Identifying common patterns via country clustering based on key macroeconomic indicators after banking crises
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Author(s)Nigar Ashurbayli-HuseynovaLink to ORCID Index: https://orcid.org/0000-0002-2641-7520
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Nigar GuliyevaLink to ORCID Index: https://orcid.org/0000-0002-1495-0686
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DOIhttp://dx.doi.org/10.21511/bbs.20(2).2025.06
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Article InfoVolume 20 2025, Issue #2, pp. 62-82
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Creative Commons Attribution 4.0 International License
Banking crises have posed recurring global challenges over the past decades. The purpose of the article is to identify common patterns among countries after banking crises by clustering them based on the trajectories of key macroeconomic indicators and underlying dynamics during critical post-crisis periods. The study analyzes 50 selected countries based on historical banking crises, data availability, and balanced regional representation. Six crisis peaks are identified: 1990, 1998, 2008, 2015, 2020, and 2023. Recovery is assessed through 12 macroeconomic indicators – GDP growth, investment, government debt, unemployment, poverty, and banking sector health – sourced from World Bank data. Z-score standardization was applied in STATA 19.5. Using Sturges’ rule and Ward’s method in STATGRAPHICS 19, the clustering revealed country groups sharing similar crisis and post-crisis recovery patterns. The analysis of these 7 formed clusters and countries belonging to each allows us to determine common patterns explained through explicit and implicit common features. Explicit characteristics cover geography, development level, crisis timing, and implicit factors are financial market exposure, banking structures, commodity dependence, policy frameworks, etc. Key findings include persistent groupings (e.g., Albania remaining in the same cluster), countries in prolonged crisis (e.g., Ukraine, Venezuela), stable pairings (e.g., Argentina-Uruguay; Azerbaijan-Iraq-Qatar), and cluster shifts (e.g., Sweden, USA, Malaysia transitioning across crises). Positive recovery cases such as Iceland, Sweden, the USA, Norway, Malaysia, and Argentina demonstrate effective resolution strategies. These insights may inform future crisis response frameworks by identifying successful policy approaches and vulnerabilities tied to institutional and structural dynamics.
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JEL Classification (Paper profile tab)G01, G21, E52
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References61
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Tables11
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Figures2
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- Figure 1. Graphic visualization of the arrangement of clusters produced by Ward’s clustering
- Figure 2. Graphic visualization of the distance at each step of the clustering process
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- Table 1. Agglomeration schedule according to Ward’s clustering method (Distance Metric: Euclidean) for 50 sample countries in 1990
- Table 2. Cluster summary for 50 sample’s countries in 1990
- Table 3. Membership table for 50 sample countries in 1990 based on Ward’s clustering
- Table 4. General cluster summary for 50 sample countries for all studied time peaks connected with six banking crises (based on 12 macro indicators analysis)
- Table 5. Results of clustering 50 sample countries in 1990–2023
- Table A1. Standardized input data in 1990
- Table A2. Standardized input data in 1998
- Table A3. Standardized input data in 2008
- Table A4. Standardized input data in 2015
- Table A5. Standardized input data in 2020
- Table A6. Standardized input data in 2023
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- Antony, T. M., & Gopal, S. (2023). Determinants of credit risk: Empirical evidence from Indian commercial banks. Banks and Bank Systems, 18(2), 88-100.
- Aryal, N. P., & Singh, G. K. (2024). Bank-specific and Macroeconomic Determinants of Credit Risk in the Banking System: A Panel Data Analysis. Financial Markets, Institutions and Risks, 8(3), 57-68.
- Curry, T., & Shibut, L. (2000). The Cost of the Savings and Loan Crisis: Truth and Consequences. FDIC Banking Review, 13(2), 26-35.
- Dachi, A., & Kasztelnik, K. (2024). Building Bridges: Implementing Governance for Sustainability in the Microfinance Banks of Developing Countries. Financial Markets, Institutions and Risks, 8(3), 1-16.
- Dahal, A. K., Budhathoki, P. B., & Bhattarai, G. (2024). Exploring the Impact of Remittance and Economic Growth on Inflation. SocioEconomic Challenges, 8(2), 126-139.
- Dematos, D. (2023). Franklin National Bank. The Tontine Coffee-House: A History of Finance.
- Di Caro, P., Pernagallo, G., Rossello, A.D., & Torrisi, B. (2019). Empirical facts characterizing banking crises: an analysis via binary time series. arXiv.
- Dias, J. G., & Ramos, S. B. (2014). The aftermath of the subprime crisis: a clustering analysis of world banking sector. Review of Quantitative Finance and Accounting, 42(2), 293-308.
