Multi-directional program efficiency: the case of Lithuanian family farms

The present paper analyses both managerial and program efficiencies of Lithuanian family farms, in the tradition of Charnes et al. (Manag Sci 27(6):668–697, 1981) but with the important difference that multi-directional efficiency analysis rather than the traditional data envelopment analysis approach is used to estimate efficiency. This enables a consideration of input-specific efficiencies. The study shows clear differences between the efficiency scores on the different inputs as well as between the farm types of crop, livestock and mixed farms respectively. We furthermore find that crop farms have the highest program efficiency, but the lowest managerial efficiency and that the mixed farms have the lowest program efficiency (yet not the highest managerial efficiency).     

Using DEA and Worst Practice DEA in Credit Risk Evaluation

The purpose of this paper is to introduce the concept of worst practice DEA, which aims at identifying worst performers by placing them on the frontier. This is particularly relevant for our application to credit risk evaluation, but this also has general relevance since the worst performers are where the largest improvement potential can be found. The paper also proposes to use a layering technique instead of the traditional cut-off point approach, since this enables incorporation of risk attitudes and risk-based pricing. Finally, it is shown how the use of a combination of normal and worst practice DEA models enable detection of self-identifiers. The results of the empirical application on credit risk evaluation validate the method. The best combination of layered normal and worst practice DEA models yields an impressive 100% bankruptcy and 78% non-bankruptcy prediction accuracy in the calibration data set, and equally convincing 100% and 67% out-of-sample classification accuracies.     

Environmental Production Factors and Efficiency of Smallholder Agricultural Households: Using Non‐parametric Conditional Frontier Methods

External environmental factors play a significant role in the agricultural production of smallholder farmers. This is especially the case in developing countries where production is less technologically intensive. These factors are mainly exogenous and affect both the farmers’ input choices and the final output levels. However, previous studies of technical efficiency of smallholder agricultural production either ignore these factors or assume separability between environmental factors and input choice, which is often not the case in developing counties. Using data on smallholder farmers in Ethiopia, we relax this separability assumption and investigate the importance of external environmental factors in farmers’ performance and efficiency variation across regions. The empirical analysis is based on a non‐parametric conditional order‐m estimation method that relaxes some of the traditional strong assumptions in efficiency modeling and, more importantly, the separability between environmental factors and the choice of physical inputs. Using data from four major agricultural regions of Ethiopia, we show the extent that environmental factors contribute to the technical efficiency and the significance of environmental factors in farmers’ performance.     

The scale of hospital production in different settings: one size does not fit all

This paper analyses the productive efficiency of 141 public hospitals from 1998 to 2004 in two Canadian provinces; one a small province with a few small cities and a generally more rural population and the other a large province that is more urban in nature, with a population who mainly live in large cities. The relative efficiencies of the hospitals, the changes in productivity during this time period, and the relationship between efficiency and the size or scale of the hospitals are investigated using data envelopment analysis. The models for the production of health care use case mix adjusted hospital discharges as the output, and nursing hours as inputs. We find clear differences between the two provinces. Making use of ‘own’ and ‘meta’ technical efficiency frontiers, we demonstrate that efficient units in the larger and more urban province are larger than non-efficient units in that province. However, efficient hospitals in the smaller and more rural province are smaller than non-efficient hospitals in that province. Overall, efficient hospitals in the larger more urban province are larger than efficient hospitals in the smaller more rural province. This has interesting policy implications—different hospitals may have different optimal sizes, or different efficient modes of operation, depending on location, the population they serve, and the policies their respective provincial governments wish to implement. In addition, there are lessons to be learned by comparing the hospitals across the two provinces, since the inefficient hospitals in the small rural province predominantly use hospitals from the large urban province as benchmarks, such that substantially larger improvement potential can be identified by inter-provincial rather than intra-provincial benchmarking analysis.     

Combining DEA Window Analysis with the Malmquist Index Approach in a Study of the Canadian Banking Industry

The banking industry in Canada is essentially an oligopoly with five large participants controlling about 90% of the market. To evaluate the industry's performance over time, we need to deal with the problem of a small number of DMU's compared to the number of relevant inputs and outputs. To overcome this problem we use data envelopment analysis (DEA) window analysis, whereby efficiency scores for the 20 year period 1981–2000 are obtained. To measure productivity changes over time, Malmquist indices can be calculated from DEA scores. Using DEA window analysis scores, however, raise the question of how to define the same period frontier in a DEA window analysis. We show that for both the adjacent and the base period Malmquist index and for all suggested definitions of same period frontier, the standard decomposition into frontier shift and catching up effects gives inappropriate results when Malmquist indices are based on DEA window analysis scores.     

Measuring Inefficiency Via Potential Improvements

In a recent paper Bogetoft and Hougaard (1999) suggest the use of a new potential improvements approach to efficiency evaluation which has the advantage of separating the issue of benchmark selection from the issue of efficiency measurement. In the present paper the potential improvements inefficiency index is reexamined and a DEA-like approach for its determination is suggested. The approach is called Multi-directional Efficiency Analysis (MEA). An empirical example on Danish dairy farms is used for illustrative purposes and comparisons with various versions of DEA.     

Estimating global frontier shifts and global Malmquist indices

The Malmquist index is a measure of productivity changes, of which an important component is the frontier shift or technological change. Often technological change can be viewed as a global phenomenon, and therefore individual or local measures of technological changes are aggregated into an overall measure, traditionally using geometric means. In this paper we propose a way of calculating global Malmquist indices and global frontier shift indices which provides a better estimation of the true frontier shift and furthermore is easy to calculate. Using simulation studies we show how this method outperforms the traditional aggregation approach, especially for sparsely populated production possibility sets and for frontiers that also change shape over time. Furthermore, our global indices can be used for unbalanced panels without disregarding any information. Finally, we show how the global indices are meaningful for calculating differences between frontiers from different groups rather than different time periods as illustrated in a small case study of bank branches in different countries.      

Economic versus environmental improvement potentials of Danish pig farms

This article demonstrates how economic and environmental improvement potentials of Danish pig farms can be estimated using Data envelopment analysis (DEA). To avoid some of the problems associated with the definition of undesirable outputs, environmental variables are included as nutrients applied with manure and nutrients removed with the crops. These environmental variables are combined with a series of economic variables, including the costs of fertilizer. Furthermore, subvector efficiency and a two‐step analysis are used in order to consider economic and environmental (technical) efficiency separately. The empirical results show considerable improvement potentials, especially on the environmental variables. Sensitivity studies validate that these results are robust to possible uncertainties in the measurement of the environmental variables.