Impact of patenting on firms' performance: an empirical investigation based on manufacturing firms in India

Abstract

The study aims to estimate the impact of R&D expenditure and patenting on the performance of firms using productivity, profitability and Tobin's q ratio as the performance indicators. The study uses firm-level data of 489 high- and medium-technology firms during the period of 2000–2010. We employ relatively a new source of data particularly in the context of India, firm-level patent granted, that has not been explored earlier. The study finds that firms patenting result in productivity improvement of firms, whereas R&D expenditure does not. The study further finds the evidence of positive impact of patenting on financial performance of firm with significant differences between foreign and domestic firms.

Keywords:

• R&D expenditure
• patent
• productivity
• profitability
• Tobin's q

Acknowledgement

The authors would like to thank two anonymous referees for the comments and insights on an earlier draft that greatly improved the analysis and presentation.

Disclosure statement

No potential conflict of interest was reported by the authors.

Funding

This work was supported by the doctoral fellowship given by University Grants Commission of India [UGC-REF. No. 18/ (NET-Dec 2009)].

Notes

1. Source: World Development Indicators.

2. New product and process innovations, number of publications are the other sources of innovative activities.

3. Oslo manual defines innovations in four ways: product, process, marketing and organizational innovation. We restrict to product and process innovation.

4. Frontier approach is the other way to measure the productivity of firms. This approach identifies the maximum attainable position given the input or the prices. Since objective of the study is to estimate the impact of technological factors on manufacturing TFP, we chose the non-frontier approach.

5. The monotonicity condition of OP insists that investment is strictly increasing in productivity. In case firm reports zero investment for a significant number of cases that can lead to the suspect on efficiency and validity of the monotonicity condition.

6. GMM and IV approaches are normally used in the case of Dynamic panel data models.

7. The formulation is extensively based on Levinsohn and Petrin (2003).

8. Chung and Pruit (1994) compare their approximate Tobin's q value with that of Linderberg and Ross (1981).

9. Sales net of indirect taxes overcome the problem of indirect taxes that does not reflect in the production capacity.

10. The depreciation rate applied to the assets when creating the accounts at the end of each financial year depends on the discretion of the company but it should comply with the prescriptive rates given in the Companies Act (1956). Furthermore, companies need to comply with Income Tax Act (1961) while depreciating their assets. Since NFA comprises of many components, the depreciation rates are different for each of these components.

11. R&D stock has been constructed through a perpetual inventory method. Specifically, we calculate R&D stock as , where R_t=stock of R&D, I_t is R&D investment made at time t and is 15% depreciation rate. The starting year of the R&D series is 1990–1991. Thus, for computing R&D stock for the base year of the study (2000), the real investment in R&D for the last 10 years has been used (Goldar 2004).

12. We measure experience of a firm in terms of the age that is equivalent to using a year trend to capture time-specific effects. Evidently, both cannot be included simultaneously in any model. Therefore, we omit age.