# **3 Methodology**

Not much quantitative research has been conducted referring to the effectiveness of the overdraft facility policy, mainly due to the unavailability of consistent data and an appropriate methodological framework.

Although there is no consensus among researchers regarding the way to establish the process to evaluate the influence of environmental variables on service efficiency levels, in this paper we have attempted to detect the repercussion of certain exogenous factors on the said efficiency levels by using a two-stage process made up of the following steps:

- 1) Obtaining the Charnes, Cooper and Rhodes (CCR) efficiency index. In order to calculate efficiency, the behavior of each unit observed is optimized, thus determining the efficient production frontier by means of linear segments based on the Decision Making Units (DMUs) that operate with the best practices. This corresponds to the set of units considered efficient in Pareto‘s terms. Therefore, the only requirement established is that each DMU should belong to the frontier envelopment (Cooper et al. 2001: 3).

- 2) Using re-sampling methods and bootstrapping techniques. Though there is currently information available on statistical inference with a reasonable level of certainty, as a result of advancement in the development of bootstrap techniques (Simar and Wilson 2000), we have opted for the traditional application of determinist mathematical models, and as a complement to them, we have used the re-sampling methods and bootstrapping techniques, in accordance with the contribution by Simar and Wilson (1998)<sup>12</sup> . The rationale behind bootstrapping is to simulate a true sampling distribution by mimicking the data-generating process, and the results throughout this paper were obtained from 2,000 bootstrap iterations.

- 3) Assessment of overdraft facility policy impact using Propensity Score Matching (PSM). The PSM was selected as the most suitable methodology to conduct the overdraft facility policy impact evaluation. The objective of the matching is to construct treatment and control groups that are as similar as possible, so that the control group would resemble what would have happened to the beneficiaries in the absence of overdraft facility policy (Rosenbaum and Rubin, 1983). The PSM estimation strategy requires multiple steps: (i) estimation of the propensity score, in terms of probability to have overdraft facility given observable variables, (ii) the review of overlap and common support conditions (i.e. which firms are matched) and (iii) assessing matching quality.

# 3.1 Data Envelopment Analysis (DEA) Frontiers and Bootstrap Procedure

According to Leibenstein (1966), a company can be categorized as technically efficient if it is able to produce maximum output given available resources. It has been acknowledged in the literature that a gap normally exists between a firm‘s actual and potential levels of economic performance<sup>13</sup> . Efficiency will be defined herein as the activity which produces maximum

> 12 Simar and Wilson noted that the DEA efficiency estimates are biased and serially correlated, which invalidates conventional inferences from the two-stage approaches. In this context, the authors proposed a procedure, based on a double bootstrap, that enables consistent inference within models explaining efficiency scores simultaneously producing standard errors and confidence intervals for these efficiency scores.

> 13 To better illustrate the relation existent between added growth of the productivity an the evidence at the company and sector levels, see Foster, Haltiwagner and Krizan (1998).

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