Journals / Ekonomik ve Sosyal Araştırmalar Dergisi / 2025 / Cilt: 21 Sayı: 2

Implications for Human Resources Management from Labour Force Participation Rates of Countries by Education Level

Eğitim Seviyesine Göre Ülkelerin İşgücüne Katılım Oranlarından İnsan Kaynakları Yönetimi İçin Çıkarımlar

Tuğçe Şimşek(GÜMÜŞHANE ÜNİVERSİTESİ, GÜMÜŞHANE İKTİSADİ VE İDARİ BİLİMLER FAKÜLTESİ)0000-0003-3256-4348
Pages
437–458
Publication Date
2025-12-29
DOI
—
🏛️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)

The findings indicate a significant decline in participation among individuals with lower education levels, particularly in countries such as Croatia and Latvia, whereas participation rates for highly educated individuals remain relatively stable.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Labour force participation rates are a key indicator of economic structure, human capital development, and employment dynamics, with education level playing a crucial role in shaping workforce accessibility and productivity.
📊
Key Findings & Data:This study examines the labour force participation rates across different education levels (basic, intermediate, high) in 34 European and surrounding countries between 2015 and 2023, aiming to provide strategic insights for human resources management (HRM).
💡
Conclusion & Impact:The findings indicate a significant decline in participation among individuals with lower education levels, particularly in countries such as Croatia and Latvia, whereas participation rates for highly educated individuals remain relatively stable.
🔬Design:Modeling & Simulation
📍Scope:2015 Verileri
⚙️Variables:İşgücüne Katılım · Eğitim Düzeyleri · İnsan Kaynakları Yönetimi

Abstract

Labour force participation rates are a key indicator of economic structure, human capital development, and employment dynamics, with education level playing a crucial role in shaping workforce accessibility and productivity. This study examines the labour force participation rates across different education levels (basic, intermediate, high) in 34 European and surrounding countries between 2015 and 2023, aiming to provide strategic insights for human resources management (HRM). Using a quantitative approach, the study employs descriptive statistics and K-means clustering to analyse workforce segmentation, while Silhouette Scores assess the degree of convergence among countries over time. The findings indicate a significant decline in participation among individuals with lower education levels, particularly in countries such as Croatia and Latvia, whereas participation rates for highly educated individuals remain relatively stable. Additionally, clustering results suggest a gradual convergence in labour force dynamics, with certain countries shifting between clusters, reflecting structural shifts in workforce composition. These results highlight the need for targeted reskilling programs, flexible employment models, and education-driven labour policies to mitigate participation disparities. The study underscores the importance of international talent mobility, workforce digitalisation, and skill development initiatives in optimising HRM strategies. By providing an education-based labour market analysis, this research contributes to data-driven workforce planning and global HRM decision-making.

Özet

İşgücüne katılım oranları ekonomik yapı, beşeri sermaye gelişimi ve istihdam dinamiklerinin önemli bir göstergesidir ve eğitim seviyesi işgücüne erişilebilirlik ve verimliliği şekillendirmede önemli bir rol oynamaktadır. Bu çalışma, insan kaynakları yönetimi (İKY) için stratejik öngörüler sağlamayı amaçlayarak, 2015-2023 yılları arasında 34 Avrupa ve çevre ülkesinde farklı eğitim seviyelerinde (temel, orta, yüksek) işgücüne katılım oranlarını incelemektedir. Nicel bir yaklaşım kullanan çalışmada, işgücü segmentasyonunu analiz etmek için tanımlayıcı istatistikler ve K-ortalamalar kümelemesi kullanılırken, Siluet Skorları zaman içinde ülkeler arasındaki yakınsama derecesini değerlendirmektedir. Bulgular, özellikle Hırvatistan ve Letonya gibi ülkelerde daha düşük eğitim seviyesine sahip bireyler arasında katılımda önemli bir düşüş olduğunu gösterirken, yüksek eğitimli bireyler için katılım oranları nispeten sabit kalmaktadır. Ayrıca, kümeleme sonuçları işgücü dinamiklerinde kademeli bir yakınsama olduğunu ve bazı ülkelerin kümeler arasında geçiş yaparak işgücü kompozisyonundaki yapısal değişimleri yansıttığını göstermektedir. Bu sonuçlar, katılım eşitsizliklerini azaltmak için hedefe yönelik yeniden beceri kazandırma programlarına, esnek istihdam modellerine ve eğitim odaklı işgücü politikalarına duyulan ihtiyacı vurgulamaktadır. Çalışma, İKY stratejilerinin optimize edilmesinde uluslararası yetenek hareketliliğinin, işgücünün dijitalleşmesinin ve beceri geliştirme girişimlerinin öneminin altını çizmektedir. Bu araştırma, eğitim temelli bir işgücü piyasası analizi sunarak, veri odaklı işgücü planlamasına ve küresel İKY karar alma süreçlerine katkıda bulunmaktadır.

