Page 44 - Contributed Paper Session (CPS) - Volume 4

CPS2111 Grant J. Cameron et al.
from by an improvement. This simple but significant requirement ensures
that the index value rises whenever one variable rises from 0 to 1 and the rest
of the variables do not fall in value. The index (; ) satisfies this property
since each is strictly positive. Notice that monotonicity supports the
incentive compatibility criterion, since it ensures that a country is not
penalized when it successfully raises its profile.
Subgroup decomposability: subgroup axioms allow the index to be
divided into salient sub-indices and linked back to the original index for policy
analysis. In the present case, the main decomposition is over the basic groups
given in partition = (1, … , ). A statistical capacity index F satisfies basic
decomposability if there exist weights ≥ 0 summing to 1 and sub-indices
() such that
() = ∑ ( ) (10)

=1
In other words, there is a collection of indices, one for each basic group of
variables, such that C can be expressed as a weighted average of these basic
indices. This is clearly the case for the nested counting index (; ), as it is
based on a weighted mean. Likewise, Equation (5) (after Proposition 1) follows
from Equation (10) by aggregating across basic groups within each dimension,
so that the overall index value is just the average of the dimensional index
values. These decompositions can help inform why one country is doing better
than another or help describe how a single country is progressing over time.
As noted above, the single country index (; ) can be expanded into an
index (; ) that covers all countries in a region or even the universe of
covered countries. The formula used to do this – Equation (7) – doubles as
another form of decomposition that expresses the aggregate index and an
average of the country indices. Since (; ) is the index of primary interest
here, the equation will not be expressed as a formal property here. However,
the fact that Equation (7) and (; ) are available allows users to have a better
understanding of regional levels and trends in statistical capacity.

3. Result
The SPI has several advantages over the SCI, particularly in terms of data
coverage. In particular, it has
i. Richer and more comprehensive dimensions covering different data
aspects ranging from data generation, curation, and dissemination to
data analysis.
ii. More indicators: the SPI has 42 indicators (of which 39 are used for
scoring), versus 25 indicators in the SCIs.
iii. More countries: the SPI covers more than 200 countries, especially
including high-income countries, while the SCI covers fewer than 150
countries and includes no high-income countries.

33 | I S I W S C 2 0 1 9