11 that place sufficient weight on the accuracy of the information on the marginal distribution would choose to analyze each margin separately, in each case drawing from the best survey.<sup>6</sup> In many cases, of course, reliable information on relevant dimensions - such as health status, anthropometrics, education and consumption - does exist in the same survey. This is the case, for instance, of the Russian RLMS survey used by Decancq (2009), the Indonesia Family Life Survey, and various LSMS-type surveys. In those cases, it is difficult to find an excuse for not complementing the marginal analysis with information on the dependency structure. Furthermore, one should not underestimate the power of the demand for better data. If there are certain aspects of well-being and deprivation which are not regularly captured in household surveys, but whose joint distribution with other dimensions (such as income) is of real policy interest, then more frequent analysis of the kind we suggest might well encourage statistical institutes, or other data providers, to collect information on them.

# _3. What about scalar multidimensional poverty indices?_

Finally, we turn briefly to the question of whether the analytical approaches we propose should completely preclude the computation of scalar multidimensional poverty indices. It is already clear that they do not preclude the “dashboard approach”, in so far as looking at the marginal distributions is inherent in looking at the joint. The three approaches described above are best seen as complementing the “dashboard approach” with information on the dependency structure between the dimensions, when that is available.

But what room do we see for the definition and computation of _scalar_ multidimensional poverty _indices_ ? We see this question as just another instance of choice given a trade-off. A scalar multidimensional index buys you a complete ordering, i.e. the ability to rank two years, or countries, or regions, even then their joint distributions (or copulas) cross. Just as in unidimensional poverty or inequality analysis, that ability to generate a complete ordering comes at a cost in terms of specific functional form assumptions, etc. In the case of multidimensional well-being and poverty, this price is higher, for two reasons.

The first reason is that the identification step (in the sense of Sen, 1976) is considerably more complex for multidimensional poverty than for unidimensional poverty. This is emphatically _not_ only because one has to define a threshold for each individual dimension. It is also because a difficult choice must be made – at the identification step – about “how many” deprivations constitute poverty. This harks back to the fundamental choice between the union and intersection approaches (e.g. Bourguignon and Chakravarty, 2003) or to the establishment of an

> 6  The question of whether data on a particular variable is indeed more reliable in one survey than in another may, in some cases, be investigated empirically. At a minimum, survey-to-survey imputation techniques can be used to assess the sensitivity of results to using information from different sources. This remains an area for future work.