IDEAS home Printed from https://ideas.repec.org/a/wly/ijfiec/v28y2023i2p1364-1375.html
   My bibliography  Save this article

The impact of corruption on commercial banks' credit risk: Evidence from a panel quantile regression

Author

Listed:
  • Hatice Jenkins
  • Ezuldeen Alshareef
  • Amer Mohamad

Abstract

This study explores the impact of countrywide corruption on the credit risk of commercial banks with different levels of credit risk. It applies the quantile regression (QR) estimation method for a panel data of 191 commercial banks, from 18 MENAP countries, between the years 2011–2018. The research finding indicates that corruption significantly exacerbates the problem of bad loans of banks. Furthermore, the QR results reveal that corruption does not affect all banks at the same level. Banks in higher quantiles (i.e., higher credit risk banks) appear to be affected more than the ones in lower quantiles (i.e., lower credit risk banks).Banks with high credit risk appear to be more vulnerable to corruption than banks with low credit risk.

Suggested Citation

  • Hatice Jenkins & Ezuldeen Alshareef & Amer Mohamad, 2023. "The impact of corruption on commercial banks' credit risk: Evidence from a panel quantile regression," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1364-1375, April.
    • Handle: RePEc:wly:ijfiec:v:28:y:2023:i:2:p:1364-1375
    DOI: 10.1002/ijfe.2481
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/ijfe.2481
    Download Restriction: no
    File URL: https://libkey.io/10.1002/ijfe.2481?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Abdelkader Boudriga & Neila Boulila Taktak & Sana Jellouli, 2010. "Bank Specific, Business and Institutional Environment Determinants of Banks Nonperforming Loans: Evidence from MENA Countries," Working Papers 547, Economic Research Forum, revised 09 Jan 2010.
    2. Roger Koenker, 2017. "Quantile Regression: 40 Years On," Annual Review of Economics, Annual Reviews, vol. 9(1), pages 155-176, September.
    3. Bahoo, Salman, 2020. "Corruption in banks: A bibliometric review and agenda," Finance Research Letters, Elsevier, vol. 35(C).
    4. Laxmi Koju & Ram Koju & Shouyang Wang, 2018. "Does Banking Management Affect Credit Risk? Evidence from the Indian Banking System," IJFS, MDPI, vol. 6(3), pages 1-11, July.
    5. Weill, Laurent, 2011. "How corruption affects bank lending in Russia," Economic Systems, Elsevier, vol. 35(2), pages 230-243, June.
    6. Benjamin A. Olken & Rohini Pande, 2012. "Corruption in Developing Countries," Annual Review of Economics, Annual Reviews, vol. 4(1), pages 479-509, July.
    7. A Das & S Ghosh, 2007. "Determinants of Credit Risk in Indian State-owned Banks: An Empirical Investigation," Economic Issues Journal Articles, Economic Issues, vol. 12(2), pages 27-46, September.
    8. Podpiera, Jiri & Weill, Laurent, 2008. "Bad luck or bad management? Emerging banking market experience," Journal of Financial Stability, Elsevier, vol. 4(2), pages 135-148, June.
    9. Fiordelisi, Franco & Marques-Ibanez, David & Molyneux, Phil, 2011. "Efficiency and risk in European banking," Journal of Banking & Finance, Elsevier, vol. 35(5), pages 1315-1326, May.
    10. Santiago Fernández de Lis & Jorge Martínez Pagés & Jesús Saurina, 2001. "Credit growth, problem loans and credit risk provisioning in Spain," BIS Papers chapters, in: Bank for International Settlements (ed.), Marrying the macro- and micro-prudential dimensions of financial stability, volume 1, pages 331-353, Bank for International Settlements.
    11. Roger Koenker, 2017. "Quantile regression 40 years on," CeMMAP working papers CWP36/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    12. Ivan A. Canay, 2011. "A simple approach to quantile regression for panel data," Econometrics Journal, Royal Economic Society, vol. 14(3), pages 368-386, October.
    13. Lan Wang & Yu Zhou & Rui Song & Ben Sherwood, 2018. "Quantile-Optimal Treatment Regimes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1243-1254, July.
    14. Peisen Liu & Houjian Li & Hua Guo, 2020. "The impact of corruption on firms’ access to bank loans: evidence from China," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 33(1), pages 1963-1984, January.
    15. Roger Koenker & Zhijie Xiao, 2004. "Unit Root Quantile Autoregression Inference," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 775-787, January.
    16. Christophe J. Godlewski, 2005. "Bank capital and credit risk taking in emerging market economies," Post-Print hal-03047755, HAL.
