Examination of Provinces in Türkiye about Sectoral Employment Share by Cluster Analysis

Öz

The significance of regional dynamics in the process of economic development and regional development has increased as a result of significant factors like competitiveness, human resource development, and observation of the global market. In this study, mathematical programming-based cluster analysis has been conducted to group the regions in Türkiye according to sectoral employment rates. A mixed integer mathematical model is presented that maximizes the smallest of the out-of-cluster distances while minimizing the largest within-cluster distance. Level 2- 26 sub-regions in Türkiye are clustered according to sectoral employment data for 2021 and 2022. As a result, two clusters were obtained for both years in our country according to employment status by gender on a sectoral basis. One of these clusters is where the employment rate of the agricultural sector is higher than other sectors, and the other is where the employment rate of the industrial and service sectors is higher. When the 2021 and 2022 clusters are compared, in total, TR22, TR32, TR33, TRC3; in men, TR21, TR22, TR32, TR52, TR81; In women, it was observed that TRC1 regions were assigned to different clusters. By implementing a successful employment policy as human resource development across the national government, it will be possible to ensure the balanced growth of provinces located in Türkiye's various geographical areas.

Anahtar Kelimeler

• Employment Rate
• Sectoral Employment Share
• Clustering Analysis
• Mathematical Programming
• Karma Tamsayılı Doğrusal Programlama

Türkiye'de İllerin Sektörel İstihdam Payının Kümeleme Analizi ile İncelenmesi

Öz

Ekonomik kalkınma ve bölgesel kalkınma sürecinde bölgesel dinamiklerin önemi; rekabet gücü, insan kaynağı gelişimi ve küresel pazarın gözlemlenmesi gibi önemli faktörlerin bir sonucu olarak artmıştır. Bu çalışmada, sektörel istihdam oranlarına göre Türkiye’deki bölgeleri gruplayabilmek için matematiksel programlama tabanlı kümele analizi yapılmıştır. Küme içi uzaklığın en büyüğünü minimize ederken küme dışı uzaklıkların en küçüğünü maksimize eden karma tamsayılı bir matematiksel model sunulmuştur. Türkiye'deki 26 Düzey 2 bölgesi, 2021 ve 2022 yılları sektörel istihdam verilerine göre kümelenmiştir. Sonuç olarak ülkemizde sektörel bazda cinsiyete göre istihdam durumuna göre her iki yıl için de iki küme elde edilmiştir. Bunlardan biri tarım sektörünün istihdam oranının diğer sektörlere göre daha yüksek olduğu diğeri ise sanayi ve hizmet sektörleri istihdam oranının daha yüksek olduğu kümelerdir. 2021 yılı ve 2022 yılı kümeleri karşılaştırıldığınde toplamda, TR22, TR32, TR33, TRC3; erkeklerde, TR21, TR22, TR32, TR52, TR81; kadınlarda ise TRC1 bölgelerinin farklı kümelere atandığı görülmüştür. Ulusal hükümet genelinde başarılı bir istihdam politikasının, insan kaynaklarının geliştirilmesinin uygulanmasıyla, Türkiye'nin çeşitli coğrafi bölgelerinde yer alan illerin dengeli büyümesinin sağlanması mümkün olacaktır.

Anahtar Kelimeler

• İstihdam Oranı
• Sektörel İstihdam Payı
• Kümeleme Analizi
• Matematiksel Programlama
• Mixed Integer Linear Programming

