EĞİTİM KAZANÇLARI ETKİLER Mİ? TÜRKİYE’DEN BİR UYGULAMA

Year 2024, Volume: 25 Issue: 4, 417 - 436, 31.12.2024

Merve Çelik Keçili , Ethem Esen

https://doi.org/10.53443/anadoluibfd.1511052

Abstract

Eğitim, kazanç farklılıkları açısından önemli bir belirleyicidir. Mincer kazanç fonksiyonunda eğitim ve kazanç düzeyindeki farklılıklar araştırılmaktadır. Bu çalışmada, Türkiye'de eğitimin getirisi, Hanehalkı Bütçe İstatistikleri mikro veri setinin 2011-2019 yıllarına ait kesit verileri kullanılarak Mincer kazanç fonksiyonu ile araştırılmaktadır. Eğitimde getiri, yarı parametrik regresyon yöntemi ve genişletilmiş kontrol fonksiyonu uygulanarak tahmin edilmektedir. Araştırmada araç değişken olarak kontrol fonksiyonu yaklaşımı kullanılmış ve kontrol fonksiyonu olarak 1997 eğitim reformu uygulanmıştır. Araştırmadan elde edilen bulgular, eğitimin söz konusu dönemdeki kazanç düzeyini olumlu yönde etkilediğini göstermektedir. Yarıparametrik regresyon tahmin sonuçları bu etkinin her yılın belirli dönemlerinde daha yüksek olduğunu göstermektedir. Kontrol fonksiyonu yaklaşımı sonuçlarına göre etki, yarı parametrik test sonuçlarının aksine bazı dönemlerde negatiftir.

Keywords

Eğitimin Getirisi, İçsellik, Semiparametrik Regresyon, Kontrol Fonksiyonu Yaklaşımı

Ethical Statement

Etik beyan bulunmamaktadır.

Supporting Institution

Anadolu University Scientific Research Projects Commission

Project Number

1901E010

DOES EDUCATION AFFECT EARNINGS? AN APPLICATION FROM TÜRKİYE

Abstract

Education is an important determination for the earnings differentials. The differences in education and the earnings level are investigated in Mincer earnings function. In this study, return to education is investigated in Türkiye by Mincer earnings function using cross sectional data of the Household Budget Statistics micro data set in the years between 2011 and 2019. The return to education is estimated using semiparametric regression method and extended applying control function approach. The control function approach is used as instrumental variable and the 1997 education reform is applied as control function in the paper. The achieved findings of the study show that the education positively affects the earnings level in concerned period. Semiparametric regression estimation results indicate that this effect is higher for certain periods in each year. According to the control function approach results, the effect is negative in some periods contrary to semiparametric test results.

Keywords

Return to Education, Endogeneity, Semiparametric Regression, Control Function Approach

