The Technical Efficiency of Nigerian Banks: Analytical Methodology

Another observation made about most of these studies is that some of them (e.g. Denizer et al, 2002) relied on a narrow set of data – short time series of an industry before and after deregulation or privatization which may not be sufficient for any meaningful conclusion. Further observation is the use of performance indicators instead of calculated efficiency scores. Efficiency indicators are observable factors which seem to determine the level of efficiency. At best, they (performance indicators) are approximations and introduce bias in the work (Eeckaut, Tulkens, and Jamar 1993.) Finally, most of the studies on banks examined the efficiency of the banking system, at the industry level, using time series. The present study will investigate firm level efficiency using cross-sectional data in pre- and postliberalization eras in Nigeria.
There are two approaches used to measure the efficiency of an entity. These are the parametric (econometric) approach and the non -parametric (mathematical programming) approach. This study will use the non -parametric frontier approach to estimate the relative efficiency of banks in Nigeria. This approach also known as Data Envelopment Analysis (DEA), is a mathematical programming technique that measures the efficiency of a decision making unit (DMU) relative to other similar DMUs with the simple restriction that all DMUs lies on or below the efficiency frontier (Seiford and Thall, 1990).
For the DEA, a parametric functional form does not have to be specified for the production function and thus, allows variable returns to scale (VRS). The focus of this methodology is both on each individual DMU and the average of the whole body of DMUs. It calculates the relative efficiency of each DMU in relation to all the other DMUs by using the actual observed values for the inputs and outputs of each DMU. It constructs the production frontier as a convex envelop of the observed points in the input/output space. The efficiency frontier is the section of the envelope of the production possibility set with a non – negative slope.
Efficiency is measured as the vertical (output orientation) or horizontal (input orientation) distance of DMUs to the efficiency frontier. If a DMU is on the production possibility set, it is defined as efficient. DEA also identifies for inefficient DMUs, the level of inefficiency for each of the bank (Charnes, Cooper, Lewin and Seiford, 1994). This is because it is a strictly deterministic technique. It ignores the error term and treats the total deviation from the production frontier as inefficiency. The degree of inefficiency shows the potential output loss due to not utilizing available resources to the fullest extent.
It uses the programming model and to that extent the assumptions of the approach are similar to those of linear programming model (Nyong, 2000). The assumptions include linearity, additivity or proportionality, independence or non – interaction among the activities and certainty or deterministic decision – making. However, DEA can cope with multiple objectives and multiple constraints unlike linear programming.
Hirshberger et al submit that DEA is better suited to evaluating management performance because it is very flexible. In contrast to regression, DEA also identifies specific DMUs that serve as a benchmark. Thus, it seems more favourable to measure efficiency compared to other methods.
In quantitative terms, if the efficiency score is 1, then, the firm is considered efficient and lies on the production possibility frontier (PPF). But any score below I is considered inefficient, and the firm lying below the PPF; and the distance to the PPF showing the level of inefficiency. The degree of inefficiency shows the potential output loss due to not utilizing available resources to the fullest extent. Here