Küresel yönetişimin değerlendirilmesi: WGI verilerine dayalı model tabanlı bir kümeleme yaklaşımı

Abstract

İyi yönetişim, toplumsal refahın ve uzun dönemli kalkınmanın temel belirleyicilerinden biri olarak geniş ölçüde kabul edilmekle birlikte, soyut ve çok boyutlu yapısı ampirik ölçüm ve ülkeler arası karşılaştırma açısından önemli güçlükler barındırmaktadır. Kavramsal tartışmalar devam etmekle birlikte, Worldwide Governance Indicators (WGI) yönetişim kalitesinin değerlendirilmesinde en yaygın kullanılan çerçevelerden biri olmayı sürdürmektedir. Bu çalışma, 2024 yılı WGI verilerini kullanarak 205 ülke için küresel ölçekte bir yönetişim performansı değerlendirmesi sunmaktadır. Yönetişim rejimleri, Gaussian karışım modellerine dayalı olasılıksal model tabanlı kümeleme yaklaşımı ile belirlenmiş; performans hiyerarşileri ise küme düzeyindeki profillere uygulanan TOPSIS yöntemi aracılığıyla oluşturulmuştur. Rejim farklılaşmasının temel belirleyicileri Random Forest yaklaşımı kullanılarak incelenmiştir. Bulgular, dünya genelinde beş farklı yönetişim rejiminin varlığını ortaya koymaktadır. Kurumsal açıdan olgun ve yüksek gelirli demokrasiler en yüksek performanslı rejimde yer alırken, kırılgan ve çatışma etkisindeki devletler en düşük performanslı rejimi oluşturmaktadır. Genel olarak sonuçlar, siyasal riskin değerlendirilmesi ve yönetişim reform önceliklerinin belirlenmesi açısından politika yapıcılar için uygulanabilir bir çerçeve sunmaktadır.

Keywords

• yönetişim göstergeleri
• model tabanlı kümeleme analizi
• siyasal risk
• random forest
• TOPSIS

Supporting Institution

yok

Project Number

yok

Ethical Statement

Yazar, üretken yapay zekâ araçlarının yalnızca İngilizce dil düzenlemesi ve üslup iyileştirmesi amacıyla kullanıldığını beyan eder. Bu kapsamda ChatGPT (OpenAI, GPT-4), dilbilgisi, açıklık ve okunabilirliği geliştirmek amacıyla kullanılmıştır. Çalışmanın bilimsel içeriği, veri analizi, sonuçların yorumlanması ve ulaşılan çıkarımlar tamamen yazar tarafından üretilmiştir.

Thanks

yok

Global governance assessment: A Model-Based Clustering approach using WGI data

Abstract

Good governance is widely recognized as a key determinant of societal welfare and long-term development, yet its abstract and multidimensional nature continues to challenge empirical measurement and cross-country comparison. Despite ongoing conceptual debates, the Worldwide Governance Indicators (WGI) remain one of the most widely used frameworks for assessing governance quality. Using the 2024 WGI data, this study provides a global assessment of governance performance across 205 countries. Governance regimes are identified through probabilistic model-based clustering using Gaussian mixture models, while performance hierarchies are established via the TOPSIS method applied to cluster-level profiles. The key drivers of regime differentiation are examined using a Random Forest approach. The results reveal five distinct governance regimes worldwide. Institutionally mature, high-income democracies occupy the highest-performing regime, whereas fragile and conflict-affected states constitute the lowest one. Overall, the findings provide a policy-relevant framework for assessing political risk and informing governance reform priorities.

Keywords

• governance indicators
• model-based clustering analysis
• political risk
• random forest
• TOPSIS

Supporting Institution

yok

Project Number

yok

Ethical Statement

The author confirms that generative artificial intelligence tools were used only for English language editing and stylistic improvement. Specifically, ChatGPT (OpenAI, GPT-4) was used to improve grammar, clarity, and readability. The scientific content, data analysis, interpretation of results, and conclusions were entirely produced by the author.

