suggested by these percentages. Indeed, these percentages implicitly assume the smallest possible weight to mortality, ie, one YLL is not worse than one PY. When assuming the normative weight used in our preferred design, we get that the fraction of well-being losses coming from mortality range from 40 percent in the North-West zone to 62 percent in the South-East zone (this fraction is even 73 percent in Lagos). Also, the upper panel shows that mortality losses are distributed more homogeneously across zones than losses coming from other-dimensions poverty, which is turn are distributed more homogeneously than well-being losses coming from monetary poverty.

More generally, non-monetary well-being losses are at least as large as monetary well-being losses in most states. We can use Figure 10 and Figure A5 in Appendix 1 to contrast the fractions of PY+YLL that respectively come from monetary poverty with that coming from non-monetary dimensions. Some individuals are both monetary poor and other-dimensions poor and we take the natural convention to attribute the corresponding poverty years (shown in red in the histograms) to both monetary losses and non-monetary losses. For instance, this implies for Nigeria that 54 percent of PY+YLL is attributed to monetary poverty and 68 percent of PY+YLL is attributed to non-monetary losses (including premature mortality). These percentages can be interpreted as follows: monetary poverty, alone, captures slightly more than half the well-being losses in Nigeria (assuming ), but non-monetary losses are larger than monetary losses in Nigeria. In all zones, monetary poverty captures less than two-thirds the well-being θθ = 1 losses, and it captures less than one quarter of well-being losses in South-West. In most zones, nonmonetary losses are larger than monetary losses.

# **Section 5: Impact on well-being comparisons**

In this section, we use two different methods to assess the impact of non-monetary dimensions on wellbeing comparisons. First, we quantify the frequency with which they reverse the well-being ranking between two states. Second, we quantify the fraction of the budget for a hypothetical social policy that should be transferred across states when allocating this budget according to our well-being indicator rather than monetary poverty.

## **Section 5.1: Quantifying re-rankings**

In Table 5 we report the fraction of all pairwise state comparisons that are reversed for different pairs of indicators. For instance, the fraction of states whose comparison is reversed when ranking them by monetary poverty rather than by mean consumption is 5 percent. This shows that the ranking of states by these two indicators is highly correlated. By comparison, when comparing two uncorrelated indicators, we expect the percentage of these “re-rankings” to be 50 percent. In the table, we consider the following list of indicators: mean consumption, monetary poverty ( ), multidimensional poverty (H) and PALE. Table 5 reveals the following findings. First, the rankings of all these indicators are highly correlated in MM HH Table 5 reveals the following findings. First, the rankings of all these indicators are highly correlated in Nigeria. This is not very surprising as it is typically the case for any two meaningful indicators of well-being. Yet, one key reason why the rankings of Nigerian states are highly correlated is that Nigerian states have highly heterogeneous well-being level (according to all three indicators). Recall for instance that monetary poverty ranges between 10 percent and 90 percent in those states. Clearly, it is unlikely to find that two indicators of well-being disagree when comparing two states with respectively 20 and 80 percent monetary poverty. This is especially true if both indicators account for monetary poverty, like is the case 29