Cilt 13 Sayı 1 (2025): Business & Management Studies: An International Journal
Makaleler

NEET’i etkileyen makroekonomik göstergeler: BRICST ülkeleri için panel veri analizi

PDF (İngilizce)
  • Eylül Kabakçı Günay
Eylül Kabakçı Günay
Doç. Dr., İzmir Demokrasi Üniversitesi, İzmir, Türkiye

Yayınlanmış 25.03.2025

Anahtar Kelimeler

  • NEET, Augmented Mean Group Estimator, BRICST, Macroeconomic Indicators
  • NEET, Arttırılmış Ortalama Grup Tahmincisi,BRICST, Makroekonomik Göstergeler

Nasıl Atıf Yapılır

NEET’i etkileyen makroekonomik göstergeler: BRICST ülkeleri için panel veri analizi. (2025). Business & Management Studies: An International Journal, 13(1), 229-242. https://doi.org/10.15295/bmij.v13i1.2516

Telif Hakkı (c) 2025 Eylül Kabakçı Günay

Creative Commons License

Bu çalışma Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License ile lisanslanmıştır.

Nasıl Atıf Yapılır

NEET’i etkileyen makroekonomik göstergeler: BRICST ülkeleri için panel veri analizi. (2025). Business & Management Studies: An International Journal, 13(1), 229-242. https://doi.org/10.15295/bmij.v13i1.2516

Öz

Bu çalışmada 1999-2023 yılı arasındaki veriler kullanılarak Brezilya, Rusya, Hindistan, Çin, Güney Afrika ve Türkiye için kişi başına düşen reel GSYİH (GDP), enflasyon oranı (INF), eğitim harcamalarının GSMH’a oranı (EDU), ve ücretli ve maaşlı çalışan işçilerin toplam işgücüne oranı (WAGE) gibi değişkenlerin NEET oranlarına olan etkisi Arttırılmış Ortalama Grup tahmincisi yardımıyla araştırılmıştır. Test sonuçlarına göre Hindistan ve Çin’de GDP’de görülen %1’lik artış NEET oranlarını sırasıyla %0.008 ve %0.0009 oranında azaltmaktadır. INF’da görülen %1’lik artış ise Rusya ve Hindistan’da NEET oranlarını %0.029 ve %0.424%oranında artırmaktadır. EDU’da görülen %1’lik artış Rusya ve Türkiye’de NEET oranlarını %2 ve %7 oranında azaltırken; Çin, Brezilya ve Güney Afrika’da NEET oranlarını sırasıyla %9, %3 ve %0.003 oranında artırmaktadır. WAGE’de görülen %1’lik bir artış, Rusya’da NEET oranlarını %0.5 Güney Afrika’da ise %0.11 oranında azaltmaktadır. Ancak Hindistan’da WAGE’de görülen %1’lik artış artış NEET oranını %1.2 oranında artırmaktadır. Çalışma, BRICST ülkeleri için söz konusu değişkenlerin NEET oranlarını azaltmak için kullanışlı araçlar olduğunu ortaya çıkarmıştır.

PDF (İngilizce)

