E-7 Ekonomilerinde Takipteki Kredilerin Belirleyicilerini Keşfetmek: Makroekonomik, Bankacılık Sektörü ve Kurumsal Boyutlar

Year 2025, Issue: 86, 427 - 446, 25.10.2025

Mümin Atalay Çetin

Abstract

Takipteki krediler (NPLs 'ler), gelişmekte olan piyasa ekonomilerinde, yalnızca finansal istikrarı sağlamak için değil, aynı zamanda sürdürülebilir kalkınmayı teşvik etmek için de kritik bir faktör olarak kabul edilmektedir. Bu çalışmanın amacı, E-7 ülkelerinde NPLs'lerin potansiyel belirleyicilerini, makroekonomik, bankacılık sektörü ve kurumsal olmak üzere üç temel boyutta analiz ederek, araştırmaktır. Veri seti 2000-2020 dönemini kapsamaktadır. Çalışmada kullanılan yöntem dinamik heterojen panel veri teknikleridir. Panel havuzlanmış ortalama grup (PMG) tahmincisinden elde edilen uzun vadeli sonuçlara göre, enflasyon, reel faiz oranı, reel efektif döviz kuru ve kamu borcunun NPLs 'ler üzerinde istatistiksel olarak anlamlı ve pozitif bir etkisi vardır. Buna karşın ekonomik büyüme, varlık getirisi, kredi büyümesi, banka verimsizliği ve kurumsal kalite NPLs ile negatif ilişkilidir. Ayrıca, uzun vadeli katsayı bulguları, kurumsal kalite, banka karlılığı, enflasyonist baskı ve işletme verimliliğinin E-7 ülkelerinde NPLs'lerin önde gelen belirleyicileri olarak ortaya çıktığını göstermektedir. Sonuç olarak bulgular, bu ekonomilerdeki politika yapıcıların ve düzenleyici otoritelerin, bankacılık sektörünün dayanıklılığını artırmak için söz konusu faktörleri finansal istikrar çerçevelerine dahil etmelerinin önemini vurgulamaktadır.

Keywords

Takipteki Krediler , E-7 Ülkeleri , Havuzlanmış ortalama grup tahmincisi

Exploring the Determinants of Non-Performing Loans in E-7 Economies: Macroeconomic, Banking Sector and Institutional Dimensions

Abstract

In emerging market economies, non-performing loans (NPLs) are recognized as a crucial element not only for maintaining financial stability but also for advancing sustainable economic development. This study aims to explore the potential drivers of NPLs in E-7 countries by examining them through three key lenses: macroeconomic conditions, banking sector characteristics, and institutional factors. The analysis is based on data spanning the years 2000 to 2020 and utilizes dynamic heterogeneous panel data methodologies. Based on the long-run estimates derived from the Pooled Mean Group (PMG) estimator, variables such as inflation, real interest rates, the real effective exchange rate, and public debt exhibit a statistically significant and positive influence on NPLs. Conversely, indicators like economic growth, return on assets, credit expansion, banking inefficiency, and institutional quality show a negative correlation with NPLs. The long-term coefficients further highlight institutional quality, bank profitability, inflationary trends, and operational efficiency as prominent determinants of NPLs levels in E-7 economies. These results underscore the necessity for policymakers and regulatory bodies in these countries to integrate these factors into their financial stability strategies to bolster the robustness of the banking sector.

