Bütünleşik BWM ve TOPSIS yöntemleri kullanılarak OPEC üyesi ülkeler için kurumsal gelişmişlik analizi

Öz

Bu çalışmada, kurumsal gelişmişlik ve doğal kaynak zenginliği bağlantısı çerçevesinde 2010-2020 dönemi için OPEC üyesi 13 ülkenin kurumsal gelişmişlik düzeyleri incelenmektedir. Literatürde kurumsal gelişmişlik göstergelerine odaklanan çalışmalarda her bir göstergenin farklı sorunlar ile arasındaki ilişki ele alınmaktadır. Ancak, kurumsal gelişmişlik analizini bütüncül bir biçimde ele alan ve çok boyutlu yapısı gereği çok kriterli karar verme yöntemlerine başvurulan çalışma sayısı sınırlıdır. Bu doğrultuda, çok kriterli karar verme yöntemlerinden BWM ve TOPSIS yöntemleri bütüncül bir yaklaşımla uygulanmış; ilk olarak göstergelerin ağırlıkları uzmanlar tarafından değerlendirilmiş ve elde edilen ağırlıklara bağlı olarak ülkelerin kurumsal gelişmişlik sıralaması belirlenmiştir. Ayrıca farklı senaryolara göre duyarlılık analizleri gerçekleştirilmiştir. Çalışmanın sonucunda, petrol zengini ülkelerin kurumsal açıdan yıllar itibariyle farklı sıralarda oldukları tespit edilmiştir. Birleşik Arap Emirlikleri, Suudi Arabistan ve Kuveyt’in kurumsal gelişmişlik sıralamasında ön planda oldukları; Ekvator Ginesi, Irak ve Venezuela’nın Libya ile birlikte başarısız bir kurumsal gelişmişlik sıralamasına sahip oldukları görülmektedir.

Anahtar Kelimeler

• Kurumsal gelişmişlik göstergeleri
• OPEC
• BWM
• TOPSIS
• Duyarlılık Analizi
• Worldwide Governance Indicators
• Sensitivity Analysis

Institutional development analysis for OPEC member countries by using integrated BWM and TOPSIS methods

Abstract

In this study, the institutional development levels of 13 OPEC member countries for the 2010-2020 period are investigated within the framework of the connection between institutional development and natural resource wealth. In studies focusing on institutional development indicators in the literature, the relationship between each indicator and different problems is discussed. However, the number of studies that deal with the analysis of institutional development in an integrated approach and which uses multi-criteria decision-making methods due to its multidimensional nature is limited. In this direction, BWM and TOPSIS methods, which are multi-criteria decision making methods, were applied with an integrated approach. First, the weights of the indicators were evaluated subjectively by experts and then the institutional development ranking of the countries was determined based on the weights obtained. In addition, sensitivity analyzes were carried out according to different scenarios. As a result of the study, it has been determined that oil-rich countries' rankings are differentiated over the years. While, United Arab Emirates, Saudi Arabia, and Kuwait are at the forefront in the ranking of institutional development; Equatorial Guinea, Iraq, Libya, and Venezuela have obtained an unsuccessful ranking of institutional development. The values of these indicators in OPEC member countries are far below the global average and the average compared to developed countries. In the selected period, decreases are striking in almost all indicators monitored. In the study, it has been seen how important the effect of the institutional structure on the economy is especially in Venezuela, Iraq, Libya and Equatorial Guinea.

Keywords

• Worldwide Governance Indicators
• Sensitivity Analysis

Kaynakça

1. Acemoglu, D. (2003). Why not a political Coase theorem? Social conflict, commitment, and politics. Journal of comparative economics, 31(4), 620-652. https://doi.org/10.1016/j.jce.2003.09.003.
2. Acemoglu, D., & Robinson, J. A. (2006). Economic backwardness in political perspective. American political science review, 100(1), 115-131. https://doi.org/10.1017/S0003055406062046.
3. Acemoglu, D., Verdier, T., & Robinson, J. A. (2004). Kleptocracy and divide-and-rule: A model of personal rule. Journal of the European Economic Association, 2(2-3), 162-192. https://doi.org/10.1162/154247604323067916.
4. Agere, S. (2000). Promoting good governance: Principles, practices and perspectives (Vol. 11). Common wealth Secretariat. https://doi.org/10.14217/9781848597129-en.
5. Ahmadi, H. B., Kusi-Sarpong, S., & Rezaei, J. (2017). Assessing the social sustainability of supply chains using Best Worst Method. Resources, Conservation and Recycling, 126, 99-106. https://doi.org/10.1016/j.resconrec.2017.07.020.
6. Akyüz, G., Tosun, Ö., & Salih, A. K. A. (2020). Performance Evaluation Of Non-Life Insurance Companies with Best-Worst Method And Topsis. Uluslararası Yönetim İktisat ve İşletme Dergisi, 16(1), 108-125. http://dx.doi.org/10.17130/ijmeb.700907.
7. Andersen, J. J., & Aslaksen, S. (2013). Oil and political survival. Journal of Development Economics, 100(1), 89-106. https://doi.org/10.1016/j.jdeveco.2012.08.008.
8. Andrews, M. (2008). The good governance agenda: Beyond indicators without theory. Oxford development studies, 36(4), 379-407. https://doi.org/10.1080/13600810802455120.