Thanks

yok

References

1. Ahlquist, J. S., and Breunig, C. (2012). Model-based clustering and typologies in the social sciences. Political Analysis, 20(1), 92-112. https://doi.org/10.1093/pan/mpr039
2. Ahmad, E., Ullah, M. A., and Arfeen, M. I. (2012). Does corruption affect economic growth? Latin American Journal of Economics, 49(2), 277–305. https://doi.org/10.7764/LAJE.49.2.277
3. Andrés, A., Otero, A., and Amavilah, V. H. (2022). Knowledge economy classification in African countries: A model-based clustering approach. Information Technology for Development, 28(2), 372–396. https://doi.org/10.1080/02681102.2021.1950597
4. Ardielli, E. (2019). Use of TOPSIS method for assessing good governance in European Union countries. Review of Economic Perspectives, 19(3), 211–231. https://doi.org/10.2478/revecp-2019-0012
5. Ardielli, E., and Vavrek, R. (2019). Evaluation of egovernment and good governance development in EU member countries. ACC Journal, 25, 7-20. https://doi.org/10.15240/tul/004/2019-2-001
6. Banfield, J. D., and Raftery, A. E. (1993). Model-based Gaussian and non-Gaussian clustering. Biometrics, 49(3), 803-821. https://doi.org/10.2307/2532201
7. Berrittella, M. (2025). Income mobility and crime: A hierarchical cluster analysis on principal components for 27 OECD countries. International Journal of Social Economics, 52(2), 287–305. https://doi.org/10.1108/IJSE-07-2023-0520
8. Bersch, K., and Botero, S. (2014). Measuring governance: Implications of conceptual choices. The European Journal of Development Research, 26(1), 124–141. https://doi.org/10.1057/ejdr.2013.49

