might be preferable to uˆ<sup>′</sup> if the two variables xˆ1 and xˆ2 are highly correlated. Indeed, the additional complexity of building indicator uˆ<sup>′</sup> might not be worth its additional benefits. Clearly, this framework aimed at minimizing well-being comparison errors can be applied to well-being measurement at large. It can also guide the improvement of monetary indicators, which also face data constraints and thus yield erroneous comparisons in practice.

# **2.7 Improving on current indicators**

It seems unlikely that the monetary approach yields indicators whose well-being comparison errors are minimized. Even though income or consumption are important determinants of well-being, several non-monetary dimensions like health and longevity are arguably at least as important.<sup>15</sup> Furthermore, longevity has been regularly shown to substantially affect cross-country well-being comparisons.<sup>16</sup> The fact that multidimensional poverty measures ignore longevity was placed under crude light during the Covid-19 crisis.<sup>17</sup> Also, consumption and income indicators capture in a noisy way the control that people exert over economic resources.<sup>18</sup> Therefore, the challenge is to build a multidimensional index making fewer well-being comparisons errors than monetary indicators.

Consider first the case when the goal is to identify low well-being across individuals within a given region, say for the purpose of targeting social spendings. For this case, the indicator should make reliable inter-personal comparisons (Objective 1). Thus, synthetic indicators are of no use and one must consider objective indicators. As a result, one must use a set of variables for which the joint distribution is available. At global level, given the current data constraints discussed in Section 2.4, this means to either ignore the monetary dimension or ignore the health dimension. The global MPI made the former choice while the World Bank’s MPM made the latter choice.<sup>19</sup> Currently, a key challenge reasoning may provide a rationale for attributing a quite high weight to education in the global MPI, even though the intrinsic value of education to well-being is disputed (Ravallion, 2011), at least if education is strongly correlated with consumption.

> 15Subjective well-being data suggests that good health and longevity are key determinant of wellbeing (Adler et al., 2017; Diener et al., 2018). The causal relationships behind the statistical association between subjective well-being, health and mortality are still an active area of research (Steptoe et al., 2015).

> 16A string of applied papers suggest that adding longevity significantly affects cross-country well-being comparisons (Becker et al., 2005; Jones and Klenow, 2016; Boarini et al., 2016).

> 17See Chapter 3 of the Poverty and Shared Prosperity Report (Bank, 2022) where the multidimensional impact of Covid-19 is analyzed through lenses different from the World Bank’s MPM.

> 18For instance, prices are imperfectly captured through unit values, rents implicitly perceived by homeowners are imperfectly captured through hedonic regressions, recall periods used in household consumption surveys affects welfare aggregates, etc.

> 19At this stage, it is an open question which of these two choices should yield the larger “signal-tonoise ratio” given the data available. On the one hand, the global MPI include a few variables capturing “standard of living”, which may serve as a noisy proxy for consumption. On the other hand, the variables capturing “health” in the global MPI provide a limited coverage of that dimension, as discussed in Section 2.4. It would be interesting to compare the correlation at household level between subjective well-being

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