A Regression Discontinuity Stochastic Frontier Model with an Application to Educational Attainment

Johnes, Geraint and Tsionas, Mike (2019) A Regression Discontinuity Stochastic Frontier Model with an Application to Educational Attainment. Stat, 8 (1): e242. ISSN 2049-1573

[thumbnail of RD-sfm]
Text (RD-sfm)
RD_sfm.pdf - Accepted Version
Available under License Creative Commons Attribution-NonCommercial.

Download (796kB)


We extend the regression discontinuity design model to the case in which the line of best fit is replaced by a stochastic frontier. The method allows causality issues to be examined in a context where the performance measure is subject to inefficiency, and where, in addition to the relationship between dependent and explanatory variables, there may be a discontinuity in the inefficiency measure at the break. In the tradition of Battese and Coelli (1995), the inefficiency scores are modelled as part of the system but we follow a novel non-parametric approach. We illustrate the method with an application to data from Texas on class size and pupil performance, exploiting a Maimonides rule discontinuity. We find that class size affects performance in the expected direction, but that there is a corresponding effect in the opposite direction on efficiency. This may contribute to the difficulty experienced by authors of earlier studies in identifying a class size effect.

Item Type:
Journal Article
Journal or Publication Title:
Additional Information:
This is the peer reviewed version of the following article: Johnes, G, Tsionas, MG. A regression discontinuity stochastic frontier model with an application to educational attainment. Stat. 2019; 8:e242. https://doi.org/10.1002/sta4.242 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/sta4.242 This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
?? educationregression discontinuitystochastic frontier ??
ID Code:
Deposited By:
Deposited On:
22 Jun 2019 09:19
Last Modified:
15 Jul 2024 19:32