Objective: As the scale and importance of official development assistance (ODA) continue to grow, the need to enhance the effectiveness of ODA policies has become more critical than ever before. In this context, it is essential to systematically classify recipient countries and establish tailored ODA policies based on these classifications. The objective of this study is to identify an appropriate methodology for categorizing developing countries using specific criteria, and to apply it to actual data, providing valuable insights for donor countries in formulating future ODA policies. Design/Methodology/Approach: The data used in this study are the basic statistics on the Sustainable Development Goals (SDGs) published annually in the SDGs Report. The analytical method employed is decision tree analysis. Results: The results indicate that the 167 countries analyzed were classified into 10 distinct nodes. The study further limited the scope to the five nodes representing the most disadvantaged developing countries and suggested future directions for aid policies for each of these nodes.

Addison, T., Hulme, D., & Kanbur, R. (2009). Poverty Dynamics: Interdisciplinary Perspectives. Oxford University Press.

Alesina, A., & Dollar, D. (2000). Who gives foreign aid to whom and why? Journal of Economic Growth, 5(1), 33-63.

Angelsen, A., & Wunder, S. (2003). Exploring the Forest—Poverty Link: Key Concepts, Issues and Research Implications. CIFOR Occasional Paper No. 40. Center for International Forestry Research.

Banerjee, A., & Duflo, E. (2011). Poor Economics: A Radical Rethinking of the Way to Fight Global Poverty. PublicAffairs.

Burnside, C., & Dollar, D. (2000). Aid, policies, and growth. American Economic Review, 90(4), 847-868.

Castells, M. (1999). The Information Age: Economy, Society and Culture Volume III: End of Millennium. Blackwell Publishing.

Chenery, H. B., & Syrquin, M. (1975). Patterns of Development, 1950-1970. Oxford University Press.

Collier, P., & Hoeffler, A. (2004). Greed and grievance in civil war. Oxford Economic Papers, 56(4), 563-595.

Collier, P. (2008). The Bottom Billion: Why the Poorest Countries are Failing and What Can Be Done About It. Oxford University Press.

Collier, P., & Dollar, D. (2002). Aid allocation and poverty reduction. European Economic Review, 46(8), 1475-1500.

Deaton, A. (2013). The Great Escape: Health, Wealth, and the Origins of Inequality. Princeton University Press.

Easterly, W. (2006). The White Man’s Burden: Why the West’s Efforts to Aid the Rest Have Done So Much Ill and So Little Good. Penguin Press.

Fernandez, L. & Ahmed, S. (2023). Clustering Developing Countries: A Data-Driven Approach for Tailored ODA Strategies. Journal of Development Economics, 162, 102950.

Forman, E. H., & Gass, S. I. (2001). The analytic hierarchy process—An exposition. Operations Research, 49(4), 469-486.

Freund, Y., & Mason, L. (1999). The alternating decision tree learning algorithm. Proceedings of the Sixteenth International Conference on Machine Learning, Bled, Slovenia, pp. 124–133.

Gama, J., Rocha, R., & Medas, P. (2003). Accurate decision trees for mining high-speed data streams. Proceedings of 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 523–528.

Garcia, M. & Johnson, K. (2023). Cluster Analysis of Developing Countries: Implications for Aid Allocation. Development Policy Review, 41(3), e12609.

Gehrke, J., Ramakrishnan, R., & Ganti, V. (2000). RainForest—a framework for fast decision tree construction of large datasets. Data Mining and Knowledge Discovery, 4(2-3), 127–162.

Hansen, H., & Tarp, F. (2001). Aid and growth regressions. Journal of Development Economics, 64(2), 547-570.

Heston, A., Summers, R., & Aten, B. (2012). Penn World Table Version 7.1. Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania.

Ho, T. K. (1998). The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8), 832–844.

Kass, G. V. (1980). An exploratory technique for investigating large quantities of categorical data. Applied Statistics, 29(2), 119–127.

Kaufmann, D., Kraay, A., & Mastruzzi, M. (2009). Governance matters VIII: aggregate and individual governance indicators 1996–2008. World Bank Policy Research Working Paper No. 4978.

Kharas, H., & Rogerson, A. (2012). Horizon 2025: Creative destruction in the aid industry. ODI Report.

