Measuring technical efficiency and productivity growth: a comparison of SFA and DEA on Norwegian grain production data

Abstract

This article compares data envelopment analysis and stochastic frontier analysis to assess efficiency and productivity growth of Norwegian grain producers. Previous studies have dealt with either one of them and less of both. For the assessment of productivity growth or regress, Malmquist productivity indices are derived from both approaches. The data cover a 10-year period. We find consistency between the approaches to the extent that: (1) there are potentials for efficiency improvements, but the magnitudes depends on the model applied and by segmentation of the data set, (2) there has been a productivity improvement in the sector, on average in the interval 30-38% in the period studied and (3) technical change has had the greatest impact on productivity, indicating that producers have a tendency to catch-up with the front runners. In general, policy-makers are warned not to be indifferent with respect to the approach used for efficiency and productivity measurement as these may give different results.

Technical efficiency of Greek olive growing farms: a robust approach with panel data

“…This is a very common practice in empirical analysis of technical efficiency (e.g. Odeck, 2007;Latruffe et al, 2008;Larsen, 2010). 6 The variables family to total labor and owned to total land may not be truly exogenous.…”

“…De Witte & Kortelainen (2013) extended the ideas of Simar (2005 and2007) to general Z vectors involving multiple continuous and discrete (both ordered and unordered) environmental factors. Moreover, they developed appropriate tools of statistical inference.…”

“…The nonparametric estimate of the conditional α-quantile output efficiency score is then [7] with The influence of exogenous (environmental) variables on the production process can be evaluated by comparing the conditional efficiency measure with the unconditional one (that means the ratio of the radial distances from the conditional and the unconditional frontiers, respectively). Specifically, Simar (2005 and2007) propose the estimation of the following smooth nonparametric regression model [8] where:…”

“…Exogenous (environmental) factors may influence the performance of production units and, thus, they may be the cause of the observed efficiency differentials. To account for heterogeneity of the operational environment, the present work employs the nonparametric conditional efficiency approach which is based on the probabilistic formulation of the production process and it incorporates the operational environment by conditioning on exogenous characteristics (Aragon et al, 2005;Daraio & Simar, 2005, 2007Daouia & Simar, 2007;De Witte & Cortelainen, 2013). The conditional efficiency approach dispenses with the separability condition and it does not require any a priori specification regarding the impact of exogenous factors 2 .…”

Do Public to Private Buyouts Improve Target's Efficiency?

“…One drawback of DEA, is that the technique attributes all deviations from the frontier to inefficiency. A frontier estimated by DEA is therefore likely to be sensitive to measurement errors or other noise in the data (Odeck, 2007). However, despite its limitations, DEA provides new ways of obtaining empirical estimates of the external relationship, such as the production function, or the efficient production possibility surfaces that are a cornerstone of modern economics (Charnes et al, 1978).…”