Referanslar

  1. Baltagi, B. H., Feng, Q., and Kao, C. (2012). A Lagrange multiplier test for cross-sectional dependence in a fixed effects panel data model. Journal of Econometrics, 170(1), 164–177. https://doi.org/10.1016/j.jeconom.2012.04.004
  2. Bardak, U., Maseda, M. R., and Rosso, F. (2015). Young people not in employment, education or training (NEET): An overview in ETF partner countries. European Training Foundation.
  3. Bell, D., and Blanchflower, D. (2011). Young people and the Great Recession. Oxford Review of Economic Policy, 27(2), 241–267.
  4. Berloffa, G., Matteazzi, E., and Villa, P. (2019). Youth inactivity and social exclusion in Southern Europe. Cambridge Journal of Economics, 43(4), 1019-1046.
  5. Bingöl, U. (2020). The macroeconomic determinants of NEET: A panel data analysis for Fragile Five countries and Russia. Yönetim Ve Ekonomi Araştırmaları Dergisi, 18(4), 173-189. https://doi.org/10.11611/yead.822305
  6. Blanchard, O. (2018). Macroeconomics: A European perspective. Pearson.
  7. Bond, S.R., and Eberhardt, M. (2013). Accounting for unobserved heterogeneity in panel time series models. Unpublished manuscript, University of Oxford. https://www.semanticscholar.org/paper/Accounting-for-unobserved-heterogeneity-in-panel-%E2%88%97-Bond-Eberhardt/2e60ef67b62aaeb2e6db945cb8d59001b587c5c3
  8. Bynner, J., and Parsons, S. (2002). Social exclusion and the transition from school to work: The case of young people not in education, employment, or training (NEET). Journal of Vocational Behavior, 60(2), 289-309.
  9. Eberhardt, M., and Bond, S. (2009). Cross-section dependence in nonstationary panel models: A novel estimator. MPRA Paper 17692. http://mpra.ub.unimuenchen.de/17692.pdf
  10. Eberhardt, M., and Teal, F. (2010). Productivity analysis in global manufacturing production. University of Oxford Discussion Paper, 515.
  11. Eurofound. (2021). NEETs in Europe: A social and economic analysis. European Foundation for the Improvement of Living and Working Conditions.
  12. Eurostat. (2024). Youth unemployment and NEET statistics. https://ec.europa.eu/eurostat/databrowser/view/lfsi_neet_a/default/table?lang=en
  13. Furlong, A. (2006). Not a very NEET solution: representing problematic labour market transitions among early school-leavers. Work, Employment and Society, 20(3), 553-569. https://doi.org/10.1177/0950017006067001
  14. Görlich, D., Stepanok, I., and Al-Hussami, F. (2013). Youth unemployment in Europe and the world: Causes, consequences, and solutions. IZA Journal of Labor Policy, 2(1), 1–25.
  15. Hämäläinen, R., Kiili, C. and Smith, B.E. (2017), Orchestrating 21st century learning in higher education: A perspective on student voice. Br J Educ Technol, 48: 1106-1118. https://doi.org/10.1111/bjet.12533
  16. Hu, D., Li, H., Li, T., Meng, L., and Nguyen, B. T. (2023). The burden of education costs in China: A struggle for all, but heavier for lower-income families. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4558282
  17. ILO. (2023). Global employment trends for youth 2024: In figures. https://www.ilo.org/resource/article/global-employment-trends-youth-2024-figures
  18. Kahn, L. (2015). The long-term labor market consequences of graduating from college in a bad economy. Labour Economics, 17(2), 303–316.
  19. Martin, J. P. (2015). Activation and active labour market policies in OECD countries: Stylised facts and evidence on their effectiveness. IZA Journal of Labor Policy, 4(1), 1–29.
  20. Maynou, L., Ordóñez, J., and Silva, J. I. (2022). Convergence and determinants of young people not in employment, education or training: An European regional analysis. Economic Modelling, 110, 105808. https://doi.org/10.1016/j.econmod.2022.105808
  21. McQuaid, R., Fuertes, V., and Richard, A. (2012). Education and skills mismatch in the labor market. Journal of Vocational Behavior, 80(3), 485–495.
  22. OECD. (2016). Employment outlook. https://www.oecd.org/en/publications/oecd-employment-outlook-2016_empl_outlook-2016-en.html
  23. O’Higgins, N. (2017). Rising youth unemployment during the Great Recession: Evidence from Europe. International Labour Review, 156(2), 247–275.
  24. Quintini, G. and S. Martin (2014). Same same but different: school-to-work transitions in emerging and advanced economies. OECD Social, Employment and Migration Working Papers, No. 154, OECD Publishing, Paris, https://doi.org/10.1787/5jzbb2t1rcwc-en
  25. Pedroni, P. (1999). Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and Statistics, 61(S1), 653–670. https://doi.org/10.1111/1468-0084.61.s1.14
  26. Pesaran, M. H., and Smith, R. (1995). Estimating long-run relationships from dynamic heterogeneous panels. Journal of Econometrics, 68(1), 79–113. https://doi.org/10.1016/0304-4076(94)01644-F
  27. Pesaran, M. H., and Shin, Y. (1996). Cointegration and speed of convergence to equilibrium. Journal of Econometrics, 71(1–2), 117–143. https://doi.org/10.1016/0304-4076(94)01697-6
  28. Pesaran, M. H. (2003). A simple panel unit root test in the presence of cross-section dependence. Cambridge Working Papers in Economics, 0346(1), Faculty of Economics, University of Cambridge. https://ideas.repec.org/p/cam/camdae/0346.html
  29. Pesaran, M. H., (2004). General diagnostic tests for cross section dependence in panels. IZA, Discussion Paper No. 1240.
  30. Ripamonti, E., and Barberis, S. (2021). The association of economic and cultural capital with the NEET rate: Differential geographical and temporal patterns. Journal for Labour Market Research, 55(13). https://doi.org/10.1186/s12651-021-00296-y
  31. Scarpetta, S., Sonnet, A., and Manfredi, T. (2010). Rising youth unemployment during the crisis. OECD Social, Employment and Migration Working Papers, 106.
  32. Westerlund, J. (2007). Testing for error correction in panel data, Oxford Bulletin of Economics and Statistics, 69, 709-748.
  33. Westerlund, J., and Edgerton, D. L. (2008). A simple test for cointegration in dependent panels with structural breaks. Oxford Bulletin of Economics and Statistics, 70(5), 665–704. https://doi.org/10.1111/j.1468-0084.2008.00513.x
  34. World Bank. (2023). World development indicators. https://databank.worldbank.org/source/world-development-indicators.

Benzer Makaleler

Bu makale için ayrıca gelişmiş bir benzerlik araması başlat yapabilirsiniz.