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The ethical constraints forced on an investment fund satisfy the fulfilment of humanitarian aims but may lower the investment profitability. Hence, when we measure the performance of ethical mutual funds we cannot disregard the ethical component.In this contribution we propose a performance indicator which considers the expected return, the investment risk, the ethical component and the subscription and redemption costs together. The performance measure proposed is obtained using a DEA approach, which allows to measure the relative efficiency of decision making units in presence of a multiple input-multiple output structure.The DEA performance indicator for ethical funds can be computed with different models, according to the nature of the ethical indicator which characterizes the socially responsible funds. In particular, a DEA model with exogenously fixed output variables and a categorical variable model seem appropriate.

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This Working Paper is published under the auspices of the Department of Applied Mathematics of the Ca' Foscari University of Venice. Opinions expressed herein are those of the authors and not those of the Department. The Working Paper series is designed to divulge preliminary or incomplete work, circulated to favour discussion and comments. Citation of this paper should consider its provisional nature.

In order to evaluate the performance of socially responsible investment (SRI) funds, we propose some models which use data envelopment analysis and can be computed in all phases of the business cycle. These models focus on the most crucial elements of an investment in mutual funds. In the literature both constant and variable returns to scale DEA models have been used to evaluate the performance of mutual funds. An empirical investigation carried out on European SRI equity funds indicates that for the funds analyzed the returns to scale are constant. Another aspect taken into account by the empirical investigation is the measurement of the degree of social responsibility of SRI equity funds in the various European countries. In addition, we have analyzed the performance of the funds considered with the different DEA models proposed, which differ in the way the ethical objective is taken into account. Moreover, the paper focuses on another crucial issue regarding socially responsible investing: the comparison of the performances between SRI and non SRI funds; the empirical study suggests that the ethical objective can be pursued without having to renounce financial rewards.

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). Data envelopment analysis (DEA) is a "data-oriented" approach for evaluating the performance of a set of entities called decision-making units (DMUs) whose performance is categorized by multiple metrics. These performance metrics are classified or termed as inputs and outputs under DEA. Although DEA has a strong link to production theory in economics, the tool is also used for benchmarking in operations management, where a set of measures is selected to benchmark the performance of manufacturing and service operations. In the circumstance of benchmarking, the efficient DMUs, as defined by DEA, may not necessarily form a "production frontier," but rather lead to a "best-practice frontier" (Cook, Tone, and Zhu, 2014).Over the years, we have seen a variety of DEA empirical applications. This handbook aims to compile state-of-the-art empirical studies and applications using DEA. It includes a collection of 18 chapters written by DEA experts.Chapter 1, by Chen, Gregoriou, and Rouah, examines the performance of chief executive officers (CEOs) of US banks and thrifts. The authors find evidence that best-practice CEOs who have a DEA efficiency score of one are rewarded with higher compensation compared to underperforming CEOs who have a DEA efficiency score greater than one. They also find DEA efficiency score to be a highly significant predictor of CEO compensation.Chapter 2, by Yu and Chen, is dedicated to describe the network operational structure of transportation organizations and the relative network data envelopment analysis model. Route-based performance evaluation, environmental factors, undesirable outputs, and multi-activity framework are incorporated into their application. v Chapter 3, by Hu and Chang, demonstrates how to use different types of DEA models to compute the total-factor energy efficiency scores with an application to energy efficiency.Chapter 4, by Growitsch, Jamasb, Müller, and Wissner, explores the impact of incorporating customers' willingness to pay for service quality in benchmarking models on cost efficiency of distribution networks.Chapter 5, by Volz, provides a brief review of previous applications of DEA to the professional baseball industry followed by two detailed applications to Major League Baseball.Chapter 6, by Cummins and Xie, examines efficiency and productivity of US property-liability (P-L) insurers using DEA. The authors estimate pure technical, scale, cost, revenue, and profit efficiency over the period 1993-2011. Insurers' adjacent year total-factor productivity changes, and their contributing factors are also investigated.Chapter 7, by Premachandra, Zhu, Watson, and Galagedera, presents a two-stage network DEA model that decomposes the overall efficiency of a decision-making unit into two components and demonstrates its applicability by assessing the relative performance of 66 large mutual fund families in the USA over the period 1993-2008. Chapter 8, by Basso and Funari, presents a comprehensive review of the literature of DEA models for the performance assessment o...