9. Ardielli, E. (2019). Use of TOPSIS method for assessing of good governance in European Union countries. Review of Economic Perspectives, 19(3), 211-231. https://doi.org/10.2478/revecp-2019-0012.
10. Auty, R. M. (1994). Industrial policy reform in six large newly industrializing countries: The resource curse thesis. World development, 22(1), 11-26. https://doi.org/10.1016/0305-750X(94)90165-1.
11. Auty, R. M. (2001). The political economy of resource-driven growth. European economic review, 45(4-6), 839-846. https://doi.org/10.1016/S0014-2921(01)00126-X.
12. Avcı, T. & Çınaroğlu, E. (2018). AHP Temelli Topsıs Yaklaşımı İle Havayolu İşletmelerinin Finansal Performans Değerlemesi. Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi, 19(1), 316-335.
13. Ayvaz, B., Boltürk, E., & Kaçtıoğlu, S. (2015). Supplier selection with TOPSIS method in fuzzy environment: an application in banking sector. Sigma Journal of Engineering and Natural Sciences, 33(3), 351-362.
14. Baldwin, R. E. (1966). Economic development and export growth. University of California Press. https://doi.org/10.1525/9780520326774.
15. Brollo, F., Nannicini, T., Perotti, R., & Tabellini, G. (2013). The political resource curse. American Economic Review, 103(5), 1759-96. https://doi.org/10.1257/aer.103.5.1759.
16. Chen, D., Gummi, U. M., Wang, J., & Mu’azu, A. (2020). Why We Feel Unsafe When We Get Rich? Review on the Empirics of Corruption, Oil Rents and Insecurity in Nigeria. Open Journal of Social Sciences, 8(5), 141-153. https://doi.org/https://doi.org/10.1111/j.1468-0297.2006.01045.x.
17. Çakır, E., & Can, M. (2019). Best-worst yöntemine dayalı ARAS yöntemi ile dış kaynak kullanım tercihinin belirlenmesi: Turizm sektöründe bir uygulama. Atatürk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 23(3), 1273-1300.
18. Çelikbilek, Y., & Tüysüz, F. (2020). An in-depth review of theory of the TOPSIS method: An experimental analysis. Journal of Management Analytics, 7(2), 281-300. https://doi.org/10.1080/23270012.2020.1748528.
19. Dinçer, S. E. (2011). Multi-criteria analysis of economic activity for European Union Member States and candidate countries: TOPSIS and WSA applications. European Journal of Social Sciences, 21(4), 563-572.
20. Ecer, F. (2020b). Multi-criteria decision making for green supplier selection using interval type-2 fuzzy AHP: a case study of a home appliance manufacturer. Operational Research, 1-35. https://doi.org/10.1007/s12351-020-00552-y.
21. Ecer, F., & Pamucar, D. (2020a). Sustainable supplier selection: A novel integrated fuzzy best worst method (F-BWM) and fuzzy CoCoSo with Bonferroni (CoCoSo’B) multi-criteria model. Journal of Cleaner Production, 266, 121981. https://doi.org/10.1016/j.jclepro.2020.121981.
22. Ecer, F., Pamucar, D., Zolfani, S. H., & Eshkalag, M. K. (2019). Sustainability assessment of OPEC countries: Application of a multiple attribute decision making tool. Journal of Cleaner Production, 241, 118324. https://doi.org/10.1016/j.jclepro.2019.118324.
23. Fearon, J. D., & Laitin, D. D. (2003). Ethnicity, insurgency, and civil war. American political science review, 97(1), 75-90. https://doi.org/10.1017/S0003055403000534.
24. Gupta, H., & Barua, M. K. (2018). A framework to overcome barriers to green innovation in SMEs using BWM and Fuzzy TOPSIS. Science of the Total Environment, 633, 122-139. https://doi.org/10.1016/j.scitotenv.2018.03.173.
25. Gupta, P., Anand, S., & Gupta, H. (2017). Developing a roadmap to overcome barriers to energy efficiency in buildings using best worst method. Sustainable Cities and Society, 31, 244-259. https://doi.org/10.1016/j.scs.2017.02.005.