9. Bi, H., and Nora, T. (2012). Estimating sovereign default risk. American Economic Review, 102(3), 161–166. https://doi.org/10.1257/aer.102.3.161
10. Biernacki, C., and Govaert, G. (1999). Choosing models in model-based clustering and discriminant analysis. Journal of Statistical Computation and Simulation, 64(1), 49–71. https://doi.org/10.1080/00949659908811966
11. Biernacki, C., and Jacques, J. (2013). A generative model for rank data based on insertion sort algorithm. Computational Statistics & Data Analysis, 58, 162–176. https://doi.org/10.1016/j.csda.2012.08.008
12. Boley, D., Gini, M., Gross, R., Han, E. H. S., Hastings, K., Karypis, G., Kumar, V., Mobasher, B., and Moore, J. (1999). Partitioning-based clustering for web document categorization. Decision Support Systems, 27(3), 329–341. https://doi.org/10.1016/S0167-9236(99)00055-X
13. Botero, J. C., Pinzon-Rondon, M. A., and Pratt, C. S. (2016). How, when and why do governance, justice and rule of law indicators fail public policy decision making in practice? Hague Journal on the Rule of Law, 8(1), 51–74. https://doi.org/10.1007/s40803-015-0020-8
14. Bouveyron, C., and Brunet-Saumard, C. (2014). Model-based clustering of high-dimensional data: A review. Computational Statistics & Data Analysis, 71, 52–78. https://doi.org/10.1016/j.csda.2012.12.008
15. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324
16. Brewer, G. A., Choi, Y., and Walker, R. M. (2007). Accountability, corruption and government effectiveness in Asia: An exploration of World Bank governance indicators. International Public Management Review, 8(2), 200–217.
17. Carmignani, F. (2003). Political instability, uncertainty and economics. Journal of Economic Surveys, 17(1), 1–54. https://doi.org/10.1111/1467-6419.00187
18. Celeux, G., and Govaert, G. (1995). Gaussian parsimonious clustering models. Pattern Recognition, 28(5), 781-793. https://doi.org/10.1016/0031-3203(94)00125-6
19. Cheong, M. Y., and Lee, H. (2008). Determining the number of clusters in cluster analysis. Journal of the Korean Statistical Society, 37(2), 135–143. https://doi.org/10.1016/j.jkss.2007.10.004
20. Colebatch, H. K. (2014). Making sense of governance. Policy and Society, 33(4), 307–316. https://doi.org/10.1016/j.polsoc.2014.10.001
21. Collier, P., Elliott, V. L., Hegre, H., Hoeffler, A., Reynal-Querol, M., and Sambanis, N. (2003). Breaking the conflict trap: Civil war and development policy. World Bank & Oxford University Press.
22. Collier, P., Hoeffler, A., and Söderbom, M. (2008). Post-conflict risks. Journal of Peace Research, 45(4), 461–478. https://doi.org/10.1177/0022343308091356
23. Davies, S., Pettersson, T., Sollenberg, M., and Öberg, M. (2025). Organized violence 1989–2024, and the challenges of identifying civilian victims. Journal of Peace Research, 62(4). https://doi.org/10.1177/00223433251345636
24. DeGennaro, R. P. (2005). Market imperfections. Journal of Financial Transformation, 14, 107-117. https://ssrn.com/abstract=775866
25. Deineko, L., and Tsyplitska, O. (2019). Institutional determinants of industrial policy: World country-cluster analysis. MIND Journal, 7, 1–16. https://doi.org/10.36228/MJ.7/2019.12
26. Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B, 39(1), 1–38.
27. Eppinger, S. D. (1991). Model-based approaches to managing concurrent engineering. Journal of Engineering Design, 2(4), 283–290. https://doi.org/10.1080/09544829108901686
28. Falkenheim, M. (2021). Governmental risk taking under market imperfections. Congressional Budget Office. https://www.cbo.gov/publication/57255
29. Fraley, C., and Raftery, A. E. (1998). How many clusters? Which clustering method? Answer via model-based cluster analysis. The Computer Journal, 41(8), 578–588. https://doi.org/10.1093/comjnl/41.8.578
30. Fraley, C., and Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611–631. https://doi.org/10.1198/016214502760047131
31. Fukuyama, F. (2016). Governance: What do we know, and how do we know it? Annual Review of Political Science, 19, 89–105. https://doi.org/10.1146/annurev-polisci-042214-044240
32. Fund for Peace. (2024). Fragile States Index 2024. Washington, DC. https://fragilestatesindex.org
33. Garcia, D., and Norli, Ø. (2012). Geographic dispersion and stock returns. Journal of Financial Economics, 106(3), 547–565. https://doi.org/10.1016/j.jfineco.2012.06.007
34. Garfinkel, M. R., and Glazer, A. (1994). Does electoral uncertainty cause economic fluctuations? American Economic Review, 84(2), 169–173. https://www.jstor.org/stable/2117823
35. Gislason, P. O., Benediktsson, J. A., and Sveinsson, J. R. (2006). Random forests for land cover classification. Pattern Recognition Letters, 27(4), 294–300. https://doi.org/10.1016/j.patrec.2005.08.011
