China Banking System. Comparison between State-owned Banks and Joint-Stock Commercial Banks - Essay Example

? Comparison between owned banks and Joint-Stock Commercial Banks China Banking System A brief history of the analysis This section involves the review stochastic frontier analysis (SFA). More so, its influential antecedents: empirical and theoretical. Furthermore, l events leading to original developments of SFA are recalled. The conclusion entails of a summary of the most important developments of SFA from 1977. The article discusses in detail many of the SFA’s developments. The empirical evidence on banking efficiency aims at highlighting features associated with the role of economic development and banking reforms in China over the given time. This is as a result of changes in China’s banking sector associated with WTO accession (2001) and the recent financial crisis (2008). It is anticipated that these reforms will have an impact on the efficiency and performance on China’s banks. Moreover, emphasis is placed upon investigating the diversity of efficiency levels between different types of banks (SOBs and JSCBs) Analysis Considering economics, the theoretical construction representing an economic process, basing on a set of variables and logical sets as well as relationships which are quantitative, can be defined as a model. A model in economics is a backdrop which is simplified thus complex processes is illustrative. Mathematical terms are not always put to use in the model. Measurements that are structural are inclusive in economic models. In model classes, parameters that underlies are structural ones. Creation of various properties comes from the changing of various parameters. Fitting, investigation and theorizing are examples of uses of model methodologically. Functionalism of economic models is in the simplification of abstraction of data under observation. The models used in selecting data, basis on sets of assumptions on the study of econometrics. As a result of ambiguity of processes in economics, simplification is necessary. Complexity in economics is due to a variety of economic activities determining factors. Diverse economic factors are in inclusive of; cooperative and individual decision making, limitation of resources, hindrances’ due to geographical and environmental factors, requirement from legal personality and institutional based laws, and random fluctuations. Thus, the making of decisions by economists, on what bases information analysis takes place and their presentation. Selection of the economic model to use is to be done on the bases of the facts economists are tabling and on their compilation. The measurement of inflation which is a concept in economics requires a behavioral model. This is for differentiation of real changes and price changes, thus inflation attributable changes in price. Models in use include; forecasting which logically relate conclusion to assumptions, proposal of policies in economics for the modification of future economic activity, is also a model, presentation of reasonable arguments for political justification of policy nationally, is also a model in use. Planning and allocation in economies that are centrally in the plan used in economic modeling is also under consideration. Predictive models in finance are in uses for a long time in trading. There is bond trading on economic bases for growth prediction in developing countries that issue them. Models in management of risk are in incorporation for economic relationships, among variables under simulation for detection of high-exposure scenarios in the future. Argumentative frameworks in application of logic are under establishment. Independent discussion and testing for mathematical application is in the application. In supporting models, arguments and policies relying on economic models have clear bases relating to soundness (Heffernan, 2009). Currently, models in economics in use do not show pretence basing on the “everything economic theories.” Pretensions under scrutiny overwhelmingly receive prevention from occurring, by computation activities that are unforeseeable such as scarcity of theories of a number of economic behaviors.’ A crucial representation of economic affairs in the drawing of conclusions from the models is of exceptional consideration. Proper construction of models removes information that goes to extreme ends thus key relationships of approximations that are of use are isolate. Model construction details vary depending on the application of the model under question, though a model that is generic, meaning that the model under observation is under general terms. In general, process of modeling is inclusive of two processes, including; generation of a model, where the model on the proposal for use is to be generated to the satisfaction by economists. Checking of accuracy, where problems are under diagnostics, and possible solution looking takes effect. Identification of accuracy is of necessity such that there is mirroring of relationships under description. Creation and diagnosing of a model is a frequent interactive procedure where there is modification and improvement of the economic model Types of models Classification of economic models depends on whether the variables are entirely deterministic. They can be listed as either non-stochastic or stochastic. Economic models are continuous or discrete choice model if only the variables are quantitative. Models can further