The influence of non-R&D channels on innovation in a developing economy: an empirical analysis in the context of India

ABSTRACT

This study investigates the capability of innovation in India from a neo-Schumpeterian perspective, mainly focusing on the non-R&D aspects. We choose this focus for three reasons: (i) Based on our exploratory analysis, the investigation of innovation needs to go beyond the R&D effort (of manufacturing firms); (ii) R&D outlays alone may not fully explain innovation, particularly in resource-scare transition economies like India; and, (iii) Motivated by the theoretical underpinnings of the neo-Schumpeterian literature highlighting the process of interaction that enable the nurturing of innovation capabilities through national innovation system (NIS). Using Indian data from 1981 to 2017, of the non-R&D aspects of inputs, such as human capital, financial capital as internal factors; openness, FDI, and remittances as external factors. Estimated results from the cointegration analysis indicate that human capital and remittances are significant, but FDI has little impact, and openness (in terms of exports and imports) has an insignificant effect on innovation.

KEYWORDS:

• Innovation
• non-R&D factors
• national innovation system
• developing economy
• India

JEL CLASSIFICATION:

• O33
• O34
• O11

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. The book goes on to explain several aspects of determinants of technological capabilities and innovation in a nation-state, and at firm level; it covers various forms of managerial, learning, and basic knowledge. Freeman’s chapter is on national innovation systems, with chapters by Fransman and Metcalfe on national and international technological effort under the themes of techno-nationalism and techno-globalism and its implications for shaping innovation capability.

2. According to Freeman, national innovation system (NIS) is an interaction between different players, to enhance the learning capabilities through the accumulation of knowledge and skills, and a variety of sources that shape the capability of innovation.

3. Due to the financial crisis of 2008 the growth rate slumped to 3.7%, but a quick reversal was made in the subsequent years, with 8.5% and 9.8% growth rates recorded for 2009–10 and 2010–11 respectively (see: Economic Survey of India, 2019–20).

4. How such interaction and knowledge flows might lead to national and regional innovation systems is discussed by Freeman (1995), Archibugi and Michie (1997b).

5. Principal component analysis reduces the number of variables to represent a single variable consisting of three steps: (i) construct a correlation matrix, (ii) extract the factor loadings, and (iii) calculate the eigenvectors and eigenvalues of the covariance matrix. See Pradhan et al. (2014).