Growing socioeconomic inequalities and deepening polarization among and within nations indicate a major risk of political, social and economic instability. Policymakers need to deepen their awareness and understanding of the circumstances and find useful guidance and examples to inspire their effective qualitative and quantitative policies. This paper empirically investigates the relative dynamic socioeconomic efficiency of thirty OECD countries using Data Envelopment Analysis (DEA) methodology. As an extension to the basic output-oriented DEA models with variable returns-to-scale, window analysis is employed. The appropriate design of window length is also proposed in the study. In the first step, the relative efficiency of the countries was measured by four economic indicators. In the second step, four new indicators were added, covering social, institutional and environmental dimensions. It has been found that, in some cases, performance rankings change very significantly and that the overall relative performance of the OECD countries increases when the set of economic indicators is extended.
Due to large and deepening development disparities among countries, comparisons across them have gained utmost importance, both in theoretical and empirical sense. At the same time, overuse of natural resources and climate change are among the most difficult issues today's world is facing. Consequently, there is a growing research interest in investigating the performance of countries, especially in terms of environmental and energy efficiency. This paper brings a literature overview on the application of data envelopment analysis (DEA) to studies that empirically explore socio-economic efficiency of OECD (Organisation for Economic Co-operation and Development) member countries. The listed papers are categorised with regard to the relevance given to economic, environmental or energy indicators. Their basic content is summarised, along with the major findings. In this way, both measurement of countries' performance and the nonparametric approach of DEA have been given deserved attention. JEL CLASSIFICATIONSc60; c67; D24; o40; o47 ARTICLE HISTORY
The aim of this paper is to use the Data Envelopment Analysis (DEA) method to measure and analyze different relative efficiencies of five Croatian shipyards. The indicators are chosen to capture different aspects of shipbuilding performance in Croatia. Window analysis is used to determine shipyard efficiency and observe possible changes in shipyard efficiency over time. This leads to identifying a subset of efficient best practice shipyards, whereas for others the magnitude of their inefficiency is ascertained along with the specified efficient input and output targets. The importance of window analysis is that its results serve as an early warning system to all inefficient shipyards. In identifying the sources of inefficiencies and formulating proposals for improving shipyard performance observed over a six-year period (2007-2012), the results presented in this paper can be used to enhance and alter decisions.
Public health services, as a preventive aspect of health care, are essential for the sustainability of the entire health care system. However, the context of public health services, which focus is primarily on prevention, is not a common setting when measuring the efficiency within nonparametric evidence-based approach. The aim of this study is to measure the efficiency of the financial performance of organizational units of the public health institute in Croatia, the Health Ecology Department in particular, during the period 2016-2018 using data envelopment analysis. Among the many reasons behind choosing this nonparametric method is the fact that it identifies the sources of inefficiency and specifies the directions and magnitudes of improvements required. Two input-oriented models-CCR under constant and BCC under variable returns-to-scale assumptionare employed for evaluating three types of efficiencytechnical, pure technical and scale efficiency. Two hypotheses are examined and empirically confirmed: first, that there is significant between-unit variability in financial performance, and second, that investments are the major source of inefficiency among the observed indicators. The results have additionally revealed that the mentioned differences are less pronounced in the case of pure technical efficiency, implying that the overall inefficiency of the Health Ecology Department units can be generally attributed to scale efficiency. Besides, only three out of twelve department units are considered efficient. The implications of the research results are aimed at further research and testing the efficiency of the entire network of public health institutes, as well as to provide policy makers with new insights when considering different modes of organizing and delivering public health services.
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