Efficiency Measurement Using a “True” Random Effects and Random Parameter Stochastic Frontier Models: An Application to Rural and Community Banks in Ghana

Michael Danquah,Alfred Barimah,Williams Ohemeng

doi:10.4236/me.2013.412093

Abstract

In this paper, we attempt to compare the results of stochastic frontier models that control for unobserved heterogeneity in the inefficiency model, and unobserved (parameter) heterogeneity in the production model respectively. We estimate a “true” random effect, and random parameter stochastic frontier models in a panel data framework. An application of these models is presented using data of rural and community banks in Ghana from 2006 to 2011. Our results show that the two models address the issue of unobserved heterogeneity, and therefore omitted unobserved heterogeneity in the production model may always show up in the estimated inefficiency.

Highlights

Estimating inefficiency within the stochastic frontier framework is common in the applied economic literature1
We estimate the baseline model in order to find out whether the treatment of unobserved heterogeneity is essential for the application to the Rural and Community Banks (RCBs) dataset in Ghana
This paper estimates a “true” random effects stochastic model that controls for unobserved heterogeneity in the inefficiency model, and a random parameter model that accounts for unobserved differences in technologies that might be inappropriately labeled as inefficiency respectively

Summary

Introduction

Estimating inefficiency within the stochastic frontier framework (i.e. based on the notion of best practice frontier) is common in the applied economic literature. Studies on inefficiency have employed the conventional stochastic frontier approaches [5,6,7,8]. In these models, the problematic modeling issue of separating unobserved heterogeneity from estimated inefficiency is not addressed. In most datasets, not all the relevant data are always available while some factors are difficult to quantify and rarely considered when empirical inefficiency comparisons are made. Given that such inputs differences are exogenously determined, the conventional stochastic frontier approaches will provide a biased measure of inefficiency. Greene [4,9,10] proposed modeling techniques in panel data framework that treat time invariant effects and separate unobserved heterogeneity from estimated inefficiency term (i.e. control for unobserved heterogeneity) [11,12]

Methods

Results

Conclusion