Journals / Ekonomi Politika ve Finans Araştırmaları Dergisi / 2026 / Cilt: 11 Sayı: 3

Analysis of Labor Market Performance in G7 Countries Using Trf-FUCOM and DOBI Methods

G7 Ülkelerinde İşgücü Piyasası Performansının TrF-FUCOM ve DOBI Yöntemleriyle Analizi

Sinan Dündar(SİVAS CUMHURİYET ÜNİVERSİTESİ)0000-0001-8061-3322
Şerife Merve Koşaroğlu(SİVAS CUMHURİYET ÜNİVERSİTESİ)0000-0002-2563-5753
Pages
1091–1117
Publication Date
2026-09-30
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Enhancing its resilience against economic uncertainty and shocks is paramount for ensuring overall economic stability.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The labor market, a complex structure with distinct dynamics, is significantly influenced by various macroeconomic factors.
📊
Key Findings & Data:This study sought to evaluate the labor market performance of G7 countries between 2000 and 2023, employing a set of labor market indicators.
💡
Conclusion & Impact:Enhancing its resilience against economic uncertainty and shocks is paramount for ensuring overall economic stability.
🔬Design:Araştırma Makalesi
📍Scope:2000 Verileri
⚙️Variables:İşgücü Piyasası Performansı · TrF-FUCOM · DOBI

Abstract

The labor market, a complex structure with distinct dynamics, is significantly influenced by various macroeconomic factors. Enhancing its resilience against economic uncertainty and shocks is paramount for ensuring overall economic stability. This study sought to evaluate the labor market performance of G7 countries between 2000 and 2023, employing a set of labor market indicators. The Trapezoidal Fuzzy Full Consistency Method (TrF-FUCOM) was utilized to rank these indicators and determine their respective weights, while the DOmbi Bonferroni (DOBI) method was applied to establish the performance ranking of the G7 countries' labor markets. Analysis revealed that unit labor costs were the most influential indicator in terms of performance ranking. This was followed by average annual wages, the hourly earnings index, the labor force participation rate, total labor force, the unemployment rate, and average annual working hours in descending order of importance. The resulting labor market performance ranking placed Japan first, followed by the United States, Germany, Canada, France, the United Kingdom, and Italy.

Özet

Belirgin dinamiklere sahip karmaşık bir yapı olan işgücü piyasası, çeşitli makroekonomik faktörlerden önemli ölçüde etkilenmektedir. Ekonomik belirsizlik ve şoklara karşı dayanıklılığını artırmak, genel ekonomik istikrarı sağlamak açısından hayati önem taşımaktadır. Bu çalışma, bir dizi işgücü piyasası göstergesini kullanarak 2000 ile 2023 yılları arasında G7 ülkelerinin işgücü piyasası performansını değerlendirmeyi amaçlamıştır. Bu göstergeleri sıralamak ve her birinin ağırlığını belirlemek için Trapezoidal Fuzzy Full Consistency Yöntemi (TrF-FUCOM) kullanılırken, G7 ülkelerinin işgücü piyasalarının performans sıralamasını oluşturmak için DOmbi Bonferroni (DOBI) yöntemi uygulanmıştır. Analizler, birim işgücü maliyetlerinin performans sıralaması açısından en etkili gösterge olduğunu ortaya koymuştur. Bunu, önem sırasına göre azalan bir şekilde, ortalama yıllık ücretler, saatlik kazanç endeksi, işgücüne katılım oranı, toplam işgücü, işsizlik oranı ve ortalama yıllık çalışma saatleri izlemiştir. Sonuçta ortaya çıkan işgücü piyasası performans sıralamasında Japonya birinci sırada yer alırken, onu sırasıyla ABD, Almanya, Kanada, Fransa, Birleşik Krallık ve İtalya izlemiştir.

🔖

Cite This Article

Dündar, S., Koşaroğlu, Ş. M., *, S. D. (2026). Analysis of Labor Market Performance in G7 Countries Using Trf-FUCOM and DOBI Methods. Ekonomi Politika ve Finans Araştırmaları Dergisi, Cilt: 11 Sayı: 3, 1091–1117. https://doi.org/10.30784/epfad.1911171

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (53)

