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Volume 12, Issue 11 (November 2025), Pages: 48-56
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Original Research Paper
Credit risk and bank efficiency in Vietnam: DEA-DDF and Bayesian Tobit approaches
Author(s):
Chau Dinh Linh *
Affiliation(s):
Business Administration Faculty, Ho Chi Minh University of Banking, Ho Chi Minh City, Vietnam
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* Corresponding Author.
Corresponding author's ORCID profile: https://orcid.org/0009-0001-9547-625X
Digital Object Identifier (DOI)
https://doi.org/10.21833/ijaas.2025.11.006
Abstract
This study investigates credit risk and technical efficiency of listed commercial banks in Vietnam using a two-stage approach. In the first stage, efficiency is measured by Data Envelopment Analysis with a Directional Distance Function (DEA-DDF), where loan loss provisions are treated as undesirable outputs, and lending, interest income, and non-interest income as desirable outputs. The results show that average efficiency improved from 0.861 in 2016 to 0.936 in 2023, with 2020 marking a key turning point when efficiency became more stable, reflecting the positive effects of banking restructuring policies. In the second stage, Bayesian Tobit regression reveals that return on assets has the strongest positive impact on efficiency, while non-interest income, non-performing loans, and the capital adequacy ratio negatively affect efficiency, suggesting challenges related to credit risk, income diversification, and conservative capital strategies. Overall, the findings provide evidence of risk-adjusted efficiency in Vietnamese banks and highlight the critical role of credit risk in shaping banking performance.
© 2025 The Authors. Published by IASE.
This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords
Credit risk, Bank efficiency, DEA-DDF, Bayesian Tobit, Vietnam
Article history
Received 19 June 2025, Received in revised form 16 September 2025, Accepted 12 October 2025
Funding
This work was supported by Ho Chi Minh University of Banking (HUB), Vietnam.
Acknowledgment
No Acknowledgment.
Compliance with ethical standards
Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Citation:
Linh CD (2025). Credit risk and bank efficiency in Vietnam: DEA-DDF and Bayesian Tobit approaches. International Journal of Advanced and Applied Sciences, 12(11): 48-56
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References (23)
- Abdulahi SM, Yitayaw M, Feyisa HL, and Mamo WB (2023). Factor affecting technical efficiency of the banking sector: Evidence from Ethiopia. Cogent Economics & Finance, 11(1): 2186039. https://doi.org/10.1080/23322039.2023.2186039
[Google Scholar]
- Barros CP, Managi S, and Matousek R (2012). The technical efficiency of the Japanese banks: Non-radial directional performance measurement with undesirable output. Omega, 40(1): 1–8. https://doi.org/10.1016/j.omega.2011.02.005
[Google Scholar]
- Berger AN and Humphrey DB (1997). Efficiency of financial institutions: International survey and directions for future research. European Journal of Operational Research, 98(2): 175–212. https://doi.org/10.1016/S0377-2217(96)00342-6
[Google Scholar]
- Berger AN and Mester LJ (1997). Inside the black box: What explains differences in the efficiencies of financial institutions? Journal of Banking & Finance, 21(7): 895-947. https://doi.org/10.1016/S0378-4266(97)00010-1
[Google Scholar]
- Casu B, Girardone C, and Molyneux P (2004). Productivity change in European banking: A comparison of parametric and non-parametric approaches. Journal of Banking & Finance, 28(10): 2521–2540. https://doi.org/10.1016/j.jbankfin.2003.10.014
[Google Scholar]
- Charnes A, Cooper WW, and Rhodes E (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6): 429–444. https://doi.org/10.1016/0377-2217(78)90138-8
[Google Scholar]
