Steps to constructing a multi-dimensional measure When measuring multi-dimensional poverty, several choices need to be made,

Steps to constructing a multi-dimensional measure When measuring multi-dimensional poverty, several choices need to be made,

some of which are common to the uni-dimensional case and some of which are specific to the multi-dimensional case. Several have already been mentioned. In what follows, we will briefly describe each practical step that needs to be taken to construct a multi-dimensional poverty measure.

Choice of the unit of analysis

A first decision regards the unit of analysis. Is it the individual, the household, cities, regions, countries? None is perfect. Frequently the household is selected (for reasons of data availability) and all members of a household identified as poor are considered poor. This ignores intra-household distribution of resources as well as variations in inherently personal variables such as nutri- tion, empowerment and health. All units have strengths and weaknesses.

Order of aggregation There are two kinds of poverty measures. Some aggregate first across people and then across dimensions (HPI). These can use data from any source, but they cannot look at the breadth of poverty each person or household suffers. Others aggregate first across all dimensions for the same person or household, and then across people. These are very appealing because they consider the multiplicity of deprivations every household or person suffers. The difficulty is that they require data from the same survey instrument. Choosing the order of aggregation influences the possible measures, as only a few measures are path independent (have the same value regardless of the order of aggregation) such as FLS’ HDI .

Choice of dimensions Another crucial choice regards the selection of dimensions. As we have seen, the capability approach does not prescribe one list of capabilities that should

be considered. The dimensions often vary depending on the context and purpose of the measure. As Sen observes (1996, pp57–58), ‘in dealing with

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extreme poverty in developing economies, we may be able to concentrate, to a great extent on a relatively small number of centrally important functionings and the corresponding basic capabilities . . . In other contexts, the list may have to be longer and more diverse.’

Nussbaum (2003) argues in favour of her list of ten central human capa- bilities (in the case of constitutional guarantees) to assure that the capability approach carries critical force. Even if hers and other conventions are useful to stir the imagination, Sen (2004) reiterates the need to engage public debate, and not fix a list from the sidelines which is deaf to public reasoning and to the particularities of that measurement context.

Alkire (2008) noted that most researchers have drawn implicitly on one or more of five selection methods. These are:

1 Existing data or convention: To select dimensions mostly because of convenience or a convention, or because these are the only data available with the requisite characteristics. If one is not gathering data directly, this is a necessary but insufficient reason.

2 Theory: To select dimensions based on implicit or explicit assumptions about what people do value or should value. This can be useful, if combined with 3 or 4.

3 Public ‘consensus’: To select dimensions using a list that has achieved a degree of legitimacy due to public consensus. Examples include human rights, the MDGs, and the Sphere project (a set of minimum standards in disaster response) or national plans. This is useful, particularly in combination with 4 or if various actors can publicly scrutinize them.

4 Ongoing deliberative participatory processes: To select dimensions on the basis of ongoing purposive participatory exercises that regularly elicit the values and perspectives of stakeholders. This is useful if participation is relatively wide and undistorted.

5 Empirical evidence regarding people’s values: To select dimensions on the basis of empirical data on consumer preferences and behaviours, or psychological studies of which values are most relevant. This can be useful in combination with 3 or 4 (but not alone).

Clearly, these methods overlap and the choice of methods will rightly vary by context.

Robeyns (2006) strongly advocates that however dimensions are selected, researchers, analysts and government officials should write up explicitly in their papers or reports the process they used in order to foster public debate and feedback. She suggests that the write-ups should explicitly formulate why the dimensions or indicators are claimed to be things people value and have reason to value. They should justify the methodology by which dimensions were selected. And they should articulate the dimensions that are important but were omitted due to feasibility considerations such as missing data.