  1. Akçoraoğlu, A. (2010). Employment, economic growth and labor market performance: The case of Turkey. Ekonomik Yaklaşım, 21(77), 101-114. http://dx.doi.org/10.5455/ey.20038
  2. Akyıldız, F. (2017). Göstergeler ne anlatır? Türkiye için iktisadi göstergeler rehberi. İstanbul: İstanbul Bilgi Üniversitesi Yayınları.
  3. Alkaya, A. and İslamoğlu, E. (2024). Aktif ve pasif işgücü piyasası politikalarının işsizlik üzerine etkisi: Seçilmiş OECD ülkeleri üzerine panel veri analizi. Sosyal Güvenlik Dergisi, 14(1), 19-40. https://doi.org/10.32331/sgd.1554611
  4. Arpaia, A. and Mourre, G. (2005). Labor market institutions and labor market performance: A survey of the literature. European Commission Directorate-General for Economic and Financial Affairs Economic Papers, 238, 1-39. https://dx.doi.org/10.2139/ssrn.871093
  5. Asaleye, A.J., Ogunjobi, J.O. and Ezenwoke, O. A. (2021). Trade openness channels and labor market performance: Evidence from Nigeria. International Journal of Social Economics, 48(11), 1589-1607. https://doi.org/10.1108/IJSE-06-2018-0320
  6. Badi, I., Pamučar, D., Stević, Ž. and Muhammad, L.J. (2023). Wind farm site selection using BWM-AHP-MARCOS method: A case study of Libya. Scientific African, 19, e01511. https://doi.org/10.1016/j.sciaf.2022.e01511
  7. Bayar, Y. (2019). İşgücü piyasası düzenlemeleri ve işsizlik arasındaki etkileşim: OECD ülkeleri örneği. HAK-İŞ Uluslararası Emek ve Toplum Dergisi, 8(21), 10-22. https://doi.org/10.31199/hakisderg.584681
  8. Bonab, S.R., Ghoushchi, S.J., Deveci, M. and Haseli, G. (2023). Logistic autonomous vehicles assessment using decision support model under spherical fuzzy set integrated Choquet integral approach. Expert Systems with Applications, 214, 119205. https://doi.org/10.1016/j.eswa.2022.119205
  9. Bošković, S., Švadlenka, L., Jovčić, S., Dobrodolac, M., Simić, V. and Bacanin, N. (2023). An alternative ranking order method accounting for two-step normalization (AROMAN)—A Case Study of the electric vehicle selection problem. IEEE Access, 11, 39496–39507. https://doi.org/10.1109/ACCESS.2023.3265818
  10. Boulhol, H. and Sicari, P. (2013). Labor market performance by age groups: A focus on France (OECD Economics Department Working Papers No. 1027). https://doi.org/10.1787/5k4c0dnhc58x-en
  11. Bouraima, M. B., Tengecha, N. A., Stević, Ž., Simić, V. and Qiu, Y. (2023). An integrated fuzzy MCDM model for prioritizing strategies for successful implementation and operation of the bus rapid transit system. Annals of Operations Research, 342, 141-172. https://doi.org/10.1007/s10479-023-05183-y
  12. Bradley, D.H. and Stephens, J.D. (2007). Employment performance in OECD countries: A test of neoliberal and institutionalist hypotheses. Comparative Political Studies, 40(12), 1486-1510. https://doi.org/10.1177/0010414006292609
  13. Brancaccio, E., Cristofaro, F.D. and Giammetti, R. (2020). A Meta-analysis on labor market deregulations and employment performance: No consensus around the IMF-OECD consensus. Review of Political Economy, 32(1), 1-21. https://doi.org/10.1080/09538259.2020.1759245
  14. Buscher, H., Dreger, C., Ramos, R. and Surinach, J. (2005). The impact of institutions on the employment performance in European labor markets (IZA Discussion Paper No. 1732). Retrieved form https://ssrn.com/abstract=807429
  15. Buttler, F., Franz, W., Schettkat, R. and Soskice, D. (2017). Institutional frameworks and labor market performance. London: Routledge.
  16. Cahuc, P. and Postel-Vinay, F. (2002). Temporary jobs, employment protection and labor market performance. Labor Economics, 9, 63-91. https://doi.org/10.1016/S0927-5371(01)00051-3
  17. Chen, Z.J. and Skuterud, M. (2018). Relative labor market performance of former international students: Evidence from the national graduates survey. Canadian Public Policy, 44(3), 207-225. Retrieved from https://www.jstor.org/stable/90025233
  18. Demidova, O., Marelli, E. and Signorelli, M. (2015). Youth labor market performances in the Russian and Italian regions. Economic Systems, 39, 43-58. https://doi.org/10.1016/j.ecosys.2014.06.003
