Creating composite indicators with DEA and robustness analysis: the case of the Technology Achievement Index

Cherchye, Laurens; Moesen, Willem; Rogge, Nicky; Puyenbroeck, Tom Van; Saisana, Michaela; Saltelli, Andrea; Liška, Roman; Tarantola, Stefano

doi:10.1057/palgrave.jors.2602445

Cited by 250 publications

(67 citation statements)

References 26 publications

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“…Using DEA to create composite indicators has previously been referred to as the 'benefit of the doubt' approach (Melyn and Moesen, 1991;Cherchye et al, 2007). For example, Cherchye et al (2008) have used this technique in the context of creating the Technology Achievement Index. Zhou et al (2007) outline the construction of composite indicators using DEA and apply their approach to modeling the development of sustainable energy in 18 countries.…”

Section: Benefits and Limitations Of Composite Measures Of Efficiencymentioning

confidence: 99%

Using Data Envelopment Analysis to Address the Challenges of Comparing Health System Efficiency

2015

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Efficiency is one of the most potent measures of health system performance and is of particular interest to policy makers because it seeks to assess the valued outcomes of a health system in relation to the resources that are sacrificed to achieve those outcomes. However, the production process of the health care system is a complex sequence, and most indicators are only able to capture part of that process; these indicators offer limited scope for analysis. While researchers have previously constructed composite indicators which combine partial measures into a single number, the weights used for aggregating data can be contentious and may not be universally applicable across systems. Data envelopment analysis (DEA) is most often used to compare the productivity of different producing entities, including health systems. In this article, we instead propose a method that relies on DEA to construct composite health system efficiency indicators from several partial efficiency measures. Among other noted benefits, this enables the construction of composite indicators where different weights are attached to partial indicators for each country, allowing countries to be viewed according to the weights that cast each in the best light. Our application of this method suggests that there is reasonable consistency among the countries that are found to be efficient.Rising health care costs and increasing concerns about fiscal sustainability have brought the issue of health system efficiency to the forefront of policy discussions. Most high-income countries are trying to identify ways in which they can secure the same health outcomes for less, while many middle and low-income countries are attempting to expand their health systems while ensuring value for money. Comparative efficiency indicators offer policy makers an important resource in their search for efficiency improvements. These types of indicators can be used to identify areas in the health system that are underperforming, as well as highlight other countries to look towards to identify potential processes that may improve the value for money of the domestic system (Papanicolas and Smith, 2013).While the concept of health system efficiency is deceptively simple -maximizing valued health system outputs relative to inputs -it becomes more difficult to operationalise when applied to a concrete situation, particularly at the system level. Among the challenges in measuring health system efficiency are defining and measuring the valued system outputs and inputs, which may differ across institutions. In practice, definitions of outputs vary and cover a range of factors such as overall performance, quality of care, health gain or volume of treatment. Thus, efficiency indicators essentially serve as a summary measure of the extent to which the inputs to the health system, in the form of expenditures and other resources, are used to secure these goals of the health system. Yet, the limitations of available metrics to measure the valued outputs and inputs at both natio...

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Section: Benefits and Limitations Of Composite Measures Of Efficiencymentioning

confidence: 99%

Using Data Envelopment Analysis to Address the Challenges of Comparing Health System Efficiency

2015

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“…A.3, e.g. These constraints, which are recommended by recent DEA applications (Cherchye et al, 2007) were added to avoid allowing regions to achieve a high score simply by assigning zero weight to all indicators for which they have a low performance, or by assigning an unreasonably high weight to a single indicator. Another apparent feature is that the aggregation method primarily affects the mid-ranking regions and, to a lesser extent, the most We finally study the impact on the ranking of the set of weights to be assigned to the indicators.…”

Section: A1 the Empirical And Measurement Frameworkmentioning

confidence: 99%

Social Multi-Criteria Evaluation for a Sustainable Economy

Munda¹

2008

306

232

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“…The Organization for Economic Co-operation and Development handbook [6] requires uncertainty and sensitivity analyses for the construction of composite indicators to test the robustness of the composite indicator and improve transparency. Those analyses have been required in several other studies as well [14][15][16]. Munda and Nardo [17] assert that because weights are one of the principal sources of technical uncertainty of the results provided, methodological discussions and sensitivity analyses are necessary to deal with uncertainty.…”

Section: Acronymmentioning

confidence: 99%

Process attribute rating and sensitivity analysis in process assessment

Jung¹

2011

J Software Evolu Process

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SUMMARYThe capability level of a process assessed on the basis of Intemational Standard ISO/IEC 15504 is determined by utilizing process attributes ratings (PAs). PA rating as a composite indicator is determined by the extent to which outcomes or the implemented practices are achieved. However, the ISO/IEC 15504 standards do not provide recommendations as to assessment methods, including the following: (i) which of the outcomes or practices implemented are evaluated as an intermediate step in rating the PA; and (ii) how a set of their achievements is aggregated into the PA rating. This study addresses a simple additive weighting method based on the performance value and weights of practices implemented as well as its rationale, in order to facilitate consistent and repeatable PA ratings. Then, sensitivity analysis is conducted to evaluate how much a change in the weight or performance value of an implemented practice wül affect and alter the current PA rating. Our sensitivity analysis can be employed in cases in which an assessor must explain his/her ratings to assessment team members, as well as the organizational unit assessed. Six examples are provided to iüustrate the applications of our sensitivity analyses.

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Creating composite indicators with DEA and robustness analysis: the case of the Technology Achievement Index

Cited by 250 publications

References 26 publications

Using Data Envelopment Analysis to Address the Challenges of Comparing Health System Efficiency

Using Data Envelopment Analysis to Address the Challenges of Comparing Health System Efficiency

Social Multi-Criteria Evaluation for a Sustainable Economy

Process attribute rating and sensitivity analysis in process assessment

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