EconPapers    
Economics at your fingertips  
 

A Bayesian nonparametric modelling to estimate student response to ICT investment

Stefano Cabras and Juan de Dios Tena ()

DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística

Abstract: This paper estimates the causal impact of investment in information andcommunication technologies (ICT) on student performances in mathematics asmeasured in the Program for International Student Assessment (PISA) 2012 for Spain.To do this we apply a new methodology in this context known as Bayesian AdditiveRegression Trees (BART) that has important advantages over more standardparametric specifications. Results indicate that ICT has a moderate positive effect onmath scores. In addition, we analyze how this effect interacts with variables related toschool features and student socioeconomic status, finding that ICT investment isespecially beneficial for students from a low socioeconomic background.

Keywords: ICT; BART; Causality; Bayesian; Statistics; Regression; trees (search for similar items in EconPapers)
Date: 2014-12
New Economics Papers: this item is included in nep-ict
References: Add references at CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link)
https://e-archivo.uc3m.es/bitstream/handle/10016/19916/ws143020.pdf?sequence=1 (application/pdf)

Related works:
Journal Article: A Bayesian non-parametric modeling to estimate student response to ICT investment (2016) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cte:wsrepe:ws143020

Access Statistics for this paper

More papers in DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística
Bibliographic data for series maintained by Ana Poveda ().

 
Page updated 2021-05-26
Handle: RePEc:cte:wsrepe:ws143020