Keywords

Non-Performing Loans , E-7 Countries , Pooled mean group estimator

References

• Abid, L., Ouertani, M.N., & Zouari-Ghorbel, S. (2014). Macroeconomic and bank-specific determinants of household's non-performing loans in Tunisia: A dynamic panel data. Procedia Economics and Finance, 13, 58-68.
• Adeola, O., & Ikpesu, F. (2017). Macroeconomic determinants of non-performing loans in Nigeria: An empirical analysis. The Journal of Developing Areas, 51(2), 31-43.
• Ahmed, S., Majeed, M.E., Thalassinos, E., & Thalassinos, Y. (2021). The impact of bank specific and macro-economic factors on non-performing loans in the banking sector: Evidence from an emerging economy. Journal of Risk and Financial Management, 14(5), 217.
• Aiyar, S., Bergthaler, W., Garrido, J. M., Ilyina, A., Jobst, A., Kang, K., et al. (2015). A strategy for resolving Europe's problem loans (IMF Staff Discussion Note. SDN/15/19). Retrieved from International Monetary Fund website: https://www.imf.org/external/pubs/ft/sdn/2015/sdn1519.pdf
• Allen, C., & Carletti, E. (2012). The roles of banks in financial systems. In N. Berger, P. Molyneux, & J. Wilson (Ed.), The Oxford handbook of banking (pp. 37-57). Oxford: Oxford University Press.
• Alnabulsi, K., Kozarević, E., & Hakimi, A. (2022). Assessing the determinants of non-performing loans under financial crisis and health crisis: Evidence from the MENA banks. Cogent Economics and Finance, 10(1), 2124665.
• Anita, S.S., Tasnova, N., & Nawar, N. (2022). Are non-performing loans sensitive to macroeconomic determinants? An empirical evidence from banking sector of SAARC countries. Future Business Journal, 8(1), 7.
• Arham, N., Salisi, M.S., Mohammed, R.U., & Tuyon, J. (2020). Impact of macroeconomic cyclical indicators and country governance on bank non-performing loans in emerging Asia. Eurasian Economic Review, 10, 707-726.
• Ayhan, F., & Kartal, M.T. (2021). The macro economic drivers of non-performing loans (NPL): Evidence from selected countries with heterogeneous panel analysis. MANAS Sosyal Araştırmalar Dergisi, 10(2), 986-999.
• Aysan, A. F., Ozturk, H., Polat, A.Y., & Saltoğlu, B. (2016). Macroeconomic drivers of loan quality in Turkey. Emerging Markets Finance and Trade, 52(1), 98-109.
• Baltagi, B., & Kao, C. (2000). Nonstationary panels, cointegration in panels and dynamic panels: A survey (Syracuse University Center for Policy Research Working Paper No. 16). Retrieved from SSRN website: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1808022
• Baltagi, B., & Pesaran, M. (2007). Heterogeneity and cross section dependence in panel data models: Theory and applications introduction. Journal of Applied Econometrics, 22(2), 229-232.
• Bank for International Settlements. (2025). Bank for International Settlements data portal [Dataset]. https://data.bis.org/
• Barbier, E., & Burgess, J. (2021). Institutional quality, governance and progress towards the SDGs. Sustainability, 13(21), 11798.
• Bardhan, S., & Mukherjee, V. (2016). Bank-specific determinants of nonperforming assets of Indian banks. International Economics and Economic Policy, 13, 483-498.
• Baudino, P., & Yun, H. (2017). Resolution of non-performing loans – policy options (Financial Stability Institute Insights on Policy Implementation No 3). Retrieved from Bank for International Settlements website: https://www.bis.org/fsi/publ/insights3.pdf
• Bayar, Y. (2019). Macroeconomic, institutional and bank-specific determinants of non-performing loans in emerging market economies: A dynamic panel regression analysis. Journal of Central Banking Theory and Practice, 8(3), 95-110.
• Blackburne III, E., & Frank, M. (2007). Estimation of nonstationary heterogeneous panels. Stata Journal, 7(2), 197-208.
• Boudriga, A., Taktak, N.B., & Jellouli, S. (2010). Bank specific, business and institutional environment determinants of banks nonperforming loans: Evidence from MENA countries (Working Paper Series No. 547). Retrieved from Economic Research Forum website: https://erf.org.eg/app/uploads/2014/08/547.pdf
• Choi, I. (2001). Unit root tests for panel data. Journal of International Money and Finance, 20(2), 249–272.
• Dimitrios, A., Helen, L., & Mike, T. (2016). Determinants of non-performing loans: Evidence from Euro-Area countries. Finance Research Letters, 18, 116-119.
• Donnery, S., Fitzpatrick, T., Greaney, D., McCann, F., & O'Keeffe, M. (2018). Resolving non-performing loans in Ireland:2010-2018. Quarterly Bulletin, Q2, 54-70.
• Driga, I., & Dura, C. (2014, October). The financial sector and the role of banks in economic development. Paper presented at the 6th International Multidisciplinary Symposium "Universitaria SIMPRO 2014", Petroşani, Romania. Retrieved from https://www.upet.ro/simpro/2014/proceedings/09%20-%20ECONOMICS%20AND%20PUBLIC%20ADMINISTRATION/9.2.pdf
