The modern field of inequality measurement grew out of the intelligent application of quantitative methods to imperfect data in the hope of illuminating important social issues.
The important social issues remain, and it is interesting to see the ways in which modern analytical techniques can throw some light on what it is possible to say about them.
(Cowell 2000: 133)
Human beings are diverse in many and important ways: they vary in age, gender, ethnicity, nationality, location, religion, relationships, abilities, personalities, occupations, leisure activities, interests, and values.
Poverty measures seek to identify legitimate, accurate, and policy-relevant comparisons across people, whilst fully respecting their basal diversity. Further, they seek to do so using data that are affected by several kinds of errors and limitations. This is no straightforward task.After a measurement methodology has been chosen, the design of poverty measures—whether unidimensional or multidimensional—also requires a series of choices. We turn to these now. The choices relate to the space of the measure, its purpose, unit of identification and analysis, dimensions, indicators, deprivation cutoffs, weights, and poverty line. Of these, ‘purpose' is particularly influential in shaping the measure. As Ravallion states succinctly, ‘One wants the method of measurement to be consistent with the purpose of measurement' (1998: 1). This chapter describes each of these normative choices in the context of multidimensional poverty measurement design and outlines alternative ways that these choices might be understood, made, and justified. Many normative theories or approaches might be used to inform measurement design, including human needs, objective lists, subjective well-being, human rights, and preference-based approaches, as well as many other less formally defined approaches.[175] Whichever are used, the normative contribution is not simply philosophical; it has a practical aim: to motivate poverty reduction.
Taken together, normative choices link the data and measurement design back to poor people's lives and values and forward to the policies that, informed by poverty analysis, will seek to improve these. For example, dimensions which contribute disproportionately to poverty might become policy ‘priorities’. Do these reflect poor people's values? Regions showing high poverty levels may be targeted geographically. Do these accord with poor communities’, taxpayers’, and experts’ understandings of who is poor? Programmes such as cash transfers may target households. Do the poorest households benefit? The headlines (and political leaders) celebrate when multidimensional poverty falls. Is this situation also applauded by those they seem to have assisted? It goes without saying that if the measure of poverty is unhinged from people’s voices and values, poverty policies are unlikely to hit the mark.
The normative choices inherent in monetary and multidimensional poverty design appear to cause consternation, particularly if measurement conventions have not yet been established.[176] In a section of The Idea of Justice named ‘The fear of non-commensurability’, Sen describes ‘non-commensurability’ as ‘a much-used philosophical concept that seems to arouse anxiety and panic’ (2009: 240). Yet setting priorities is no weakness. As Sen points out, ‘the need for selection and discrimination is neither an embarrassment, nor a unique difficulty, for the conceptualization of functionings and capabilities’ (1992: 46 and 44).
Building on Sen in their extremely relevant work Disadvantage, Wolff and De-Shalit elaborate additional conceptual insights that are relevant to addressing the ‘indexing problem’ in measurement. Defining poverty as clustered disadvantage, their policy goal is ‘a society in which disadvantages do not cluster, a society where there is no clear answer to the question of who is the worst off. To achieve this, governments need to give special attention to the way patterns of disadvantage form and persist, and to take steps to break up such clusters’ (2007: 10).
They argue that because disadvantages are interconnected and must be solved by policies that break up such clusters, and also because key policy decisions such as budget allocation require ‘some sort of overall assessment of disadvantage’, then ‘an overall index of disadvantage seems inescapable’ (95, 89). They then proceed to address how such an index could be legitimately constructed, and we will return to their work in following sections.Given that multidimensional poverty measurement remains a relatively new field of endeavour, a clear overview of the judgements and comparisons that normative choices draw upon, using the capability approach as a springboard, may prove useful.[177] To motivate the discussion we begin by sharing a birds-eye view of how the Adjusted Headcount Ratio (M0) can—if a set of assumptions about the normative choices are fulfilled—reflect capability poverty.4,5
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