Statistical inference for the Russell measure of technical efficiency

Abstract

Data envelopment analysis (DEA) has become a popular approach to nonparametric efficiency measurement. The statistical inference using bootstrap methods is readily available for the radial DEA estimator; however it is missing for the Russell measure, the nonradial DEA estimator. We propose a bootstrap based procedure for making statistical inference about the individual Russell measures of technical efficiency. We perform simulations to examine finite sample properties of the proposed estimator. Finally, we present an empirical study using proposed bootstrap procedure.

Keywords:

• Nonradial efficiency
• Russell measure
• data envelopment analysis
• frontier efficiency models
• statistical inference
• bootstrap

Acknowledgements

The authors wish to thank two anonymous Referees and the Editor for their valuable comments. The usual disclaimers apply. The authors acknowledge the Lebesgue Center of Mathematics who supported the work of Second Author according to the program PIA-ANR-11-LABX-0020-01 during his postdoc at Agrocampus Ouest, when a substantial part of this project was carried out.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 Though we do not directly set the correlation between λ₁ and λ₂, our experiments with different constellations gave us a sense that setting the correlation between λ₁₀ and λ₂₀ directly influenced the correlation between λ₁ and λ₂.