Skip to main content

Sensitivity analysis of efficiency rankings to distributional assumptions: applications to Japanese water utilities

Buy Article:

$55.00 plus tax (Refund Policy)

Abstract:

This article examines the robustness of efficiency score rankings across four distributional assumptions for trans-log stochastic production-frontier models, using data from 1221 Japanese water utilities (for 2004 and 2005). One-sided error terms considered include the half-normal, truncated normal, exponential and gamma distributions. Results are compared for homoscedastic and doubly heteroscedastic models, where we also introduce a doubly heteroscedastic variable mean model, and examine the sensitivity of the nested models to a stronger heteroscedasticity correction for the one-sided error component. The results support three conclusions regarding the sensitivity of efficiency rankings to distributional assumptions. When four standard distributional assumptions are applied to a homoscedastic stochastic frontier model, the efficiency rankings are quite consistent. When those assumptions are applied to a doubly heteroscedastic stochastic frontier model, the efficiency rankings are consistent when proper and sufficient arguments for the variance functions are included in the model. When a more general model, like a variable mean model is estimated, efficiency rankings are quite sensitive to heteroscedasticity correction schemes.

Keywords: C16; C21; Japanese water utilities; L95; heteroscedasticity; stochastic production frontier models

Document Type: Research Article

DOI: https://doi.org/10.1080/00036846.2012.663475

Affiliations: 1: Department of Economics,St. Andrew's University, Momoyama Gakuin University, Osaka, Japan 2: Department of Economics,Public Utility Research Center, University of Florida, Matherly Hall 318Gainesville, 32611-7142, USA

Publication date: 2013-06-01

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more