Consistent weight restrictions in data envelopment analysis

It has recently been shown that the incorporation of weight restrictions in models of data envelopment analysis (DEA) may induce free or unlimited production of output vectors in the underlying production technology, which is expressly disallowed by standard production assumptions. This effect may either result in an infeasible multiplier model with weight restrictions or remain undetected by normal efficiency computations. The latter is potentially troubling because even if the efficiency scores appear unproblematic, they may still be assessed in an erroneous model of production technology. Two approaches to testing the existence of free and unlimited production have recently been developed: computational and analytical. While the latter is more straightforward than the former, its application is limited only to unlinked weight restrictions. In this paper we develop several new analytical conditions for a larger class of unlinked and linked weight restrictions

Multiple goals and ownership structure : effects on the performance of Spanish savings banks.

Spanish savings banks (SBs) are financial institutions with a wide mission that includes different stakeholders’ goals. Profit maximization is only one among several goals, and the widespread use of cost or profit efficiency as the only comparative performance measure may prove to be insufficient in this context. To overcome this problem, we build an aggregate performance index for organizations with multiple goals. Furthermore, we show how the ownership structure of SBs influences their economic behavior in two basic ways: (1) the performance level and (2) their goal priorities. In particular, we distinguish two types of ownership structures in our application, namely, organizations controlled by Public Administrations and those controlled by insiders (i.e. managers and workers). Our results indicate that each type has different priorities and differ in their performance indexes. More specifically, the empirical analysis shows that insider-controlled SBs favor goals related to profit maximization and the universal access to financial services and, furthermore, they perform better. In contrast, contributing to regional development becomes the most favored goal when Public Administrations have a majority in the bank.

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.

Opening the 'black box' of efficiency measurement: input allocation in multi-output settings

We develop a new Data Envelopment Analysis (DEA)-based methodology for measuring the efficiency of Decision Making Units (DMUs) characterized by multiple inputs and multiple outputs. The distinguishing feature of our method is that it explicitly includes information about output-specific inputs and joint inputs in the efficiency evaluation. This contributes to opening the „black box? of efficiency measurement in two different ways. First, including information on the input allocation substantially increases the discriminatory power of the efficiency measurement. Second, it allows to decompose the efficiency value of a DMU into output-specific efficiency values which facilitates the identification of the outputs the manager should focus on to remedy the observed inefficiency. We demonstrate the usefulness and managerial implications of our methodology by means of a unique dataset collected from the Activity Based Costing (ABC) system of a large service company with 290 DMUs.

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/

Measuring Hospital Efficiency through Data Envelopment Analysis when Policy-makers’ Preferences Matter. An Application to a sample of Italian NHS hospitals

In this paper we show how both the choice of specific constraints on input and output weights (in accordance with health care policy-makers’ preferences) and the consideration of exogenous variables outside the control of hospital management (and linked to past policy-makers’ decisions) can affect the measurement of hospital technical efficiency using the Data Envelopment Analysis (DEA). Considering these issues, the DEA method is applied to measure the efficiency of 85 (public and private) hospitals in Veneto, a Northern region of Italy. The empirical analysis allows us to verify the role of weight restrictions and of demand in measuring the efficiency of hospitals operating within a National Health Service (NHS). We find that the imposition of a lower bound on the virtual weight of acute care discharges weighted by case-mix (in order to consider policy-maker objectives) reduces average hospital efficiency. Moreover, we show that, in many cases, low efficiency scores are attributable to external factors, which are not fully controlled by the hospital management; especially for public hospitals low total efficiency scores can be mainly explained by past policy-makers’ decisions on the size of the hospitals or their role within the regional health care service. Finally, non-profit private hospitals exhibit a higher total inefficiency while both non-profit and for-profit hospitals are characterised by higher levels of scale inefficiency than public ones.Hospital performance, Technical efficiency, Data envelopment analysis, NationalHealth Service

Creating Composite Indicators with DEA and Robustness Analysis: the case of the Technology Achievement Index

Composite indicators are regularly used for benchmarking countries’ performance, but equally often stir controversies about the unavoidable subjectivity that is connected with their construction. Data Envelopment Analysis helps to overcome some key limitations, viz., the undesirable dependence of final results from the preliminary normalization of sub-indicators, and, more cogently, from the subjective nature of the weights used for aggregating. Still, subjective decisions remain, and such modelling uncertainty propagates onto countries’ composite indicator values and relative rankings. Uncertainty and sensitivity analysis are therefore needed to assess robustness of final results and to analyze how much each individual source of uncertainty contributes to the output variance. The current paper reports on these issues, using the Technology Achievement Index as an illustration.factor is more important in explaining the observed progress.composite indicators, aggregation, weighting, Internal Market

Creating composite indicators with DEA and robustness analysis: The case of the technology achievement index.

Composite indicators are regularly used for benchmarking countries’ performance, but equally often stir controversies about the unavoidable subjectivity that is connected with their construction. Data Envelopment Analysis helps to overcome some key limitations, viz., the undesirable dependence of final results from the preliminary normalization of sub-indicators, and, more cogently, from the subjective nature of the weights used for aggregating. Still, subjective decisions remain, and such modelling uncertainty propagates onto countries’ composite indicator values and relative rankings. Uncertainty and sensitivity analysis are therefore needed to assess robustness of final results and to analyze how much each individual source of uncertainty contributes to the output variance. The current paper reports on these issues, using the Technology Achievement Index as an illustration.Indexes; Indicators; Robustness; Technology;

Opening the 'black box' of efficiency measurement: input allocation in multi-output settings.

We develop a new Data Envelopment Analysis (DEA)-based methodology for measuring the efficiency of Decision Making Units (DMUs) characterized by multiple inputs and multiple outputs. The distinguishing feature of our method is that it explicitly includes information about output-specific inputs and joint inputs in the efficiency evaluation. This contributes to opening the „black box‟ of efficiency measurement in two different ways. First, including information on the input allocation substantially increases the discriminatory power of the efficiency measurement. Second, it allows to decompose the efficiency value of a DMU into output-specific efficiency values which facilitates the identification of the outputs the manager should focus on to remedy the observed inefficiency. We demonstrate the usefulness and managerial implications of our methodology by means of a unique dataset collected from the Activity Based Costing (ABC) system of a large service company with 290 DMUs.