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Comparing the Performance of National Educational Systems: Inequality Versus Achievement?

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Abstract

We measure performance change for the educational systems of 28 countries which participated in the Trends in International Mathematics and Science Study in years 2007 and 2011 for eighth grade basic education students. We consider simultaneously variables related to academic achievement and inequality in the discipline of mathematics. From a methodological point of view, we use the global Malmquist–Luenberger productivity index due to the presence of bad outputs. To the best of our knowledge, this methodology had not been applied previously in the field of education despite these desirable which are particularly useful in this field. Results indicate that the countries participating in the study not only chose different paths to improve their educational performance but, in addition, results varied remarkably among them. They also suggest that, on average, educational performance deteriorated between 2007 and 2011, although we also found (successful) efforts in several countries to improve equality.

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Notes

  1. Among the international studies we may find the Programme for International Student Assessment (PISA), the Teaching and Learning International Survey (TALIS), the Third Regional Comparative and Explanatory Study (TERCE), the International Computer and Information Literacy Study (ICILS), the Progress in International Reading Literacy Study (PIRLS), the Trends in International Mathematics and Science Study (TIMSS) and the International Civic and Citizenship Study (ICCS).

  2. We refer to inequality in terms of academic performance. As we will show throughout the paper we model inequality as a bad output in the context of frontier estimation and directional distance functions.

  3. Education provision is considered to be efficient if its producers make the best possible use of available inputs.

  4. A good output is a result variable (produced simultaneously with good outputs) that, when its value increases, has a positive impact on the process under evaluation and, therefore, is a desirable result. In the context of education, examples of good outputs could be students’ academic achievement, the percentage of students who obtain the maximum qualification, or the achievement of certain competences. In contrast, a bad output is a result variable that, when its value increases, negatively impacts on the process under evaluation and, therefore, is a non-desirable result. In the context of education a bad output could be dispersion in the academic results obtained or the percentage of students not reaching a minimum threshold of qualification. A typical example of a bad output in environmental economics would be pollution.

  5. If we were explicitly interested in conducting a dynamic analysis in the context of Data Envelopment Analysis, we could consider approaches such as those by Nemoto and Goto (1999, 2003), Chen and Dalen (2010) and Kao (2013). We thank an anonymous reviewer for this comment.

  6. Readers interested in a more detailed mathematical formulation, or a graphic illustration of this methodology may refer to Oh (2010, p. 186).

  7. For an extensive review of their use in the literature, see Emrouznejad et al. (2008) and, more recently, Emrouznejad and Yang (2017). We are grateful to an anonymous reviewer for this comment.

  8. These directions should be selected exogenously from the model in order to characterize the desirability of the movements in a specific direction according, for instance, to their specific prices or the statement of strategic goals. A good example of different possible directions can be found in Picazo-Tadeo et al. (2012).

  9. The literature on efficiency identifies mainly two types of technologies for these cases. First, strong disposability, which implies that the bad output may be reduced while maintaining the level of good outputs. In the context of education, this could imply reducing inequality without affecting the best perfomers’ level of learning. Second, weak disposability involves a more restrictive technology, for which reducing the bad output implies accepting reductions in the good output. In the context of education, this would imply that inequality cannot be reduced without eroding the best performers’ average learning levels.

  10. See also the recent relevant contribution by Dakpo et al. (2016) and Epure and Lafuente (2015).

  11. See http://www.un.org/millenniumgoals/education.shtml, accessed November 2nd, 2017.

  12. Software alternatives are freely available to estimate both global and local bandwidths. In the case of global bandwidth, the KernSmooth package for R considers the dpik function for the direct plug-in; for the local bandwidths, we considered R’s locfit package and the locfit function.

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Authors and Affiliations

  1. Department of Business, Universitat Autònoma de Barcelona, Barcelona, Spain

    Víctor Giménez & Diego Prior

  2. Facultad de Economía y Empresa, Universidad Diego Portales, Santiago de Chile, Chile

    Claudio Thieme

  3. Departament d’Economia, Universitat Jaume I, Campus del Riu Sec, 12071, Castelló de la Plana, Spain

    Emili Tortosa-Ausina

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  1. Víctor Giménez
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  2. Claudio Thieme
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  3. Diego Prior
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  4. Emili Tortosa-Ausina
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Correspondence to Emili Tortosa-Ausina.

Additional information

We are grateful to four anonymous reviewers whose comments have greatly contributed to improving the quality of the paper. Claudio Thieme and Emili Tortosa-Ausina thank FONDECYT (National Fund of Scientific and Technological Development, Grants #1121164 and #1151313) for generous financial support. Víctor Giménez, Diego Prior and Emili Tortosa-Ausina acknowledge the financial support of the Spanish Ministerio de Economía y Competitividad (ECO2013-46954-C3-2-R, ECO2013-44115-P and ECO2014-55221-P). Emili Tortosa-Ausina also acknowledges the financial support of Generalitat Valenciana (PROMETEOII/2014/046) and Universitat Jaume I (P1.1B2014-17). The usual disclaimer applies.

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Giménez, V., Thieme, C., Prior, D. et al. Comparing the Performance of National Educational Systems: Inequality Versus Achievement?. Soc Indic Res 141, 581–609 (2019). https://doi.org/10.1007/s11205-018-1855-x

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