🔖

Cite This Article

Şimşek, T. (2025). Implications for Human Resources Management from Labour Force Participation Rates of Countries by Education Level. Ekonomik ve Sosyal Araştırmalar Dergisi, Cilt: 21 Sayı: 2, 437–458. https://ajindex.com/articles/1217928

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (53)

  1. Akdemir, B., & Duman, M. Ç. (2017). KADIN ÇALIŞANLARIN PERFORMANSINDA CAM TAVAN SENDROMU ENGELİ! International Journal of Academic Value Studies, 3(15), 517–526. www.javstudies.com
  2. Angrist, N., Djankov, S., Goldberg, P. K., & Patrinos, H. A. (2021). Measuring human capital using global learning data. Nature, 592(7854), 403–408. https://doi.org/10.1038/s41586-021-03323-7
  3. Becker, G. S. (1964). Human Capital: A Theoretical and Empirical Analysis, with Special Reference to Education. University of Chicago Press.
  4. Becker, R. (2019). Economic change and continuous vocational training in the work history: a longitudinal multilevel analysis of the employees’ participation in further training and the effects on their occupational careers in Germany, 1970–2008. Empirical Research in Vocational Education and Training, 11(1), 13–14. https://doi.org/10.1186/s40461-019-0079-x
  5. Becker, R., & Blossfeld, H.-P. (2017). Entry of men into the labour market in West Germany and their career mobility (1945–2008). Journal for Labour Market Research, 50(1), 113–130. https://doi.org/10.1007/s12651-017-0224-6
  6. Buchmann, M., & Malti, T. (2012). The future of young women’s economic role in a globalized economy: New opportunities, persisting constraints. New Directions for Youth Development, 2012(135), 77–86. https://doi.org/10.1002/yd.20030
  7. Burdorf, A., Fernandes, R. C. P., & Robroek, S. J. W. (2023). Health and inclusive labour force participation. The Lancet, 402(10410), 1382–1392. https://doi.org/10.1016/S0140-6736(23)00868-1
  8. Busse, R., Sun, L., & Zhu, V. (2015). Comparing value orientations of German and Chinese managers: impacts of demographic and business-related factors. Asia Pacific Business Review, 21(2), 170–187. https://doi.org/10.1080/13602381.2014.891400
  9. Carraher, S. M., Gibson, J. W., & Buckley, M. R. (2006). Compensation satisfaction in the Baltics and the USA. Baltic Journal of Management, 1(1), 7–23. https://doi.org/10.1108/17465260610640840
  10. Celebi, M. E., Kingravi, H. A., & Vela, P. A. (2013). A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert Systems with Applications, 40(1), 200–210. https://doi.org/10.1016/j.eswa.2012.07.021
  11. Chi, D. (2021). Research on the Application of K-Means Clustering Algorithm in Student Achievement. 2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE), 435–438. https://doi.org/10.1109/ICCECE51280.2021.9342164
  12. Ehsan, R., & Sloam, J. (2020). Resources, Values, Identity: Young Cosmopolitans and the Referendum on British Membership of the European Union. Parliamentary Affairs, 73(1), 46–65. https://doi.org/10.1093/pa/gsy035
  13. El Ouirdi, M., El Ouirdi, A., Segers, J., & Pais, I. (2016). Technology adoption in employee recruitment: The case of social media in Central and Eastern Europe. Computers in Human Behavior, 57, 240–249. https://doi.org/10.1016/j.chb.2015.12.043
  14. Englehardt, C. S., & Simmons, P. R. (2002). Organizational flexibility for a changing world. Leadership & Organization Development Journal, 23(3), 113–121. https://doi.org/10.1108/01437730210424057
  15. Fa’rifah, R. Y., & Pramesti, D. (2022). Cluster Analysis of Inclusive Economic Development Using K-Means Algorithm. Jurnal Varian, 5(2), 171–178. https://doi.org/10.30812/varian.v5i2.1894
  16. Fahim, A. M., Salem, A.-B. M., Torkey, F. A., & Ramadan, M. A. (2006). An efficient enhanced k-means clustering algorithm. Journal of Zhejiang University-SCIENCE A, 7(10), 1626–1633. https://doi.org/10.1631/jzus.2006.A1626
  17. Gibson, J. L. (2015). The Effects of Workforce Trends and Changes on Organizational Recruiting: A Practical Perspective. Industrial and Organizational Psychology, 8(3), 383–387. https://doi.org/10.1017/iop.2015.54
  18. Gurgu, E., & Savu, C. (2014). Human capital in the new economy. A post-revolutionary Romanian radiography. Contemporary Readings in Law and Social Justice, 6(1), 510.
