Overall, we find that well-being rankings are highly correlated but accounting for non-monetary dimension may imply substantial re-rankings, especially when giving an intermediate weight to mortality.

# **Section 5.2: Quantifying budget transfers across states**

Quantifying re-rankings is a binary method to probe changes in well-being comparisons. Indeed, a ranking between two states is either reversed or not. The limitation of this method is that, when the ranking is not reversed, the changes in the relative well-being levels of the two states being compared is not accounted for. The method used in this subsection accounts for such changes.

The next method is based on a hypothetical social policy. Assume that the Nigerian government has a budget for social protection policies. Further assume that this budget is distributed across states proportionally to needs as captured by a well-being indicator. In the case of poverty, this budget could be distributed proportionally to the number of poor individuals in each state. When contrasting monetary poverty with multidimensional poverty, our method reports the fraction of the total budget that must be redistributed across states when using the latter instead of the former. Consider the following simplified example with two states, where state A has 0 monetary poor and 1 multidimensionally poor individual while state B has 1 monetary poor and 3 multidimensionally poor individuals. Under monetary poverty, state B receives 100 percent of the budget, while under multidimensional poverty state B receives 75 percent of the budget and state A 25 percent. The fraction of the total budget that must be transferred is thus 25 percent, from state B to state A. We use a straightforward adaptation of this method that accounts for the fact that poverty _decreases_ with well-being while PALE _increases_ with well-being, which we describe in detail in Appendix 5.

Table 6 shows the fraction of the total budget that must be transferred when comparing two of the following list of indicators: monetary poverty ( ), multidimensional poverty (H) and PALE. The fraction transferred corresponds to 6 percent of the budget when moving from monetary poverty to PALE MM HH (baseline design). Again, the table suggests for the baseline design that moving from monetary poverty to multidimensional poverty impacts these transfers more than moving from multidimensional poverty to PALE. Then, the fraction transferred increases to 11 percent of the budget when moving from monetary poverty to PALE (preferred design). The table suggests again that this increase is more driven by the change in normative weight given to mortality than by the different designs for multidimensional poverty.

_Table 6: Fraction of (hypothetical) budget reallocated across states between pairs of well-being indicators_

||**Baseline**|**Preferred**|
|---|---|---|
|<br>|**(%)**|**(%)**|
|HM-VS- PALE<br> <br>|5.6|11.3|
|<br>HM-VS-H<br>|4.7|6.0|
|<br> <br>H-VS- PALE<br>_Source:_Authors’ estimates b|2.2<br>ased on data from NLSS 2019|13.5|

_Note_ : The table considers a hypothetical policy that allocates a budget across Nigerian states proportionally to (minus) the state’s total wellbeing (see Appendix 5 for details). The table presents the fraction of the budget that must be re-allocated across states when changing from one well-being measure to another: monetary poverty rate ( ), multidimensional poverty rate ( ) and poverty-adjusted life-expectancy (PALE). MM HH HH 31