tantly, they could also improve inter-personal comparisons across men. Such advantage is easier to grasp when thinking of another type of individual level data that depends less on household level circumstances. This is arguably the case of (self reported) disabilities. The extreme disability of one senior individual may drastically affect her well-being, but it needs not necessarily have the same impact on other household members. If such disability is accounted at the household level as a moderate deprivation, this leads to many identification mistakes. For instance, the (“extremely”) disabled person may not be identified as poor if her household does not face other types of deprivations. Alternatively, her relatives may wrongly be identified as poor if the household’s moderate disability deprivation pushes the household beyond the identification threshold. Interestingly, accounting at individual level for deprivations that affect one category of individual (e.g., seniors) may improve well-being comparisons across another category of individuals (say children).

# **3.6 Beyond ad-hoc weights**

One key data constraint is that non-monetary data typically do not contain information on the relevant preference u. As a result, selecting appropriate values for the weights of a multidimensional indicator is challenging for practitioners. This explains the popularity of the “equal-weights” procedure, which allows accounting for non-monetary data without requiring any information on u.<sup>60</sup> The main risk is that equal weights deviate too much from the weights that would best reflect preference u (Ravallion, 2012).<sup>61</sup> Another risk is that the “equal-weights” procedure decreases the perceived legitimacy of multidimensional poverty measures because of this obviously ad-hoc procedure.

Improving on this issue requires some information on the relevant preference u. This information can potentially be taken from different sources (Decancq and Lugo, 2013). Subjective well-being data may be one source of information on u, as suggested by Decancq et al. (2015) or by in Decancq et al. (2019). Ideally, the survey that collects all relevant outcomes also collects information on life satisfaction or happiness. Preference elicitation surveys may provide another sources of information on u, as done in Decancq and Nys (2021). These surveys ask subjects to rank pairs of achievement vectors in one

> 60In spite of its paternalistic weights, it could be that the World Bank’s MPM has a larger signal-tonoise ratio than the fraction of extremely monetary poor individuals. That is, the additional information coming from its non-monetary variables may more than compensate for the bias coming from using equal-weights wˆ<sup>eq</sup> , which are bound to be different than weights w informed from individual preferences.

> 61There are other limitations associated with the “equal-weights” procedure. For instance, this procedure provides incentives to ignore relevant information. The reason is that the only way through which the practitioner can influence the weight attributed to a given variable is by changing the number of dimensions and/or changing the number of dichotomous variables considered within the same dimension. Under the equal-weights procedure, there is an incentive not to include one variable if another variable belonging to the same dimension is already included and has a clearly different importance for well-being. Indeed, the aggregation with equal weights would not look plausible if both variables were simultaneously included.

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