STI Indicators: Measuring STI is fundamental for the formulation of national innovation strategies.  Absence of “relevant” indicators is often considered a major obstacle for the design and implementation of STI policies in developing countries. The existing indicators are failing to serve their very own purpose in the policy – making process, because the existing indicators do not capture the relationship between STI activities and socioeconomic development. As a matter of fact, macro level indicators do not always reflect capabilities to achieve microlevel developmental targets.  The issues are becoming more cross-disciplinary in nature. The notion of linear relation between R&D activities (between R&D input and output) is vanishing and R&D as a process (input, output, outcome and impact) is becoming more and more of a complex system. The conventional STI indicators limit itself only to inputs and outputs; neglecting the monitoring of outcomes and impacts of STI process. The idea of harnessing the efforts in STI to achieve the development goals demands for new forms of indicators.  With this background, this work attempts to develop a conceptual framework for cross-cutting STI indicators interlaced between STI capabilities and developmental targets.

R&D Statistics are very important to formulate accurate indicators to monitor country’s R&D efforts. Accurate data-collection ensures reliability of the R&D indicators; and these indicators are very important to craft effective public policies. To produce R&D statistics, the methodology proposed by the OECD in the Frascati Manual (FM) is used extensively in both OECD and Non-OECD developing countries, even though it was originally written for R&D surveys in just OECD member countries. The characteristics of research systems in developing countries differ significantly from the OECD countries. Developing countries face problems when trying to apply the FM standards to the situation in their country. Hence, UNESCO Institute of Statistics (UIS) has come-up with an annex to FM – to provide suggestions on how the concepts in the FM should be interpreted to ensure that data better reflect the characteristics of R&D activity in developing countries while still maintaining international comparability. However, in Indian context, there are still several challenges related to R&D data, particularly in terms of its reliability, accessibility and availability. This study compares the R&D data-collection methodology in different countries to learn how the FM methodology is adapted in various national setups to construct their data-collection models to achieve reliable R&D statistics.

For more details, please contact, Prof. Subbiah Arunachalam, Prof. Abinandanan or M Madhan