focuses on key final products needed for policy; and (iv) Dissemination Practices and Openness (DPO), which evaluates the extent to which products are publicly disseminated. It is easy to see that each of these dimensions is centrally related to the statistical capacity of an NSS.<sup>11</sup> We return to more discussion on these four dimensions in Section 4.1.

As with any measurement exercise, there will likely be dimensions missing from the list. One dimension that could be particularly relevant for measuring statistical capacity is _flexibility_ , or the ability of an NSS to adapt to changing circumstances.<sup>12</sup> For example, in response to a change in national priorities, the NSS may need to alter the schedule or scope of data production. Alternatively, as new technologies for data gathering are being developed, international guidelines may change, requiring the NSS to alter its methods. However, to fully capture the missing dimension of flexibility, one needs data about circumstances that have not yet have happened, and this directly conflicts with the implementability desideratum.<sup>13</sup> Likewise, the absence of such data in the present context leads to the use of more observable variables.<sup>14</sup> A second missing dimension might be _statistical infrastructure_ as represented by the underlying array of inputs that the NSS 11 It may be useful to briefly discuss whether one dimension is inherently more important than another. For instance, since earlier dimensions are needed for the latter dimensions, they can be arguably viewed as being more important. Yet, from the point of view of stakeholders, the opposite could also be seen as true. Notice that both the first and last dimensions could be considered to be _process_ dimensions, while the middle two are _product_ dimensions. We could posit that process and product are equally important to evaluating statistical capacity, while making a judgment that the individual components are also of equal importance. If so, then the four dimensions would be consistent with Atkinson et al (2002) who observe that “the interpretation of a set of indicators is greatly eased where the individual components have degrees of importance that, while not necessarily exactly equal, are not grossly different.” 12 For fundamental discussions of flexibility in the context of production, see, for example, Kreps (1979).

13 Parts of this aspect might be captured in the technology or product dimensions: if the country is able to produce a best practice census, then it is plausible that it could produce comparably sophisticated products if and when needed. An analogy can be seen with Sen’s notion of capability, which evaluates a person’s well-being via sets of available options, or capability sets, which require information on alternatives that have not yet been chosen. The difficulty of obtaining counterfactual information has led many researchers to measure capability using actual achievements or functionings.

14 One related critique is that since the variables are based on past data, the resulting measure cannot be “forward looking” enough to convey the future possibilities for a given NSS. This inevitable consequence is analogous to the challenge of measuring “vulnerability to poverty” using household survey data. See Dang and Lanjouw (2017) for a recent study that addresses this challenge.

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