Assessing Bank Performance Using Malmquist Productivity Index Approach and One-Step System GMM Dynamic Panel Data Model ()
ABSTRACT
This article evaluated the total factor productivity
of Ethiopian banks from 2011 to 2020 using the DEA-based Malmquist productivity index and one-step system GMM dynamic data approaches. The study covered the 14
banks that were operational during the study period and examined the
regressive, stable, and progressive nature of their productivity taking into
account both the production and intermediary role of banks. We used constant
returns to scale to compare the efficiency
and productivity and establish a benchmark for bank performance.
Interest expense, operating non-interest expense, and deposits were used as
input variables and interest income, operating non-interest income, and loan
and advances as output variables to analyze the productivity change of banks in
their production role while deposits and loans were used as input and output variables, respectively to
study productivity change of banks in their intermediation role. The study
concludes nominal efficiency change both due to improved operations and
management practices as well as increased economies of scale and deterioration
in technological efficiency. We also conclude nominal regress in total bank
factor productivity during the study period and a regressive, and progressive
impact of technology, and improved management practices on the productivity of
Ethiopian banks, respectively. Consequently, we suggest a thorough feasibility
study in the technology choice of banks.
Share and Cite:
Bayiley, Y. (2022) Assessing Bank Performance Using Malmquist Productivity Index Approach and One-Step System GMM Dynamic Panel Data Model.
Open Journal of Business and Management,
10, 798-821. doi:
10.4236/ojbm.2022.102045.