Kim, H. & Davis, M. (2023). Reassessing the World Bank's Country Classification System: A Critical Analysis. Global Policy, 14(2), 228-241.

Knack, S. (2004). Does foreign aid promote democracy? International Studies Quarterly, 48(1), 251-266.

Kohavi, R., & Sommerfield, D. (1998). Targeting business users with decision table classifiers. Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, AAAI Press, pp. 249–253.

Kosack, S., & Tobin, J. (2006). Funding self-sustaining development: The role of aid, FDI and government in economic success. International Organization, 60(1), 205-243.

Lee, S. et al. (2022). Beyond GDP: A Comprehensive Framework for Categorizing Developing Nations. World Development, 150, 105722.

Langley, P., & Sage, S. (1994). Oblivious decision trees and cases. Working Notes of the AAAI-94 Workshop on Case-Based Reasoning, Seattle, WA: AAAI Press, pp. 113–117.

Last, M., Maimon, O., & Minkov, E. (2002). Improving stability of decision trees. International Journal of Pattern Recognition and Artificial Intelligence, 16(2), 145–159

Lim, X., Loh, W. Y., & Shih, X. (2000). A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Machine Learning, 40(3), 203–228.

Lopez de Mantras, R. (1991). A distance-based attribute selection measure for decision tree induction. Machine Learning, 6, 81–92.

Moyo, D. (2009). Dead Aid: Why Aid is Not Working and How There is a Better Way for Africa. Farrar, Straus and Giroux.

Nguyen, T. & Wilson, E. (2022). Beyond Income: Multidimensional Poverty and Development Classification. Oxford Development Studies, 50(3), 321-339.

Osei, R. & Miller, G. (2022). Redefining Development Categories: An Analysis of Sub-Saharan African Nations. African Development Review, 34(3), 448-463.

Patel, N. (2023). Socioeconomic Indicators and Their Role in Developing Country Classification. International Journal of Social Economics, 50(7), 1022-1040.

Pritchett, L., & Woolcock, M. (2004). Solutions when the solution is the problem: Arraying the disarray in development. World Development, 32(2), 191-212.

Ravallion, M. (2001). Growth, inequality and poverty: Looking beyond averages. World Development, 29(11), 1803-1815.

Rodriguez, C. et al. (2022). A New Taxonomy of Developing Nations: Implications for Aid Effectiveness. Development and Change, 53(5), 1206-1234.

Sachs, J. D. (2005). The End of Poverty: Economic Possibilities for Our Time. Penguin Press.

Sen, A. (1999). Development as Freedom. Oxford University Press.

Smith, J. & Brown, A. (2023). Rethinking Development Classifications: A Multidimensional Approach. Journal of International Development, 35(4), 567-589.

Todaro, M. P., & Smith, S. C. (2011). Economic Development. Addison-Wesley.

United Nations. (2015). Transforming our world: the 2030 Agenda for Sustainable Development.

United Nations. (2014). World Economic Situation and Prospects. United Nations Publications.

Vaidya, O. S., & Kumar, S. (2006). Analytic hierarchy process: An overview of applications. European Journal of Operational Research, 169(1), 1-29.

Wang, Y. & Thompson, R. (2022). Machine Learning Approaches to Classify Developing Countries for Targeted ODA. Data Science for Social Good, 7(2), 145-168.

Williamson, J. (1990). What Washington means by policy reform. In J. Williamson (Ed.), Latin American Adjustment: How Much Has Happened?. Institute for International Economics.

World Bank. (1998). Assessing aid: What works, what doesn’t, and why. World Bank Policy Research Report.

World Bank. (2006). Global Monitoring Report 2006: Strengthening Mutual Accountability - Aid, Trade, and Governance. World Bank Publications.

World Bank. (2023). World Development Report 2023: Data for Better Lives.

OECD. (2019). Development Co-operation Report 2019: A Fairer, Greener, Safer Tomorrow.

OECD. (2021). Aid Effectiveness 2021: Progress in Implementing the Paris Declaration.

Sustainable Development Solutions Network. (2023). Sustainable Development Report 2023: Implementing the SDG Stimulus.

UNDP. (2022). Human Development Report 2022: Uncertain Times, Unsettled Lives.

UNCTAD. (2020). Trade and Development Report 2020: From Global Pandemic to Prosperity for All.