On the suitability of ratio variables in data envelopment analysis: An application to education with methodological extensions

It is well known that the use of ratio variables is inconsistent with the fundamental assumptions of convex Data Envelopment Analysis (DEA).

However, in this paper, we establish a general result demonstrating the equivalence between DEA models with ratio variables under variable returns to scale and DEA models with volume (non-ratio) variables under constant returns to scale, provided that all variables are ratio variables sharing a common denominator.

This significant result enables the development of a framework for evaluating the efficiency of the Organisation for Economic Co-operation and Development (OECD) countries based on the results of the Programme for International Student Assessment (PISA) report, using mean performance scores as outputs.

In this framework, we give some methodological innovations, such as the incorporation of the index of economic social and cultural status (ESCS) as an input, thereby enabling fairer comparisons with countries with a lower socio-economic level.

Furthermore, we introduce different methods for estimating directions of improvement and calculating targets appropriate to the difficulty of improving each performance score.

Finally, we review and introduce several novel contributions to emerging methodologies that can complement classical radial and directional models, such as efficient frontier estimation with adaptive constrained enveloping splines (ACES), stochastic chance-constrained models, and fuzzy models.

All these methodologies can be used to analyse data from other PISA or similar reports, allowing non-specialists to implement DEA appropriately.