26. Gylfason, T. (2001). Natural resources, education, and economic development. European economic review, 45(4-6), 847-859. https://doi.org/10.1016/S0014-2921(01)00127-1.
27. Hodler, R. (2006). The curse of natural resources in fractionalized countries. European Economic Review, 50(6), 1367-1386. https://doi.org/10.1016/j.euroecorev.2005.05.004.
28. Hwang, C. L., & Yoon, K. (1981). Methods for multiple attribute decision making. In M. Beckmann, & H. P. Künzi (Eds.), Multiple attribute decision making (pp. 58–191). Berlin: Springer
29. Ilgaz, A. (2018). Lojistik sektöründe personel seçim kriterlerinin Ahp ve Topsis yöntemleri ile değerlendirilmesi. Süleyman Demirel Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 1(32), 586-605.
30. Karande, P., & Chakraborty, S. (2012). Application of multi-objective optimization on the basis of ratio analysis (MOORA) method for materials selection. Materials & Design, 37, 317-324. https://doi.org/10.1016/j.matdes.2012.01.013.
31. Karl, T. L. (1997). The paradox of plenty. University of California Press. https://doi.org/10.1525/9780520918696.
32. Kaufmann, D., Kraay, A., & Mastruzzi, M. (2011). The worldwide governance indicators: Methodology and analytical issues1. Hague journal on the rule of law, 3(2), 220-246. https://doi.org/10.1017/S1876404511200046.
33. Kaufmann, D., Kraay, A., & Zoido-Lobatón, P. (1999). Aggregating governance indicators (Vol. 2195). world Bank publications.
34. Kheybari, S., Kazemi, M., & Rezaei, J. (2019). Bioethanol facility location selection using best-worst method. Applied energy, 242, 612-623. https://doi.org/10.1016/j.apenergy.2019.03.054.
35. Koca, G. & Akçakaya, E.D.U. (2021). Giyilebilir Teknolojik Ürünlerin Tasarımında Etkili Olan Faktörlerin Best-Worst Metodu (BWM) İle Değerlendirilmesi. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, 8(1), 136-150. https://doi.org/10.35193/bseufbd.847791.
36. Liou, J. J., & Tzeng, G. H. (2012). Comments on “Multiple criteria decision making (MCDM) methods in economics: an overview”. Technological and Economic Development of Economy, 18(4), 672-695. https://doi.org/10.3846/20294913.2012.753489
37. Luong, P. J., & Weinthal, E. (2010). Oil is not a curse: Ownership structure and institutions in Soviet successor states. Cambridge University Press.
38. Mahdavy, H., & Cook, M. A. (1970). The patterns and problems of economic development in rentier states: the case of Iran. Life, 1000(1), 129-135.
39. Mateusz, P., Danuta, M., Małgorzata, Ł., Mariusz, B., & Kesra, N. (2018). TOPSIS and VIKOR methods in study of sustainable development in the EU countries. Procedia Computer Science, 126, 1683-1692. https://doi.org/10.1016/j.procs.2018.08.109.
40. Mehlum, H., Moene, K., & Torvik, R. (2006). Institutions and the resource curse. The economic journal, 116(508), 1-20. https://doi.org/10.1111/j.1468-0297.2006.01045.x.
41. Mi, X., Tang, M., Liao, H., Shen, W., & Lev, B. (2019). The state-of-the-art survey on integrations and applications of the best worst method in decision making: Why, what, what for and what's next?. Omega, 87, 205-225. https://doi.org/10.1016/j.omega.2019.01.009.
42. Nabeeh, N. A., Smarandache, F., Abdel-Basset, M., El-Ghareeb, H. A., & Aboelfetouh, A. (2019). An integrated neutrosophic-topsis approach and its application to personnel selection: A new trend in brain processing and analysis. IEEE Access, 7, 29734-29744. https://doi.org/10.1109/ACCESS.2019.2899841.
43. Norman, C. S. (2009). Rule of law and the resource curse: abundance versus intensity. Environmental and Resource Economics, 43(2), 183-207. https://doi.org/10.1007/s10640-008-9231-y.
