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Bank efficiency and undesirable output: An analysis of non-performing loans in the Brazilian banking sector

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  • Takahashi, Fábio Lucas
  • Vasconcelos, Marcos Roberto

Abstract

This study aims to analyze the impact of non-performing loans (NPLs) on the technical efficiency of banks in the Brazilian banking sector and to identify determinants of bank efficiency. The Directional Distance Function (DDF) method is used to measure banks’ technical efficiency and identify the factors that affect it. The results show that NPLs negatively impact efficiency, compromising banking operations and reducing the ability to produce new loans, affecting profitability. From 2003 to 2019, foreign banks were, on average, more efficient than domestic public and private banks. During COVID-19 (2020–2022), federal public banks were the most efficient. The analysis also suggests that the low efficiency of domestic public banks is associated with the lower technical quality of state public banks. The practical implications of this study are that banks must manage their NPLs effectively to improve their efficiency and profitability. This study's originality lies in analyzing the determinants of bank efficiency in Brazil, which can help banks improve their efficiency and performance.

Suggested Citation

  • Takahashi, Fábio Lucas & Vasconcelos, Marcos Roberto, 2024. "Bank efficiency and undesirable output: An analysis of non-performing loans in the Brazilian banking sector," Finance Research Letters, Elsevier, vol. 59(C).
    • Handle: RePEc:eee:finlet:v:59:y:2024:i:c:s1544612323010231
    DOI: 10.1016/j.frl.2023.104651
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    1. Phung, Quang Thanh & Van Vu, Huong & Tran, Huy Phuoc, 2022. "Do non-performing loans impact bank efficiency?," Finance Research Letters, Elsevier, vol. 46(PB).
    2. George Assaf, A. & Matousek, Roman & Tsionas, Efthymios G., 2013. "Turkish bank efficiency: Bayesian estimation with undesirable outputs," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 506-517.
    3. Berger, Allen N. & Humphrey, David B., 1997. "Efficiency of financial institutions: International survey and directions for future research," European Journal of Operational Research, Elsevier, vol. 98(2), pages 175-212, April.
    4. Fare, Rolf & Grosskopf, Shawna, 2004. "Modeling undesirable factors in efficiency evaluation: Comment," European Journal of Operational Research, Elsevier, vol. 157(1), pages 242-245, August.
    5. Barros, Carlos Pestana & Wanke, Peter, 2014. "Banking efficiency in Brazil," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 28(C), pages 54-65.
    6. Fukuyama, Hirofumi & Matousek, Roman, 2017. "Modelling bank performance: A network DEA approach," European Journal of Operational Research, Elsevier, vol. 259(2), pages 721-732.
    7. Tan, Yong, 2016. "The impacts of risk and competition on bank profitability in China," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 40(C), pages 85-110.
    8. Mamun, Abdullah & Meier, Garrett & Wilson, Craig, 2023. "How do noninterest income activities affect bank holding company performance?," Finance Research Letters, Elsevier, vol. 53(C).
    9. Tabak, Benjamin M. & Fazio, Dimas M. & Cajueiro, Daniel O., 2013. "Systemically important banks and financial stability: The case of Latin America," Journal of Banking & Finance, Elsevier, vol. 37(10), pages 3855-3866.
    10. Huang, Tai-Hsin & Chung, Ming-Tai, 2017. "Do undesirables matter on the examination of banking efficiency using stochastic directional distance functions," The Quarterly Review of Economics and Finance, Elsevier, vol. 65(C), pages 194-211.
    11. Fukuyama, Hirofumi & Matousek, Roman, 2011. "Efficiency of Turkish banking: Two-stage network system. Variable returns to scale model," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 21(1), pages 75-91, February.
    12. Ahamed, M. Mostak & Ho, Shirley J. & Mallick, Sushanta K. & Matousek, Roman, 2021. "Inclusive banking, financial regulation and bank performance: Cross-country evidence," Journal of Banking & Finance, Elsevier, vol. 124(C).
    13. Barth, James R. & Lin, Chen & Ma, Yue & Seade, Jesús & Song, Frank M., 2013. "Do bank regulation, supervision and monitoring enhance or impede bank efficiency?," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 2879-2892.
    14. Barth, James R. & Caprio, Gerard Jr. & Levine, Ross, 2004. "Bank regulation and supervision: what works best?," Journal of Financial Intermediation, Elsevier, vol. 13(2), pages 205-248, April.
    15. Tecles, Patricia Langsch & Tabak, Benjamin M., 2010. "Determinants of bank efficiency: The case of Brazil," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1587-1598, December.
    16. Felisitas Defung & Ruhul Salim & Harry Bloch, 2016. "Has regulatory reform had any impact on bank efficiency in Indonesia? A two-stage analysis," Applied Economics, Taylor & Francis Journals, vol. 48(52), pages 5060-5074, November.
    17. Demirgüç-Kunt, Asli & Pedraza, Alvaro & Ruiz-Ortega, Claudia, 2021. "Banking sector performance during the COVID-19 crisis," Journal of Banking & Finance, Elsevier, vol. 133(C).
    18. Rolf Färe & Shawna Grosskopf, 2000. "Theory and Application of Directional Distance Functions," Journal of Productivity Analysis, Springer, vol. 13(2), pages 93-103, March.
    19. Chambers, Robert G. & Chung, Yangho & Fare, Rolf, 1996. "Benefit and Distance Functions," Journal of Economic Theory, Elsevier, vol. 70(2), pages 407-419, August.
    20. Hsiao, Hsing-Chin & Chang, Hsihui & Cianci, Anna M. & Huang, Li-Hua, 2010. "First Financial Restructuring and operating efficiency: Evidence from Taiwanese commercial banks," Journal of Banking & Finance, Elsevier, vol. 34(7), pages 1461-1471, July.
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    More about this item

    Keywords

    Bank efficiency; Two-stage analysis; Directional distance function; Non-performing loans; Brazilian banks;
    All these keywords.

    JEL classification:

    • B23 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Econometrics; Quantitative and Mathematical Studies
    • B26 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Financial Economics
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • G20 - Financial Economics - - Financial Institutions and Services - - - General

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