• European Central Bank. (2016). What are non-performing loans (NPLs)? Retrieved from https://www.ecb.europa.eu/ecb-and-you/explainers/tell-me/html/npl.en.html
• Eyraud, L., Bunda, I., Jack, J., Jardak, T., Ouedraogo, R., Wang, Z., & Wezel, T. (2021). Resolving nonperforming loans in Sub-Saharan Africa in the aftermath of the COVID-19 crisis (Departmental Paper No 2021/014). Retrieved from International Monetary Fund website: https://www.imf.org/en/Publications/Departmental-Papers-Policy-Papers/Issues/2021/06/07/Resolving-Nonperforming-Loans-in-Sub-Saharan-Africa-in-the-Aftermath-of-the-COVID-19-Crisis-50333
• Federal Reserve Bank. (2025). Federal Reserve economic data [FRED Dataset]. https://fred.stlouisfed.org/
• Fofack, H. (2005). Nonperforming loans in Sub-Saharan Africa: Causal analysis and macroeconomic implications (Policy Research Working Paper 3769). Retrieved from World Bank website: https://documents.worldbank.org/en/publication/documents-reports/documentdetail/446961468104639856/nonperforming-loans-in-sub-saharan-africa-causal-analysis-and-macroeconomic-implications
• Goyal, S., Singhal, N., Mishra, N., & Verma, S.K. (2023). The impact of macroeconomic and institutional environment on NPL of developing and developed countries. Future Business Journal, 9(1), 45.
• Hakimi, A., Boussaada, R., & Karmani, M. (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 and Economics, 27(4), 4383-4398.
• Hurlin, C., & Mignon, V. (2007). Second generation panel unit root tests. Retrieved from HAL open science website: https://shs.hal.science/file/index/docid/159842/filename/UnitRoot_Ev5.pdf
• Im, K., Pesaran, M., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115(1), 53-74.
• International Monetary Fund. (2025). IMF data [Dataset]. https://data.imf.org/en/Datasets#t=coveo117bcfc4&sort=%40idata_publication_date%20descending
• Jakubik, P., & Kadioglu, E. (2022). Factors affecting bank loan quality: A panel analysis of emerging markets. International Economics and Economic Policy, 19(3), 437-458.
• Jalali, H., Munyonga, T., Isiksal, A., & Assi, A. (2023, September). Examining the determinants of non-performing loans and bank performance in E7 countries. Paper presented at 2023 International Conference on Sustainable Islamic Business and Finance (SIBF), Bahrain. Retrieved from https://ieeexplore.ieee.org/document/10379952.
• Kaufmann, D., Kraay, A., & Zoido-Lobaton, P. (2000). Aggregating governance indicators (Policy Research Working Paper No. 2195). Retrieved from World Bank website: https://documents,worldbank,org/en/publication/documents-reports/documentdetail/167911468766840406
• Khemraj, T., & Pasha, S. (2009). The determinants of non-performing loans: An econometric case study of Guyana (Munich Personel Repec Archive Paper 53128). Retrieved from MPRA website: https://mpra.ub.uni-muenchen.de/53128/1/MPRA_paper_53128.pdf
• Klein, N. (2013). Non-performing loans in CESEE: Determinants and impact on macroeconomic performance (IMF Working Paper WP/13/72). Retrieved from International Monetary Fund website: https://www.imf.org/external/pubs/ft/wp/2013/wp1372.pdf
• Koju, L., Koju, R., & Wang, S. (2018). Macroeconomic and bank-specific determinants of non-performing loans: Evidence from Nepalese banking system. Journal of Central Banking Theory and Practice, 7(3), 111-138.
• Kumar, M., Al-Romaihi, M.A., & Aktan, B. (2023). Do the macro and global economic factors drive the nonperforming loans in GCC economies? Journal of Financial Economic Policy, 15(3), 190-207.
• Kumar, R.R., Stauvermann, P.J, Arvind, P., & Prasad, S.S. (2018). Determinants of non-performing loans in banking sector in small developing island states. Accounting Research Journal, 31(2), 192-213.
• Kuzucu, N., & Kuzucu, S. (2019). What drives non-performing loans? Evidence from emerging and advanced economies during pre-and post-global financial crisis. Emerging Markets Finance and Trade, 55(8), 1694-1708.
• Lee, Y., Yahya, M., Habibullah, M., & Ashhari, Z. (2020). Non-performing loans in European Union: Country governance dimensions. Journal of Financial Economic Policy, 12(2), 209-226.
• Maddala, G., & Wu, S. (1999). A comparative study of unit root tests with panel data and a new simple test. Oxford Bulletin of Economics and Statistics, 61(S1), 631–652.
• Makri, V., Tsagkanos, A., & Bellas, A. (2014). Determinants of non-performing loans: The case of Eurozone. Panoeconomicus, 2014(2), 193-206.
• Malik, Y., Latif, K., Khan, Z., Butt, H., Hussain, M., & Nadeem, M. (2020). Symmetric and asymmetric impact of oil price, FDI and economic growth on carbon emission in Pakistan: Evidence from ARDL and non-linear ARDL approach. Science of the Total Environment, 726, 138421.
• Mdaghri, A.A. (2022). How does bank liquidity creation affect non-performing loans in the MENA region? International Journal of Emerging Markets, 17(7), 1635-1658.
