Consistency Conditions for Regulatory Analysis of Financial Institutions: A Comparison of Frontier Efficiency Methods

Abstract

We propose a set of consistency conditions that frontier efficiencymeasures should meet to be most usefd for re@atory analysis or other purposes. The efficiency estimates shotid be consistent in their efficiency levels, rankings, and identification of best and worst fus, consistent over time and with competitiveconditions in the market, and consistent with standard nonfrontier measures of performance. We provide evidence on these conditions by evaluating and comparing efficiency estimates on U.S. bank efficiency from variants of all four of the major approaches-DEA, SFA, TFA, and DFA-and fmd mixed results.

Financial deregulation and profit efficiency: A nonparametric analysis of Indian banks

“…In latter half of the sample period, as profit considerations of banks gained prominence, more number of banks performed relatively close to the benchmark which resulted in some improvement in profit efficiency, particularly after 2000. These results are in sharp contrast to the findings of Bauer et al (1998) who observed that X-inefficiency is the major source of performance problems among financial institutions.…”

“…However, the major measurement problem is distinguishing variations in technical efficiency from random error (Bauer et al, 1998). To overcome this shortcoming, we employ the non-parametric Data Envelopment Analysis (DEA) in our exercise.…”

Regulation and Measuring Cost-Efficiency with Panel Data Models: Application to Electricity Distribution Utilities

“…Therefore, the mutual consistency conditions proposed by Bauer et al (1998) Cost frontier models can also be used by the regulator to "predict" the costs of individual companies. Three specifications OLS, fixed effects (FE) and random effects (RE)…”

Assessing the Efficiency of commercial Tunisian Banks using Fuzzy Data Envelopment Analysis

“…Every respondent describes his judgment about the innovation degree in his bank by the following linguistic terms; Not at all satisfied, unsatisfied, moderately satisfied, satisfied, and very satisfied. These linguistic expressions were converted into fuzzy numbers as (3,4,5), (6,7,8), (9,10,11), (12,13,14) and (15,16,17), respectively. In order to establish the imprecise value of the innovation level for each bank, we used the following aggregation function.…”