- Federal Deposit Insurance Corporation (FDIC). (1997). History of the Eighties: Lessons for the Future. Vol. 1: An Examination of the Banking Crises of the 1980s and Early 1990s. Washington, DC: FDIC.
- Foo, J., & Witkowska, D. (2024). The 2020 COVID-19 Financial Crisis Impact on the European Stock Markets and Economies. A Preliminary Analysis. Folia Oeconomica Stetinensia, 24(1).
- Gentsoudi, V. (2023). The Impact of Effective Leadership on Public Sector’s Financial Instruments: Empirical Evidence from Greece. Business Ethics and Leadership, 7(2), 47-54.
- Gerber, J. (Ed.). (2019). The Latin American Debt Crisis, 1982–1989. In A Great Deal of Ruin: Financial Crises since 1929 (pp. 92-117). Cambridge University Press.
- Göbel, M., & Araújo, T. (2020). Indicators of economic crises: a data-driven clustering approach. Applied Network Science, 5, 44.
- Haugh, D., Ollivaud, P., & Turner, D. (2009). The macroeconomic consequences of banking crises in OECD countries (OECD Economics Department Working Papers No. 683). Paris: OECD Publishing.
- IMF. (1998a). The Asian Crisis: Causes and Cures. Finance & Development, 35(2).
- IMF. (1998b). Leading Indicators of Banking Crises: Was Asia Different? (IMF Working Papers No. WPIEA0911998).
- James, H. (2024). The IMF and the European Debt Crisis. Washington, DC: International Monetary Fund.
- Kaufman, L., & Rousseeuw, P. J. (1990). Finding groups in data: An introduction to cluster analysis. New York: Wiley.
- Khactu, D. N. (1981). Review of The Failure of the Franklin National Bank: Challenge to the International Banking System, by J. E. Spero. Southern Economic Journal, 48(2), 536-539.
- Kuzior, A., Koldovskiy, A., & Rekunenko I. (2024a). Optimizing Financial Market Stability through AI-Based Risk Management. Materials Research Proceedings, 45, 223-231.
- Kuzior, A., Kostenko, A., Sotnyk, I., Yulija, C., Tuliakov, O., & Podmanicka, M. (2023). Socioeconomic sustainability and the solidarity economy formation under the war conditions in Ukraine. Financial and Credit Activity-Problems of Theory and Practice, 5(52), 256-267.
- Kuzior, A., Vysochyna, A., Augustyniak, W., & Remsei, S. (2024b). Forecasting of macroeconomic stability post-pandemic recovery: The case of European countries. Journal of International Studies, 17(4), 56-79.
- Lucotte, Y. (2015). Euro area banking fragmentation in the aftermath of the crisis: a cluster analysis. Applied Economics Letters, 22(13), 1046-1050.
- Lyeonov, S., Vysochyna, A., Śmiech, L., & Rabe, M. (2024). Economic growth and national security: Patterns of European countries’ resistance to global turbulences. Journal of International Studies, 17(3), 182-204.
- Makarenko, I., Cavagnetto, S., Kozmenko, O., Kozmenko, S., & Bilan, Y. (2024). Sustainability transparency in sovereign wealth funds: benchmark and case study approach. Financial and Credit Activity Problems of Theory and Practice, 2(55), 424-439.
- Mitchell, M. N. (2020). Data Management Using Stata: A Practical Handbook (2nd ed.). College Station, TX: Stata Press.
- Murshudli, F. F., & Mursalov, M. M. (2020). Anti-crisis Regulation of Banking Business: Case of Azerbaijan. In Poliakov, J. (Ed.), Anti-crisis management: state, region, enterprise (pp. 220-241). Riga: Publishing House “Baltija Publishing”.
- Murshudli, F., & Zapotichna, R. (2020). Multinational Banking in the Times of Coronavirus Outbreak: Trends and Challenges. Economic and Social Development: Book of Proceedings, 1/4, 135-146.
- Murshudli, F., Kornyliuk, R., & Dilbazi, E. (2019). Foreign Banks in Transition Economies: Trends and Impact on Systemic Stability. Economic and Social Development: Book of Proceedings, 795-806.
- Murshudli, F., Sheludko, S., & Dilbazi, E. (2020a). Banking Business Internationalization in Developing Countries (Evidence from Azerbaijan and Ukraine). Financial and credit activity problems of theory and practice, 2(33), 48-57.