  19. Destefanis, S. and Mastromatteo, G. (2010). Labor-market performance in the OECD: Some recent cross-country evidence. International Journal of Manpower, 31(7), 713-731. https://doi.org/10.1108/01437721011081563
  20. Dornbusch, R., Fischer, S. and Startz, R. (2007). Makroekonomi (Çev. Salih Ak). Ankara: Gazi Kitabevi.
  21. Ecer, F. and Pamucar, D. (2022). A novel LOPCOW‐DOBI multi‐criteria sustainability performance assessment methodology: An application in developing country banking sector. Omega, 112, 102690. https://doi.org/10.1016/j.omega.2022.102690
  22. Eichhorst, W. and Konle-Seidl, R. (2016). Evaluating labor market policy (IZA Discussion Paper Series No. 9966). Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2790004
  23. Erikli, S. and Türkoğlu, S.P. (2021). OECD ülkelerinin işgücü piyasası performans analizi. Sosyal Siyaset Konferansları Dergisi, 80, 299-320. Retrieved form https://izlik.org/JA68XK45UG
  24. Epstein, J., Duerr, D., Kenworthy, L. and Ragin, C. (2008). Comparative employment performance: A fuzzy-set analysis. In Kenworthy, L. & Hicks, A. (Eds.), Method and substance in macrocomparative analysis (pp. 67-90). London: Palgrave Macmillan.
  25. Helmdag, J. (2025). The three faces of partisanship: Uncovering country-specific partisan dynamics in labor market policy reforms (Doctoral dissertation). University of Greifswald, Germany.
  26. Hijzen, A., Kappeler, A., Pak, M. and Schwellnus, C. (2018). Labor market resilience: The role of structural and macroeconomic policies. In J. de Haan and J. Parlevliet (Eds.), Structural reforms: moving the economy forward (pp. 173-198). Cham: Springer International Publishing.
  27. Garg, H., Majumder, P., and Nath, M. (2022). A hybrid trapezoidal fuzzy FUCOM-AHP approach and their application to identification of monkeypox risk factors. Computational and Applied Mathematics, 41, 379. https://doi.org/10.1007/s40314-022-02085-w
  28. Güllü, G. (2025). Seçilmiş OECD ülkelerinde karşılaştırmalı emek piyasaları analizi. Ankara: Savaş Kitap ve Yayınevi.
  29. Günsan, N. and Yergin, H. (2021). Impact of active labor policies on employment. Journal of Social, Humanities and Administrative Sciences, 4(12): 1161-1180. https://doi.org/10.26677/TR1010.2021.899
  30. Kahraman, Y.R. (2002). Robust sensitivity analysis for multi-attribute deterministic hierarchical value models (Doctoral dissertation). Air Force Institute of Technology Graduate School of Engineering and Management, Ohio.
  31. Klinger, S. and Rothe, T. (2012). The impact of labor market reforms and economic performance on the matching of the short-term and the long-term unemployed. Scottish Journal of Political Economy, 59, 90-114. https://doi.org/10.1111/j.1467-9485.2011.00570.x
  32. Koca, D. (2022). 2000-2020 yılları arasında G7 ülkeleri ve Türkiye’nin işgücü piyasası yapısının ve aktif işgücü piyasası politikalarının karşılaştırmalı analizi. Sosyal Siyaset Konferansları Dergisi, 83, 101-140. https://doi.org/10.26650/jspc.2022.83.1008072
  33. Kumar, S., Maity, S.R. and Patnaik, L. (2023). A novel BWM integrated MABAC decision-making approach to optimize the wear parameter of CrN/TiAlSiN coating. Journal of Industrial and Management Optimization, 19(4), 2676–2703. https://doi.org/10.3934/jimo.2022061
  34. Majumder, P. (2023). An integrated trapezoidal fuzzy FUCOM with single-valued neutrosophic fuzzy MARCOS and GMDH method to determine the alternatives weight and its applications in efficiency analysis of water treatment plant. Expert Systems with Applications, 225, 120087. https://doi.org/10.1016/j.eswa.2023.120087
  35. Matić, B., Marinković, M., Jovanović, S., Sremac, S. and Stević, Ž. (2022). Intelligent novel IMF D-SWARA—rough MARCOS algorithm for selection construction machinery for sustainable construction of road infrastructure. Buildings, 12(7), 1059. https://doi.org/10.3390/buildings12071059
