considered negligible compared to one year of life lost (θ = 0), PALE boils down to LE.

Observe that PALE is a hybrid index. The multidimensional head-count ratio H is an “objective” index, which relies on the joint distribution between dimensions capturing quality of life. Yet, longevity is aggregated into PALE in a synthetic way, assuming that all individuals face the same mortality risks summarized in LE. Arguably, PALE has a larger signal-to-noise ratio than H when performing cross-country comparisons (Objective 2). At least this should be the case if LE captures mortality risks with limited noise and if longevity captures an important dimension of well-being.

Baland et al. (2021) show empirically that monetary poverty measures that ignore mortality lead to substantially biased well-being comparisons. They find that mortality ˆ is not dwarfed by poverty even when assuming a conservatively low age threshold (a = 50 years) and the largest plausible weight to monetary poverty (θ = 1).<sup>36</sup> Furthermore, the correlation between monetary poverty and mortality is far from perfect. Depending on the value attributed to the weight θ, they find that between 8 and 27% of country trends are reversed over five-years periods when integrating mortality.

This points to one key open question: what is the range of plausible values for parameter θ? Narrowing down this range within the [0, 1] interval would allow sharpening the conclusions obtained when integrating mortality.<sup>37</sup> There are at least two routes one can take to investigate plausible values for parameter θ.<sup>38</sup> One could try to survey people’s attitudes on these two parameters.<sup>39</sup> Another route would be to calibrate values for θ based on a parametric expression for a Bernoulli utility function, as illustrated in the Appendices of Baland et al. (2023) and applied for a large set of countries in Decerf et al. (2024). This approach could be further refined by calibrating the parametric expression for the Bernoulli utility from the Value of Statistical Life literature (Kniesner and Viscusi, 2019), as for instance done in Becker et al. (2005); Jones and Klenow (2016); Boarini et al. (2016).

However, besides the selection of its parameter’s value, the PALE index is ready-touse. The same holds for other indices based on a premature mortality threshold.<sup>40</sup> The mortality data required are often available at country level and sometimes at subnational level. Accounting for longevity could very well be the lowest hanging fruit for practitioners

> 36Decerf and Fonton (2023) also find that mortality is sizable compared to multidimensional poverty when contrasting well-being across Nigerian states in 2019.

> 37Baland et al. (2023) study the theoretical conditions and document the empirical frequency with which poverty comparisons are reversed for all values of θ ∈ [0, 1].

> 38The value for θ depends on the poverty standard considered. A more austere poverty line implies a larger weight to poverty and thus a larger value for θ.

> 39For instance, one could ask to non-poor individuals how many years of their remaining life they would be willing to spend in poverty to extend their longevity by one year.

> 40It could still be interesting to investigate how to extend these indices for contexts where some information is available on the joint distribution between mortality and income, e.g., information on selective mortality, i.e., the differential mortality risks faced by the poor and the non-poor. Another question is how to extend these indices for gap-sensitive poverty indices.

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