Project Number

1901E010

References

• Arce, I., Sperlich, S. A., & Fernandez, A. (2012). The semiparametric Juhn-Murphy- Pierce decomposition of the gender Pay gap with an application to Spain. In R. Mukherjee (Ed.), Wages and employment: Economics, structure and gender differences (p. 1-21). New York: Nova Science.
• Aydemir, A., & Kırdar, M. (2013). Estimates of the return to schooling in a developing country: Evidence from a major policy reform in Turkey (MPRA No. 51938). Retrieved from https://mpra.ub.uni-muenchen.de/51938/
• Azam, M. (2012). changes in wage structure in urban India, 1983–2004: A quantile regression decomposition. World Development, 40(6), 1135–1150. doi: 10.1016/j.worlddev.2012.02.002.
• Behrman, J., & Birdsall, N. (1983). The quality of schooling: Quantity alone is misleading. American Economic Review, 73(5), 928–946.
• Card, D., & Krueger, A. B. (1992). Does school quality matter? Returns to education and the characteristics of public schools in the United States. Journal of Political Economy, 100(1), 1–40.
• Dacuycuy, L. (2005). Is the earnings−schooling relationship linear? A semiparametric analysis. Economics Bulletin, 3(37), 1–8.
• Filiztekin, A. (2011). Social returns to education in a developing country (MPRA No. 35124). Retrieved from https://mpra.ub.uni-muenchen.de/35124/1/MPRA_paper_35124.pdf
• Furno, M. (2013). Quantile regression and structural change in the Italian wage equation. Economic Modelling, 30, 420–434. doi: 10.1016/j.econmod.2012.09.031.
• Gabbriellini, C. (2015). The determinants of the Italian wages (Unpublished Ph.D. thesis). Universita di Pisa, Pisa.
• Gujarati, D. (2015). Econometrics by example. New York: Palgrave Macmillan.
• Hastie, T., &Tibshirani, RJ. (1990). Generalized additive models. London: Chapman & Hall.
• Isfahani, D., Tunali, I., & Ragui, A. (2009). A comparative study of returns to education of urban men in Egypt, Iran, and Turkey. Middle East Development Journal, 1(2), 145–187.
• Keele, L. J. (2008). Semiparametric regression for the social sciences. Chichester: John Wiley.
• Kharbanda, V. (2014). Three essays on the labor market (Unpublished Ph.D. thesis). Graduate College of The University of Iowa, Iowa.
• Krueger, A. B. (1993). How computers have changed the wage structure: Evidence from microdata, 1984-1989. Quarterly Journal of Economics, 108(1), 33–60.
• MEB [Milli Eğitim Bakanlığı]. (2022). Eğitim Raporları. Retrieved from http://www.meb.gov.tr/egitim-raporlar%C4%B1/duyuru/6560.
• Mincer, J. (1974). Schooling, experience and earnings. National Bureau of Economic Research.
• Murphy, K. M., & Welch, F. (1990). Empirical age-earning profiles. Journal of Labor Economics, 8, 202–229.
• Newey, W. K., Powell, J. L., & Vella, F. (1999). Nonparametric Estimation of Triangular Simultaneous Equations Models. Econometrica, 67(3), 565–603. doi: 10.1111/1468-0262.00037.
• Ozabaci, D., Henderson, D. J., & Su, L. (2014). Additive nonparametric regression in the presence of endogenous regressors. Journal of Business & Economic Statistics, 32(4), 555–575. doi: 10.1080/07350015.2014.917590.
• Patrinos, H. A., Psacharopoulos, G., & Tansel, A. (2021). private and social returns to ınvestment in education: the case of Turkey with alternative methods. Applied Economics, 53(14), 1638–1658. doi: 10.1080/00036846.2020.1841086.
• Psacharopoulos, G. (1981). Returns to education: An updated international comparison. Comparative Education, 17(3), 321–341.
• Schafgans, M. (1998). Ethnic wage differences in Malaysia: Parametric and Semiparametric Estimation of the Chinese-Malay Wage Gap. Journal of Applied Economics. 13(5), 481–504.
• Seltzer, A. J., & Frank, J. (2007). Promotion tournaments and white collar careers: Evidence from Williams Deacon’s Bank, 1890 1941. Oxford Economic Papers, 59 (Supplement 1), i49–i72. Doi: 10.1093/oep/gpm030.
• Smith, J., & Welch, F. (1979). Inequality: Race differences in the distribution of earnings. International Economic Review, 20, 515–526.
• Stanfors, M., & Burnette, J. (2015). Estimating historical wage profiles. Historical Methods: A Journal of Quantitative and Interdisciplinary History, 48(1), 35–51. doi: 10.1080/01615440.2014.947397.
• Tansel, A., & Daoud, Y. (2011). Comparative essay on returns to education in Palestine and Türkiye. SSRN Electronic Journal. doi: 10.2139/ssrn.1904864.
• Tansel, A., & Bodur, F. B. (2012). Wage inequality and returns to education in Turkey: A quantile regression analysis. Review of Development Economics, 16(1), 107–121. doi: 10.1111/j.1467-9361.2011.00655.x.
• Tokatlıoğlu, Y., & Doğan, N. (2021). Return of education for women across socio-economic status: Using quantile regression and machado-mata decomposition methods for Turkey. Ege Academic Review, 21(2), 93–110.
• TURKSTAT [Turkish Statistical Institute] (2019). Labor force statistics micro data set 2019. Retrieved from https://www.tuik.gov.tr/Kurumsal/Mikro_Veri.
• TURKSTAT- Turkish Statistical Institute (2022a). Population projections. Retrieved from https://tuikweb.tuik.gov.tr/PreTablo.do?alt_id=1027.
• TURKSTAT- Turkish Statistical Institute (2022b). Household budget statistics micro data set (2011-2019). Retrieved from https://www.tuik.gov.tr/