Açığa Dikkat Et: Avrupa’da Kamu Eğitim Harcamaları ve Toplumsal Cinsiyete Duyarlı İstihdam

Year 2025, Volume: 9 Issue: Toplumsal Cinsiyet Özel Sayısı, 429 - 440, 27.09.2025

İrem Yendi Çetin

https://doi.org/10.25295/fsecon.1707222

Abstract

Toplumsal cinsiyet temelli istihdam farklarının azaltılması, sürdürülebilir kalkınma ve eşitlikçi ekonomik büyüme açısından önem taşımaktadır. Eğitim politikaları ise, kadınların beşerî sermayelerini artırarak işgücü piyasasına erişimlerini kolaylaştıran temel araçlar arasında yer almaktadır. Kamu eğitim harcamalarının kadın ve erkek istihdam oranları arasındaki fark üzerindeki etkisi hem harcamanın türüne hem de hedeflenen eğitim düzeyine göre farklılık gösterebilir. Bu çerçevede, çalışmada, Avrupa ülkelerinde kamu eğitim harcamalarının toplumsal cinsiyet temelli istihdam açığı üzerindeki etkileri Panel ARDL yöntemiyle analiz edilmektedir. Çalışmada, 30 Avrupa ülkesinin 2014–2022 yılları arasındaki verileri kullanılarak analiz yapılmıştır. Elde edilen bulgular, özellikle ortaöğretim ve yükseköğretime yönelik kamu harcamalarının uzun vadede toplumsal cinsiyet temelli istihdam farkını azaltmada anlamlı ve istatistiksel olarak güçlü bir etkiye sahip olduğunu ortaya koymaktadır. Buna karşılık, eğitim sübvansiyonlarının uzun vadeli etkisi istatistiksel olarak anlamlı bulunmamıştır. Sonuçlar, kamu eğitim politikalarının yalnızca toplam harcama miktarına odaklanmakla kalmayıp, harcamanın niteliği, hedef kitlesi ve eğitim düzeyi gibi boyutlarının da dikkate alınması gerektiğine işaret etmektedir. Bu bağlamda çalışma, kamu eğitim harcamalarının toplumsal cinsiyet eşitliği üzerindeki ayrıştırılmış etkilerine ilişkin literatürdeki boşluğu doldurmayı ve cinsiyete duyarlı kamu politikalarının tasarımına yönelik kanıta dayalı katkılar sunmayı amaçlamaktadır.

Keywords

Toplumsal Cinsiyete Duyarlı İstihdam Açığı , Kamu Eğitim Harcamaları , Panel ARDL

Mind The Gap: Public Education Expenditures and Gender Employment in Europe

Abstract

Reducing gender-based employment gaps is vital for sustainable development and equitable economic growth. Education policies are among the key tools for increasing women's human capital and facilitating their access to the labor market. The impact of public education expenditures on the gender gap in employment rates may vary depending on both the type of expenditure and the targeted level of education. In this context, the study analyzes the effects of public education expenditures on the gender-based employment gap in European countries using the Panel ARDL method. The study analyzes data from 30 European countries between 2014 and 2022. The findings reveal that public spending on secondary and higher education, in particular, has a significant and statistically strong effect on reducing the gender-based employment gap in the long term. In contrast, the long-term effect of education subsidies was not found to be statistically significant. The results indicate that public education policies should not only focus on the total amount of spending but also consider the quality of spending, the target audience, and the level of education. In this context, the study aims to fill the gap in the literature on the disaggregated effects of public education spending on gender equality and to provide evidence-based contributions to the design of gender-sensitive public policies.

