692 research outputs found

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

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    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.

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    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;

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

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    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

    Cognitive strategic groups and long-run efficiency evaluation : the case of Spanish savings banks

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    In the framework of Cognitive Approach, this paper proposes a new method to identify strategic groups (SG) using Data Envelopment Analysis (DEA) methods. Two assumptions are maintained in the SG literature: first, firms grouped together value inputs and outputs similarly, and, second, some degree of stability in those valuations should be identified. Virtual weights obtained from DEA are extremely useful in the valuation of the strategic variables, but a problem emerges when longitudinal analysis is performed. This problem is addressed by defining a long run DEA evaluation. SGs are determined by means of Cluster Analysis, using virtual outputs and virtual inputs as variables and Spanish savings banks as observations. The traditional method of determining SGs by clustering on the original variables is also applied and the results are compared. It is shown that the long run DEA weights approach has advantages over the traditional methodology

    Cognitive strategic groups and long-run efficiency evaluation : the case of Spanish savings banks

    Get PDF
    In the framework of Cognitive Approach, this paper proposes a new method to identify strategic groups (SG) using Data Envelopment Analysis (DEA) methods. Two assumptions are maintained in the SG literature: first, firms grouped together value inputs and outputs similarly, and, second, some degree of stability in those valuations should be identified. Virtual weights obtained from DEA are extremely useful in the valuation of the strategic variables, but a problem emerges when longitudinal analysis is performed. This problem is addressed by defining a long run DEA evaluation. SGs are determined by means of Cluster Analysis, using virtual outputs and virtual inputs as variables and Spanish savings banks as observations. The traditional method of determining SGs by clustering on the original variables is also applied and the results are compared. It is shown that the long run DEA weights approach has advantages over the traditional methodology.

    Assessing the Efficiency of Mother-to-Child HIV Prevention in Low- and Middle-Income Countries using Data Envelopment Analysis

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    AIDS is one of the most significant health care problems worldwide. Due to the difficulty and costs involved in treating HIV, preventing infection is of paramount importance in controlling the AIDS epidemic. The main purpose of this paper is to explore the potential of using Data Envelopment Analysis (DEA) to establish international comparisons on the efficiency implementation of HIV prevention programmes. To this effect we use data from 52 low- and middle-income countries regarding the prevention of mother-to-child transmission of HIV. Our results indicate that there is a remarkable variation in efficiency of prevention services across nations, suggesting that a better use of resources could lead to more and improved services, and ultimately, prevent the infection of thousands of children. These results also demonstrate the potential strategic role of DEA for the efficient and effective planning of scarce resources to fight the epidemic.HIV Prevention; DEA; Mother-to-Child HIV Transmission.

    Benchmarking the Health Sector in Germany – An Application of Data Envelopment Analysis

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    At present, a first round of hospital benchmarking as required by German law on health care reform takes place. After extensive discussions between hospitals and insurance companies, which are jointly responsible to deliver benchmarking results, a method with some peculiar characteristics was chosen. In this paper it is argued that the deficiencies of said method could be overcome by using Data Envelopment Analysis (DEA). The reasons that make DEA an advisable tool for policy decisions within the context of relative performance evaluation in the health care sector are discussed. In order to illustrate the potential of nonparametric frontier estimation for hospital benchmarking in Germany, a comparison of hospitals, which provide the same basic clinical care, is carried out. Controlling for differences in the case mix and for possible heterogeneity of the services which hospitals provide, substantial productivity differences can be detected. Beyond simply identifying inefficient providers DEA leads to additional insight about the reasons of inefficiency and to useful management implications.Health care reform benchmarking relative performance evaluation Data Envelopment Analysis

    Modeling Blank Data Entries in Data Envelopment Analysis

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    We show how Data Envelopment Analysis (DEA) can handle missing data. When blank data entries are coded by appropriate dummy values, the DEA model automatically excludes the missing data from the analysis. We extend this result to weight-restricted DEA models by presenting a simple modification to the usual weight restrictions, which automatically relaxes the weight restriction in case of missing data. Our approach is illustrated by a case study, describing an application to international sustainable development indices.Data Envelopment Analysis, Weight Restrictions, Missing Data, Blank Entries
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