KaydedilenlerDerginizi ekleyin

Dergiler / İktisat Politikası Araştırmaları Dergisi / 2026 / Cilt: 13 Sayı: 1

Examining Education, Culture, Health, and Workforce Characteristics of Turkish Provinces: A Cluster-Based Analysis

Elif Mutlu(IZMIR BAKIRCAY UNIVERSITY)0009-0004-1034-0567
Serhat Peker(IZMIR BAKIRCAY UNIVERSITY)0000-0002-6876-3982
Ümit Kuvvetli(IZMIR BAKIRCAY UNIVERSITY)0000-0002-9567-3675
Sayfa
277–291
Yayın Tarihi
2026-02-19
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Overall, the findings provide a nuanced spatial classification of provinces and offer policy-relevant insights for designing cluster-specific regional development strategies and more equitable resource allocation.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:This study examines the education, culture, health, and workforce characteristics of Turkish provinces through a cluster-based analytical approach.
📊
Temel Bulgular & Veri:Using data from the Turkish Statistical Institute (TUİK) and the Ministry of Health, we assess provincial-level performance across a diverse set of social development indicators.
💡
Sonuç & Katkı:Overall, the findings provide a nuanced spatial classification of provinces and offer policy-relevant insights for designing cluster-specific regional development strategies and more equitable resource allocation.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Cluster Analysis · Data-Driven Policy · Regional Development

Abstract

This study examines the education, culture, health, and workforce characteristics of Turkish provinces through a cluster-based analytical approach. Using data from the Turkish Statistical Institute (TUİK) and the Ministry of Health, we assess provincial-level performance across a diverse set of social development indicators. The clustering analysis identifies groups of provinces with similar profiles, highlighting regional disparities and patterns in the distribution of public services and labour market conditions. The results show that metropolitan provinces form a highly developed cluster characterised by strong economic capacity and population growth, while several clusters reflect structurally disadvantaged regions with low economic performance, negative net migration, and limited social infrastructure. In addition, some provinces exhibit relatively high education and cultural indicators despite weaker economic outcomes, underscoring the heterogeneous nature of regional development in Türkiye. Overall, the findings provide a nuanced spatial classification of provinces and offer policy-relevant insights for designing cluster-specific regional development strategies and more equitable resource allocation.

🔖

Bu Makaleye Atıf Yap

Mutlu, E., Peker, S., Kuvvetli, Ü. (2026). Examining Education, Culture, Health, and Workforce Characteristics of Turkish Provinces: A Cluster-Based Analysis. İktisat Politikası Araştırmaları Dergisi, Cilt: 13 Sayı: 1, 277–291. https://doi.org/10.26650/JEPR1785376

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (37)

