The matching percentiles method brings with it several advantages for creating a set of indices of this nature.
First, the matching percentiles method overcomes the problem of sampling bias. This is pervasive when a new data source only covers a limited and unrepresentative sample of countries, as country scores on the new indicator will reflect not only a difference in scaling (β) but also a difference in the constant (α). A further advantage of the matching percentiles technique is that it allows us to keep adding successive waves of indicators, even with very small samples, that can be used to continually ‘refine’ the country scores simply by using information on relative rankings. Whereas regression based techniques of aggregation encounter difficulties in incorporating small sample sources due to difficulties estimating α and β when the sample size is very low, no such difficulties affect the matching percentiles technique. This is critically important for a set of indices of this nature, where the present data remain incomplete, such that it will be necessary to keep adding new indicators in future years as successive data source become available, even where such sources cover relatively few countries.