Data Envelopment Analysis and its Applications. Proceedings of the 13th International Conference of DEA
In recent decades, increased economic pressure and growing societal expectations have led to the introduction of performance-based funding models for universities. In this respect, a great scholarly attention has paid to how to evaluate universities performance correctly. This allows national governments to design and apply various taxonomies to facilitate the development of efficient programmes for the advancement of higher education. The wide spread approach used for that purpose is DEA. This paper provides a review of different approaches how to take into account universities heterogeneity when applying DEA to construct the typologies of university by showing statistically their similarities and differences. The authors use the modified DEA proposed by Aleskerov & Petrushchenko (2013) to evaluate performance scores of Russian technical universities. This proposed typology divides universities into specific groups with a description taking into account their heterogeneity.
This research studies the short-term effects of the Russian Excellence Initiative Project 5–100 on participating universities. To trace the effect, we develop a quasi-experimental methodology. A control group of universities comparable to the Project 5–100 universities at the starting point of the program’s implementation was singled out using propensity score matching. Data envelopment analysis was conducted, and the Malmquist productivity index was calculated to trace how and why the efficiency of the “participants” and “nonparticipants” of the Project 5–100 has changed. We find statistically significant positive effects of the policy both on the productivity and on the efficiency of the participating universities.
In this paper we provide the methodology for evaluating ef- fectiveness of international sanctions using Data Envelopment Analysis (DEA), which we use for generating the network matrix for further anal- ysis. DEA is a non-parametric technique used to compare performance of similar units, such as departments or organizations. DEA has wide applications in all industries, and has been successfully used to compare performance of hospitals, banks, universities, etc. The most important advantage of this technique is that it can handle multiple input and out- put variables, even those not generally comparable to each other. We use the ”Threat and Imposition of Sanctions (TIES)” Data 4.0 for analysis. This database contains the largest number of cases of international sanctions (1412 from the years 1945-2005) imposed by some countries on others, takes into account simultaneous sanction imposition, and also estimates the cost of all sanctions - both for those who receive and those who impose them. As input variables for DEA model we use the impact of sender commitment, anticipated target and sender economic costs, and actual target and sender economic costs. As the output variable, we use the outcome of sanctions for senders. We describe how to use DEA cross-efficiency outputs to build the network of sanction episodes. Our proposed combination of DEA and network methodology allows us to cluster sanction episodes depending on their outcomes, and provides explanations of higher efficiency of one group of sanction episodes over the others.
Data Envelopment Analysis (DEA) models and Free Disposal Hull (FDH) models were proposed almost simultaneously in the scientific literature. In DEA models, the constraints generate a convex set. For this reason, optimization methods and software are widely used for modeling and computations for these models. Non-convexity of production possibility set of FDH models refrained significantly the development of these models. In this paper, two methods are proposed for two-and three-dimensional frontier visualization for DEA and FDH models. Computational experiments using real-life datasets documented reliability and effectiveness of proposed methods.
In data envelopment analysis, methods for constructing sections of the frontier have been recently proposed to visualize the production possibility set. The aim of this paper is to develop, prove and test the methods for the visualization of production possibility sets using parallel computations. In this paper, a general scheme of the algorithms for constructing sections (visualization) of production possibility set is proposed. In fact, the algorithm breaks the original large-scale problems into parallel threads, working independently, then the piecewise solution is combined into a global solution. An algorithm for constructing a generalized production function is described in detail.
DEA-analysis is performed based on publicly available data on 94 world largest fashion retailers. Standard clusterization of coefficients obtained from DEA-analysis gives clusters that are analyzed with respect to homogeneity and fit to the types of strategic behavior outlined in strategic management.
In this paper we describe the Data Envelopment Analysis (DEA) research design and its applications for effectiveness evaluation of company marketing strategies. We argue that DEA is an efficient instrument for use in academia and industry to compare a company’s business performance with its competitors’. This comparison provides the company with information on the closest competitors, including evaluating strategies with similar costs, but more efficient outcomes (sales). Furthermore, DEA provides suggestions on the optimal marketing mix to achieve superior performance.