Abstract
This paper applies a data envelopment analysis (DEA) to study the efficiency and productivity changes in the Swiss cantonal bank sector in the period 2006-2014. The efficiency analysis is conducted by applying the production input-oriented DEA variable returns to scale model in a three-stage procedure. The productivity is studied by estimating a DEA-based Malmquist Productivity Index (MPI) that provides evidence of increasing productivity growth on average for the sector in the studied period. The main source of productivity growth as per the components of the Banker, Charnes and Cooper (BCC) MPI model is related to a frontier-shift (technological innovation) rather than to improvements in the technical efficiency. The decreasing average DEA scores in the post-global financial crisis period of 2008-2014 further support this finding. In the second stage of the efficiency analysis, the environmental factors influencing the productivity growth are analysed by conducting a general method of moments (GMM) regression. The results provide evidence of a positive and statistically significant relationship between the stock of residential buildings per canton and technical efficiency. In the third stage, the environmental variables from the second-stage regression are included within the constraints of the first-stage DEA model as proposed by Ray [1]. The third-stage DEA scores support the evidence of slightly decreasing average post-global crisis technical efficiency. The overall average technical efficiency in the Swiss cantonal banking sector, however, remains at a relatively high level in the studied period.
Highlights
Analysis, Bank Efficiency, Bank Productivity, Malmquist Productivity Index study efficiency and productivity in the Swiss cantonal bank sector by using data envelopment analysis (DEA) efficiency scores and a DEA-based Malmquist Productivity Index
The productivity is analysed by applying the innovative method of DEA-based Malmquist Productivity Index for the cantonal banking sector that distinguishes between technical efficiency changes and technological changes
The DEA technical efficiency scores on the 24 Swiss cantonal banks calculated by applying the input-oriented BCC production approach are listed in Table 2 below
Summary
Improving efficiency and productivity is a goal associated with higher competitiveness and improved services to the clients In this context, the findings of the paper should contribute to the public discussion on efficiency and productivity in the Swiss banking sector. The paper uses the production input-oriented DEA variable returns to scale model to estimate technical efficiency scores for the 24 cantonal banks in a first-stage. In the second stage of the DEA model, the effects of environmental factors like stock of residential buildings, unemployment, and economy development on bank efficiency and productivity are analysed by applying the general method of moments (GMM). The productivity is analysed by applying the innovative method of DEA-based Malmquist Productivity Index for the cantonal banking sector that distinguishes between technical efficiency changes and technological changes
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