The Measurement Of Total Factor Productivity Of Chinese Commercial Banks

Modern commercial banks are the institutions which collect and allocate capital among every country's economic activities. They are the most important part of a country's financial system. Commercial banks are not normal enterprises but financial enterprises which take cash capital as their input and output. Their business activities make their role played in the national economy. The performing efficiency of the commercial banks of a country directly reflect the speed of capital liquidation of the country and the utility of the capital among liquidation. From the efficiency of the industry of commercial banks, we can get some information about the national economy. So it's meaningful to do some research on the efficiency of commercial banks, especially in the background that Chinese government fulfill the promise made to WTO which open Chinese financial market completely.The analysis of productivity is an important tool to explore the source of growth and is also the main methodology to decide the quality of the growth. The total factor productivity(TFP) is relative to the single factor productivity. According to the general definition of productivity which is the ratio of output to input or average product, if input which is the research subject only include one factor such as labor and capital, the resulting productivity is called single factor productivity. If input which is the research subject includes all factors which contains labor,capital,resources and so on, the resulting productivity is TFP. TFP is a comprehensive indicator of managerial efficiency of social economic system.Some researchers discovered that scale economic efficiency and scope economic efficiency don't impact commercial banks' obviously. The cost inefficiency owes 5% to them and owes 20% to TFP. Thus it's especially important to research the TFP of industry of commercial banks. The measures of TFP mainly includes Solow measure of productivity change based on the aggregation production function, Jorgenson index, DEA-Malmquist index and SFA-Malmquist index. Data envelopment analysis(DEA) was introduced by Charnes, W.W.Cooper and E.Rhodes at 1978. DEA takes linear programming technique as a tool to measure the efficiency of decision making units(DMU) with multiple inputs and outputs. DEA is a nonparametric approach which isn't specified a particular functional form. We can't put DEA in the measure of TFP directly, but with the combination of Malmquist index. Malmquist(1953) introduced a quantity index, defined as the amount by which one consumption bundle must be radially scaled in order to generate the same utility level provided by some base consumption bundle. In the past decade or so, beginning with the influential work of Caves, Christensen and Diewert(1982), the Malmquist quantity index frequently has been applied to the measurement of productivity change. This application has been spurred by the work of Fare et al. (1989), who showed how t use nonparametric linear programming techniques to calculate the Malmquist productivity index. This ostensibly has made the Malmquist productivity index a useful applied index, rather than just a theoretical index.The use of traditional DEA to measure Malmquist index means that the result also has the DEA shortcomings. A key drawback to the nonparametric approaches is that DEA assumes that there is no random error. There is assumed to be:(a) no measurement error in constructing the frontier; (b) no luck that temporarily gives a decision making unit better measured performance one year from the next, and (c) no inaccuracies created by accounting rules than would make measured outputs and inputs deviate from economic outputs and inputs. Any of these errors that did appear in an inefficient unit's data may be reflected as a change in its measured efficiency. What may be more problematical is that any of these errors in one of the units on the efficient frontier may alter the measured efficiency of all the units that are compared to this unit or linear combinations involving this unit. And the different environments that decision making units face will weak the measure's explaining power.So this paper applied three-stage DEA introduced by Fried, Lovell, Schmidt and Yaisawarng to measure the Malmquist index. We adjust inputs of every commercial banks in every year. If some commercial banks face relative better environment and own relative better luckiness, we will increase the quantity of inputs. Then the result accounts for the impact of environmental factors and statistical noise.This study examines the productivity growth of 14 commercial banks of China during the period of 2004～2008. The results indicate that Chinese commercial banks experienced productivity growth in average, however, the degree of growth is very little. And the growth which did occur due to the improvement in technical efficiency, even in some years that the technology decreased.