Measuring income inequalities beyond the Gini coefficient

Growing interest in the analysis of interrelationships between income distribution and economic growth has recently stimulated new theoretical and empirical research. Measures such as the head-count ratio for the poverty index or the widely used Gini coefficient are aggregated indicators describing the general extent of inequality without deeper insights into income distribution among households. To derive an indicator accounting for income distribution among income groups, we propose a new approach based on an output oriented DEA model where the input value is unitized to 1 for each country and weights restrictions imposed so as to favour a higher income share in the lower quantiles. We demonstrate the merits of this approach on the quintile income breakdown data of 29 European countries. Prioritizing lower income groups’ welfare, countries such as Slovenia and Slovakia can be equally favoured by the new proposed indicator while being assessed differently by the Gini index. An intertemporal analysis reveals a slight deterioration of income distribution in the majority of 29 European countries over the period of 2007–2016 in a Rawlsian sense.

Performance measurement in data envelopment analysis without slacks: an application to electricity distribution companies

In performance measurement of the firms using tools such as data envelopment analysis (DEA) models, weak efficient units are almost appeared as reference points in the models. To avoid zero weights or equivalently non-zero slacks in DEA assessment, weights restrictions are used frequently. In DEA literature, two-stage procedures are developed to deal with non-zero slacks based on restricting input/output weights in multiplier formulating the CCR DEA model. In this paper, a single-stage approach for efficiency evaluation is developed to ensure zero slacks in target setting and avoid weights dissimilarity. A real case on electricity distribution companies in Iran has been given to demonstrate the applicability of the proposed approach.

Introducing Weights Restrictions in Data Envelopment Analysis Models for Mutual Funds

Data envelopment analysis has been applied in a number of papers to measure the performance of mutual funds, besides a great many applications on the more diverse fields of performance evaluation. The data envelopment analysis models proposed in the mutual funds literature do not generally set restrictions on the weights assigned to the input and output variables. In this paper, we study the effects of the introduction of different weight restrictions on the results of the performance evaluation of mutual funds. In addition, we provide a unified matrix representation for three widely used approaches on weight restrictions: virtual weight restrictions with constraints on all decision-making units (DMUs) (on all funds); virtual weight restrictions with constraints only on the target unit; assurance regions. Using the unified matrix representation of the weights constraints, we formulate the data envelopment analysis (DEA ) efficiency model and express the efficient frontier in a unified way for the different weight restrictions considered. We investigate the effects of the different weight restrictions on the performance evaluation by means of an empirical application on a set of European mutual funds. Moreover, we study the behaviour of the fund performance scores as the restrictions on the weights become increasingly strict.

Uniformization of frontiers in non-radial ZSG-DEA models: an application to airport revenues

We propose in this paper an extension to the Zero Sum Gains Data Envelopment Analysis model (ZSG-DEA). The proposed approach takes into account, simultaneously, non-radial projections and cone-ratio weights restrictions. We developed an iterative approximate algorithm to solve this model, as in the case study it is oriented only to the constant sum output. The theoretical approach is applied to the concession of discounts and surcharges problem, in terms of airport fees.

Multicriteria approach to data envelopment analysis

With the aim of making Data Envelopment Analysis (DEA) more acceptable to the managers' community, the Weights Restrictions approaches were born. They allow DEA to not dispose of any data and permit the Decision Maker (DM) to have some management over the method. The purpose of this paper is to suggest a Weights Restrictions DEA model that incorporates the DM preference. In order to perform that, we employed the MACBETH methodology as a tool to find out the bounds of the weights to be used in a Weights Restrictions approach named Virtual Weights Restrictions. Our proposal achieved an outcome that has an expressive correlation with three widely used decision-aids methodologies: the ELECTRE III, the SMART and the PROMETHEE I and II. In addition, our approach was able to join the most significant outcomes of all the above three Multicriteria decision-aids methodologies in one unique outcome.