- Chung YH, Färe R, and Grosskopf S (1997). Productivity and undesirable outputs: A directional distance function approach. Journal of Environmental Management, 51(3): 229–240. https://doi.org/10.1006/jema.1997.0146
[Google Scholar]
- Fiordelisi F, Marques-Ibanez D, and Molyneux P (2011). Efficiency and risk in European banking. Journal of Banking & Finance, 35(5): 1315–1326. https://doi.org/10.1016/j.jbankfin.2010.10.005
[Google Scholar]
- Fukuyama H and Weber WL (2017). Measuring bank performance with a dynamic network Luenberger indicator. Annals of Operations Research, 250: 85–104. https://doi.org/10.1007/s10479-015-1922-5
[Google Scholar]
- Huang TH and Chung MT (2017). Do undesirables matter on the examination of banking efficiency using stochastic directional distance functions. The Quarterly Review of Economics and Finance, 65: 194-211. https://doi.org/10.1016/j.qref.2016.09.007
[Google Scholar]
- Istaiteyeh R, Milhem MM, and Elsayed A (2024). Efficiency assessment and determinants of performance: A study of Jordan’s banks using DEA and Tobit regression. Economies, 12(2): 37. https://doi.org/10.3390/economies12020037
[Google Scholar]
- Le C, Šević A, Tzeremes PG, and Ngo T (2022). Bank efficiency in Vietnam: Do scale expansion strategies and non-performing loans matter? International Journal of Finance & Economics, 27: 822–843. https://doi.org/10.1002/ijfe.2179
[Google Scholar]
- Le TDQ (2018). Bank risk, capitalisation and technical efficiency in the Vietnamese banking system. Australasian Accounting, Business and Finance Journal, 12(3): 41–61. https://doi.org/10.14453/aabfj.v12i3.4
[Google Scholar]
- Liu L, Moon HR, and Schorfheide F (2023). Forecasting with a panel Tobit model. Quantitative Economics, 14: 117-159. https://doi.org/10.3982/QE1505
[Google Scholar]
- Louzis DP, Vouldis AT, and Metaxas VL (2012). Macroeconomic and bank-specific determinants of non-performing loans in Greece: A comparative study of mortgage, business and consumer loan portfolios. Journal of Banking & Finance, 36(4): 1012–1027. https://doi.org/10.1016/j.jbankfin.2011.10.012
[Google Scholar]
- Maji SG and Saha R (2024). Does intellectual capital influence banks' efficiency? Evidence from India using panel data Tobit model. Managerial Finance, 50(4): 697–717. https://doi.org/10.1108/MF-05-2023-0303
[Google Scholar]
- Minh NK, Long GT, and Hung NV (2013). Efficiency and super-efficiency of commercial banks in Vietnam: Performances and determinants. Asia-Pacific Journal of Operational Research, 30(1): 1250047. https://doi.org/10.1142/S0217595912500479
[Google Scholar]
- Rashidi SF (2023). Interval congestion in commercial bank branch. Journal of Mathematical Extension, 17(2): 1-17. https://doi.org/10.30495/JME.2023.2461
[Google Scholar]
- Sang MN (2017). Income diversification and bank efficiency in Vietnam. Journal of Economics and Development, 19(3): 52–67. https://doi.org/10.33301/JED.2017.19.03.04
[Google Scholar]
- Simar L and Wilson PW (2007). Estimation and inference in two-stage, semi-parametric models of production processes. Journal of Econometrics, 136(1): 31–64. https://doi.org/10.1016/j.jeconom.2005.07.009
[Google Scholar]
- Stewart C, Matousek R, and Nguyen TN (2016). Efficiency in the Vietnamese banking system: A DEA double bootstrap approach. Research in International Business and Finance, 36: 96–111. https://doi.org/10.1016/j.ribaf.2015.09.006
[Google Scholar]
- Thanh BD, Ha DT, and Nhung PTH (2023). How non-interest income matters for operation efficiency? A Bayesian analysis of Vietnam banks. In: Ngoc Thach N, Kreinovich V, Ha DT, and Trung ND (Eds.), Optimal transport statistics for economics and related topics. Studies in systems, decision and control, 483: 211–234. Springer, Cham, Switzerland. https://doi.org/10.1007/978-3-031-35763-3_15
[Google Scholar]
- Vu H and Nahm D (2013). The determinants of profit efficiency of banks in Vietnam. Journal of the Asia Pacific Economy, 18(4): 615-631. https://doi.org/10.1080/13547860.2013.803847
[Google Scholar]
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