44. Ogwang, T., Vanclay, F., & van den Assem, A. (2019). Rent-seeking practices, local resource curse, and social conflict in Uganda’s emerging oil economy. Land, 8(4), 53. https://doi.org/10.3390/land8040053.
45. Özdemir, M. (2015). TOPSIS. B.F. Yıldırım, E. Önder (Ed.), Operasyonel, yönetsel ve stratejik problemlerin çözümünde çok kriterli karar verme yöntemleri (133-176). Bursa. Dora Basım-Yayın.
46. Rezaei, J. (2015a). Best-worst multi-criteria decision-making method. Omega, 53, 49-57. https://doi.org/10.1016/j.omega.2014.11.009.
47. Rezaei, J., Nispeling, T., Sarkis, J., & Tavasszy, L. (2016). A supplier selection life cycle approach integrating traditional and environmental criteria using the best worst method. Journal of Cleaner Production, 135, 577-588. https://doi.org/10.1016/j.jclepro.2016.06.125.
48. Rezaei, J., van Roekel, W. S., & Tavasszy, L. (2018). Measuring the relative importance of the logistics performance index indicators using Best Worst Method. Transport Policy, 68, 158-169. https://doi.org/10.1016/j.tranpol.2018.05.007.
49. Robinson, J. A., Torvik, R., & Verdier, T. (2006). Political foundations of the resource curse. Journal of development Economics, 79(2), 447-468. https://doi.org/10.1016/j.jdeveco.2006.01.008.
50. Ross, M. L. (1999). The political economy of the resource curse. World politics, 51(2), 297-322. https://doi.org/10.1017/S0043887100008200.
51. Ross, M. L. (2001). Does oil hinder democracy?. World politics, 53(3), 325-361. https://doi.org/10.1353/wp.2001.0011.
52. Ross, M. L. (2015). What have we learned about the resource curse?. Annual review of political science, 18, 239-259. https://doi.org/10.1146/annurev-polisci-052213-040359.
53. Rosser, A. (2006). The political economy of the resource curse: A literature survey. https://opendocs.ids.ac.uk/opendocs/handle/20.500.12413/4061.
54. Roy, P. K., & Shaw, K. (2021). A multicriteria credit scoring model for SMEs using hybrid BWM and TOPSIS. Financial Innovation, 7(1), 1-27. https://doi.org/10.1186/s40854-021-00295-5.
55. Sachs, J. D., & Warner, A. M. (1999). The big push, natural resource booms and growth. Journal of development economics, 59(1), 43-76. https://doi.org/10.1016/S0304-3878(99)00005-X.
56. Sachs, J. D., & Warner, A. M. (2001). The curse of natural resources. European economic review, 45(4-6), 827-838. https://doi.org/10.1016/S0014-2921(01)00125-8.
57. Salimi, N., & Rezaei, J. (2018). Evaluating firms’ R&D performance using best worst method. Evaluation and program planning, 66, 147-155. https://doi.org/10.1016/j.evalprogplan.2017.10.002.
58. Ture, H., Dogan, S., & Kocak, D. (2019). Assessing Euro 2020 strategy using multi-criteria decision making methods: VIKOR and TOPSIS. Social Indicators Research, 142(2), 645-665. https://doi.org/10.1007/s11205-018-1938-8.
59. Wang, P., Zhu, Z., & Huang, S. (2017). The use of improved TOPSIS method based on experimental design and Chebyshev regression in solving MCDM problems. Journal of Intelligent Manufacturing, 28(1), 229-243. https://doi.org/10.1007/s10845-014-0973-9.
60. Yıldız, A., Ayyıldız, E., Gümüş, A. T., & Özkan, C. (2019). Ülkelerin yaşam kalitelerine göre değerlendirilmesi için hibrit pisagor bulanık AHP-TOPSIS metodolojisi: Avrupa Birliği örneği. Avrupa Bilim ve Teknoloji Dergisi, (17), 1383-1391. https://doi.org/10.31590/ejosat.658021.
61. You, P., Guo, S., Zhao, H., & Zhao, H. (2017). Operation performance evaluation of power grid enterprise using a hybrid BWM-TOPSIS method. Sustainability, 9(12), 2329. https://doi.org/10.3390/su9122329.
62. Zanger, S. C. (2000). Good governance and European aid: The impact of political conditionality. European Union Politics, 1(3), 293-317. https://doi.org/10.1177/1465116500001003002.