  19. Hanushek, E. A., & Kimko, D. D. (2000). Schooling, Labor-Force Quality, and the Growth of Nations. American Economic Review, 90(5), 1184–1208. https://doi.org/10.1257/aer.90.5.1184
  20. Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31(8), 651–666. https://doi.org/10.1016/j.patrec.2009.09.011
  21. Jarinto, K., & Ridsomboon, L. (2024). “Clustering by Employee Personality”, Modern Working World Perspectives on Work Efficiency in the Organizations. WSEAS Transactions on Business and Economics, 21, 288–298. https://doi.org/10.37394/23207.2024.21.26
  22. Jegede, O., & Muchie, M. (2024). Introduction to the Special Issue: Leveraging Global Value Chains for innovation and industrialization in Africa. African Journal of Science, Technology, Innovation and Development, 16(4), 451–458. https://doi.org/10.1080/20421338.2024.2361952
  23. Kalleberg, A. L. (2001). Organizing Flexibility: The Flexible Firm in a New Century. British Journal of Industrial Relations, 39(4), 479–504. https://doi.org/10.1111/1467-8543.00211
  24. Kanungo, T., Mount, D. M., Netanyahu, N. S., Piatko, C. D., Silverman, R., & Wu, A. Y. (2002). An efficient k-means clustering algorithm: analysis and implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 881–892. https://doi.org/10.1109/TPAMI.2002.1017616
  25. Karaboga, D., & Ozturk, C. (2011). A novel clustering approach: Artificial Bee Colony (ABC) algorithm. Applied Soft Computing, 11(1), 652–657. https://doi.org/10.1016/j.asoc.2009.12.025
  26. Kumari, R. (2018). Economic growth, disparity, and determinants of female labor force participation. World Journal of Entrepreneurship, Management and Sustainable Development, 14(2), 138–152. https://doi.org/10.1108/WJEMSD-03-2017-0009
  27. Lu, H., Hou, L., Zhou, W., Shen, L., Jin, S., Wang, M., Shang, S., Cong, X., Jin, X., & Dou, D. (2021). Trends, composition and distribution of nurse workforce in China: a secondary analysis of national data from 2003 to 2018. BMJ Open, 11(10), 1–10. https://doi.org/10.1136/bmjopen-2020-047348
  28. Lv, W. (2011). Educational funds, educational level and human capital - Empirical analysis based on inter-province panel data of China. 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce, AIMSEC 2011 - Proceedings, 1997, 2987–2991. https://doi.org/10.1109/AIMSEC.2011.6010691
  29. Maket, L. J., Lamaon, L. G., & Kwonyike, J. (2015). Managing Diversity through Workplace Flexibility for Organizational Performance. International Journal of Academic Research in Business and Social Sciences, 5(4). https://doi.org/10.6007/IJARBSS/v5-i4/1581
  30. Marrocu, E., & Paci, R. (2012). Education or Creativity: What Matters Most for Economic Performance? Economic Geography, 88(4), 369–401. https://doi.org/10.1111/j.1944-8287.2012.01161.x
  31. Mok, K. H., Yu, K. M., & Ku, Y. wen. (2013). After massification: The quest for entrepreneurial universities and technological advancement in Taiwan. Journal of Higher Education Policy and Management, 35(3), 264–279. https://doi.org/10.1080/1360080X.2013.786857
  32. Muttaqin, M. F. J. (2022). Cluster Analysis Using K-Means Method to Classify Sumatera Regency and City Based on Human Development Index Indicator. Seminar Nasional Official Statistics, 2022(1), 967–976. https://doi.org/10.34123/semnasoffstat.v2022i1.1299
  33. Muttaqin, M. F. J., & Zulkarnain. (2020). Cluster Analysis Using K-Means Method to Classify Indonesia Regency/City based on Human Development Index Indicator. Proceedings of the 3rd Asia Pacific Conference on Research in Industrial and Systems Engineering 2020, 81–85. https://doi.org/10.1145/3400934.3400951
  34. Neethirajan, S. (2023). Artificial Intelligence and Sensor Technologies in Dairy Livestock Export: Charting a Digital Transformation. Sensors, 23(16). https://doi.org/10.3390/s23167045
  35. Osmancevic, S., Großschädl, F., & Lohrmann, C. (2023). Cultural competence among nursing students and nurses working in acute care settings: a cross-sectional study. BMC Health Services Research, 23(1), 1–7. https://doi.org/10.1186/s12913-023-09103-5