    17. Berger, Allen N. & Udell, Gregory F., 2004. "The institutional memory hypothesis and the procyclicality of bank lending behavior," Journal of Financial Intermediation, Elsevier, vol. 13(4), pages 458-495, October.
    18. David Powell, 2020. "Quantile Treatment Effects in the Presence of Covariates," The Review of Economics and Statistics, MIT Press, vol. 102(5), pages 994-1005, December.
    19. Joel F. Houston & Liangliang Jiang & Chen Lin & Yue Ma, 2014. "Political Connections and the Cost of Bank Loans," Journal of Accounting Research, Wiley Blackwell, vol. 52(1), pages 193-243, March.
    20. Agarwal, Sumit & Qian, Wenlan & Seru, Amit & Zhang, Jian, 2020. "Disguised corruption: Evidence from consumer credit in China," Journal of Financial Economics, Elsevier, vol. 137(2), pages 430-450.
    21. Fofack, Hippolyte L., 2005. "Nonperforming loans in Sub-Saharan Africa : causal analysis and macroeconomic implications," Policy Research Working Paper Series 3769, The World Bank.
    22. Vicente Salas & Jesús Saurina, 2002. "Credit Risk in Two Institutional Regimes: Spanish Commercial and Savings Banks," Journal of Financial Services Research, Springer;Western Finance Association, vol. 22(3), pages 203-224, December.
    23. Rajeev Goel & Iftekhar Hasan, 2011. "Economy-wide corruption and bad loans in banking: international evidence," Applied Financial Economics, Taylor & Francis Journals, vol. 21(7), pages 455-461.
    24. Kato, Kengo & F. Galvao, Antonio & Montes-Rojas, Gabriel V., 2012. "Asymptotics for panel quantile regression models with individual effects," Journal of Econometrics, Elsevier, vol. 170(1), pages 76-91.
    25. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    26. Murharsito & Fitri Ella Fauziah & Emanuel Kristijadi & Rr. Iramani, 2017. "Provincial corruption and local development bank performance," Economic Journal of Emerging Markets, Universitas Islam Indonesia, vol. 9(1), pages 66-73, April.
    27. Park, Junghee, 2012. "Corruption, soundness of the banking sector, and economic growth: A cross-country study," Journal of International Money and Finance, Elsevier, vol. 31(5), pages 907-929.
    28. Festic, Mejra & Kavkler, Alenka & Repina, Sebastijan, 2011. "The macroeconomic sources of systemic risk in the banking sectors of five new EU member states," Journal of Banking & Finance, Elsevier, vol. 35(2), pages 310-322, February.
    29. Djalilov, Khurshid & Piesse, Jenifer, 2019. "Bank regulation and efficiency: Evidence from transition countries," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 308-322.
    30. Khemaies Bougatef, 2016. "How corruption affects loan portfolio quality in emerging markets?," Journal of Financial Crime, Emerald Group Publishing Limited, vol. 23(4), pages 769-785, October.
    31. Murharsito Murharsito & Fitri Ella Fauziah & Emanuel Kristijadi & Rr. Iramani, 2017. "Provincial corruption and local development bank performance," Economic Journal of Emerging Markets, Universitas Islam Indonesia, vol. 9(1), pages 66-73.
    32. Christan RA Setyobudi & Dyah Setyaningrum, 2019. "E-government and corruption perception index: a cross-country study," Jurnal Akuntansi dan Auditing Indonesia, Accounting Department, Faculty of Business and Economics, Universitas Islam Indonesia, vol. 23(1), pages 11-20.
    33. Lamarche, Carlos, 2010. "Robust penalized quantile regression estimation for panel data," Journal of Econometrics, Elsevier, vol. 157(2), pages 396-408, August.
    34. Roger Koenker, 2017. "Quantile regression 40 years on," CeMMAP working papers 36/17, Institute for Fiscal Studies.
    35. Liu, Tengdong & Hammoudeh, Shawkat & Thompson, Mark A., 2013. "A momentum threshold model of stock prices and country risk ratings: Evidence from BRICS countries," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 27(C), pages 99-112.
    36. Christan RA Setyobudi & Dyah Setyaningrum, 2019. "E-Government And Corruption Perception Index: A Cross-Country Study," Jurnal Akuntansi dan Auditing Indonesia, Accounting Department, Faculty of Business and Economics, Universitas Islam Indonesia, vol. 23(1), pages 11-20, Juni.
    37. Tran Hung Son & Nguyen Thanh Liem & Nguyen Vinh Khuong, 2020. "Corruption, nonperforming loans, and economic growth: International evidence," Cogent Business & Management, Taylor & Francis Journals, vol. 7(1), pages 1735691-173, January.
