تاثیر عوامل اقتصاد کلان و نظارتی بر روی بهره وری بانک: تجزیه و تحلیل غیر پارامتری از سیستم بانکی هنگ کنگ
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|18262||2006||24 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Banking & Finance, Volume 30, Issue 5, May 2006, Pages 1443–1466
This paper assesses the relative technical efficiency of institutions operating in a market that has been significantly affected by environmental and market factors in recent years, the Hong Kong banking system. These environmental factors are specifically incorporated into the efficiency analysis using the innovative slacks-based, second stage Tobit regression approach advocated by Fried et al. [Fried, H.O., Schmidt, S.S., Yaisawarng, S., 1999. Incorporating the operating environment into a nonparametric measure of technical efficiency. Journal of Productivity Analysis 12, 249–267]. A further innovation is that we also employ Tone’s [Tone, K., 2001. A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research 130, 498–509] slacks-based model (SBM) to conduct the data envelopment analysis (DEA), in addition to the more traditional approach attributable to Banker, Charnes and Cooper (BCC) [Banker, R.D., Charnes, A., Cooper, W.W., 1984. Some models for estimating technical and scale efficiencies in data envelopment analysis. Management Science 30, 1078–1092]. The results indicate: high levels of technical inefficiency for many institutions; considerable variations in efficiency levels and trends across size groups and banking sectors; and also differential impacts of environmental factors on different size groups and financial sectors. Surprisingly, the accession of Hong Kong to the People’s Republic of China, episodes of financial deregulation, and the 1997/1998 South East Asian crisis do not seem to have had a significant independent impact on relative efficiency. However, the results suggest that the impact of the last-mentioned may have come via the adverse developments in the macroeconomy and in the housing market.
The concept of efficiency in banking has been considered widely in the literature, utilising both non-parametric and parametric techniques (Hall, 2001). However, there has been an on-going debate over whether the estimated efficiency scores (‘scale efficiencies’ or ‘X-efficiencies’) are biased, not only due to the techniques utilised to estimate them, but also due to endogenous and/or exogenous factors affecting the bank sample. With respect to the former, for example, McAllister and McManus (1993) argue that the minimum efficient scale (MES) for banks can change as the total asset size of the banks in the sample increases, due to possible differences in the asset portfolios between the smaller and larger banks. With respect to the latter, it has long been recognised that external/environmental factors can have a significant impact on relative efficiency scores. There have recently been advances made, however, in respect of how researchers incorporate the potential impact of environmental, economic and regulatory factors on bank efficiencies (see, for example, parametric studies by Akhigbe and McNulty (2003), Berger and Mester (2003), Chaffai et al. (2001) and Dietsch and Lozano-Vivas (2000), and non-parametric studies by Lozano-Vivas et al. (2002)). In the former set of studies, the external variables (which are added as control variables to the functional form equation) are assumed to have a direct effect on the production/cost structure. Hence, each bank is assumed to face a different production/cost frontier. In the latter set of non-parametric studies, the external factor variables are typically introduced as non-discretionary inputs and/or outputs, having a direct effect on the efficient production frontier. A drawback of this particular non-parametric approach, however, is that there is no standard statistical test to determine whether the researcher has utilised the correct set of non-controllable inputs or outputs. In this paper, therefore, we utilise an innovative non-parametric approach to examine the impact of external/environmental factors on an evolving banking market. Specifically, this is undertaken using an approach that allows a second statistical stage of analysis of the effects of external factors to be determined. These impacts are then incorporated into a revised non-parametric efficiency analysis. We maintain that any analysis of specific financial service sectors in individual countries, or any comparison of financial institutions across a range of different countries, needs to take account of the various exogenous factors specific to those sectors/countries. The paper is organised as follows. In Section 2, we provide a review of the changing nature of banking in Hong Kong and the effects of the Asian financial crisis and the colony’s handover. Section 3 provides a brief literature review. Section 4 discusses the three-stage DEA methodology, based on Fried et al. (1999), used to account for potential environmental and market influences on bank efficiency. This section also outlines the slacks-based measure (SBM) of efficiency proposed by Tone (2001) and contrasts this with the more conventional Banker, Charnes and Cooper (BCC, 1984) approach to DEA. Section 5 discusses the profit-oriented approach to the data set utilised. Section 6 presents the Stage 1, 2 and 3 results. These are the results from the initial Stage 1 DEA analysis, the subsequent Stage 2 regression analysis which quantifies the impact of environmental factors on efficiency, and the Stage 3 DEA analysis which utilises inputs adjusted to take account of the influence of environmental and market factors. To the authors’ knowledge, this is the first paper to apply this type of three-stage approach to the study of a financial service sector. This study also extends the Fried et al. (1999) procedure by incorporating Tone’s (2001) slacks-based measure (SBM). Section 6 also contrasts the SBM efficiency results with the BCC efficiency scores, both across asset size groups and across different sectors of the banking system. Section 7 concludes.
