being as represented by the concept of utility, and the goodness of a social state depends on the sum total of utilities associated with that state [regardless of how these utilities are distributed among individuals (Sen 1999)].

Widespread poverty in the developing world remains a serious challenge for the development community and may have prompted the inclusion of poverty eradication among the MDGs. The World Bank has declared poverty reduction its overarching objective and a benchmark measure of its performance as a development institution. In view of these considerations, we emphasize poverty-focused evaluation in this paper. If development is about _empowering people_ to take charge of their destinies (Wolfensohn 1998), then _poverty_ must be seen as the _deprivation of basic capabilities_ to lead the kind of life one has reason to value (Sen 1999). In this perspective, the identification of poverty must go beyond lowness of income. This invitation however does not deny the fact that inadequate income can lead to capability deprivation. The notion of relative deprivation plays an important role in shaping the evaluative framework discussed here.

In the context of applied policy analysis, it is not enough to have an analytical or evaluative framework, available data must be processed according to that framework in order to draw relevant conclusions. Invariably, one needs a computing platform to do the job. We propose to show that the syntax of EViews can easily be exploited to do the job at hand. EViews stands for _Econometric Views,_ a Windows version of a software designed by Quantitative Micro Software (QMS) for the processing of time series data and the conduct of econometric analysis. We find that many people who are already using EViews for other purposes and interested in distributional issues may not necessarily be familiar with the fundamental concepts and techniques of distributional analysis. In addition, depending on their level of mastery of the software, they may not even think of EViews as appropriate for handling household survey data. Yet the current version of the software (Version 4.1) can handle 4 million observations per series (variable) and the total number of observations (i.e. number of variables times the number of observations per variable) is limited only by the available Random Access Memory (RAM). In addition the syntax is sufficiently straitforward to allow the user to plot common curves such as cumulative distributions, concentration or Lorenz curves. It is also easy to compute indices of social welfare, inequality and poverty.

The outline of the paper is as follows. Section 2 presents a poverty-focused evaluation framework based on the _maximin principle._ The concept of relative deprivation underpins the distribution of social weights in the formulation of aggregative judgments. Section 3 focuses on the implications of the traditional approach to poverty analysis based on the use of a poverty line. In particular, it is shown that when the distribution of welfare is truncated on the basis of a poverty line, the Gini coefficient can be additively decomposed into two components, one measuring the within group inequality and the other the inter-group inequality. Impact analysis is reviewed in Section 4. In that Section, we show how the ranking of socioeconomic situations can vary with the specification of social weights. The basic EViews routines used to produce illustrations are provided in boxes. Concluding remarks are presented in the last Section of the paper.

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