Various issues with R&D Statistics compilation in India
- India’s research and development (R&D) expenditure-GDP ratio is 0.7% which is much below the world average of 1.8%. India also has various issues with collecting data from the private corporate sector on gross domestic expenditure on R&D (GERD). Therefore, transforming India’s R&D statistics to truly reflect the R&D ecosystem calls for short-term and medium-term measures.
- India’s R&D expenditure-GDP ratio has been relatively low compared to other developed countries. According to the National Science and Technology Management Information System (NSTMIS), the R&D expenditure-GDP ratio in India was around 0.7% in 2018-19.
- While this represents an increase from previous years, it is still significantly lower than the R&D expenditure-GDP ratios of many other countries, including the United States, Japan, and South Korea, which all invest over 3% of their GDP in R&D.
- The main reason is the low investment in R&D by the corporate sector. While the corporate sector accounts for about two-thirds of gross domestic expenditure on R&D (GERD) in leading economies, its share in India is just 37%.
- However, evidence suggests that India’s GERD data are incomplete and inaccurate.
- For instance, a 2022 info brief of the National Science Foundation (NSF) of the United States on Foreign R&D by U.S.-based multinational corporations (MNCs) shows a spend of $9.5 billion (₹649.7 billion) on R&D in India in 2018.
- But the latest Research and Development Statistics, published by the Department of Science and Technology (DST) in 2020 has provided an estimate of ₹60.9 billion in R&D spending in 2017-18 by foreign MNCs, which is only about 10% of what U.S. firms have reported to have spent in India on R&D.
Issues with the current system
- NSTMIS compiles GERD statistics in India. And it faces challenges in collecting data from the private corporate sector.
- One issue with compiling statistics on GERD is the challenge of defining what exactly counts as research and development. Different countries and organisations may have different definitions and criteria for what qualifies as R&D spending, which can lead to discrepancies in reported figures.
- The method used by NSTIMS for the identification of R&D performing firms does not capture all the R&D performing firms due to poor registration and categorisation of firms.
- For example, SigTuple Technologies, which is a leading start-up in India focusing on artificial intelligence-based HealthTech and has filed 19 patents as of 2021, is unlisted in government databases.
- For those firms which do not respond to the survey by NSTIMS, the data is collected from secondary sources such as annual reports and the Prowess database.
- This method will work only if firms disclose their R&D spending. However, some firms do not report any spending on R&D in spite of their declarations and have patents granted in India.
- Additionally, accurately measuring R&D spending can be difficult, as it may involve estimating the value of intangible assets such as intellectual property.
- In the short term, the NSTMIS should use the patents granted data, both in India and the U.S., in addition to its current method to identify R&D-performing enterprises.
- Annual R&D estimates can be prepared from mandatory disclosures that the enterprises are required to make to the Ministry of Corporate Affairs instead of confining R&D statistics to the responses to the surveys.
- In order to ensure compliance and proper reporting, technologies can be used like in the case of revamped income-tax return forms where various sections are interlinked.
- Proper disclosure of information to regulatory agencies, including R&D spending data, should be made an essential component of the environmental, social and governance (ESG) ranking of enterprises.
- While GERD statistics can provide valuable insights into the state of research and development in a country, they may not capture all relevant factors, such as the quality or impact of the research being funded. It is important to interpret statistical data in context and to consider other sources of information when making decisions related to research policy and funding.