An application of the DEA double bootstrap to examine sources of efficiency in Bangladesh rice farming

In this article we examine sources of technical efficiency for rice farming in Bangladesh. The motivation for the analysis is the need to close the rice yield gap to enable food security. We employ the DEA double bootstrap of Simar and Wilson (2007) to estimate and explain technical efficiency. This technique overcomes severe limitations inherent in using the two-stage DEA approach commonly employed in the efficiency literature. From a policy perspective our results show that potential efficiency gains to reduce the yield gap are greater than previously found. Statistically positive influences on technical efficiency are education, extension and credit, with age being a negative influence.

Ranking efficiency units in DEA using bootstrapping an applied analysis for Slovenian farm data

This article explores how data envelopment analysis (DEA), along with a smoothed bootstrap method, can be used in applied analysis to obtain more reliable efficiency rankings for farms. The main focus is the smoothed homogeneous bootstrap procedure introduced by Simar and Wilson (1998) to implement statistical inference for the original efficiency point estimates. Two main model specifications, constant and variable returns to scale, are investigated along with various choices regarding data aggregation. The coefficient of separation (CoS), a statistic that indicates the degree of statistical differentiation within the sample, is used to demonstrate the findings. The CoS suggests a substantive dependency of the results on the methodology and assumptions employed. Accordingly, some observations are made on how to conduct DEA in order to get more reliable efficiency rankings, depending on the purpose for which they are to be used. In addition, attention is drawn to the ability of the SLICE MODEL, implemented in GAMS, to enable researchers to overcome the computational burdens of conducting DEA (with bootstrapping).

The use of bootstrapped Malmquist indices to reassess productivity change findings: an application to a sample of Polish farms

This article assesses the extent to which sampling variation affects findings about Malmquist productivity change derived using data envelopment analysis (DEA), in the first stage by calculating productivity indices and in the second stage by investigating the farm-specific change in productivity. Confidence intervals for Malmquist indices are constructed using Simar and Wilson's (1999) bootstrapping procedure. The main contribution of this article is to account in the second stage for the information in the second stage provided by the first-stage bootstrap. The DEA SEs of the Malmquist indices given by bootstrapping are employed in an innovative heteroscedastic panel regression, using a maximum likelihood procedure. The application is to a sample of 250 Polish farms over the period 1996 to 2000. The confidence intervals' results suggest that the second half of 1990s for Polish farms was characterized not so much by productivity regress but rather by stagnation. As for the determinants of farm productivity change, we find that the integration of the DEA SEs in the second-stage regression is significant in explaining a proportion of the variance in the error term. Although our heteroscedastic regression results differ with those from the standard OLS, in terms of significance and sign, they are consistent with theory and previous research.

Productivity change in Polish agriculture: an illustration of a bootstrapping procedure applied to Malmquist indices

This article illustrates the usefulness of applying bootstrap procedures to total factor productivity Malmquist indices, derived with data envelopment analysis (DEA), for a sample of 250 Polish farms during 1996-2000. The confidence intervals constructed as in Simar and Wilson suggest that the common portrayal of productivity decline in Polish agriculture may be misleading. However, a cluster analysis based on bootstrap confidence intervals reveals that important policy conclusions can be drawn regarding productivity enhancement.

Modelling requirements of supplier selection for benchmarking service provision

Supplier selection has a great impact on supply chain management. The quality of supplier selection also affects profitability of organisations which work in the supply chain. As suppliers can provide variety of services and customers demand higher quality of service provision, the organisation is facing challenges for making the right choice of supplier for the right needs. The existing methods for supplier selection, such as data envelopment analysis (DEA) and analytical hierarchy process (AHP) can automatically perform selection of competitive suppliers and further decide winning supplier(s). However, these methods are not capable of determining the right selection criteria which should be derived from the business strategy. An ontology model described in this paper integrates the strengths of DEA and AHP with new mechanisms which ensure the right supplier to be selected by the right criteria for the right customer's needs.

An environmental uncertainty-based diagnostic reference tool for evaluating the performance of supply chain value streams

This research has responded to the need for diagnostic reference tools explicitly linking the influence of environmental uncertainty and performance within the supply chain. Uncertainty is a key factor influencing performance and an important measure of the operating environment. We develop and demonstrate a novel reference methodology based on data envelopment analysis (DEA) for examining the performance of value streams within the supply chain with specific reference to the level of environmental uncertainty they face. In this paper, using real industrial data, 20 product supply value streams within the European automotive industry sector are evaluated. Two are found to be efficient. The peer reference groups for the underperforming value streams are identified and numerical improvement targets are derived. The paper demonstrates how DEA can be used to guide supply chain improvement efforts through role-model identification and target setting, in a way that recognises the multiple dimensions/outcomes of the supply chain process and the influence of its environmental conditions. We have facilitated the contextualisation of environmental uncertainty and its incorporation into a specific diagnostic reference tool.

Evaluating the sustainable intensification of arable farms

Sustainable Intensification (SI) of agriculture has recently received widespread political attention, in both the UK and internationally. The concept recognises the need to simultaneously raise yields, increase input use efficiency and reduce the negative environmental impacts of farming systems to secure future food production and to sustainably use the limited resources for agriculture. The objective of this paper is to outline a policy-making tool to assess SI at a farm level. Based on the method introduced by Kuosmanen and Kortelainen (2005), we use an adapted Data Envelopment Analysis (DEA) to consider the substitution possibilities between economic value and environmental pressures generated by farming systems in an aggregated index of Eco-Efficiency. Farm level data, specifically General Cropping Farms (GCFs) from the East Anglian River Basin Catchment (EARBC), UK were used as the basis for this analysis. The assignment of weights to environmental pressures through linear programming techniques, when optimising the relative Eco-Efficiency score, allows the identification of appropriate production technologies and practices (integrating pest management, conservation farming, precision agriculture, etc.) for each farm and therefore indicates specific improvements that can be undertaken towards SI. Results are used to suggest strategies for the integration of farming practices and environmental policies in the framework of SI of agriculture. Paths for improving the index of Eco-Efficiency and therefore reducing environmental pressures are also outlined.