Keywords

Gender Sensitive Employment Gap , Public Education Expenditures , Panel ARDL

References

• Acosta-Ballesteros, J., Osorno-Del Rosal, M. D. P., & Rodríguez-Rodríguez, O. M. (2024). Gender occupational segregation: A new approach to quantifying the effect of educational segregation. Applied Economics, 56(21), 2075–2089. https://doi.org/10.1080/00036846.2024.2423067
• Afonso, A., & Blanco-Arana, M. C. (2025). The persistence of gender pay and employment gaps in European countries. Comparative Economic Studies, Advance online publication, 1–29. https://doi.org/10.1057/s41294-025-00252-6
• Aslam, M., & Kingdon, G. G. (2008). Gender and household education expenditure in Pakistan. Applied Economics, 40(20), 2573–2591. https://doi.org/10.1080/00036840600970252
• Azam, M., & Kingdon, G. G. (2013). Are girls the fairer sex in India? Revisiting intra-household allocation of education expenditure. World Development, 42, 143-164. https://doi.org/10.1016/j.worlddev.2012.09.003
• Berg, J., Hilal, A., El, S., & Horne, R. (2021). World employment and social outlook: Trends 2021. International Labour Organization, 130. https://doi.org/10.54394/dspl5113
• Blackburne, E. F., & Frank, M. W. (2007). Estimation of nonstationary heterogeneous panels. Stata Journal, 7(2), 197–208. https://doi.org/10.1177/1536867x0700700204
• Bussemakers, C., Van Oosterhout, K., Kraaykamp, G., & Spierings, N. (2017). Women’s worldwide education–employment connection: a multilevel analysis of the moderating impact of economic, political, and cultural contexts. World Development, 99, 28-41. https://doi.org/10.1016/j.worlddev.2017.07.002
• Cavalcanti, T. V. de V., & Tavares, J. (2016). The output cost of gender discrimination: A model-based macroeconomic estimate. The Economic Journal, 126(590), 109–134. https://doi.org/10.1111/ecoj.12303
• Cubas, G. (2016). Distortions, infrastructure, and female labor supply in developing countries. European Economic Review, 87, 194–216. https://doi.org/10.1016/j.euroecorev.2016.05.006
• Eurostat. (2025). Employment- Annual Statistics. Retrieved April 15, 2025 from https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Employment_-_annual_statistics
• Gharehgozli, O., & Atal, V. (2021). Gender wage gap: Within and across borders. Studies in Business and Economics, 16(1), 29–38. https://doi.org/10.2478/sbe-2021-0025
• Klasen, S., & Lamanna, F. (2009). The impact of gender inequality in education and employment on economic growth: New evidence for a panel of countries. Feminist Economics, 15(3), 91–132. https://doi.org/10.1080/13545700902893106
• Law, Y. S., & Wye, C. K. (2023). The effects of fertility on female labour force participation in OECD countries: the role of education and health. Studies in Economics and Econometrics, 47(3), 280-302. https://doi.org/10.1080/03796205.2023.2218055
• Lepinteur, A., & Nieto, A. (2025). All about the money? The gendered effect of education on industrial and occupational sorting. Labour Economics, 92, 102670. https://doi.org/10.1016/j.labeco.2024.102670
• Levin, A., Lin, C. F., & Chu, C. S. J. (2002). Unit root tests in panel data: Asymptotic and finite-sample properties. Journal of Econometrics, 108(1), 1–24. https://doi.org/10.1016/s0304-4076(01)00098-7
• Maddala, G. S., & Wu, S. (1999). A comparative study of unit root tests with panel data and a new simple test. Oxford Bulletin of Economics and Statistics, 61(S1), 631–652. https://doi.org/10.1111/1468-0084.0610s1631
• Mbodji, Y. C. (2023). Effects of public expenditure on education on gender inequality in education in sub‐Saharan Africa (SSA). Review of Education, 11(2), e3398. https://doi.org/10.1002/rev3.3398
• Mukhopadhyay, U. (2022). Gender gap in schooling and wages: Effects of foreign capital and education subsidies. Review of Economics, 73(1), 3–26. https://doi.org/10.1515/roe-2021-0043
• Mukhopadhyay, U. (2024). Gender-neutral vs gender-specific education subsidy: effects on schooling and earning inequality. International Journal of Social Economics. https://doi.org/10.1108/ijse-07-2023-0554
• Pedroni, P. (2004). Panel cointegration: Asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Econometric Theory, 20(3), 579–625. https://doi.org/10.1017/s0266466604203073
• Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels. Cambridge Working Papers. Economics, 1240(1), 1. https://doi.org/10.2139/ssrn.572504
• Pesaran, M. H., & 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
• Pesaran, M. H., & Yamagata, T. (2008). Testing slope homogeneity in large panels. Journal of econometrics, 142(1), 50-93. https://doi.org/10.1016/j.jeconom.2007.05.010
• Pesaran, M. H., Shin, Y., & Smith, R. P. (1999). Pooled mean group estimation of dynamic heterogeneous panels. Journal of the American Statistical Association, 94(446), 621–634. https://doi.org/10.1080/01621459.1999.10474156
• Peter, F., Schober, P., & Spiess, C. K. (2024). Information intervention on long-term earnings prospects and the gender gap in major choice. European Sociological Review, 40(2), 258–275. https://doi.org/10.1093/esr/jcad055
• Rashmi, R., Malik, B. K., Mohanty, S. K., Mishra, U. S., & Subramanian, S. V. (2022). Predictors of the gender gap in household educational spending among school and college-going children in India. Humanities and Social Sciences Communications, 9(1), 1–11. https://doi.org/10.1057/s41599-022-01350-x
• Rubery, J., & Tavora, I. (2020). The Covid-19 crisis and gender equality: risks and opportunities. Social policy in the European Union: State of play, 71-96. https://doi.org/10.1787/ee4cd4f4-en
• Singh, S. P., Singh, P., & Komaraiah, J. B. (2023). Gender discrimination and inequalities in education expenditure in India. International Journal of Social Economics, 50(9), 1323-1340. https://doi.org/10.1108/ijse-08-2022-0537
• Tekgüç, H., Eryar, D., & Cindoğlu, D. (2017). Women’s tertiary education masks the gender wage gap in Turkey. Journal of Labor Research, 38, 263–286. https://doi.org/10.1007/s12122-017-9243-x
• Voumik, L. C., Rahman, M. H., Islam, M. A., Chowdhury, M. A. S., & Zimon, G. (2023). The impact of female education, trade openness, per capita GDP, and urbanization on women’s employment in South Asia: application of CS-ARDL model. Systems, 11(2), 97. https://doi.org/10.3390/systems11020097