Exploring Bank Efficiency in Indonesia: A Dual Method Approach Using Data Envelopment Analysis and Stochastic Frontier Analysis
Abstract
This study evaluates the efficiency of 76 commercial banks in Indonesia over the period 2020-2021, using both Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA). The dataset includes key inputs such as interest cost and labor cost, while outputs for DEA are split into interest income and non-interest income, and the total income is used for SFA. DEA is applied with both Charnes-Cooper-Rhodes (CCR) and Banker-Charnes-Cooper (BCC) models, with output orientation to maximize interest income and non-interest income. The CCR model assumes constant returns to scale (CRS), while the BCC model assumes variable returns to scale (VRS), allowing for scale differences among banks. For SFA, both the Cobb-Douglas and Translog production functions are used to model the relationship between inputs and outputs, with the former assuming a simpler linear relationship and the latter accounting for non-linearities. The results show high correlation between the efficiency scores obtained from both DEA and SFA, suggesting that both methods produce similar rankings of bank performance. However, SFA’s flexibility in capturing inefficiencies through random noise makes it a more robust method for analyzing bank performance in volatile environments. Spearman’s rank correlation is employed to assess the relationship between the efficiency rankings from both methods, revealing strong consistency in their assessments.
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