Analisis Strategi Efisiensi Perbankan Indonesia dalam Menghadapi Era Masyarakat Ekonomi ASEAN
Date
2022Author
Mulyana, Rahmat
Achsani, Noer Azam
Maulana, Nur Ahmad
Andati, Trias
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The study is motivated by the need for more efficient Indonesian banks when
the ASEAN economic cooperation scheme in the banking sector is carried out in
2020. Both quantitative and qualitative methods were deployed to answer the
research questions. The quantitative analyses are stochastic frontier analysis (SFA)
and panel data regression with Indonesian banking population data for 2007–2016.
Analysis at the ASEAN level uses a sample of the 20 largest banks in 2014-2016.
A desk study, surveys, focus group discussions (FGD), and analytic network
process (ANP) were used for qualitative analysis. Both types of analysis are
complementary to each other so that the results can answer questions and research
objectives comprehensively. Some conclusions related to banking management and
regulatory policies have been made and are expected to support Indonesia's banking
industry to be more competitive in the future.
Throughout the last ten years up to 2016, the efficiency of Indonesian banking
has not experienced an increase. Indonesian banking efficiency is normally
distributed with an average value of 67.7%, with a minimum value of 41.1% and a
maximum of 93.3%. The stochastic frontier model shows that all input and output
factors, as well as price variables, are significant. The magnitude of variables, from
the biggest to the lowest, is operating costs (0.30), labor costs (0.26), and fixed
assets (0.05) . Bank size does not affect efficiency, while differences in ownership
and type of Islamic or conventional bank significantly affect efficiency.
The average value of bank efficiency per country is as follows: Singapore
(93.1%), Malaysia (75.2%), Philippines (75.2%), Thailand (75.0%), Indonesia
(65.7%), and Vietnam (64.2%). Indonesia's banking condition is characterized by
high profit and low efficiency, which is a feature of the "Quiet-Life Hypothesis."
This condition makes Indonesian banks less likely to be more efficient because their
profitability is high enough even in an unefficient condition. A separate study is
needed to develop an effective incentive system.
At the national level, determinants analysis of banking efficiency at the
national level shows that BOPO, ROE, and NIM are significant at 1% level,
affecting efficiency. Banks with greater NIM conditions tend to be less efficient
due to "quiet-life" conditions, where in a wider margin position there is less
incentive for banks to be more efficient.
The determinants model of banking efficiency at the ASEAN level shows that
the variable 3-month deposit rate (3MRATE) is significant in all models with a
greater magnitude than many other macroeconomic variables. Another significant
determinant is NPL for TOBIT and PLS models, with a negative relationship. It is
certainly very logical that the banking industry, which has many loan problems,
will be increasingly inefficient. This research supports previous studies that show
higher deposit rates tend to make a banking system less efficient.
The strategy map has a weight of 30.0% for the financial perspective, 24.4%
for the customer perspective, 19.4% for the internal perspective, and for the learning
and growth perspective, 26.2%. The financial perspective has a short time
dimension; the results are expected in the near future. The overall strategy map
model is a management tool to identify various strategic objectives in order to
increase efficiency.
Both the SFA model and the strategy map model provide in-line conclusions
in identifying important factors for efficiency. It can be seen that the higher the
weight of a strategy in the strategy map, the more linkages with input factors in the
estimated banking cost function. Better management of labor costs, bank
operational costs, and asset utilization is the key to improving banking efficiency.
This was confirmed by both the SFA model and the strategy map model.
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