Abstract

This paper analyses the incidence and intensity of low performance among 15-year old students in the OECD countries according to PISA 2012. Taking level 2 of proficiency as the baseline competence, we approach the measurement of low performance by applying a multidimensional poverty measure that permits interpreting educational poverty as a welfare loss. We use a conventional welfare evaluation function to derive an index that combines the incidence, intensity and inequality of educational poverty. The results show that OECD countries differ in educational poverty much more than in PISA average scores and also that they present different mixes of incidence and intensity.

Highlights

  • The OECD’s Programme for International Student Assessment (PISA) provides the richest and most comprehensive database for the evaluation of the educational achievements of 15 year-old students in three different subjects: mathematics, reading, and science

  • How to cite this paper: Villar, A. (2016) Educational Poverty as a Welfare Loss: Low Performance in the OECD According to PISA 2012

  • The purpose of this paper is contributing to such a measurement by interpreting low performance as educational poverty and applying the tools that are usual in the welfare analysis of inequality and poverty

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Summary

Introduction

The OECD’s Programme for International Student Assessment (PISA) provides the richest and most comprehensive database for the evaluation of the educational achievements of 15 year-old students in three different subjects: mathematics, reading, and science. The age of the students is very close to the end of compulsory education for most of the participating countries. Those results, are a good proxy of the basic knowledge ensured by the different countries to their citizens. Sixty-five countries and large economies participated in the. How to cite this paper: Villar, A. (2016) Educational Poverty as a Welfare Loss: Low Performance in the OECD According to PISA 2012.

Villar
The Model
From the Model to the Data
The Results
Final Comments
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