Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/200355 
Erscheinungsjahr: 
2017
Schriftenreihe/Nr.: 
Budapest Working Papers on the Labour Market No. BWP - 2017/4
Verlag: 
Hungarian Academy of Sciences, Institute of Economics, Centre for Economic and Regional Studies, Budapest
Zusammenfassung: 
Educational systems can be characterized by a complex structure: students, classes and teachers, schools and principals, and providers of education. The added value of schools is likely influenced by all these levels and, especially, by interactions between them. We illustrate the ability of Machine Learning (ML) methods (Regression Trees, Random Forests and Boosting) to model this complex 'education production function' using Hungarian data. We find that, in contrast to ML methods, classical regression approaches fail to identify relevant nonlinear interactions such as the role of school principals to accommodate district size policies. We visualize nonlinear interaction effects in a way that can be easily interpreted.
Schlagwörter: 
machine learning
education production function
interaction effects
non-linear effects
JEL: 
C5
C18
C49
I21
H75
ISBN: 
978-615-5594-99-1
Dokumentart: 
Working Paper

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