Kaynakça

1. Agarwal, G. and Kempe, D. (2008). Modularity-maximizing graph communities via mathematical programming. The European Physical Journal B, 66, 409-418. https://doi.org/10.1140/epjb/e2008-00425-1
2. Ágoston, K. C. and Nagy, M. E. (2023). Mixed integer linear programming formulation for K-means clustering problem. Central European Journal of Operations Research, 32, 1-17. https://doi.org/10.1007/s10100-023-00881-1
3. Benati, S. and García, S. (2014). A mixed integer linear model for clustering with variable selection. Computers & Operations Research, 43, 280-285. http://dx.doi.org/10.1016/j.cor.2013.10.005
4. Benati, S., García, S. and Puerto, J. (2018). Mixed integer linear programming and heuristic methods for feature selection in clustering. Journal of the Operational Research Society, 69(9), 1379-1395. https://doi.org/10.1080/01605682.2017.1398206
5. Cafieri, S. and Hansen, P. (2014). In models, algorithms and technologies for network analysis: From the third international conference on network analysis. Switzerland: Springer International Publishing.
6. Caramia, M. and Pizzari, E. (2022). Clustering, location, and allocation in two stage supply chain for waste management: A fractional programming approach. Computers & Industrial Engineering, 169, 108297. https://doi.org/10.1016/j.cie.2022.108297 Çamlica, Z. and Şenkayas, H. (2020). Kümelenme potansiyelinin belirlenmesi: TR32 bölgesi imalat sektörlerinde bir uygulama. Yaşar Üniversitesi E-Dergisi, 15, 88-105. https://doi.org/10.19168/jyasar.657876
7. Ferreira, L. and Hitchcock, D. B. (2009). A comparison of hierarchical methods for clustering functional data. Communications in Statistics-Simulation and Computation, 38(9), 1925-1949. https://doi.org/10.1080/03610910903168603
8. Florez, L. and Cortissoz, J. C. (2017). Using workers compatibility to predict labor productivity through cluster analysis. Procedia Engineering, 196, 359-365. doi: 10.1016/j.proeng.2017.07.211

