Suchergebnisse

Data Envelopment Analysis (DEA) is often used by regulators to create a pseudo-competitive environment for sectors with natural monopolies. In addition to develop a theoretically well-behaved model, regulators need to take into account several other factors, such as the political agenda and the historical context of the regulation. This sometimes results in some unconventional approaches, which furthermore are not easily changed. In this paper, we discuss the model used for DEA-based benchmark regulation of the Danish water sector. More specifically, we look at the characteristics of the method the regulator uses to take into account differences in the companies' environmental conditions. We show how the approach currently used to control for differences in environmental conditions seemingly does not sufficiently control for the actual differences as intended since second stage analysis still reveals significant correlations between the efficiency scores and these external factors. To explain this, we reconsider the second stage analysis, using permutation-based approaches and also accounting for the fact that only those companies that in the DEA assign weights to those output measures adjusted for environmental conditions, will benefit from the adjustments.

Data Envelopment Analysis (DEA) is often used by regulators to create a pseudo-competitive environment for sectors with natural monopolies. In addition to develop a theoretically well-behaved model, regulators need to take into account several other factors, such as the political agenda and the historical context of the regulation. This sometimes results in some unconventional approaches, which furthermore are not easily changed. In this paper, we discuss the model used for DEA-based benchmark regulation of the Danish water sector. More specifically, we look at the characteristics of the method the regulator uses to take into account differences in the companies' environmental conditions. We show how the approach currently used to control for differences in environmental conditions seemingly does not sufficiently control for the actual differences as intended since second stage analysis still reveals significant correlations between the efficiency scores and these external factors. To explain this, we reconsider the second stage analysis, using permutation-based approaches and also accounting for the fact that only those companies that in the DEA assign weights to those output measures adjusted for environmental conditions, will benefit from the adjustments.

PurposeMulti-directional efficiency analysis (MEA) is an alternative methodology to data envelopment analysis (DEA) that investigates the improvement potentials in each input and output dimension and identifies a benchmark proportional to these potential improvements. This results in a more nuanced picture of the sources of the inefficiency providing opportunities for additional conclusions about which variables the inefficiency is mainly located on. MEA provides insights into not only the level of the inefficiency but also the patterns within the inefficiency, i.e. its sources and location. This paper applies this methodology to Bangladeshi banks to understand the differences in the inefficiency patterns between different subgroups.Design/methodology/approachThis paper analyses the difference in the pattern of inefficiency between the older family-dominated banks and the newer non-family-owned banks in Bangladesh using the recently developed MEAs technology, which enables analysis of patterns within inefficiencies rather than only levels of (in)efficiency. The empirical results show that whilst there are few significant differences in the levels of variable-specific efficiency scores between the two subgroups, there are clearer differences on the inefficiency contributions from particular outputs in most of the study period and also on most variables in the time window of 2007–2009. This finding provides clues to differences in business models and management practice between the two types of banks in Bangladesh.FindingsThe empirical results show that whilst there are few significant differences in the levels of variable-specific efficiency scores between the two subgroups (older family-dominated banks and the newer non-family-owned banks), there are clearer differences on the inefficiency contributions from particular outputs in most of the study period and also on most variables in the time window of 2007–2009, during the Global Financial Crisis (GFCs). This finding provides clues to differences in business models and management practice between the two types of banks in Bangladesh.Practical implicationsDEA is a conventional tool for benchmarking in management science. However, conventional benchmarking exercises based on DEA do not reveal significant differences in the sources of inefficiency that show differences in business models. While DEA remains the most utilized technique in the efficiency literature, we think that a more flexible and deeper analysis requires something like MEA.Originality/valueThe contribution is twofold. First, examination of performances of family-owned firms is a well-established but analysis of performances of family-dominated banks is relative scarce. Secondly, isolating the sources of inefficiency which differs between types of banks even if there is no difference in inefficiency levels is absolutely new for a complete data set of conventional banks in Bangladesh. It turns out that there are few (significant) differences between the groups in terms of the inefficiency levels, whereas clear patterns emerge in terms of differences in inefficiency contributions between family-dominated and non-family-owned banks, during the Global Financial Crisis