36. Gormley, I. C., Murphy, T. B., and Raftery, A. E. (2023). Model-based clustering. Annual Review of Statistics and Its Application, 10(1), 573–595. https://doi.org/10.1146/annurev-statistics-033121-115326
37. Gribincea, A. (2017). Ethics, social responsibility, and corruption as risk factors. Annals of Spiru Haret University – Economic Series, 17(1), 21–33. https://doi.org/10.26458/1711
38. Hansen, H. K. (2010). Managing corruption risks. Review of International Political Economy, 18(2), 251–275. https://doi.org/10.1080/09692291003791448
39. He, J., Harris, J. R., Sawada, M., and Behnia, P. (2015). A comparison of classification algorithms using Landsat-7 and Landsat-8 data for mapping lithology in Canada’s Arctic. International Journal of Remote Sensing, 36(8), 2252–2276. https://doi.org/10.1080/01431161.2015.1035410
40. Hirshleifer, J. (2001). The dark side of the force: Economic foundations of conflict theory. Cambridge University Press.
41. Hourdakis, N., Argyriou, M., Petrakis, E. G., and Milios, E. E. (2010). Hierarchical clustering in medical document collections: The BIC-Means method. Journal of Digital Information Management, 8(2), 71–77.
42. Hufty, M. (2011). Investigating policy processes: The Governance Analytical Framework (GAF). In U. Wiesmann & H. Hurni (Eds.), Research for sustainable development: Foundations, experiences, and perspectives (pp. 403–424). Geographica Bernensia. https://ssrn.com/abstract=2019005
43. Hwang, C. L., and Yoon, K. (1981). Methods for multiple attribute decision making. In C. L. Hwang and K. Yoon (Eds.), Multiple attribute decision making (pp. 58–191). Springer. https://doi.org/10.1007/978-3-642-48318-9_3 Jain, A. K., and Dubes, R. C. (1988). Algorithms for clustering data. Prentice-Hall.
44. Jang, J., and Hitchcock, D. B. (2012). Model-based cluster analysis of democracies. Journal of Data Science, 10(2), 297–319. https://doi.org/10.6339/JDS.2012.10(2).1018
45. Jankovic-Milic, V., and Džunić, M. (2017). Measuring Governance: The Application of Grey Relational Analysis on World Governance Indicators. In Tools and Techniques for Economic Decision Analysis,104-128. IGI Global Scientific Publishing. https://doi.org/10.4018/978-1-5225-0959-2.ch005
46. Jens, C. E. (2017). Political uncertainty and investment: Causal evidence from U.S. gubernatorial elections. Journal of Financial Economics, 124(3), 563–579. https://doi.org/10.2139/ssrn.2176855
47. Kamal, M. A., Hasanat, S. S., Jing, W., and Hasnat, H. (2019). Does the quality of institutions in host countries affect the location choice of Chinese OFDI? Evidence from Asia and Africa. Emerging Markets Finance and Trade, 56(1), 208–227. https://doi.org/10.1080/1540496X.2019.1610876
48. Knack, S., and Manning, N. (2000). Towards consensus on governance indicators (Policy Research Working Paper No. 34724). World Bank.
49. Koçak, D., and Özer, M. A. (2022). Comparing the quality of governance across the European Union member countries: a grey relational analysis approach. Policy Studies, 43(5), 1135–1155. https://doi.org/10.1080/01442872.2021.1994135
50. Kurtz, M., and Schrank, A. (2007). Growth and governance: A defense. Journal of Politics, 69(2), 563–569. https://doi.org/10.1111/j.1468-2508.2007.00551.x
51. Lai, Y. L., Liu, T. Y., and Hwang, C. L. (1994). TOPSIS for MODM. European Journal of Operational Research, 76, 486–501. https://doi.org/10.1016/0377-2217(94)90282-8
52. Langbein, L., and Knack, S. (2010). The Worldwide Governance Indicators: Six, one, or none? Journal of Development Studies, 46(2), 350–370. https://doi.org/10.1080/00220380902952399
53. Lee, G., and Scott, C. (2012). EM algorithms for multivariate Gaussian mixture models with truncated and censored data. Computational Statistics & Data Analysis, 56(9), 2816–2829. https://doi.org/10.1016/j.csda.2012.03.003
54. Li, Y., and Rengifo, E. W. (2018). The impact of institutions and exchange rate volatility on China’s outward FDI. Emerging Markets Finance and Trade, 54(12), 2778–2798. https://doi.org/10.1080/1540496X.2017.1412302
55. Manolopoulou, I., Kepler, T. B., and Merl, D. M. (2012). Mixtures of Gaussian wells: Theory, computation, and application. Computational Statistics & Data Analysis, 56(12), 3809–3820. https://doi.org/10.1016/j.csda.2012.03.027
56. Mizrahi, S., Vigoda-Gadot, E., and Cohen, N. (2021). How well do they manage a crisis? The government’s effectiveness during the COVID-19 pandemic. Public Administration Review, 81(6), 1120–1130. https://doi.org/10.1111/puar.13370
57. Özer, M. A., Koçak, D. and Türe, H. (2020). Yönetişim göstergeleri doğrultusunda ülkelerin performanslarının değerlendirilmesi. Ankara Hacı Bayram Veli Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 22(1), 25–53.
