ESTIMATING NON-CONCAVE METAFRONTIERS USING DATA ENVELOPE ANALYSIS

In this article we propose non-concave metafrontiers for estimating the inefficiency among production functions which do not necessarily belong to the same technology. In this case, estimating a joint production by literature approaches might be inappropriate. We call this inefficiency technological inefficiency and suggest Data Envelopment Analysis to construct a metafrontier production function which consists only of parts of different (group) frontier production functions. Thus, in contrast to the common literature our metafrontier does not need any assumptions additional to the group production functions. We illustrate our approach by means of a large sample of differently diversified crop farms. Results show that the literature approach overestimates the technological inefficiency in our sample for 75% of the observations and on average up to 7%-points in a diversification class of farms.Efficiency analysis, Metafrontier production function, Data Envelopment Analysis, Production Economics,

Product Specialization, Efficiency and Productivity Change in the Spanish Insurance Industry

In this paper we analyze the levels of technical efficiency and productivity growth attained by Spanish insurance companies during a period of deregulation. We compute Malmquist productivity indexes using the estimates of parametric distance function for several specialized insurance branches. In this way, we show that branch specialization matters a great deal and that firms combining two or three product lines (Health, Property-Liabilities and Life) perform better than firms operating in one insurance line exclusively. In the light of these results, we recommend that the remaining restrictions coming from the European Third Directives on the operations of multi-branch firms should be removed. Moreover, from a management point of view, it would be appropriate to encourage the creation of multi-branch insurance firms. However, in all cases, the estimated scores indicate low productivity growth (less than 2% per year) compared with a huge increase in insurance activity (premiums were multiplied by nearly 3 in a decade).Efficiency, parametric Malmquist index, output specialization, Spanish insurance

Canonical correlation analysis and DEA for azorean agriculture efficiency

In this paper we will document the application of canonical correlation analysis to variable aggregation using the correlations of the original variables with the canonical variates. A case study, about farms in Terceira Island, with a small data set is presented. In this data set of 30 farms we intend to use 17 input variables and 2 output variables to measure DEA efficiency. Without any data reduction procedure several problems known as “curse of dimensionality” are expected. With the data reduction procedures suggested it was possible to conclude quite acceptable and domain consistent conclusions.N/

COOPER-framework: A Unified Standard Process for Non-parametric Projects

Practitioners assess performance of entities in increasingly large and complicated datasets. If non-parametric models, such as Data Envelopment Analysis, were ever considered as simple push-button technologies, this is impossible when many variables are available or when data have to be compiled from several sources. This paper introduces by the ‘COOPER-framework’ a comprehensive model for carrying out non-parametric projects. The framework consists of six interrelated phases: Concepts and objectives, On structuring data, Operational models, Performance comparison model, Evaluation, and Result and deployment. Each of the phases describes some necessary steps a researcher should examine for a well defined and repeatable analysis. The COOPER-framework provides for the novice analyst guidance, structure and advice for a sound non-parametric analysis. The more experienced analyst benefits from a check list such that important issues are not forgotten. In addition, by the use of a standardized framework non-parametric assessments will be more reliable, more repeatable, more manageable, faster and less costly.DEA, non-parametric efficiency, unified standard process, COOPER-framework.

How to deal with S-shaped curve in DEA

経済学 / EconomicsIn DEA we are often puzzled by the big difference in CRS and VRS scores, and by the convex production possibility set syndrome in spite of the S-shaped curve often observed in many real data. In this paper we perform a challenge to these subjects.http://www.grips.ac.jp/list/facultyinfo/tone_kaoru

Transit Costs and Cost Efficiency: Bootstrapping Nonparametric Frontiers.

This paper explores a selection of recently proposed bootstrapping techniques to estimate non-parametric convex (DEA) cost frontiers and efficiency scores for transit firms. Using a sample of Norwegian bus operators, the key results can be summarised as follows: (i) the bias implied by uncorrected cost efficiency measures is numerically important (close to 25%), (ii) the bootstrapped-based test rejects the constant returns to scale hypothesis (iii) explaining patterns of efficiency scores using a two-stage bootstrapping approach detects only one significant covariate, in contrast to earlier results highlighting, e.g., the positive impact of high-powered contract types. Finally, comparing the average inefficiency obtained for the Norwegian data set with an analogous estimate for a smaller French sample illustrates how the estimated differences in average efficiency almost disappear once sample size differences are accounted for.

Projections onto Efficient Frontiers: Theoretical and Computational Extensions to DEA

Data Envelopment Analysis (DEA) has been widely studied in the literature since its inception in 1978 and is a key analytical technique used in Wharton's performance analysis for retail delivery systems. The methodology behind the classical DEA, the oriented method, is to hold inputs (outputs) constant and to determine how much of an improvement in the output (input) dimensions is necessary in order to become efficient. The authors extend this methodology in two substantive ways. First, a method is developed that determines the shortest projection from an inefficient DMU to the efficient frontier in both the input and output space simultaneously, and second, introduces the notion of the "observable" frontier and its subsequent projection. The observable frontier is the portion of the frontier that has been experienced by other DMUs, and thus the projection onto this portion of the frontier guarantees a recommendation that has already been demonstrated by an existing DMU or a convex combination of existing DMUs. A numerical example is used to illustrate the importance of these two methodological extensions.