incorrect comparisons coming from data constraints. Loosely speaking, this indicator yields the largest “signal-to-noise ratio” when filtering available data.

The review focuses on global multidimensional poverty measures. First, multidimensional poverty measures are arguably the multidimensional indicators most used for policy making, perhaps because of the simplicity of the Alkire-Foster (AF) methodology. Second, global applications face the most stringent data constraints. However, most of the improvement opportunities we identify still make sense at the national or regional level. The conceptual framework proposed can also guide well-being measurement practices beyond poverty.

We identify three sources of potential improvements: “wasting” less of the available data, better filtering the data used, and better harnessing the added-value of multidimensional poverty measures. The first two sources relate to the construction of the indicator while the third source relates to the use of the indicator.

The main source of potential improvements relates to “wasting” less data. Ironically, in spite of stringent data constraints, large amounts of relevant data are simply ignored. To a large extent, this problem originates from conceptual rigidities constraining the indices used to aggregate across dimensions. We identify three ways of reducing the current “waste” of data.

First, the AF methodology to identify the multidimensionally poor is based on dichotomous deprivation statuses, i.e., either being deprived or non-deprived in a given variable. However, not all variables are dichotomous. For instance, this is neither the case of consumption, which is a cardinal variable, nor the case of some health or education outcomes, which are captured by categorical variables. Dichotomozing such variables leads to a loss of information. We identify in the literature two conceptually sound solutions to reduce this loss of information and show how these solutions can be implemented even in the absence of information on the relevant preference.

Second, in spite of its large intrinsic value, longevity is ignored by all global multidimensional poverty measures. Mortality data are often available but these data are not used. One difficulty is that the joint distribution between mortality and other achievements is almost always missing. However, this does not mean that indicators that ignore mortality yield better cross-regions comparisons, quite the contrary. There exists several conceptually sound indices that integrate mortality into poverty measures while assuming that all individuals within a given region face the same mortality risks. These indices are “hybrid” in the sense that they account for the joint distribution between some but not all variables considered.<sup>2</sup>

> 2For instance, assume that the joint distribution between two variables x1 and x2 (say income and education) is known, but the joint distribution between variable x3 (mortality) and the other two variables is unknown. Consider an index that aggregates x1, x2 and x3 while taking into account the joint distribution between x1 and x2. This index is “hybrid” in the sense that it accounts for the joint distribution between some but not all variables considered.

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