Evaluation of Banking Competition between Two Countries - Assignment Example

Evaluation of Banking Competition between Two Countries Introduction The paper aims at evaluating the banking competition between two countries. The paper would scrutinize the internal and external competition environments in the light of empirical and reliable banking literature. There must be a certain approach to draw conclusions about the competitive and/or market structure analysis involving the firms’ conduct in the specified countries. In this regard, the Panzar-Rosse approach suits the best. The approach utilizes the most reliable, tested and rigorous methods to analyse the market and economic backdrop with the help of H-statistic. This set of statistical methods aids in the instrumentation of a unique symmetric and unbiased estimator to calculate the central moment for a given distribution. For instance, the estimator h can be evaluated as: h = n2m3 m (n – 2)(n – 1) where m3 is a given moment and n denotes frequency. Countries Chosen for Analysis The countries chosen for analysis are: 1. Belgium 2. Denmark Reasons for the selection: 1. Both the countries are members of the developed world. Sufficient data is available to carry out the required estimation over an extended period of time. For example, CL, NA, OBS, DEP, etc. were available for Antwerps Beroepskrediet (which is a Belgian cooperative bank) over the years 1998, 2001 and 2004. In the case of Denmark too, similar variety and quantity of critical data were available. 2. The countries are important members of the EU. Both of them share the compact regional economy of the Western Europe. Apart from availability of data, the Belgian and Danish banks are facing several challenges due to expansion of the EU. 3. Both the countries have advanced following the capitalist model of development. The geographical vicinity between them might have caused mutual influence and serious undercurrents in the bilateral relationships. 4. In spite of the fact that the countries are closely related to each other due to the well-knitted European economy, they still value their individual existence and economic status. Assessment of the Issue In the sphere of analysis of banking competition, J. A. Bikker and J. W. B. Bos have eloquently remarked, “In observing trends, we distinguish original causes, subsequent changes in banking behavior and in the structure of financial markets, and final consequences, aware all the while, that this classification may be somewhat arbitrary.” (Bikker, J. A. and Bos, J. W. B., 2008) In this way, country specific banking behaviour can be put in correlation with financial markets which are profoundly influenced by the bond markets and the quantity of national assets. With the lapse of time, apart from qualitative analysis, quantitative methods too have emerged as tools of critical importance in modern financial research (McCrary, S. A., 2010). Theory of the Panzar-Rosse Approach The Panzar-Rosse revenue test to estimate the competitive circumstances and parameters in the realm of banking depends on certain empirical observations. Variations and fluctuations in the factor input values affect the bank-level revenues. The causative of the upshot is empirically observed and the results thus obtained aid in evaluating the competitive conditions through Panzar-Rosse approach. Panzar-Rosse approach further takes in account the tendencies of adjustments towards the market equilibrium, degree of bias in revenue equations and effects o f the factor input estimation shocks. Fixed Effects (FE) analysis of the static revenue equations culminates at measured H-statistic. The values obtained by implementing the H-statistic ultimately help the analysts to determine how competitive the banking sector is. Description of the Sampled Data The individual bank data are obtained from the Bankscope database and made available in both Stata and ASCII format. The data have several significant data-fields. Specialisation of the respective banks has been specified. Varieties of banking structures have been counted. These varieties include the cooperative banks, commercial banks, savings banks, investment banks, etc. Values like those of the CL, NA, EQU, NII, etc. have been properly tabulated. The number of the bank-year observations is high. Processing the raw data thus made available, the following information is obtained in a tabular form: Country Nobs Nbank ĤF ĤG Belgium 251 62 0.586 0.762 Denmark 397 96 0.058 0.13 Table – 1 where Nobs = Number of the bank-year observations utilized in each estimation Nbank = Number of the banks (with data available) ĤF is the FE (Fixed Effects) estimated Panzar-Rosse H-Static ĤG is the GMM (Generalized Method of Moments) estimated Panzar-Rosse H-Static Estimation of the H-statistic In estimating the H-statistic for the countries under examination, the following equation has been deployed: Estimation Method Used: Fixed Effects (FE) method has been explicitly utilised. FE method is a statistical modelling which represents the quantities observed in the terms of descriptive variables. These descriptive variables would be treated such that none of those amounts were random. Formally, the deployed model is: yi,t = φ0 + xi,tφ + zi,γ + ui,t + αi, (3) where yi,t = dependent variable which is observed at time t for individual i xi,t = a regressor (time-variant) zi, = another regressor (time-invariant) αi = the individual effect unobserved and ui,t = the tern of error In the present thesis, αi is the institutional factor under country-level data with historic significance. Further, for the Fixed Effects, αi is assumed to be dependent on xi,t and zi. The FE method has been utilised to find out the H-statistic for Belgium and Denmark (refer to Table – 1). Utilisation of the FE method is preferred because: 1. It aids in structuring and solving the revenue equations when the factor input price fluctuations are non-instantaneous. 