Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/200355
Authors: 
Schiltz, Fritz
Masci, Chiara
Agasisti, Tommaso
Horn, Dániel
Year of Publication: 
2017
Series/Report no.: 
Budapest Working Papers on the Labour Market BWP - 2017/4
Abstract: 
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.
Subjects: 
machine learning
education production function
interaction effects
non-linear effects
JEL: 
C5
C18
C49
I21
H75
ISBN: 
978-615-5594-99-1
Document Type: 
Working Paper

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