  1. Altuntas, S., Selim, S., Altuntas, F. (2022). A hierarchical clustering based panel data approach: A case study of regional incentives, International Journal of Information Management Data Insights, 2 (2), 100098, https://doi.org/10.1016/j.jjimei.2022.100098. google scholar
  2. Atalay, M. (2020). Tourism-based classification of Turkish provinces using cluster analysis. Tourism Economics and Management, 4(1), 22–38. google scholar
  3. Bayram, C., & Ergül, İ. (2019). Human capital investments and income inequality: A provincial analysis for Turkey. Sosyoekonomi, 27(40), 167–184.google scholar
  4. Çelik, Ş. (2010). Türkiye'de sağlık hizmetlerinin illere göre değerlendirilmesi: Kümeleme analizi uygulaması. Anadolu University Journal of Social Sciences, 10(1), 105–120. google scholar
  5. Çilan, T., & Demirhan, H. (2018). Determining the development levels of provinces by using cluster analysis. Gümüşhane Üniversitesi Sosyal Bilimler Elektronik Dergisi, 9(25), 68–84. google scholar
  6. Cengiz, Ö., & Öztürk, A. (2012). Türkiye’de eğitim düzeylerine göre illerin kümelenmesi: K-ortalama analizi uygulaması. İktisadi ve İdari Bilimler Dergisi, 26(2), 1–16. google scholar
  7. Dolu, A. & Kuvvetli, U. (2023). Türkiye’de Kentlerin Sosyo-Ekonomik Gelişmişlik Düzeylerinin Karşılaştırılması. International Journal of Public Finance. 8(1), 85 – 106. google scholar
  8. Eren, H., & Ömürbek, N. (2019). Evaluation of healthcare performance in Turkey’s provinces: A cluster analysis approach. Journal of Business Research - Turk, 11(2), 1044–1059. google scholar
  9. Gençoğlu, P. (2015). Assessment of provincial differences in healthcare service provision in Turkey. Hacettepe Sağlık İdaresi Dergisi, 18(2), 223–242.google scholar
  10. Gürsoy, F., & Badur, B. (2022). Inter-provincial migration flows in Turkey: A network and clustering approach. Ege Academic Review, 22(2), 149–160.google scholar
  11. Hamarat, B. (2009). Türkiye’de illerin sosyoekonomik yapılarına göre sınıflandırılması: Kümeleme analizi uygulaması. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 14(2), 345–360. google scholar
  12. Karabulut, Y., Yılmaz, H., & Çetin, T. (2018). Türkiye illerinin sosyo-ekonomik gelişmişlik düzeylerinin kümeleme analizi yöntemiyle incelenmesi. Gaziantep University Journal of Social Sciences, 17(3), 949–965.google scholar
  13. Kandemir, E., & Çelik, Ş. (2014). Türkiye’de hastane ve yatak kapasitesine göre illerin sınıflandırılması: Bulanık kümeleme analizi. Afyon Kocatepe Üniversitesi İİBF Dergisi, 16(2), 251–266. google scholar
  14. Kaufman, L., & Rousseeuw, P. J. (2009). Finding groups in data: an introduction to cluster analysis. John Wiley & Sons. google scholar
  15. Köse, A. (2019). Evaluation of statistical regions with hierarchical cluster analysis according to data on health service demand, production and capacity. Uluslararası İktisadi ve İdari İncelemeler Dergisi, 1(23), 15–28. google scholar
  16. OECD. (2023). Education policy outlook: Turkey 2023. OECD Publishing. google scholar
  17. Özkubat, G., & Selim, S. (2019). Türkiye’de İllerin Sosyo-Ekonomik Gelişmişliği: Bir Mekânsal Ekonometrik Analiz. Alphanumeric Journal, 7(2), 449-470. google scholar
  18. Republic of Turkey Ministry of Health. (2019). Turkey Health Statistics Yearbook 2019. https://www.saglik.gov.tr/TR,67115/saglik-istatistikleri-yilligi-2019.html google scholar
  19. Servi, T., & Erişoğlu, Ü. (2020). Türkiye’deki Şehirlerin Sosyo-Ekonomik Gelişmişlik Düzeylerinin İstatistiksel Analizi. Al Farabi Uluslararası Sosyal Bilimler Dergisi, 5(2), 174-186. google scholar
  20. Şen, H., Çemrek, F., & Özaydın, Ö. (2006). Türkiye’deki İllerin Sosyo-Ekonomik Gelişmşlik Düzeylerinin Belirlenmesi. Sosyal Ekonomik Araştırmalar Dergisi, 6(11), 155-171. google scholar
  21. Şimşek, A. B. (2024). Provincial classification of healthcare services in Turkey: A density-based clustering approach. Health Systems and Policy Research, 11(1), 1–9. google scholar
  22. Tansel, A., & Güngör, N. D. (1997). Educational composition of the Turkish workforce, 1988–1994. METU Studies in Development, 24(1), 1–29. google scholar
  23. Tekin, B. (2015). Evaluation of primary healthcare services using cluster analysis. Gazi Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 17(2), 309–330. google scholar
  24. Tuncer, G. (2019). Türkiye’de Sosyo-Ekonomik Gelişmişliğin Mekansal Eşitsizliği. Ekonomi Maliye İşletme Dergisi, 2(2), 69-80. google scholar
  25. Turkish Statistical Institute (TÜİK). (2024). Regional Statistics Database. https://data.tuik.gov.tr google scholar
  26. Turkish Statistical Institute (TÜİK). (2023). Provincial level labour force statistics. https://data.tuik.gov.tr/Bulten/Index?p=Provincial-Level-Labour-Force-Statistics-2023-53838 google scholar
  27. Tüzüntürk, S. (2024). Clustering Turkish provinces based on educational attainment levels. Journal of Education and Research, 34(1), 55–72. google scholar
  28. Ullmann, A., & Hennig, C. (2021). Stability and validation of clustering results: A comparative review. Journal of Classification, 38(1), 1–42. google scholar
  29. Ünel, F.B. (2024). Türkiye’deki sosyo-ekonomik gelişmişlik endeksi (SEGE) ile sosyal, ekonomik, kültürel verilerin sıralı en küçük kareler yöntemi ile analiz edilmesi. Türkiye Coğrafi Bilgi Sistemleri Dergisi, 6(1), 47-58. google scholar
  30. United Nations Development Programme. (2023). Human Development Report 2023–2024: Breaking the gridlock—Reimagining cooperation in a polarized world. google scholar
  31. United Nations. (n.d.). Transforming our world: The 2030 Agenda for Sustainable Development. https://sdgs.un.org/2030agenda. google scholar
  32. Wegmann, L., & Zipperling, J. (2021). How to choose a clustering algorithm: A framework for systematic algorithm selection. Data Science Journal, 20(1), 1–19.google scholar
  33. Yavan, S., & Gazeloğlu, C. (2022). Yerel yönetimlerde cinsiyete duyarlı bütçelemenin hiyerarşik kümeleme analiziyle incelenmesi: Aydın ilçe belediyeleri örneği. Çağdaş Yerel Yönetimler, 31(1), 165-200. google scholar
  34. Yıldırım, H. H., Kaya, S., & Ünal, E. (2020). Distribution and planning of health workforce in Turkey: A regional comparison. Health Policy and Planning, 35(5), 601–610. google scholar
  35. Yılancı, V. (2010). Socioeconomic classification of Turkish provinces using fuzzy clustering. İktisat ve Finans Dergisi, 25(292), 45–62. google scholar
  36. Yıldırım, U. T. (2025). Socio-Economic Development and Voter Preferences in Türkiye: The 28 May 2023 Presidential Election. Akademik Araştırmalar Ve Çalışmalar Dergisi (AKAD), 17(32), 165-180. google scholar
  37. Yıldız, A. (2018). Classification of Turkish provinces according to basic health indicators using clustering analysis. Journal of Health and Society, 28(4), 77–91.google scholar