Estimation of cost efficiency in non-parametric frontier models
The paper proposes a bootstrap methodology for estimating cost efficiency in data envelopment analysis. We consider the conventional concept of Fare, Grosskopf and Lovellcost efficiency, for which our algorithm re-samples “naive” input-oriented efficiency scores, rescales original inputs to bring them to the frontier, and then re-estimates cost efficiency scores for the rescaled inputs. Next, we examine Tone cost efficiency, where input prices vary across producers. Here we show that the direct modification on bootstrap algorithms by Simar and Wilson are applicable. We consider cases both with the absence and presence of environmental variables (i.e. input variables not directly controlled by firms). The bootstrap methodology exploits these assumptions: 1) the sample are i.i.d. random variables with the continuous joint probability density function with support over production set; 2) the frontier is smooth; and 3) the probability of observing firms on the frontier approaches unity with an increase in sample. The results of simulations for a multi-input, multi-output Cobb–Douglas production function with correlated outputs, and correlated technical and cost efficiency, show consistency of our proposed algorithm, even for small samples. Finally, we offer real data estimates for the Japanese banking industry in 2013. Our package “rDEA,” developed in the R language, is available from the GitHub and CRAN repository.
This publication refl ects the results of the authors’ research aimed at fi nding ways to reduce the complexity of appraising the investment attractiveness of potential recipients of investments. The purpose of the research is to create a methodology that will eff ectively manage not only the process of determining the recipients of investments but also the development of organizations to increase their investment attractiveness. The authors provide an overview of the most signifi cant publications that consider existing methods for appraising investment attractiveness, based on both fi nancial statements and the market value of a business. In the main part of the article, the authors conclude that data envelopment analysis (DEA) may be used to aggregate several diff erent criteria of the investment attractiveness appraising in one number. The section that presents the empirical results of the study contains a description of a number of indicators of Russian oil refi ning companies and their aggregation based both on the method of expert assessments and a formal approach using the DEA. The examples only apply criteria calculated based on organizations’ fi nancial statements. It is emphasized that in real practice the fi rst method is a very expensive and time-consuming procedure in comparison with the second, which provides a formalized agreement of the criteria used in the former method, and takes into account the situation in the entire market segment under study. It is shown that the calculations made based on these two methods give approximately the same results. This indicates that the methodology proposed by the authors can be considered as an eff ective alternative to existing expert approaches to appraising investment attractiveness. In the fi nal part of the article, recommendations are formulated for improving the proposed methodological approach by including a number of market indicators that characterize the activities of potential recipients of investments.
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.
A “Network Analysis” section was arranged at the XVIIIth Interna- tional Academic Conference on Economic and Social Development at the Higher School of Economics on 11–12 April 2017. For the third year, this section invited scholars from sociology, political science, management, mathematics, and linguistics who use network analysis in their research projects. During the sessions, speakers discussed the development of mathematical models used in network analysis, studies of collaboration and communication networks, networks’ in- uence on individual attributes, identifcation of latent relationships and regularities, and application of network analysis for the study of concept networks.
The speakers in this section were E. V. Artyukhova (HSE), G. V. Gra- doselskaya (HSE), M. Е. Erofeeva (HSE), D. G. Zaitsev (HSE), S. A. Isaev (Adidas), V. A. Kalyagin (HSE), I. A. Karpov (HSE), A. P. Koldanov (HSE), I. I. Kuznetsov (HSE), S. V. Makrushin (Fi- nancial University), V. D. Matveenko (HSE), A. A. Milekhina (HSE), S. P. Moiseev (HSE), Y. V. Priestley (HSE), A. V. Semenov (HSE), I. B. Smirnov (HSE), D. A. Kharkina (HSE, St. Petersburg), C. F. Fey (Aalto University School of Business), and F. López-Iturriaga (Uni- versity of Valladolid).
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.