    38. Zhike Lv & Ting Xu, 2017. "A panel data quantile regression analysis of the impact of corruption on tourism," Current Issues in Tourism, Taylor & Francis Journals, vol. 20(6), pages 603-616, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ghosh, Saibal, 2019. "Loan delinquency in banking systems: How effective are credit reporting systems?," Research in International Business and Finance, Elsevier, vol. 47(C), pages 220-236.
    2. Syed Faizan Iftikhar, 2015. "Financial Reforms and Financial Fragility: A Panel Data Analysis," IJFS, MDPI, vol. 3(2), pages 1-18, April.
    3. Vasiliki Makri & Athanasios Tsagkanos & Athanasios Bellas, 2014. "Determinants of Non-Performing Loans: The Case of Eurozone," Panoeconomicus, Savez ekonomista Vojvodine, Novi Sad, Serbia, vol. 61(2), pages 193-206, March.
    4. Maryem Naili & Younes Lahrichi, 2022. "The determinants of banks' credit risk: Review of the literature and future research agenda," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 334-360, January.
    5. Imad Jabbouri & Maryem Naili, 2020. "Determinants of Nonperforming Loans in Emerging Markets: Evidence from the MENA Region," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 22(04), pages 1-33, February.
    6. Ghosh, Saibal, 2018. "Bad luck, Bad policy or Bad banking? Understanding the financial management behavior of MENA banks," Journal of Multinational Financial Management, Elsevier, vol. 47, pages 110-128.
    7. Frantisek Cech & Jozef Barunik, 2017. "Measurement of Common Risk Factors: A Panel Quantile Regression Model for Returns," Working Papers IES 2017/20, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Sep 2017.
    8. Abuzayed, Bana & Ben Ammar, Mouldi & Molyneux, Philip & Al-Fayoumi, Nedal, 2024. "Corruption, lending and bank performance," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 802-830.
    9. Pallavi Chavan & Leonardo Gambacorta, 2016. "Bank lending and loan quality: the case of India," BIS Working Papers 595, Bank for International Settlements.
    10. Samaresh Bardhan & Rajesh Sharma & Vivekananda Mukherjee, 2019. "Threshold Effect of Bank-specific Determinants of Non-performing Assets: An Application in Indian Banking," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 18(1_suppl), pages 1-34, April.
    11. Panagiotidis, Theodore & Printzis, Panagiotis, 2021. "Investment and uncertainty: Are large firms different from small ones?," Journal of Economic Behavior & Organization, Elsevier, vol. 184(C), pages 302-317.
    12. Abdelaziz Hakimi & Rim Boussaada & Majdi Karmani, 2022. "Is the relationship between corruption, government stability and non‐performing loans non‐linear? A threshold analysis for the MENA region," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4383-4398, October.
    13. Chen, Zhao & Cheng, Vivian Xinyi & Liu, Xu, 2024. "Reprint: Hypothesis testing on high dimensional quantile regression," Journal of Econometrics, Elsevier, vol. 239(2).
    14. Aneta Kosztowniak, 2022. "Credit Policy of Commercial Banks in EU and the Asset Quality of Non-Financial Corporate Loan Portfolio in 2009-2021," European Research Studies Journal, European Research Studies Journal, vol. 0(1), pages 563-582.
    15. Chong-Chuo Chang & Oshamah Lin Lin & Oshamah Yu-Cheng Chang & Oshamah Kun-Zhan Hsu, 2023. "Impact of Financial Liberalization on Firm Risk," Advances in Decision Sciences, Asia University, Taiwan, vol. 27(3), pages 14-45, September.
    16. Pallavi Chavan & Leonardo Gambacorta, 2019. "Bank lending and loan quality: an emerging economy perspective," Empirical Economics, Springer, vol. 57(1), pages 1-29, July.
    17. Denis Chetverikov & Bradley Larsen & Christopher Palmer, 2016. "IV Quantile Regression for Group‐Level Treatments, With an Application to the Distributional Effects of Trade," Econometrica, Econometric Society, vol. 84, pages 809-833, March.
    18. Battagliola, Maria Laura & Sørensen, Helle & Tolver, Anders & Staicu, Ana-Maria, 2022. "A bias-adjusted estimator in quantile regression for clustered data," Econometrics and Statistics, Elsevier, vol. 23(C), pages 165-186.
    19. Laxmi Koju & Ram Koju & Shouyang Wang, 2018. "Does Banking Management Affect Credit Risk? Evidence from the Indian Banking System," IJFS, MDPI, vol. 6(3), pages 1-11, July.
    20. Manuel Arellano & Stéphane Bonhomme, 2016. "Nonlinear panel data estimation via quantile regressions," Econometrics Journal, Royal Economic Society, vol. 19(3), pages 61-94, October.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:ijfiec:v:28:y:2023:i:2:p:1364-1375. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/1076-9307/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.