  36. Palpacuer, F. (2000). Competence-Based Strategies and Global Production Networks a Discussion of Current Changes and Their Implications for Employment. Competition & Change, 4(4), 353–400. https://doi.org/10.1177/102452940000400401
  37. Patzina, A., & Wydra-Somaggio, G. (2020). Early careers of dropouts from vocational training: Signals, human capital formation, and training firms. European Sociological Review, 36(5), 741–759. https://doi.org/10.1093/esr/jcaa011
  38. Peneder, M. (2007). A sectoral taxonomy of educational intensity. Empirica, 34(3), 189–212. https://doi.org/10.1007/s10663-007-9035-2
  39. Perez‐Arce, F., & Prados, M. J. (2021). THE DECLINE IN THE U.S. LABOR FORCE PARTICIPATION RATE: A LITERATURE REVIEW. Journal of Economic Surveys, 35(2), 615–652. https://doi.org/10.1111/joes.12402
  40. Pokharel, M., Bhatta, J., & Paudel, N. (2021). Comparative Analysis of K-Means and Enhanced K-Means Algorithms for Clustering. NUTA Journal, 8(1–2), 79–87. https://doi.org/10.3126/nutaj.v8i1-2.44044
  41. Schim, S. M., Doorenbos, A. Z., & Borse, N. N. (2005). Cultural competence among Ontario and Michigan healthcare providers. Journal of Nursing Scholarship, 37(4), 354–360. https://doi.org/10.1111/j.1547-5069.2005.00061.x
  42. Schömann, K., & Becker, R. (1995). Participation in Further Education over the Life Course: A Longitudinal Study of Three Birth Cohorts in the Federal Republic of Germany. European Sociological Review, 11(2), 187–208. https://doi.org/10.1093/oxfordjournals.esr.a036356
  43. Shamsuddinova, S. (2024). The European Youth Guarantee scheme: A viable solution to youth unemployment? International Review of Education, 70(5), 819–847. https://doi.org/10.1007/s11159-024-10075-9
  44. Sozen, H. C., Varoglu, D., Yeloglu, H. O., & Basim, H. N. (2016). Human or Social Resources Management: Which Conditions Force HR Departments to Select the Right Employees for Organizational Social Capital. European Management Review, 13(1), 3–18. https://doi.org/10.1111/emre.12063
  45. Stegmaier, J., Krekel, E. M., & Bellmann, L. (2010). Aus- und Weiterbildung - Komplemente oder Substitute? REPORT - Zeitschrift Für Weiterbildungsforschung, 93, 41–54. https://doi.org/10.3278/REP1001W041
  46. Tahsin, R., Rantu, S. B. A., Rahman, M., Salman, S., & Karim, M. R. (2025). Towards the adoption of AI, IoT, and Blockchain technologies in Bangladesh’s maritime industry: Challenges and insights. Results in Engineering, 25(December 2024), 103825. https://doi.org/10.1016/j.rineng.2024.103825
  47. Wahyuni, S. N., Khanom, N. N., & Astuti, Y. (2023). K-Means Algorithm Analysis for Election Cluster Prediction. JOIV: International Journal on Informatics Visualization, 7(1), 1. https://doi.org/10.30630/joiv.7.1.1107
  48. Wang, C. (2023). Development of Student Biochemical Index Monitoring System Based on K-means Cluster Analysis. 2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS), 1–5. https://doi.org/10.1109/ICICACS57338.2023.10100254
  49. Weir-Smith, G. (2018). Spatiotemporal Variation of South African Jobless Trends: Policy Directions. Professional Geographer, 70(1), 94–102. https://doi.org/10.1080/00330124.2017.1325748
  50. Yang, L., Peng, H., Yang, Y., Ouyang, L., & Li, Y. (2020). Situation and Countermeasures of the Management Team of the Elderly Care Institutions from the Perspective of the Combination of Medical and Health Care: A Cross-Sectional Study. Journal of Healthcare Engineering, 2020. https://doi.org/10.1155/2020/8826007
  51. Zamani, F. E., Kusnandar, T., Silmi, F. E., & Rachman, R. (2023). Analysis of Public Service Satisfaction using Artificial Intelligence K-Means Cluster. Majalah Bisnis & IPTEK, 16(1), 181–187. https://doi.org/10.55208/bistek.v16i1.428
  52. Zuzana, W. (2017). Comparison of requirements for brand managers responsible for competitiveness of brands: A cross-national study in the US and the Czech Republic. Journal of Competitiveness, 9(4), 148–163. https://doi.org/10.7441/joc.2017.04.10
  53. Wahyuni, S. N., Khanom, N. N., & Astuti, Y. (2023). K-Means Algorithm Analysis for Election Cluster Prediction. JOIV: International Journal on Informatics Visualization, 7(1), 1. https://doi.org/10.30630/joiv.7.1.1107