# **4 Discussion of opportunities**

We summarize in Table 3 the opportunities identified, the challenge to which they relate and whether they may help improve inter-personal comparisons (Objective 1) or crossregions comparisons (Objective 2).<sup>74</sup>

**Table 3:** Improvement opportunities for multidimensional poverty measures.

<!-- Start of picture text --> Challenges Opportunities Objectives<br>Waste less 3.2 Do not needlessly dichotomize variables<br>available data (trichotomize categorical variables) 1 & 2<br>(PX identification for consumption) 1 & 2<br>Cover more dimensions through hybrid index<br>3.3 Synthetic integration of mortality-longevity only 2<br>3.4 Assume missing joint-distribution M vs OD<br>3.4.1 Assume overlap between HM and HOD only 2<br>3.4.2 Impute consumption into OD survey 1 & 2<br><!-- End of picture text -->

|Challenges|Opportunities|Objectives|
|---|---|---|
|Waste less<br>available data|3.2 Do not needlessly dichotomize variables<br>(trichotomize categorical variables)<br>(PX identifcation for consumption)<br>Cover more dimensions through hybrid index<br>3.3 Synthetic integration of mortality-longevity<br>3.4 Assume missing joint-distribution M vs OD<br>3.4.1 Assume overlap between HM and HOD<br>3.4.2 Impute consumption into OD survey<br>3.4.3 Total consumption|1 & 2<br>1 & 2<br>only 2<br>only 2<br>1 & 2<br>1 & 2|
|Better “flter”<br>available data|3.5 Use most fned-grained unit of analysis<br>(take advantage of individual-level data)<br>3.6 Select weights from information on preference<br>(SWB data or preference-elicitation surveys)|1 & 2<br>1 & 2|
|Beyond dim-by-dim<br>policy making|3.7 Develop best practices for MPM analysis|1 & 2|

<!-- Start of picture text --> 3.4.3 Total consumption 1 & 2<br>Better “filter” 3.5 Use most fined-grained unit of analysis 1 & 2<br>available data (take advantage of individual-level data)<br>3.6 Select weights from information on preference 1 & 2<br><!-- End of picture text --> <!-- Start of picture text --> (SWB data or preference-elicitation surveys)<br>Beyond dim-by-dim 3.7 Develop best practices for MPM analysis 1 & 2<br><!-- End of picture text --> <!-- Start of picture text --> policy making<br><!-- End of picture text --> Many opportunities presented in this review are complementary and are thus worth pursuing in parallel. Some opportunities are substitutes for one another, like the three alternative methods to deal with the missing joint distribution between monetary and non-monetary outcomes. Among these three methods, the first (overlap) is the simplest and the second (imputation) is conceptually the most promising. This does not disqualify the third method (total consumption), which could be very useful if the second method proves impractical or unreliable.

For what it is worth, our own expectation is that the largest increase in signal-to-noise ratio will come from the integration of additional data. This could in particular be the case for the methods to go beyond dichotomous variables (section 3.2), the integration of longevity (section 3.3) and the methods to bypass the missing joint distribution (section 3.4). It is unclear whether the methods suggested to go beyond equal-weights (section 3.6)

> 74Recall that Objective 1 aims at improving the targeting of policies across individuals or across households, while Objective 2 aims at improving the targeting of policies across regions or geographic areas (see Section 2.6).

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