  36. Mešić, A., Miškić, S., Stević, Ž. and Mastilo, Z. (2022). Hybrid MCDM solutions for evaluation of the logistics performance index of the Western Balkan countries. Economics, 10(1), 13–34. https://doi.org/10.2478/eoik-2022-0004
  37. Milinković, A., Brkljač, D., Škorić, S., Stević, Ž., Danilevičius, A. and Das, D.K. (2025). Evaluation of potentials for urban planning using the fuzzy FUCOM-IMF SWARA-Fuzzy OPARA model. Buildings, 15(5), 803. https://doi.org/10.3390/buildings15050803
  38. Murat, S. (2012). Japon çalışma hayatı. Sosyal Siyaset Konferansları Dergisi, 37-38, 189-203. https://izlik.org/JA27YE59BP
  39. Pamučar, D., Stević, Ž. and Sremac, S. (2018). A new model for determining weight coefficients of criteria in MCDM Models: Full consistency method (FUCOM). Symmetry, 10(9), 393. https://doi.org/10.3390/sym10090393
  40. Pamucar, D. and Ecer, F. (2020). Prioritizing the weights of the evaluation criteria under fuzziness: The fuzzy full consistency method – FUCOM-F. Facta Universitatis Series Mechanical Engineering, 18(3), 419–437. https://doi.org/10.22190/FUME200602034P
  41. Paul, A., Ghosh, S., Smarandache, F. and Majumder, P. (2025). Integrating trapezoidal neutrosophic FUCOM-TOPSIS in SVNS for enhanced seismic hazard evaluation of Northeast India. Informatica, 37(1), 109-157. https://doi.org/10.15388/25-INFOR589
  42. Perugini, C. and Signorelli, M. (2007). Labor market performance differentials and dynamics in EU-15 countries and regions. The European Journal of Comparative Economics, 4(2), 209-262. Retrieved from https://research.unipg.it/handle/11391/124431
  43. Puška, A., Božanić, D., Nedeljković, M. and Janošević, M. (2022). Green supplier selection in an uncertain environment in agriculture using a hybrid MCDM model: Z-numbers–fuzzy LMAW–fuzzy CRADIS model. Axioms, 11(9), 427. https://doi.org/10.3390/axioms11090427
  44. Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49–57. https://doi.org/10.1016/j.omega.2014.11.009
  45. Rohit, Kumar, K., Bhardwaj, R. and Kaur, G. (2025). Rainfall analysis using FUCOM weighted logarithmic distance measure based on probabilistic dual hesitant preference values. Water Resources Management, 39(1), 207–226. https://doi.org/10.1007/s11269-024-03967-w
  46. Saaty, T. (2004). Decision making—The analytic hierarchy and network processes (AHP/ANP). Journal of Systems and Systems Engineering, 13(1), 1–35. https://doi.org/10.1007/s11518-006-0151-5
  47. Sharma, P., Nila, B., Pamucar, D. and Roy, J. (2025). Integrating LOPCOW-DOBI method and possibilistic programming for two-stage decision making in resilient food supply chain network. Journal of Industrial Information Integration, 46, 100847. https://doi.org/10.1016/j.jii.2025.100847
  48. Tvrdon, M. (2015). Labor market performance in EU member states: A panel regression approach. Journal of Economics, Business and Management, 3(1), 34-37. Retrieved from https://www.joebm.com
  49. Wang, C. and Shen, J. (2022). Settlement intention of migrants in urban China: The effects of labor-market performance, employment status, and social integration. Applied Geography, 147, 102773. https://doi.org/10.1016/j.apgeog.2022.102773
  50. Wei, Q. and Zhou, C. (2023). A multi-criteria decision-making framework for electric vehicle supplier selection of government agencies and public bodies in China. Environmental Science and Pollution Research, 30(4), 10540–10559. https://doi.org/10.1007/s11356-022-22783-6
  51. Vella, F. (1994). Gender roles and human capital investment: The relationship between traditional attitudes and female labor market performance. Economica, 61(242), 191-211. https://doi.org/10.2307/2554957
  52. Yazdani, M., Chatterjee, P., Pamucar, D. and Doval, M. (2019). A risk-based integrated decision-making model for green supplier selection: A case study of a construction company in Spain. Kybernetes, 49(4), 1229–1252. https://doi.org/10.1108/K-09-2018-0509
  53. Zhang, X., Wang, C., Li, E. and Xu, C. (2014). Assessment model of ecoenvironmental vulnerability based on improved Entropy weight method. The Scientific World Journal, 2014(1), 797814. https://doi.org/10.1155/2014/797814