2. This method implemented to the static revenue equations helps culmination at H-statistic. 3. Controls biasing that determine the banking environment conditions. 4. In the case of the developed countries like Belgium and Denmark. FE estimation will help partial adjustment modelling in a non-instantaneous adjustment environment. What Does H-statistic mean? We know that When we have H < 0, it denotes complete monopoly. In the sphere of monopolistic competition, the value of H is between zero and one. When H = 1, we have perfect competition. Assessment of the Development of Competition Study of H-estimations both through FE and GMM techniques provides us with a way to assess the development of competitions. Analysing the H-estimation through FE and GMM techniques (please refer to Table - 1), it can be said that the Belgian banking industry is more competitive in comparison with that of Denmark because the H-statistic for Belgium is nearer to one. The ĤF value for Belgium is 0.586 which is relatively high. Moreover, the ĤG is still higher at 0.762. Integration of the weaker economies of Europe in the EU has not created much pressure on the country. On the other hand, Denmark has highly monopolistic competition and it appears to be moving towards monopoly. The ĤF value of the country is as low as 0.058. We get similar results by deploying the GMM method as well, and hence the conclusion is reaffirmed. Competition Differences Competition differences can be caused by several complicated socio-economic and monetary factors. In any part of the world, a regional economy is a blend of strong and weak economies. For example, in Northern America, the USA is a strong economy while its neighbour, Mexico, is a weaker one. In such circumstances, the two economies may influence each other. In the case of Europe, both Belgium and Denmark have to handle the pressure of European integration. Hence, the external competitions would increase. However, inside Belgium, the H-statistic implies that the banking sector is more dynamic. In the case of Denmark, H-statistic signals onset of monopoly. Apart from these countries, if we look at the strong economies like Japan and the USA, we would find that competitions in the banking industries have been rather high all during the early 1990s which finally resulted into positive growth. Moreover, the competition differences between them did not affect the bilateral relationships adversely. In this way, competition differences can suggest which countries have stronger economy, lesser monopoly and relatively stable financial management at the national level. Significance of Competition Differences across the Countries In the 1990s, researchers obtained 0 < H < 1 for Britain, France, Spain, and Germany. Under similar researches, H was found to be relatively low in the case of Italy. The information can be regarded as a proof of successful recovery of the British economy and stronger economic status of post unification Germany. Towards stronger integration under the banner of the EU, these countries took positive measures to lessen competition among them. But the companies were encouraged to rival each other in the realm of the financial markets which is rather necessary for economic growth. When H-estimation tends towards one, the internal fiscal condition appears to be healthy and non-monopolistic. Country Nobs Nbank ĤF Britain 394 112 0.435 Germany 7740 1950 0.431 Italy 3224 763 0.413 Spain 413 99 0.571 Table – 2 Limitations of H-statistic In general, H-statistic can throw some light on the internal situation of an economy with reference to the banking industry. There is no considerable universal approach and technique … the FE method of estimation suits only the developed countries. Therefore, since estimation methods for developed and developing countries are different, direct comparison is hampered. Specifically in the case of competition development between Belgium and Denmark, H-statistic involved hectic calculations and large amount of sampled data, which could be difficult to obtain. Although banking industry is crucial in the evaluation of the economy of a country comprehensively, H-estimation can give results that are somewhat tentative in the broader sense. The calculations involved are lengthy and often they surpass the limits of statistical techniques. Conclusion Although H-statistic does not have a universal approach, comparison between the developed economies is greatly helped by it and the related revenue equations (Matheison, D. J., & Schinasi, G. J., 2002). In case of an underdeveloped country, large amount of banking industry data is usually not procurable. Therefore, H-statistic cannot be utilised much in the realm of developmental and welfare economics. However, once the required set of sampled data is furnished, we can judge the external and internal competition environments of a country. The amount of procurable data for Belgian and Danish banking industries has been rather sufficient and comprehensive. Both the countries have rich banking traditions over a substantial time period. Since many other sectors of economy depend on the banking industry, correct H-estimation can render valuable information to assess the overall economy of a country. References Bikker, J. A., & Bos, J. W. (2008). Bank Performance: A Theoretical and Empirical Framework for the Analysis of Profitability, Competition and Efficiency (Routledge International Studies in Money and Banking) (1 ed.). New York: Routledge. Matheison, D. J., & Schinasi, G. J. (2002). International Capital Markets: Developments, Prospects, and Key Policy Issues (International Capital Markets Development, Prospects and Key Policy Issues). Washington: International Monetary Fund. McCrary, S. A. (2010). Mastering Corporate Finance Essentials: The Critical Quantitative Methods and Tools in Finance (Wiley Finance). New York, NY: Wiley. Read More