58. Pauw, W. P., Kempa, L., Moslener, U., Grüning, C., and Çevik, C. (2021). A focus on market imperfections can help governments mobilize private investments in adaptation. Climate and Development, 14(1), 91–97. https://doi.org/10.1080/17565529.2021.1885337
59. Pierre, J., and Peters, G. B. (2000). Governance, politics and the state. St. Martin’s Press.
60. Plattner, M. F. (2013). Reflections on "Governance". Journal of Democracy, 24(4), 17-28. https://doi.org/10.1353/jod.2013.0058
61. Praça, S., and Taylor, M. M. (2014). Inching toward accountability: The evolution of Brazil's anticorruption institutions, 1985–2010. Latin American Politics and Society, 56(2), 27–48. https://doi.org/10.1111/j.1548-2456.2014.00230.x
62. Ren, L., Zhang, Y., Wang, Y., and Sun, Z. (2007). Comparative analysis of a novel M-TOPSIS method and TOPSIS. Applied Mathematics Research Express, 2007(1), abm005. https://doi.org/10.1093/amrx/abm005
63. Roe, M. J., and Siegel, J. I. (2011). Political instability: Effects on financial development, roots in the severity of economic inequality. Journal of Comparative Economics, 39(3), 279–309. https://doi.org/10.1016/j.jce.2011.02.001
64. Sarkar, A. (2013). A TOPSIS method to evaluate technologies. International Journal of Quality & Reliability Management, 31(1), 2–13. https://doi.org/10.1108/IJQRM-03-2013-0042
65. Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464.
66. Scrucca, L., Fop, M., Murphy, T. B., and Raftery, A. E. (2016). mclust 5: Clustering, classification and density estimation using Gaussian finite mixture models. The R Journal, 8(1), 289–317. https://doi.org/10.32614/RJ-2016-021
67. Shah, S. H., Ahmad, M. H., and Ahmed, Q. M. (2015). The nexus between sectoral FDI and institutional quality: Empirical evidence from Pakistan. Applied Economics, 48(17), 1591–1601. https://doi.org/10.1080/00036846.2015.1103039
68. Shahabadi, A., Omidi, V., and Jahandideh, M. (2024). The impact of various inequalities on social unrest in Middle Eastern countries. Macroeconomics Research Letters, 19. https://doi.org/10.22080/iejm.2024.26686.2042
69. Srivastava, M. (2009). Good governance—Concept, meaning and features: A detailed study. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1528449
70. Tamanaha, B. Z. (2008). The dark side of the relationship between the rule of law and liberalism. NYU Journal of Law & Liberty, 33. (St. John’s Legal Studies Research Paper No. 08-0096)
71. Thomas, M. A. (2007). The Governance Bank. International Affairs, 83(4), 720–745. https://doi.org/10.1111/j.1468-2346.2007.00649.x
72. Thomas, M. A. (2010). What do the Worldwide Governance Indicators measure? The European Journal of Development Research, 22, 31–54. https://doi.org/10.1057/ejdr.2009.32
73. Transparency International. (2024). Corruption Perceptions Index 2024. https://www.transparency.org/en/cpi/2024
74. Ture, H., Dogan, S., and Kocak, D. (2019). Assessing Euro 2020 strategy using multi-criteria decision making methods: VIKOR and TOPSIS. Social Indicators Research, 142, 645–665. https://doi.org/10.1007/s11205-018-1938-8
75. Utzig, M. (2023). Diversification of good governance in European Union countries using the TOPSIS method. Journal of Modern Science, 4(53), 467.
76. Van de Walle, S. (2006). The state of the world’s bureaucracies. Journal of Comparative Policy Analysis, 8(4), 437–448. https://doi.org/10.1080/13876980600971409
77. Visbal-Cadavid, D., Delahoz-Domínguez, E., and Mendoza-Mendoza, A. (2025). A multiple factor analysis and hierarchical clustering of global logistics governance and development. Decision Analytics Journal, 15. https://doi.org/10.1016/j.dajour.2025.100579
78. Wei, C. P., Lee, Y. H., and Hsu, C. M. (2003). Empirical comparison of fast partitioning-based clustering algorithms for large data sets. Expert Systems with Applications, 24(4), 351–363. https://doi.org/10.1016/S0957-4174(02)00185-9
79. World Bank. (n.d.). Worldwide Governance Indicators: Frequently asked questions. https://www.worldbank.org/en/publication/worldwide-governance-indicators/frequently-asked-questions
80. World Bank. (2011). World Development Report 2011: Conflict, security, and development. Washington, DC: World Bank.
81. World Bank. (2024a). Worldwide Governance Indicators. https://info.worldbank.org/governance/wgi/
82. World Bank. (2024b). World Bank country and lending groups. https://datahelpdesk.worldbank.org/knowledgebase/articles/906519
83. Yoon, K., and Hwang, C. L. (1985). Manufacturing plant location analysis by multiple attribute decision making: Part I, single-plant strategy. International Journal of Production Research, 23(2), 345–359. https://doi.org/10.1080/00207548508904712