| About1000trust and investment corporations had been established nationwide in China by1989since the founding of the first trust and investment corporation in October1979. Trust industry in China has been moving ahead through the "trial and error" process from fixing the trust industry in1982by the State Council to the establishment of trust legislation and legal systems. Compared with other financial industries-banking business, insurance and securities-trust industry has immature development, low social approval and weak market impact, but trust industry serves as a bridge and important intermediary in China’s financial system and economic development, and will have nonnegligible potential among financial industries in the future. Therefore it is especially important to evaluate the efficiency and productivity of China’s trust companies, measure changes in technical efficiency and total factor productivity of China’s trust companies in recent years, and find out the main influencing factors.This dissertation follows the research approach from theory to practice. In theory, this dissertation analyses the connotation and function of trust and points out that trust relationship as a special institutional arrangement plays a unique function in modern society. This is the basis for the existence and development of trust companies. In practice, this dissertation reviews the five stages of development of China’s trust industry which shows a distinctive feature of institutional change in trust industry. This dissertation defines the efficiency and productivity of trust company as trust company technical efficiency and total factor productivity respectively.Technical efficiency studies the efficiency disparity of different trust companies under the same production frontiers in the same period. It is relative efficiency, with value between0and1. This is static and microscopic analysis. Total factor productivity is the change of productivity of different trust companies in different periods using indices, which is dynamic and macro analysis. Technical efficiency and total factor productivity are closely related to each other, and we can link them together by Malmquist-Luenberger Productivity Index. This dissertation transits from the same frontier production to different frontier production by means of technological progress, and transits the static measurement of technical efficiency to the dynamic measurement of total factor productivity, and thus carries on the research deeply and systemically from the micro level to macro level, and from static point of view to dynamic point of view. The study of total factor productivity, as it were, goes one step further in the study of efficiency. It considered not only the changes of trust companies’efficiency, but also technological progress of trust industry. From this level and aspect, efficiency and total factor productivity have a certain logically successive relationship.Measurement of Technical Efficiency.The selection of input and output indices is most crucial in the measurement of technical efficiency. This dissertation selects labor force, capital fund and operating expenses as input indices in KS test and T test, and selects trust income and net income of trust companies as output indices. On one hand, this dissertation found out that the average technical efficiency of trust companies is pretty low after measuring and decomposing technical efficiency of China’s trust companies by DEA. On the other hand, considering that the non-performing assets of trust companies’can have an effect on technical efficiency, this dissertation joins non-performing assets of trust company into output index as unexpected output,and measures trust companies’technical efficiency based on directional distance functions. As we can found in trust companies’efficiency ranking of the same year by comparing the two methods, due to the encumbrance of non-performing assets, some trust companies’efficiency ranking had a certain extent drop, such as China and Thailand trust and Shandong trust, after unexpected output is taken into consideration.Measurement of Total Factor Productivity. Malmquist productivity index and Malmquist-Luenberger productivity index are two models commonly applied to the study of productivity. Based on Malmquist productivity index model, total factor productivity of China’s trust companies in2004-2010presents a rising trend, and it came mainly from technological changes rather than technical efficiency changes. Based on Malmquist-Luenberger productivity index model, total factor productivity of trust company is remeasured in view of unexpected output. This dissertation found that the mean-variance of Malmquist productivity index is somewhat higher than the results of Malmquist-Luenberger productivity index in2004-2007through comparative analysis of two groups of measurement results. This suggests that the non-performing assets can reduce the growth level of trust companies’productivity to some extent. However, in2007-2010, two total factor productivity variation curves get closer gradually and finally intersect without obvious trend. This suggests that the effect of non-performing assets on total factor productivity of trust industry declines year by year. In particular, non-performing assets of the whole trust industry slumped sharply after CBRC (China Banking Regulatory Commission) promulgated "management method of trust companies’net capital"The Influence Factor Analysis of Technical Efficiency and Total Factor Productivity. By measuring the technical efficiency and total factor productivity of China’s trust companies, this dissertation found out that significant differences exist between different trust companies and we need to use the regression model to find out the long-term substantial factors of technical efficiency and total factor productivity fluctuation of trust companies. On one hand, this dissertation has made empirical analysis of effect factors of panel Tobit model and system GMM model on trust companies’technical efficiency.The ultimate results shows that asset turnover ratio, expense ratio and capital adequacy ratio are negatively correlated to comprehensive technical efficiency, and ROA(return on assets) and market share is positively correlated to comprehensive technical efficiency. On the other hand, this dissertation uses the random forest algorithm to select the major influence factors of total factor productivity and build panel econometric model in three aspects, including macro, industry and micro, and found out that capital market, real estate market, the ROA and trust assets market share are positively correlated to total factor productivity of trust industry and company size is negatively correlated to total factor productivity and property right variables are not significant.On the basis of the analysis above, this dissertation gives policy recommendations in four aspects aimed at accelerating the formation of trust company core competitiveness, strengthening risk control of trust companies, advancing actively products and business innovation, and deepening strategic cooperation with financial institutions, in order to enhance the efficiency and productivity of trust company and to ensure the healthy, sustainable development of trust industry in the long run. |
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