be classified according to their proposed function that is qualitative or quantitative. Further still a model can be termed as partial, non-equilibrium, or equilibrium in reference to the ambit of the model. In addition, models can be representative or rational agent models based on characteristics of the economic agent (Heffernan, 2009). Stochastic models- their formulation is done via the stochastic process. This model is economical and observes time. Many econometrics are based on statistics to test and formulate hypotheses concerning the estimate and process parameters for them. Autoregressive models are the most used; they were popularized by Wold and Tinbergen. They are unique in that they utterly relate past and current values; they include autoregressive conditional heteroskedasticity, GARCH models used to model heteroskedasticity and the autoregressive moving average models. Non-stochastic mathematical models- models involved in the theory of social choice are excellent examples and are qualitative or those containing financial rationalization variables which are quantitative models. However, functional relationships can be only qualitative when the economic predictions assert economic variables direction of movement. Such as a case, where the price of an item (x) increases, the demand which is directly proportional increases. Therefore, instead of the functions the economists will use two-dimensional graph. Qualitative models- occasionally, qualitative models are used though most of economic models require mathematical applications (quantitative analysis). In scenario planning, for example, future events are played out thus classified as qualitative analysis. This analysis lacks precision. In many fields of economics, there’s application of quantitative modeling. Methodologies, which are independent, have evolved and as a result, the taxonomy model is no longer naturally available. Below are a few illustrations of model construction (Aschauer, 1989). Accounting models- they are based on the fact that each credit is a debit. The accounting model defines the conversational principle in a symbolic form. The sum algebraic inflows =sinks- sources The above is the basis of national income and is known as certainly true for money principle. By convention, accounting models are true. Hence, any experiment to confirm them should be equals to the exact value. Otherwise, the experiment would be termed as fraud illustrating that the experiment was improperly conducted. Contained and optimality models – this is another method based of utility or profit maximization. An ideal example of the comparative model is comparative statics of taxation for the profit-maximization firm. The profit of the firm will be given by This is where P(x) the price offered by the company in the Market at a standard rate of X. thus from this, we can clued that xp(x) is the revenue gained after the sale of the commodity. On the other hand, C(x) is the bridging cost of the product under the cost of x. However, T is the taxation rate per product. The assumption states that, for a firm to produce products at a rate of x, if that rate is the rate of the firm’s profit. If we differentiate the equation above, we will maximize the value of x. Assume the above equation is an implicitly function, thus we use the implicitly function theorem with respect to x. thus the second derivative will turn out to be as shown in the equation below. From the above illustration, it is despicable that it is a second order condition for the local maximum. For the firms’ in question to maximize he profit, something must be done to ensure that the taxation is minimal. If the process above fails, we conclude that the profit maximization hypothesis in the firm is false. The firms under examination should change their mode of operation to maximize the profit and reduce the amount of payable tax. This is through adoption of alternative econometric models. An example of other models is the bound rationality model. Aggregate models – the econometrics model deals with aggregates such as output, interest rates, and the price level, this is because the tools are under macroeconomics. The examiner takes the output as a vector quantity of the goods and services. These include items such as computers, cars, planes, banking services, and secretarial tools. In the model, the vector quantities must be maintained. A larger part of the model is computationally hard to deal with; it is also hard to use for qualitative analysis. For this reason, the best qualitative analysis method is Stochastic frontier model. Standard error function This is the deviation of a statistic. It may be measured in reference to the mean or any other measures of central tendency. Standard error of the mean SEx= S divide by the root of n that is In the above case, s is the sample of standard deviation, while n is the number of observation. It is also evident that the standard deviation is the square root of the variance. Through this function, we can create different relationships between different statistical data. This is in the case of the two groups of banks. Te two entries must be variables. It examines the dependencies between the variables. Below is an illustration of the function, where X(s) and X(t) are different. C(s,t)= corr(X(s), X(t)). Corr is the article for correlation. The stochastic variable is a scalar quantity. If they are not stochastic, complex correlation functions can be