Abstract
This study examines the evolving convergence of bank cost efficiency in ASEAN from 1994 to 2022, focusing on the determinants influencing this trend during distinct periods. We aim to unravel the progression of convergence drivers and their policy implications. We employ the true fixed effect stochastic frontier approach (SFA) to estimate bank cost efficiency and identify a movement toward uniform efficiency across ASEAN banks using β-convergence and σ-convergence analyses. By examining conditional β-convergence in various sub-periods, we found that the initial convergence (1994–2000) was driven by economic growth and regulation, particularly aiding less developed economies. Post-crisis (2001–2009), banks in wealthier countries led efficiency gains, albeit with growing barriers from market concentration and regulatory intensity. As the sector matured (2010–2015), traditional drivers like GDP growth and regulation attenuated in influence, while bank size and national wealth supported further convergence. In the latest phase (2016–2022), traditional convergence drivers waned, signalling a shift in the banking landscape shaped by potential new factors such as fintech and consumer behaviour changes. The evolving nature of these determinants highlights the need for ASEAN banking policies to adapt proactively. Policymakers should consider flexible, forward-looking regulations that support efficiency convergence and sectoral integration amidst a rapidly changing financial landscape.
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Data Availability
The datasets used in this research are available in the Harvard Dataverse repository. Zhang, Tiantian, 2023, "Data set for ASEAN bank efficiency 2023", https://doi.org/10.7910/DVN/OFIBXJ, Harvard Dataverse.
Notes
See Shephard (1970) for details.
Another reason for choosing the Cobb-Douglas cost function is that as pointed by (Greene 2008) a simpler model setup with fewer parameters to be estimated may help cope with the problem of “wrong skewness” of the residual term in the production/cost function. However, there is no theoretical basis for this in the literature. The translog function does result in ‘wrong skewness’ for the residual in our sample, while the Cobb-Douglas function does not.
Although the Phillips and Sul (2007) convergence test is a popular method currently used to identify club convergence in time series or panel data, it does not directly address the conditional convergence that is the focal point of our study. Our selected methodology is specifically chosen for its ability to evaluate the influence of specific factors and conditions on the convergence process, which aligns with the primary aim of our analysis.
GDPP and ΔGDP are included in our model to capture distinct macroeconomic conditions—wealth and economic expansion, respectively. Following the precedents set by Pancurova and Lyócsa (2013), we recognize their separate influences on banking efficiency. A correlation test between GDPP and ΔGDP shows a coefficient of 0.0354 and statistically insignificant, suggesting multicollinearity is not a concern in our analysis.
Formerly known as BankScope database of Bureau van Dijk.
We use consolidated financial reports in our analysis only when unconsolidated ones are unavailable, affecting less than 3% of our data. A dummy variable was added to assess the impact of using consolidated reports. While it moderately affects total costs at a 10% significance level, its correlation with cost efficiency is minimal, at just 0.0081, indicating a negligible effect on efficiency estimates.
Per the Monetary Authority of Singapore, of the 120 commercial banks in Singapore, only six are local, and merely a fifth of foreign banks provide comprehensive banking services. Public access to the financial reports of foreign subsidiaries or representative offices is restricted. Nonetheless, due to the competitive nature of Singapore's banking sector, the banks in operation should reflect the industry's overall efficiency. Therefore, we regard our smaller sample as indicative of the broader efficiency trends within the Singaporean banking industry.
Labour cost is determined by dividing personnel expenses by the total number of employees. In cases where data on personnel expenses or employee numbers is missing, we assume that a bank's employment growth rate mirrors its total asset growth rate in real terms. Additionally, we use the ratio of personnel to operational expenses from the nearest year with available data. Fixed asset costs are calculated as the ratio of non-personnel operating expenses to fixed assets, with the operating expenses representing capital maintenance, as suggested by Shen et al. (2009).
Calculated as exp(0.108/-0.507) = 0.8078.
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Acknowledgements
The authors would like to express their sincere gratitude to the anonymous referees for their valuable comments and suggestions. Their insightful feedback was instrumental in refining both the content and presentation of this manuscript. We appreciate the time and effort they dedicated to reviewing our work, which has undoubtedly enhanced the quality and clarity of the final publication. Any remaining errors are the responsibility of the authors.
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Zhang, T., Liu, Z. Navigating Integration Through Rising Complexity: Convergence Dynamics in the ASEAN Banking Markets. Open Econ Rev (2024). https://doi.org/10.1007/s11079-024-09771-8
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DOI: https://doi.org/10.1007/s11079-024-09771-8