Selection of variables

We consider the selection of inputs and outputs which is crucial in the banking efficiency measurement. In the banking sector, similar to other sectors, a consensus is reached on the classification of some factors as inputs and outputs. However, the classification of others particularly deposit is unclear and controversial. The debate on bank deposit which in the DEA literature is termed as a flexible measure or dual role factors (see Toloo, 2012; Toloo, Allahyar, & Hančlová, 2018) is that, depending on the operational activities of the bank, in one hand, deposit could be regarded as an input (intermediation approach) and on the other hand as an output (production approach) or as a major component involved in the creation of added value (value-added approach). The first two are the main competing approaches as identified in Berger & Humphrey (1997) and so we explain them.

Intermediation approach: This approach involves examining how banks are

organizationally efficient by using labor and capital in conjunction with financial liabilities; deposits to produce loans, mortgage, and other means of financing (e.g., investment). It therefore perceives banks as financial intermediaries between savers and investors and considers banks’ liabilities as input. Efficiency of a bank by this approach signifies a strong indication of the strength of its lending ability which in turn is linked to the bank‘s ability to operate as a going concern (Paradi, Rouatt, & Zhu, 2011). One key important advantage of this approach is that it is better suited in capturing the management decisions to minimize the cost of financing mix, hence, it is seen more appropriate for evaluating the performance of financial institutions as a whole. According to a survey on banking efficiency by Fethi &

Pasiouras (2010), the intermediation approach has become a dominant approach used in the performance of whole banks since most banks are essentially financial intermediaries, whose main activities is to borrow funds from depositors and lend to others.

Production approach: This approach is of the view that banks are producers of services

and product using capital, labour and other resources as inputs to produce loans and deposit account services including the number of transactions or document processing as outputs to customers. The production approach is a significant dimension of bank performance at the branch level. At the bank branch level, transactions are made face-face to customers and the branch is seen as a ‘factory’ of service rendering services in the form of transactions, loan processing or customer deposit account services. The approach is therefore recommended for bank branch performance, also, due to the fact that managers have limited control on making decision on financial mix (Berger & Humphrey, 1997)

It is important to note that different researchers select different measures using different approaches. There is no general consensus on the best approach to use in literature and the exact classification of deposit as input or outputs is even subject to controversy within a particular approach. For instance, although the intermediation approach is argued with deposit intake as input, it is too simplistic. Paradi, Rouatt, & Zhu (2011) argue that it is unfair to bank branches the classification of deposit as input since a significant amount of revenue is generated from deposits which further unfairly penalizes branches from taking in customers and their funds. Consequently, some studies consider deposit as an additional output in line with the value-added approach. In order to select the most appropriate bank features for this thesis, we follow Mostafa (2009) where 26 research papers done on the banking industry in different countries are surveyed. Reference is made to Toloo & Tichý (2015) on the percentage

Figure 3.2. Input and output measures with banks as DMUs

of frequent selection of these banking measures presented in Mostafa (2009). Generally, employees are considered as an input variable and reasonably as fixed input. However,

Assets Employees Personnel expense Equity Intermediary Banking DMU

Input measures Output measures

Deposit from banks Net interest income Net fees and comm. Operating income Loans

deposit as an input usually under the production approach, 26.92% of the surveyed papers measure it as output with the intermediate approach27_{. Figure 3.2 summarizes the approach}

adopted in this chapter. Assets, employees and equity are the most important measures considered as inputs and while loans, operating income and revenues (from interest and commissions) in addition to deposit are used here as output. Table 3.3 shows the descriptive statistics for the input and output measures. The specific sub regional descriptive details are also given in Appendix B. All the inputs and outputs variables are measured in millions of Euros. Employees - measured as the number of banking professionals and the non-banking staff is given in actual figures. As a result, the raw data are scaled for uniformity and to reduce round-off errors in the DEA models from excessively large values (Thanassoulis, 2001).

Table 3.3. Descriptive statistics for input and output measures

Variables Mean SD Min Max

Inputs Employees 14000 29277.42 217 193863 Assets 140792.80 308512.70 10017.70 1994193 Personnel expenses 1009.31 2270.90 14.2 16061 Equity 8537.6 1046.2 226.3 100077 Outputs

Net interest revenue 1834.79 4001.07 52.3 33267

Loans 66053.43 126218.9 305.6 758505

Deposit from banks 18740.07 40327.94 58 263121 Net fees & comm. 776.52 1714.09 6.6 12765 Operating income 1220.27 2781.87 20.29 19805

In order to assess the performance and complexity of the RRDEA model compared to the RDEA model under the GRC, uncertainties compelling volatilities in banks specific variables were considered. Banking sector uncertainties may originate from forecast values of loans and deposit, missing values, and measurement errors, etc. A DMU is classified as uncertain if any of its inputs or outputs data is uncertain. Now we could consider the robust approach of Bertsimas & Sim (2004) to select the appropriate robust parameter Γ . For each DMU with ∈

and ∈ , the percentage of perturbation, of the nominal data is set to 0.01 and 0.05. For the choice of appropriate robustness level, it suffices to select Γ and Γ according to the number of uncertain input and output indices (see Sadjadi & Omrani, 2008, Omrani, 2013).

Since the variable employee is given as fixed, there are three sources of uncertainties arising from the inputs and five sources of uncertainties for the output measures. To ensure full

27 _{These performance measures are known as flexible measures (see Toloo, 2012; 2014) or dual-role factors (see}

protection, Γ is set to 8; i.e. Γ = 3 and Γ = 5 which implies that the uncertain parameters are protected 100% taking their worst-case value in the uncertainty set.