**5. Results**

The distributions of efficiency scores for each stage, as well as that of the overall score, are negatively skewed, as shown in Table 2, indicating clustering of individual banks' scores at higher efficiency levels (see Figure 1).

The measured efficiency at the deposit mobilization stage was 93%, which indicates that, at this stage of operations, banks were highly efficient, possibly reflecting managerial performance metrics that reward deposit creation.

At the loan financing (second) stage, the efficiency level was 0.85 on average, and most measurements were clustered in the interval of 0.80–1.0. The banks were somewhat less efficient in the loan financing stage. This inefficiency was mainly caused by high non-performing loans because of adverse borrower selection. Our results were found to be consistent with the findings of Hamid et al. (2017); Zago and Dongili (2011).

<sup>2</sup> It may be argued that, instead of the non-performing loans (NPLs), the loan loss provisions are an alternative representation of the undesirable output in the model. Note, however, that the loan loss provisions are also calculated on the basis of non-performing loans (Bholat et al. 2016). The previous studies employed non-performing loans as an undesirable output in measuring efficiency through directional distance functions. See, for example, (Akther et al. 2013; Barros et al. 2012; Zhu et al. 2015). We followed the same convention and used NPLs to represent undesirable outputs in this study.


**Table 2.** Descriptive information of efficiency scores \*.

Note: \* The model was estimated including non-performing loans (NPLs) as an undesirable output.

**Figure 1.** Distributions of measured efficiency scores.

A non-parametric Kolmogorov–Smirnov test was used to measure the equality of distributions of efficiency scores with and without the inclusion of undesirable outputs in modeling. A significant difference at the 1% level of significance was found. This implied that, once non-performing loans (NPLs) were taken into account, the technical efficiency of banks increased significantly, suggesting that an important aspect of banking production (i.e., credit quality) needs to be considered when evaluating banks' performances. Several studies employed this test to investigate the equality of distributions (Banker et al. 2010; Johnes et al. 2014; Titko et al. 2014).

By contrast, the efficiency scores calculated from the model that did not take NPLs into account had greater dispersion, and more of the mass of the distribution was concentrated in the lower efficiency range, represented by the interval of 0.25–0.75, compared to the distribution obtained upon including NPLs in the model (see Figure 2).

**Figure 2.** Comparative distribution analysis of efficiency scores with and without non-performing loans (NPLs).

The same non-parametric test was employed to measure the equality of distributions for pre-crisis and post-crisis efficiency scores (Figure 3), also finding a significant difference at the 1% level.

The distribution of efficiency scores pre-crisis were more skewed to the left. In addition, the mass of the distribution was concentrated at higher efficiency levels. By contrast, the efficiency scores post-crisis were concentrated in the lower efficiency range, represented by the interval of 0.70–0.90. The overall average efficiency score pre-crisis was 0.85; however, it dropped to 0.74 post-crisis (see Table 5). This reconfirmed that the global financial crisis hit banking efficiency in emerging economies.

**Figure 3.** Comparative distribution analysis of efficiency scores pre-crisis and post-crisis.

Table 3 presents the technical efficiency scores for stage I (deposit mobilization) and stage II (loan financing), and their regional and national breakdown. The overall bank efficiency was 0.80, which resulted from a much higher average efficiency (93%) in the deposit mobilization stage than in the loan financing stage (85%). The most inefficient banks were in Africa and the Middle East where the average efficiency score was only 0.55. This was followed by South Asia and Emerging Europe, where the average efficiency scores were 0.67 and 0.68, respectively. The average overall bank efficiency scores for some countries were very low, for example, in Egypt (0.37), United Arab Emirates (UAE; 0.55), and Pakistan (0.61).


**Table 3.** Regional and national bank efficiency patterns. UAE—United Arab Emirates.

Despite overall higher efficiency in the deposit mobilization stage, banks in some countries were very inefficient in mobilizing deposits. For example, the first-stage efficiency scores in Egypt, Czech Republic, and Greece were 0.60, 0.69, and 0.75, respectively. This suggested that there was huge potential for saving the productive input resources used by the banks, while achieving the same level of mobilization of deposits.

For the (second) loan financing stage—where the bank's risk-taking behavior may be manifested in the accumulation of non-performing loans—the average efficiency score was 85%, which is much lower than the first-stage efficiency score. In this second stage, the efficiency scores of banks in Malaysia, Thailand, Pakistan, India, Egypt, UAE, and South Africa were quite low. Again, the Egyptian banks had the lowest average efficiency score with only 52%.

Next, we discuss the regional comparisons of the overall average efficiency along with the stage-wise average efficiency. The Latin American banks were found to be the leaders in emerging economies and registered an overall average efficiency of 0.93, which was the result of an average efficiency of 0.95 at the deposit mobilization stage, and 0.97 at the loan financing stage. This was followed by East Asian and Southeast Asian banks, which registered good average efficiency scores of 0.85 and 0.83, respectively.

An important question is how taking into account bad loans as an undesirable output impacts the efficiency measurements. Zago and Dongili (2011) argued that "recognizing banks' efforts to reduce bad loans increases their efficiency". Our results (Table 4) showed that the overall efficiency scores after allowing for non-performing loans were higher at 0.80, compared to only 0.69 when NPLs were excluded. This was also true for all regions where the non-performing loans were a higher proportion of the banks' loan portfolios. However, for Latin America, where the proportion on NLPs was only about half the average proportion of NPLs across all regions, the average efficiency scores with and without the inclusion of non-performing loans were quite similar. This suggests that it is important to incorporate non-performing loans, in addition to the undesirable output DEA formulation, for measuring bank efficiency in countries and regions with higher proportions of bad loans exist in the banks' loan portfolios.


**Table 4.** Average overall efficiency scores with and without non-performing loans (NPLs).

Table 5 shows that the overall average efficiency score during the period of 1999–2007 before the global financial crisis was 0.85. However, it dropped to 0.74 during the post-crisis period (2008–2013). A closer look at the stage-wise efficiency scores suggests that efficiency declines in the post-crisis period were more pronounced for the loan financing stage and were largely concentrated in the countries of South Asia, Africa and the Middle East, and emerging Europe—regions where the proportion of NPLs was higher. This suggests that non-performing loans had a role in the efficiency declines during the post crisis period.


**Table 5.** Average efficiency and NPLs pre- and post-crisis by region.