used to bring out the relationship. Such functions are: Cij (s,s1)=corr(Xi(s), Xj(s1)) Stochastic Frontier Analysis This is an economic modeling used to measure efficiency. The model has a starting point which is at the stochastic production frontier. The model is a product designed by leading economists named Aigner, Schmidt, and Lovel back in 1977. Other participants of the model design are Broeck and Meeusem. The production model has a basic equation as illustrated in the pictorial below. The Basic equation is applicable only in production instances where the components are random. This is where yi represents the scalar quantity of the output from the producer. However, i=1…n. x is the vector of the input used by the producers. In this, i= to a function f(xi, b) of the production frontier. B is the vector representing the technology parameters under estimation. On the other hand, TE denotes the technical efficiency. After the efficiency calculation, the analyzer should analyze the value of TE. If the value is equals to one, then the firm under examination obtains maximum feasibility output. On the other hand, if the value of TE is less than one, then firm is operating with feasibility below the output. From the analysis of the performance of the two categories of banks in China, it is evident that the State owned banks are performing poorly compared to the joint-stock commercial banks. This is because of these banks do appreciate and uphold both the economic and technological changes prevailing in the world’s economy. It is the same ideology that has been leading to development f other smaller banks within the region. These banks are highly aggressive to change. They also work with an intension of growth thus their technical efficiency (TE) is always greater than one, unlike the State owned. Reports from different bank analysis show that the model of stochastic frontier, also explains the rigidness of the State owned banks (Aschauer, 1989). Stochastic components that describe random shocks impacting the production and efficiency processes can also be illustrated by extension of the function. However, shocks are not direct attributors of the production process or the relationship between the firm under examination and technology. Shocks may arise from factors such as weather changes, plain luck, and economic adversities. Exp{vi} denotes these effects. However, it is crucial to keep in mind that different firms face different shocks. This is because they may be located in different economic environments thus open to different challenges within their areas of operation. There is also an assumption that the shocks are random and are external factors affecting the performance of an enterprise. This is because these shocks are not under control of the firm under examination. Below is the equation illustrating the stochastic production frontier. There is an assumption that TEi is the stochastic variable. It has a fixed distribution function which is common to all producers. The variable (TEi) can also be written as TEi=exp{-Ui}. The value of Ui is always less than zero because we require TEi to be less than or equal to one. Therefore, we achieve this equation: We now assume that the function f(xi, B) takes a log-linear form. Thus the model later takes the form below. In the equation above, Vi are the external factors or the shocks affecting the banks (the firm in question). The value of Vi may either have a negative or positive effect on the operation of the sector in question. On the other side, Ui is a non-negative technical inefficiency element. The combination of the two forms a compound error term. With the equation, the distribution and the effect of shock and the technical efficiency of any firm can be determined. With the analysis above, one can easily analyze technical efficiency of the two groups of banks. References Afriat, S. N. 1972. Efficiency estimation of production functions. International Economic Review. 13 (3) 568 – 598 Aigner D.J., Lovell C.A.K., and Schmidt P. 1977. Formulation and estimation of stochastic frontier production functions. Journal of Econometrics.6 21.37. Aigner, D.J., Lovell, C.A.K.; Schmidt, P. 1977. Formulation and estimation of stochastic frontier production functions. Journal of Econometrics. Aschauer, D.A. 1989. Is Public Expenditure Productive? Journal of Monetary Economics. 23 177 - 200. Chen, C.H. and Shih, H.T. 2004. Banking and Insurance in the New China: Competition and the Challenge of Accession to the WTO Cheltenham. Claessens, S., Demirguc-Kunt, A., and Huizinga, H., 2001. How Does Foreign Entry Affect Domestic Banking Markets? Journal of Banking and Finance 25(5): 891-911. Coelli, T.J., Rao, D.S.P.; O'Donnell, C.J.; Battese, G.E. 2005. An Introduction to Efficiency and Productivity Analysis, 2nd Edition. Springer. Fu, X., Heffernan, S. 2009. The effects of reform on China’s bank structure and performance, Journal of Banking & Finance, Volume 33, Issue 1, 39-52. Gelb, A. 1989. Financial policies, growth, and efficiency. Washington: Working Paper WPS 202. Girardin, E., 1997. Banking Sector Reform and Credit Control in China, OECD Publishing. Goldsmith, R. W. 1969. Financial structure and development. Yale Univ. New Haven Press. Goodhart, C. and Xu, C. 1996. The Rise of China as an Economic Power. National Institute Economic Review, 155: 56-80. Kumbhakar, S.C.; Lovell, C.A.K. 2000. Stochastic Frontier analysis. Cambridge: Cambridge University Press. Read More