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CPS2111 Grant J. Cameron et al.
The World Bank’s Statistical Capacity Index (SCI) is one such tool that has
3
been widely employed.
Several international and national agencies have adopted the SCI for
measuring progress in statistical capacity building and related investments.
The United Nations, for example, uses the SCI to measure trends in the
development of national statistical capacity (United Nations, 2016). The SCI is
used to evaluate the efficiency of statistical support provided to a country as
well as the need to further develop its statistical capacity (PARIS21, 2002).
Some regional organizations use the SCI to identify areas of improvement in
their member countries (OIC, 2012), while researchers use the SCI as a
benchmark to validate their new statistical indexes (Sanga et al., 2011). The
World Bank mainstreamed the SCI in its monitoring and assessment
framework and has adopted it as a baseline indicator in various projects at the
4
country level . The SCI is based on publicly available data, and this has various
advantages over other indexes of statistical capacity. A key advantage of the
SCI is that it can provide assessment of a country’s statistical capacity in an
internationally comparable and cost-effective manner.
Existing efforts in building indexes to assess statistical capacity have
focused on the practical aspects such as data collection, organization, and
legal issues, paying little attention to the underlying theoretical principles that
are indispensable for the construction of a reliable, transparent, and consistent
statistical capacity index. For example, the UNECE, in a recent Global
Assessment report, discusses only the legal basis, description of the statistical
system, data source, and processing of the target country (UNECE, 2014). The
FAO, in its guidelines for assessing country capacity in producing agricultural
statistics, provides instructions on completing the questionnaires and on
compiling the assessment indicator (FAO, 2014), but pays no attention to the
axiomatic principles of these indicators. The U.S. Census Bureau developed
and recently updated (2017) the Tool for Assessing Statistical Capacity (TASC)
with a primary objective of measuring the overall capacity of an NSS by
providing a breakdown of the areas of strength and weakness. However, the
focus of this instrument is on measuring the capacity of an NSS to conduct
5
household-based surveys and censuses . To our knowledge, only Sanga,
Dosso, and Gui-Diby (2011) discuss the technical framework behind the
African Statistical Development Index (ASDI).

3 For brevity, we refer to both the Statistical Capacity Indicators and the Statistical Capacity Index as the SCI
in the rest of the paper. We will make it clear where we refer to either the indicators or the index. We
similarly refer
4 For other recent examples that use the SCI, see: Beegle et al. (2016) for an analysis of the relationship
between good governance and statistical capacity in African countries; Tapsoba, Noumon, and York
(2017) for the impacts of statistical capacity on reducing procyclical fiscal policy; and UNICEF (2018) for
the role of statistical capacity in tracking the SDG for child development.
5 We return to provide more discussion on the SCI and these other methods in Section 2 below.
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