Conceptual and practical challenges are inevitable when trying to understand and model the fundamentals of innovation at the national level worldwide. Now in its 16th edition, the Global Innovation Index (GII) 2023, considers these conceptual challenges and deals with practical issues – related to data quality and methodological choices – by grouping economy-level data for 132 economies across 80 indicators into 21 sub-pillars, seven pillars, two sub-indices and, finally, an overall index.

This appendix offers detailed insights into the practical issues as they relate to the construction of the GII, analyzing the statistical soundness of the calculations and assumptions used to arrive at the final index rankings. Statistical soundness should be regarded as a necessary but not sufficient condition for a sound GII, since the correlations underpinning the majority of the statistical analyses carried out herein need not “necessarily represent the real influence of the individual indicators on the phenomenon being measured” (OECD/EC JRC, 2008: 26). Consequently, the development of the GII must be informed by a dynamic, iterative dialogue between the principles of statistical and conceptual soundness; or, to put it another way, between the theoretical understanding of innovation and the empirical observation of the data underlying the variables.

The European Commission’s Competence Centre on Composite Indicators and Scoreboards (COIN) at the Joint Research Centre (JRC) in Ispra, Italy, has been invited to audit the GII for a 13th consecutive year. As in previous editions, the present JRC-COIN audit focuses on the statistical soundness of the multilevel structure of the index, as well as on the impact of key modeling assumptions on the results. The independent statistical assessment of the GII provided by the JRC-COIN guarantees the transparency and reliability of the index for both policymakers and other stakeholders, thus facilitating more accurate priority setting and policy formulation in the innovation field. As in past GII reports, the JRC-COIN analysis complements the economy rankings with confidence intervals for the GII, the Innovation Input Sub-Index and the Innovation Output Sub-Index, in order to allow a better appreciation of the robustness of these rankings to the choice of computation methodology. Finally, through the use of data envelopment analysis, the JRC-COIN analysis includes an assessment of the added value of the GII, together with a measure of the “distance to the efficiency frontier” of innovation.