نتیجه گیری انگلیسی
This paper assesses the relative technical efficiency of institutions in the Hong Kong banking system using both the BCC and the SBM approaches, and an innovative profit-based DEA specification. The results indicate quite clearly that the failure to incorporate slacks formally and directly into the efficiency analysis (as in the BCC approach) can sometimes produce inflated and misleading indications of relative efficiency, even though the rank correlation between the two sets of results is relatively high. Both approaches, however, suggested that banks in Hong Kong may have been affected by a range of external/environmental factors outside the control of the institutions’ management. In order to incorporate the possible impact of these factors in the efficiency analysis, therefore, the second stage Tobit approach advocated by Fried et al. (1999) was adopted and a subsequent Stage 3 DEA efficiency analysis conducted using the transformed input data. This Stage 3 efficiency analysis generally supported the hypothesis that the Hong Kong banking system had indeed been affected by external factors (mainly macroeconomic and housing market factors), but indicated that different sized banks and different institutional sectors had been differentially affected. Interestingly, the accession of Hong Kong to the PRC, episodes of financial deregulation, and the 1997/1998 South East Asian crisis were found not to have had a significant independent influence on relative efficiency levels in the Hong Kong banking system. In the case of the last mentioned, however, it would appear that the impact of the crisis was manifested via adverse developments in the macroeconomy and in the housing market. One of the most striking results to emerge from the Stage 3 analysis was the finding of a very strong size–efficiency relationship, with the largest institutions clearly outperforming their smaller competitors. This result clearly has important implications for future merger policies, although it has been stressed that the recent marked improvement in the relative performance of the smallest Group D institutions merits further investigation. The Stage 3 results also indicated that the CB and BHHC sectors have consistently outperformed the IB and NBCI sectors in terms of technical efficiency. Furthermore, the fact that the former sectors have performed particularly well after 1997/1998 (once external factors are controlled for) may suggest that these sectors have adapted most successfully to the deregulated and post-PRC accession environment. The use of a more conventional intermediation-based DEA specification confirmed the potential impact of environmental factors on the relative efficiency of the Hong Kong banking system. However, in line with ex-ante expectations, the intermediation-based approach generally produced less discrimination across different asset size groups and different banking sectors, particularly in the case of the Stage 3 (adjusted) results. This result tends to support the assertion of Berger and Mester (2003) that a profit-based approach is better able to capture the diversity of strategic responses by financial firms in the face of dynamic changes in competitive and environmental conditions. The key message to emerge from this paper, however, is that the failure to account for the impact of external factors can have a marked impact on relative efficiency scores and ranks and on trends in efficiency levels over time, both across the sector as a whole, and across differential size and institutional groupings. This is a particularly significant issue if such results are to be used to inform policy analysis, in the area of mergers and consolidation, for example. An important issue for future research in this respect will be to investigate the size–efficiency relationship in respect of scale efficiency in order to establish whether the superior technical efficiency of the larger Hong Kong banks is offset by scale inefficiencies.