Data Envelopment Analysis Models of Investment Funds

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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

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

Data Envelopment Analysis as a Complement to Marginal Analysis

The consideration in the present study is mainly conceptual. The objective is to show how Data Envelopment Analysis (DEA) can be used to reveal the true input-output relations in an industry. In the estimation of a production function it is assumed that all firms use the existing technology efficiently. However, in the real world the observed firms produce homogeneous outputs with differences in factor intensities and in managerial capacity. Hence, inefficiencies are hidden in the estimated production functions. In order to overcome this drawback of the parametric approach and to reveal the true nature of the input-output relations in production, given the available technology, the DEA approach is applied. In this study DEA is applied in order to select the farms that utilize efficiently the existing technology, allowing the estimation of a production function that reveals the true input-output relations in sheep-goat farming, using farm accounting data from a sample of 108 sheep-goat farms.Research Methods/ Statistical Methods,

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

A Robust Data Envelopment Analysis for Evaluating Technical Efficiency of Indonesian High Schools

The main purpose of this study is to evaluate the technical efficiency of high school education in Indonesia by applying Data Envelopment Analysis (DEA), which is the most frequently used to measure the efficiency scores. However, this study uses a robust approach to face the complex problem of the traditional DEA, which may lead to biased results. Besides, it is a powerful approach to estimate technical efficiency when outliers contaminate the data set. Statistical data from general senior secondary schools in the period 2015/2016 is analyzed, using 34 provinces as decision-making units (DMUs), with eight input and six output variables. The results indicate that the average efficiency score of Indonesia's major political subdivisions in managing high school education is 0.936. Furthermore, as many as 32.35 percents of provinces achieve efficient performances, with an efficiency score equal to one, while 17 provinces have above average efficiency scores. The results also indicate that efficiency scores from robust data envelopment analysis provide better accuracy. Overall, application of robust data envelopment analysis (RDEA) is appropriate for measuring the efficiency of provincial performance in organizing secondary education

Cost Allocation and Convex Data Envelopment

This paper considers allocation rules. First, we demonstrate that costs allocated by the Aumann-Shapley and the Friedman-Moulin cost allocation rules are easy to determine in practice using convex envelopment of registered cost data and parametric programming. Second, from the linear programming problems involved it becomes clear that the allocation rules, technically speaking, allocate the non-zero value of the dual variable for a convexity constraint on to the output vector. Hence, the allocation rules can also be used to allocate inefficiencies in non-parametric efficiency measurement models such as Data Envelopment Analysis (DEA). The convexity constraint of the BCC model introduces a non-zero slack in the objective function of the multiplier problem and we show that the cost allocation rules discussed in this paper can be used as candidates to allocate this slack value on to the input (or output) variables and hence enable a full allocation of the inefficiency on to the input (or output) variables as in the CCR model.cost allocation; convex envelopment; data envelopment analysis; slack allocation

ORGANIC OR CONVENTIONAL? OPTIMAL DAIRY FARMING TECHNOLOGY UNDER THE EU MILK QUOTA SYSTEM AND ORGANIC SUBSIDIES

Organic farming, EU milk quota, Data Envelopment Analysis, subsidy payments, Agricultural and Food Policy,

The Human Development Index Adjusted for Efficient Resource Utilization

human development index, data envelopment analysis, efficiency, congestion and scale economics

Valuing Environmental Factors in Cost-Benefit Analysis Using Data Envelopment Analysis

Environmental cost-benefit analysis (ECBA) refers to social evaluation of investment projects and policies that involve significant environmental impacts. Valuation of the environmental impacts in monetary terms forms one of the critical steps in ECBA. We propose a new approach for environmental valuation within ECBA framework that is based on data envelopment analysis (DEA) and does not demand any price estimation for environmental impacts using traditional revealed or stated preference methods. We show that DEA can be modified to the context of CBA by using absolute shadow prices instead of traditionally used relative prices. We also discuss how the approach can be used for sensitive analysis which is an important part of ECBA. We illustrate the application of the DEA approach to ECBA by means of a hypothetical numerical example where a household considers investment to a new sport utility vehicle.Cost-Benefit Analysis, Data Envelopment Analysis, Eco-Efficiency, Environmental Valuation, Environmental Performance, Performance Measurement