Fifth, data-constraints prevent from adequately covering several dimensions or properly accounting for joint distribution. The issue arises in our design for the nutrition indicators considered as part of the health dimension. We do not have data on bad nutrition outcomes except between 15 and 49 years old and below 5 years old. For the large fraction of the population outside of these two age windows, we conservatively assume that their nutrition status is normal. Moreover, because nutrition outcomes come from a separate survey, we had to input the nutrition outcomes from DHS to NLSS (see Appendix 3 for details on the imputation model). This imputation is not expected to have a large impact on results given the small fraction of the population for which we have bad nutrition status (see Table A1 in Appendix 1). However, one should bear in mind that our results suffer from this limitation.

## **Aggregating quality with quantity of life**

For the reasons exposed in Section 2.2, we account for the direct impact of mortality on quantity of life using PALE. There only remains to select a value for its normative weight , which defines the trade-off between quantity of life and quality of life. θθ There are several ways one could select a value for . For instance, the selection could be grounded in the views of the individuals themselves. Such views should be collected through dedicated surveys. θθ Alternatively, parametric values for could be deduced from assuming a particular Bernouilli (period) utility function, as illustrated in Appendix 2. θθ For our purposes, we simply assume two different values for the baseline and preferred designs. We conservatively assume for the baseline design. From a policy making perspective, this choice provides the smallest plausible weight to mortality. The reason is that implies that being dead θθ = 1 provides the same well-being level as being (multidimensionally) poor. Arguably, no policy maker would θθ = 1 explicitly ground its decisions on the assumption that poor individuals would be better-off dead. Providing the smallest plausible weight to mortality is conservative when probing question 1, namely how much is lost when ignoring non-monetary dimensions of well-being. We then assume for our preferred design. We argue that is an implausible weight for mortality, so smaller values must be considered. The “middle of the road” value implies that one θθ = 0.5 θθ = 1 year of life lost is equivalent to two ( years of life spent in poverty. θθ = 0.5 = 1/θθ)

# **Section 4: Incidence of low well-being in Nigeria in 2019**

In this section, we present the data sources, the context and the basic results describing the _incidence_ of low well-being in Nigeria in 2019. This incidence is measured by our indicators H and PALE. We focus on the respective contributions of alternative dimensions and the impact of some assumptions underlying as moderately deprived when one of its members has an extreme form of deprivation. This alternative is clearly less attractive because it could also lead to different types of identification errors. For instance, the heavily disabled individual would incorrectly be considered non-poor if her disability is the only form of deprivation afflicting her household. Or, all other members of her household would incorrectly be considered poor if the household has only one other moderate form of deprivation.

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