- Murshudli, F., Zapotichna, R., & Dilbazi, E. (2020b). International Banking Business and Bank Strategy: Global Trends and Benchmarks for Post-Soviet States. Economic Annals-XXI, 185(9-10), 27-38.
- Nikhil, M.N., Chakraborty, S., Lithin, B.M., Ledwani, S., & Satyakam (2023). Modeling Indian Bank Nifty volatility using univariate GARCH models. Banks and Bank Systems, 18(1), 127-138.
- Pyle, D. H. (1995). The U.S. savings and loan crisis. In Handbooks in Operations Research and Management Science, 9, 1105-1125.
- Richardson, K. (2024). An Analysis of the Great Disruption of COVID-19 Pandemic. Socio Economic Challenges, 8(1), 219-239.
- Rossi, S. (2023). The banking crisis of credit suisse: origins, consequences, and reform proposals. Investigación Económica, 82(325), 21-36.
- Ruščákováa, A., & Semančíkováa, J. (2016). The European debt crisis: a brief discussion of its causes and possible solutions. Procedia – Social and Behavioral Sciences, 220, 399-406.
- Saher, L., Tabák, L., Lyeonov, S., & Vasa, L. (2024). Inclusive growth: Literature review. Journal of International Studies, 17(1), 205-232.
- Scott, D. W. (2009). Sturges’ rule. Wiley Interdisciplinary Reviews: Computational Statistics, 1(3), 303-306.
- Stata. (n.d.). Egen – Extensions to generate. Stata Manuals.
- Steenbergen, E. F., Jansen, W. S., & Ellemers, N. (2023). How Executive Boards Set the Stage for Unethical Behavior in the Financial Sector. Business Ethics and Leadership, 7(4), 108-127.
- Tekin, B., & Eraslan, M. (2024). How do Macroeconomic Indicators Shape Nonperforming Loans? Financial Markets, Institutions and Risks, 8(2), 50-69.
- Tiutiunyk, I.V., Zolkover, A.O., Lyeonov, S.V., & Ryabushka, L.B. (2022). The impact of economic shadowing on social development: challenges for macroeconomic stability. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, 1, 183-191.
- Vasylieva, T. A., & Chmutova, I. M. (2015). Empirical model of a bank life cycle. Actual Problems of Economics, 172(10), 352-361.
- Ward, J. H. Jr. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58(301), 236-244.
- Weinberg, J. (2007). The Great Recession and Its Aftermath. Federal Reserve History.
- World Bank. (2011). The World Bank Group’s Response to the Global Economic Crisis: Phase 1.
- World Bank. (n.d.). Economies by region.
- World Bank data. (n.d.a). Bank nonperforming loans to total gross loans (%).
- World Bank data. (n.d.b). Central government debt, total (% of GDP).
- World Bank data. (n.d.c). Foreign direct investment, net inflows (% of GDP).
- World Bank data. (n.d.d). GDP per capita growth (annual %).
- World Bank data. (n.d.e). Gini index.
- World Bank data. (n.d.f). Gross capital formation (annual % growth).
- World Bank data. (n.d.g). Inflation, consumer prices (annual %).
- World Bank data. (n.d.h). Interest payments (% of expense).
- World Bank data. (n.d.i). Monetary Sector credit to private sector (% GDP).
- World Bank data. (n.d.j). Poverty headcount ratio at societal poverty line (% of population).
- World Bank data. (n.d.k). Real interest rate (%).
- World Bank data. (n.d.l). Unemployment, total (% of total labor force) (national estimate).
- Yetongnon, T. M. H., & Kasztelnik, K. (2024). Innovative Leadership for Social Impact: A Study of Benin’s Accounting, Finance and Economic Strategy. Business Ethics and Leadership, 8(3), 1-25.
- Yoshimori, M. (2024). Rising Tides Lift All Boats: The Sustained Dominance and Global Impact of the US Dollar as the Reserve Currency. SocioEconomic Challenges, 8(3), 77-92.
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Muhtar Lutfi ,
Pricylia Chintya Dewi Buntuang ,
Yoberth Kornelius ,
Erdiyansyah ,
Bakri Hasanuddin
doi: http://dx.doi.org/10.21511/ppm.18(3).2020.40
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Amgad S.D. Khaled ,
Nabil Mohamed Alabsy ,
Eissa A. Al-Homaidi 
,
Abdulmalek M.M. Saeed
doi: http://dx.doi.org/10.21511/im.16(4).2020.11
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Halyna Kushniruk
doi: http://dx.doi.org/10.21511/ppm.18(2).2020.17
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