The choice of dimensions is interconnected with the choice of weights

POVERTY AND INEQUALITY MEASUREMENT

between dimensions (addressed later). For example, dimensions might be chosen such that they are of relatively equal weight. This, indeed, is the recommendation given by Atkinson et al (2002, p25) in their work on social indicators in Europe: ‘the interpretation of the set of indicators is greatly eased where the individual components have degrees of importance that, while not necessarily exactly equal, are not grossly different’.

Choice of variables/indicator(s) for dimensions Once the selection of dimensions has been accomplished, it is necessary to select the variables or indicators within each dimension that are appropriate to the purpose of the measure. One consideration is whether to choose indicators of resources, functionings, utility or (where possible) capability. For example, if the underlying framework is that of the capability approach, the researcher might seek indicators of functionings rather than resources or basic needs. ‘[B]asic needs are usually defined in terms of means rather than outcomes, for instance, as living in the proximity of providers of health care services (but not necessarily being in good health), as the number of years of achieved schooling (but not necessarily being literate), as living in a democracy (but not necessarily partici- pating in the life of the community), and so on’ (Duclos and Araar, 2006, Part I). Other things being equal, one will select indicators that are a strong proxy for the dimension and are not highly correlated with other indicators in the measure. If we select more than one indicator per dimension, we must decide whether to combine them into a dimensional index, or use each indicator directly.

Choice of poverty lines As in the uni-dimensional case, poverty lines need to be selected. A desirable method is to set a poverty line for each indicator or dimension. In the multi- dimensional context, absolute lines have typically been used. They reflect a value judgement about poverty – but are often based on some national or international consensus. Examples of these are the thresholds set by the MDGs. However they are set, it is important to make explicit the process through which the values of the poverty lines were decided. Analysis of the sensitivity and robustness of rankings to changes in one or more poverty line are also essential.

Choice of the identification criterion In the uni-dimensional case, identification was easy: anyone who earned less than the poverty line was poor; anyone who did not was not poor. In the multi- dimensional case, we have to decide the range or number of dimensions used to identify a poor person. If a person is deprived in any one dimension, do we consider them multi-dimensionally poor? If we say yes we take the union approach. If a person must instead be deprived in all dimensions before we consider them poor, we are taking the intersection approach. It is also possible to take an intermediary counting approach – for example everyone who is deprived in any 3 of 7 dimensions. If the dimensions are not equally weighted, we use the weighted sum of dimensions instead (Alkire and Foster, 2007).

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Choice of weights In the capability approach, because capabilities are of intrinsic value, the relative weights on different capabilities or dimensions are value judgements. Weights can be applied in three ways in multi-dimensional poverty measures: (i) between capabilities and dimensions (the relative weight of nutrition and education for example), (ii) within dimensions (if more than one indicator of mobility is used for example), and (iii) among people in the distribution (to give greater priority to the most disadvantaged, for example). 30

Weights between dimensions can represent:

1 the enduring importance of a capability relative to other capabilities (long term) or

2 the priority of expanding one capability relative to others in planning and policy.

In practice, weights on indicators that are aggregated within dimensions are often set as equal (which is a value judgement), or else are generated by a statistical process. Weights among people are often accomplished through the choice of aggregation such as the FGT-2 in income space, rather than through explicit distributional weights.

Weights are also implicitly influenced by technical issues, such as the number and choice of dimensions/indicators, the poverty line and, where relevant, the transformation function. For example, the use of a very low poverty line for one dimension (few people are deprived) and a high poverty line for another (many are deprived) will implicitly give more weight to the dimension in which many people are deprived.

In summary, ‘since any choice of weights should be open to public debate, it is crucial that the judgements that are implicit in such weighting be made as clear and comprehensible as possible and thus be open to public scrutiny’ (Anand and Sen, 1997, p6). 31

Choice of the poverty measure When these decisions have been made, it is possible to select the methodology by which a composite index will be created. The next section introduces two measures – the HPI, as an example of a measure that aggregates first across people, and the Alkire and Foster (2007), which aggregates first across deprivations for each person or household.