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CPS2460 Mustafa Dinc et al.
data. Together, these six steps comprise the core theoretical elements of our
proposed measurement technology.

Characteristics of the Statistical Performance Index (SPI)
Constructing a measure of statistical capacity entails many distinct choices
that can appear to be arbitrary and unrelated to one another if no context is
provided. A set of desired characteristics, or criteria, can provide the guiding
principles that help organize these choices to obtain a relevant and useful
measurement tool. SPI is designed to satisfy seven criteria. The SPI should be:
1. Simple. It must be understandable and easy to describe
2. Coherent. It must conform to a common-sense notion of what is
being measured
3. Motivated. It must fit the purpose for which it is being developed
4. Rigorous. It must be technically solid
5. Implementable. It must be operationally viable
6. Replicable. It must be easily replicable
7. Incentive Compatible. It must respect country incentives

The SPI also satisfies three axioms. The symmetry axiom requires that the
index value is unaffected when variable levels are switched. The dominance
axiom requires 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 subgroup
decomposability axiom allows the index to be divided into salient sub-indices
and linked back to the original index for policy analysis.

SPI Dimensions
The production process for statistical outputs has certain similarities to the
traditional production model from economics and begins with a technology
that is used in generating the statistical products, and the level of this
technology is clearly a relevant component of statistical capacity. The resulting
statistical outputs might be divided into two general categories. First are the
intermediate products, which have direct use for specialists but require
additional processing to create products suitable for general use. For example,
a census can be helpful for policy analysts but must be processed to obtain
useful statistics. Second are the final products, which are available in a form
that can be understood by the public. The key macro statistics of a country
would naturally be viewed as final products. Even after the products have been
created, their existence does not imply that potential users will actually have
access to them. Statistical products may be available to only a few users, or
available to all. The final dimension then covers the extent to which statistical
products are disseminated.
This simple framework helps to identify four coherent dimensions for a
measure of statistical capacity, namely: (i) Methodology, Standards and

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