The Brazilian, Russian, Indian, Chinese and South African (BRICS) Heads of State in
2014, in Fortaleza Brazil called for the closer cooperation of their statistical agencies and
experts to promote the identification of common data methodologies that can be employed to
analyse social indicators which measure a common set of challenges in their countries. The
study examines the possibility of using data produced locally within Brazil, India and South
Africa specifically to assess the singular but complex phenomenon of learners dropping out of
school. Although the countries share a common challenge, the reasons behind the challenge
differ based on the countries’ varied backgrounds. In addition, each of the countries measure
school dropout rates differently but in essence only considers the number of learners who
dropout, whilst not describing the determinants of this dropout. This study employs Amartya
Sen’s Capability Approach to identify these determinants by identifying the central freedom
affecting the learner, viz., the learner’s real freedom to complete school and attain employment
and an improved quality of life. This freedom is tested in terms of a Capability Set of
functionings that learners aspire to attain or conduct, viz., being physically well, being
financially secure, being mentally well, being taught in infrastructure of a suitable standard,
being in a conducive home learning environment, travelling to school in a safe manner, feeling
free to express themselves in school and lastly, effectively participating in school activities in
a meaningful way. These broad functionings are further defined in terms of themes and subthemes
and thereafter datasets from the above mentioned 3 countries are identified in terms of
questions that are appropriate to assess the performance of the country. However, the key
additional step of this study is to qualify the selection of data variables per sub-theme in terms
of the associated level of data quality. By applying data quality theory, a set of dimensions are
identified, which are applicable to a data user working with a publicly released dataset. The
selected datasets are checked in terms of relevance internationally and amongst Brazil, India
and South Africa in terms of their data collection policy priorities. South Africa’s Statistical
Assessment Framework was found highly useful, as the framework shared many of the
identified data quality dimensions and assisted in developing the framework practically. In
applying the newly constructed Public Data Quality Assessment Framework, the identified
datasets were assessed in terms of the data quality dimensions and their level of data quality
was rated. South Africa’s surveys produced by Statistics South Africa were rated strongest.
Ultimately, relevant data can be sourced from the BRICS, however the variables identified are
nuanced and pertain to the priorities of the countries. Greater effort is need to promote
collaboration amongst the BRICS to produce comparable data, informed by common
methodologies and data quality standards.