Abstract
The main aim of the paper is to analyse the effect of country and school factors on a new measure of educational equity defined as the country proportion of resilient students, i.e. those who, despite their disadvantaged socioeconomic background, are able to obtain good educational results. We construct a cross country panel dataset by merging the five editions of OECD PISA (Programme for International Student Assessment). The panel analysis allows to exploit country and time level variation in the proportion of resilient students controlling for systematic and institutional differences. Our findings suggest that educational funding can help disadvantaged students to obtain the opportunities that they are otherwise lacking. In addition, this effect seems to be heterogeneous, and particularly driven by those countries whose economic development (in terms of per capita GDP) is lower.
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Notes
Programme for International Students Assessment (PISA) is a triennial international survey conducted by the Organisation for Economic Cooperation and Development (OECD).
Progress in International Reading Literacy Study (PIRLS) is a recurring study conducted by the International Association for the Evaluation of Educational Achievement (IEA).
The index of quality of school educational resources (SCMATEDU) was derived from six items measuring school principals’ perceptions of potential factors hindering instruction at their school. These factors are: (i) shortage or inadequacy of science laboratory equipment; (ii) shortage or inadequacy of instructional materials; (iii) shortage or inadequacy of computers for instruction; (iv) lack or inadequacy of Internet connectivity; (v) shortage or inadequacy of computer software for instruction; and (vi) shortage or inadequacy of library materials. As all items were inverted for scaling, higher values on this index indicate better quality of educational resources.
The main domain of each PISA edition is used to assess the student performance, i.e. reading for PISA 2000 and 2009, mathematics for PISA 2003 and 2012, science for PISA 2006.
As a robustness check, we estimated the Eq. (1) separately by country, and the main results of the final model (in terms of both sign and significance) remain nearly unchanged—we do not report them here because of space constraints, though they are available on request from the authors.
An alternative way to compute the share of resilient students in each country would be to divide the number of resilient students by the number of disadvantaged students. This choice does not influence the ranking of the countries according to the share of resilient students since the number of disadvantaged students in a country is a constant proportion (based on a predetermined threshold) of the total student sample. Indeed the correlation coefficient between the two measures is very high (0.9).
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Acknowledgments
A previous version of this paper has been presented at the Public Economic Theory (PET) Conference, Seattle July 11th–13th 2014. We are grateful to participants for their useful comments and suggestions. Usual disclaimers apply.
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Agasisti, T., Longobardi, S. & Regoli, A. A cross-country panel approach to exploring the determinants of educational equity through PISA data. Qual Quant 51, 1243–1260 (2017). https://doi.org/10.1007/s11135-016-0328-z
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DOI: https://doi.org/10.1007/s11135-016-0328-z