Scale properties in data envelopment analysis

Recently there has been some discussion in the literature concerning the nature of scale properties in the Data Envelopment Model (DEA). It has been argued that DEA may not be able to provide reliable estimates of the optimal scale size. We argue in this paper that DEA is well suited to estimate optimal scale size, if DEA is augmented with two additional maintained hypotheses which imply that the DEA-frontier is consistent with smooth curves along rays in input and in output space that obey the Regular Ultra Passum (RUP) law (Frisch 1965). A necessary condition for a smooth curve passing through all vertices to obey the RUP-law is presented. If this condition is satisfied then upper and lower bounds for the marginal product at each vertex are presented. It is shown that any set of feasible marginal products will correspond to a smooth curve passing through all points with a monotonic decreasing scale elasticity. The proof is constructive in the sense that an estimator of the curve is provided with the desired properties. A typical DEA based return to scale analysis simply reports whether or not a DMU is at the optimal scale based on point estimates of scale efficiency. A contribution of this paper is that we provide a method which allows us to determine in what interval optimal scale is located.DEA; efficiency

data envelopment analysis

Thesis(Master) -- KDI School: Master of Public Policy, 2020This paper analyzes the efficiency of internet-only banks in South Korea during the period starting from 2017 to 2019. To use the data envelopment analysis, we examine the efficiency rate of Korea’s banking industry. The data envelopment analysis(DEA) was generally used to apply to measure the efficiency of banks. From the perspective taken, variables alter to inputs and outputs. Herein the operating and intermediation approaches were applied. Analysis on two internet-only banks in Korea, the results indicate that Kakao bank efficiencies tend to increase. However, the K-bank’s efficiencies were goes down as time goes by. Considering this situation, the role of government is gaining importance in promoting innovation enabled by new technologies.Chapter 1 Introduction Chapter 2 Literature review Chapter 3 Research Method and Data Chapter 4 Results Chapter 5 ConclusionmasterpublishedSeo-Jin LE

Data Envelopment Analysis Models of Investment Funds

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Evaluating Greek equity funds using data envelopment analysis

This study assesses the relative performance of Greek equity funds employing a non-parametric method, specifically Data Envelopment Analysis (DEA). Using an original sample of cost and operational attributes we explore the e¤ect of each variable on funds' operational efficiency for an oligopolistic and bank-dominated fund industry. Our results have significant implications for the investors' fund selection process since we are able to identify potential sources of inefficiencies for the funds. The most striking result is that the percentage of assets under management affects performance negatively, a conclusion which may be related to the structure of the domestic stock market. Furthermore, we provide evidence against the notion of funds' mean-variance efficiency

Uncertain Data Envelopment Analysis

Data Envelopment Analysis (DEA) is a nonparametric, data driven method to conduct relative performance measurements among a set of decision making units (DMUs). Efficiency scores are computed based on assessing input and output data for each DMU by means of linear programming. Traditionally, these data are assumed to be known precisely. We instead consider the situation in which data is uncertain, and in this case, we demonstrate that efficiency scores increase monotonically with uncertainty. This enables inefficient DMUs to leverage uncertainty to counter their assessment of being inefficient. Using the framework of robust optimization, we propose an uncertain DEA (uDEA) model for which an optimal solution determines 1) the maximum possible efficiency score of a DMU over all permissible uncertainties, and 2) the minimal amount of uncertainty that is required to achieve this efficiency score. We show that the uDEA model is a proper generalization of traditional DEA and provide a first-order algorithm to solve the uDEA model with ellipsoidal uncertainty sets. Finally, we present a case study applying uDEA to the problem of deciding efficiency of radiotherapy treatments

Leading advertisers efficiency evaluated by data envelopment analysis

In this paper we analyze the problem of measuring the advertising efficiency of the Leading US Advertisers during the period 2001-2006. We use the DEA (Data Envelopment Analysis) approach that enables to evaluate the relative efficiency in case of multiple inputs and outputs. In particular, the classical CCR-DEA model is first implemented in each year considered; a windows analysis approach is then used in order to better capture the dynamics of efficiency. Finally, the effect on efficiency of advertising spending over time, is captured by Adstock as an additional variable of the DEA model. The dynamics of Adstock is described by a finite difference equation.

Modeling Blank Data Entries in Data Envelopment Analysis

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

STRUCTURING PRODUCT-MARKETS: AN APPROACH BASED ON CUSTOMER VALUE

We offer an efficiency-based approach to derive market partitions and respective benchmarks using Data Envelopment Analysis. Product efficiency is measured as an output to input value from the customer’s perspective. Products offering a maximum customer value relative to alternatives represent benchmarks for different sub-markets. The framework is applied to data on compact cars. relevant product segments.Customer Value, Product-Market Structuring, Market Partitioning, Data Envelopment Analysis, Product Efficiency, Frontier Functions