9. Gürler, C. (2023). Ülkelerin turizm potansiyellerine göre K-ortalamalar yöntemi kullanılarak kümelenmesi. MANAS Sosyal Araştırmalar Dergisi, 12(1), 353-363. https://doi.org/10.33206/mjss.1082471
10. Hansen, P. and Jaumard, B. (1997). Cluster analysis and mathematical programming. Mathematical programming, 79(1-3), 191-215. http:// doi.org/10.1007/BF02614317
11. Karlilar, S. and Kiral, G. (2019). Kadın işgücüne katılımı ve ekonomik büyüme arasındaki ilişki: Ülke grupları için panel veri analizi. Third Sector Social Economic Review, 54(2), 935-948. http:doi.org/10.15659/3.sektor-sosyal-ekonomi.19.04.1099
12. Klein, G. and Aronson, J. E. (1991). Optimal clustering: A model and method. Naval Research Logistics (NRL), 38(3), 447-461. https://doi.org/10.1002/1520-6750(199106)38:3<447::AID-NAV3220380312>3.0.CO;2-0
13. Kusiak, A. (1984). Analysis of integer programming formulations of clustering problems. Image and Vision Computing, 2(1), 35-40. https://doi.org/10.1016/0262-8856(84)90042-8
14. McDermott, A. M., Heffernan, M. and Beynon, M. J. (2013). When the nature of employment matters in the employment relationship: A cluster analysis of psychological contracts and organizational commitment in the non-profit sector. The International Journal of Human Resource Management, 24(7), 1490-1518. https://doi.org/10.1080/09585192.2012.723635
15. Mehrotra, A. and Trick, M. A. (1998). Cliques and clustering: A combinatorial approach. Operations Research Letters, 22(1), 1-12. https://doi.org/10.1016/S0167-6377(98)00006-6
16. Mokhtarzadeh, M., Tavakkoli-Moghaddam, R., Triki, C. and Rahimi, Y. (2021). A hybrid of clustering and meta-heuristic algorithms to solve a p-mobile hub location–allocation problem with the depreciation cost of hub facilities. Engineering Applications of Artificial Intelligence, 98, 104-121. https://doi.org/10.1016/j.engappai.2020.104121
17. Muntaner, C., Chung, H., Benach, J. and Ng, E. (2012). Hierarchical cluster analysis of labour market regulations and population health: a taxonomy of low-and middle-income countries. BMC Public Health, 12(1), 1-15. Erişim adresi: http://www.biomedcentral.com/1471-2458/12/286
18. Pașnicu, D., Pîrciog, S., Ghinăraru, C. and Tudose, G. (2014). Cluster analysis of the EU countries in terms of labor market indicators. International Conferans on Procedia of Economics and Business Administration, 0(0), 222-229. https://icesba.eu/RePEc/icb/wpaper/ICESBA2014_27PASNICU_ P222-229.pdf
19. Pirim, H., Ekşioğlu, B., Perkins, A. D. and Yüceer, Ç. (2012). Clustering of high throughput gene expression data. Computers & Operations Research, 39(12), 3046-3061. doi: 10.1016/j.cor.2012.03.008
20. Pirim, H., Eksioglu, B. and Glover, F. W. (2018). A novel mixed integer linear programming model for clustering relational networks. Journal of Optimization Theory and Applications, 176, 492-508. https://doi.org/10.1007/s10957-017-1213-1
21. Puerto, J., Rodríguez-Madrena, M. and Scozzari, A. (2020). Clustering and portfolio selection problems: A unified framework. Computers & Operations Research, 117, 104891. https://doi.org/10.1016/j.cor.2020.104891
22. Rao, M. R. (1971). Cluster analysis and mathematical programming. Journal of the American Statistical Association, 66(335), 622-626. Retrieved from https://www.tandfonline.com/doi/abs/10.1080/01621459.1971.10482319
23. Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster Analysis. Journal of Computational and Applied Mathematics, 20, 53-65. https://doi.org/10.1016/0377-0427(87)90125-7
24. Sağlam, B., Salman, F. S., Sayın, S. and Türkay, M. (2006). A mixed-integer programming approach to the clustering problem with an application in customer segmentation. European Journal of Operational Research, 173(3), 866-879. https://doi.org/10.1016/j.ejor.2005.04.048
25. Seki, İ. and Arslan, M. (2018). TRC2 (Diyarbakır–Şanlıurfa) Bölgesi kümelenme potansiyeli analizi. Al Farabi Uluslararası Sosyal Bilimler Dergisi, 2(1), 32-53. Retrieved from https://dergipark.org.tr/en/download/article-file/431072
26. Sema, A. Y. (2012). Türkiye’de işsizliğin nedenleri: istihdam politikaları üzerine bir değerlendirme. Yönetim ve Ekonomi Dergisi, 19(2), 321-341. Retrieved from https://dergipark.org.tr/en/download/article-file/146108
27. Vinod, H. D. (1969). Integer programming and the theory of grouping. Journal of the American Statistical association, 64(326), 506-519. Retrieved from https://www.tandfonline.com/doi/abs/10.1080/01621459.1969.10500990
28. Yamaç, B. (2019). Tekstil sektöründe kümelenme Türkiye tekstil sektörü örneği. Kahramanmaraş Sütçü İmam Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 9(2), 215-232. Retrieved from http://iibfdergisi.ksu.edu.tr/en/download/article-file/906847
29. Yaprakli, S. and Aslan, Ö. F. (2023). Tra1 bölgesi illerinde (Erzurum, Erzincan, Bayburt) kümelenme potansiyeli ve yerel rekabet gücü: Üç yıldız analizine dayalı bir saha araştırması. Anadolu Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 24(2), 429-458. https://doi.org/10.53443/anadoluibfd.1115658
30. Wang, S., Zhang, W., Bie, Y., Wang, K. and Diabat, A. (2019). Mixed-integer second-order cone programming model for bus route clustering problem. Transportation Research Part C: Emerging Technologies, 102, 351-369. https://doi.org/10.1016/j.trc.2019.03.019
31. Xu, G., Tsoka, S. and Papageorgiou, L. G. (2007). Finding community structures in complex networks using mixed integer optimisation. The European Physical Journal B, 60, 231-239. Retrieved from https://link.springer.com/article/10.1140/epjb/e2007-00331-0