Logo image
Estimation and efficiency measurement in stochastic production frontiers with ordinal outcomes
Journal article   Peer reviewed

Estimation and efficiency measurement in stochastic production frontiers with ordinal outcomes

W. Griffiths, X. Zhang and X. Zhao
Journal of Productivity Analysis, Vol.42(1), pp.67-84
2014
url
Link to Published Version *Subscription may be requiredView

Abstract

We consider Bayesian estimation of a stochastic production frontier with ordered categorical output, where the inefficiency error is assumed to follow an exponential distribution, and where output, conditional on the inefficiency error, is modelled as an ordered probit model. Gibbs sampling algorithms are provided for estimation with both cross-sectional and panel data, with panel data being our main focus. A Monte Carlo study and a comparison of results from an example where data are used in both continuous and categorical form supports the usefulness of the approach. New efficiency measures are suggested to overcome a lack-of-invariance problem suffered by traditional efficiency measures. Potential applications include health and happiness production, university research output, financial credit ratings, and agricultural output recorded in broad bands. In our application to individual health production we use data from an Australian panel survey to compute posterior densities for marginal effects, outcome probabilities, and a number of within-sample and out-of-sample efficiency measures.

Details

UN Sustainable Development Goals (SDGs)

This output has contributed to the advancement of the following goals:

#9 Industry, Innovation and Infrastructure

Source: InCites

Metrics

InCites Highlights

These are selected metrics from InCites Benchmarking & Analytics tool, related to this output

Collaboration types
Domestic collaboration
Citation topics
6 Social Sciences
6.10 Economics
6.10.502 Data Envelopment Analysis
Web Of Science research areas
Business
Economics
Social Sciences, Mathematical Methods
ESI research areas
Economics & Business
Logo image