• Menegaki, A. (2019). The ARDL method in the energy-growth nexus field; Best implementation strategies. Economies, 7(4), 105.
• Nasim, A., Nasir, M., & Downing, G. (2025). Determinants of bank efficiency in developed (G7) and developing (E7) countries: Role of regulatory and economic environment. Review of Quantitative Finance and Accounting, 65, 257-294.
• Ofria, F., & Mucciardi, M. (2022). Government failures and non-performing loans in European countries: A spatial approach. Journal of Economic Studies, 49(5), 876-887.
• Pesaran, M. (2004). General diagnostic tests for cross section dependence in panels (CESifo Working Paper No. 1229). Retrieved from Center for Economic Studies and Ifo Institute website: https://www.ifo.de/en/cesifo/publications/2004/working-paper/general-diagnostic-tests-cross-section-dependence-panels
• Pesaran, M. (2007). A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics, 22(2), 265-312.
• Pesaran, M. (2021). General diagnostic tests for cross-sectional dependence in panels. Empirical Economics, 60(1), 13-50.
• Pesaran, M., & Smith, R.P. (1995). Estimating long-run relationships from dynamic heterogenous panels. Journal of Econometrics, 68(1), 79-113.
• Pesaran, M., Shin, Y., & Smith, R. (1999). Pooled mean group estimation of dynamic heterogenous panels. Journal of the American Statistical Association, 94(446), 621-634.
• Rachid, S. (2019). The determiants of non-performing loans: Do institutions matter? A comparative analysis of the MENA and CEE countries (Munich Personal RePEc Archive 96428). Retrieved from MPRA website: https://mpra.ub.uni-muenchen.de/96428/1/MPRA_paper_96428.pdf
• Rachman, R., Kadarusman, Y., Kevin, A., & Robertus, S. (2018). Bank-specific factors affecting non-performing loans in developing countries: Case study of Indonesia. The Journal of Asian Finance, Economics and Business, 5(2), 35-42.
• Salas, M., Lamothe, P., Delgado, E., Fernández-Miguélez, A., & Valcarce, L. (2024). Determinants of nonperforming loans: A global data analysis. Computational Economics, 64(5), 2695-2716.
• Saliba, C., Farmanesh, P., & Athari, S.A. (2023). Does country risk impact the banking sectors’ non-performing loans? Evidence from BRICS emerging economies. Financial Innovation, 9(1), 86.
• Shrestha, N. (2020). Detecting multicollinearity in regression analysis. American Journal of Applied Mathematics and Statistics, 8(2), 39-42.
• Stoever, J. (2012). On comprehensive wealth, institutional quality and sustainable development-quantifying the effect of institutional quality on sustainability. Journal of Economic Behavior & Organization, 81(3), 794-801.
• Syed, A.A, Kamal, M.A., & Ullah, A. (2022). Do shadow economy, institutional regulatory framework, government stability, and corruption affect non‐performing banking loans in emerging market economies: A dynamic common correlated effect (DCCE) approach. International Social Science Journal, 72(246), 979-997.
• Tao, R., Umar, M., Naseer, A., & Razi, U. (2021). The dynamic effect of eco-innovation and environmental taxes on carbon neutrality target in emerging seven (E7) economies. Journal of Environmental Management, 299, 113525.
• Tatarici, L., Kubinschi, M., & Barnea, D. (2020). Determinants of non-performing loans for the EEC region. A financial stability perspective. Management & Marketing, 15(4), 621-642.
• Umar, M., & Sun, G. (2018). Determinants of non-performing loans in Chinese banks. Journal of Asia Business Studies, 12(3), 273-289.
• Us, V. (2018). The determinants of nonperforming loans before and after the crisis: Challenges and policy implications for Turkish banks. Emerging Markets Finance and Trade, 54(7), 1608-1622.
• Us, V. (2020). A panel VAR approach on analyzing non-performing loans in the Turkish banking sector. Bankacılık Düzenleme ve Denetleme Kurulu (BDDK) Bankacılık ve Finansal Piyasalar Dergisi, 14(1), 1-38.
• World Bank. (2025a). World development indicators [Dataset]. https://databank.worldbank.org/source/world-development-indicators
• World Bank. (2025b). Global financial database [Dataset]. https://www.worldbank.org/en/publication/gfdr/data/global-financial-development-database#:~:text=The%20Global%20Financial%20Development%20Database,annual%20data%2C%20starting%20from%201960
• World Bank. (2025c). World governance indicators [Dataset]. https://www.worldbank.org/en/publication/worldwide-governance-indicators
• Xu, P., Hussain, M., Ye, C., Wang, J., Wang, C., Geng, J., et al. (2022). Natural resources, economic policies, energy structure, and ecological footprints' nexus in emerging seven countries. Resources Policy, 77, 102747.
• Zheng, C., Bhowmik, P.K., & Sarker, N. (2020). Industry-specific and macroeconomic determinants of non-performing loans: A comparative analysis of ARDL and VECM. Sustainability, 12(1), 325.