investigating dynamic features of Chinese logistics TFP based on slack based measure(SBM) and Luenberger index(LTFP) methods; as well as establishes an endogenous econometric model of logistics Green TFP drawing upon two-stage least squares(2SLS) approach to analyze the impact factors to TFP change. Research finds that the LTFP is lower than traditional Malmquist index. This indicates that the research overestimates TFP change if unsatisfied output is ignored, and Potter hypothesis is inapplicable to logistics industry at the moment. With regard to spatial distribution, Eastern China appears the fastest growth of logistics Green TFP, followed by Western China and then Mid China,and the last is Northeast. Both TEC and TC are correlated to TFP change: the former is a driven force. In terms of time distribution, the kernel density estimation of cumulative productivity index finds that the change of logistics Green TFP features a‘catch-up’ effect. The 2SLS based econometric model can efficiently control endogeneity by using instrumental variables so the coefficient of all impact factors is higher than that generated by generalized least squares method(GLS)based fixed effects model. This means that the existence of endogeneity underestimates the significance of impact factors to logistics Green TFP.(3) To study price change and allocative efficiency, research on cost efficiency is necessary. This paper hence builds up a price index system of logistics input and output elements, follows Kumbhakar, Brissimis, Delis and Tsionas and Schmidt’s cost function based approaches and applies Markov Chain Monte Carlo(MCMC) method to evaluate the cost efficiency of logistics firms. This research framework is able to avoid the negative impact of element share deviation ignored by traditional single-equation through the use of convergence test so the measurement is more accurate. Research finds that the reason leads to low cost efficiency of listed logistics firms is low technical efficiency(TE). The distribution of allocative efficiency is centralized, while distribution of TE is dispersed and the distance of different types of companies’ TE is significant.(4) Cost efficiency reflects the distance between actual costs and minimal costs, which is a static index. At last, paper develops cost malmquist(CM) index based on cost function to illustrate the dynamic features of cost efficiency and further identify the relations between PC, AEC and IM index(Input-oriented Malmquist index) through a two-stages decomposition. The evidence shows that the CM of listed logistics firms is more significant comparing with IM in productivity setback. The driven forces for CM setback are both PC and AEC, but the former is more significant than the latter.Based on production function and cost function, this paper builds up a new research framework to research the static and dynamic features of Logistics Green TFP and cost efficiency. The theoretical contributions are:(1) this paper adopts SBM and Luenberger index to evaluate Chinese Logistics Green TFP which illustrates the space-time feature of logistics development. In order to avoid endogeneity and make the measurement accurate, it applies 2SLS method to analyze the impact factors of logistics Green TFP change.(2) This paper establishes a price index system for Chinese logistics based on the prices of input and output elements, so it is able to further explore traditional TFP studies from the perspective of cost efficiency. In terms of cost function, it follows the concept of Schmit ’s model to modify Ferrier and Lovell’s approach and conduct exploratory study on Greene problem. The symmetric random errors are added to share equations for the purpose of minimizing statistical errors and sets the factor’s divergences of share deviation with which this research can identify the reasons resulting in low cost efficiency.(3) This paper develops the CM index based on cost function to estimate the impact of PC and AEC on logistics productivity. Theoretically, this research extends the application of efficiency and productivity theories to logistics field and builds a basis for the measurement of Green TFP and cost efficiency. The empirical contributions of this paper are:(1) provides a theoretical basis for the main tasks proposed by the ‘State Council on the Issuance of Mid- and Long-Term Planning for Logistics Industry Development(2014-2020)’;(2) provides empirical evidence for identifying the internal and external factors driving Chinese logistics Green TFP change, which could be a reference for industry policy-making;(3) helps both Chinese government and logistics firms to understand the status of cost management skills, resource allocation etc. This could be a clue for improving firm’s operation and profitability in the future.