Comparing efficiency across markets: An extension and critique of the methodology

Ruben Chumpitaz, Kristiaan Kerstens, Nicholas Paparoidamis and Matthias Staat

European Journal of Operational Research, 2010, vol. 205, issue 3, 719-728

Abstract: The use of non-parametric frontier methods for the evaluation of product market efficiency in heterogeneous markets seems to have gained some popularity recently. However, the statistical properties of these frontier estimators have been largely ignored. The main point is that non-parametric frontier estimators are biased and that the degree of bias depends on specific sample properties, most importantly sample size and number of dimensions of the model. To investigate the effect of this bias on comparing market efficiency, this contribution estimates the efficiency for several datasets for two main product categories. Following (Zhang, Y., Bartels, R., 1998. The effect of sample size on the mean efficiency in DEA with an application to electricity distribution in Australia, Sweden and New Zealand. Journal of Productivity Analysis, 9(3), 187-204.), these results comprise re-estimates for the larger samples limiting their size to that of the smaller samples when the model dimensions for different samples are identical. Furthermore, sample sizes are adjusted to mitigate the eventual differences in dimensions in specification. This allows comparing market efficiency for different markets on a more equal footing, since it reduces the bias effect to a minimum making the comparison of market efficiency possible. However, the article also points out the critical limitations of this [Zhang, Y., Bartels, R., (1998). The effect of sample size on the mean efficiency in DEA with an application to electricity distribution in Australia, Sweden and New Zealand. Journal of Productivity Analysis 9 (3), 187-204] approach in certain respects. Apart from reporting these negative results, we also offer some suggestions for future work.

Keywords: Market; efficiency; Heterogeneous; product; markets; Bias; Monte; Carlo; simulation (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (2)

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